release: langchain 1.2.4 (#34755 )

chore(langchain): add agent name metadata (#34743 )
Adding `lc_agent_name` to default agent config so that said metadata can be used in LS for a nicer tracing devx <img width="801" height="304" alt="Screenshot 2026-01-13 at 5 17 07 PM" src="https://github.com/user-attachments/assets/0c72a52d-4b56-4ace-bf27-89680ebb4e39" />
2026-02-03 15:55:44 +00:00 · 2026-01-14 14:24:31 -05:00 · 2026-01-14 14:57:35 +00:00 · 2026-01-13 15:59:08 -05:00 · 2026-01-13 10:55:21 -05:00 · 2026-01-13 10:25:50 -05:00
858 changed files with 72756 additions and 35948 deletions
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -26,7 +26,7 @@
  // Use 'forwardPorts' to make a list of ports inside the container available locally.
  // "forwardPorts": [],
  // Run commands after the container is created
-  "postCreateCommand": "uv sync && echo 'LangChain (Python) dev environment ready!'",
+  "postCreateCommand": "cd libs/langchain_v1 && uv sync && echo 'LangChain (Python) dev environment ready!'",
  // Configure tool-specific properties.
  "customizations": {
    "vscode": {
@@ -42,7 +42,7 @@
        "GitHub.copilot-chat"
      ],
      "settings": {
-        "python.defaultInterpreterPath": ".venv/bin/python",
+        "python.defaultInterpreterPath": "libs/langchain_v1/.venv/bin/python",
        "python.formatting.provider": "none",
        "[python]": {
          "editor.formatOnSave": true,
--- a/.dockerignore
+++ b/.dockerignore
@@ -0,0 +1,34 @@
+# Git
+.git
+.github
+
+# Python
+__pycache__
+*.pyc
+*.pyo
+.venv
+.mypy_cache
+.pytest_cache
+.ruff_cache
+*.egg-info
+.tox
+
+# IDE
+.idea
+.vscode
+
+# Worktree
+worktree
+
+# Test artifacts
+.coverage
+htmlcov
+coverage.xml
+
+# Build artifacts
+dist
+build
+
+# Misc
+*.log
+.DS_Store
--- a/.github/CODE_OF_CONDUCT.md
+++ b/.github/CODE_OF_CONDUCT.md
@@ -1,132 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-conduct@langchain.dev.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@@ -1,6 +0,0 @@
-# Contributing to LangChain
-
-Hi there! Thank you for even being interested in contributing to LangChain.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
-
-To learn how to contribute to LangChain, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -8,16 +8,15 @@ body:
      value: |
        Thank you for taking the time to file a bug report.

-        Use this to report BUGS in LangChain. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).

-        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
-        if there's another way to solve your problem:
+        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:

-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference](https://reference.langchain.com/python/),
+        * [Documentation](https://docs.langchain.com/oss/python/langchain/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
        * [LangChain ChatBot](https://chat.langchain.com/)
        * [GitHub search](https://github.com/langchain-ai/langchain),
+        * [LangChain Forum](https://forum.langchain.com/),
  - type: checkboxes
    id: checks
    attributes:
@@ -36,16 +35,48 @@ body:
          required: true
        - label: This is not related to the langchain-community package.
          required: true
-        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
-          required: true
        - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
          required: true
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Which `langchain` package(s) is this bug related to? Select at least one.
+
+        Note that if the package you are reporting for is not listed here, it is not in this repository (e.g. `langchain-google-genai` is in [`langchain-ai/langchain-google`](https://github.com/langchain-ai/langchain-google/)).
+
+        Please report issues for other packages to their respective repositories.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general
  - type: textarea
    id: reproduction
    validations:
      required: true
    attributes:
-      label: Example Code
+      label: Example Code (Python)
      description: |
        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.

@@ -53,15 +84,12 @@ body:

        **Important!**

-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
-        * INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
+        * Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+        * Reduce your code to the minimum required to reproduce the issue if possible.

+        (This will be automatically formatted into code, so no need for backticks.)
+      render: python
      placeholder: |
-        The following code:
-
-        ```python
        from langchain_core.runnables import RunnableLambda

        def bad_code(inputs) -> int:
@@ -69,17 +97,14 @@ body:

          chain = RunnableLambda(bad_code)
          chain.invoke('Hello!')
-        ```
  - type: textarea
-    id: error
-    validations:
-      required: false
    attributes:
      label: Error Message and Stack Trace (if applicable)
      description: |
-        If you are reporting an error, please include the full error message and stack trace.
-      placeholder: |
-        Exception + full stack trace
+        If you are reporting an error, please copy and paste the full error message and
+        stack trace.
+        (This will be automatically formatted into code, so no need for backticks.)
+      render: shell
  - type: textarea
    id: description
    attributes:
@@ -99,9 +124,7 @@ body:
    attributes:
      label: System Info
      description: |
-        Please share your system info with us. Do NOT skip this step and please don't trim
-        the output. Most users don't include enough information here and it makes it harder
-        for us to help you.
+        Please share your system info with us.

        Run the following command in your terminal and paste the output here:

@@ -113,8 +136,6 @@ body:
        from langchain_core import sys_info
        sys_info.print_sys_info()
        ```
-
-        alternatively, put the entire output of `pip freeze` here.
      placeholder: |
        python -m langchain_core.sys_info
    validations:
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,9 +1,15 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: 📚 Documentation
-    url: https://github.com/langchain-ai/docs/issues/new?template=langchain.yml
+  - name: 📚 Documentation issue
+    url: https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml
    about: Report an issue related to the LangChain documentation
  - name: 💬 LangChain Forum
    url:  https://forum.langchain.com/
    about: General community discussions and support
+  - name: 📚 LangChain Documentation
+    url: https://docs.langchain.com/oss/python/langchain/overview
+    about: View the official LangChain documentation
+  - name: 📚 API Reference Documentation
+    url: https://reference.langchain.com/python/
+    about: View the official LangChain API reference documentation
--- a/.github/ISSUE_TEMPLATE/feature-request.yml
+++ b/.github/ISSUE_TEMPLATE/feature-request.yml
@@ -13,11 +13,11 @@ body:
        Relevant links to check before filing a feature request to see if your request has already been made or
        if there's another way to achieve what you want:

-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference](https://reference.langchain.com/python/),
+        * [Documentation](https://docs.langchain.com/oss/python/langchain/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
        * [LangChain ChatBot](https://chat.langchain.com/)
        * [GitHub search](https://github.com/langchain-ai/langchain),
+        * [LangChain Forum](https://forum.langchain.com/),
  - type: checkboxes
    id: checks
    attributes:
@@ -34,6 +34,40 @@ body:
          required: true
        - label: This is not related to the langchain-community package.
          required: true
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Which `langchain` package(s) is this request related to? Select at least one.
+
+        Note that if the package you are requesting for is not listed here, it is not in this repository (e.g. `langchain-google-genai` is in `langchain-ai/langchain`).
+
+        Please submit feature requests for other packages to their respective repositories.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general
  - type: textarea
    id: feature-description
    validations:
--- a/.github/ISSUE_TEMPLATE/privileged.yml
+++ b/.github/ISSUE_TEMPLATE/privileged.yml
@@ -18,3 +18,33 @@ body:
    attributes:
      label: Issue Content
      description: Add the content of the issue here.
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Please select package(s) that this issue is related to.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general
--- a/.github/ISSUE_TEMPLATE/task.yml
+++ b/.github/ISSUE_TEMPLATE/task.yml
@@ -25,13 +25,13 @@ body:
      label: Task Description
      description: |
        Provide a clear and detailed description of the task.
-        
+
        What needs to be done? Be specific about the scope and requirements.
      placeholder: |
        This task involves...
-        
+
        The goal is to...
-        
+
        Specific requirements:
        - ...
        - ...
@@ -43,7 +43,7 @@ body:
      label: Acceptance Criteria
      description: |
        Define the criteria that must be met for this task to be considered complete.
-        
+
        What are the specific deliverables or outcomes expected?
      placeholder: |
        This task will be complete when:
@@ -58,15 +58,15 @@ body:
      label: Context and Background
      description: |
        Provide any relevant context, background information, or links to related issues/PRs.
-        
+
        Why is this task needed? What problem does it solve?
      placeholder: |
        Background:
        - ...
-        
+
        Related issues/PRs:
        - #...
-        
+
        Additional context:
        - ...
    validations:
@@ -77,15 +77,45 @@ body:
      label: Dependencies
      description: |
        List any dependencies or blockers for this task.
-        
+
        Are there other tasks, issues, or external factors that need to be completed first?
      placeholder: |
        This task depends on:
        - [ ] Issue #...
        - [ ] PR #...
        - [ ] External dependency: ...
-        
+
        Blocked by:
        - ...
    validations:
      required: false
+  - type: checkboxes
+    id: package
+    attributes:
+      label: Package (Required)
+      description: |
+        Please select package(s) that this task is related to.
+      options:
+        - label: langchain
+        - label: langchain-openai
+        - label: langchain-anthropic
+        - label: langchain-classic
+        - label: langchain-core
+        - label: langchain-cli
+        - label: langchain-model-profiles
+        - label: langchain-tests
+        - label: langchain-text-splitters
+        - label: langchain-chroma
+        - label: langchain-deepseek
+        - label: langchain-exa
+        - label: langchain-fireworks
+        - label: langchain-groq
+        - label: langchain-huggingface
+        - label: langchain-mistralai
+        - label: langchain-nomic
+        - label: langchain-ollama
+        - label: langchain-perplexity
+        - label: langchain-prompty
+        - label: langchain-qdrant
+        - label: langchain-xai
+        - label: Other / not sure / general
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,28 +1,30 @@
 (Replace this entire block of text)

-Thank you for contributing to LangChain! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
+Read the full contributing guidelines: https://docs.langchain.com/oss/python/contributing/overview
+
+Thank you for contributing to LangChain! Follow these steps to have your pull request considered as ready for review.
+
+1. PR title: Should follow the format: TYPE(SCOPE): DESCRIPTION

- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
  - Examples:
+    - fix(anthropic): resolve flag parsing error
    - feat(core): add multi-tenant support
-    - fix(cli): resolve flag parsing error
-    - docs(openai): update API usage examples
-  - Allowed `{TYPE}` values:
-    - feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
-  - Allowed `{SCOPE}` values (optional):
-    - core, cli, langchain, standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant, xai, infra
-  - Once you've written the title, please delete this checklist item; do not include it in the PR.
+    - test(openai): update API usage tests
+  - Allowed TYPE and SCOPE values: https://github.com/langchain-ai/langchain/blob/master/.github/workflows/pr_lint.yml#L15-L33

- [ ] **PR message**: ***Delete this entire checklist*** and replace with
-  - **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable to a relevant issue.
-  - **Issue:** the issue # it fixes, if applicable (e.g. Fixes #123)
-  - **Dependencies:** any dependencies required for this change
+2. PR description:

- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. **We will not consider a PR unless these three are passing in CI.** See [contribution guidelines](https://docs.langchain.com/oss/python/contributing) for more.
+  - Write 1-2 sentences summarizing the change.
+  - If this PR addresses a specific issue, please include "Fixes #ISSUE_NUMBER" in the description to automatically close the issue when the PR is merged.
+  - If there are any breaking changes, please clearly describe them.
+  - If this PR depends on another PR being merged first, please include "Depends on #PR_NUMBER" inthe description.
+
+3. Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified.
+
+  - We will not consider a PR unless these three are passing in CI.

 Additional guidelines:

- Most PRs should not touch more than one package.
- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests. Likewise, please do not update the `uv.lock` files unless you are adding a required dependency.
- Changes should be backwards compatible.
- Make sure optional dependencies are imported within a function.
+  - We ask that if you use generative AI for your contribution, you include a disclaimer.
+  - PRs should not touch more than one package unless absolutely necessary.
+  - Do not update the `uv.lock` files unless or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer.
--- a/.github/actions/poetry_setup/action.yml
+++ b/.github/actions/poetry_setup/action.yml
@@ -1,93 +0,0 @@
-# An action for setting up poetry install with caching.
-# Using a custom action since the default action does not
-# take poetry install groups into account.
-# Action code from:
-# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
-name: poetry-install-with-caching
-description: Poetry install with support for caching of dependency groups.
-
-inputs:
-  python-version:
-    description: Python version, supporting MAJOR.MINOR only
-    required: true
-
-  poetry-version:
-    description: Poetry version
-    required: true
-
-  cache-key:
-    description: Cache key to use for manual handling of caching
-    required: true
-
-  working-directory:
-    description: Directory whose poetry.lock file should be cached
-    required: true
-
-runs:
-  using: composite
-  steps:
-    - uses: actions/setup-python@v5
-      name: Setup python ${{ inputs.python-version }}
-      id: setup-python
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    - uses: actions/cache@v4
-      id: cache-bin-poetry
-      name: Cache Poetry binary - Python ${{ inputs.python-version }}
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
-      with:
-        path: |
-          /opt/pipx/venvs/poetry
-        # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
-        key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
-
-    - name: Refresh shell hashtable and fixup softlinks
-      if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
-      shell: bash
-      env:
-        POETRY_VERSION: ${{ inputs.poetry-version }}
-        PYTHON_VERSION: ${{ inputs.python-version }}
-      run: |
-        set -eux
-
-        # Refresh the shell hashtable, to ensure correct `which` output.
-        hash -r
-
-        # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
-        # Delete and recreate the softlinks pipx expects to have.
-        rm /opt/pipx/venvs/poetry/bin/python
-        cd /opt/pipx/venvs/poetry/bin
-        ln -s "$(which "python$PYTHON_VERSION")" python
-        chmod +x python
-        cd /opt/pipx_bin/
-        ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
-        chmod +x poetry
-
-        # Ensure everything got set up correctly.
-        /opt/pipx/venvs/poetry/bin/python --version
-        /opt/pipx_bin/poetry --version
-
-    - name: Install poetry
-      if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
-      shell: bash
-      env:
-        POETRY_VERSION: ${{ inputs.poetry-version }}
-        PYTHON_VERSION: ${{ inputs.python-version }}
-      # Install poetry using the python version installed by setup-python step.
-      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
-
-    - name: Restore pip and poetry cached dependencies
-      uses: actions/cache@v4
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
-        WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
-      with:
-        path: |
-          ~/.cache/pip
-          ~/.cache/pypoetry/virtualenvs
-          ~/.cache/pypoetry/cache
-          ~/.cache/pypoetry/artifacts
-          ${{ env.WORKDIR }}/.venv
-        key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles(format('{0}/**/poetry.lock', env.WORKDIR)) }}
--- a/.github/actions/uv_setup/action.yml
+++ b/.github/actions/uv_setup/action.yml
@@ -27,7 +27,7 @@ runs:
  using: composite
  steps:
    - name: Install uv and set the python version
-      uses: astral-sh/setup-uv@v6
+      uses: astral-sh/setup-uv@v7
      with:
        version: ${{ env.UV_VERSION }}
        python-version: ${{ inputs.python-version }}
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -1,330 +0,0 @@
-# Global Development Guidelines for LangChain Projects
-
-## Core Development Principles
-
-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
-
-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
-
-❌ **Bad - Breaking Change:**
-
-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
-```
-
-✅ **Good - Stable Interface:**
-
-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
-
-**Before making ANY changes to public APIs:**
-
- Check if the function/class is exported in `__init__.py`
- Look for existing usage patterns in tests and examples
- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
- Mark experimental features clearly with docstring admonitions (using MkDocs Material, like `!!! warning`)
-
-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
- Avoid unnecessary abstraction or premature optimization
- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
- Unit tests: `tests/unit_tests/` (no network calls allowed)
- Integration tests: `tests/integration_tests/` (network calls permitted)
- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
- [ ] Tests fail when your new logic is broken
- [ ] Happy path is covered
- [ ] Edge cases and error conditions are tested
- [ ] Use fixtures/mocks for external dependencies
- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
- [ ] Does the test suite fail if your new logic is broken?
- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
- No `eval()`, `exec()`, or `pickle` on user-controlled input
- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
- Remove unreachable/commented code before committing
- Race conditions or resource leaks (file handles, sockets, threads).
- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args and Returns sections for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level.
-
-    Returns:
-        True if email was sent successfully, False otherwise.
-
-    Raises:
-        InvalidEmailError: If the email address format is invalid.
-        SMTPConnectionError: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
- Types go in function signatures, NOT in docstrings
- Focus on "why" rather than "what" in descriptions
- Document all parameters, return values, and exceptions
- Keep descriptions concise but clear
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications.
-
-        Args:
-            data: List of data items to process.
-
-        Returns:
-            ProcessingResult with details of the operation.
-        """
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
- Reduce code duplication through shared utilities
- Make unit testing easier
- Improve separation of concerns (single responsibility)
- Make unit testing easier through dependency injection
- Add clarity without adding complexity
- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
-
-```bash
-# Run unit tests (no network)
-make test
-
-# Don't run integration tests, as API keys must be set
-
-# Run specific test file
-uv run --group test pytest tests/unit_tests/test_specific.py
-```
-
-### Code Quality
-
-```bash
-# Lint code
-make lint
-
-# Format code
-make format
-
-# Type checking
-uv run --group lint mypy .
-```
-
-### Dependency Management Patterns
-
-**Local Development Dependencies:**
-
-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
-
-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
-
-```python
-from langchain_core.tools import tool
-
-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
-
-    Args:
-        query: The search query string.
-    """
-    # Implementation here
-    return results
-```
-
-## Commit Standards
-
-**Use Conventional Commits format for PR titles:**
-
- `feat(core): add multi-tenant support`
- `!fix(cli): resolve flag parsing error` (breaking change uses exclamation mark)
- `docs: update API usage examples`
- `docs(openai): update API usage examples`
-
-## Framework-Specific Guidelines
-
- Follow the existing patterns in `langchain_core` for base abstractions
- Implement proper streaming support where applicable
- Avoid deprecated components
-
-### Partner Integrations
-
- Follow the established patterns in existing partner libraries
- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
- Include comprehensive integration tests
- Document API key requirements and authentication
-
---
-
-## Quick Reference Checklist
-
-Before submitting code changes:
-
- [ ] **Breaking Changes**: Verified no public API changes
- [ ] **Type Hints**: All functions have complete type annotations
- [ ] **Tests**: New functionality is fully tested
- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
- [ ] **Documentation**: Google-style docstrings for public functions
- [ ] **Code Quality**: `make lint` and `make format` pass
- [ ] **Architecture**: Suggested improvements where applicable
- [ ] **Commit Message**: Follows Conventional Commits format
--- a/.github/pr-file-labeler.yml
+++ b/.github/pr-file-labeler.yml
@@ -7,13 +7,12 @@ core:
      - any-glob-to-any-file:
          - "libs/core/**/*"

-langchain:
+langchain-classic:
  - changed-files:
      - any-glob-to-any-file:
          - "libs/langchain/**/*"
-          - "libs/langchain_v1/**/*"

-v1:
+langchain:
  - changed-files:
      - any-glob-to-any-file:
          - "libs/langchain_v1/**/*"
@@ -28,6 +27,11 @@ standard-tests:
      - any-glob-to-any-file:
          - "libs/standard-tests/**/*"

+model-profiles:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/model-profiles/**/*"
+
 text-splitters:
  - changed-files:
      - any-glob-to-any-file:
@@ -39,6 +43,81 @@ integration:
      - any-glob-to-any-file:
          - "libs/partners/**/*"

+anthropic:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/anthropic/**/*"
+
+chroma:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/chroma/**/*"
+
+deepseek:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/deepseek/**/*"
+
+exa:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/exa/**/*"
+
+fireworks:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/fireworks/**/*"
+
+groq:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/groq/**/*"
+
+huggingface:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/huggingface/**/*"
+
+mistralai:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/mistralai/**/*"
+
+nomic:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/nomic/**/*"
+
+ollama:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/ollama/**/*"
+
+openai:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/openai/**/*"
+
+perplexity:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/perplexity/**/*"
+
+prompty:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/prompty/**/*"
+
+qdrant:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/qdrant/**/*"
+
+xai:
+  - changed-files:
+      - any-glob-to-any-file:
+          - "libs/partners/xai/**/*"
+
 # Infrastructure and DevOps
 infra:
  - changed-files:
@@ -69,16 +148,5 @@ documentation:
  - changed-files:
      - any-glob-to-any-file:
          - "**/*.md"
-          - "**/*.rst"
          - "**/README*"

-# Security related changes
-security:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "**/*security*"
-          - "**/*auth*"
-          - "**/*credential*"
-          - "**/*secret*"
-          - "**/*token*"
-          - ".github/workflows/security*"
--- a/.github/pr-title-labeler.yml
+++ b/.github/pr-title-labeler.yml
@@ -1,41 +0,0 @@
-# PR title labeler config
-#
-# Labels PRs based on conventional commit patterns in titles
-#
-# Format: type(scope): description or type!: description (breaking)
-
-add-missing-labels: true
-clear-prexisting: false
-include-commits: false
-include-title: true
-label-for-breaking-changes: breaking
-
-label-mapping:
-  documentation: ["docs"]
-  feature: ["feat"]
-  fix: ["fix"]
-  infra: ["build", "ci", "chore"]
-  integration:
-    [
-      "anthropic",
-      "chroma",
-      "deepseek",
-      "exa",
-      "fireworks",
-      "groq",
-      "huggingface",
-      "mistralai",
-      "nomic",
-      "ollama",
-      "openai",
-      "perplexity",
-      "prompty",
-      "qdrant",
-      "xai",
-    ]
-  linting: ["style"]
-  performance: ["perf"]
-  refactor: ["refactor"]
-  release: ["release"]
-  revert: ["revert"]
-  tests: ["test"]
--- a/.github/scripts/check_diff.py
+++ b/.github/scripts/check_diff.py
@@ -30,6 +30,7 @@ LANGCHAIN_DIRS = [
    "libs/text-splitters",
    "libs/langchain",
    "libs/langchain_v1",
+    "libs/model-profiles",
 ]

 # When set to True, we are ignoring core dependents
@@ -130,29 +131,20 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
        return _get_pydantic_test_configs(dir_)

    if job == "codspeed":
-        py_versions = ["3.12"]  # 3.13 is not yet supported
+        py_versions = ["3.13"]
    elif dir_ == "libs/core":
-        py_versions = ["3.10", "3.11", "3.12", "3.13"]
+        py_versions = ["3.10", "3.11", "3.12", "3.13", "3.14"]
    # custom logic for specific directories
-
-    elif dir_ == "libs/langchain" and job == "extended-tests":
+    elif dir_ in {"libs/partners/chroma"}:
        py_versions = ["3.10", "3.13"]
-    elif dir_ == "libs/langchain_v1":
-        py_versions = ["3.10", "3.13"]
-    elif dir_ in {"libs/cli"}:
-        py_versions = ["3.10", "3.13"]
-
-    elif dir_ == ".":
-        # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
-        py_versions = ["3.10", "3.12"]
    else:
-        py_versions = ["3.10", "3.13"]
+        py_versions = ["3.10", "3.14"]

    return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]


 def _get_pydantic_test_configs(
-    dir_: str, *, python_version: str = "3.11"
+    dir_: str, *, python_version: str = "3.12"
 ) -> List[Dict[str, str]]:
    with open("./libs/core/uv.lock", "rb") as f:
        core_uv_lock_data = tomllib.load(f)
@@ -306,7 +298,9 @@ if __name__ == "__main__":
                if not filename.startswith(".")
            ] != ["README.md"]:
                dirs_to_run["test"].add(f"libs/partners/{partner_dir}")
-                dirs_to_run["codspeed"].add(f"libs/partners/{partner_dir}")
+                # Skip codspeed for partners without benchmarks or in IGNORED_PARTNERS
+                if partner_dir not in IGNORED_PARTNERS:
+                    dirs_to_run["codspeed"].add(f"libs/partners/{partner_dir}")
            # Skip if the directory was deleted or is just a tombstone readme
        elif file.startswith("libs/"):
            # Check if this is a root-level file in libs/ (e.g., libs/README.md)
--- a/.github/scripts/get_min_versions.py
+++ b/.github/scripts/get_min_versions.py
@@ -98,7 +98,7 @@ def _check_python_version_from_requirement(
        return True
    else:
        marker_str = str(requirement.marker)
-        if "python_version" or "python_full_version" in marker_str:
+        if "python_version" in marker_str or "python_full_version" in marker_str:
            python_version_str = "".join(
                char
                for char in marker_str
--- a/.github/workflows/_compile_integration_test.yml
+++ b/.github/workflows/_compile_integration_test.yml
@@ -35,7 +35,7 @@ jobs:
    timeout-minutes: 20
    name: "Python ${{ inputs.python-version }}"
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
        uses: "./.github/actions/uv_setup"
--- a/.github/workflows/_lint.yml
+++ b/.github/workflows/_lint.yml
@@ -38,7 +38,7 @@ jobs:
    timeout-minutes: 20
    steps:
      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6

      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
        uses: "./.github/actions/uv_setup"
@@ -47,6 +47,12 @@ jobs:
          cache-suffix: lint-${{ inputs.working-directory }}
          working-directory: ${{ inputs.working-directory }}

+      # - name: "🔒 Verify Lockfile is Up-to-Date"
+      #   working-directory: ${{ inputs.working-directory }}
+      #   run: |
+      #     unset UV_FROZEN
+      #     uv lock --check
+
      - name: "📦 Install Lint & Typing Dependencies"
        working-directory: ${{ inputs.working-directory }}
        run: |
--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -19,7 +19,7 @@ on:
        required: true
        type: string
        description: "From which folder this pipeline executes"
-        default: "libs/langchain"
+        default: "libs/langchain_v1"
      release-version:
        required: true
        type: string
@@ -54,7 +54,7 @@ jobs:
      version: ${{ steps.check-version.outputs.version }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: Set up Python + uv
        uses: "./.github/actions/uv_setup"
@@ -77,7 +77,7 @@ jobs:
        working-directory: ${{ inputs.working-directory }}

      - name: Upload build
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
@@ -105,7 +105,7 @@ jobs:
    outputs:
      release-body: ${{ steps.generate-release-body.outputs.release-body }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          repository: langchain-ai/langchain
          path: langchain
@@ -149,8 +149,8 @@ jobs:
            fi
          fi

-          # if PREV_TAG is empty, let it be empty
-          if [ -z "$PREV_TAG" ]; then
+          # if PREV_TAG is empty or came out to 0.0.0, let it be empty
+          if [ -z "$PREV_TAG" ] || [ "$PREV_TAG" = "$PKG_NAME==0.0.0" ]; then
            echo "No previous tag found - first release"
          else
            # confirm prev-tag actually exists in git repo with git tag
@@ -179,8 +179,8 @@ jobs:
          PREV_TAG: ${{ steps.check-tags.outputs.prev-tag }}
        run: |
          PREAMBLE="Changes since $PREV_TAG"
-          # if PREV_TAG is empty, then we are releasing the first version
-          if [ -z "$PREV_TAG" ]; then
+          # if PREV_TAG is empty or 0.0.0, then we are releasing the first version
+          if [ -z "$PREV_TAG" ] || [ "$PREV_TAG" = "$PKG_NAME==0.0.0" ]; then
            PREAMBLE="Initial release"
            PREV_TAG=$(git rev-list --max-parents=0 HEAD)
          fi
@@ -206,9 +206,9 @@ jobs:
      id-token: write

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v7
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
@@ -237,7 +237,7 @@ jobs:
      contents: read
    timeout-minutes: 20
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      # We explicitly *don't* set up caching here. This ensures our tests are
      # maximally sensitive to catching breakage.
@@ -258,7 +258,7 @@ jobs:
        with:
          python-version: ${{ env.PYTHON_VERSION }}

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v7
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
@@ -377,6 +377,7 @@ jobs:
          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
+          LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
        run: make integration_tests
        working-directory: ${{ inputs.working-directory }}

@@ -393,6 +394,7 @@ jobs:
    runs-on: ubuntu-latest
    permissions:
      contents: read
+    if: false # temporarily skip
    strategy:
      matrix:
        partner: [openai, anthropic]
@@ -409,8 +411,9 @@ jobs:
      AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
      AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
      AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
+      LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      # We implement this conditional as Github Actions does not have good support
      # for conditionally needing steps. https://github.com/actions/runner/issues/491
@@ -428,7 +431,7 @@ jobs:
        with:
          python-version: ${{ env.PYTHON_VERSION }}

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v7
        if: startsWith(inputs.working-directory, 'libs/core')
        with:
          name: dist
@@ -442,7 +445,7 @@ jobs:
            git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
            | awk '{print $2}' \
            | sed 's|refs/tags/||' \
-            | grep -E '[0-9]+\.[0-9]+\.[0-9]+([a-zA-Z]+[0-9]+)?$' \
+            | grep -E '[0-9]+\.[0-9]+\.[0-9]+$' \
            | sort -Vr \
            | head -n 1
          )"
@@ -475,7 +478,7 @@ jobs:
      - release-notes
      - test-pypi-publish
      - pre-release-checks
-      - test-prior-published-packages-against-new-core
+      # - test-prior-published-packages-against-new-core
    runs-on: ubuntu-latest
    permissions:
      # This permission is used for trusted publishing:
@@ -490,14 +493,14 @@ jobs:
        working-directory: ${{ inputs.working-directory }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: Set up Python + uv
        uses: "./.github/actions/uv_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v7
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
@@ -530,14 +533,14 @@ jobs:
        working-directory: ${{ inputs.working-directory }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: Set up Python + uv
        uses: "./.github/actions/uv_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}

-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v7
        with:
          name: dist
          path: ${{ inputs.working-directory }}/dist/
--- a/.github/workflows/_test.yml
+++ b/.github/workflows/_test.yml
@@ -33,7 +33,7 @@ jobs:
    name: "Python ${{ inputs.python-version }}"
    steps:
      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6

      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
        uses: "./.github/actions/uv_setup"
--- a/.github/workflows/_test_pydantic.yml
+++ b/.github/workflows/_test_pydantic.yml
@@ -13,7 +13,7 @@ on:
        required: false
        type: string
        description: "Python version to use"
-        default: "3.11"
+        default: "3.12"
      pydantic-version:
        required: true
        type: string
@@ -36,7 +36,7 @@ jobs:
    name: "Pydantic ~=${{ inputs.pydantic-version }}"
    steps:
      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6

      - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
        uses: "./.github/actions/uv_setup"
@@ -51,7 +51,9 @@ jobs:

      - name: "🔄 Install Specific Pydantic Version"
        shell: bash
-        run: VIRTUAL_ENV=.venv uv pip install pydantic~=${{ inputs.pydantic-version }}
+        env:
+          PYDANTIC_VERSION: ${{ inputs.pydantic-version }}
+        run: VIRTUAL_ENV=.venv uv pip install "pydantic~=$PYDANTIC_VERSION"

      - name: "🧪 Run Core Tests"
        shell: bash
--- a/.github/workflows/auto-label-by-package.yml
+++ b/.github/workflows/auto-label-by-package.yml
@@ -0,0 +1,107 @@
+name: Auto Label Issues by Package
+
+on:
+  issues:
+    types: [opened, edited]
+
+jobs:
+  label-by-package:
+    permissions:
+      issues: write
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Sync package labels
+        uses: actions/github-script@v8
+        with:
+          script: |
+            const body = context.payload.issue.body || "";
+
+            // Extract text under "### Package" (handles " (Required)" suffix and being last section)
+            const match = body.match(/### Package[^\n]*\n([\s\S]*?)(?:\n###|$)/i);
+            if (!match) return;
+
+            const packageSection = match[1].trim();
+
+            // Mapping table for package names to labels
+            const mapping = {
+              "langchain": "langchain",
+              "langchain-openai": "openai",
+              "langchain-anthropic": "anthropic",
+              "langchain-classic": "langchain-classic",
+              "langchain-core": "core",
+              "langchain-cli": "cli",
+              "langchain-model-profiles": "model-profiles",
+              "langchain-tests": "standard-tests",
+              "langchain-text-splitters": "text-splitters",
+              "langchain-chroma": "chroma",
+              "langchain-deepseek": "deepseek",
+              "langchain-exa": "exa",
+              "langchain-fireworks": "fireworks",
+              "langchain-groq": "groq",
+              "langchain-huggingface": "huggingface",
+              "langchain-mistralai": "mistralai",
+              "langchain-nomic": "nomic",
+              "langchain-ollama": "ollama",
+              "langchain-perplexity": "perplexity",
+              "langchain-prompty": "prompty",
+              "langchain-qdrant": "qdrant",
+              "langchain-xai": "xai",
+            };
+
+            // All possible package labels we manage
+            const allPackageLabels = Object.values(mapping);
+            const selectedLabels = [];
+
+            // Check if this is checkbox format (multiple selection)
+            const checkboxMatches = packageSection.match(/- \[x\]\s+([^\n\r]+)/gi);
+            if (checkboxMatches) {
+              // Handle checkbox format
+              for (const match of checkboxMatches) {
+                const packageName = match.replace(/- \[x\]\s+/i, '').trim();
+                const label = mapping[packageName];
+                if (label && !selectedLabels.includes(label)) {
+                  selectedLabels.push(label);
+                }
+              }
+            } else {
+              // Handle dropdown format (single selection)
+              const label = mapping[packageSection];
+              if (label) {
+                selectedLabels.push(label);
+              }
+            }
+
+            // Get current issue labels
+            const issue = await github.rest.issues.get({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number
+            });
+
+            const currentLabels = issue.data.labels.map(label => label.name);
+            const currentPackageLabels = currentLabels.filter(label => allPackageLabels.includes(label));
+
+            // Determine labels to add and remove
+            const labelsToAdd = selectedLabels.filter(label => !currentPackageLabels.includes(label));
+            const labelsToRemove = currentPackageLabels.filter(label => !selectedLabels.includes(label));
+
+            // Add new labels
+            if (labelsToAdd.length > 0) {
+              await github.rest.issues.addLabels({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                labels: labelsToAdd
+              });
+            }
+
+            // Remove old labels
+            for (const label of labelsToRemove) {
+              await github.rest.issues.removeLabel({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                name: label
+              });
+            }
--- a/.github/workflows/check_core_versions.yml
+++ b/.github/workflows/check_core_versions.yml
@@ -18,7 +18,7 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: "✅ Verify pyproject.toml & version.py Match"
        run: |
--- a/.github/workflows/check_diffs.yml
+++ b/.github/workflows/check_diffs.yml
@@ -47,7 +47,7 @@ jobs:
    if: ${{ !contains(github.event.pull_request.labels.*.name, 'ci-ignore') }}
    steps:
      - name: "📋 Checkout Code"
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
      - name: "🐍 Setup Python 3.11"
        uses: actions/setup-python@v6
        with:
@@ -141,7 +141,7 @@ jobs:
      run:
        working-directory: ${{ matrix.job-configs.working-directory }}
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

      - name: "🐍 Set up Python ${{ matrix.job-configs.python-version }} + UV"
        uses: "./.github/actions/uv_setup"
@@ -182,17 +182,16 @@ jobs:
        job-configs: ${{ fromJson(needs.build.outputs.codspeed) }}
      fail-fast: false
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6

-      # We have to use 3.12 as 3.13 is not yet supported
      - name: "📦 Install UV Package Manager"
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
        with:
-          python-version: "3.12"
+          python-version: "3.13"

      - uses: actions/setup-python@v6
        with:
-          python-version: "3.12"
+          python-version: "3.13"

      - name: "📦 Install Test Dependencies"
        run: uv sync --group test
--- a/.github/workflows/integration_tests.yml
+++ b/.github/workflows/integration_tests.yml
@@ -23,10 +23,8 @@ permissions:
  contents: read

 env:
-  POETRY_VERSION: "1.8.4"
  UV_FROZEN: "true"
  DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/xai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
-  POETRY_LIBS: ("libs/partners/aws")

 jobs:
  # Generate dynamic test matrix based on input parameters or defaults
@@ -60,7 +58,6 @@ jobs:
          echo $matrix
          echo "matrix=$matrix" >> $GITHUB_OUTPUT
  # Run integration tests against partner libraries with live API credentials
-  # Tests are run with Poetry or UV depending on the library's setup
  build:
    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
    name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.working-directory }}"
@@ -74,14 +71,14 @@ jobs:
        working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          path: langchain
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          repository: langchain-ai/langchain-google
          path: langchain-google
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          repository: langchain-ai/langchain-aws
          path: langchain-aws
@@ -95,17 +92,7 @@ jobs:
          mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
          mv langchain-aws/libs/aws langchain/libs/partners/aws

-      - name: "🐍 Set up Python ${{ matrix.python-version }} + Poetry"
-        if: contains(env.POETRY_LIBS, matrix.working-directory)
-        uses: "./langchain/.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: langchain/${{ matrix.working-directory }}
-          cache-key: scheduled
-
      - name: "🐍 Set up Python ${{ matrix.python-version }} + UV"
-        if: "!contains(env.POETRY_LIBS, matrix.working-directory)"
        uses: "./langchain/.github/actions/uv_setup"
        with:
          python-version: ${{ matrix.python-version }}
@@ -123,15 +110,7 @@ jobs:
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: ${{ secrets.AWS_REGION }}

-      - name: "📦 Install Dependencies (Poetry)"
-        if: contains(env.POETRY_LIBS, matrix.working-directory)
-        run: |
-          echo "Running scheduled tests, installing dependencies with poetry..."
-          cd langchain/${{ matrix.working-directory }}
-          poetry install --with=test_integration,test
-
-      - name: "📦 Install Dependencies (UV)"
-        if: "!contains(env.POETRY_LIBS, matrix.working-directory)"
+      - name: "📦 Install Dependencies"
        run: |
          echo "Running scheduled tests, installing dependencies with uv..."
          cd langchain/${{ matrix.working-directory }}
@@ -176,6 +155,7 @@ jobs:
          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
+          LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
        run: |
          cd langchain/${{ matrix.working-directory }}
          make integration_tests
--- a/.github/workflows/pr_lint.yml
+++ b/.github/workflows/pr_lint.yml
@@ -26,11 +26,13 @@
 #   * revert     — reverts a previous commit
 #   * release    — prepare a new release
 #
-# Allowed Scopes (optional):
-#   core, cli, langchain, langchain_v1, langchain_legacy, standard-tests,
-#   text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq,
-#   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
-#   xai, infra
+# Allowed Scope(s) (optional):
+#   core, cli, langchain, langchain-classic, model-profiles,
+#   standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa,
+#   fireworks, groq, huggingface, mistralai, nomic, ollama, openai,
+#   perplexity, prompty, qdrant, xai, infra, deps
+#
+# Multiple scopes can be used by separating them with a comma.
 #
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.
@@ -79,8 +81,8 @@ jobs:
            core
            cli
            langchain
-            langchain_v1
-            langchain_legacy
+            langchain-classic
+            model-profiles
            standard-tests
            text-splitters
            docs
@@ -100,6 +102,7 @@ jobs:
            qdrant
            xai
            infra
+            deps
          requireScope: false
          disallowScopes: |
            release
--- a/.github/workflows/v03_api_doc_build.yml
+++ b/.github/workflows/v03_api_doc_build.yml
@@ -23,12 +23,12 @@ jobs:
    permissions:
      contents: read
    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          ref: v0.3
          path: langchain

-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
        with:
          repository: langchain-ai/langchain-api-docs-html
          path: langchain-api-docs-html
--- a/.github/workflows/v1_changes.md
+++ b/.github/workflows/v1_changes.md
@@ -1,8 +0,0 @@
-With the deprecation of v0 docs, the following files will need to be migrated/supported
-in the new docs repo:
-
- run_notebooks.yml: New repo should run Integration tests on code snippets?
- people.yml: Need to fix and somehow display on the new docs site
-  - Subsequently, `.github/actions/people/`
- _test_doc_imports.yml
- check-broken-links.yml
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,8 @@
 .vs/
 .claude/
 .idea/
+#Emacs backup
+*~
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -161,3 +163,6 @@ node_modules

 prof
 virtualenv/
+scratch/
+
+.langgraph_api/
--- a/.mcp.json
+++ b/.mcp.json
@@ -0,0 +1,8 @@
+{
+  "mcpServers": {
+    "docs-langchain": {
+      "type": "http",
+      "url": "https://docs.langchain.com/mcp"
+    }
+  }
+}
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,4 +1,24 @@
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: no-commit-to-branch # prevent direct commits to protected branches
+        args: ["--branch", "master"]
+      - id: check-yaml # validate YAML syntax
+        args: ["--unsafe"] # allow custom tags
+      - id: check-toml # validate TOML syntax
+      - id: end-of-file-fixer # ensure files end with a newline
+      - id: trailing-whitespace # remove trailing whitespace from lines
+        exclude: \.ambr$
+
+  # Text normalization hooks for consistent formatting
+  - repo: https://github.com/sirosen/texthooks
+    rev: 0.6.8
+    hooks:
+      - id: fix-smartquotes # replace curly quotes with straight quotes
+      - id: fix-spaces # replace non-standard spaces (e.g., non-breaking) with regular spaces
+
+  # Per-package format and lint hooks for the monorepo
  - repo: local
    hooks:
      - id: core
--- a/.vscode/extensions.json
+++ b/.vscode/extensions.json
@@ -6,8 +6,6 @@
    "ms-toolsai.jupyter",
    "ms-toolsai.jupyter-keymap",
    "ms-toolsai.jupyter-renderers",
-    "ms-toolsai.vscode-jupyter-cell-tags",
-    "ms-toolsai.vscode-jupyter-slideshow",
    "yzhang.markdown-all-in-one",
    "davidanson.vscode-markdownlint",
    "bierner.markdown-mermaid",
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -1,253 +1,58 @@
-# Global Development Guidelines for LangChain Projects
+# Global development guidelines for the LangChain monorepo

-## Core Development Principles
+This document provides context to understand the LangChain Python project and assist with development.

-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
+## Project architecture and context

-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
+### Monorepo structure

-❌ **Bad - Breaking Change:**
+This is a Python monorepo with multiple independently versioned packages that use `uv`.

-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
+```txt
+langchain/
+├── libs/
+│   ├── core/             # `langchain-core` primitives and base abstractions
+│   ├── langchain/        # `langchain-classic` (legacy, no new features)
+│   ├── langchain_v1/     # Actively maintained `langchain` package
+│   ├── partners/         # Third-party integrations
+│   │   ├── openai/       # OpenAI models and embeddings
+│   │   ├── anthropic/    # Anthropic (Claude) integration
+│   │   ├── ollama/       # Local model support
+│   │   └── ... (other integrations maintained by the LangChain team)
+│   ├── text-splitters/   # Document chunking utilities
+│   ├── standard-tests/   # Shared test suite for integrations
+│   ├── model-profiles/   # Model configuration profiles
+│   └── cli/              # Command-line interface tools
+├── .github/              # CI/CD workflows and templates
+├── .vscode/              # VSCode IDE standard settings and recommended extensions
+└── README.md             # Information about LangChain
 ```

-✅ **Good - Stable Interface:**
+- **Core layer** (`langchain-core`): Base abstractions, interfaces, and protocols. Users should not need to know about this layer directly.
+- **Implementation layer** (`langchain`): Concrete implementations and high-level public utilities
+- **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
+- **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations

-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
+### Development tools & commands**

-**Before making ANY changes to public APIs:**
+- `uv` – Fast Python package installer and resolver (replaces pip/poetry)
+- `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
+- `ruff` – Fast Python linter and formatter
+- `mypy` – Static type checking
+- `pytest` – Testing framework

- Check if the function/class is exported in `__init__.py`
- Look for existing usage patterns in tests and examples
- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`

-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
- Avoid unnecessary abstraction or premature optimization
- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
- Unit tests: `tests/unit_tests/` (no network calls allowed)
- Integration tests: `tests/integration_tests/` (network calls permitted)
- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
- [ ] Tests fail when your new logic is broken
- [ ] Happy path is covered
- [ ] Edge cases and error conditions are tested
- [ ] Use fixtures/mocks for external dependencies
- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
- [ ] Does the test suite fail if your new logic is broken?
- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
- No `eval()`, `exec()`, or `pickle` on user-controlled input
- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
- Remove unreachable/commented code before committing
- Race conditions or resource leaks (file handles, sockets, threads).
- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args section for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
-
-    Returns:
-        True if email was sent successfully, False otherwise.
-
-    Raises:
-        InvalidEmailError: If the email address format is invalid.
-        SMTPConnectionError: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
- Types go in function signatures, NOT in docstrings
- Focus on "why" rather than "what" in descriptions
- Document all parameters, return values, and exceptions
- Keep descriptions concise but clear
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications."""
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
- Reduce code duplication through shared utilities
- Make unit testing easier
- Improve separation of concerns (single responsibility)
- Make unit testing easier through dependency injection
- Add clarity without adding complexity
- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
+Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.

 ```bash
 # Run unit tests (no network)
 make test

-# Don't run integration tests, as API keys must be set
-
 # Run specific test file
 uv run --group test pytest tests/unit_tests/test_specific.py
 ```

-### Code Quality
-
 ```bash
 # Lint code
 make lint
@@ -259,66 +64,118 @@ make format
 uv run --group lint mypy .
 ```

-### Dependency Management Patterns
+#### Key config files

-**Local Development Dependencies:**
+- pyproject.toml: Main workspace configuration with dependency groups
+- uv.lock: Locked dependencies for reproducible builds
+- Makefile: Development tasks

-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
+#### Commit standards

-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
+Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.

-```python
-from langchain_core.tools import tool
+#### Pull request guidelines

-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
+- Always add a disclaimer to the PR description mentioning how AI agents are involved with the contribution.
+- Describe the "why" of the changes, why the proposed solution is the right one. Limit prose.
+- Highlight areas of the proposed changes that require careful review.
+
+## Core development principles
+
+### Maintain stable public interfaces
+
+CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.
+
+**Before making ANY changes to public APIs:**
+
+- Check if the function/class is exported in `__init__.py`
+- Look for existing usage patterns in tests and examples
+- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
+- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+
+Ask: "Would this change break someone's code if they used it last week?"
+
+### Code quality standards
+
+All Python code MUST include type hints and return types.
+
+```python title="Example"
+def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
+    """Single line description of the function.
+
+    Any additional context about the function can go here.

    Args:
-        query: The search query string.
+        users: List of user identifiers to filter.
+        known_users: Set of known/valid user identifiers.
+
+    Returns:
+        List of users that are not in the known_users set.
    """
-    # Implementation here
-    return results
 ```

-## Commit Standards
+- Use descriptive, self-explanatory variable names.
+- Follow existing patterns in the codebase you're modifying
+- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense

-**Use Conventional Commits format for PR titles:**
+### Testing requirements

- `feat(core): add multi-tenant support`
- `fix(cli): resolve flag parsing error`
- `docs: update API usage examples`
- `docs(openai): update API usage examples`
+Every new feature or bugfix MUST be covered by unit tests.

-## Framework-Specific Guidelines
+- Unit tests: `tests/unit_tests/` (no network calls allowed)
+- Integration tests: `tests/integration_tests/` (network calls permitted)
+- We use `pytest` as the testing framework; if in doubt, check other existing tests for examples.
+- The testing file structure should mirror the source code structure.

- Follow the existing patterns in `langchain-core` for base abstractions
- Use `langchain_core.callbacks` for execution tracking
- Implement proper streaming support where applicable
- Avoid deprecated components like legacy `LLMChain`
+**Checklist:**

-### Partner Integrations
+- [ ] Tests fail when your new logic is broken
+- [ ] Happy path is covered
+- [ ] Edge cases and error conditions are tested
+- [ ] Use fixtures/mocks for external dependencies
+- [ ] Tests are deterministic (no flaky tests)
+- [ ] Does the test suite fail if your new logic is broken?

- Follow the established patterns in existing partner libraries
- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
- Include comprehensive integration tests
- Document API key requirements and authentication
+### Security and risk assessment

---
+- No `eval()`, `exec()`, or `pickle` on user-controlled input
+- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
+- Remove unreachable/commented code before committing
+- Race conditions or resource leaks (file handles, sockets, threads).
+- Ensure proper resource cleanup (file handles, connections)

-## Quick Reference Checklist
+### Documentation standards

-Before submitting code changes:
+Use Google-style docstrings with Args section for all public functions.

- [ ] **Breaking Changes**: Verified no public API changes
- [ ] **Type Hints**: All functions have complete type annotations
- [ ] **Tests**: New functionality is fully tested
- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
- [ ] **Documentation**: Google-style docstrings for public functions
- [ ] **Code Quality**: `make lint` and `make format` pass
- [ ] **Architecture**: Suggested improvements where applicable
- [ ] **Commit Message**: Follows Conventional Commits format
+```python title="Example"
+def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
+    """Send an email to a recipient with specified priority.
+
+    Any additional context about the function can go here.
+
+    Args:
+        to: The email address of the recipient.
+        msg: The message body to send.
+        priority: Email priority level.
+
+    Returns:
+        `True` if email was sent successfully, `False` otherwise.
+
+    Raises:
+        InvalidEmailError: If the email address format is invalid.
+        SMTPConnectionError: If unable to connect to email server.
+    """
+```
+
+- Types go in function signatures, NOT in docstrings
+  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
+- Focus on "why" rather than "what" in descriptions
+- Document all parameters, return values, and exceptions
+- Keep descriptions concise but clear
+- Ensure American English spelling (e.g., "behavior", not "behaviour")
+
+## Additional resources
+
+- **Documentation:** https://docs.langchain.com/oss/python/langchain/overview and source at https://github.com/langchain-ai/docs or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
+- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -1,253 +1,58 @@
-# Global Development Guidelines for LangChain Projects
+# Global development guidelines for the LangChain monorepo

-## Core Development Principles
+This document provides context to understand the LangChain Python project and assist with development.

-### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
+## Project architecture and context

-**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
+### Monorepo structure

-❌ **Bad - Breaking Change:**
+This is a Python monorepo with multiple independently versioned packages that use `uv`.

-```python
-def get_user(id, verbose=False):  # Changed from `user_id`
-    pass
+```txt
+langchain/
+├── libs/
+│   ├── core/             # `langchain-core` primitives and base abstractions
+│   ├── langchain/        # `langchain-classic` (legacy, no new features)
+│   ├── langchain_v1/     # Actively maintained `langchain` package
+│   ├── partners/         # Third-party integrations
+│   │   ├── openai/       # OpenAI models and embeddings
+│   │   ├── anthropic/    # Anthropic (Claude) integration
+│   │   ├── ollama/       # Local model support
+│   │   └── ... (other integrations maintained by the LangChain team)
+│   ├── text-splitters/   # Document chunking utilities
+│   ├── standard-tests/   # Shared test suite for integrations
+│   ├── model-profiles/   # Model configuration profiles
+│   └── cli/              # Command-line interface tools
+├── .github/              # CI/CD workflows and templates
+├── .vscode/              # VSCode IDE standard settings and recommended extensions
+└── README.md             # Information about LangChain
 ```

-✅ **Good - Stable Interface:**
+- **Core layer** (`langchain-core`): Base abstractions, interfaces, and protocols. Users should not need to know about this layer directly.
+- **Implementation layer** (`langchain`): Concrete implementations and high-level public utilities
+- **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
+- **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations

-```python
-def get_user(user_id: str, verbose: bool = False) -> User:
-    """Retrieve user by ID with optional verbose output."""
-    pass
-```
+### Development tools & commands**

-**Before making ANY changes to public APIs:**
+- `uv` – Fast Python package installer and resolver (replaces pip/poetry)
+- `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
+- `ruff` – Fast Python linter and formatter
+- `mypy` – Static type checking
+- `pytest` – Testing framework

- Check if the function/class is exported in `__init__.py`
- Look for existing usage patterns in tests and examples
- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`

-🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
-
-### 2. Code Quality Standards
-
-**All Python code MUST include type hints and return types.**
-
-❌ **Bad:**
-
-```python
-def p(u, d):
-    return [x for x in u if x not in d]
-```
-
-✅ **Good:**
-
-```python
-def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
-    """Filter out users that are not in the known users set.
-
-    Args:
-        users: List of user identifiers to filter.
-        known_users: Set of known/valid user identifiers.
-
-    Returns:
-        List of users that are not in the known_users set.
-    """
-    return [user for user in users if user not in known_users]
-```
-
-**Style Requirements:**
-
- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
- Avoid unnecessary abstraction or premature optimization
- Follow existing patterns in the codebase you're modifying
-
-### 3. Testing Requirements
-
-**Every new feature or bugfix MUST be covered by unit tests.**
-
-**Test Organization:**
-
- Unit tests: `tests/unit_tests/` (no network calls allowed)
- Integration tests: `tests/integration_tests/` (network calls permitted)
- Use `pytest` as the testing framework
-
-**Test Quality Checklist:**
-
- [ ] Tests fail when your new logic is broken
- [ ] Happy path is covered
- [ ] Edge cases and error conditions are tested
- [ ] Use fixtures/mocks for external dependencies
- [ ] Tests are deterministic (no flaky tests)
-
-Checklist questions:
-
- [ ] Does the test suite fail if your new logic is broken?
- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
- [ ] Do tests use fixtures or mocks where needed?
-
-```python
-def test_filter_unknown_users():
-    """Test filtering unknown users from a list."""
-    users = ["alice", "bob", "charlie"]
-    known_users = {"alice", "bob"}
-
-    result = filter_unknown_users(users, known_users)
-
-    assert result == ["charlie"]
-    assert len(result) == 1
-```
-
-### 4. Security and Risk Assessment
-
-**Security Checklist:**
-
- No `eval()`, `exec()`, or `pickle` on user-controlled input
- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
- Remove unreachable/commented code before committing
- Race conditions or resource leaks (file handles, sockets, threads).
- Ensure proper resource cleanup (file handles, connections)
-
-❌ **Bad:**
-
-```python
-def load_config(path):
-    with open(path) as f:
-        return eval(f.read())  # ⚠️ Never eval config
-```
-
-✅ **Good:**
-
-```python
-import json
-
-def load_config(path: str) -> dict:
-    with open(path) as f:
-        return json.load(f)
-```
-
-### 5. Documentation Standards
-
-**Use Google-style docstrings with Args section for all public functions.**
-
-❌ **Insufficient Documentation:**
-
-```python
-def send_email(to, msg):
-    """Send an email to a recipient."""
-```
-
-✅ **Complete Documentation:**
-
-```python
-def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
-    """
-    Send an email to a recipient with specified priority.
-
-    Args:
-        to: The email address of the recipient.
-        msg: The message body to send.
-        priority: Email priority level (`'low'`, `'normal'`, `'high'`).
-
-    Returns:
-        True if email was sent successfully, False otherwise.
-
-    Raises:
-        InvalidEmailError: If the email address format is invalid.
-        SMTPConnectionError: If unable to connect to email server.
-    """
-```
-
-**Documentation Guidelines:**
-
- Types go in function signatures, NOT in docstrings
- Focus on "why" rather than "what" in descriptions
- Document all parameters, return values, and exceptions
- Keep descriptions concise but clear
-
-📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
-
-### 6. Architectural Improvements
-
-**When you encounter code that could be improved, suggest better designs:**
-
-❌ **Poor Design:**
-
-```python
-def process_data(data, db_conn, email_client, logger):
-    # Function doing too many things
-    validated = validate_data(data)
-    result = db_conn.save(validated)
-    email_client.send_notification(result)
-    logger.log(f"Processed {len(data)} items")
-    return result
-```
-
-✅ **Better Design:**
-
-```python
-@dataclass
-class ProcessingResult:
-    """Result of data processing operation."""
-    items_processed: int
-    success: bool
-    errors: List[str] = field(default_factory=list)
-
-class DataProcessor:
-    """Handles data validation, storage, and notification."""
-
-    def __init__(self, db_conn: Database, email_client: EmailClient):
-        self.db = db_conn
-        self.email = email_client
-
-    def process(self, data: List[dict]) -> ProcessingResult:
-        """Process and store data with notifications."""
-        validated = self._validate_data(data)
-        result = self.db.save(validated)
-        self._notify_completion(result)
-        return result
-```
-
-**Design Improvement Areas:**
-
-If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
-
- Reduce code duplication through shared utilities
- Make unit testing easier
- Improve separation of concerns (single responsibility)
- Make unit testing easier through dependency injection
- Add clarity without adding complexity
- Prefer dataclasses for structured data
-
-## Development Tools & Commands
-
-### Package Management
-
-```bash
-# Add package
-uv add package-name
-
-# Sync project dependencies
-uv sync
-uv lock
-```
-
-### Testing
+Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.

 ```bash
 # Run unit tests (no network)
 make test

-# Don't run integration tests, as API keys must be set
-
 # Run specific test file
 uv run --group test pytest tests/unit_tests/test_specific.py
 ```

-### Code Quality
-
 ```bash
 # Lint code
 make lint
@@ -259,66 +64,118 @@ make format
 uv run --group lint mypy .
 ```

-### Dependency Management Patterns
+#### Key config files

-**Local Development Dependencies:**
+- pyproject.toml: Main workspace configuration with dependency groups
+- uv.lock: Locked dependencies for reproducible builds
+- Makefile: Development tasks

-```toml
-[tool.uv.sources]
-langchain-core = { path = "../core", editable = true }
-langchain-tests = { path = "../standard-tests", editable = true }
-```
+#### Commit standards

-**For tools, use the `@tool` decorator from `langchain_core.tools`:**
+Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.

-```python
-from langchain_core.tools import tool
+#### Pull request guidelines

-@tool
-def search_database(query: str) -> str:
-    """Search the database for relevant information.
+- Always add a disclaimer to the PR description mentioning how AI agents are involved with the contribution.
+- Describe the "why" of the changes, why the proposed solution is the right one. Limit prose.
+- Highlight areas of the proposed changes that require careful review.
+
+## Core development principles
+
+### Maintain stable public interfaces
+
+CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.
+
+**Before making ANY changes to public APIs:**
+
+- Check if the function/class is exported in `__init__.py`
+- Look for existing usage patterns in tests and examples
+- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
+- Mark experimental features clearly with docstring warnings (using MkDocs Material admonitions, like `!!! warning`)
+
+Ask: "Would this change break someone's code if they used it last week?"
+
+### Code quality standards
+
+All Python code MUST include type hints and return types.
+
+```python title="Example"
+def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
+    """Single line description of the function.
+
+    Any additional context about the function can go here.

    Args:
-        query: The search query string.
+        users: List of user identifiers to filter.
+        known_users: Set of known/valid user identifiers.
+
+    Returns:
+        List of users that are not in the known_users set.
    """
-    # Implementation here
-    return results
 ```

-## Commit Standards
+- Use descriptive, self-explanatory variable names.
+- Follow existing patterns in the codebase you're modifying
+- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense

-**Use Conventional Commits format for PR titles:**
+### Testing requirements

- `feat(core): add multi-tenant support`
- `fix(cli): resolve flag parsing error`
- `docs: update API usage examples`
- `docs(openai): update API usage examples`
+Every new feature or bugfix MUST be covered by unit tests.

-## Framework-Specific Guidelines
+- Unit tests: `tests/unit_tests/` (no network calls allowed)
+- Integration tests: `tests/integration_tests/` (network calls permitted)
+- We use `pytest` as the testing framework; if in doubt, check other existing tests for examples.
+- The testing file structure should mirror the source code structure.

- Follow the existing patterns in `langchain-core` for base abstractions
- Use `langchain_core.callbacks` for execution tracking
- Implement proper streaming support where applicable
- Avoid deprecated components like legacy `LLMChain`
+**Checklist:**

-### Partner Integrations
+- [ ] Tests fail when your new logic is broken
+- [ ] Happy path is covered
+- [ ] Edge cases and error conditions are tested
+- [ ] Use fixtures/mocks for external dependencies
+- [ ] Tests are deterministic (no flaky tests)
+- [ ] Does the test suite fail if your new logic is broken?

- Follow the established patterns in existing partner libraries
- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
- Include comprehensive integration tests
- Document API key requirements and authentication
+### Security and risk assessment

---
+- No `eval()`, `exec()`, or `pickle` on user-controlled input
+- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
+- Remove unreachable/commented code before committing
+- Race conditions or resource leaks (file handles, sockets, threads).
+- Ensure proper resource cleanup (file handles, connections)

-## Quick Reference Checklist
+### Documentation standards

-Before submitting code changes:
+Use Google-style docstrings with Args section for all public functions.

- [ ] **Breaking Changes**: Verified no public API changes
- [ ] **Type Hints**: All functions have complete type annotations
- [ ] **Tests**: New functionality is fully tested
- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
- [ ] **Documentation**: Google-style docstrings for public functions
- [ ] **Code Quality**: `make lint` and `make format` pass
- [ ] **Architecture**: Suggested improvements where applicable
- [ ] **Commit Message**: Follows Conventional Commits format
+```python title="Example"
+def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
+    """Send an email to a recipient with specified priority.
+
+    Any additional context about the function can go here.
+
+    Args:
+        to: The email address of the recipient.
+        msg: The message body to send.
+        priority: Email priority level.
+
+    Returns:
+        `True` if email was sent successfully, `False` otherwise.
+
+    Raises:
+        InvalidEmailError: If the email address format is invalid.
+        SMTPConnectionError: If unable to connect to email server.
+    """
+```
+
+- Types go in function signatures, NOT in docstrings
+  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
+- Focus on "why" rather than "what" in descriptions
+- Document all parameters, return values, and exceptions
+- Keep descriptions concise but clear
+- Ensure American English spelling (e.g., "behavior", not "behaviour")
+
+## Additional resources
+
+- **Documentation:** https://docs.langchain.com/oss/python/langchain/overview and source at https://github.com/langchain-ai/docs or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
+- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)
--- a/MIGRATE.md
+++ b/MIGRATE.md
@@ -1,8 +0,0 @@
-# Migrating
-
-Please see the following guides for migrating LangChain code:
-
-* Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
-* Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
-* Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
-* Upgrade to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)
--- a/README.md
+++ b/README.md
@@ -1,47 +1,43 @@
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-dark.svg">
-    <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-light.svg">
-    <img alt="LangChain Logo" src=".github/images/logo-dark.svg" width="80%">
-  </picture>
-</p>
+<div align="center">
+  <a href="https://www.langchain.com/">
+    <picture>
+      <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-dark.svg">
+      <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-light.svg">
+      <img alt="LangChain Logo" src=".github/images/logo-dark.svg" width="80%">
+    </picture>
+  </a>
+</div>

-<p align="center">
-    The platform for reliable agents.
-</p>
+<div align="center">
+  <h3>The platform for reliable agents.</h3>
+</div>

-<p align="center">
-  <a href="https://opensource.org/licenses/MIT" target="_blank">
-      <img src="https://img.shields.io/pypi/l/langchain-core?style=flat-square" alt="PyPI - License">
-  </a>
-  <a href="https://pypistats.org/packages/langchain-core" target="_blank">
-      <img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads">
-  </a>
-  <a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank">
-      <img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square" alt="Open in Dev Containers">
-  </a>
-  <a href="https://codespaces.new/langchain-ai/langchain" target="_blank">
-      <img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">
-  </a>
-  <a href="https://codspeed.io/langchain-ai/langchain" target="_blank">
-      <img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed Badge">
-  </a>
-  <a href="https://twitter.com/langchainai" target="_blank">
-      <img src="https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI" alt="Twitter / X">
-  </a>
-</p>
+<div align="center">
+  <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License"></a>
+  <a href="https://pypistats.org/packages/langchain" target="_blank"><img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads"></a>
+  <a href="https://pypi.org/project/langchain/#history" target="_blank"><img src="https://img.shields.io/pypi/v/langchain?label=%20" alt="Version"></a>
+  <a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank"><img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode" alt="Open in Dev Containers"></a>
+  <a href="https://codespaces.new/langchain-ai/langchain" target="_blank"><img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20"></a>
+  <a href="https://codspeed.io/langchain-ai/langchain" target="_blank"><img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed Badge"></a>
+  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
+</div>

-LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
+LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development – all while future-proofing decisions as the underlying technology evolves.

 ```bash
 pip install langchain
 ```

+If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
+
 ---

-**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview).
+**Documentation**:

-If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows.
+- [docs.langchain.com](https://docs.langchain.com/oss/python/langchain/overview) – Comprehensive documentation, including conceptual overviews and guides
+- [reference.langchain.com/python](https://reference.langchain.com/python) – API reference docs for LangChain packages
+
+**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.

 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
@@ -52,23 +48,28 @@ LangChain helps developers build applications powered by LLMs through a standard

 Use LangChain for:

- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
+- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more.
+- **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly – LangChain's abstractions keep you moving without losing momentum.
+- **Rapid prototyping**. Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle.
+- **Production-ready features**. Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices.
+- **Vibrant community and ecosystem**. Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community.
+- **Flexible abstraction layers**. Work at the level of abstraction that suits your needs - from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity.

-## LangChain’s ecosystem
+## LangChain ecosystem

 While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.

 To improve your LLM application development, pair LangChain with:

- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio).
+- [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
+- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview) – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
+- [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio).
+- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks

 ## Additional resources

- [Learn](https://docs.langchain.com/oss/python/learn): Use cases, conceptual overviews, and more.
- [API Reference](https://reference.langchain.com/python): Detailed reference on
-navigating base packages and integrations for LangChain.
- [LangChain Forum](https://forum.langchain.com): Connect with the community and share all of your technical questions, ideas, and feedback.
- [Chat LangChain](https://chat.langchain.com): Ask questions & chat with our documentation.
+- [API Reference](https://reference.langchain.com/python) – Detailed reference on navigating base packages and integrations for LangChain.
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues.
+- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – Our community guidelines and standards for participation.
+- [LangChain Academy](https://academy.langchain.com/) – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -1,80 +0,0 @@
-# Security Policy
-
-LangChain has a large ecosystem of integrations with various external resources like local and remote file systems, APIs and databases. These integrations allow developers to create versatile applications that combine the power of LLMs with the ability to access, interact with and manipulate external resources.
-
-## Best practices
-
-When building such applications, developers should remember to follow good security practices:
-
-* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc., as appropriate for your application.
-* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it's safest to assume that any LLM able to use those credentials may in fact delete data.
-* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It's best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
-
-Risks of not doing so include, but are not limited to:
-
-* Data corruption or loss.
-* Unauthorized access to confidential information.
-* Compromised performance or availability of critical resources.
-
-Example scenarios with mitigation strategies:
-
-* A user may ask an agent with access to the file system to delete files that should not be deleted or read the content of files that contain sensitive information. To mitigate, limit the agent to only use a specific directory and only allow it to read or write files that are safe to read or write. Consider further sandboxing the agent by running it in a container.
-* A user may ask an agent with write access to an external API to write malicious data to the API, or delete data from that API. To mitigate, give the agent read-only API keys, or limit it to only use endpoints that are already resistant to such misuse.
-* A user may ask an agent with access to a database to drop a table or mutate the schema. To mitigate, scope the credentials to only the tables that the agent needs to access and consider issuing READ-ONLY credentials.
-
-If you're building applications that access external resources like file systems, APIs or databases, consider speaking with your company's security team to determine how to best design and secure your applications.
-
-## Reporting OSS Vulnerabilities
-
-LangChain is partnered with [huntr by Protect AI](https://huntr.com/) to provide
-a bounty program for our open source projects.
-
-Please report security vulnerabilities associated with the LangChain
-open source projects at [huntr](https://huntr.com/bounties/disclose/?target=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain&validSearch=true).
-
-Before reporting a vulnerability, please review:
-
-1) In-Scope Targets and Out-of-Scope Targets below.
-2) The [langchain-ai/langchain](https://docs.langchain.com/oss/python/contributing/code#repository-structure) monorepo structure.
-3) The [Best Practices](#best-practices) above to understand what we consider to be a security vulnerability vs. developer responsibility.
-
-### In-Scope Targets
-
-The following packages and repositories are eligible for bug bounties:
-
-* langchain-core
-* langchain (see exceptions)
-* langchain-community (see exceptions)
-* langgraph
-* langserve
-
-### Out of Scope Targets
-
-All out of scope targets defined by huntr as well as:
-
-* **langchain-experimental**: This repository is for experimental code and is not
-  eligible for bug bounties (see [package warning](https://pypi.org/project/langchain-experimental/)), bug reports to it will be marked as interesting or waste of
-  time and published with no bounty attached.
-* **tools**: Tools in either langchain or langchain-community are not eligible for bug
-  bounties. This includes the following directories
-  * libs/langchain/langchain/tools
-  * libs/community/langchain_community/tools
-  * Please review the [Best Practices](#best-practices)
-    for more details, but generally tools interact with the real world. Developers are
-    expected to understand the security implications of their code and are responsible
-    for the security of their tools.
-* Code documented with security notices. This will be decided on a case-by-case basis, but likely will not be eligible for a bounty as the code is already
-  documented with guidelines for developers that should be followed for making their
-  application secure.
-* Any LangSmith related repositories or APIs (see [Reporting LangSmith Vulnerabilities](#reporting-langsmith-vulnerabilities)).
-
-## Reporting LangSmith Vulnerabilities
-
-Please report security vulnerabilities associated with LangSmith by email to `security@langchain.dev`.
-
-* LangSmith site: [https://smith.langchain.com](https://smith.langchain.com)
-* SDK client: [https://github.com/langchain-ai/langsmith-sdk](https://github.com/langchain-ai/langsmith-sdk)
-
-### Other Security Concerns
-
-For any other security concerns, please contact us at `security@langchain.dev`.
--- a/libs/Makefile
+++ b/libs/Makefile
@@ -0,0 +1,20 @@
+# Makefile for libs/ directory
+# Contains targets that operate across multiple packages
+
+LANGCHAIN_DIRS = core text-splitters langchain langchain_v1 model-profiles
+
+.PHONY: lock check-lock
+
+# Regenerate lockfiles for all core packages
+lock:
+	@for dir in $(LANGCHAIN_DIRS); do \
+		echo "=== Locking $$dir ==="; \
+		(cd $$dir && uv lock); \
+	done
+
+# Verify all lockfiles are up-to-date
+check-lock:
+	@for dir in $(LANGCHAIN_DIRS); do \
+		echo "=== Checking $$dir ==="; \
+		(cd $$dir && uv lock --check) || exit 1; \
+	done
--- a/libs/cli/README.md
+++ b/libs/cli/README.md
@@ -1,6 +1,30 @@
 # langchain-cli

-This package implements the official CLI for LangChain. Right now, it is most useful
-for getting started with LangChain Templates!
+[![PyPI - Version](https://img.shields.io/pypi/v/langchain-cli?label=%20)](https://pypi.org/project/langchain-cli/#history)
+[![PyPI - License](https://img.shields.io/pypi/l/langchain-cli)](https://opensource.org/licenses/MIT)
+[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-cli)](https://pypistats.org/packages/langchain-cli)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)
+
+## Quick Install
+
+```bash
+pip install langchain-cli
+```
+
+## 🤔 What is this?
+
+This package implements the official CLI for LangChain. Right now, it is most useful for getting started with LangChain Templates!
+
+## 📖 Documentation

 [CLI Docs](https://github.com/langchain-ai/langchain/blob/master/libs/cli/DOCS.md)
+
+## 📕 Releases & Versioning
+
+See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
+
+## 💁 Contributing
+
+As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+
+For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).
--- a/libs/cli/langchain_cli/integration_template/README.md
+++ b/libs/cli/langchain_cli/integration_template/README.md
@@ -19,8 +19,8 @@ And you should configure credentials by setting the following environment variab
 ```python
 from __module_name__ import Chat__ModuleName__

-llm = Chat__ModuleName__()
-llm.invoke("Sing a ballad of LangChain.")
+model = Chat__ModuleName__()
+model.invoke("Sing a ballad of LangChain.")
 ```

 ## Embeddings
@@ -41,6 +41,6 @@ embeddings.embed_query("What is the meaning of life?")
 ```python
 from __module_name__ import __ModuleName__LLM

-llm = __ModuleName__LLM()
-llm.invoke("The meaning of life is")
+model = __ModuleName__LLM()
+model.invoke("The meaning of life is")
 ```
--- a/libs/cli/langchain_cli/integration_template/docs/chat.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/chat.ipynb
@@ -1,262 +1,264 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# Chat__ModuleName__\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import Chat__ModuleName__\n",
+        "\n",
+        "model = Chat__ModuleName__(\n",
+        "    model=\"model-name\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = model.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | model\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this model provider\n",
+        "\n",
+        "E.g. creating/using finetuned models via this provider. Delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# Chat__ModuleName__\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your __ModuleName__ API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import Chat__ModuleName__\n",
-    "\n",
-    "llm = Chat__ModuleName__(\n",
-    "    model=\"model-name\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this model provider\n",
-    "\n",
-    "E.g. creating/using finetuned models via this provider. Delete if not relevant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/libs/cli/langchain_cli/integration_template/docs/llms.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/llms.ipynb
@@ -1,236 +1,238 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "67db2992",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "67db2992",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9597802c",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__LLM\n",
+        "\n",
+        "- [ ] TODO: Make sure API reference link is correct\n",
+        "\n",
+        "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bc51e756",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4b6e1ca6",
+      "metadata": {},
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "196c2b41",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "809c6577",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "59c710c4",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0a760037",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a0562a13",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__LLM\n",
+        "\n",
+        "model = __ModuleName__LLM(\n",
+        "    model=\"model-name\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0ee90032",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "- [ ] TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "035dea0f",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "input_text = \"__ModuleName__ is an AI company that \"\n",
+        "\n",
+        "completion = model.invoke(input_text)\n",
+        "completion"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "add38532",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
+        "\n",
+        "- TODO: Run cells so output can be seen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "078e9db2",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import PromptTemplate\n",
+        "\n",
+        "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
+        "\n",
+        "chain = prompt | model\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e99eef30",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this model provider\n",
+        "\n",
+        "E.g. creating/using finetuned models via this provider. Delete if not relevant"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9bdfcef",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.11.1 64-bit",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+      }
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "9597802c",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__LLM\n",
-    "\n",
-    "- [ ] TODO: Make sure API reference link is correct\n",
-    "\n",
-    "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc51e756",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your __ModuleName__ API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b6e1ca6",
-   "metadata": {},
-   "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "196c2b41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "809c6577",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59c710c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a760037",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a0562a13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__LLM\n",
-    "\n",
-    "llm = __ModuleName__LLM(\n",
-    "    model=\"model-name\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee90032",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "- [ ] TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "input_text = \"__ModuleName__ is an AI company that \"\n",
-    "\n",
-    "completion = llm.invoke(input_text)\n",
-    "completion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "add38532",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "078e9db2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e99eef30",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this model provider\n",
-    "\n",
-    "E.g. creating/using finetuned models via this provider. Delete if not relevant"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9bdfcef",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.11.1 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/libs/cli/langchain_cli/integration_template/docs/retrievers.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/retrievers.ipynb
@@ -155,7 +155,7 @@
    "\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
   ]
  },
  {
@@ -185,7 +185,7 @@
    "chain = (\n",
    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
-    "    | llm\n",
+    "    | model\n",
    "    | StrOutputParser()\n",
    ")"
   ]
--- a/libs/cli/langchain_cli/integration_template/docs/stores.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/stores.ipynb
@@ -1,204 +1,204 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
+  "cells": [
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__ByteStore\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__ByteStore\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This will help you get started with __ModuleName__ [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all __ModuleName__ByteStore features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.__module_name__ByteStore.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/stores/in_memory/ for an example.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "- TODO: (Optional) A short introduction to the underlying technology/API.\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Fill in table features.\n",
+        "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+        "- TODO: Make sure API reference links are correct.\n",
+        "\n",
+        "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/_package_name_) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
+        "| [__ModuleName__ByteStore](https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__&label=%20) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Update with relevant info.\n",
+        "\n",
+        "To create a __ModuleName__ byte store, you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "- TODO: Update with relevant info, or omit if the service does not require any credentials.\n",
+        "\n",
+        "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
+        "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your __ModuleName__ API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU __package_name__"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our byte store:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from __module_name__ import __ModuleName__ByteStore\n",
+        "\n",
+        "kv_store = __ModuleName__ByteStore(\n",
+        "    # params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Usage\n",
+        "\n",
+        "- TODO: Run cells so output can be seen.\n",
+        "\n",
+        "You can set data under keys like this using the `mset` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "kv_store.mset(\n",
+        "    [\n",
+        "        [\"key1\", b\"value1\"],\n",
+        "        [\"key2\", b\"value2\"],\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "kv_store.mget(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And you can delete data using the `mdelete` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "kv_store.mdelete(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "kv_store.mget(\n",
+        "    [\n",
+        "        \"key1\",\n",
+        "        \"key2\",\n",
+        "    ]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## TODO: Any functionality specific to this key-value store provider\n",
+        "\n",
+        "E.g. extra initialization. Delete if not relevant."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__ByteStore features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.5"
    }
-   },
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__ByteStore\n",
-    "---"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__ByteStore\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This will help you get started with __ModuleName__ [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all __ModuleName__ByteStore features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.__module_name__ByteStore.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/stores/in_memory/ for an example.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "- TODO: (Optional) A short introduction to the underlying technology/API.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/_package_name_) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [__ModuleName__ByteStore](https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To create a __ModuleName__ byte store, you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info, or omit if the service does not require any credentials.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your __ModuleName__ API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our byte store:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__ByteStore\n",
-    "\n",
-    "kv_store = __ModuleName__ByteStore(\n",
-    "    # params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "- TODO: Run cells so output can be seen.\n",
-    "\n",
-    "You can set data under keys like this using the `mset` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kv_store.mset(\n",
-    "    [\n",
-    "        [\"key1\", b\"value1\"],\n",
-    "        [\"key2\", b\"value2\"],\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can delete data using the `mdelete` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kv_store.mdelete(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this key-value store provider\n",
-    "\n",
-    "E.g. extra initialization. Delete if not relevant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__ByteStore features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/libs/cli/langchain_cli/integration_template/docs/tools.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/tools.ipynb
@@ -1,271 +1,271 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "10238e62-3465-4973-9279-606cbb7ccf16",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "10238e62-3465-4973-9279-606cbb7ccf16",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: __ModuleName__\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a6f91f20",
+      "metadata": {},
+      "source": [
+        "# __ModuleName__\n",
+        "\n",
+        "- TODO: Make sure API reference link is correct.\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "- TODO: Make sure links and features are correct\n",
+        "\n",
+        "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/__module_name__) |  Package latest |\n",
+        "| :--- | :--- | :---: | :---: | :---: |\n",
+        "| [__ModuleName__](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community&label=%20) |\n",
+        "\n",
+        "### Tool features\n",
+        "\n",
+        "- TODO: Add feature table if it makes sense\n",
+        "\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- TODO: Add any additional deps\n",
+        "\n",
+        "The integration lives in the `langchain-community` package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f85b4089",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install --quiet -U langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b15e9266",
+      "metadata": {},
+      "source": [
+        "### Credentials\n",
+        "\n",
+        "- TODO: Add any credentials that are needed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
+        "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
+      "metadata": {},
+      "source": [
+        "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "- TODO: Fill in instantiation params\n",
+        "\n",
+        "Here we show how to instantiate an instance of the __ModuleName__ tool, with "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.tools import __ModuleName__\n",
+        "\n",
+        "\n",
+        "tool = __ModuleName__(...)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "74147a1a",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
+        "\n",
+        "- TODO: Describe what the tool args are, fill them in, run cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tool.invoke({...})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d6e73897",
+      "metadata": {},
+      "source": [
+        "### [Invoke with ToolCall](/docs/concepts/tool_calling/#tool-execution)\n",
+        "\n",
+        "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
+        "\n",
+        "- TODO: Fill in tool args and run cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f90e33a7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
+        "model_generated_tool_call = {\n",
+        "    \"args\": {...},  # TODO: FILL IN\n",
+        "    \"id\": \"1\",\n",
+        "    \"name\": tool.name,\n",
+        "    \"type\": \"tool_call\",\n",
+        "}\n",
+        "tool.invoke(model_generated_tool_call)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "- TODO: Add user question and run cells\n",
+        "\n",
+        "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
+        "\n",
+        "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+        "\n",
+        "<ChatModelTabs customVarName=\"llm\" />\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# | output: false\n",
+        "# | echo: false\n",
+        "\n",
+        "# !pip install -qU langchain langchain-openai\n",
+        "from langchain.chat_models import init_chat_model\n",
+        "\n",
+        "model = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bea35fa1",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "tools = [tool]\n",
+        "agent = create_react_agent(model, tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "example_query = \"...\"\n",
+        "\n",
+        "events = agent.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4ac8146c",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "poetry-venv-311",
+      "language": "python",
+      "name": "poetry-venv-311"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "a6f91f20",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Make sure links and features are correct\n",
-    "\n",
-    "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/__module_name__) |  Package latest |\n",
-    "| :--- | :--- | :---: | :---: | :---: |\n",
-    "| [__ModuleName__](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Tool features\n",
-    "\n",
-    "- TODO: Add feature table if it makes sense\n",
-    "\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Add any additional deps\n",
-    "\n",
-    "The integration lives in the `langchain-community` package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f85b4089",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --quiet -U langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b15e9266",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
-    "\n",
-    "- TODO: Add any credentials that are needed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
-    "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
-   "metadata": {},
-   "source": [
-    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "- TODO: Fill in instantiation params\n",
-    "\n",
-    "Here we show how to instantiate an instance of the __ModuleName__ tool, with "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.tools import __ModuleName__\n",
-    "\n",
-    "\n",
-    "tool = __ModuleName__(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74147a1a",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
-    "\n",
-    "- TODO: Describe what the tool args are, fill them in, run cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool.invoke({...})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6e73897",
-   "metadata": {},
-   "source": [
-    "### [Invoke with ToolCall](/docs/concepts/tool_calling/#tool-execution)\n",
-    "\n",
-    "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
-    "\n",
-    "- TODO: Fill in tool args and run cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f90e33a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
-    "model_generated_tool_call = {\n",
-    "    \"args\": {...},  # TODO: FILL IN\n",
-    "    \"id\": \"1\",\n",
-    "    \"name\": tool.name,\n",
-    "    \"type\": \"tool_call\",\n",
-    "}\n",
-    "tool.invoke(model_generated_tool_call)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
-   "metadata": {},
-   "source": [
-    "## Use within an agent\n",
-    "\n",
-    "- TODO: Add user question and run cells\n",
-    "\n",
-    "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
-    "\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "# !pip install -qU langchain langchain-openai\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "\n",
-    "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bea35fa1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "tools = [tool]\n",
-    "agent = create_react_agent(llm, tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "example_query = \"...\"\n",
-    "\n",
-    "events = agent.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ac8146c",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-311",
-   "language": "python",
-   "name": "poetry-venv-311"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb
@@ -295,7 +295,7 @@
   "source": [
    "## TODO: Any functionality specific to this vector store\n",
    "\n",
-    "E.g. creating a persisten database to save to your disk, etc."
+    "E.g. creating a persistent database to save to your disk, etc."
   ]
  },
  {
--- a/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py
+++ b/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py
@@ -36,20 +36,20 @@ class Chat__ModuleName__(BaseChatModel):

    # TODO: Populate with relevant params.
    Key init args — completion params:
-        model: str
+        model:
            Name of __ModuleName__ model to use.
-        temperature: float
+        temperature:
            Sampling temperature.
-        max_tokens: int | None
+        max_tokens:
            Max number of tokens to generate.

    # TODO: Populate with relevant params.
    Key init args — client params:
-        timeout: float | None
+        timeout:
            Timeout for requests.
-        max_retries: int
+        max_retries:
            Max number of retries.
-        api_key: str | None
+        api_key:
            __ModuleName__ API key. If not passed in will be read from env var
            __MODULE_NAME___API_KEY.

@@ -60,7 +60,7 @@ class Chat__ModuleName__(BaseChatModel):
        ```python
        from __module_name__ import Chat__ModuleName__

-        llm = Chat__ModuleName__(
+        model = Chat__ModuleName__(
            model="...",
            temperature=0,
            max_tokens=None,
@@ -77,7 +77,7 @@ class Chat__ModuleName__(BaseChatModel):
            ("system", "You are a helpful translator. Translate the user sentence to French."),
            ("human", "I love programming."),
        ]
-        llm.invoke(messages)
+        model.invoke(messages)
        ```

        ```python
@@ -87,7 +87,7 @@ class Chat__ModuleName__(BaseChatModel):
    # TODO: Delete if token-level streaming isn't supported.
    Stream:
        ```python
-        for chunk in llm.stream(messages):
+        for chunk in model.stream(messages):
            print(chunk.text, end="")
        ```

@@ -96,7 +96,7 @@ class Chat__ModuleName__(BaseChatModel):
        ```

        ```python
-        stream = llm.stream(messages)
+        stream = model.stream(messages)
        full = next(stream)
        for chunk in stream:
            full += chunk
@@ -110,13 +110,13 @@ class Chat__ModuleName__(BaseChatModel):
    # TODO: Delete if native async isn't supported.
    Async:
        ```python
-        await llm.ainvoke(messages)
+        await model.ainvoke(messages)

        # stream:
-        # async for chunk in (await llm.astream(messages))
+        # async for chunk in (await model.astream(messages))

        # batch:
-        # await llm.abatch([messages])
+        # await model.abatch([messages])
        ```

        ```python
@@ -137,8 +137,8 @@ class Chat__ModuleName__(BaseChatModel):

            location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

-        llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
-        ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
+        model_with_tools = model.bind_tools([GetWeather, GetPopulation])
+        ai_msg = model_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
        ai_msg.tool_calls
        ```

@@ -162,8 +162,8 @@ class Chat__ModuleName__(BaseChatModel):
            punchline: str = Field(description="The punchline to the joke")
            rating: int | None = Field(description="How funny the joke is, from 1 to 10")

-        structured_llm = llm.with_structured_output(Joke)
-        structured_llm.invoke("Tell me a joke about cats")
+        structured_model = model.with_structured_output(Joke)
+        structured_model.invoke("Tell me a joke about cats")
        ```

        ```python
@@ -176,8 +176,8 @@ class Chat__ModuleName__(BaseChatModel):
    JSON mode:
        ```python
        # TODO: Replace with appropriate bind arg.
-        json_llm = llm.bind(response_format={"type": "json_object"})
-        ai_msg = json_llm.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
+        json_model = model.bind(response_format={"type": "json_object"})
+        ai_msg = json_model.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
        ai_msg.content
        ```

@@ -204,7 +204,7 @@ class Chat__ModuleName__(BaseChatModel):
                },
            ],
        )
-        ai_msg = llm.invoke([message])
+        ai_msg = model.invoke([message])
        ai_msg.content
        ```

@@ -235,7 +235,7 @@ class Chat__ModuleName__(BaseChatModel):
    # TODO: Delete if token usage metadata isn't supported.
    Token usage:
        ```python
-        ai_msg = llm.invoke(messages)
+        ai_msg = model.invoke(messages)
        ai_msg.usage_metadata
        ```

@@ -247,8 +247,8 @@ class Chat__ModuleName__(BaseChatModel):
    Logprobs:
        ```python
        # TODO: Replace with appropriate bind arg.
-        logprobs_llm = llm.bind(logprobs=True)
-        ai_msg = logprobs_llm.invoke(messages)
+        logprobs_model = model.bind(logprobs=True)
+        ai_msg = logprobs_model.invoke(messages)
        ai_msg.response_metadata["logprobs"]
        ```

@@ -257,7 +257,7 @@ class Chat__ModuleName__(BaseChatModel):
        ```
    Response metadata
        ```python
-        ai_msg = llm.invoke(messages)
+        ai_msg = model.invoke(messages)
        ai_msg.response_metadata
        ```

--- a/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py
+++ b/libs/cli/langchain_cli/integration_template/integration_template/retrievers.py
@@ -65,7 +65,7 @@ class __ModuleName__Retriever(BaseRetriever):
        Question: {question}\"\"\"
        )

-        llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+        model = ChatOpenAI(model="gpt-3.5-turbo-0125")

        def format_docs(docs):
            return "\\n\\n".join(doc.page_content for doc in docs)
@@ -73,7 +73,7 @@ class __ModuleName__Retriever(BaseRetriever):
        chain = (
            {"context": retriever | format_docs, "question": RunnablePassthrough()}
            | prompt
-            | llm
+            | model
            | StrOutputParser()
        )

--- a/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py
+++ b/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py
@@ -37,16 +37,16 @@ class __ModuleName__VectorStore(VectorStore):

    # TODO: Populate with relevant params.
    Key init args — indexing params:
-        collection_name: str
+        collection_name:
            Name of the collection.
-        embedding_function: Embeddings
+        embedding_function:
            Embedding function to use.

    # TODO: Populate with relevant params.
    Key init args — client params:
-        client: Client | None
+        client:
            Client to use.
-        connection_args: dict | None
+        connection_args:
            Connection arguments.

    # TODO: Replace with relevant init params.
--- a/libs/cli/langchain_cli/integration_template/pyproject.toml
+++ b/libs/cli/langchain_cli/integration_template/pyproject.toml
@@ -36,6 +36,9 @@ dev-dependencies = [
 [tool.ruff.lint]
 select = ["E", "F", "I", "T201"]

+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
 [tool.ruff.lint.per-file-ignores]
 "docs/**" = [ "ALL",]

--- a/libs/cli/langchain_cli/namespaces/migrate/generate/utils.py
+++ b/libs/cli/langchain_cli/namespaces/migrate/generate/utils.py
@@ -65,7 +65,7 @@ def is_subclass(class_obj: type, classes_: list[type]) -> bool:
        classes_: A list of classes to check against.

    Returns:
-        True if `class_obj` is a subclass of any class in `classes_`, False otherwise.
+        True if `class_obj` is a subclass of any class in `classes_`, `False` otherwise.
    """
    return any(
        issubclass(class_obj, kls)
--- a/libs/cli/langchain_cli/utils/git.py
+++ b/libs/cli/langchain_cli/utils/git.py
@@ -6,9 +6,8 @@ import hashlib
 import logging
 import re
 import shutil
-from collections.abc import Sequence
 from pathlib import Path
-from typing import Any, TypedDict
+from typing import TYPE_CHECKING, Any, TypedDict

 from git import Repo

@@ -18,6 +17,9 @@ from langchain_cli.constants import (
    DEFAULT_GIT_SUBDIRECTORY,
 )

+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
 logger = logging.getLogger(__name__)


@@ -182,7 +184,7 @@ def parse_dependencies(
    inner_branches = _list_arg_to_length(branch, num_deps)

    return list(
-        map(  # type: ignore[call-overload]
+        map(  # type: ignore[call-overload, unused-ignore]
            parse_dependency_string,
            inner_deps,
            inner_repos,
--- a/libs/cli/pyproject.toml
+++ b/libs/cli/pyproject.toml
@@ -20,12 +20,13 @@ description = "CLI for interacting with LangChain"
 readme = "README.md"

 [project.urls]
-homepage = "https://docs.langchain.com/"
-repository = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
-changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-cli%3D%3D1%22"
-twitter = "https://x.com/LangChainAI"
-slack = "https://www.langchain.com/join-community"
-reddit = "https://www.reddit.com/r/LangChain/"
+Homepage = "https://docs.langchain.com/"
+Documentation = "https://docs.langchain.com/"
+Source = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
+Changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-cli%3D%3D1%22"
+Twitter = "https://x.com/LangChain"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"

 [project.scripts]
 langchain = "langchain_cli.cli:app"
@@ -37,19 +38,21 @@ dev = [
    "pytest-watcher>=0.3.4,<1.0.0"
 ]
 lint = [
-    "ruff>=0.13.1,<0.14",
-    "mypy>=1.18.1,<1.19"
+    "ruff>=0.14.11,<0.15.0"
 ]
 test = [
    "langchain-core",
-    "langchain"
+    "langchain-classic"
+]
+typing = [
+    "mypy>=1.19.1,<1.20",
+    "langchain-classic"
 ]
-typing = ["langchain"]
 test_integration = []

 [tool.uv.sources]
 langchain-core = { path = "../core", editable = true }
-langchain = { path = "../langchain", editable = true }
+langchain-classic = { path = "../langchain", editable = true }

 [tool.ruff.format]
 docstring-code-format = true
@@ -63,10 +66,6 @@ ignore = [
    "FIX002",  # Line contains TODO
    "PERF203", # Rarely useful
    "PLR09",   # Too many something (arg, statements, etc)
-    "RUF012",  # Doesn't play well with Pydantic
-    "TC001",   # Doesn't play well with Pydantic
-    "TC002",   # Doesn't play well with Pydantic
-    "TC003",   # Doesn't play well with Pydantic
    "TD002",   # Missing author in TODO
    "TD003",   # Missing issue link in TODO

@@ -75,7 +74,6 @@ ignore = [
 ]
 unfixable = [
    "B028",    # People should intentionally tune the stacklevel
-    "PLW1510", # People should intentionally set the check argument
 ]

 flake8-annotations.allow-star-arg-any = true
@@ -88,6 +86,9 @@ pyupgrade.keep-runtime-typing = true
 convention = "google"
 ignore-var-parameters = true  # ignore missing documentation for *args and **kwargs parameters

+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
 [tool.ruff.lint.per-file-ignores]
 "tests/**" = [ "D1", "S", "SLF",]
 "scripts/**" = [ "INP", "S",]
--- a/libs/cli/tests/unit_tests/migrate/cli_runner/case.py
+++ b/libs/cli/tests/unit_tests/migrate/cli_runner/case.py
@@ -1,9 +1,11 @@
 from __future__ import annotations

 from dataclasses import dataclass
+from typing import TYPE_CHECKING

-from .file import File
-from .folder import Folder
+if TYPE_CHECKING:
+    from tests.unit_tests.migrate.cli_runner.file import File
+    from tests.unit_tests.migrate.cli_runner.folder import Folder


@dataclass
--- a/libs/cli/tests/unit_tests/migrate/cli_runner/folder.py
+++ b/libs/cli/tests/unit_tests/migrate/cli_runner/folder.py
@@ -1,8 +1,11 @@
 from __future__ import annotations

-from pathlib import Path
+from typing import TYPE_CHECKING

-from .file import File
+from tests.unit_tests.migrate.cli_runner.file import File
+
+if TYPE_CHECKING:
+    from pathlib import Path


 class Folder:
--- a/libs/cli/tests/unit_tests/migrate/generate/test_langchain_migration.py
+++ b/libs/cli/tests/unit_tests/migrate/generate/test_langchain_migration.py
@@ -1,5 +1,5 @@
 import pytest
-from langchain._api import suppress_langchain_deprecation_warning as sup2
+from langchain_classic._api import suppress_langchain_deprecation_warning as sup2
 from langchain_core._api import suppress_langchain_deprecation_warning as sup1

 from langchain_cli.namespaces.migrate.generate.generic import (
--- a/libs/cli/uv.lock
+++ b/libs/cli/uv.lock
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+    { name = "langchain-deepseek", marker = "extra == 'deepseek'" },
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+
 [[package]]
 name = "zstandard"
 version = "0.25.0"
--- a/libs/core/README.md
+++ b/libs/core/README.md
@@ -1,7 +1,14 @@
 # 🦜🍎️ LangChain Core

-[![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)
+[![PyPI - Version](https://img.shields.io/pypi/v/langchain-core?label=%20)](https://pypi.org/project/langchain-core/#history)
+[![PyPI - License](https://img.shields.io/pypi/l/langchain-core)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-core)](https://pypistats.org/packages/langchain-core)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)
+
+Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
+
+To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
+[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.

 ## Quick Install

@@ -9,16 +16,14 @@
 pip install langchain-core
 ```

-## What is it?
+## 🤔 What is this?

-LangChain Core contains the base abstractions that power the the LangChain ecosystem.
+LangChain Core contains the base abstractions that power the LangChain ecosystem.

 These abstractions are designed to be as modular and simple as possible.

 The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.

-For full documentation see the [API reference](https://reference.langchain.com/python/).
-
 ## ⛰️ Why build on top of LangChain Core?

 The LangChain ecosystem is built on top of `langchain-core`. Some of the benefits:
@@ -27,12 +32,16 @@ The LangChain ecosystem is built on top of `langchain-core`. Some of the benefit
 - **Stability**: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
 - **Battle-tested**: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.

+## 📖 Documentation
+
+For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview).
+
 ## 📕 Releases & Versioning

-See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning Policy](https://docs.langchain.com/oss/python/versioning).
+See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.

 ## 💁 Contributing

 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

-For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing).
+For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).
--- a/libs/core/langchain_core/_api/init.py
+++ b/libs/core/langchain_core/_api/init.py
@@ -13,20 +13,20 @@ from typing import TYPE_CHECKING
 from langchain_core._import_utils import import_attr

 if TYPE_CHECKING:
-    from .beta_decorator import (
+    from langchain_core._api.beta_decorator import (
        LangChainBetaWarning,
        beta,
        suppress_langchain_beta_warning,
        surface_langchain_beta_warnings,
    )
-    from .deprecation import (
+    from langchain_core._api.deprecation import (
        LangChainDeprecationWarning,
        deprecated,
        suppress_langchain_deprecation_warning,
        surface_langchain_deprecation_warnings,
        warn_deprecated,
    )
-    from .path import as_import_path, get_relative_path
+    from langchain_core._api.path import as_import_path, get_relative_path

 __all__ = (
    "LangChainBetaWarning",
@@ -58,6 +58,20 @@ _dynamic_imports = {


 def __getattr__(attr_name: str) -> object:
+    """Dynamically import and return an attribute from a submodule.
+
+    This function enables lazy loading of API functions from submodules, reducing
+    initial import time and circular dependency issues.
+
+    Args:
+        attr_name: Name of the attribute to import.
+
+    Returns:
+        The imported attribute object.
+
+    Raises:
+        AttributeError: If the attribute is not a valid dynamic import.
+    """
    module_name = _dynamic_imports.get(attr_name)
    result = import_attr(attr_name, module_name, __spec__.parent)
    globals()[attr_name] = result
@@ -65,4 +79,9 @@ def __getattr__(attr_name: str) -> object:


 def __dir__() -> list[str]:
+    """Return a list of available attributes for this module.
+
+    Returns:
+        List of attribute names that can be imported from this module.
+    """
    return list(__all__)
--- a/libs/core/langchain_core/_api/beta_decorator.py
+++ b/libs/core/langchain_core/_api/beta_decorator.py
@@ -125,7 +125,7 @@ def beta(
            _name = _name or obj.__qualname__
            old_doc = obj.__doc__

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                """Finalize the annotation of a class."""
                # Can't set new_doc on some extension objects.
                with contextlib.suppress(AttributeError):
@@ -168,7 +168,7 @@ def beta(
                    emit_warning()
                obj.fdel(instance)

-            def finalize(_wrapper: Callable[..., Any], new_doc: str) -> Any:
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> Any:
                """Finalize the property."""
                return property(fget=_fget, fset=_fset, fdel=_fdel, doc=new_doc)

@@ -181,7 +181,7 @@ def beta(
            wrapped = obj
            old_doc = wrapped.__doc__

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str, /) -> T:
                """Wrap the wrapped function using the wrapper and update the docstring.

                Args:
--- a/libs/core/langchain_core/_api/deprecation.py
+++ b/libs/core/langchain_core/_api/deprecation.py
@@ -28,6 +28,27 @@ from pydantic.v1.fields import FieldInfo as FieldInfoV1
 from langchain_core._api.internal import is_caller_internal


+def _build_deprecation_message(
+    *,
+    alternative: str = "",
+    alternative_import: str = "",
+) -> str:
+    """Build a simple deprecation message for `__deprecated__` attribute.
+
+    Args:
+        alternative: An alternative API name.
+        alternative_import: A fully qualified import path for the alternative.
+
+    Returns:
+        A deprecation message string for IDE/type checker display.
+    """
+    if alternative_import:
+        return f"Use {alternative_import} instead."
+    if alternative:
+        return f"Use {alternative} instead."
+    return "Deprecated."
+
+
 class LangChainDeprecationWarning(DeprecationWarning):
    """A class for issuing deprecation warnings for LangChain users."""

@@ -81,60 +102,57 @@ def deprecated(
 ) -> Callable[[T], T]:
    """Decorator to mark a function, a class, or a property as deprecated.

-    When deprecating a classmethod, a staticmethod, or a property, the
-    `@deprecated` decorator should go *under* `@classmethod` and
-    `@staticmethod` (i.e., `deprecated` should directly decorate the
-    underlying callable), but *over* `@property`.
+    When deprecating a classmethod, a staticmethod, or a property, the `@deprecated`
+    decorator should go *under* `@classmethod` and `@staticmethod` (i.e., `deprecated`
+    should directly decorate the underlying callable), but *over* `@property`.

-    When deprecating a class `C` intended to be used as a base class in a
-    multiple inheritance hierarchy, `C` *must* define an `__init__` method
-    (if `C` instead inherited its `__init__` from its own base class, then
-    `@deprecated` would mess up `__init__` inheritance when installing its
-    own (deprecation-emitting) `C.__init__`).
+    When deprecating a class `C` intended to be used as a base class in a multiple
+    inheritance hierarchy, `C` *must* define an `__init__` method (if `C` instead
+    inherited its `__init__` from its own base class, then `@deprecated` would mess up
+    `__init__` inheritance when installing its own (deprecation-emitting) `C.__init__`).

-    Parameters are the same as for `warn_deprecated`, except that *obj_type*
-    defaults to 'class' if decorating a class, 'attribute' if decorating a
-    property, and 'function' otherwise.
+    Parameters are the same as for `warn_deprecated`, except that *obj_type* defaults to
+    'class' if decorating a class, 'attribute' if decorating a property, and 'function'
+    otherwise.

    Args:
-        since:
-            The release at which this API became deprecated.
-        message:
-            Override the default deprecation message. The %(since)s,
-            %(name)s, %(alternative)s, %(obj_type)s, %(addendum)s,
-            and %(removal)s format specifiers will be replaced by the
+        since: The release at which this API became deprecated.
+        message: Override the default deprecation message.
+
+            The `%(since)s`, `%(name)s`, `%(alternative)s`, `%(obj_type)s`,
+            `%(addendum)s`, and `%(removal)s` format specifiers will be replaced by the
            values of the respective arguments passed to this function.
-        name:
-            The name of the deprecated object.
-        alternative:
-            An alternative API that the user may use in place of the
-            deprecated API. The deprecation warning will tell the user
-            about this alternative if provided.
-        alternative_import:
-            An alternative import that the user may use instead.
-        pending:
-            If `True`, uses a `PendingDeprecationWarning` instead of a
-            DeprecationWarning. Cannot be used together with removal.
-        obj_type:
-            The object type being deprecated.
-        addendum:
-            Additional text appended directly to the final message.
-        removal:
-            The expected removal version. With the default (an empty
-            string), a removal version is automatically computed from
-            since. Set to other Falsy values to not schedule a removal
-            date. Cannot be used together with pending.
-        package:
-            The package of the deprecated object.
+        name: The name of the deprecated object.
+        alternative: An alternative API that the user may use in place of the deprecated
+            API.
+
+            The deprecation warning will tell the user about this alternative if
+            provided.
+        alternative_import: An alternative import that the user may use instead.
+        pending: If `True`, uses a `PendingDeprecationWarning` instead of a
+            `DeprecationWarning`.
+
+            Cannot be used together with removal.
+        obj_type: The object type being deprecated.
+        addendum: Additional text appended directly to the final message.
+        removal: The expected removal version.
+
+            With the default (an empty string), a removal version is automatically
+            computed from since. Set to other Falsy values to not schedule a removal
+            date.
+
+            Cannot be used together with pending.
+        package: The package of the deprecated object.

    Returns:
        A decorator to mark a function or class as deprecated.

-    ```python
-    @deprecated("1.4.0")
-    def the_function_to_deprecate():
-        pass
-    ```
+    Example:
+        ```python
+        @deprecated("1.4.0")
+        def the_function_to_deprecate():
+            pass
+        ```
    """
    _validate_deprecation_params(
        removal, alternative, alternative_import, pending=pending
@@ -204,7 +222,7 @@ def deprecated(
            _name = _name or obj.__qualname__
            old_doc = obj.__doc__

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                """Finalize the deprecation of a class."""
                # Can't set new_doc on some extension objects.
                with contextlib.suppress(AttributeError):
@@ -223,6 +241,11 @@ def deprecated(
                obj.__init__ = functools.wraps(obj.__init__)(  # type: ignore[misc]
                    warn_if_direct_instance
                )
+                # Set __deprecated__ for PEP 702 (IDE/type checker support)
+                obj.__deprecated__ = _build_deprecation_message(  # type: ignore[attr-defined]
+                    alternative=alternative,
+                    alternative_import=alternative_import,
+                )
                return obj

        elif isinstance(obj, FieldInfoV1):
@@ -234,7 +257,7 @@ def deprecated(
                raise ValueError(msg)
            old_doc = obj.description

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                return cast(
                    "T",
                    FieldInfoV1(
@@ -255,7 +278,7 @@ def deprecated(
                raise ValueError(msg)
            old_doc = obj.description

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                return cast(
                    "T",
                    FieldInfo(
@@ -313,14 +336,17 @@ def deprecated(
                    if _name == "<lambda>":
                        _name = set_name

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                """Finalize the property."""
-                return cast(
-                    "T",
-                    _DeprecatedProperty(
-                        fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
-                    ),
+                prop = _DeprecatedProperty(
+                    fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
                )
+                # Set __deprecated__ for PEP 702 (IDE/type checker support)
+                prop.__deprecated__ = _build_deprecation_message(  # type: ignore[attr-defined]
+                    alternative=alternative,
+                    alternative_import=alternative_import,
+                )
+                return cast("T", prop)

        else:
            _name = _name or cast("type | Callable", obj).__qualname__
@@ -331,7 +357,7 @@ def deprecated(
            wrapped = obj
            old_doc = wrapped.__doc__

-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str, /) -> T:
                """Wrap the wrapped function using the wrapper and update the docstring.

                Args:
@@ -343,6 +369,11 @@ def deprecated(
                """
                wrapper = functools.wraps(wrapped)(wrapper)
                wrapper.__doc__ = new_doc
+                # Set __deprecated__ for PEP 702 (IDE/type checker support)
+                wrapper.__deprecated__ = _build_deprecation_message(  # type: ignore[attr-defined]
+                    alternative=alternative,
+                    alternative_import=alternative_import,
+                )
                return cast("T", wrapper)

        old_doc = inspect.cleandoc(old_doc or "").strip("\n")
@@ -398,7 +429,7 @@ def deprecated(

@contextlib.contextmanager
 def suppress_langchain_deprecation_warning() -> Generator[None, None, None]:
-    """Context manager to suppress LangChainDeprecationWarning."""
+    """Context manager to suppress `LangChainDeprecationWarning`."""
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", LangChainDeprecationWarning)
        warnings.simplefilter("ignore", LangChainPendingDeprecationWarning)
@@ -421,35 +452,33 @@ def warn_deprecated(
    """Display a standardized deprecation.

    Args:
-        since:
-            The release at which this API became deprecated.
-        message:
-            Override the default deprecation message. The %(since)s,
-            %(name)s, %(alternative)s, %(obj_type)s, %(addendum)s,
-            and %(removal)s format specifiers will be replaced by the
+        since: The release at which this API became deprecated.
+        message: Override the default deprecation message.
+
+            The `%(since)s`, `%(name)s`, `%(alternative)s`, `%(obj_type)s`,
+            `%(addendum)s`, and `%(removal)s` format specifiers will be replaced by the
            values of the respective arguments passed to this function.
-        name:
-            The name of the deprecated object.
-        alternative:
-            An alternative API that the user may use in place of the
-            deprecated API. The deprecation warning will tell the user
-            about this alternative if provided.
-        alternative_import:
-            An alternative import that the user may use instead.
-        pending:
-            If `True`, uses a `PendingDeprecationWarning` instead of a
-            DeprecationWarning. Cannot be used together with removal.
-        obj_type:
-            The object type being deprecated.
-        addendum:
-            Additional text appended directly to the final message.
-        removal:
-            The expected removal version. With the default (an empty
-            string), a removal version is automatically computed from
-            since. Set to other Falsy values to not schedule a removal
-            date. Cannot be used together with pending.
-        package:
-            The package of the deprecated object.
+        name: The name of the deprecated object.
+        alternative: An alternative API that the user may use in place of the
+            deprecated API.
+
+            The deprecation warning will tell the user about this alternative if
+            provided.
+        alternative_import: An alternative import that the user may use instead.
+        pending: If `True`, uses a `PendingDeprecationWarning` instead of a
+            `DeprecationWarning`.
+
+            Cannot be used together with removal.
+        obj_type: The object type being deprecated.
+        addendum: Additional text appended directly to the final message.
+        removal: The expected removal version.
+
+            With the default (an empty string), a removal version is automatically
+            computed from since. Set to other Falsy values to not schedule a removal
+            date.
+
+            Cannot be used together with pending.
+        package: The package of the deprecated object.
    """
    if not pending:
        if not removal:
@@ -534,8 +563,8 @@ def rename_parameter(
    """Decorator indicating that parameter *old* of *func* is renamed to *new*.

    The actual implementation of *func* should use *new*, not *old*. If *old* is passed
-    to *func*, a DeprecationWarning is emitted, and its value is used, even if *new* is
-    also passed by keyword.
+    to *func*, a `DeprecationWarning` is emitted, and its value is used, even if *new*
+    is also passed by keyword.

    Args:
        since: The version in which the parameter was renamed.
--- a/libs/core/langchain_core/_api/internal.py
+++ b/libs/core/langchain_core/_api/internal.py
@@ -1,4 +1,5 @@
 import inspect
+from typing import cast


 def is_caller_internal(depth: int = 2) -> bool:
@@ -16,7 +17,7 @@ def is_caller_internal(depth: int = 2) -> bool:
                return False
        # Directly access the module name from the frame's global variables
        module_globals = frame.f_globals
-        caller_module_name = module_globals.get("__name__", "")
+        caller_module_name = cast("str", module_globals.get("__name__", ""))
        return caller_module_name.startswith("langchain")
    finally:
        del frame
--- a/libs/core/langchain_core/agents.py
+++ b/libs/core/langchain_core/agents.py
@@ -5,12 +5,10 @@

 !!! warning
    New agents should be built using the
-    [langgraph library](https://github.com/langchain-ai/langgraph), which provides a
+    [`langchain` library](https://pypi.org/project/langchain/), which provides a
    simpler and more flexible way to define agents.

-    Please see the
-    [migration guide](https://python.langchain.com/docs/how_to/migrate_agent/) for
-    information on how to migrate existing agents to modern langgraph agents.
+    See docs on [building agents](https://docs.langchain.com/oss/python/langchain/agents).

 Agents use language models to choose a sequence of actions to take.

@@ -54,37 +52,39 @@ class AgentAction(Serializable):
    """The input to pass in to the Tool."""
    log: str
    """Additional information to log about the action.
-    This log can be used in a few ways. First, it can be used to audit
-    what exactly the LLM predicted to lead to this (tool, tool_input).
-    Second, it can be used in future iterations to show the LLMs prior
-    thoughts. This is useful when (tool, tool_input) does not contain
-    full information about the LLM prediction (for example, any `thought`
-    before the tool/tool_input)."""
+
+    This log can be used in a few ways. First, it can be used to audit what exactly the
+    LLM predicted to lead to this `(tool, tool_input)`.
+
+    Second, it can be used in future iterations to show the LLMs prior thoughts. This is
+    useful when `(tool, tool_input)` does not contain full information about the LLM
+    prediction (for example, any `thought` before the tool/tool_input).
+    """
    type: Literal["AgentAction"] = "AgentAction"

    # Override init to support instantiation by position for backward compat.
    def __init__(self, tool: str, tool_input: str | dict, log: str, **kwargs: Any):
-        """Create an AgentAction.
+        """Create an `AgentAction`.

        Args:
            tool: The name of the tool to execute.
-            tool_input: The input to pass in to the Tool.
+            tool_input: The input to pass in to the `Tool`.
            log: Additional information to log about the action.
        """
        super().__init__(tool=tool, tool_input=tool_input, log=log, **kwargs)

    @classmethod
    def is_lc_serializable(cls) -> bool:
-        """AgentAction is serializable.
+        """`AgentAction` is serializable.

        Returns:
-            True
+            `True`
        """
        return True

    @classmethod
    def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.

        Returns:
            `["langchain", "schema", "agent"]`
@@ -100,19 +100,23 @@ class AgentAction(Serializable):
 class AgentActionMessageLog(AgentAction):
    """Representation of an action to be executed by an agent.

-    This is similar to AgentAction, but includes a message log consisting of
-    chat messages. This is useful when working with ChatModels, and is used
-    to reconstruct conversation history from the agent's perspective.
+    This is similar to `AgentAction`, but includes a message log consisting of
+    chat messages.
+
+    This is useful when working with `ChatModels`, and is used to reconstruct
+    conversation history from the agent's perspective.
    """

    message_log: Sequence[BaseMessage]
-    """Similar to log, this can be used to pass along extra
-    information about what exact messages were predicted by the LLM
-    before parsing out the (tool, tool_input). This is again useful
-    if (tool, tool_input) cannot be used to fully recreate the LLM
-    prediction, and you need that LLM prediction (for future agent iteration).
+    """Similar to log, this can be used to pass along extra information about what exact
+    messages were predicted by the LLM before parsing out the `(tool, tool_input)`.
+
+    This is again useful if `(tool, tool_input)` cannot be used to fully recreate the
+    LLM prediction, and you need that LLM prediction (for future agent iteration).
+
    Compared to `log`, this is useful when the underlying LLM is a
-    ChatModel (and therefore returns messages rather than a string)."""
+    chat model (and therefore returns messages rather than a string).
+    """
    # Ignoring type because we're overriding the type from AgentAction.
    # And this is the correct thing to do in this case.
    # The type literal is used for serialization purposes.
@@ -120,12 +124,12 @@ class AgentActionMessageLog(AgentAction):


 class AgentStep(Serializable):
-    """Result of running an AgentAction."""
+    """Result of running an `AgentAction`."""

    action: AgentAction
-    """The AgentAction that was executed."""
+    """The `AgentAction` that was executed."""
    observation: Any
-    """The result of the AgentAction."""
+    """The result of the `AgentAction`."""

    @property
    def messages(self) -> Sequence[BaseMessage]:
@@ -134,19 +138,22 @@ class AgentStep(Serializable):


 class AgentFinish(Serializable):
-    """Final return value of an ActionAgent.
+    """Final return value of an `ActionAgent`.

-    Agents return an AgentFinish when they have reached a stopping condition.
+    Agents return an `AgentFinish` when they have reached a stopping condition.
    """

    return_values: dict
    """Dictionary of return values."""
    log: str
    """Additional information to log about the return value.
+
    This is used to pass along the full LLM prediction, not just the parsed out
-    return value. For example, if the full LLM prediction was
-    `Final Answer: 2` you may want to just return `2` as a return value, but pass
-    along the full string as a `log` (for debugging or observability purposes).
+    return value.
+
+    For example, if the full LLM prediction was `Final Answer: 2` you may want to just
+    return `2` as a return value, but pass along the full string as a `log` (for
+    debugging or observability purposes).
    """
    type: Literal["AgentFinish"] = "AgentFinish"

@@ -156,12 +163,12 @@ class AgentFinish(Serializable):

    @classmethod
    def is_lc_serializable(cls) -> bool:
-        """Return True as this class is serializable."""
+        """Return `True` as this class is serializable."""
        return True

    @classmethod
    def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.

        Returns:
            `["langchain", "schema", "agent"]`
@@ -204,7 +211,7 @@ def _convert_agent_observation_to_messages(
        observation: Observation to convert to a message.

    Returns:
-        AIMessage that corresponds to the original tool invocation.
+        `AIMessage` that corresponds to the original tool invocation.
    """
    if isinstance(agent_action, AgentActionMessageLog):
        return [_create_function_message(agent_action, observation)]
@@ -227,7 +234,7 @@ def _create_function_message(
        observation: the result of the tool invocation.

    Returns:
-        FunctionMessage that corresponds to the original tool invocation.
+        `FunctionMessage` that corresponds to the original tool invocation.
    """
    if not isinstance(observation, str):
        try:
--- a/libs/core/langchain_core/caches.py
+++ b/libs/core/langchain_core/caches.py
@@ -1,18 +1,18 @@
-"""Cache classes.
+"""Optional caching layer for language models.

-!!! warning
-    Beta Feature!
+Distinct from provider-based [prompt caching](https://docs.langchain.com/oss/python/langchain/models#prompt-caching).

-**Cache** provides an optional caching layer for LLMs.
+!!! warning "Beta feature"

-Cache is useful for two reasons:
+    This is a beta feature. Please be wary of deploying experimental code to production
+    unless you've taken appropriate precautions.

- It can save you money by reducing the number of API calls you make to the LLM
+A cache is useful for two reasons:
+
+1. It can save you money by reducing the number of API calls you make to the LLM
    provider if you're often requesting the same completion multiple times.
- It can speed up your application by reducing the number of API calls you make
-    to the LLM provider.
-
-Cache directly competes with Memory. See documentation for Pros and Cons.
+2. It can speed up your application by reducing the number of API calls you make to the
+    LLM provider.
 """

 from __future__ import annotations
@@ -34,8 +34,8 @@ class BaseCache(ABC):

    The cache interface consists of the following methods:

-    - lookup: Look up a value based on a prompt and llm_string.
-    - update: Update the cache based on a prompt and llm_string.
+    - lookup: Look up a value based on a prompt and `llm_string`.
+    - update: Update the cache based on a prompt and `llm_string`.
    - clear: Clear the cache.

    In addition, the cache interface provides an async version of each method.
@@ -47,43 +47,46 @@ class BaseCache(ABC):

    @abstractmethod
    def lookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Look up based on prompt and llm_string.
+        """Look up based on `prompt` and `llm_string`.

        A cache implementation is expected to generate a key from the 2-tuple
-        of prompt and llm_string (e.g., by concatenating them with a delimiter).
+        of `prompt` and `llm_string` (e.g., by concatenating them with a delimiter).

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.
+
                This is used to capture the invocation parameters of the LLM
                (e.g., model name, temperature, stop tokens, max tokens, etc.).
-                These invocation parameters are serialized into a string
-                representation.
+
+                These invocation parameters are serialized into a string representation.

        Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
-            The cached value is a list of Generations (or subclasses).
+            On a cache miss, return `None`. On a cache hit, return the cached value.
+            The cached value is a list of `Generation` (or subclasses).
        """

    @abstractmethod
    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string.
+        """Update cache based on `prompt` and `llm_string`.

        The prompt and llm_string are used to generate a key for the cache.
        The key should match that of the lookup method.

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.
+
                This is used to capture the invocation parameters of the LLM
                (e.g., model name, temperature, stop tokens, max tokens, etc.).
+
                These invocation parameters are serialized into a string
                representation.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                (or subclasses).
        """

@@ -92,45 +95,49 @@ class BaseCache(ABC):
        """Clear cache that can take additional keyword arguments."""

    async def alookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Async look up based on prompt and llm_string.
+        """Async look up based on `prompt` and `llm_string`.

        A cache implementation is expected to generate a key from the 2-tuple
-        of prompt and llm_string (e.g., by concatenating them with a delimiter).
+        of `prompt` and `llm_string` (e.g., by concatenating them with a delimiter).

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.
+
                This is used to capture the invocation parameters of the LLM
                (e.g., model name, temperature, stop tokens, max tokens, etc.).
+
                These invocation parameters are serialized into a string
                representation.

        Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
-            The cached value is a list of Generations (or subclasses).
+            On a cache miss, return `None`. On a cache hit, return the cached value.
+            The cached value is a list of `Generation` (or subclasses).
        """
        return await run_in_executor(None, self.lookup, prompt, llm_string)

    async def aupdate(
        self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
    ) -> None:
-        """Async update cache based on prompt and llm_string.
+        """Async update cache based on `prompt` and `llm_string`.

        The prompt and llm_string are used to generate a key for the cache.
        The key should match that of the look up method.

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.
+
                This is used to capture the invocation parameters of the LLM
                (e.g., model name, temperature, stop tokens, max tokens, etc.).
+
                These invocation parameters are serialized into a string
                representation.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                (or subclasses).
        """
        return await run_in_executor(None, self.update, prompt, llm_string, return_val)
@@ -150,10 +157,9 @@ class InMemoryCache(BaseCache):
            maxsize: The maximum number of items to store in the cache.
                If `None`, the cache has no maximum size.
                If the cache exceeds the maximum size, the oldest items are removed.
-                Default is None.

        Raises:
-            ValueError: If maxsize is less than or equal to 0.
+            ValueError: If `maxsize` is less than or equal to `0`.
        """
        self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
        if maxsize is not None and maxsize <= 0:
@@ -162,28 +168,28 @@ class InMemoryCache(BaseCache):
        self._maxsize = maxsize

    def lookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Look up based on prompt and llm_string.
+        """Look up based on `prompt` and `llm_string`.

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.

        Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
+            On a cache miss, return `None`. On a cache hit, return the cached value.
        """
        return self._cache.get((prompt, llm_string), None)

    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
-        """Update cache based on prompt and llm_string.
+        """Update cache based on `prompt` and `llm_string`.

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                (or subclasses).
        """
        if self._maxsize is not None and len(self._cache) == self._maxsize:
@@ -196,30 +202,30 @@ class InMemoryCache(BaseCache):
        self._cache = {}

    async def alookup(self, prompt: str, llm_string: str) -> RETURN_VAL_TYPE | None:
-        """Async look up based on prompt and llm_string.
+        """Async look up based on `prompt` and `llm_string`.

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.

        Returns:
-            On a cache miss, return None. On a cache hit, return the cached value.
+            On a cache miss, return `None`. On a cache hit, return the cached value.
        """
        return self.lookup(prompt, llm_string)

    async def aupdate(
        self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
    ) -> None:
-        """Async update cache based on prompt and llm_string.
+        """Async update cache based on `prompt` and `llm_string`.

        Args:
-            prompt: a string representation of the prompt.
-                In the case of a Chat model, the prompt is a non-trivial
+            prompt: A string representation of the prompt.
+                In the case of a chat model, the prompt is a non-trivial
                serialization of the prompt into the language model.
            llm_string: A string representation of the LLM configuration.
-            return_val: The value to be cached. The value is a list of Generations
+            return_val: The value to be cached. The value is a list of `Generation`
                (or subclasses).
        """
        self.update(prompt, llm_string, return_val)
--- a/libs/core/langchain_core/callbacks/base.py
+++ b/libs/core/langchain_core/callbacks/base.py
@@ -5,13 +5,12 @@ from __future__ import annotations
 import logging
 from typing import TYPE_CHECKING, Any

-from typing_extensions import Self
-
 if TYPE_CHECKING:
    from collections.abc import Sequence
    from uuid import UUID

    from tenacity import RetryCallState
+    from typing_extensions import Self

    from langchain_core.agents import AgentAction, AgentFinish
    from langchain_core.documents import Document
@@ -22,7 +21,7 @@ _LOGGER = logging.getLogger(__name__)


 class RetrieverManagerMixin:
-    """Mixin for Retriever callbacks."""
+    """Mixin for `Retriever` callbacks."""

    def on_retriever_error(
        self,
@@ -32,12 +31,12 @@ class RetrieverManagerMixin:
        parent_run_id: UUID | None = None,
        **kwargs: Any,
    ) -> Any:
-        """Run when Retriever errors.
+        """Run when `Retriever` errors.

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -49,12 +48,12 @@ class RetrieverManagerMixin:
        parent_run_id: UUID | None = None,
        **kwargs: Any,
    ) -> Any:
-        """Run when Retriever ends running.
+        """Run when `Retriever` ends running.

        Args:
            documents: The documents retrieved.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -69,6 +68,7 @@ class LLMManagerMixin:
        chunk: GenerationChunk | ChatGenerationChunk | None = None,
        run_id: UUID,
        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
        **kwargs: Any,
    ) -> Any:
        """Run on new output token. Only available when streaming is enabled.
@@ -78,8 +78,9 @@ class LLMManagerMixin:
        Args:
            token: The new token.
            chunk: The new generated chunk, containing content and other information.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
+            tags: The tags.
            **kwargs: Additional keyword arguments.
        """

@@ -89,14 +90,16 @@ class LLMManagerMixin:
        *,
        run_id: UUID,
        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
        **kwargs: Any,
    ) -> Any:
        """Run when LLM ends running.

        Args:
            response: The response which was generated.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
+            tags: The tags.
            **kwargs: Additional keyword arguments.
        """

@@ -106,14 +109,16 @@ class LLMManagerMixin:
        *,
        run_id: UUID,
        parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
        **kwargs: Any,
    ) -> Any:
        """Run when LLM errors.

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
+            tags: The tags.
            **kwargs: Additional keyword arguments.
        """

@@ -133,8 +138,8 @@ class ChainManagerMixin:

        Args:
            outputs: The outputs of the chain.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -150,8 +155,8 @@ class ChainManagerMixin:

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -167,8 +172,8 @@ class ChainManagerMixin:

        Args:
            action: The agent action.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -184,8 +189,8 @@ class ChainManagerMixin:

        Args:
            finish: The agent finish.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -205,8 +210,8 @@ class ToolManagerMixin:

        Args:
            output: The output of the tool.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -222,8 +227,8 @@ class ToolManagerMixin:

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -252,8 +257,8 @@ class CallbackManagerMixin:
        Args:
            serialized: The serialized LLM.
            prompts: The prompts.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -279,8 +284,8 @@ class CallbackManagerMixin:
        Args:
            serialized: The serialized chat model.
            messages: The messages.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -301,13 +306,13 @@ class CallbackManagerMixin:
        metadata: dict[str, Any] | None = None,
        **kwargs: Any,
    ) -> Any:
-        """Run when the Retriever starts running.
+        """Run when the `Retriever` starts running.

        Args:
-            serialized: The serialized Retriever.
+            serialized: The serialized `Retriever`.
            query: The query.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -329,8 +334,8 @@ class CallbackManagerMixin:
        Args:
            serialized: The serialized chain.
            inputs: The inputs.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -353,8 +358,8 @@ class CallbackManagerMixin:
        Args:
            serialized: The serialized chain.
            input_str: The input string.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            inputs: The inputs.
@@ -377,8 +382,8 @@ class RunManagerMixin:

        Args:
            text: The text.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -394,8 +399,8 @@ class RunManagerMixin:

        Args:
            retry_state: The retry state.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -413,15 +418,12 @@ class RunManagerMixin:

        Args:
            name: The name of the custom event.
-            data: The data for the custom event. Format will match
-                the format specified by the user.
+            data: The data for the custom event. Format will match the format specified
+                by the user.
            run_id: The ID of the run.
-            tags: The tags associated with the custom event
-                (includes inherited tags).
-            metadata: The metadata associated with the custom event
-                (includes inherited metadata).
-
-        !!! version-added "Added in version 0.2.15"
+            tags: The tags associated with the custom event (includes inherited tags).
+            metadata: The metadata associated with the custom event (includes inherited
+                metadata).
        """


@@ -433,7 +435,7 @@ class BaseCallbackHandler(
    CallbackManagerMixin,
    RunManagerMixin,
 ):
-    """Base callback handler for LangChain."""
+    """Base callback handler."""

    raise_error: bool = False
    """Whether to raise an error if an exception occurs."""
@@ -478,7 +480,7 @@ class BaseCallbackHandler(


 class AsyncCallbackHandler(BaseCallbackHandler):
-    """Async callback handler for LangChain."""
+    """Base async callback handler."""

    async def on_llm_start(
        self,
@@ -501,8 +503,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        Args:
            serialized: The serialized LLM.
            prompts: The prompts.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -528,8 +530,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        Args:
            serialized: The serialized chat model.
            messages: The messages.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -556,8 +558,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        Args:
            token: The new token.
            chunk: The new generated chunk, containing content and other information.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -575,8 +577,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            response: The response which was generated.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -594,10 +596,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
+
                - response (LLMResult): The response which was generated before
                    the error occurred.
        """
@@ -618,8 +621,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        Args:
            serialized: The serialized chain.
            inputs: The inputs.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -638,8 +641,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            outputs: The outputs of the chain.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -657,8 +660,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -680,8 +683,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        Args:
            serialized: The serialized tool.
            input_str: The input string.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            inputs: The inputs.
@@ -701,8 +704,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            output: The output of the tool.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -720,8 +723,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -739,8 +742,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            text: The text.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -757,8 +760,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            retry_state: The retry state.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            **kwargs: Additional keyword arguments.
        """

@@ -775,8 +778,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            action: The agent action.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -794,8 +797,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            finish: The agent finish.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -816,8 +819,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        Args:
            serialized: The serialized retriever.
            query: The query.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            metadata: The metadata.
            **kwargs: Additional keyword arguments.
@@ -836,8 +839,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            documents: The documents retrieved.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -855,8 +858,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):

        Args:
            error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
            tags: The tags.
            **kwargs: Additional keyword arguments.
        """
@@ -882,13 +885,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
                (includes inherited tags).
            metadata: The metadata associated with the custom event
                (includes inherited metadata).
-
-        !!! version-added "Added in version 0.2.15"
        """


 class BaseCallbackManager(CallbackManagerMixin):
-    """Base callback manager for LangChain."""
+    """Base callback manager."""

    def __init__(
        self,
@@ -937,8 +938,9 @@ class BaseCallbackManager(CallbackManagerMixin):
    def merge(self, other: BaseCallbackManager) -> Self:
        """Merge the callback manager with another callback manager.

-        May be overwritten in subclasses. Primarily used internally
-        within merge_configs.
+        May be overwritten in subclasses.
+
+        Primarily used internally within `merge_configs`.

        Returns:
            The merged callback manager of the same type as the current object.
@@ -965,28 +967,29 @@ class BaseCallbackManager(CallbackManagerMixin):
                #    ['tag2', 'tag1']
            ```
        """  # noqa: E501
-        manager = self.__class__(
+        # Combine handlers and inheritable_handlers separately, using sets
+        # to deduplicate (order not preserved)
+        combined_handlers = list(set(self.handlers) | set(other.handlers))
+        combined_inheritable = list(
+            set(self.inheritable_handlers) | set(other.inheritable_handlers)
+        )
+
+        return self.__class__(
            parent_run_id=self.parent_run_id or other.parent_run_id,
-            handlers=[],
-            inheritable_handlers=[],
+            handlers=combined_handlers,
+            inheritable_handlers=combined_inheritable,
            tags=list(set(self.tags + other.tags)),
            inheritable_tags=list(set(self.inheritable_tags + other.inheritable_tags)),
            metadata={
                **self.metadata,
                **other.metadata,
            },
+            inheritable_metadata={
+                **self.inheritable_metadata,
+                **other.inheritable_metadata,
+            },
        )

-        handlers = self.handlers + other.handlers
-        inheritable_handlers = self.inheritable_handlers + other.inheritable_handlers
-
-        for handler in handlers:
-            manager.add_handler(handler)
-
-        for handler in inheritable_handlers:
-            manager.add_handler(handler, inherit=True)
-        return manager
-
    @property
    def is_async(self) -> bool:
        """Whether the callback manager is async."""
@@ -1001,7 +1004,7 @@ class BaseCallbackManager(CallbackManagerMixin):

        Args:
            handler: The handler to add.
-            inherit: Whether to inherit the handler. Default is True.
+            inherit: Whether to inherit the handler.
        """
        if handler not in self.handlers:
            self.handlers.append(handler)
@@ -1028,7 +1031,7 @@ class BaseCallbackManager(CallbackManagerMixin):

        Args:
            handlers: The handlers to set.
-            inherit: Whether to inherit the handlers. Default is True.
+            inherit: Whether to inherit the handlers.
        """
        self.handlers = []
        self.inheritable_handlers = []
@@ -1044,7 +1047,7 @@ class BaseCallbackManager(CallbackManagerMixin):

        Args:
            handler: The handler to set.
-            inherit: Whether to inherit the handler. Default is True.
+            inherit: Whether to inherit the handler.
        """
        self.set_handlers([handler], inherit=inherit)

@@ -1057,7 +1060,7 @@ class BaseCallbackManager(CallbackManagerMixin):

        Args:
            tags: The tags to add.
-            inherit: Whether to inherit the tags. Default is True.
+            inherit: Whether to inherit the tags.
        """
        for tag in tags:
            if tag in self.tags:
@@ -1087,7 +1090,7 @@ class BaseCallbackManager(CallbackManagerMixin):

        Args:
            metadata: The metadata to add.
-            inherit: Whether to inherit the metadata. Default is True.
+            inherit: Whether to inherit the metadata.
        """
        self.metadata.update(metadata)
        if inherit:
--- a/libs/core/langchain_core/callbacks/file.py
+++ b/libs/core/langchain_core/callbacks/file.py
@@ -132,7 +132,7 @@ class FileCallbackHandler(BaseCallbackHandler):
        Args:
            text: The text to write to the file.
            color: Optional color for the text. Defaults to `self.color`.
-            end: String appended after the text. Defaults to `""`.
+            end: String appended after the text.
            file: Optional file to write to. Defaults to `self.file`.

        Raises:
@@ -239,7 +239,7 @@ class FileCallbackHandler(BaseCallbackHandler):
            text: The text to write.
            color: Color override for this specific output. If `None`, uses
                `self.color`.
-            end: String appended after the text. Defaults to `""`.
+            end: String appended after the text.
            **kwargs: Additional keyword arguments.

        """
--- a/libs/core/langchain_core/callbacks/manager.py
+++ b/libs/core/langchain_core/callbacks/manager.py
@@ -6,14 +6,12 @@ import asyncio
 import atexit
 import functools
 import logging
-import uuid
 from abc import ABC, abstractmethod
 from collections.abc import Callable
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager, contextmanager
 from contextvars import copy_context
 from typing import TYPE_CHECKING, Any, TypeVar, cast
-from uuid import UUID

 from langsmith.run_helpers import get_tracing_context
 from typing_extensions import Self, override
@@ -39,12 +37,13 @@ from langchain_core.tracers.context import (
    tracing_v2_callback_var,
 )
 from langchain_core.tracers.langchain import LangChainTracer
-from langchain_core.tracers.schemas import Run
 from langchain_core.tracers.stdout import ConsoleCallbackHandler
 from langchain_core.utils.env import env_var_is_set
+from langchain_core.utils.uuid import uuid7

 if TYPE_CHECKING:
    from collections.abc import AsyncGenerator, Coroutine, Generator, Sequence
+    from uuid import UUID

    from tenacity import RetryCallState

@@ -52,6 +51,7 @@ if TYPE_CHECKING:
    from langchain_core.documents import Document
    from langchain_core.outputs import ChatGenerationChunk, GenerationChunk, LLMResult
    from langchain_core.runnables.config import RunnableConfig
+    from langchain_core.tracers.schemas import Run

 logger = logging.getLogger(__name__)

@@ -229,7 +229,24 @@ def shielded(func: Func) -> Func:

    @functools.wraps(func)
    async def wrapped(*args: Any, **kwargs: Any) -> Any:
-        return await asyncio.shield(func(*args, **kwargs))
+        # Capture the current context to preserve context variables
+        ctx = copy_context()
+
+        # Create the coroutine
+        coro = func(*args, **kwargs)
+
+        # For Python 3.11+, create task with explicit context
+        # For older versions, fallback to original behavior
+        try:
+            # Create a task with the captured context to preserve context variables
+            task = asyncio.create_task(coro, context=ctx)  # type: ignore[call-arg, unused-ignore]
+            # `call-arg` used to not fail 3.9 or 3.10 tests
+            return await asyncio.shield(task)
+        except TypeError:
+            # Python < 3.11 fallback - create task normally then shield
+            # This won't preserve context perfectly but is better than nothing
+            task = asyncio.create_task(coro)
+            return await asyncio.shield(task)

    return cast("Func", wrapped)

@@ -487,7 +504,7 @@ class BaseRunManager(RunManagerMixin):

        """
        return cls(
-            run_id=uuid.uuid4(),
+            run_id=uuid7(),
            handlers=[],
            inheritable_handlers=[],
            tags=[],
@@ -1313,7 +1330,7 @@ class CallbackManager(BaseCallbackManager):
        managers = []
        for i, prompt in enumerate(prompts):
            # Can't have duplicate runs with the same run ID (if provided)
-            run_id_ = run_id if i == 0 and run_id is not None else uuid.uuid4()
+            run_id_ = run_id if i == 0 and run_id is not None else uuid7()
            handle_event(
                self.handlers,
                "on_llm_start",
@@ -1367,7 +1384,7 @@ class CallbackManager(BaseCallbackManager):
                run_id_ = run_id
                run_id = None
            else:
-                run_id_ = uuid.uuid4()
+                run_id_ = uuid7()
            handle_event(
                self.handlers,
                "on_chat_model_start",
@@ -1416,7 +1433,7 @@ class CallbackManager(BaseCallbackManager):

        """
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
        handle_event(
            self.handlers,
            "on_chain_start",
@@ -1471,7 +1488,7 @@ class CallbackManager(BaseCallbackManager):

        """
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        handle_event(
            self.handlers,
@@ -1520,7 +1537,7 @@ class CallbackManager(BaseCallbackManager):
            The callback manager for the retriever run.
        """
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        handle_event(
            self.handlers,
@@ -1566,9 +1583,6 @@ class CallbackManager(BaseCallbackManager):

        Raises:
            ValueError: If additional keyword arguments are passed.
-
-        !!! version-added "Added in version 0.2.14"
-
        """
        if not self.handlers:
            return
@@ -1580,7 +1594,7 @@ class CallbackManager(BaseCallbackManager):
            )
            raise ValueError(msg)
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        handle_event(
            self.handlers,
@@ -1802,7 +1816,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                run_id_ = run_id
                run_id = None
            else:
-                run_id_ = uuid.uuid4()
+                run_id_ = uuid7()

            if inline_handlers:
                inline_tasks.append(
@@ -1886,7 +1900,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                run_id_ = run_id
                run_id = None
            else:
-                run_id_ = uuid.uuid4()
+                run_id_ = uuid7()

            for handler in self.handlers:
                task = ahandle_event(
@@ -1948,7 +1962,7 @@ class AsyncCallbackManager(BaseCallbackManager):
            The async callback manager for the chain run.
        """
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        await ahandle_event(
            self.handlers,
@@ -1996,7 +2010,7 @@ class AsyncCallbackManager(BaseCallbackManager):
            The async callback manager for the tool run.
        """
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        await ahandle_event(
            self.handlers,
@@ -2042,13 +2056,11 @@ class AsyncCallbackManager(BaseCallbackManager):

        Raises:
            ValueError: If additional keyword arguments are passed.
-
-        !!! version-added "Added in version 0.2.14"
        """
        if not self.handlers:
            return
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        if kwargs:
            msg = (
@@ -2090,7 +2102,7 @@ class AsyncCallbackManager(BaseCallbackManager):
            The async callback manager for the retriever run.
        """
        if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()

        await ahandle_event(
            self.handlers,
@@ -2555,9 +2567,6 @@ async def adispatch_custom_event(
        This is due to a limitation in asyncio for python <= 3.10 that prevents
        LangChain from automatically propagating the config object on the user's
        behalf.
-
-    !!! version-added "Added in version 0.2.15"
-
    """
    # Import locally to prevent circular imports.
    from langchain_core.runnables.config import (  # noqa: PLC0415
@@ -2630,9 +2639,6 @@ def dispatch_custom_event(
        foo_ = RunnableLambda(foo)
        foo_.invoke({"a": "1"}, {"callbacks": [CustomCallbackManager()]})
        ```
-
-    !!! version-added "Added in version 0.2.15"
-
    """
    # Import locally to prevent circular imports.
    from langchain_core.runnables.config import (  # noqa: PLC0415
--- a/libs/core/langchain_core/callbacks/stdout.py
+++ b/libs/core/langchain_core/callbacks/stdout.py
@@ -104,7 +104,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
        Args:
            text: The text to print.
            color: The color to use for the text.
-            end: The end character to use. Defaults to "".
+            end: The end character to use.
            **kwargs: Additional keyword arguments.
        """
        print_text(text, color=color or self.color, end=end)
--- a/libs/core/langchain_core/callbacks/usage.py
+++ b/libs/core/langchain_core/callbacks/usage.py
@@ -24,7 +24,7 @@ class UsageMetadataCallbackHandler(BaseCallbackHandler):
        from langchain_core.callbacks import UsageMetadataCallbackHandler

        llm_1 = init_chat_model(model="openai:gpt-4o-mini")
-        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-20241022")

        callback = UsageMetadataCallbackHandler()
        result_1 = llm_1.invoke("Hello", config={"callbacks": [callback]})
@@ -43,7 +43,7 @@ class UsageMetadataCallbackHandler(BaseCallbackHandler):
          'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
        ```

-    !!! version-added "Added in version 0.3.49"
+    !!! version-added "Added in `langchain-core` 0.3.49"

    """

@@ -95,7 +95,7 @@ def get_usage_metadata_callback(
    """Get usage metadata callback.

    Get context manager for tracking usage metadata across chat model calls using
-    `AIMessage.usage_metadata`.
+    [`AIMessage.usage_metadata`][langchain.messages.AIMessage.usage_metadata].

    Args:
        name: The name of the context variable.
@@ -109,7 +109,7 @@ def get_usage_metadata_callback(
        from langchain_core.callbacks import get_usage_metadata_callback

        llm_1 = init_chat_model(model="openai:gpt-4o-mini")
-        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-20241022")

        with get_usage_metadata_callback() as cb:
            llm_1.invoke("Hello")
@@ -134,7 +134,7 @@ def get_usage_metadata_callback(
        }
        ```

-    !!! version-added "Added in version 0.3.49"
+    !!! version-added "Added in `langchain-core` 0.3.49"

    """
    usage_metadata_callback_var: ContextVar[UsageMetadataCallbackHandler | None] = (
--- a/libs/core/langchain_core/chat_history.py
+++ b/libs/core/langchain_core/chat_history.py
@@ -121,7 +121,7 @@ class BaseChatMessageHistory(ABC):
        This method may be deprecated in a future release.

        Args:
-            message: The human message to add to the store.
+            message: The `HumanMessage` to add to the store.
        """
        if isinstance(message, HumanMessage):
            self.add_message(message)
@@ -129,7 +129,7 @@ class BaseChatMessageHistory(ABC):
            self.add_message(HumanMessage(content=message))

    def add_ai_message(self, message: AIMessage | str) -> None:
-        """Convenience method for adding an AI message string to the store.
+        """Convenience method for adding an `AIMessage` string to the store.

        !!! note
            This is a convenience method. Code should favor the bulk `add_messages`
@@ -138,7 +138,7 @@ class BaseChatMessageHistory(ABC):
        This method may be deprecated in a future release.

        Args:
-            message: The AI message to add.
+            message: The `AIMessage` to add.
        """
        if isinstance(message, AIMessage):
            self.add_message(message)
@@ -153,7 +153,7 @@ class BaseChatMessageHistory(ABC):

        Raises:
            NotImplementedError: If the sub-class has not implemented an efficient
-                add_messages method.
+                `add_messages` method.
        """
        if type(self).add_messages != BaseChatMessageHistory.add_messages:
            # This means that the sub-class has implemented an efficient add_messages
@@ -173,7 +173,7 @@ class BaseChatMessageHistory(ABC):
        in an efficient manner to avoid unnecessary round-trips to the underlying store.

        Args:
-            messages: A sequence of BaseMessage objects to store.
+            messages: A sequence of `BaseMessage` objects to store.
        """
        for message in messages:
            self.add_message(message)
@@ -182,7 +182,7 @@ class BaseChatMessageHistory(ABC):
        """Async add a list of messages.

        Args:
-            messages: A sequence of BaseMessage objects to store.
+            messages: A sequence of `BaseMessage` objects to store.
        """
        await run_in_executor(None, self.add_messages, messages)

--- a/libs/core/langchain_core/document_loaders/base.py
+++ b/libs/core/langchain_core/document_loaders/base.py
@@ -27,7 +27,7 @@ class BaseLoader(ABC):  # noqa: B024
    """Interface for Document Loader.

    Implementations should implement the lazy-loading method using generators
-    to avoid loading all Documents into memory at once.
+    to avoid loading all documents into memory at once.

    `load` is provided just for user convenience and should not be overridden.
    """
@@ -35,38 +35,40 @@ class BaseLoader(ABC):  # noqa: B024
    # Sub-classes should not implement this method directly. Instead, they
    # should implement the lazy load method.
    def load(self) -> list[Document]:
-        """Load data into Document objects.
+        """Load data into `Document` objects.

        Returns:
-            the documents.
+            The documents.
        """
        return list(self.lazy_load())

    async def aload(self) -> list[Document]:
-        """Load data into Document objects.
+        """Load data into `Document` objects.

        Returns:
-            the documents.
+            The documents.
        """
        return [document async for document in self.alazy_load()]

    def load_and_split(
        self, text_splitter: TextSplitter | None = None
    ) -> list[Document]:
-        """Load Documents and split into chunks. Chunks are returned as Documents.
+        """Load `Document` and split into chunks. Chunks are returned as `Document`.

-        Do not override this method. It should be considered to be deprecated!
+        !!! danger
+
+            Do not override this method. It should be considered to be deprecated!

        Args:
-            text_splitter: TextSplitter instance to use for splitting documents.
-                Defaults to RecursiveCharacterTextSplitter.
+            text_splitter: `TextSplitter` instance to use for splitting documents.
+                Defaults to `RecursiveCharacterTextSplitter`.

        Raises:
-            ImportError: If langchain-text-splitters is not installed
-                and no text_splitter is provided.
+            ImportError: If `langchain-text-splitters` is not installed
+                and no `text_splitter` is provided.

        Returns:
-            List of Documents.
+            List of `Document`.
        """
        if text_splitter is None:
            if not _HAS_TEXT_SPLITTERS:
@@ -86,10 +88,10 @@ class BaseLoader(ABC):  # noqa: B024
    # Attention: This method will be upgraded into an abstractmethod once it's
    #            implemented in all the existing subclasses.
    def lazy_load(self) -> Iterator[Document]:
-        """A lazy loader for Documents.
+        """A lazy loader for `Document`.

        Yields:
-            the documents.
+            The `Document` objects.
        """
        if type(self).load != BaseLoader.load:
            return iter(self.load())
@@ -97,10 +99,10 @@ class BaseLoader(ABC):  # noqa: B024
        raise NotImplementedError(msg)

    async def alazy_load(self) -> AsyncIterator[Document]:
-        """A lazy loader for Documents.
+        """A lazy loader for `Document`.

        Yields:
-            the documents.
+            The `Document` objects.
        """
        iterator = await run_in_executor(None, self.lazy_load)
        done = object()
@@ -115,7 +117,7 @@ class BaseBlobParser(ABC):
    """Abstract interface for blob parsers.

    A blob parser provides a way to parse raw data stored in a blob into one
-    or more documents.
+    or more `Document` objects.

    The parser can be composed with blob loaders, making it easy to reuse
    a parser independent of how the blob was originally loaded.
@@ -128,25 +130,25 @@ class BaseBlobParser(ABC):
        Subclasses are required to implement this method.

        Args:
-            blob: Blob instance
+            blob: `Blob` instance

        Returns:
-            Generator of documents
+            Generator of `Document` objects
        """

    def parse(self, blob: Blob) -> list[Document]:
-        """Eagerly parse the blob into a document or documents.
+        """Eagerly parse the blob into a `Document` or list of `Document` objects.

        This is a convenience method for interactive development environment.

-        Production applications should favor the lazy_parse method instead.
+        Production applications should favor the `lazy_parse` method instead.

        Subclasses should generally not over-ride this parse method.

        Args:
-            blob: Blob instance
+            blob: `Blob` instance

        Returns:
-            List of documents
+            List of `Document` objects
        """
        return list(self.lazy_parse(blob))
--- a/libs/core/langchain_core/document_loaders/blob_loaders.py
+++ b/libs/core/langchain_core/document_loaders/blob_loaders.py
@@ -28,7 +28,7 @@ class BlobLoader(ABC):
    def yield_blobs(
        self,
    ) -> Iterable[Blob]:
-        """A lazy loader for raw data represented by LangChain's Blob object.
+        """A lazy loader for raw data represented by LangChain's `Blob` object.

        Returns:
            A generator over blobs
--- a/libs/core/langchain_core/document_loaders/langsmith.py
+++ b/libs/core/langchain_core/document_loaders/langsmith.py
@@ -11,16 +11,17 @@ from typing_extensions import override

 from langchain_core.document_loaders.base import BaseLoader
 from langchain_core.documents import Document
+from langchain_core.tracers._compat import pydantic_to_dict


 class LangSmithLoader(BaseLoader):
-    """Load LangSmith Dataset examples as Documents.
+    """Load LangSmith Dataset examples as `Document` objects.

-    Loads the example inputs as the Document page content and places the entire example
-    into the Document metadata. This allows you to easily create few-shot example
-    retrievers from the loaded documents.
+    Loads the example inputs as the `Document` page content and places the entire
+    example into the `Document` metadata. This allows you to easily create few-shot
+    example retrievers from the loaded documents.

-    ??? note "Lazy load"
+    ??? note "Lazy loading example"

        ```python
        from langchain_core.document_loaders import LangSmithLoader
@@ -34,9 +35,6 @@ class LangSmithLoader(BaseLoader):
        ```python
        # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
        ```
-
-    !!! version-added "Added in version 0.2.34"
-
    """

    def __init__(
@@ -69,15 +67,14 @@ class LangSmithLoader(BaseLoader):
            format_content: Function for converting the content extracted from the example
                inputs into a string. Defaults to JSON-encoding the contents.
            example_ids: The IDs of the examples to filter by.
-            as_of: The dataset version tag OR
-                timestamp to retrieve the examples as of.
-                Response examples will only be those that were present at the time
-                of the tagged (or timestamped) version.
+            as_of: The dataset version tag or timestamp to retrieve the examples as of.
+                Response examples will only be those that were present at the time of
+                the tagged (or timestamped) version.
            splits: A list of dataset splits, which are
-                divisions of your dataset such as 'train', 'test', or 'validation'.
+                divisions of your dataset such as `train`, `test`, or `validation`.
                Returns examples only from the specified splits.
-            inline_s3_urls: Whether to inline S3 URLs. Defaults to `True`.
-            offset: The offset to start from. Defaults to 0.
+            inline_s3_urls: Whether to inline S3 URLs.
+            offset: The offset to start from.
            limit: The maximum number of examples to return.
            metadata: Metadata to filter by.
            filter: A structured filter string to apply to the examples.
@@ -122,14 +119,14 @@ class LangSmithLoader(BaseLoader):
            for key in self.content_key:
                content = content[key]
            content_str = self.format_content(content)
-            metadata = example.dict()
+            metadata = pydantic_to_dict(example)
            # Stringify datetime and UUID types.
            for k in ("dataset_id", "created_at", "modified_at", "source_run_id", "id"):
                metadata[k] = str(metadata[k]) if metadata[k] else metadata[k]
            yield Document(content_str, metadata=metadata)


-def _stringify(x: str | dict) -> str:
+def _stringify(x: str | dict[str, Any]) -> str:
    if isinstance(x, str):
        return x
    try:
--- a/libs/core/langchain_core/documents/init.py
+++ b/libs/core/langchain_core/documents/init.py
@@ -1,7 +1,28 @@
-"""Documents module.
+"""Documents module for data retrieval and processing workflows.

-**Document** module is a collection of classes that handle documents
-and their transformations.
+This module provides core abstractions for handling data in retrieval-augmented
+generation (RAG) pipelines, vector stores, and document processing workflows.
+
+!!! warning "Documents vs. message content"
+    This module is distinct from `langchain_core.messages.content`, which provides
+    multimodal content blocks for **LLM chat I/O** (text, images, audio, etc. within
+    messages).
+
+    **Key distinction:**
+
+    - **Documents** (this module): For **data retrieval and processing workflows**
+        - Vector stores, retrievers, RAG pipelines
+        - Text chunking, embedding, and semantic search
+        - Example: Chunks of a PDF stored in a vector database
+
+    - **Content Blocks** (`messages.content`): For **LLM conversational I/O**
+        - Multimodal message content sent to/from models
+        - Tool calls, reasoning, citations within chat
+        - Example: An image sent to a vision model in a chat message (via
+            [`ImageContentBlock`][langchain.messages.ImageContentBlock])
+
+    While both can represent similar data types (text, files), they serve different
+    architectural purposes in LangChain applications.
 """

 from typing import TYPE_CHECKING
@@ -9,9 +30,9 @@ from typing import TYPE_CHECKING
 from langchain_core._import_utils import import_attr

 if TYPE_CHECKING:
-    from .base import Document
-    from .compressor import BaseDocumentCompressor
-    from .transformers import BaseDocumentTransformer
+    from langchain_core.documents.base import Document
+    from langchain_core.documents.compressor import BaseDocumentCompressor
+    from langchain_core.documents.transformers import BaseDocumentTransformer

 __all__ = ("BaseDocumentCompressor", "BaseDocumentTransformer", "Document")

--- a/libs/core/langchain_core/documents/base.py
+++ b/libs/core/langchain_core/documents/base.py
@@ -1,4 +1,16 @@
-"""Base classes for media and documents."""
+"""Base classes for media and documents.
+
+This module contains core abstractions for **data retrieval and processing workflows**:
+
+- `BaseMedia`: Base class providing `id` and `metadata` fields
+- `Blob`: Raw data loading (files, binary data) - used by document loaders
+- `Document`: Text content for retrieval (RAG, vector stores, semantic search)
+
+!!! note "Not for LLM chat messages"
+    These classes are for data processing pipelines, not LLM I/O. For multimodal
+    content in chat messages (images, audio in conversations), see
+    `langchain.messages` content blocks instead.
+"""

 from __future__ import annotations

@@ -19,27 +31,23 @@ PathLike = str | PurePath


 class BaseMedia(Serializable):
-    """Use to represent media content.
+    """Base class for content used in retrieval and data processing workflows.

-    Media objects can be used to represent raw data, such as text or binary data.
+    Provides common fields for content that needs to be stored, indexed, or searched.

-    LangChain Media objects allow associating metadata and an optional identifier
-    with the content.
-
-    The presence of an ID and metadata make it easier to store, index, and search
-    over the content in a structured way.
+    !!! note
+        For multimodal content in **chat messages** (images, audio sent to/from LLMs),
+        use `langchain.messages` content blocks instead.
    """

    # The ID field is optional at the moment.
    # It will likely become required in a future major release after
-    # it has been adopted by enough vectorstore implementations.
+    # it has been adopted by enough VectorStore implementations.
    id: str | None = Field(default=None, coerce_numbers_to_str=True)
    """An optional identifier for the document.

    Ideally this should be unique across the document collection and formatted
    as a UUID, but this will not be enforced.
-
-    !!! version-added "Added in version 0.2.11"
    """

    metadata: dict = Field(default_factory=dict)
@@ -47,15 +55,14 @@ class BaseMedia(Serializable):


 class Blob(BaseMedia):
-    """Blob represents raw data by either reference or value.
+    """Raw data abstraction for document loading and file processing.

-    Provides an interface to materialize the blob in different representations, and
-    help to decouple the development of data loaders from the downstream parsing of
-    the raw data.
+    Represents raw bytes or text, either in-memory or by file reference. Used
+    primarily by document loaders to decouple data loading from parsing.

-    Inspired by: https://developer.mozilla.org/en-US/docs/Web/API/Blob
+    Inspired by [Mozilla's `Blob`](https://developer.mozilla.org/en-US/docs/Web/API/Blob)

-    Example: Initialize a blob from in-memory data
+    ???+ example "Initialize a blob from in-memory data"

        ```python
        from langchain_core.documents import Blob
@@ -73,7 +80,7 @@ class Blob(BaseMedia):
            print(f.read())
        ```

-    Example: Load from memory and specify mime-type and metadata
+    ??? example "Load from memory and specify MIME type and metadata"

        ```python
        from langchain_core.documents import Blob
@@ -85,7 +92,7 @@ class Blob(BaseMedia):
        )
        ```

-    Example: Load the blob from a file
+    ??? example "Load the blob from a file"

        ```python
        from langchain_core.documents import Blob
@@ -105,13 +112,13 @@ class Blob(BaseMedia):
    """

    data: bytes | str | None = None
-    """Raw data associated with the blob."""
+    """Raw data associated with the `Blob`."""
    mimetype: str | None = None
-    """MimeType not to be confused with a file extension."""
+    """MIME type, not to be confused with a file extension."""
    encoding: str = "utf-8"
    """Encoding to use if decoding the bytes into a string.

-    Use utf-8 as default encoding, if decoding to string.
+    Uses `utf-8` as default encoding if decoding to string.
    """
    path: PathLike | None = None
    """Location where the original content was found."""
@@ -125,9 +132,9 @@ class Blob(BaseMedia):
    def source(self) -> str | None:
        """The source location of the blob as string if known otherwise none.

-        If a path is associated with the blob, it will default to the path location.
+        If a path is associated with the `Blob`, it will default to the path location.

-        Unless explicitly set via a metadata field called "source", in which
+        Unless explicitly set via a metadata field called `'source'`, in which
        case that value will be used instead.
        """
        if self.metadata and "source" in self.metadata:
@@ -211,15 +218,15 @@ class Blob(BaseMedia):
        """Load the blob from a path like object.

        Args:
-            path: path like object to file to be read
+            path: Path-like object to file to be read
            encoding: Encoding to use if decoding the bytes into a string
-            mime_type: if provided, will be set as the mime-type of the data
-            guess_type: If `True`, the mimetype will be guessed from the file extension,
-                        if a mime-type was not provided
-            metadata: Metadata to associate with the blob
+            mime_type: If provided, will be set as the MIME type of the data
+            guess_type: If `True`, the MIME type will be guessed from the file
+                extension, if a MIME type was not provided
+            metadata: Metadata to associate with the `Blob`

        Returns:
-            Blob instance
+            `Blob` instance
        """
        if mime_type is None and guess_type:
            mimetype = mimetypes.guess_type(path)[0] if guess_type else None
@@ -245,17 +252,17 @@ class Blob(BaseMedia):
        path: str | None = None,
        metadata: dict | None = None,
    ) -> Blob:
-        """Initialize the blob from in-memory data.
+        """Initialize the `Blob` from in-memory data.

        Args:
-            data: the in-memory data associated with the blob
+            data: The in-memory data associated with the `Blob`
            encoding: Encoding to use if decoding the bytes into a string
-            mime_type: if provided, will be set as the mime-type of the data
-            path: if provided, will be set as the source from which the data came
-            metadata: Metadata to associate with the blob
+            mime_type: If provided, will be set as the MIME type of the data
+            path: If provided, will be set as the source from which the data came
+            metadata: Metadata to associate with the `Blob`

        Returns:
-            Blob instance
+            `Blob` instance
        """
        return cls(
            data=data,
@@ -276,6 +283,10 @@ class Blob(BaseMedia):
 class Document(BaseMedia):
    """Class for storing a piece of text and associated metadata.

+    !!! note
+        `Document` is for **retrieval workflows**, not chat I/O. For sending text
+        to an LLM in a conversation, use message types from `langchain.messages`.
+
    Example:
        ```python
        from langchain_core.documents import Document
@@ -298,12 +309,12 @@ class Document(BaseMedia):

    @classmethod
    def is_lc_serializable(cls) -> bool:
-        """Return True as this class is serializable."""
+        """Return `True` as this class is serializable."""
        return True

    @classmethod
    def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.

        Returns:
            ["langchain", "schema", "document"]
@@ -311,10 +322,10 @@ class Document(BaseMedia):
        return ["langchain", "schema", "document"]

    def __str__(self) -> str:
-        """Override __str__ to restrict it to page_content and metadata.
+        """Override `__str__` to restrict it to page_content and metadata.

        Returns:
-            A string representation of the Document.
+            A string representation of the `Document`.
        """
        # The format matches pydantic format for __str__.
        #
--- a/libs/core/langchain_core/documents/compressor.py
+++ b/libs/core/langchain_core/documents/compressor.py
@@ -21,14 +21,14 @@ class BaseDocumentCompressor(BaseModel, ABC):

    This abstraction is primarily used for post-processing of retrieved documents.

-    Documents matching a given query are first retrieved.
+    `Document` objects matching a given query are first retrieved.

    Then the list of documents can be further processed.

    For example, one could re-rank the retrieved documents using an LLM.

    !!! note
-        Users should favor using a RunnableLambda instead of sub-classing from this
+        Users should favor using a `RunnableLambda` instead of sub-classing from this
        interface.

    """
@@ -43,9 +43,9 @@ class BaseDocumentCompressor(BaseModel, ABC):
        """Compress retrieved documents given the query context.

        Args:
-            documents: The retrieved documents.
+            documents: The retrieved `Document` objects.
            query: The query context.
-            callbacks: Optional callbacks to run during compression.
+            callbacks: Optional `Callbacks` to run during compression.

        Returns:
            The compressed documents.
@@ -61,9 +61,9 @@ class BaseDocumentCompressor(BaseModel, ABC):
        """Async compress retrieved documents given the query context.

        Args:
-            documents: The retrieved documents.
+            documents: The retrieved `Document` objects.
            query: The query context.
-            callbacks: Optional callbacks to run during compression.
+            callbacks: Optional `Callbacks` to run during compression.

        Returns:
            The compressed documents.
--- a/libs/core/langchain_core/documents/transformers.py
+++ b/libs/core/langchain_core/documents/transformers.py
@@ -16,8 +16,8 @@ if TYPE_CHECKING:
 class BaseDocumentTransformer(ABC):
    """Abstract base class for document transformation.

-    A document transformation takes a sequence of Documents and returns a
-    sequence of transformed Documents.
+    A document transformation takes a sequence of `Document` objects and returns a
+    sequence of transformed `Document` objects.

    Example:
        ```python
@@ -57,10 +57,10 @@ class BaseDocumentTransformer(ABC):
        """Transform a list of documents.

        Args:
-            documents: A sequence of Documents to be transformed.
+            documents: A sequence of `Document` objects to be transformed.

        Returns:
-            A sequence of transformed Documents.
+            A sequence of transformed `Document` objects.
        """

    async def atransform_documents(
@@ -69,10 +69,10 @@ class BaseDocumentTransformer(ABC):
        """Asynchronously transform a list of documents.

        Args:
-            documents: A sequence of Documents to be transformed.
+            documents: A sequence of `Document` objects to be transformed.

        Returns:
-            A sequence of transformed Documents.
+            A sequence of transformed `Document` objects.
        """
        return await run_in_executor(
            None, self.transform_documents, documents, **kwargs
--- a/libs/core/langchain_core/embeddings/fake.py
+++ b/libs/core/langchain_core/embeddings/fake.py
@@ -18,7 +18,8 @@ class FakeEmbeddings(Embeddings, BaseModel):

    This embedding model creates embeddings by sampling from a normal distribution.

-    Do not use this outside of testing, as it is not a real embedding model.
+    !!! danger "Toy model"
+        Do not use this outside of testing, as it is not a real embedding model.

    Instantiate:
        ```python
@@ -72,7 +73,8 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
    This embedding model creates embeddings by sampling from a normal distribution
    with a seed based on the hash of the text.

-    Do not use this outside of testing, as it is not a real embedding model.
+    !!! danger "Toy model"
+        Do not use this outside of testing, as it is not a real embedding model.

    Instantiate:
        ```python
--- a/libs/core/langchain_core/example_selectors/length_based.py
+++ b/libs/core/langchain_core/example_selectors/length_based.py
@@ -11,7 +11,7 @@ from langchain_core.prompts.prompt import PromptTemplate


 def _get_length_based(text: str) -> int:
-    return len(re.split("\n| ", text))
+    return len(re.split(r"\n| ", text))


 class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
@@ -29,7 +29,7 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
    max_length: int = 2048
    """Max length for the prompt, beyond which examples are cut."""

-    example_text_lengths: list[int] = Field(default_factory=list)  # :meta private:
+    example_text_lengths: list[int] = Field(default_factory=list)
    """Length of each example."""

    def add_example(self, example: dict[str, str]) -> None:
--- a/libs/core/langchain_core/example_selectors/semantic_similarity.py
+++ b/libs/core/langchain_core/example_selectors/semantic_similarity.py
@@ -41,7 +41,7 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
    """Optional keys to filter input to. If provided, the search is based on
    the input variables instead of all variables."""
    vectorstore_kwargs: dict[str, Any] | None = None
-    """Extra arguments passed to similarity_search function of the vectorstore."""
+    """Extra arguments passed to similarity_search function of the `VectorStore`."""

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
@@ -154,12 +154,12 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
            examples: List of examples to use in the prompt.
            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
+            k: Number of examples to select.
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            example_keys: If provided, keys to filter examples to.
            vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store

        Returns:
@@ -198,12 +198,12 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
            examples: List of examples to use in the prompt.
            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
+            k: Number of examples to select.
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            example_keys: If provided, keys to filter examples to.
            vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store

        Returns:
@@ -285,14 +285,13 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
            examples: List of examples to use in the prompt.
            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
+            k: Number of examples to select.
+            fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            example_keys: If provided, keys to filter examples to.
            vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store

        Returns:
@@ -333,14 +332,13 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
            examples: List of examples to use in the prompt.
            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
-            k: Number of examples to select. Default is 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-                Default is 20.
+            k: Number of examples to select.
+            fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            example_keys: If provided, keys to filter examples to.
            vectorstore_kwargs: Extra arguments passed to similarity_search function
-                of the vectorstore.
+                of the `VectorStore`.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store

        Returns:
--- a/libs/core/langchain_core/exceptions.py
+++ b/libs/core/langchain_core/exceptions.py
@@ -16,9 +16,10 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
    """Exception that output parsers should raise to signify a parsing error.

    This exists to differentiate parsing errors from other code or execution errors
-    that also may arise inside the output parser. OutputParserExceptions will be
-    available to catch and handle in ways to fix the parsing error, while other
-    errors will be raised.
+    that also may arise inside the output parser.
+
+    `OutputParserException` will be available to catch and handle in ways to fix the
+    parsing error, while other errors will be raised.
    """

    def __init__(
@@ -28,23 +29,23 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
        llm_output: str | None = None,
        send_to_llm: bool = False,  # noqa: FBT001,FBT002
    ):
-        """Create an OutputParserException.
+        """Create an `OutputParserException`.

        Args:
            error: The error that's being re-raised or an error message.
-            observation: String explanation of error which can be passed to a
-                model to try and remediate the issue.
+            observation: String explanation of error which can be passed to a model to
+                try and remediate the issue.
            llm_output: String model output which is error-ing.

            send_to_llm: Whether to send the observation and llm_output back to an Agent
-                after an OutputParserException has been raised.
+                after an `OutputParserException` has been raised.
+
                This gives the underlying model driving the agent the context that the
                previous output was improperly structured, in the hopes that it will
                update the output to the correct format.
-                Defaults to `False`.

        Raises:
-            ValueError: If `send_to_llm` is True but either observation or
+            ValueError: If `send_to_llm` is `True` but either observation or
                `llm_output` are not provided.
        """
        if isinstance(error, str):
@@ -67,11 +68,11 @@ class ErrorCode(Enum):
    """Error codes."""

    INVALID_PROMPT_INPUT = "INVALID_PROMPT_INPUT"
-    INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"
+    INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"  # Used in JS; not Py (yet)
    MESSAGE_COERCION_FAILURE = "MESSAGE_COERCION_FAILURE"
-    MODEL_AUTHENTICATION = "MODEL_AUTHENTICATION"
-    MODEL_NOT_FOUND = "MODEL_NOT_FOUND"
-    MODEL_RATE_LIMIT = "MODEL_RATE_LIMIT"
+    MODEL_AUTHENTICATION = "MODEL_AUTHENTICATION"  # Used in JS; not Py (yet)
+    MODEL_NOT_FOUND = "MODEL_NOT_FOUND"  # Used in JS; not Py (yet)
+    MODEL_RATE_LIMIT = "MODEL_RATE_LIMIT"  # Used in JS; not Py (yet)
    OUTPUT_PARSING_FAILURE = "OUTPUT_PARSING_FAILURE"


@@ -87,6 +88,6 @@ def create_message(*, message: str, error_code: ErrorCode) -> str:
    """
    return (
        f"{message}\n"
-        "For troubleshooting, visit: https://python.langchain.com/docs/"
-        f"troubleshooting/errors/{error_code.value} "
+        "For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain"
+        f"/errors/{error_code.value} "
    )
--- a/libs/core/langchain_core/indexing/init.py
+++ b/libs/core/langchain_core/indexing/init.py
@@ -1,7 +1,7 @@
 """Code to help indexing data into a vectorstore.

 This package contains helper logic to help deal with indexing data into
-a vectorstore while avoiding duplicated content and over-writing content
+a `VectorStore` while avoiding duplicated content and over-writing content
 if it's unchanged.
 """

--- a/libs/core/langchain_core/indexing/api.py
+++ b/libs/core/langchain_core/indexing/api.py
@@ -6,16 +6,9 @@ import hashlib
 import json
 import uuid
 import warnings
-from collections.abc import (
-    AsyncIterable,
-    AsyncIterator,
-    Callable,
-    Iterable,
-    Iterator,
-    Sequence,
-)
 from itertools import islice
 from typing import (
+    TYPE_CHECKING,
    Any,
    Literal,
    TypedDict,
@@ -29,6 +22,16 @@ from langchain_core.exceptions import LangChainException
 from langchain_core.indexing.base import DocumentIndex, RecordManager
 from langchain_core.vectorstores import VectorStore

+if TYPE_CHECKING:
+    from collections.abc import (
+        AsyncIterable,
+        AsyncIterator,
+        Callable,
+        Iterable,
+        Iterator,
+        Sequence,
+    )
+
 # Magic UUID to use as a namespace for hashing.
 # Used to try and generate a unique UUID for each document
 # from hashing the document content and metadata.
@@ -239,6 +242,17 @@ def _delete(
    vector_store: VectorStore | DocumentIndex,
    ids: list[str],
 ) -> None:
+    """Delete documents from a vector store or document index by their IDs.
+
+    Args:
+        vector_store: The vector store or document index to delete from.
+        ids: List of document IDs to delete.
+
+    Raises:
+        IndexingException: If the delete operation fails.
+        TypeError: If the `vector_store` is neither a `VectorStore` nor a
+            `DocumentIndex`.
+    """
    if isinstance(vector_store, VectorStore):
        delete_ok = vector_store.delete(ids)
        if delete_ok is not None and delete_ok is False:
@@ -298,61 +312,59 @@ def index(
    For the time being, documents are indexed using their hashes, and users
    are not able to specify the uid of the document.

-    !!! warning "Behavior changed in 0.3.25"
+    !!! warning "Behavior changed in `langchain-core` 0.3.25"
+
        Added `scoped_full` cleanup mode.

    !!! warning

        * In full mode, the loader should be returning
-          the entire dataset, and not just a subset of the dataset.
-          Otherwise, the auto_cleanup will remove documents that it is not
-          supposed to.
+            the entire dataset, and not just a subset of the dataset.
+            Otherwise, the auto_cleanup will remove documents that it is not
+            supposed to.
        * In incremental mode, if documents associated with a particular
-          source id appear across different batches, the indexing API
-          will do some redundant work. This will still result in the
-          correct end state of the index, but will unfortunately not be
-          100% efficient. For example, if a given document is split into 15
-          chunks, and we index them using a batch size of 5, we'll have 3 batches
-          all with the same source id. In general, to avoid doing too much
-          redundant work select as big a batch size as possible.
+            source id appear across different batches, the indexing API
+            will do some redundant work. This will still result in the
+            correct end state of the index, but will unfortunately not be
+            100% efficient. For example, if a given document is split into 15
+            chunks, and we index them using a batch size of 5, we'll have 3 batches
+            all with the same source id. In general, to avoid doing too much
+            redundant work select as big a batch size as possible.
        * The `scoped_full` mode is suitable if determining an appropriate batch size
-          is challenging or if your data loader cannot return the entire dataset at
-          once. This mode keeps track of source IDs in memory, which should be fine
-          for most use cases. If your dataset is large (10M+ docs), you will likely
-          need to parallelize the indexing process regardless.
+            is challenging or if your data loader cannot return the entire dataset at
+            once. This mode keeps track of source IDs in memory, which should be fine
+            for most use cases. If your dataset is large (10M+ docs), you will likely
+            need to parallelize the indexing process regardless.

    Args:
        docs_source: Data loader or iterable of documents to index.
        record_manager: Timestamped set to keep track of which documents were
            updated.
-        vector_store: VectorStore or DocumentIndex to index the documents into.
-        batch_size: Batch size to use when indexing. Default is 100.
-        cleanup: How to handle clean up of documents. Default is None.
+        vector_store: `VectorStore` or DocumentIndex to index the documents into.
+        batch_size: Batch size to use when indexing.
+        cleanup: How to handle clean up of documents.

            - incremental: Cleans up all documents that haven't been updated AND
-              that are associated with source ids that were seen during indexing.
-              Clean up is done continuously during indexing helping to minimize the
-              probability of users seeing duplicated content.
+                that are associated with source IDs that were seen during indexing.
+                Clean up is done continuously during indexing helping to minimize the
+                probability of users seeing duplicated content.
            - full: Delete all documents that have not been returned by the loader
-              during this run of indexing.
-              Clean up runs after all documents have been indexed.
-              This means that users may see duplicated content during indexing.
+                during this run of indexing.
+                Clean up runs after all documents have been indexed.
+                This means that users may see duplicated content during indexing.
            - scoped_full: Similar to Full, but only deletes all documents
-              that haven't been updated AND that are associated with
-              source ids that were seen during indexing.
+                that haven't been updated AND that are associated with
+                source IDs that were seen during indexing.
            - None: Do not delete any documents.
        source_id_key: Optional key that helps identify the original source
-            of the document. Default is None.
+            of the document.
        cleanup_batch_size: Batch size to use when cleaning up documents.
-            Default is 1_000.
        force_update: Force update documents even if they are present in the
            record manager. Useful if you are re-indexing with updated embeddings.
-            Default is False.
        key_encoder: Hashing algorithm to use for hashing the document content and
-            metadata. Default is "sha1".
-            Other options include "blake2b", "sha256", and "sha512".
+            metadata. Options include "blake2b", "sha256", and "sha512".

-            !!! version-added "Added in version 0.3.66"
+            !!! version-added "Added in `langchain-core` 0.3.66"

        key_encoder: Hashing algorithm to use for hashing the document.
            If not provided, a default encoder using SHA-1 will be used.
@@ -366,10 +378,10 @@ def index(
            When changing the key encoder, you must change the
            index as well to avoid duplicated documents in the cache.
        upsert_kwargs: Additional keyword arguments to pass to the add_documents
-            method of the VectorStore or the upsert method of the DocumentIndex.
+            method of the `VectorStore` or the upsert method of the DocumentIndex.
            For example, you can use this to specify a custom vector_field:
            upsert_kwargs={"vector_field": "embedding"}
-            !!! version-added "Added in version 0.3.10"
+            !!! version-added "Added in `langchain-core` 0.3.10"

    Returns:
        Indexing result which contains information about how many documents
@@ -378,10 +390,10 @@ def index(
    Raises:
        ValueError: If cleanup mode is not one of 'incremental', 'full' or None
        ValueError: If cleanup mode is incremental and source_id_key is None.
-        ValueError: If vectorstore does not have
+        ValueError: If `VectorStore` does not have
            "delete" and "add_documents" required methods.
        ValueError: If source_id_key is not None, but is not a string or callable.
-        TypeError: If `vectorstore` is not a VectorStore or a DocumentIndex.
+        TypeError: If `vectorstore` is not a `VectorStore` or a DocumentIndex.
        AssertionError: If `source_id` is None when cleanup mode is incremental.
            (should be unreachable code).
    """
@@ -418,7 +430,7 @@ def index(
                raise ValueError(msg)

        if type(destination).delete == VectorStore.delete:
-            # Checking if the vectorstore has overridden the default delete method
+            # Checking if the VectorStore has overridden the default delete method
            # implementation which just raises a NotImplementedError
            msg = "Vectorstore has not implemented the delete method"
            raise ValueError(msg)
@@ -469,11 +481,11 @@ def index(
        ]

        if cleanup in {"incremental", "scoped_full"}:
-            # source ids are required.
+            # Source IDs are required.
            for source_id, hashed_doc in zip(source_ids, hashed_docs, strict=False):
                if source_id is None:
                    msg = (
-                        f"Source ids are required when cleanup mode is "
+                        f"Source IDs are required when cleanup mode is "
                        f"incremental or scoped_full. "
                        f"Document that starts with "
                        f"content: {hashed_doc.page_content[:100]} "
@@ -482,7 +494,7 @@ def index(
                    raise ValueError(msg)
                if cleanup == "scoped_full":
                    scoped_full_cleanup_source_ids.add(source_id)
-            # source ids cannot be None after for loop above.
+            # Source IDs cannot be None after for loop above.
            source_ids = cast("Sequence[str]", source_ids)

        exists_batch = record_manager.exists(
@@ -541,7 +553,7 @@ def index(
        # If source IDs are provided, we can do the deletion incrementally!
        if cleanup == "incremental":
            # Get the uids of the documents that were not returned by the loader.
-            # mypy isn't good enough to determine that source ids cannot be None
+            # mypy isn't good enough to determine that source IDs cannot be None
            # here due to a check that's happening above, so we check again.
            for source_id in source_ids:
                if source_id is None:
@@ -639,61 +651,59 @@ async def aindex(
    For the time being, documents are indexed using their hashes, and users
    are not able to specify the uid of the document.

-    !!! warning "Behavior changed in 0.3.25"
+    !!! warning "Behavior changed in `langchain-core` 0.3.25"
+
        Added `scoped_full` cleanup mode.

    !!! warning

        * In full mode, the loader should be returning
-          the entire dataset, and not just a subset of the dataset.
-          Otherwise, the auto_cleanup will remove documents that it is not
-          supposed to.
+            the entire dataset, and not just a subset of the dataset.
+            Otherwise, the auto_cleanup will remove documents that it is not
+            supposed to.
        * In incremental mode, if documents associated with a particular
-          source id appear across different batches, the indexing API
-          will do some redundant work. This will still result in the
-          correct end state of the index, but will unfortunately not be
-          100% efficient. For example, if a given document is split into 15
-          chunks, and we index them using a batch size of 5, we'll have 3 batches
-          all with the same source id. In general, to avoid doing too much
-          redundant work select as big a batch size as possible.
+            source id appear across different batches, the indexing API
+            will do some redundant work. This will still result in the
+            correct end state of the index, but will unfortunately not be
+            100% efficient. For example, if a given document is split into 15
+            chunks, and we index them using a batch size of 5, we'll have 3 batches
+            all with the same source id. In general, to avoid doing too much
+            redundant work select as big a batch size as possible.
        * The `scoped_full` mode is suitable if determining an appropriate batch size
-          is challenging or if your data loader cannot return the entire dataset at
-          once. This mode keeps track of source IDs in memory, which should be fine
-          for most use cases. If your dataset is large (10M+ docs), you will likely
-          need to parallelize the indexing process regardless.
+            is challenging or if your data loader cannot return the entire dataset at
+            once. This mode keeps track of source IDs in memory, which should be fine
+            for most use cases. If your dataset is large (10M+ docs), you will likely
+            need to parallelize the indexing process regardless.

    Args:
        docs_source: Data loader or iterable of documents to index.
        record_manager: Timestamped set to keep track of which documents were
            updated.
-        vector_store: VectorStore or DocumentIndex to index the documents into.
-        batch_size: Batch size to use when indexing. Default is 100.
-        cleanup: How to handle clean up of documents. Default is None.
+        vector_store: `VectorStore` or DocumentIndex to index the documents into.
+        batch_size: Batch size to use when indexing.
+        cleanup: How to handle clean up of documents.

            - incremental: Cleans up all documents that haven't been updated AND
-              that are associated with source ids that were seen during indexing.
-              Clean up is done continuously during indexing helping to minimize the
-              probability of users seeing duplicated content.
+                that are associated with source IDs that were seen during indexing.
+                Clean up is done continuously during indexing helping to minimize the
+                probability of users seeing duplicated content.
            - full: Delete all documents that have not been returned by the loader
-              during this run of indexing.
-              Clean up runs after all documents have been indexed.
-              This means that users may see duplicated content during indexing.
+                during this run of indexing.
+                Clean up runs after all documents have been indexed.
+                This means that users may see duplicated content during indexing.
            - scoped_full: Similar to Full, but only deletes all documents
-              that haven't been updated AND that are associated with
-              source ids that were seen during indexing.
+                that haven't been updated AND that are associated with
+                source IDs that were seen during indexing.
            - None: Do not delete any documents.
        source_id_key: Optional key that helps identify the original source
-            of the document. Default is None.
+            of the document.
        cleanup_batch_size: Batch size to use when cleaning up documents.
-            Default is 1_000.
        force_update: Force update documents even if they are present in the
            record manager. Useful if you are re-indexing with updated embeddings.
-            Default is False.
        key_encoder: Hashing algorithm to use for hashing the document content and
-            metadata. Default is "sha1".
-            Other options include "blake2b", "sha256", and "sha512".
+            metadata. Options include "blake2b", "sha256", and "sha512".

-            !!! version-added "Added in version 0.3.66"
+            !!! version-added "Added in `langchain-core` 0.3.66"

        key_encoder: Hashing algorithm to use for hashing the document.
            If not provided, a default encoder using SHA-1 will be used.
@@ -707,10 +717,10 @@ async def aindex(
            When changing the key encoder, you must change the
            index as well to avoid duplicated documents in the cache.
        upsert_kwargs: Additional keyword arguments to pass to the add_documents
-            method of the VectorStore or the upsert method of the DocumentIndex.
+            method of the `VectorStore` or the upsert method of the DocumentIndex.
            For example, you can use this to specify a custom vector_field:
            upsert_kwargs={"vector_field": "embedding"}
-            !!! version-added "Added in version 0.3.10"
+            !!! version-added "Added in `langchain-core` 0.3.10"

    Returns:
        Indexing result which contains information about how many documents
@@ -719,10 +729,10 @@ async def aindex(
    Raises:
        ValueError: If cleanup mode is not one of 'incremental', 'full' or None
        ValueError: If cleanup mode is incremental and source_id_key is None.
-        ValueError: If vectorstore does not have
+        ValueError: If `VectorStore` does not have
            "adelete" and "aadd_documents" required methods.
        ValueError: If source_id_key is not None, but is not a string or callable.
-        TypeError: If `vector_store` is not a VectorStore or DocumentIndex.
+        TypeError: If `vector_store` is not a `VectorStore` or DocumentIndex.
        AssertionError: If `source_id_key` is None when cleanup mode is
            incremental or `scoped_full` (should be unreachable).
    """
@@ -763,7 +773,7 @@ async def aindex(
            type(destination).adelete == VectorStore.adelete
            and type(destination).delete == VectorStore.delete
        ):
-            # Checking if the vectorstore has overridden the default adelete or delete
+            # Checking if the VectorStore has overridden the default adelete or delete
            # methods implementation which just raises a NotImplementedError
            msg = "Vectorstore has not implemented the adelete or delete method"
            raise ValueError(msg)
@@ -821,11 +831,11 @@ async def aindex(
        ]

        if cleanup in {"incremental", "scoped_full"}:
-            # If the cleanup mode is incremental, source ids are required.
+            # If the cleanup mode is incremental, source IDs are required.
            for source_id, hashed_doc in zip(source_ids, hashed_docs, strict=False):
                if source_id is None:
                    msg = (
-                        f"Source ids are required when cleanup mode is "
+                        f"Source IDs are required when cleanup mode is "
                        f"incremental or scoped_full. "
                        f"Document that starts with "
                        f"content: {hashed_doc.page_content[:100]} "
@@ -834,7 +844,7 @@ async def aindex(
                    raise ValueError(msg)
                if cleanup == "scoped_full":
                    scoped_full_cleanup_source_ids.add(source_id)
-            # source ids cannot be None after for loop above.
+            # Source IDs cannot be None after for loop above.
            source_ids = cast("Sequence[str]", source_ids)

        exists_batch = await record_manager.aexists(
@@ -894,7 +904,7 @@ async def aindex(
        if cleanup == "incremental":
            # Get the uids of the documents that were not returned by the loader.

-            # mypy isn't good enough to determine that source ids cannot be None
+            # mypy isn't good enough to determine that source IDs cannot be None
            # here due to a check that's happening above, so we check again.
            for source_id in source_ids:
                if source_id is None:
--- a/libs/core/langchain_core/indexing/base.py
+++ b/libs/core/langchain_core/indexing/base.py
@@ -25,7 +25,7 @@ class RecordManager(ABC):
    The record manager abstraction is used by the langchain indexing API.

    The record manager keeps track of which documents have been
-    written into a vectorstore and when they were written.
+    written into a `VectorStore` and when they were written.

    The indexing API computes hashes for each document and stores the hash
    together with the write time and the source id in the record manager.
@@ -37,7 +37,7 @@ class RecordManager(ABC):
    already been indexed, and to only index new documents.

    The main benefit of this abstraction is that it works across many vectorstores.
-    To be supported, a vectorstore needs to only support the ability to add and
+    To be supported, a `VectorStore` needs to only support the ability to add and
    delete documents by ID. Using the record manager, the indexing API will
    be able to delete outdated documents and avoid redundant indexing of documents
    that have already been indexed.
@@ -45,13 +45,13 @@ class RecordManager(ABC):
    The main constraints of this abstraction are:

    1. It relies on the time-stamps to determine which documents have been
-       indexed and which have not. This means that the time-stamps must be
-       monotonically increasing. The timestamp should be the timestamp
-       as measured by the server to minimize issues.
+        indexed and which have not. This means that the time-stamps must be
+        monotonically increasing. The timestamp should be the timestamp
+        as measured by the server to minimize issues.
    2. The record manager is currently implemented separately from the
-       vectorstore, which means that the overall system becomes distributed
-       and may create issues with consistency. For example, writing to
-       record manager succeeds, but corresponding writing to vectorstore fails.
+        vectorstore, which means that the overall system becomes distributed
+        and may create issues with consistency. For example, writing to
+        record manager succeeds, but corresponding writing to `VectorStore` fails.
    """

    def __init__(
@@ -460,7 +460,7 @@ class UpsertResponse(TypedDict):
 class DeleteResponse(TypedDict, total=False):
    """A generic response for delete operation.

-    The fields in this response are optional and whether the vectorstore
+    The fields in this response are optional and whether the `VectorStore`
    returns them or not is up to the implementation.
    """

@@ -508,8 +508,6 @@ class DocumentIndex(BaseRetriever):
    1. Storing document in the index.
    2. Fetching document by ID.
    3. Searching for document using a query.
-
-    !!! version-added "Added in version 0.2.29"
    """

    @abc.abstractmethod
@@ -520,40 +518,40 @@ class DocumentIndex(BaseRetriever):
        if it is provided. If the ID is not provided, the upsert method is free
        to generate an ID for the content.

-        When an ID is specified and the content already exists in the vectorstore,
+        When an ID is specified and the content already exists in the `VectorStore`,
        the upsert method should update the content with the new data. If the content
-        does not exist, the upsert method should add the item to the vectorstore.
+        does not exist, the upsert method should add the item to the `VectorStore`.

        Args:
-            items: Sequence of documents to add to the vectorstore.
+            items: Sequence of documents to add to the `VectorStore`.
            **kwargs: Additional keyword arguments.

        Returns:
            A response object that contains the list of IDs that were
-            successfully added or updated in the vectorstore and the list of IDs that
+            successfully added or updated in the `VectorStore` and the list of IDs that
            failed to be added or updated.
        """

    async def aupsert(
        self, items: Sequence[Document], /, **kwargs: Any
    ) -> UpsertResponse:
-        """Add or update documents in the vectorstore. Async version of upsert.
+        """Add or update documents in the `VectorStore`. Async version of `upsert`.

        The upsert functionality should utilize the ID field of the item
        if it is provided. If the ID is not provided, the upsert method is free
        to generate an ID for the item.

-        When an ID is specified and the item already exists in the vectorstore,
+        When an ID is specified and the item already exists in the `VectorStore`,
        the upsert method should update the item with the new data. If the item
-        does not exist, the upsert method should add the item to the vectorstore.
+        does not exist, the upsert method should add the item to the `VectorStore`.

        Args:
-            items: Sequence of documents to add to the vectorstore.
+            items: Sequence of documents to add to the `VectorStore`.
            **kwargs: Additional keyword arguments.

        Returns:
            A response object that contains the list of IDs that were
-            successfully added or updated in the vectorstore and the list of IDs that
+            successfully added or updated in the `VectorStore` and the list of IDs that
            failed to be added or updated.
        """
        return await run_in_executor(
@@ -570,7 +568,7 @@ class DocumentIndex(BaseRetriever):
        Calling delete without any input parameters should raise a ValueError!

        Args:
-            ids: List of ids to delete.
+            ids: List of IDs to delete.
            **kwargs: Additional keyword arguments. This is up to the implementation.
                For example, can include an option to delete the entire index,
                or else issue a non-blocking delete etc.
@@ -588,7 +586,7 @@ class DocumentIndex(BaseRetriever):
        Calling adelete without any input parameters should raise a ValueError!

        Args:
-            ids: List of ids to delete.
+            ids: List of IDs to delete.
            **kwargs: Additional keyword arguments. This is up to the implementation.
                For example, can include an option to delete the entire index.

--- a/libs/core/langchain_core/indexing/in_memory.py
+++ b/libs/core/langchain_core/indexing/in_memory.py
@@ -23,8 +23,6 @@ class InMemoryDocumentIndex(DocumentIndex):

    It provides a simple search API that returns documents by the number of
    counts the given query appears in the document.
-
-    !!! version-added "Added in version 0.2.29"
    """

    store: dict[str, Document] = Field(default_factory=dict)
@@ -64,10 +62,10 @@ class InMemoryDocumentIndex(DocumentIndex):
        """Delete by IDs.

        Args:
-            ids: List of ids to delete.
+            ids: List of IDs to delete.

        Raises:
-            ValueError: If ids is None.
+            ValueError: If IDs is None.

        Returns:
            A response object that contains the list of IDs that were successfully
--- a/libs/core/langchain_core/language_models/init.py
+++ b/libs/core/langchain_core/language_models/init.py
@@ -1,43 +1,32 @@
-"""Language models.
+"""Core language model abstractions.

-**Language Model** is a type of model that can generate text or complete
-text prompts.
+LangChain has two main classes to work with language models: chat models and
+"old-fashioned" LLMs (string-in, string-out).

-LangChain has two main classes to work with language models: **Chat Models**
-and "old-fashioned" **LLMs**.
-
-**Chat Models**
+**Chat models**

 Language models that use a sequence of messages as inputs and return chat messages
-as outputs (as opposed to using plain text). These are traditionally newer models (
-older models are generally LLMs, see below). Chat models support the assignment of
-distinct roles to conversation messages, helping to distinguish messages from the AI,
-users, and instructions such as system messages.
+as outputs (as opposed to using plain text).

-The key abstraction for chat models is `BaseChatModel`. Implementations
-should inherit from this class. Please see LangChain how-to guides with more
-information on how to implement a custom chat model.
+Chat models support the assignment of distinct roles to conversation messages, helping
+to distinguish messages from the AI, users, and instructions such as system messages.

-To implement a custom Chat Model, inherit from `BaseChatModel`. See
-the following guide for more information on how to implement a custom Chat Model:
+The key abstraction for chat models is
+[`BaseChatModel`][langchain_core.language_models.BaseChatModel]. Implementations should
+inherit from this class.

-https://python.langchain.com/docs/how_to/custom_chat_model/
+See existing [chat model integrations](https://docs.langchain.com/oss/python/integrations/chat).

-**LLMs**
+**LLMs (legacy)**

 Language models that takes a string as input and returns a string.
-These are traditionally older models (newer models generally are Chat Models,
-see below).

-Although the underlying models are string in, string out, the LangChain wrappers
-also allow these models to take messages as input. This gives them the same interface
-as Chat Models. When messages are passed in as input, they will be formatted into a
-string under the hood before being passed to the underlying model.
+These are traditionally older models (newer models generally are chat models).

-To implement a custom LLM, inherit from `BaseLLM` or `LLM`.
-Please see the following guide for more information on how to implement a custom LLM:
-
-https://python.langchain.com/docs/how_to/custom_llm/
+Although the underlying models are string in, string out, the LangChain wrappers also
+allow these models to take messages as input. This gives them the same interface as
+chat models. When messages are passed in as input, they will be formatted into a string
+under the hood before being passed to the underlying model.
 """

 from typing import TYPE_CHECKING
@@ -66,6 +55,10 @@ if TYPE_CHECKING:
        ParrotFakeChatModel,
    )
    from langchain_core.language_models.llms import LLM, BaseLLM
+    from langchain_core.language_models.model_profile import (
+        ModelProfile,
+        ModelProfileRegistry,
+    )

 __all__ = (
    "LLM",
@@ -81,6 +74,8 @@ __all__ = (
    "LanguageModelInput",
    "LanguageModelLike",
    "LanguageModelOutput",
+    "ModelProfile",
+    "ModelProfileRegistry",
    "ParrotFakeChatModel",
    "SimpleChatModel",
    "get_tokenizer",
@@ -103,6 +98,8 @@ _dynamic_imports = {
    "GenericFakeChatModel": "fake_chat_models",
    "ParrotFakeChatModel": "fake_chat_models",
    "LLM": "llms",
+    "ModelProfile": "model_profile",
+    "ModelProfileRegistry": "model_profile",
    "BaseLLM": "llms",
    "is_openai_data_block": "_utils",
 }
--- a/libs/core/langchain_core/language_models/_utils.py
+++ b/libs/core/langchain_core/language_models/_utils.py
@@ -35,7 +35,7 @@ def is_openai_data_block(
            different type, this function will return False.

    Returns:
-        True if the block is a valid OpenAI data block and matches the filter_
+        `True` if the block is a valid OpenAI data block and matches the filter_
        (if provided).

    """
@@ -89,7 +89,8 @@ class ParsedDataUri(TypedDict):
 def _parse_data_uri(uri: str) -> ParsedDataUri | None:
    """Parse a data URI into its components.

-    If parsing fails, return None. If either MIME type or data is missing, return None.
+    If parsing fails, return `None`. If either MIME type or data is missing, return
+    `None`.

    Example:
        ```python
@@ -138,7 +139,8 @@ def _normalize_messages(
            directly; this may change in the future
    - LangChain v0 standard content blocks for backward compatibility

-    !!! warning "Behavior changed in 1.0.0"
+    !!! warning "Behavior changed in `langchain-core` 1.0.0"
+
        In previous versions, this function returned messages in LangChain v0 format.
        Now, it returns messages in LangChain v1 format, which upgraded chat models now
        expect to receive when passing back in message history. For backward
--- a/libs/core/langchain_core/language_models/base.py
+++ b/libs/core/langchain_core/language_models/base.py
@@ -12,13 +12,14 @@ from typing import (
    Literal,
    TypeAlias,
    TypeVar,
+    cast,
 )

 from pydantic import BaseModel, ConfigDict, Field, field_validator
 from typing_extensions import TypedDict, override

-from langchain_core.caches import BaseCache
-from langchain_core.callbacks import Callbacks
+from langchain_core.caches import BaseCache  # noqa: TC001
+from langchain_core.callbacks import Callbacks  # noqa: TC001
 from langchain_core.globals import get_verbose
 from langchain_core.messages import (
    AIMessage,
@@ -86,19 +87,41 @@ def get_tokenizer() -> Any:
    return GPT2TokenizerFast.from_pretrained("gpt2")


+_GPT2_TOKENIZER_WARNED = False
+
+
 def _get_token_ids_default_method(text: str) -> list[int]:
-    """Encode the text into token IDs."""
-    # get the cached tokenizer
+    """Encode the text into token IDs using the fallback GPT-2 tokenizer."""
+    global _GPT2_TOKENIZER_WARNED  # noqa: PLW0603
+    if not _GPT2_TOKENIZER_WARNED:
+        warnings.warn(
+            "Using fallback GPT-2 tokenizer for token counting. "
+            "Token counts may be inaccurate for non-GPT-2 models. "
+            "For accurate counts, use a model-specific method if available.",
+            stacklevel=3,
+        )
+        _GPT2_TOKENIZER_WARNED = True
+
    tokenizer = get_tokenizer()

-    # tokenize the text using the GPT-2 tokenizer
-    return tokenizer.encode(text)
+    # Pass verbose=False to suppress the "Token indices sequence length is longer than
+    # the specified maximum sequence length" warning from HuggingFace. This warning is
+    # about GPT-2's 1024 token context limit, but we're only using the tokenizer for
+    # counting, not for model input.
+    return cast("list[int]", tokenizer.encode(text, verbose=False))


 LanguageModelInput = PromptValue | str | Sequence[MessageLikeRepresentation]
+"""Input to a language model."""
+
 LanguageModelOutput = BaseMessage | str
+"""Output from a language model."""
+
 LanguageModelLike = Runnable[LanguageModelInput, LanguageModelOutput]
+"""Input/output interface for a language model."""
+
 LanguageModelOutputVar = TypeVar("LanguageModelOutputVar", AIMessage, str)
+"""Type variable for the output of a language model."""


 def _get_verbosity() -> bool:
@@ -123,16 +146,20 @@ class BaseLanguageModel(
    * If instance of `BaseCache`, will use the provided cache.

    Caching is not currently supported for streaming methods of models.
-
    """
+
    verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)
    """Whether to print out response text."""
+
    callbacks: Callbacks = Field(default=None, exclude=True)
    """Callbacks to add to the run trace."""
+
    tags: list[str] | None = Field(default=None, exclude=True)
    """Tags to add to the run trace."""
+
    metadata: dict[str, Any] | None = Field(default=None, exclude=True)
    """Metadata to add to the run trace."""
+
    custom_get_token_ids: Callable[[str], list[int]] | None = Field(
        default=None, exclude=True
    )
@@ -146,7 +173,7 @@ class BaseLanguageModel(
    def set_verbose(cls, verbose: bool | None) -> bool:  # noqa: FBT001
        """If verbose is `None`, set it.

-        This allows users to pass in None as verbose to access the global setting.
+        This allows users to pass in `None` as verbose to access the global setting.

        Args:
            verbose: The verbosity setting to use.
@@ -186,22 +213,29 @@ class BaseLanguageModel(
        1. Take advantage of batched calls,
        2. Need more output from the model than just the top generated value,
        3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).

        Args:
-            prompts: List of PromptValues. A PromptValue is an object that can be
-                converted to match the format of any language model (string for pure
-                text generation models and BaseMessages for chat models).
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            prompts: List of `PromptValue` objects.
+
+                A `PromptValue` is an object that can be converted to match the format
+                of any language model (string for pure text generation models and
+                `BaseMessage` objects for chat models).
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+            callbacks: `Callbacks` to pass through.
+
+                Used for executing additional functionality, such as logging or
+                streaming, throughout generation.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-            prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generation` objects for
+                each input prompt and additional model provider-specific output.

        """

@@ -223,22 +257,29 @@ class BaseLanguageModel(
        1. Take advantage of batched calls,
        2. Need more output from the model than just the top generated value,
        3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).

        Args:
-            prompts: List of PromptValues. A PromptValue is an object that can be
-                converted to match the format of any language model (string for pure
-                text generation models and BaseMessages for chat models).
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            prompts: List of `PromptValue` objects.
+
+                A `PromptValue` is an object that can be converted to match the format
+                of any language model (string for pure text generation models and
+                `BaseMessage` objects for chat models).
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+            callbacks: `Callbacks` to pass through.
+
+                Used for executing additional functionality, such as logging or
+                streaming, throughout generation.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Returns:
-            An `LLMResult`, which contains a list of candidate Generations for each
-            input prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generation` objects for
+                each input prompt and additional model provider-specific output.

        """

@@ -256,15 +297,14 @@ class BaseLanguageModel(
        return self.lc_attributes

    def get_token_ids(self, text: str) -> list[int]:
-        """Return the ordered ids of the tokens in a text.
+        """Return the ordered IDs of the tokens in a text.

        Args:
            text: The string input to tokenize.

        Returns:
-            A list of ids corresponding to the tokens in the text, in order they occur
-            in the text.
-
+            A list of IDs corresponding to the tokens in the text, in order they occur
+                in the text.
        """
        if self.custom_get_token_ids is not None:
            return self.custom_get_token_ids(text)
@@ -275,6 +315,9 @@ class BaseLanguageModel(

        Useful for checking if an input fits in a model's context window.

+        This should be overridden by model-specific implementations to provide accurate
+        token counts via model-specific tokenizers.
+
        Args:
            text: The string input to tokenize.

@@ -293,9 +336,17 @@ class BaseLanguageModel(

        Useful for checking if an input fits in a model's context window.

+        This should be overridden by model-specific implementations to provide accurate
+        token counts via model-specific tokenizers.
+
        !!! note
-            The base implementation of `get_num_tokens_from_messages` ignores tool
-            schemas.
+
+            * The base implementation of `get_num_tokens_from_messages` ignores tool
+                schemas.
+            * The base implementation of `get_num_tokens_from_messages` adds additional
+                prefixes to messages in represent user roles, which will add to the
+                overall token count. Model-specific implementations may choose to
+                handle this differently.

        Args:
            messages: The message inputs to tokenize.
--- a/libs/core/langchain_core/language_models/chat_models.py
+++ b/libs/core/langchain_core/language_models/chat_models.py
@@ -5,7 +5,6 @@ from __future__ import annotations
 import asyncio
 import inspect
 import json
-import typing
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator, Callable, Iterator, Sequence
 from functools import cached_property
@@ -33,6 +32,7 @@ from langchain_core.language_models.base import (
    LangSmithParams,
    LanguageModelInput,
 )
+from langchain_core.language_models.model_profile import ModelProfile
 from langchain_core.load import dumpd, dumps
 from langchain_core.messages import (
    AIMessage,
@@ -73,6 +73,7 @@ from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
 from langchain_core.utils.utils import LC_ID_PREFIX, from_env

 if TYPE_CHECKING:
+    import builtins
    import uuid

    from langchain_core.output_parsers.base import OutputParserLike
@@ -88,7 +89,10 @@ def _generate_response_from_error(error: BaseException) -> list[ChatGeneration]:
            try:
                metadata["body"] = response.json()
            except Exception:
-                metadata["body"] = getattr(response, "text", None)
+                try:
+                    metadata["body"] = getattr(response, "text", None)
+                except Exception:
+                    metadata["body"] = None
        if hasattr(response, "headers"):
            try:
                metadata["headers"] = dict(response.headers)
@@ -240,79 +244,52 @@ def _format_ls_structured_output(ls_structured_output_format: dict | None) -> di


 class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
-    """Base class for chat models.
+    r"""Base class for chat models.

    Key imperative methods:
        Methods that actually call the underlying model.

-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | Method                    | Input                                                          | Output                                                              | Description                                                                                      |
-        +===========================+================================================================+=====================================================================+==================================================================================================+
-        | `invoke`                  | str | list[dict | tuple | BaseMessage] | PromptValue           | BaseMessage                                                         | A single chat model call.                                                                        |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `ainvoke`                 | '''                                                            | BaseMessage                                                         | Defaults to running invoke in an async executor.                                                 |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `stream`                  | '''                                                            | Iterator[BaseMessageChunk]                                          | Defaults to yielding output of invoke.                                                           |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `astream`                 | '''                                                            | AsyncIterator[BaseMessageChunk]                                     | Defaults to yielding output of ainvoke.                                                          |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `astream_events`          | '''                                                            | AsyncIterator[StreamEvent]                                          | Event types: 'on_chat_model_start', 'on_chat_model_stream', 'on_chat_model_end'.                 |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `batch`                   | list[''']                                                      | list[BaseMessage]                                                   | Defaults to running invoke in concurrent threads.                                                |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `abatch`                  | list[''']                                                      | list[BaseMessage]                                                   | Defaults to running ainvoke in concurrent threads.                                               |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `batch_as_completed`      | list[''']                                                      | Iterator[tuple[int, Union[BaseMessage, Exception]]]                 | Defaults to running invoke in concurrent threads.                                                |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
-        | `abatch_as_completed`     | list[''']                                                      | AsyncIterator[tuple[int, Union[BaseMessage, Exception]]]            | Defaults to running ainvoke in concurrent threads.                                               |
-        +---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
+        This table provides a brief overview of the main imperative methods. Please see the base `Runnable` reference for full documentation.

-        This table provides a brief overview of the main imperative methods. Please see the base Runnable reference for full documentation.
+        | Method                 | Input                                                        | Output                                                     | Description                                                                      |
+        | ---------------------- | ------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------------------------------- |
+        | `invoke`               | `str` \| `list[dict | tuple | BaseMessage]` \| `PromptValue` | `BaseMessage`                                              | A single chat model call.                                                        |
+        | `ainvoke`              | `'''`                                                        | `BaseMessage`                                              | Defaults to running `invoke` in an async executor.                               |
+        | `stream`               | `'''`                                                        | `Iterator[BaseMessageChunk]`                               | Defaults to yielding output of `invoke`.                                         |
+        | `astream`              | `'''`                                                        | `AsyncIterator[BaseMessageChunk]`                          | Defaults to yielding output of `ainvoke`.                                        |
+        | `astream_events`       | `'''`                                                        | `AsyncIterator[StreamEvent]`                               | Event types: `on_chat_model_start`, `on_chat_model_stream`, `on_chat_model_end`. |
+        | `batch`                | `list[''']`                                                  | `list[BaseMessage]`                                        | Defaults to running `invoke` in concurrent threads.                              |
+        | `abatch`               | `list[''']`                                                  | `list[BaseMessage]`                                        | Defaults to running `ainvoke` in concurrent threads.                             |
+        | `batch_as_completed`   | `list[''']`                                                  | `Iterator[tuple[int, Union[BaseMessage, Exception]]]`      | Defaults to running `invoke` in concurrent threads.                              |
+        | `abatch_as_completed`  | `list[''']`                                                  | `AsyncIterator[tuple[int, Union[BaseMessage, Exception]]]` | Defaults to running `ainvoke` in concurrent threads.                             |

    Key declarative methods:
-        Methods for creating another Runnable using the ChatModel.
-
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | Method                           | Description                                                                                               |
-        +==================================+===========================================================================================================+
-        | `bind_tools`                     | Create ChatModel that can call tools.                                                                     |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `with_structured_output`         | Create wrapper that structures model output using schema.                                                 |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `with_retry`                     | Create wrapper that retries model calls on failure.                                                       |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `with_fallbacks`                 | Create wrapper that falls back to other models on failure.                                                |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `configurable_fields`            | Specify init args of the model that can be configured at runtime via the RunnableConfig.                  |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
-        | `configurable_alternatives`      | Specify alternative models which can be swapped in at runtime via the RunnableConfig.                     |
-        +----------------------------------+-----------------------------------------------------------------------------------------------------------+
+        Methods for creating another `Runnable` using the chat model.

        This table provides a brief overview of the main declarative methods. Please see the reference for each method for full documentation.

+        | Method                       | Description                                                                                |
+        | ---------------------------- | ------------------------------------------------------------------------------------------ |
+        | `bind_tools`                 | Create chat model that can call tools.                                                     |
+        | `with_structured_output`     | Create wrapper that structures model output using schema.                                  |
+        | `with_retry`                 | Create wrapper that retries model calls on failure.                                        |
+        | `with_fallbacks`             | Create wrapper that falls back to other models on failure.                                 |
+        | `configurable_fields`        | Specify init args of the model that can be configured at runtime via the `RunnableConfig`. |
+        | `configurable_alternatives`  | Specify alternative models which can be swapped in at runtime via the `RunnableConfig`.    |
+
    Creating custom chat model:
        Custom chat model implementations should inherit from this class.
        Please reference the table below for information about which
        methods and properties are required or optional for implementations.

-        +----------------------------------+--------------------------------------------------------------------+-------------------+
-        | Method/Property                  | Description                                                        | Required/Optional |
-        +==================================+====================================================================+===================+
+        | Method/Property                  | Description                                                        | Required          |
+        | -------------------------------- | ------------------------------------------------------------------ | ----------------- |
        | `_generate`                      | Use to generate a chat result from a prompt                        | Required          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
        | `_llm_type` (property)           | Used to uniquely identify the type of the model. Used for logging. | Required          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
        | `_identifying_params` (property) | Represent model parameterization for tracing purposes.             | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
        | `_stream`                        | Use to implement streaming                                         | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
        | `_agenerate`                     | Use to implement a native async method                             | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
        | `_astream`                       | Use to implement async version of `_stream`                        | Optional          |
-        +----------------------------------+--------------------------------------------------------------------+-------------------+
-
-        Follow the guide for more information on how to implement a custom Chat Model:
-        [Guide](https://python.langchain.com/docs/how_to/custom_chat_model/).

    """  # noqa: E501

@@ -327,9 +304,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):

    - If `True`, will always bypass streaming case.
    - If `'tool_calling'`, will bypass streaming case only when the model is called
-      with a `tools` keyword argument. In other words, LangChain will automatically
-      switch to non-streaming behavior (`invoke`) only when the tools argument is
-      provided. This offers the best of both worlds.
+        with a `tools` keyword argument. In other words, LangChain will automatically
+        switch to non-streaming behavior (`invoke`) only when the tools argument is
+        provided. This offers the best of both worlds.
    - If `False` (Default), will always use streaming case if available.

    The main reason for this flag is that code might be written using `stream` and
@@ -349,23 +326,43 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
    Supported values:

    - `'v0'`: provider-specific format in content (can lazily-parse with
-      `.content_blocks`)
-    - `'v1'`: standardized format in content (consistent with `.content_blocks`)
+        `content_blocks`)
+    - `'v1'`: standardized format in content (consistent with `content_blocks`)

-    Partner packages (e.g., `langchain-openai`) can also use this field to roll out
-    new content formats in a backward-compatible way.
+    Partner packages (e.g.,
+    [`langchain-openai`](https://pypi.org/project/langchain-openai)) can also use this
+    field to roll out new content formats in a backward-compatible way.

-    !!! version-added "Added in version 1.0"
+    !!! version-added "Added in `langchain-core` 1.0.0"

    """

+    profile: ModelProfile | None = Field(default=None, exclude=True)
+    """Profile detailing model capabilities.
+
+    !!! warning "Beta feature"
+
+        This is a beta feature. The format of model profiles is subject to change.
+
+    If not specified, automatically loaded from the provider package on initialization
+    if data is available.
+
+    Example profile data includes context window sizes, supported modalities, or support
+    for tool calling, structured output, and other features.
+
+    !!! version-added "Added in `langchain-core` 1.1.0"
+    """
+
    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    @cached_property
    def _serialized(self) -> dict[str, Any]:
-        return dumpd(self)
+        # self is always a Serializable object in this case, thus the result is
+        # guaranteed to be a dict since dumps uses the default callback, which uses
+        # obj.to_json which always returns TypedDict subclasses
+        return cast("dict[str, Any]", dumpd(self))

    # --- Runnable methods ---

@@ -468,7 +465,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):

        # Check if a runtime streaming flag has been passed in.
        if "stream" in kwargs:
-            return kwargs["stream"]
+            return bool(kwargs["stream"])

        if "streaming" in self.model_fields_set:
            streaming_value = getattr(self, "streaming", None)
@@ -554,7 +551,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                        ):
                            if block["type"] != index_type:
                                index_type = block["type"]
-                                index = index + 1
+                                index += 1
                            if "index" not in block:
                                block["index"] = index
                    run_manager.on_llm_new_token(
@@ -686,7 +683,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                    ):
                        if block["type"] != index_type:
                            index_type = block["type"]
-                            index = index + 1
+                            index += 1
                        if "index" not in block:
                            block["index"] = index
                await run_manager.on_llm_new_token(
@@ -737,7 +734,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):

    # --- Custom methods ---

-    def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:  # noqa: ARG002
+    def _combine_llm_outputs(self, _llm_outputs: list[dict | None], /) -> dict:
        return {}

    def _convert_cached_generations(self, cache_val: list) -> list[ChatGeneration]:
@@ -864,24 +861,29 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
        1. Take advantage of batched calls,
        2. Need more output from the model than just the top generated value,
        3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).

        Args:
            messages: List of list of messages.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+            callbacks: `Callbacks` to pass through.
+
+                Used for executing additional functionality, such as logging or
+                streaming, throughout generation.
            tags: The tags to apply.
            metadata: The metadata to apply.
            run_name: The name of the run.
            run_id: The ID of the run.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-            prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generations` for each
+                input prompt and additional model provider-specific output.

        """
        ls_structured_output_format = kwargs.pop(
@@ -982,24 +984,29 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
        1. Take advantage of batched calls,
        2. Need more output from the model than just the top generated value,
        3. Are building chains that are agnostic to the underlying language model
-           type (e.g., pure text completion models vs chat models).
+            type (e.g., pure text completion models vs chat models).

        Args:
            messages: List of list of messages.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+            callbacks: `Callbacks` to pass through.
+
+                Used for executing additional functionality, such as logging or
+                streaming, throughout generation.
            tags: The tags to apply.
            metadata: The metadata to apply.
            run_name: The name of the run.
            run_id: The ID of the run.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-            prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generations` for each
+                input prompt and additional model provider-specific output.

        """
        ls_structured_output_format = kwargs.pop(
@@ -1141,7 +1148,15 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
        if check_cache:
            if llm_cache:
                llm_string = self._get_llm_string(stop=stop, **kwargs)
-                prompt = dumps(messages)
+                normalized_messages = [
+                    (
+                        msg.model_copy(update={"id": None})
+                        if getattr(msg, "id", None) is not None
+                        else msg
+                    )
+                    for msg in messages
+                ]
+                prompt = dumps(normalized_messages)
                cache_val = llm_cache.lookup(prompt, llm_string)
                if isinstance(cache_val, list):
                    converted_generations = self._convert_cached_generations(cache_val)
@@ -1184,7 +1199,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                    ):
                        if block["type"] != index_type:
                            index_type = block["type"]
-                            index = index + 1
+                            index += 1
                        if "index" not in block:
                            block["index"] = index
                if run_manager:
@@ -1259,7 +1274,15 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
        if check_cache:
            if llm_cache:
                llm_string = self._get_llm_string(stop=stop, **kwargs)
-                prompt = dumps(messages)
+                normalized_messages = [
+                    (
+                        msg.model_copy(update={"id": None})
+                        if getattr(msg, "id", None) is not None
+                        else msg
+                    )
+                    for msg in messages
+                ]
+                prompt = dumps(normalized_messages)
                cache_val = await llm_cache.alookup(prompt, llm_string)
                if isinstance(cache_val, list):
                    converted_generations = self._convert_cached_generations(cache_val)
@@ -1302,7 +1325,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                    ):
                        if block["type"] != index_type:
                            index_type = block["type"]
-                            index = index + 1
+                            index += 1
                        if "index" not in block:
                            block["index"] = index
                if run_manager:
@@ -1497,9 +1520,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):

    def bind_tools(
        self,
-        tools: Sequence[
-            typing.Dict[str, Any] | type | Callable | BaseTool  # noqa: UP006
-        ],
+        tools: Sequence[builtins.dict[str, Any] | type | Callable | BaseTool],
        *,
        tool_choice: str | None = None,
        **kwargs: Any,
@@ -1518,35 +1539,43 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):

    def with_structured_output(
        self,
-        schema: typing.Dict | type,  # noqa: UP006
+        schema: builtins.dict[str, Any] | type,
        *,
        include_raw: bool = False,
        **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, typing.Dict | BaseModel]:  # noqa: UP006
+    ) -> Runnable[LanguageModelInput, builtins.dict[str, Any] | BaseModel]:
        """Model wrapper that returns outputs formatted to match the given schema.

        Args:
            schema: The output schema. Can be passed in as:

-                - an OpenAI function/tool schema,
-                - a JSON Schema,
-                - a `TypedDict` class,
-                - or a Pydantic class.
+                - An OpenAI function/tool schema,
+                - A JSON Schema,
+                - A `TypedDict` class,
+                - Or a Pydantic class.

                If `schema` is a Pydantic class then the model output will be a
                Pydantic instance of that class, and the model-generated fields will be
                validated by the Pydantic class. Otherwise the model output will be a
-                dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool`
-                for more on how to properly specify types and descriptions of
-                schema fields when specifying a Pydantic or `TypedDict` class.
+                dict and will not be validated.
+
+                See `langchain_core.utils.function_calling.convert_to_openai_tool` for
+                more on how to properly specify types and descriptions of schema fields
+                when specifying a Pydantic or `TypedDict` class.

            include_raw:
-                If `False` then only the parsed structured output is returned. If
-                an error occurs during model output parsing it will be raised. If `True`
-                then both the raw model response (a BaseMessage) and the parsed model
-                response will be returned. If an error occurs during output parsing it
-                will be caught and returned as well. The final output is always a dict
-                with keys `'raw'`, `'parsed'`, and `'parsing_error'`.
+                If `False` then only the parsed structured output is returned.
+
+                If an error occurs during model output parsing it will be raised.
+
+                If `True` then both the raw model response (a `BaseMessage`) and the
+                parsed model response will be returned.
+
+                If an error occurs during output parsing it will be caught and returned
+                as well.
+
+                The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
+                `'parsing_error'`.

        Raises:
            ValueError: If there are any unsupported `kwargs`.
@@ -1554,20 +1583,21 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                `with_structured_output()`.

        Returns:
-            A Runnable that takes same inputs as a `langchain_core.language_models.chat.BaseChatModel`.
+            A `Runnable` that takes same inputs as a
+                `langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is
+                `False` and `schema` is a Pydantic class, `Runnable` outputs an instance
+                of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is
+                `False` then `Runnable` outputs a `dict`.

-            If `include_raw` is False and `schema` is a Pydantic class, Runnable outputs
-            an instance of `schema` (i.e., a Pydantic object).
+                If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys:

-            Otherwise, if `include_raw` is False then Runnable outputs a dict.
+                - `'raw'`: `BaseMessage`
+                - `'parsed'`: `None` if there was a parsing error, otherwise the type
+                    depends on the `schema` as described above.
+                - `'parsing_error'`: `BaseException | None`

-            If `include_raw` is True, then Runnable outputs a dict with keys:
+        ???+ example "Pydantic schema (`include_raw=False`)"

-            - `'raw'`: BaseMessage
-            - `'parsed'`: None if there was a parsing error, otherwise the type depends on the `schema` as described above.
-            - `'parsing_error'`: BaseException | None
-
-        Example: Pydantic schema (include_raw=False):
            ```python
            from pydantic import BaseModel

@@ -1579,10 +1609,10 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                justification: str


-            llm = ChatModel(model="model-name", temperature=0)
-            structured_llm = llm.with_structured_output(AnswerWithJustification)
+            model = ChatModel(model="model-name", temperature=0)
+            structured_model = model.with_structured_output(AnswerWithJustification)

-            structured_llm.invoke(
+            structured_model.invoke(
                "What weighs more a pound of bricks or a pound of feathers"
            )

@@ -1592,7 +1622,8 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
            # )
            ```

-        Example: Pydantic schema (include_raw=True):
+        ??? example "Pydantic schema (`include_raw=True`)"
+
            ```python
            from pydantic import BaseModel

@@ -1604,12 +1635,12 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                justification: str


-            llm = ChatModel(model="model-name", temperature=0)
-            structured_llm = llm.with_structured_output(
+            model = ChatModel(model="model-name", temperature=0)
+            structured_model = model.with_structured_output(
                AnswerWithJustification, include_raw=True
            )

-            structured_llm.invoke(
+            structured_model.invoke(
                "What weighs more a pound of bricks or a pound of feathers"
            )
            # -> {
@@ -1619,7 +1650,8 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
            # }
            ```

-        Example: Dict schema (include_raw=False):
+        ??? example "Dictionary schema (`include_raw=False`)"
+
            ```python
            from pydantic import BaseModel
            from langchain_core.utils.function_calling import convert_to_openai_tool
@@ -1633,10 +1665,10 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):


            dict_schema = convert_to_openai_tool(AnswerWithJustification)
-            llm = ChatModel(model="model-name", temperature=0)
-            structured_llm = llm.with_structured_output(dict_schema)
+            model = ChatModel(model="model-name", temperature=0)
+            structured_model = model.with_structured_output(dict_schema)

-            structured_llm.invoke(
+            structured_model.invoke(
                "What weighs more a pound of bricks or a pound of feathers"
            )
            # -> {
@@ -1645,8 +1677,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
            # }
            ```

-        !!! warning "Behavior changed in 0.2.26"
-                Added support for TypedDict class.
+        !!! warning "Behavior changed in `langchain-core` 0.2.26"
+
+            Added support for `TypedDict` class.

        """  # noqa: E501
        _ = kwargs.pop("method", None)
@@ -1745,9 +1778,12 @@ def _gen_info_and_msg_metadata(
    }


+_MAX_CLEANUP_DEPTH = 100
+
+
 def _cleanup_llm_representation(serialized: Any, depth: int) -> None:
    """Remove non-serializable objects from a serialized object."""
-    if depth > 100:  # Don't cooperate for pathological cases
+    if depth > _MAX_CLEANUP_DEPTH:  # Don't cooperate for pathological cases
        return

    if not isinstance(serialized, dict):
--- a/libs/core/langchain_core/language_models/fake_chat_models.py
+++ b/libs/core/langchain_core/language_models/fake_chat_models.py
@@ -1,4 +1,4 @@
-"""Fake ChatModel for testing purposes."""
+"""Fake chat models for testing purposes."""

 import asyncio
 import re
@@ -19,7 +19,7 @@ from langchain_core.runnables import RunnableConfig


 class FakeMessagesListChatModel(BaseChatModel):
-    """Fake `ChatModel` for testing purposes."""
+    """Fake chat model for testing purposes."""

    responses: list[BaseMessage]
    """List of responses to **cycle** through in order."""
@@ -57,7 +57,7 @@ class FakeListChatModelError(Exception):


 class FakeListChatModel(SimpleChatModel):
-    """Fake ChatModel for testing purposes."""
+    """Fake chat model for testing purposes."""

    responses: list[str]
    """List of responses to **cycle** through in order."""
--- a/libs/core/langchain_core/language_models/llms.py
+++ b/libs/core/langchain_core/language_models/llms.py
@@ -1,4 +1,7 @@
-"""Base interface for large language models to expose."""
+"""Base interface for traditional large language models (LLMs) to expose.
+
+These are traditionally older models (newer models generally are chat models).
+"""

 from __future__ import annotations

@@ -58,6 +61,8 @@ if TYPE_CHECKING:

 logger = logging.getLogger(__name__)

+_background_tasks: set[asyncio.Task] = set()
+

@functools.lru_cache
 def _log_error_once(msg: str) -> None:
@@ -74,8 +79,8 @@ def create_base_retry_decorator(

    Args:
        error_types: List of error types to retry on.
-        max_retries: Number of retries. Default is 1.
-        run_manager: Callback manager for the run. Default is None.
+        max_retries: Number of retries.
+        run_manager: Callback manager for the run.

    Returns:
        A retry decorator.
@@ -91,13 +96,17 @@ def create_base_retry_decorator(
            if isinstance(run_manager, AsyncCallbackManagerForLLMRun):
                coro = run_manager.on_retry(retry_state)
                try:
-                    loop = asyncio.get_event_loop()
-                    if loop.is_running():
-                        # TODO: Fix RUF006 - this task should have a reference
-                        #  and be awaited somewhere
-                        loop.create_task(coro)  # noqa: RUF006
-                    else:
+                    try:
+                        loop = asyncio.get_event_loop()
+                    except RuntimeError:
                        asyncio.run(coro)
+                    else:
+                        if loop.is_running():
+                            task = loop.create_task(coro)
+                            _background_tasks.add(task)
+                            task.add_done_callback(_background_tasks.discard)
+                        else:
+                            asyncio.run(coro)
                except Exception as e:
                    _log_error_once(f"Error in on_retry: {e}")
            else:
@@ -153,7 +162,7 @@ def get_prompts(
    Args:
        params: Dictionary of parameters.
        prompts: List of prompts.
-        cache: Cache object. Default is None.
+        cache: Cache object.

    Returns:
        A tuple of existing prompts, llm_string, missing prompt indexes,
@@ -189,7 +198,7 @@ async def aget_prompts(
    Args:
        params: Dictionary of parameters.
        prompts: List of prompts.
-        cache: Cache object. Default is None.
+        cache: Cache object.

    Returns:
        A tuple of existing prompts, llm_string, missing prompt indexes,
@@ -292,7 +301,10 @@ class BaseLLM(BaseLanguageModel[str], ABC):

    @functools.cached_property
    def _serialized(self) -> dict[str, Any]:
-        return dumpd(self)
+        # self is always a Serializable object in this case, thus the result is
+        # guaranteed to be a dict since dumps uses the default callback, which uses
+        # obj.to_json which always returns TypedDict subclasses
+        return cast("dict[str, Any]", dumpd(self))

    # --- Runnable methods ---

@@ -644,9 +656,12 @@ class BaseLLM(BaseLanguageModel[str], ABC):

        Args:
            prompts: The prompts to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-                If stop tokens are not supported consider raising NotImplementedError.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+
+                If stop tokens are not supported consider raising `NotImplementedError`.
            run_manager: Callback manager for the run.

        Returns:
@@ -664,9 +679,12 @@ class BaseLLM(BaseLanguageModel[str], ABC):

        Args:
            prompts: The prompts to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-                If stop tokens are not supported consider raising NotImplementedError.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+
+                If stop tokens are not supported consider raising `NotImplementedError`.
            run_manager: Callback manager for the run.

        Returns:
@@ -698,11 +716,14 @@ class BaseLLM(BaseLanguageModel[str], ABC):

        Args:
            prompt: The prompt to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
            run_manager: Callback manager for the run.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Yields:
            Generation chunks.
@@ -724,11 +745,14 @@ class BaseLLM(BaseLanguageModel[str], ABC):

        Args:
            prompt: The prompt to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
            run_manager: Callback manager for the run.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Yields:
            Generation chunks.
@@ -839,10 +863,14 @@ class BaseLLM(BaseLanguageModel[str], ABC):

        Args:
            prompts: List of string prompts.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+            callbacks: `Callbacks` to pass through.
+
+                Used for executing additional functionality, such as logging or
+                streaming, throughout generation.
            tags: List of tags to associate with each prompt. If provided, the length
                of the list must match the length of the prompts list.
            metadata: List of metadata dictionaries to associate with each prompt. If
@@ -852,8 +880,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                length of the list must match the length of the prompts list.
            run_id: List of run IDs to associate with each prompt. If provided, the
                length of the list must match the length of the prompts list.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Raises:
            ValueError: If prompts is not a list.
@@ -861,8 +890,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                `run_name` (if provided) does not match the length of prompts.

        Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-                prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generations` for each
+                input prompt and additional model provider-specific output.
        """
        if not isinstance(prompts, list):
            msg = (
@@ -1109,10 +1138,14 @@ class BaseLLM(BaseLanguageModel[str], ABC):

        Args:
            prompts: List of string prompts.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of these substrings.
-            callbacks: Callbacks to pass through. Used for executing additional
-                functionality, such as logging or streaming, throughout generation.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+            callbacks: `Callbacks` to pass through.
+
+                Used for executing additional functionality, such as logging or
+                streaming, throughout generation.
            tags: List of tags to associate with each prompt. If provided, the length
                of the list must match the length of the prompts list.
            metadata: List of metadata dictionaries to associate with each prompt. If
@@ -1122,16 +1155,17 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                length of the list must match the length of the prompts list.
            run_id: List of run IDs to associate with each prompt. If provided, the
                length of the list must match the length of the prompts list.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Raises:
            ValueError: If the length of `callbacks`, `tags`, `metadata`, or
                `run_name` (if provided) does not match the length of prompts.

        Returns:
-            An LLMResult, which contains a list of candidate Generations for each input
-                prompt and additional model provider-specific output.
+            An `LLMResult`, which contains a list of candidate `Generations` for each
+                input prompt and additional model provider-specific output.
        """
        if isinstance(metadata, list):
            metadata = [
@@ -1387,11 +1421,6 @@ class LLM(BaseLLM):
        `astream` will use `_astream` if provided, otherwise it will implement
        a fallback behavior that will use `_stream` if `_stream` is implemented,
        and use `_acall` if `_stream` is not implemented.
-
-    Please see the following guide for more information on how to
-    implement a custom LLM:
-
-    https://python.langchain.com/docs/how_to/custom_llm/
    """

    @abstractmethod
@@ -1408,12 +1437,16 @@ class LLM(BaseLLM):

        Args:
            prompt: The prompt to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-                If stop tokens are not supported consider raising NotImplementedError.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+
+                If stop tokens are not supported consider raising `NotImplementedError`.
            run_manager: Callback manager for the run.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Returns:
            The model output as a string. SHOULD NOT include the prompt.
@@ -1434,12 +1467,16 @@ class LLM(BaseLLM):

        Args:
            prompt: The prompt to generate from.
-            stop: Stop words to use when generating. Model output is cut off at the
-                first occurrence of any of the stop substrings.
-                If stop tokens are not supported consider raising NotImplementedError.
+            stop: Stop words to use when generating.
+
+                Model output is cut off at the first occurrence of any of these
+                substrings.
+
+                If stop tokens are not supported consider raising `NotImplementedError`.
            run_manager: Callback manager for the run.
-            **kwargs: Arbitrary additional keyword arguments. These are usually passed
-                to the model provider API call.
+            **kwargs: Arbitrary additional keyword arguments.
+
+                These are usually passed to the model provider API call.

        Returns:
            The model output as a string. SHOULD NOT include the prompt.
--- a/libs/core/langchain_core/language_models/model_profile.py
+++ b/libs/core/langchain_core/language_models/model_profile.py
@@ -0,0 +1,85 @@
+"""Model profile types and utilities."""
+
+from typing_extensions import TypedDict
+
+
+class ModelProfile(TypedDict, total=False):
+    """Model profile.
+
+    !!! warning "Beta feature"
+
+        This is a beta feature. The format of model profiles is subject to change.
+
+    Provides information about chat model capabilities, such as context window sizes
+    and supported features.
+    """
+
+    # --- Input constraints ---
+
+    max_input_tokens: int
+    """Maximum context window (tokens)"""
+
+    image_inputs: bool
+    """Whether image inputs are supported."""
+    # TODO: add more detail about formats?
+
+    image_url_inputs: bool
+    """Whether [image URL inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    pdf_inputs: bool
+    """Whether [PDF inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+    # TODO: add more detail about formats? e.g. bytes or base64
+
+    audio_inputs: bool
+    """Whether [audio inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+    # TODO: add more detail about formats? e.g. bytes or base64
+
+    video_inputs: bool
+    """Whether [video inputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+    # TODO: add more detail about formats? e.g. bytes or base64
+
+    image_tool_message: bool
+    """Whether images can be included in tool messages."""
+
+    pdf_tool_message: bool
+    """Whether PDFs can be included in tool messages."""
+
+    # --- Output constraints ---
+
+    max_output_tokens: int
+    """Maximum output tokens"""
+
+    reasoning_output: bool
+    """Whether the model supports [reasoning / chain-of-thought](https://docs.langchain.com/oss/python/langchain/models#reasoning)"""
+
+    image_outputs: bool
+    """Whether [image outputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    audio_outputs: bool
+    """Whether [audio outputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    video_outputs: bool
+    """Whether [video outputs](https://docs.langchain.com/oss/python/langchain/models#multimodal)
+    are supported."""
+
+    # --- Tool calling ---
+    tool_calling: bool
+    """Whether the model supports [tool calling](https://docs.langchain.com/oss/python/langchain/models#tool-calling)"""
+
+    tool_choice: bool
+    """Whether the model supports [tool choice](https://docs.langchain.com/oss/python/langchain/models#forcing-tool-calls)"""
+
+    # --- Structured output ---
+    structured_output: bool
+    """Whether the model supports a native [structured output](https://docs.langchain.com/oss/python/langchain/models#structured-outputs)
+    feature"""
+
+
+ModelProfileRegistry = dict[str, ModelProfile]
+"""Registry mapping model identifiers or names to their ModelProfile."""
--- a/libs/core/langchain_core/load/init.py
+++ b/libs/core/langchain_core/load/init.py
@@ -6,7 +6,7 @@ from langchain_core._import_utils import import_attr

 if TYPE_CHECKING:
    from langchain_core.load.dump import dumpd, dumps
-    from langchain_core.load.load import loads
+    from langchain_core.load.load import InitValidator, loads
    from langchain_core.load.serializable import Serializable

 # Unfortunately, we have to eagerly import load from langchain_core/load/load.py
@@ -15,11 +15,19 @@ if TYPE_CHECKING:
 # the `from langchain_core.load.load import load` absolute import should also work.
 from langchain_core.load.load import load

-__all__ = ("Serializable", "dumpd", "dumps", "load", "loads")
+__all__ = (
+    "InitValidator",
+    "Serializable",
+    "dumpd",
+    "dumps",
+    "load",
+    "loads",
+)

 _dynamic_imports = {
    "dumpd": "dump",
    "dumps": "dump",
+    "InitValidator": "load",
    "loads": "load",
    "Serializable": "serializable",
 }
--- a/libs/core/langchain_core/load/_validation.py
+++ b/libs/core/langchain_core/load/_validation.py
@@ -0,0 +1,174 @@
+"""Validation utilities for LangChain serialization.
+
+Provides escape-based protection against injection attacks in serialized objects. The
+approach uses an allowlist design: only dicts explicitly produced by
+`Serializable.to_json()` are treated as LC objects during deserialization.
+
+## How escaping works
+
+During serialization, plain dicts (user data) that contain an `'lc'` key are wrapped:
+
+```python
+{"lc": 1, ...}  # user data that looks like LC object
+# becomes:
+{"__lc_escaped__": {"lc": 1, ...}}
+```
+
+During deserialization, escaped dicts are unwrapped and returned as plain dicts,
+NOT instantiated as LC objects.
+"""
+
+from typing import Any
+
+from langchain_core.load.serializable import (
+    Serializable,
+    to_json_not_implemented,
+)
+
+_LC_ESCAPED_KEY = "__lc_escaped__"
+"""Sentinel key used to mark escaped user dicts during serialization.
+
+When a plain dict contains 'lc' key (which could be confused with LC objects),
+we wrap it as {"__lc_escaped__": {...original...}}.
+"""
+
+
+def _needs_escaping(obj: dict[str, Any]) -> bool:
+    """Check if a dict needs escaping to prevent confusion with LC objects.
+
+    A dict needs escaping if:
+
+    1. It has an `'lc'` key (could be confused with LC serialization format)
+    2. It has only the escape key (would be mistaken for an escaped dict)
+    """
+    return "lc" in obj or (len(obj) == 1 and _LC_ESCAPED_KEY in obj)
+
+
+def _escape_dict(obj: dict[str, Any]) -> dict[str, Any]:
+    """Wrap a dict in the escape marker.
+
+    Example:
+        ```python
+        {"key": "value"}  # becomes {"__lc_escaped__": {"key": "value"}}
+        ```
+    """
+    return {_LC_ESCAPED_KEY: obj}
+
+
+def _is_escaped_dict(obj: dict[str, Any]) -> bool:
+    """Check if a dict is an escaped user dict.
+
+    Example:
+        ```python
+        {"__lc_escaped__": {...}}  # is an escaped dict
+        ```
+    """
+    return len(obj) == 1 and _LC_ESCAPED_KEY in obj
+
+
+def _serialize_value(obj: Any) -> Any:
+    """Serialize a value with escaping of user dicts.
+
+    Called recursively on kwarg values to escape any plain dicts that could be confused
+    with LC objects.
+
+    Args:
+        obj: The value to serialize.
+
+    Returns:
+        The serialized value with user dicts escaped as needed.
+    """
+    if isinstance(obj, Serializable):
+        # This is an LC object - serialize it properly (not escaped)
+        return _serialize_lc_object(obj)
+    if isinstance(obj, dict):
+        if not all(isinstance(k, (str, int, float, bool, type(None))) for k in obj):
+            # if keys are not json serializable
+            return to_json_not_implemented(obj)
+        # Check if dict needs escaping BEFORE recursing into values.
+        # If it needs escaping, wrap it as-is - the contents are user data that
+        # will be returned as-is during deserialization (no instantiation).
+        # This prevents re-escaping of already-escaped nested content.
+        if _needs_escaping(obj):
+            return _escape_dict(obj)
+        # Safe dict (no 'lc' key) - recurse into values
+        return {k: _serialize_value(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple)):
+        return [_serialize_value(item) for item in obj]
+    if isinstance(obj, (str, int, float, bool, type(None))):
+        return obj
+
+    # Non-JSON-serializable object (datetime, custom objects, etc.)
+    return to_json_not_implemented(obj)
+
+
+def _is_lc_secret(obj: Any) -> bool:
+    """Check if an object is a LangChain secret marker."""
+    expected_num_keys = 3
+    return (
+        isinstance(obj, dict)
+        and obj.get("lc") == 1
+        and obj.get("type") == "secret"
+        and "id" in obj
+        and len(obj) == expected_num_keys
+    )
+
+
+def _serialize_lc_object(obj: Any) -> dict[str, Any]:
+    """Serialize a `Serializable` object with escaping of user data in kwargs.
+
+    Args:
+        obj: The `Serializable` object to serialize.
+
+    Returns:
+        The serialized dict with user data in kwargs escaped as needed.
+
+    Note:
+        Kwargs values are processed with `_serialize_value` to escape user data (like
+        metadata) that contains `'lc'` keys. Secret fields (from `lc_secrets`) are
+        skipped because `to_json()` replaces their values with secret markers.
+    """
+    if not isinstance(obj, Serializable):
+        msg = f"Expected Serializable, got {type(obj)}"
+        raise TypeError(msg)
+
+    serialized: dict[str, Any] = dict(obj.to_json())
+
+    # Process kwargs to escape user data that could be confused with LC objects
+    # Skip secret fields - to_json() already converted them to secret markers
+    if serialized.get("type") == "constructor" and "kwargs" in serialized:
+        serialized["kwargs"] = {
+            k: v if _is_lc_secret(v) else _serialize_value(v)
+            for k, v in serialized["kwargs"].items()
+        }
+
+    return serialized
+
+
+def _unescape_value(obj: Any) -> Any:
+    """Unescape a value, processing escape markers in dict values and lists.
+
+    When an escaped dict is encountered (`{"__lc_escaped__": ...}`), it's
+    unwrapped and the contents are returned AS-IS (no further processing).
+    The contents represent user data that should not be modified.
+
+    For regular dicts and lists, we recurse to find any nested escape markers.
+
+    Args:
+        obj: The value to unescape.
+
+    Returns:
+        The unescaped value.
+    """
+    if isinstance(obj, dict):
+        if _is_escaped_dict(obj):
+            # Unwrap and return the user data as-is (no further unescaping).
+            # The contents are user data that may contain more escape keys,
+            # but those are part of the user's actual data.
+            return obj[_LC_ESCAPED_KEY]
+
+        # Regular dict - recurse into values to find nested escape markers
+        return {k: _unescape_value(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [_unescape_value(item) for item in obj]
+    return obj
--- a/libs/core/langchain_core/load/dump.py
+++ b/libs/core/langchain_core/load/dump.py
@@ -1,10 +1,26 @@
-"""Dump objects to json."""
+"""Serialize LangChain objects to JSON.
+
+Provides `dumps` (to JSON string) and `dumpd` (to dict) for serializing
+`Serializable` objects.
+
+## Escaping
+
+During serialization, plain dicts (user data) that contain an `'lc'` key are escaped
+by wrapping them: `{"__lc_escaped__": {...original...}}`. This prevents injection
+attacks where malicious data could trick the deserializer into instantiating
+arbitrary classes. The escape marker is removed during deserialization.
+
+This is an allowlist approach: only dicts explicitly produced by
+`Serializable.to_json()` are treated as LC objects; everything else is escaped if it
+could be confused with the LC format.
+"""

 import json
 from typing import Any

 from pydantic import BaseModel

+from langchain_core.load._validation import _serialize_value
 from langchain_core.load.serializable import Serializable, to_json_not_implemented
 from langchain_core.messages import AIMessage
 from langchain_core.outputs import ChatGeneration
@@ -17,7 +33,7 @@ def default(obj: Any) -> Any:
        obj: The object to serialize to json if it is a Serializable object.

    Returns:
-        A json serializable object or a SerializedNotImplemented object.
+        A JSON serializable object or a SerializedNotImplemented object.
    """
    if isinstance(obj, Serializable):
        return obj.to_json()
@@ -25,6 +41,20 @@ def default(obj: Any) -> Any:


 def _dump_pydantic_models(obj: Any) -> Any:
+    """Convert nested Pydantic models to dicts for JSON serialization.
+
+    Handles the special case where a `ChatGeneration` contains an `AIMessage`
+    with a parsed Pydantic model in `additional_kwargs["parsed"]`. Since
+    Pydantic models aren't directly JSON serializable, this converts them to
+    dicts.
+
+    Args:
+        obj: The object to process.
+
+    Returns:
+        A copy of the object with nested Pydantic models converted to dicts, or
+            the original object unchanged if no conversion was needed.
+    """
    if (
        isinstance(obj, ChatGeneration)
        and isinstance(obj.message, AIMessage)
@@ -38,17 +68,23 @@ def _dump_pydantic_models(obj: Any) -> Any:


 def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
-    """Return a json string representation of an object.
+    """Return a JSON string representation of an object.
+
+    Note:
+        Plain dicts containing an `'lc'` key are automatically escaped to prevent
+        confusion with LC serialization format. The escape marker is removed during
+        deserialization.

    Args:
        obj: The object to dump.
-        pretty: Whether to pretty print the json. If true, the json will be
-            indented with 2 spaces (if no indent is provided as part of kwargs).
-            Default is False.
-        **kwargs: Additional arguments to pass to json.dumps
+        pretty: Whether to pretty print the json.
+
+            If `True`, the json will be indented by either 2 spaces or the amount
+            provided in the `indent` kwarg.
+        **kwargs: Additional arguments to pass to `json.dumps`

    Returns:
-        A json string representation of the object.
+        A JSON string representation of the object.

    Raises:
        ValueError: If `default` is passed as a kwarg.
@@ -56,30 +92,29 @@ def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
    if "default" in kwargs:
        msg = "`default` should not be passed to dumps"
        raise ValueError(msg)
-    try:
-        obj = _dump_pydantic_models(obj)
-        if pretty:
-            indent = kwargs.pop("indent", 2)
-            return json.dumps(obj, default=default, indent=indent, **kwargs)
-        return json.dumps(obj, default=default, **kwargs)
-    except TypeError:
-        if pretty:
-            indent = kwargs.pop("indent", 2)
-            return json.dumps(to_json_not_implemented(obj), indent=indent, **kwargs)
-        return json.dumps(to_json_not_implemented(obj), **kwargs)
+
+    obj = _dump_pydantic_models(obj)
+    serialized = _serialize_value(obj)
+
+    if pretty:
+        indent = kwargs.pop("indent", 2)
+        return json.dumps(serialized, indent=indent, **kwargs)
+    return json.dumps(serialized, **kwargs)


 def dumpd(obj: Any) -> Any:
    """Return a dict representation of an object.

-    !!! note
-        Unfortunately this function is not as efficient as it could be because it first
-        dumps the object to a json string and then loads it back into a dictionary.
+    Note:
+        Plain dicts containing an `'lc'` key are automatically escaped to prevent
+        confusion with LC serialization format. The escape marker is removed during
+        deserialization.

    Args:
        obj: The object to dump.

    Returns:
-        dictionary that can be serialized to json using json.dumps
+        Dictionary that can be serialized to json using `json.dumps`.
    """
-    return json.loads(dumps(obj))
+    obj = _dump_pydantic_models(obj)
+    return _serialize_value(obj)
--- a/libs/core/langchain_core/load/load.py
+++ b/libs/core/langchain_core/load/load.py
@@ -1,11 +1,83 @@
-"""Load LangChain objects from JSON strings or objects."""
+"""Load LangChain objects from JSON strings or objects.
+
+## How it works
+
+Each `Serializable` LangChain object has a unique identifier (its "class path"), which
+is a list of strings representing the module path and class name. For example:
+
+- `AIMessage` -> `["langchain_core", "messages", "ai", "AIMessage"]`
+- `ChatPromptTemplate` -> `["langchain_core", "prompts", "chat", "ChatPromptTemplate"]`
+
+When deserializing, the class path from the JSON `'id'` field is checked against an
+allowlist. If the class is not in the allowlist, deserialization raises a `ValueError`.
+
+## Security model
+
+The `allowed_objects` parameter controls which classes can be deserialized:
+
+- **`'core'` (default)**: Allow classes defined in the serialization mappings for
+    langchain_core.
+- **`'all'`**: Allow classes defined in the serialization mappings. This
+    includes core LangChain types (messages, prompts, documents, etc.) and trusted
+    partner integrations. See `langchain_core.load.mapping` for the full list.
+- **Explicit list of classes**: Only those specific classes are allowed.
+
+For simple data types like messages and documents, the default allowlist is safe to use.
+These classes do not perform side effects during initialization.
+
+!!! note "Side effects in allowed classes"
+
+    Deserialization calls `__init__` on allowed classes. If those classes perform side
+    effects during initialization (network calls, file operations, etc.), those side
+    effects will occur. The allowlist prevents instantiation of classes outside the
+    allowlist, but does not sandbox the allowed classes themselves.
+
+Import paths are also validated against trusted namespaces before any module is
+imported.
+
+### Injection protection (escape-based)
+
+During serialization, plain dicts that contain an `'lc'` key are escaped by wrapping
+them: `{"__lc_escaped__": {...}}`. During deserialization, escaped dicts are unwrapped
+and returned as plain dicts, NOT instantiated as LC objects.
+
+This is an allowlist approach: only dicts explicitly produced by
+`Serializable.to_json()` (which are NOT escaped) are treated as LC objects;
+everything else is user data.
+
+Even if an attacker's payload includes `__lc_escaped__` wrappers, it will be unwrapped
+to plain dicts and NOT instantiated as malicious objects.
+
+## Examples
+
+```python
+from langchain_core.load import load
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.messages import AIMessage, HumanMessage
+
+# Use default allowlist (classes from mappings) - recommended
+obj = load(data)
+
+# Allow only specific classes (most restrictive)
+obj = load(
+    data,
+    allowed_objects=[
+        ChatPromptTemplate,
+        AIMessage,
+        HumanMessage,
+    ],
+)
+```
+"""

 import importlib
 import json
 import os
-from typing import Any
+from collections.abc import Callable, Iterable
+from typing import Any, Literal, cast

 from langchain_core._api import beta
+from langchain_core.load._validation import _is_escaped_dict, _unescape_value
 from langchain_core.load.mapping import (
    _JS_SERIALIZABLE_MAPPING,
    _OG_SERIALIZABLE_MAPPING,
@@ -44,35 +116,209 @@ ALL_SERIALIZABLE_MAPPINGS = {
    **_JS_SERIALIZABLE_MAPPING,
 }

+# Cache for the default allowed class paths computed from mappings
+# Maps mode ("all" or "core") to the cached set of paths
+_default_class_paths_cache: dict[str, set[tuple[str, ...]]] = {}
+
+
+def _get_default_allowed_class_paths(
+    allowed_object_mode: Literal["all", "core"],
+) -> set[tuple[str, ...]]:
+    """Get the default allowed class paths from the serialization mappings.
+
+    This uses the mappings as the source of truth for what classes are allowed
+    by default. Both the legacy paths (keys) and current paths (values) are included.
+
+    Args:
+        allowed_object_mode: either `'all'` or `'core'`.
+
+    Returns:
+        Set of class path tuples that are allowed by default.
+    """
+    if allowed_object_mode in _default_class_paths_cache:
+        return _default_class_paths_cache[allowed_object_mode]
+
+    allowed_paths: set[tuple[str, ...]] = set()
+    for key, value in ALL_SERIALIZABLE_MAPPINGS.items():
+        if allowed_object_mode == "core" and value[0] != "langchain_core":
+            continue
+        allowed_paths.add(key)
+        allowed_paths.add(value)
+
+    _default_class_paths_cache[allowed_object_mode] = allowed_paths
+    return _default_class_paths_cache[allowed_object_mode]
+
+
+def _block_jinja2_templates(
+    class_path: tuple[str, ...],
+    kwargs: dict[str, Any],
+) -> None:
+    """Block jinja2 templates during deserialization for security.
+
+    Jinja2 templates can execute arbitrary code, so they are blocked by default when
+    deserializing objects with `template_format='jinja2'`.
+
+    Note:
+        We intentionally do NOT check the `class_path` here to keep this simple and
+        future-proof. If any new class is added that accepts `template_format='jinja2'`,
+        it will be automatically blocked without needing to update this function.
+
+    Args:
+        class_path: The class path tuple being deserialized (unused).
+        kwargs: The kwargs dict for the class constructor.
+
+    Raises:
+        ValueError: If `template_format` is `'jinja2'`.
+    """
+    _ = class_path  # Unused - see docstring for rationale. Kept to satisfy signature.
+    if kwargs.get("template_format") == "jinja2":
+        msg = (
+            "Jinja2 templates are not allowed during deserialization for security "
+            "reasons. Use 'f-string' template format instead, or explicitly allow "
+            "jinja2 by providing a custom init_validator."
+        )
+        raise ValueError(msg)
+
+
+def default_init_validator(
+    class_path: tuple[str, ...],
+    kwargs: dict[str, Any],
+) -> None:
+    """Default init validator that blocks jinja2 templates.
+
+    This is the default validator used by `load()` and `loads()` when no custom
+    validator is provided.
+
+    Args:
+        class_path: The class path tuple being deserialized.
+        kwargs: The kwargs dict for the class constructor.
+
+    Raises:
+        ValueError: If template_format is `'jinja2'`.
+    """
+    _block_jinja2_templates(class_path, kwargs)
+
+
+AllowedObject = type[Serializable]
+"""Type alias for classes that can be included in the `allowed_objects` parameter.
+
+Must be a `Serializable` subclass (the class itself, not an instance).
+"""
+
+InitValidator = Callable[[tuple[str, ...], dict[str, Any]], None]
+"""Type alias for a callable that validates kwargs during deserialization.
+
+The callable receives:
+
+- `class_path`: A tuple of strings identifying the class being instantiated
+    (e.g., `('langchain', 'schema', 'messages', 'AIMessage')`).
+- `kwargs`: The kwargs dict that will be passed to the constructor.
+
+The validator should raise an exception if the object should not be deserialized.
+"""
+
+
+def _compute_allowed_class_paths(
+    allowed_objects: Iterable[AllowedObject],
+    import_mappings: dict[tuple[str, ...], tuple[str, ...]],
+) -> set[tuple[str, ...]]:
+    """Return allowed class paths from an explicit list of classes.
+
+    A class path is a tuple of strings identifying a serializable class, derived from
+    `Serializable.lc_id()`. For example: `('langchain_core', 'messages', 'AIMessage')`.
+
+    Args:
+        allowed_objects: Iterable of `Serializable` subclasses to allow.
+        import_mappings: Mapping of legacy class paths to current class paths.
+
+    Returns:
+        Set of allowed class paths.
+
+    Example:
+        ```python
+        # Allow a specific class
+        _compute_allowed_class_paths([MyPrompt], {}) ->
+            {("langchain_core", "prompts", "MyPrompt")}
+
+        # Include legacy paths that map to the same class
+        import_mappings = {("old", "Prompt"): ("langchain_core", "prompts", "MyPrompt")}
+        _compute_allowed_class_paths([MyPrompt], import_mappings) ->
+            {("langchain_core", "prompts", "MyPrompt"), ("old", "Prompt")}
+        ```
+    """
+    allowed_objects_list = list(allowed_objects)
+
+    allowed_class_paths: set[tuple[str, ...]] = set()
+    for allowed_obj in allowed_objects_list:
+        if not isinstance(allowed_obj, type) or not issubclass(
+            allowed_obj, Serializable
+        ):
+            msg = "allowed_objects must contain Serializable subclasses."
+            raise TypeError(msg)
+
+        class_path = tuple(allowed_obj.lc_id())
+        allowed_class_paths.add(class_path)
+        # Add legacy paths that map to the same class.
+        for mapping_key, mapping_value in import_mappings.items():
+            if tuple(mapping_value) == class_path:
+                allowed_class_paths.add(mapping_key)
+    return allowed_class_paths
+

 class Reviver:
-    """Reviver for JSON objects."""
+    """Reviver for JSON objects.
+
+    Used as the `object_hook` for `json.loads` to reconstruct LangChain objects from
+    their serialized JSON representation.
+
+    Only classes in the allowlist can be instantiated.
+    """

    def __init__(
        self,
+        allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
        secrets_map: dict[str, str] | None = None,
        valid_namespaces: list[str] | None = None,
-        secrets_from_env: bool = True,  # noqa: FBT001,FBT002
+        secrets_from_env: bool = False,  # noqa: FBT001,FBT002
        additional_import_mappings: dict[tuple[str, ...], tuple[str, ...]]
        | None = None,
        *,
        ignore_unserializable_fields: bool = False,
+        init_validator: InitValidator | None = default_init_validator,
    ) -> None:
        """Initialize the reviver.

        Args:
-            secrets_map: A map of secrets to load. If a secret is not found in
-                the map, it will be loaded from the environment if `secrets_from_env`
-                is True.
-            valid_namespaces: A list of additional namespaces (modules)
-                to allow to be deserialized.
+            allowed_objects: Allowlist of classes that can be deserialized.
+                - `'core'` (default): Allow classes defined in the serialization
+                    mappings for `langchain_core`.
+                - `'all'`: Allow classes defined in the serialization mappings.
+
+                    This includes core LangChain types (messages, prompts, documents,
+                    etc.) and trusted partner integrations. See
+                    `langchain_core.load.mapping` for the full list.
+                - Explicit list of classes: Only those specific classes are allowed.
+            secrets_map: A map of secrets to load.
+                If a secret is not found in the map, it will be loaded from the
+                environment if `secrets_from_env` is `True`.
+            valid_namespaces: Additional namespaces (modules) to allow during
+                deserialization, beyond the default trusted namespaces.
            secrets_from_env: Whether to load secrets from the environment.
-                Defaults to `True`.
-            additional_import_mappings: A dictionary of additional namespace mappings
+            additional_import_mappings: A dictionary of additional namespace mappings.
+
                You can use this to override default mappings or add new mappings.

+                When `allowed_objects` is `None` (using defaults), paths from these
+                mappings are also added to the allowed class paths.
            ignore_unserializable_fields: Whether to ignore unserializable fields.
-                Defaults to `False`.
+            init_validator: Optional callable to validate kwargs before instantiation.
+
+                If provided, this function is called with `(class_path, kwargs)` where
+                `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+                The validator should raise an exception if the object should not be
+                deserialized, otherwise return `None`.
+
+                Defaults to `default_init_validator` which blocks jinja2 templates.
        """
        self.secrets_from_env = secrets_from_env
        self.secrets_map = secrets_map or {}
@@ -91,7 +337,26 @@ class Reviver:
            if self.additional_import_mappings
            else ALL_SERIALIZABLE_MAPPINGS
        )
+        # Compute allowed class paths:
+        # - "all" -> use default paths from mappings (+ additional_import_mappings)
+        # - Explicit list -> compute from those classes
+        if allowed_objects in ("all", "core"):
+            self.allowed_class_paths: set[tuple[str, ...]] | None = (
+                _get_default_allowed_class_paths(
+                    cast("Literal['all', 'core']", allowed_objects)
+                ).copy()
+            )
+            # Add paths from additional_import_mappings to the defaults
+            if self.additional_import_mappings:
+                for key, value in self.additional_import_mappings.items():
+                    self.allowed_class_paths.add(key)
+                    self.allowed_class_paths.add(value)
+        else:
+            self.allowed_class_paths = _compute_allowed_class_paths(
+                cast("Iterable[AllowedObject]", allowed_objects), self.import_mappings
+            )
        self.ignore_unserializable_fields = ignore_unserializable_fields
+        self.init_validator = init_validator

    def __call__(self, value: dict[str, Any]) -> Any:
        """Revive the value.
@@ -142,6 +407,20 @@ class Reviver:
            [*namespace, name] = value["id"]
            mapping_key = tuple(value["id"])

+            if (
+                self.allowed_class_paths is not None
+                and mapping_key not in self.allowed_class_paths
+            ):
+                msg = (
+                    f"Deserialization of {mapping_key!r} is not allowed. "
+                    "The default (allowed_objects='core') only permits core "
+                    "langchain-core classes. To allow trusted partner integrations, "
+                    "use allowed_objects='all'. Alternatively, pass an explicit list "
+                    "of allowed classes via allowed_objects=[...]. "
+                    "See langchain_core.load.mapping for the full allowlist."
+                )
+                raise ValueError(msg)
+
            if (
                namespace[0] not in self.valid_namespaces
                # The root namespace ["langchain"] is not a valid identifier.
@@ -149,13 +428,11 @@ class Reviver:
            ):
                msg = f"Invalid namespace: {value}"
                raise ValueError(msg)
-            # Has explicit import path.
+            # Determine explicit import path
            if mapping_key in self.import_mappings:
                import_path = self.import_mappings[mapping_key]
                # Split into module and name
                import_dir, name = import_path[:-1], import_path[-1]
-                # Import module
-                mod = importlib.import_module(".".join(import_dir))
            elif namespace[0] in DISALLOW_LOAD_FROM_PATH:
                msg = (
                    "Trying to deserialize something that cannot "
@@ -163,9 +440,16 @@ class Reviver:
                    f"{mapping_key}."
                )
                raise ValueError(msg)
-            # Otherwise, treat namespace as path.
            else:
-                mod = importlib.import_module(".".join(namespace))
+                # Otherwise, treat namespace as path.
+                import_dir = namespace
+
+            # Validate import path is in trusted namespaces before importing
+            if import_dir[0] not in self.valid_namespaces:
+                msg = f"Invalid namespace: {value}"
+                raise ValueError(msg)
+
+            mod = importlib.import_module(".".join(import_dir))

            cls = getattr(mod, name)

@@ -177,6 +461,10 @@ class Reviver:
            # We don't need to recurse on kwargs
            # as json.loads will do that for us.
            kwargs = value.get("kwargs", {})
+
+            if self.init_validator is not None:
+                self.init_validator(mapping_key, kwargs)
+
            return cls(**kwargs)

        return value
@@ -186,43 +474,81 @@ class Reviver:
 def loads(
    text: str,
    *,
+    allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
    secrets_map: dict[str, str] | None = None,
    valid_namespaces: list[str] | None = None,
-    secrets_from_env: bool = True,
+    secrets_from_env: bool = False,
    additional_import_mappings: dict[tuple[str, ...], tuple[str, ...]] | None = None,
    ignore_unserializable_fields: bool = False,
+    init_validator: InitValidator | None = default_init_validator,
 ) -> Any:
    """Revive a LangChain class from a JSON string.

    Equivalent to `load(json.loads(text))`.

+    Only classes in the allowlist can be instantiated. The default allowlist includes
+    core LangChain types (messages, prompts, documents, etc.). See
+    `langchain_core.load.mapping` for the full list.
+
+    !!! warning "Beta feature"
+
+        This is a beta feature. Please be wary of deploying experimental code to
+        production unless you've taken appropriate precautions.
+
    Args:
        text: The string to load.
-        secrets_map: A map of secrets to load. If a secret is not found in
-            the map, it will be loaded from the environment if `secrets_from_env`
-            is True.
-        valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized.
+        allowed_objects: Allowlist of classes that can be deserialized.
+
+            - `'core'` (default): Allow classes defined in the serialization mappings
+                for `langchain_core`.
+            - `'all'`: Allow classes defined in the serialization mappings.
+
+                This includes core LangChain types (messages, prompts, documents, etc.)
+                and trusted partner integrations. See `langchain_core.load.mapping` for
+                the full list.
+
+            - Explicit list of classes: Only those specific classes are allowed.
+            - `[]`: Disallow all deserialization (will raise on any object).
+        secrets_map: A map of secrets to load.
+
+            If a secret is not found in the map, it will be loaded from the environment
+            if `secrets_from_env` is `True`.
+        valid_namespaces: Additional namespaces (modules) to allow during
+            deserialization, beyond the default trusted namespaces.
        secrets_from_env: Whether to load secrets from the environment.
-            Defaults to `True`.
-        additional_import_mappings: A dictionary of additional namespace mappings
+        additional_import_mappings: A dictionary of additional namespace mappings.
+
            You can use this to override default mappings or add new mappings.

+            When `allowed_objects` is `None` (using defaults), paths from these
+            mappings are also added to the allowed class paths.
        ignore_unserializable_fields: Whether to ignore unserializable fields.
-            Defaults to `False`.
+        init_validator: Optional callable to validate kwargs before instantiation.
+
+            If provided, this function is called with `(class_path, kwargs)` where
+            `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+            The validator should raise an exception if the object should not be
+            deserialized, otherwise return `None`.
+
+            Defaults to `default_init_validator` which blocks jinja2 templates.

    Returns:
        Revived LangChain objects.
+
+    Raises:
+        ValueError: If an object's class path is not in the `allowed_objects` allowlist.
    """
-    return json.loads(
-        text,
-        object_hook=Reviver(
-            secrets_map,
-            valid_namespaces,
-            secrets_from_env,
-            additional_import_mappings,
-            ignore_unserializable_fields=ignore_unserializable_fields,
-        ),
+    # Parse JSON and delegate to load() for proper escape handling
+    raw_obj = json.loads(text)
+    return load(
+        raw_obj,
+        allowed_objects=allowed_objects,
+        secrets_map=secrets_map,
+        valid_namespaces=valid_namespaces,
+        secrets_from_env=secrets_from_env,
+        additional_import_mappings=additional_import_mappings,
+        ignore_unserializable_fields=ignore_unserializable_fields,
+        init_validator=init_validator,
    )


@@ -230,46 +556,112 @@ def loads(
 def load(
    obj: Any,
    *,
+    allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
    secrets_map: dict[str, str] | None = None,
    valid_namespaces: list[str] | None = None,
-    secrets_from_env: bool = True,
+    secrets_from_env: bool = False,
    additional_import_mappings: dict[tuple[str, ...], tuple[str, ...]] | None = None,
    ignore_unserializable_fields: bool = False,
+    init_validator: InitValidator | None = default_init_validator,
 ) -> Any:
    """Revive a LangChain class from a JSON object.

-    Use this if you already have a parsed JSON object,
-    eg. from `json.load` or `orjson.loads`.
+    Use this if you already have a parsed JSON object, eg. from `json.load` or
+    `orjson.loads`.
+
+    Only classes in the allowlist can be instantiated. The default allowlist includes
+    core LangChain types (messages, prompts, documents, etc.). See
+    `langchain_core.load.mapping` for the full list.
+
+    !!! warning "Beta feature"
+
+        This is a beta feature. Please be wary of deploying experimental code to
+        production unless you've taken appropriate precautions.

    Args:
        obj: The object to load.
-        secrets_map: A map of secrets to load. If a secret is not found in
-            the map, it will be loaded from the environment if `secrets_from_env`
-            is True.
-        valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized.
+        allowed_objects: Allowlist of classes that can be deserialized.
+
+            - `'core'` (default): Allow classes defined in the serialization mappings
+                for `langchain_core`.
+            - `'all'`: Allow classes defined in the serialization mappings.
+
+                This includes core LangChain types (messages, prompts, documents, etc.)
+                and trusted partner integrations. See `langchain_core.load.mapping` for
+                the full list.
+
+            - Explicit list of classes: Only those specific classes are allowed.
+            - `[]`: Disallow all deserialization (will raise on any object).
+        secrets_map: A map of secrets to load.
+
+            If a secret is not found in the map, it will be loaded from the environment
+            if `secrets_from_env` is `True`.
+        valid_namespaces: Additional namespaces (modules) to allow during
+            deserialization, beyond the default trusted namespaces.
        secrets_from_env: Whether to load secrets from the environment.
-            Defaults to `True`.
-        additional_import_mappings: A dictionary of additional namespace mappings
+        additional_import_mappings: A dictionary of additional namespace mappings.
+
            You can use this to override default mappings or add new mappings.

+            When `allowed_objects` is `None` (using defaults), paths from these
+            mappings are also added to the allowed class paths.
        ignore_unserializable_fields: Whether to ignore unserializable fields.
-            Defaults to `False`.
+        init_validator: Optional callable to validate kwargs before instantiation.
+
+            If provided, this function is called with `(class_path, kwargs)` where
+            `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+            The validator should raise an exception if the object should not be
+            deserialized, otherwise return `None`.
+
+            Defaults to `default_init_validator` which blocks jinja2 templates.

    Returns:
        Revived LangChain objects.
+
+    Raises:
+        ValueError: If an object's class path is not in the `allowed_objects` allowlist.
+
+    Example:
+        ```python
+        from langchain_core.load import load, dumpd
+        from langchain_core.messages import AIMessage
+
+        msg = AIMessage(content="Hello")
+        data = dumpd(msg)
+
+        # Deserialize using default allowlist
+        loaded = load(data)
+
+        # Or with explicit allowlist
+        loaded = load(data, allowed_objects=[AIMessage])
+
+        # Or extend defaults with additional mappings
+        loaded = load(
+            data,
+            additional_import_mappings={
+                ("my_pkg", "MyClass"): ("my_pkg", "module", "MyClass"),
+            },
+        )
+        ```
    """
    reviver = Reviver(
+        allowed_objects,
        secrets_map,
        valid_namespaces,
        secrets_from_env,
        additional_import_mappings,
        ignore_unserializable_fields=ignore_unserializable_fields,
+        init_validator=init_validator,
    )

    def _load(obj: Any) -> Any:
        if isinstance(obj, dict):
-            # Need to revive leaf nodes before reviving this node
+            # Check for escaped dict FIRST (before recursing).
+            # Escaped dicts are user data that should NOT be processed as LC objects.
+            if _is_escaped_dict(obj):
+                return _unescape_value(obj)
+
+            # Not escaped - recurse into children then apply reviver
            loaded_obj = {k: _load(v) for k, v in obj.items()}
            return reviver(loaded_obj)
        if isinstance(obj, list):
--- a/libs/core/langchain_core/load/mapping.py
+++ b/libs/core/langchain_core/load/mapping.py
@@ -1,21 +1,19 @@
 """Serialization mapping.

-This file contains a mapping between the lc_namespace path for a given
-subclass that implements from Serializable to the namespace
+This file contains a mapping between the `lc_namespace` path for a given
+subclass that implements from `Serializable` to the namespace
 where that class is actually located.

 This mapping helps maintain the ability to serialize and deserialize
 well-known LangChain objects even if they are moved around in the codebase
 across different LangChain versions.

-For example,
+For example, the code for the `AIMessage` class is located in
+`langchain_core.messages.ai.AIMessage`. This message is associated with the
+`lc_namespace` of `["langchain", "schema", "messages", "AIMessage"]`,
+because this code was originally in `langchain.schema.messages.AIMessage`.

-The code for AIMessage class is located in langchain_core.messages.ai.AIMessage,
-This message is associated with the lc_namespace
-["langchain", "schema", "messages", "AIMessage"],
-because this code was originally in langchain.schema.messages.AIMessage.
-
-The mapping allows us to deserialize an AIMessage created with an older
+The mapping allows us to deserialize an `AIMessage` created with an older
 version of LangChain where the code was in a different location.
 """

@@ -275,6 +273,11 @@ SERIALIZABLE_MAPPING: dict[tuple[str, ...], tuple[str, ...]] = {
        "chat_models",
        "ChatGroq",
    ),
+    ("langchain_xai", "chat_models", "ChatXAI"): (
+        "langchain_xai",
+        "chat_models",
+        "ChatXAI",
+    ),
    ("langchain", "chat_models", "fireworks", "ChatFireworks"): (
        "langchain_fireworks",
        "chat_models",
@@ -529,16 +532,6 @@ SERIALIZABLE_MAPPING: dict[tuple[str, ...], tuple[str, ...]] = {
        "structured",
        "StructuredPrompt",
    ),
-    ("langchain_sambanova", "chat_models", "ChatSambaNovaCloud"): (
-        "langchain_sambanova",
-        "chat_models",
-        "ChatSambaNovaCloud",
-    ),
-    ("langchain_sambanova", "chat_models", "ChatSambaStudio"): (
-        "langchain_sambanova",
-        "chat_models",
-        "ChatSambaStudio",
-    ),
    ("langchain_core", "prompts", "message", "_DictMessagePromptTemplate"): (
        "langchain_core",
        "prompts",
--- a/libs/core/langchain_core/load/serializable.py
+++ b/libs/core/langchain_core/load/serializable.py
@@ -25,9 +25,9 @@ class BaseSerialized(TypedDict):
    id: list[str]
    """The unique identifier of the object."""
    name: NotRequired[str]
-    """The name of the object. Optional."""
+    """The name of the object."""
    graph: NotRequired[dict[str, Any]]
-    """The graph of the object. Optional."""
+    """The graph of the object."""


 class SerializedConstructor(BaseSerialized):
@@ -52,7 +52,7 @@ class SerializedNotImplemented(BaseSerialized):
    type: Literal["not_implemented"]
    """The type of the object. Must be `'not_implemented'`."""
    repr: str | None
-    """The representation of the object. Optional."""
+    """The representation of the object."""


 def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
@@ -61,7 +61,7 @@ def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
    Args:
        value: The value.
        key: The key.
-        model: The pydantic model.
+        model: The Pydantic model.

    Returns:
        Whether the value is different from the default.
@@ -92,20 +92,24 @@ class Serializable(BaseModel, ABC):

    It relies on the following methods and properties:

-    - `is_lc_serializable`: Is this class serializable?
-        By design, even if a class inherits from Serializable, it is not serializable by
-        default. This is to prevent accidental serialization of objects that should not
-        be serialized.
-    - `get_lc_namespace`: Get the namespace of the langchain object.
+    - [`is_lc_serializable`][langchain_core.load.serializable.Serializable.is_lc_serializable]: Is this class serializable?
+
+        By design, even if a class inherits from `Serializable`, it is not serializable
+        by default. This is to prevent accidental serialization of objects that should
+        not be serialized.
+    - [`get_lc_namespace`][langchain_core.load.serializable.Serializable.get_lc_namespace]: Get the namespace of the LangChain object.
+
        During deserialization, this namespace is used to identify
        the correct class to instantiate.
+
        Please see the `Reviver` class in `langchain_core.load.load` for more details.
-        During deserialization an additional mapping is handle
-        classes that have moved or been renamed across package versions.
-    - `lc_secrets`: A map of constructor argument names to secret ids.
-    - `lc_attributes`: List of additional attribute names that should be included
-      as part of the serialized representation.
-    """
+        During deserialization an additional mapping is handle classes that have moved
+        or been renamed across package versions.
+
+    - [`lc_secrets`][langchain_core.load.serializable.Serializable.lc_secrets]: A map of constructor argument names to secret ids.
+    - [`lc_attributes`][langchain_core.load.serializable.Serializable.lc_attributes]: List of additional attribute names that should be included
+        as part of the serialized representation.
+    """  # noqa: E501

    # Remove default BaseModel init docstring.
    def __init__(self, *args: Any, **kwargs: Any) -> None:
@@ -116,24 +120,25 @@ class Serializable(BaseModel, ABC):
    def is_lc_serializable(cls) -> bool:
        """Is this class serializable?

-        By design, even if a class inherits from Serializable, it is not serializable by
-        default. This is to prevent accidental serialization of objects that should not
-        be serialized.
+        By design, even if a class inherits from `Serializable`, it is not serializable
+        by default. This is to prevent accidental serialization of objects that should
+        not be serialized.

        Returns:
-            Whether the class is serializable. Default is False.
+            Whether the class is serializable. Default is `False`.
        """
        return False

    @classmethod
    def get_lc_namespace(cls) -> list[str]:
-        """Get the namespace of the langchain object.
+        """Get the namespace of the LangChain object.

-        For example, if the class is `langchain.llms.openai.OpenAI`, then the
-        namespace is ["langchain", "llms", "openai"]
+        For example, if the class is
+        [`langchain.llms.openai.OpenAI`][langchain_openai.OpenAI], then the namespace is
+        `["langchain", "llms", "openai"]`

        Returns:
-            The namespace as a list of strings.
+            The namespace.
        """
        return cls.__module__.split(".")

@@ -141,8 +146,7 @@ class Serializable(BaseModel, ABC):
    def lc_secrets(self) -> dict[str, str]:
        """A map of constructor argument names to secret ids.

-        For example,
-            {"openai_api_key": "OPENAI_API_KEY"}
+        For example, `{"openai_api_key": "OPENAI_API_KEY"}`
        """
        return {}

@@ -151,6 +155,7 @@ class Serializable(BaseModel, ABC):
        """List of attribute names that should be included in the serialized kwargs.

        These attributes must be accepted by the constructor.
+
        Default is an empty dictionary.
        """
        return {}
@@ -194,7 +199,7 @@ class Serializable(BaseModel, ABC):
            ValueError: If the class has deprecated attributes.

        Returns:
-            A json serializable object or a SerializedNotImplemented object.
+            A JSON serializable object or a `SerializedNotImplemented` object.
        """
        if not self.is_lc_serializable():
            return self.to_json_not_implemented()
@@ -269,7 +274,7 @@ class Serializable(BaseModel, ABC):
        """Serialize a "not implemented" object.

        Returns:
-            SerializedNotImplemented.
+            `SerializedNotImplemented`.
        """
        return to_json_not_implemented(self)

@@ -284,8 +289,8 @@ def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool:

    Returns:
        Whether the field is useful. If the field is required, it is useful.
-        If the field is not required, it is useful if the value is not None.
-        If the field is not required and the value is None, it is useful if the
+        If the field is not required, it is useful if the value is not `None`.
+        If the field is not required and the value is `None`, it is useful if the
        default value is different from the value.
    """
    field = type(inst).model_fields.get(key)
@@ -344,10 +349,10 @@ def to_json_not_implemented(obj: object) -> SerializedNotImplemented:
    """Serialize a "not implemented" object.

    Args:
-        obj: object to serialize.
+        obj: Object to serialize.

    Returns:
-        SerializedNotImplemented
+        `SerializedNotImplemented`
    """
    id_: list[str] = []
    try:
--- a/libs/core/langchain_core/messages/init.py
+++ b/libs/core/langchain_core/messages/init.py
@@ -9,6 +9,9 @@ if TYPE_CHECKING:
    from langchain_core.messages.ai import (
        AIMessage,
        AIMessageChunk,
+        InputTokenDetails,
+        OutputTokenDetails,
+        UsageMetadata,
    )
    from langchain_core.messages.base import (
        BaseMessage,
@@ -87,10 +90,12 @@ __all__ = (
    "HumanMessage",
    "HumanMessageChunk",
    "ImageContentBlock",
+    "InputTokenDetails",
    "InvalidToolCall",
    "MessageLikeRepresentation",
    "NonStandardAnnotation",
    "NonStandardContentBlock",
+    "OutputTokenDetails",
    "PlainTextContentBlock",
    "ReasoningContentBlock",
    "RemoveMessage",
@@ -104,6 +109,7 @@ __all__ = (
    "ToolCallChunk",
    "ToolMessage",
    "ToolMessageChunk",
+    "UsageMetadata",
    "VideoContentBlock",
    "_message_from_dict",
    "convert_to_messages",
@@ -145,6 +151,7 @@ _dynamic_imports = {
    "HumanMessageChunk": "human",
    "NonStandardAnnotation": "content",
    "NonStandardContentBlock": "content",
+    "OutputTokenDetails": "ai",
    "PlainTextContentBlock": "content",
    "ReasoningContentBlock": "content",
    "RemoveMessage": "modifier",
@@ -154,12 +161,14 @@ _dynamic_imports = {
    "SystemMessage": "system",
    "SystemMessageChunk": "system",
    "ImageContentBlock": "content",
+    "InputTokenDetails": "ai",
    "InvalidToolCall": "tool",
    "TextContentBlock": "content",
    "ToolCall": "tool",
    "ToolCallChunk": "tool",
    "ToolMessage": "tool",
    "ToolMessageChunk": "tool",
+    "UsageMetadata": "ai",
    "VideoContentBlock": "content",
    "AnyMessage": "utils",
    "MessageLikeRepresentation": "utils",
--- a/Show More
+++ b/Show More