better tests

.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": "cd libs/langchain_v1 && uv sync && echo 'LangChain (Python) dev environment ready!'",
+  "postCreateCommand": "uv sync && echo 'LangChain (Python) dev environment ready!'",
   // Configure tool-specific properties.
   "customizations": {
     "vscode": {
@@ -42,7 +42,7 @@
         "GitHub.copilot-chat"
       ],
       "settings": {
-        "python.defaultInterpreterPath": "libs/langchain_v1/.venv/bin/python",
+        "python.defaultInterpreterPath": ".venv/bin/python",
         "python.formatting.provider": "none",
         "[python]": {
           "editor.formatOnSave": true,

.dockerignore

@@ -1,34 +0,0 @@
-# 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

.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,132 @@
+# 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

.github/CONTRIBUTING.md

@@ -0,0 +1,6 @@
+# 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).

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangChain. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
+description: Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the LangChain forum.
 labels: ["bug"]
 type: bug
 body:
@@ -8,15 +8,16 @@ body:
       value: |
         Thank you for taking the time to file a bug report.
 
-        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        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/).
 
-        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
+        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:
 
-        * [Documentation](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference Documentation](https://reference.langchain.com/python/),
+        * [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/),
         * [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:
@@ -35,48 +36,16 @@ 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: Reproduction Steps / Example Code (Python)
+      label: Example Code
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
 
@@ -84,12 +53,15 @@ body:
 
         **Important!**
 
-        * 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.
+        * 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 ```).
 
-        (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:
@@ -97,14 +69,17 @@ 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 copy and paste the full error message and
-        stack trace.
-        (This will be automatically formatted into code, so no need for backticks.)
-      render: shell
+        If you are reporting an error, please include the full error message and stack trace.
+      placeholder: |
+        Exception + full stack trace
   - type: textarea
     id: description
     attributes:
@@ -124,7 +99,9 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us.
+        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.
 
         Run the following command in your terminal and paste the output here:
 
@@ -136,6 +113,8 @@ 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:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,15 +1,9 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
+  - name: 📚 Documentation
+    url: https://github.com/langchain-ai/docs/issues/new?template=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
-  - 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

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,5 +1,5 @@
 name: "✨ Feature Request"
-description: Request a new feature or enhancement for LangChain. For questions, please use the LangChain forum (below).
+description: Request a new feature or enhancement for LangChain. For questions, please use the LangChain forum.
 labels: ["feature request"]
 type: feature
 body:
@@ -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:
 
-        * [Documentation](https://docs.langchain.com/oss/python/langchain/overview),
-        * [API Reference Documentation](https://reference.langchain.com/python/),
+        * [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/),
         * [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:
@@ -34,40 +34,6 @@ 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:

.github/ISSUE_TEMPLATE/privileged.yml

@@ -18,33 +18,3 @@ 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

.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:
@@ -77,45 +77,15 @@ 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

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,30 +1,28 @@
 (Replace this entire block of text)
 
-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
+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.**
 
+- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
   - Examples:
-    - fix(anthropic): resolve flag parsing error
     - feat(core): add multi-tenant support
-    - 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
+    - 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.
 
-2. PR description:
+- [ ] **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
 
-  - 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.
+- [ ] **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.
 
 Additional guidelines:
 
-  - 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.
+- 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.

.github/actions/poetry_setup/action.yml

@@ -0,0 +1,93 @@
+# 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)) }}

.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@v7
+      uses: astral-sh/setup-uv@v6
       with:
         version: ${{ env.UV_VERSION }}
         python-version: ${{ inputs.python-version }}

.github/copilot-instructions.md

@@ -0,0 +1,330 @@
+# 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

.github/pr-file-labeler.yml

@@ -7,12 +7,13 @@ core:
       - any-glob-to-any-file:
           - "libs/core/**/*"
 
-langchain-classic:
+langchain:
   - changed-files:
       - any-glob-to-any-file:
           - "libs/langchain/**/*"
+          - "libs/langchain_v1/**/*"
 
-langchain:
+v1:
   - changed-files:
       - any-glob-to-any-file:
           - "libs/langchain_v1/**/*"
@@ -27,11 +28,6 @@ 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:
@@ -43,80 +39,16 @@ integration:
       - any-glob-to-any-file:
           - "libs/partners/**/*"
 
-anthropic:
+# Infrastructure and DevOps
+infra:
   - 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/**/*"
+          - ".github/**/*"
+          - "Makefile"
+          - ".pre-commit-config.yaml"
+          - "scripts/**/*"
+          - "docker/**/*"
+          - "Dockerfile*"
 
 github_actions:
   - changed-files:
@@ -131,3 +63,22 @@ dependencies:
           - "uv.lock"
           - "**/requirements*.txt"
           - "**/poetry.lock"
+
+# Documentation
+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*"

.github/pr-title-labeler.yml

@@ -0,0 +1,41 @@
+# 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"]

.github/scripts/check_diff.py

@@ -30,7 +30,6 @@ LANGCHAIN_DIRS = [
     "libs/text-splitters",
     "libs/langchain",
     "libs/langchain_v1",
-    "libs/model-profiles",
 ]
 
 # When set to True, we are ignoring core dependents

.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" in marker_str or "python_full_version" in marker_str:
+        if "python_version" or "python_full_version" in marker_str:
             python_version_str = "".join(
                 char
                 for char in marker_str

.github/workflows/_compile_integration_test.yml

@@ -35,7 +35,7 @@ jobs:
     timeout-minutes: 20
     name: "Python ${{ inputs.python-version }}"
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/_lint.yml

@@ -38,7 +38,7 @@ jobs:
     timeout-minutes: 20
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"
@@ -47,12 +47,6 @@ jobs:
           cache-suffix: lint-${{ 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: |

.github/workflows/_release.yml

@@ -19,7 +19,7 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-        default: "libs/langchain_v1"
+        default: "libs/langchain"
       release-version:
         required: true
         type: string
@@ -54,7 +54,7 @@ jobs:
       version: ${{ steps.check-version.outputs.version }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - 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@v6
+        uses: actions/upload-artifact@v4
         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@v6
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain
           path: langchain
@@ -149,8 +149,8 @@ jobs:
             fi
           fi
 
-          # if PREV_TAG is empty or came out to 0.0.0, let it be empty
-          if [ -z "$PREV_TAG" ] || [ "$PREV_TAG" = "$PKG_NAME==0.0.0" ]; then
+          # if PREV_TAG is empty, let it be empty
+          if [ -z "$PREV_TAG" ]; 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 $PREV_TAG"
-          # if PREV_TAG is empty or 0.0.0, then we are releasing the first version
-          if [ -z "$PREV_TAG" ] || [ "$PREV_TAG" = "$PKG_NAME==0.0.0" ]; then
+          # if PREV_TAG is empty, then we are releasing the first version
+          if [ -z "$PREV_TAG" ]; then
             PREAMBLE="Initial release"
             PREV_TAG=$(git rev-list --max-parents=0 HEAD)
           fi
@@ -206,9 +206,9 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v5
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -237,7 +237,7 @@ jobs:
       contents: read
     timeout-minutes: 20
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       # 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@v7
+      - uses: actions/download-artifact@v5
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -377,7 +377,6 @@ 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 }}
 
@@ -394,10 +393,9 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       contents: read
-    if: false # temporarily skip
     strategy:
       matrix:
-        partner: [openai, anthropic]
+        partner: [anthropic]
       fail-fast: false # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -411,9 +409,8 @@ 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@v6
+      - uses: actions/checkout@v5
 
       # We implement this conditional as Github Actions does not have good support
       # for conditionally needing steps. https://github.com/actions/runner/issues/491
@@ -431,7 +428,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v5
         if: startsWith(inputs.working-directory, 'libs/core')
         with:
           name: dist
@@ -445,7 +442,7 @@ jobs:
             git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
             | awk '{print $2}' \
             | sed 's|refs/tags/||' \
-            | grep -E '[0-9]+\.[0-9]+\.[0-9]+$' \
+            | grep -E '[0-9]+\.[0-9]+\.[0-9]+([a-zA-Z]+[0-9]+)?$' \
             | sort -Vr \
             | head -n 1
           )"
@@ -471,67 +468,6 @@ jobs:
           uv pip install ../../core/dist/*.whl
           make integration_tests
 
-  # Test external packages that depend on langchain-core/langchain against the new release
-  # Only runs for core and langchain_v1 releases to catch breaking changes before publish
-  test-dependents:
-    name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.package.path }}"
-    needs:
-      - build
-      - release-notes
-      - test-pypi-publish
-      - pre-release-checks
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    # Only run for core or langchain_v1 releases
-    if: startsWith(inputs.working-directory, 'libs/core') || startsWith(inputs.working-directory, 'libs/langchain_v1')
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.11", "3.13"]
-        package:
-          - name: deepagents
-            repo: langchain-ai/deepagents
-            path: libs/deepagents
-    # No API keys needed for now - deepagents `make test` only runs unit tests
-
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          path: langchain
-
-      - uses: actions/checkout@v6
-        with:
-          repository: ${{ matrix.package.repo }}
-          path: ${{ matrix.package.name }}
-
-      - name: Set up Python + uv
-        uses: "./langchain/.github/actions/uv_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - uses: actions/download-artifact@v7
-        with:
-          name: dist
-          path: dist/
-
-      - name: Install ${{ matrix.package.name }} with local packages
-        # External dependents don't have [tool.uv.sources] pointing to this repo,
-        # so we install the package normally then override with the built wheel.
-        run: |
-          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
-
-          # Install the package with test dependencies
-          uv sync --group test
-
-          # Override with the built wheel from this release
-          uv pip install $GITHUB_WORKSPACE/dist/*.whl
-
-      - name: Run ${{ matrix.package.name }} tests
-        run: |
-          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
-          make test
-
   publish:
     # Publishes the package to PyPI
     needs:
@@ -539,10 +475,7 @@ jobs:
       - release-notes
       - test-pypi-publish
       - pre-release-checks
-      - test-dependents
-      # - test-prior-published-packages-against-new-core
-    # Run if all needed jobs succeeded or were skipped (test-dependents only runs for core/langchain_v1)
-    if: ${{ !cancelled() && !failure() }}
+      - test-prior-published-packages-against-new-core
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:
@@ -557,14 +490,14 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v5
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -597,14 +530,14 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - name: Set up Python + uv
         uses: "./.github/actions/uv_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v5
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/

.github/workflows/_test.yml

@@ -33,7 +33,7 @@ jobs:
     name: "Python ${{ inputs.python-version }}"
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/_test_pydantic.yml

@@ -36,7 +36,7 @@ jobs:
     name: "Pydantic ~=${{ inputs.pydantic-version }}"
     steps:
       - name: "📋 Checkout Code"
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
 
       - name: "🐍 Set up Python ${{ inputs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"

.github/workflows/auto-label-by-package.yml

@@ -1,107 +0,0 @@
-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
-              });
-            }

.github/workflows/check_core_versions.yml

@@ -18,7 +18,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - name: "✅ Verify pyproject.toml & version.py Match"
         run: |

.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@v6
+        uses: actions/checkout@v5
       - 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@v6
+      - uses: actions/checkout@v5
 
       - name: "🐍 Set up Python ${{ matrix.job-configs.python-version }} + UV"
         uses: "./.github/actions/uv_setup"
@@ -182,7 +182,7 @@ jobs:
         job-configs: ${{ fromJson(needs.build.outputs.codspeed) }}
       fail-fast: false
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
 
       - name: "📦 Install UV Package Manager"
         uses: astral-sh/setup-uv@v7

.github/workflows/integration_tests.yml

@@ -1,8 +1,8 @@
 # Routine integration tests against partner libraries with live API credentials.
 #
-# Uses `make integration_tests` within each library being tested.
+# Uses `make integration_tests` for each library in the matrix.
 #
-# Runs daily with the option to trigger manually.
+# Runs daily. Can also be triggered manually for immediate updates.
 
 name: "⏰ Integration Tests"
 run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.10, 3.13' }})"
@@ -24,29 +24,17 @@ permissions:
 
 env:
   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"]
+  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"]'
 
 jobs:
   # Generate dynamic test matrix based on input parameters or defaults
   # Only runs on the main repo (for scheduled runs) or when manually triggered
   compute-matrix:
-    # Defend against forks running scheduled jobs, but allow manual runs from forks
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
-
     runs-on: ubuntu-latest
     name: "📋 Compute Test Matrix"
     outputs:
       matrix: ${{ steps.set-matrix.outputs.matrix }}
-      python-version-min-3-11: ${{ steps.set-matrix.outputs.python-version-min-3-11 }}
     steps:
       - name: "🔢 Generate Python & Library Matrix"
         id: set-matrix
@@ -59,16 +47,9 @@ jobs:
           # python-version should default to 3.10 and 3.13, but is overridden to [PYTHON_VERSION_FORCE] if set
           # working-directory should default to DEFAULT_LIBS, but is overridden to [WORKING_DIRECTORY_FORCE] if set
           python_version='["3.10", "3.13"]'
-          python_version_min_3_11='["3.11", "3.13"]'
           working_directory="$DEFAULT_LIBS"
           if [ -n "$PYTHON_VERSION_FORCE" ]; then
             python_version="[\"$PYTHON_VERSION_FORCE\"]"
-            # Bound forced version to >= 3.11 for packages requiring it
-            if [ "$(echo "$PYTHON_VERSION_FORCE >= 3.11" | bc -l)" -eq 1 ]; then
-              python_version_min_3_11="[\"$PYTHON_VERSION_FORCE\"]"
-            else
-              python_version_min_3_11='["3.11"]'
-            fi
           fi
           if [ -n "$WORKING_DIRECTORY_FORCE" ]; then
             working_directory="[\"$WORKING_DIRECTORY_FORCE\"]"
@@ -76,10 +57,8 @@ jobs:
           matrix="{\"python-version\": $python_version, \"working-directory\": $working_directory}"
           echo $matrix
           echo "matrix=$matrix" >> $GITHUB_OUTPUT
-          echo "python-version-min-3-11=$python_version_min_3_11" >> $GITHUB_OUTPUT
-
   # Run integration tests against partner libraries with live API credentials
-  integration-tests:
+  build:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.working-directory }}"
     runs-on: ubuntu-latest
@@ -92,30 +71,18 @@ jobs:
         working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           path: langchain
-
-      # These libraries exist outside of the monorepo and need to be checked out separately
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain-google
           path: langchain-google
-      - name: "🔐 Authenticate to Google Cloud"
-        id: "auth"
-        uses: google-github-actions/auth@v3
-        with:
-          credentials_json: "${{ secrets.GOOGLE_CREDENTIALS }}"
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain-aws
           path: langchain-aws
-      - name: "🔐 Configure AWS Credentials"
-        uses: aws-actions/configure-aws-credentials@v5
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ secrets.AWS_REGION }}
+
       - name: "📦 Organize External Libraries"
         run: |
           rm -rf \
@@ -130,27 +97,27 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: "📦 Install Dependencies"
-        # Partner packages use [tool.uv.sources] in their pyproject.toml to resolve
-        # langchain-core/langchain to local editable installs, so `uv sync` automatically
-        # tests against the versions from the current branch (not published releases).
+      - name: "🔐 Authenticate to Google Cloud"
+        id: "auth"
+        uses: google-github-actions/auth@v3
+        with:
+          credentials_json: "${{ secrets.GOOGLE_CREDENTIALS }}"
 
-        # TODO: external google/aws don't have local resolution since they live in
-        # separate repos, so they pull `core`/`langchain_v1` from PyPI. We should update
-        # their dev groups to use git source dependencies pointing to the current
-        # branch's latest commit SHA to fully test against local langchain changes.
+      - name: "🔐 Configure AWS Credentials"
+        uses: aws-actions/configure-aws-credentials@v5
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
+
+      - name: "📦 Install Dependencies"
         run: |
           echo "Running scheduled tests, installing dependencies with uv..."
           cd langchain/${{ matrix.working-directory }}
           uv sync --group test --group test_integration
 
       - name: "🚀 Run Integration Tests"
-        # WARNING: All secrets below are available to every matrix job regardless of
-        # which package is being tested. This is intentional for simplicity, but means
-        # any test file could technically access any key. Only use for trusted code.
         env:
-          LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
-
           AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
@@ -211,59 +178,3 @@ jobs:
           # grep will exit non-zero if the target message isn't found,
           # and `set -e` above will cause the step to fail.
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
-
-  # Test dependent packages against local packages to catch breaking changes
-  test-dependents:
-    # Defend against forks running scheduled jobs, but allow manual runs from forks
-    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
-
-    name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.package.path }}"
-    runs-on: ubuntu-latest
-    needs: [compute-matrix]
-    timeout-minutes: 30
-    strategy:
-      fail-fast: false
-      matrix:
-        # deepagents requires Python >= 3.11, use bounded version from compute-matrix
-        python-version: ${{ fromJSON(needs.compute-matrix.outputs.python-version-min-3-11) }}
-        package:
-          - name: deepagents
-            repo: langchain-ai/deepagents
-            path: libs/deepagents
-
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          path: langchain
-
-      - uses: actions/checkout@v6
-        with:
-          repository: ${{ matrix.package.repo }}
-          path: ${{ matrix.package.name }}
-
-      - name: "🐍 Set up Python ${{ matrix.python-version }} + UV"
-        uses: "./langchain/.github/actions/uv_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: "📦 Install ${{ matrix.package.name }} with Local"
-        # Unlike partner packages (which use [tool.uv.sources] for local resolution),
-        # external dependents live in separate repos and need explicit overrides to
-        # test against the langchain versions from the current branch, as their
-        # pyproject.toml files point to released versions.
-        run: |
-          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
-
-          # Install the package with test dependencies
-          uv sync --group test
-
-          # Override langchain packages with local versions
-          uv pip install \
-            -e $GITHUB_WORKSPACE/langchain/libs/core \
-            -e $GITHUB_WORKSPACE/langchain/libs/langchain_v1
-
-      # No API keys needed for now - deepagents `make test` only runs unit tests
-      - name: "🚀 Run ${{ matrix.package.name }} Tests"
-        run: |
-          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
-          make test

.github/workflows/pr_labeler_file.yml

@@ -8,7 +8,7 @@ on:
   # Safe since we're not checking out or running the PR's code
   # Never check out the PR's head in a pull_request_target job
   pull_request_target:
-    types: [opened, synchronize, reopened]
+    types: [opened, synchronize, reopened, edited]
 
 jobs:
   labeler:

.github/workflows/pr_lint.yml

@@ -26,13 +26,11 @@
 #   * revert     — reverts a previous commit
 #   * release    — prepare a new release
 #
-# 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.
+# Allowed Scopes (optional):
+#   core, cli, langchain, langchain_v1, langchain-classic, standard-tests,
+#   text-splitters, docs, anthropic, chroma, deepseek, exa, fireworks, groq,
+#   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
+#   xai, infra
 #
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.
@@ -81,8 +79,8 @@ jobs:
             core
             cli
             langchain
+            langchain_v1
             langchain-classic
-            model-profiles
             standard-tests
             text-splitters
             docs
@@ -102,7 +100,6 @@ jobs:
             qdrant
             xai
             infra
-            deps
           requireScope: false
           disallowScopes: |
             release

.github/workflows/tag-external-contributions.yml

@@ -1,148 +0,0 @@
-# Automatically tag issues and pull requests as "external" or "internal"
-# based on whether the author is a member of the langchain-ai
-# GitHub organization.
-#
-# Setup Requirements:
-# 1. Create a GitHub App with permissions:
-#    - Repository: Issues (write), Pull requests (write)
-#    - Organization: Members (read)
-# 2. Install the app on your organization and this repository
-# 3. Add these repository secrets:
-#    - ORG_MEMBERSHIP_APP_ID: Your app's ID
-#    - ORG_MEMBERSHIP_APP_PRIVATE_KEY: Your app's private key
-#
-# The GitHub App token is required to check private organization membership.
-# Without it, the workflow will fail.
-
-name: Tag External Contributions
-
-on:
-  issues:
-    types: [opened]
-  pull_request_target:
-    types: [opened]
-
-jobs:
-  tag-external:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-
-    steps:
-      - name: Generate GitHub App token
-        id: app-token
-        uses: actions/create-github-app-token@v1
-        with:
-          app-id: ${{ secrets.ORG_MEMBERSHIP_APP_ID }}
-          private-key: ${{ secrets.ORG_MEMBERSHIP_APP_PRIVATE_KEY }}
-
-      - name: Check if contributor is external
-        id: check-membership
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ steps.app-token.outputs.token }}
-          script: |
-            const { owner, repo } = context.repo;
-            const author = context.payload.sender.login;
-
-            try {
-              // Check if the author is a member of the langchain-ai organization
-              // This requires org:read permissions to see private memberships
-              const membership = await github.rest.orgs.getMembershipForUser({
-                org: 'langchain-ai',
-                username: author
-              });
-
-              // Check if membership is active (not just pending invitation)
-              if (membership.data.state === 'active') {
-                console.log(`User ${author} is an active member of langchain-ai organization`);
-                core.setOutput('is-external', 'false');
-              } else {
-                console.log(`User ${author} has pending membership in langchain-ai organization`);
-                core.setOutput('is-external', 'true');
-              }
-            } catch (error) {
-              if (error.status === 404) {
-                console.log(`User ${author} is not a member of langchain-ai organization`);
-                core.setOutput('is-external', 'true');
-              } else {
-                console.error('Error checking membership:', error);
-                console.log('Status:', error.status);
-                console.log('Message:', error.message);
-                // If we can't determine membership due to API error, assume external for safety
-                core.setOutput('is-external', 'true');
-              }
-            }
-
-      - name: Add external label to issue
-        if: steps.check-membership.outputs.is-external == 'true' && github.event_name == 'issues'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const { owner, repo } = context.repo;
-            const issue_number = context.payload.issue.number;
-
-            await github.rest.issues.addLabels({
-              owner,
-              repo,
-              issue_number,
-              labels: ['external']
-            });
-
-            console.log(`Added 'external' label to issue #${issue_number}`);
-
-      - name: Add external label to pull request
-        if: steps.check-membership.outputs.is-external == 'true' && github.event_name == 'pull_request_target'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const { owner, repo } = context.repo;
-            const pull_number = context.payload.pull_request.number;
-
-            await github.rest.issues.addLabels({
-              owner,
-              repo,
-              issue_number: pull_number,
-              labels: ['external']
-            });
-
-            console.log(`Added 'external' label to pull request #${pull_number}`);
-
-      - name: Add internal label to issue
-        if: steps.check-membership.outputs.is-external == 'false' && github.event_name == 'issues'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const { owner, repo } = context.repo;
-            const issue_number = context.payload.issue.number;
-
-            await github.rest.issues.addLabels({
-              owner,
-              repo,
-              issue_number,
-              labels: ['internal']
-            });
-
-            console.log(`Added 'internal' label to issue #${issue_number}`);
-
-      - name: Add internal label to pull request
-        if: steps.check-membership.outputs.is-external == 'false' && github.event_name == 'pull_request_target'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const { owner, repo } = context.repo;
-            const pull_number = context.payload.pull_request.number;
-
-            await github.rest.issues.addLabels({
-              owner,
-              repo,
-              issue_number: pull_number,
-              labels: ['internal']
-            });
-
-            console.log(`Added 'internal' label to pull request #${pull_number}`);

.github/workflows/v03_api_doc_build.yml

@@ -23,12 +23,12 @@ jobs:
     permissions:
       contents: read
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           ref: v0.3
           path: langchain
 
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           repository: langchain-ai/langchain-api-docs-html
           path: langchain-api-docs-html

.github/workflows/v1_changes.md

@@ -0,0 +1,8 @@
+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

.gitignore

@@ -163,6 +163,3 @@ node_modules
 
 prof
 virtualenv/
-scratch/
-
-.langgraph_api/

.pre-commit-config.yaml

@@ -1,24 +1,4 @@
 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
@@ -117,15 +97,3 @@ repos:
         entry: make -C libs/partners/qdrant format lint
         files: ^libs/partners/qdrant/
         pass_filenames: false
-      - id: core-version
-        name: check core version consistency
-        language: system
-        entry: make -C libs/core check_version
-        files: ^libs/core/(pyproject\.toml|langchain_core/version\.py)$
-        pass_filenames: false
-      - id: langchain-v1-version
-        name: check langchain version consistency
-        language: system
-        entry: make -C libs/langchain_v1 check_version
-        files: ^libs/langchain_v1/(pyproject\.toml|langchain/__init__\.py)$
-        pass_filenames: false

.vscode/extensions.json

@@ -6,6 +6,8 @@
     "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",

AGENTS.md

@@ -1,58 +1,255 @@
-# Global development guidelines for the LangChain monorepo
+# Global Development Guidelines for LangChain Projects
 
-This document provides context to understand the LangChain Python project and assist with development.
+## Core Development Principles
 
-## Project architecture and context
+### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
 
-### Monorepo structure
+**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
 
-This is a Python monorepo with multiple independently versioned packages that use `uv`.
+❌ **Bad - Breaking Change:**
 
-```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
+```python
+def get_user(id, verbose=False):  # Changed from `user_id`
+    pass
 ```
 
-- **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
+✅ **Good - Stable Interface:**
 
-### Development tools & commands**
+```python
+def get_user(user_id: str, verbose: bool = False) -> User:
+    """Retrieve user by ID with optional verbose output."""
+    pass
+```
 
-- `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
+**Before making ANY changes to public APIs:**
 
-This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`
+- 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`)
 
-Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.
+🧠 *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
+  - 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")
+
+📌 *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
 
 ```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
@@ -64,119 +261,66 @@ make format
 uv run --group lint mypy .
 ```
 
-#### Key config files
+### Dependency Management Patterns
 
-- pyproject.toml: Main workspace configuration with dependency groups
-- uv.lock: Locked dependencies for reproducible builds
-- Makefile: Development tasks
+**Local Development Dependencies:**
 
-#### Commit standards
-
-Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.
-
-#### Pull request guidelines
-
-- 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.
-You should warn the developer for any function signature changes, regardless of whether they look breaking or not.
-
-**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:
-        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.
-    """
+```toml
+[tool.uv.sources]
+langchain-core = { path = "../core", editable = true }
+langchain-tests = { path = "../standard-tests", editable = true }
 ```
 
-- 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
+**For tools, use the `@tool` decorator from `langchain_core.tools`:**
 
-### Testing requirements
+```python
+from langchain_core.tools import tool
 
-Every new feature or bugfix MUST be covered by unit tests.
-
-- 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.
-
-**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)
-- [ ] Does the test suite fail if your new logic is broken?
-
-### 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)
-
-### Documentation standards
-
-Use Google-style docstrings with Args section for all public functions.
-
-```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.
+@tool
+def search_database(query: str) -> str:
+    """Search the database for relevant information.
 
     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.
+        query: The search query string.
     """
+    # Implementation here
+    return results
 ```
 
-- 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")
+## Commit Standards
 
-## Additional resources
+**Use Conventional Commits format for PR titles:**
 
-- **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)
+- `feat(core): add multi-tenant support`
+- `fix(cli): resolve flag parsing error`
+- `docs: update API usage examples`
+- `docs(openai): update API usage examples`
+
+## Framework-Specific Guidelines
+
+- 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`
+
+### 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

CLAUDE.md

@@ -1,58 +1,255 @@
-# Global development guidelines for the LangChain monorepo
+# Global Development Guidelines for LangChain Projects
 
-This document provides context to understand the LangChain Python project and assist with development.
+## Core Development Principles
 
-## Project architecture and context
+### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
 
-### Monorepo structure
+**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
 
-This is a Python monorepo with multiple independently versioned packages that use `uv`.
+❌ **Bad - Breaking Change:**
 
-```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
+```python
+def get_user(id, verbose=False):  # Changed from `user_id`
+    pass
 ```
 
-- **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
+✅ **Good - Stable Interface:**
 
-### Development tools & commands**
+```python
+def get_user(user_id: str, verbose: bool = False) -> User:
+    """Retrieve user by ID with optional verbose output."""
+    pass
+```
 
-- `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
+**Before making ANY changes to public APIs:**
 
-This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`
+- 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`)
 
-Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.
+🧠 *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
+  - 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")
+
+📌 *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
 
 ```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
@@ -64,119 +261,66 @@ make format
 uv run --group lint mypy .
 ```
 
-#### Key config files
+### Dependency Management Patterns
 
-- pyproject.toml: Main workspace configuration with dependency groups
-- uv.lock: Locked dependencies for reproducible builds
-- Makefile: Development tasks
+**Local Development Dependencies:**
 
-#### Commit standards
-
-Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.
-
-#### Pull request guidelines
-
-- 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.
-You should warn the developer for any function signature changes, regardless of whether they look breaking or not.
-
-**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:
-        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.
-    """
+```toml
+[tool.uv.sources]
+langchain-core = { path = "../core", editable = true }
+langchain-tests = { path = "../standard-tests", editable = true }
 ```
 
-- 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
+**For tools, use the `@tool` decorator from `langchain_core.tools`:**
 
-### Testing requirements
+```python
+from langchain_core.tools import tool
 
-Every new feature or bugfix MUST be covered by unit tests.
-
-- 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.
-
-**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)
-- [ ] Does the test suite fail if your new logic is broken?
-
-### 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)
-
-### Documentation standards
-
-Use Google-style docstrings with Args section for all public functions.
-
-```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.
+@tool
+def search_database(query: str) -> str:
+    """Search the database for relevant information.
 
     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.
+        query: The search query string.
     """
+    # Implementation here
+    return results
 ```
 
-- 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")
+## Commit Standards
 
-## Additional resources
+**Use Conventional Commits format for PR titles:**
 
-- **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)
+- `feat(core): add multi-tenant support`
+- `fix(cli): resolve flag parsing error`
+- `docs: update API usage examples`
+- `docs(openai): update API usage examples`
+
+## Framework-Specific Guidelines
+
+- 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`
+
+### 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

MIGRATE.md

@@ -0,0 +1,9 @@
+# Migrating
+
+Please see the following guides for migrating LangChain code:
+
+* Migrate to [LangChain v1.0](https://docs.langchain.com/oss/python/migrate/langchain-v1)
+* 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/)

README.md

@@ -1,43 +1,50 @@
-<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>
+<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>
+
+<p align="center">
+    The platform for reliable agents.
+</p>
+
+<p align="center">
+  <a href="https://opensource.org/licenses/MIT" target="_blank">
+      <img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License">
   </a>
-</div>
+  <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://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">
-  <h3>The platform for reliable agents.</h3>
-</div>
-
-<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 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.
+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.
 
 ```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**:
+**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview).
 
-- [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.
+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.
 
 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
@@ -48,28 +55,24 @@ 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.
-- **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.
+- **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.
 
-## LangChain ecosystem
+## LangChain’s 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:
 
-- [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
+- [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).
 
 ## Additional resources
 
-- [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.
+- [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.
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues.
+- [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.

SECURITY.md

@@ -0,0 +1,80 @@
+# 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`.

libs/Makefile

@@ -1,20 +0,0 @@
-# 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

libs/cli/README.md

@@ -3,7 +3,7 @@
 [![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)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
 
 ## Quick Install
 

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 persistent database to save to your disk, etc."
+    "E.g. creating a persisten database to save to your disk, etc."
    ]
   },
   {

libs/cli/langchain_cli/integration_template/pyproject.toml

@@ -36,9 +36,6 @@ 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",]
 

libs/cli/langchain_cli/utils/git.py

@@ -6,8 +6,9 @@ import hashlib
 import logging
 import re
 import shutil
+from collections.abc import Sequence
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, TypedDict
+from typing import Any, TypedDict
 
 from git import Repo
 
@@ -17,9 +18,6 @@ from langchain_cli.constants import (
     DEFAULT_GIT_SUBDIRECTORY,
 )
 
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
 logger = logging.getLogger(__name__)
 
 

libs/cli/pyproject.toml

@@ -24,7 +24,7 @@ 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"
+Twitter = "https://x.com/LangChainAI"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 
@@ -38,16 +38,14 @@ dev = [
     "pytest-watcher>=0.3.4,<1.0.0"
 ]
 lint = [
-    "ruff>=0.14.11,<0.15.0"
+    "ruff>=0.13.1,<0.14",
+    "mypy>=1.18.1,<1.19"
 ]
 test = [
     "langchain-core",
     "langchain-classic"
 ]
-typing = [
-    "mypy>=1.19.1,<1.20",
-    "langchain-classic"
-]
+typing = ["langchain-classic"]
 test_integration = []
 
 [tool.uv.sources]
@@ -66,6 +64,10 @@ 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
 
@@ -74,6 +76,7 @@ ignore = [
 ]
 unfixable = [
     "B028",    # People should intentionally tune the stacklevel
+    "PLW1510", # People should intentionally set the check argument
 ]
 
 flake8-annotations.allow-star-arg-any = true
@@ -86,9 +89,6 @@ 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",]

libs/cli/tests/unit_tests/migrate/cli_runner/case.py

@@ -1,11 +1,9 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING
 
-if TYPE_CHECKING:
-    from tests.unit_tests.migrate.cli_runner.file import File
-    from tests.unit_tests.migrate.cli_runner.folder import Folder
+from .file import File
+from .folder import Folder
 
 
 @dataclass

libs/cli/tests/unit_tests/migrate/cli_runner/folder.py

@@ -1,11 +1,8 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from pathlib import Path
 
-from tests.unit_tests.migrate.cli_runner.file import File
-
-if TYPE_CHECKING:
-    from pathlib import Path
+from .file import File
 
 
 class Folder:

libs/cli/uv.lock

@@ -2,15 +2,6 @@ version = 1
 revision = 3
 requires-python = ">=3.10.0, <4.0.0"
 
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-version = "0.0.4"
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-]
-
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -152,17 +143,16 @@ wheels = [
 
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 name = "fastapi"
-version = "0.128.0"
+version = "0.118.0"
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 ]
 
 [[package]]

libs/core/Makefile

@@ -1,4 +1,4 @@
-.PHONY: all format lint test tests test_watch integration_tests help extended_tests check_version
+.PHONY: all format lint test tests test_watch integration_tests help extended_tests
 
 # Default target executed when no arguments are given to make.
 all: help
@@ -31,9 +31,6 @@ test_profile:
 check_imports: $(shell find langchain_core -name '*.py')
 	uv run --group test python ./scripts/check_imports.py $^
 
-check_version:
-	uv run python ./scripts/check_version.py
-
 extended_tests:
 	uv run --group test pytest --only-extended --disable-socket --allow-unix-socket $(TEST_FILE)
 
@@ -72,7 +69,6 @@ help:
 	@echo '----'
 	@echo 'format                       - run code formatters'
 	@echo 'lint                         - run linters'
-	@echo 'check_version                - validate version consistency'
 	@echo 'test                         - run unit tests'
 	@echo 'tests                        - run unit tests'
 	@echo 'test TEST_FILE=<test_file>   - run all tests in file'

libs/core/README.md

@@ -3,7 +3,7 @@
 [![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)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 
@@ -34,7 +34,7 @@ The LangChain ecosystem is built on top of `langchain-core`. Some of the benefit
 
 ## 📖 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).
+For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/).
 
 ## 📕 Releases & Versioning
 

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 langchain_core._api.beta_decorator import (
+    from .beta_decorator import (
         LangChainBetaWarning,
         beta,
         suppress_langchain_beta_warning,
         surface_langchain_beta_warnings,
     )
-    from langchain_core._api.deprecation import (
+    from .deprecation import (
         LangChainDeprecationWarning,
         deprecated,
         suppress_langchain_deprecation_warning,
         surface_langchain_deprecation_warnings,
         warn_deprecated,
     )
-    from langchain_core._api.path import as_import_path, get_relative_path
+    from .path import as_import_path, get_relative_path
 
 __all__ = (
     "LangChainBetaWarning",
@@ -58,20 +58,6 @@ _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
@@ -79,9 +65,4 @@ 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__)

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(_: Callable[..., Any], new_doc: str, /) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
                 """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(_: Callable[..., Any], new_doc: str, /) -> Any:
+            def finalize(_wrapper: 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:

libs/core/langchain_core/_api/deprecation.py

@@ -28,27 +28,6 @@ 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."""
 
@@ -102,57 +81,60 @@ 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.
 
-    Example:
-        ```python
-        @deprecated("1.4.0")
-        def the_function_to_deprecate():
-            pass
-        ```
+    ```python
+    @deprecated("1.4.0")
+    def the_function_to_deprecate():
+        pass
+    ```
     """
     _validate_deprecation_params(
         removal, alternative, alternative_import, pending=pending
@@ -222,7 +204,7 @@ def deprecated(
             _name = _name or obj.__qualname__
             old_doc = obj.__doc__
 
-            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
                 """Finalize the deprecation of a class."""
                 # Can't set new_doc on some extension objects.
                 with contextlib.suppress(AttributeError):
@@ -241,11 +223,6 @@ 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):
@@ -257,7 +234,7 @@ def deprecated(
                 raise ValueError(msg)
             old_doc = obj.description
 
-            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
                 return cast(
                     "T",
                     FieldInfoV1(
@@ -278,7 +255,7 @@ def deprecated(
                 raise ValueError(msg)
             old_doc = obj.description
 
-            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
                 return cast(
                     "T",
                     FieldInfo(
@@ -336,17 +313,14 @@ def deprecated(
                     if _name == "<lambda>":
                         _name = set_name
 
-            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
                 """Finalize the property."""
-                prop = _DeprecatedProperty(
-                    fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
+                return cast(
+                    "T",
+                    _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__
@@ -357,7 +331,7 @@ 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:
@@ -369,11 +343,6 @@ 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")
@@ -429,7 +398,7 @@ 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)
@@ -452,33 +421,35 @@ 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:
@@ -563,8 +534,8 @@ 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.

libs/core/langchain_core/_api/internal.py

@@ -1,5 +1,4 @@
 import inspect
-from typing import cast
 
 
 def is_caller_internal(depth: int = 2) -> bool:
@@ -17,7 +16,7 @@ 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 = cast("str", module_globals.get("__name__", ""))
+        caller_module_name = module_globals.get("__name__", "")
         return caller_module_name.startswith("langchain")
     finally:
         del frame

libs/core/langchain_core/agents.py

@@ -52,33 +52,31 @@ 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
 
@@ -100,23 +98,19 @@ 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
-    chat model (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.
@@ -124,12 +118,12 @@ 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]:
@@ -138,22 +132,19 @@ 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"
 
@@ -163,7 +154,7 @@ 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
@@ -211,7 +202,7 @@ 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)]
@@ -234,7 +225,7 @@ 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:

libs/core/langchain_core/caches.py

@@ -2,9 +2,8 @@
 
 Distinct from provider-based [prompt caching](https://docs.langchain.com/oss/python/langchain/models#prompt-caching).
 
-!!! warning "Beta feature"
-
-    This is a beta feature. Please be wary of deploying experimental code to production
+!!! warning
+    This is a beta feature! Please be wary of deploying experimental code to production
     unless you've taken appropriate precautions.
 
 A cache is useful for two reasons:
@@ -50,18 +49,17 @@ class BaseCache(ABC):
         """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
                 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.
@@ -80,10 +78,8 @@ class BaseCache(ABC):
                 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 `Generation`
@@ -98,17 +94,15 @@ class BaseCache(ABC):
         """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
                 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.
 
@@ -131,10 +125,8 @@ class BaseCache(ABC):
                 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 `Generation`

libs/core/langchain_core/callbacks/base.py

@@ -5,12 +5,13 @@ 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
@@ -21,7 +22,7 @@ _LOGGER = logging.getLogger(__name__)
 
 
 class RetrieverManagerMixin:
-    """Mixin for `Retriever` callbacks."""
+    """Mixin for Retriever callbacks."""
 
     def on_retriever_error(
         self,
@@ -31,12 +32,12 @@ 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 ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -48,12 +49,12 @@ 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 ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -68,7 +69,6 @@ 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.
@@ -78,9 +78,8 @@ class LLMManagerMixin:
         Args:
             token: The new token.
             chunk: The new generated chunk, containing content and other information.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
-            tags: The tags.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -90,16 +89,14 @@ 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 ID of the current run.
-            parent_run_id: The ID of the parent run.
-            tags: The tags.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -109,16 +106,14 @@ 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 ID of the current run.
-            parent_run_id: The ID of the parent run.
-            tags: The tags.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -138,8 +133,8 @@ class ChainManagerMixin:
 
         Args:
             outputs: The outputs of the chain.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -155,8 +150,8 @@ class ChainManagerMixin:
 
         Args:
             error: The error that occurred.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -172,8 +167,8 @@ class ChainManagerMixin:
 
         Args:
             action: The agent action.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -189,8 +184,8 @@ class ChainManagerMixin:
 
         Args:
             finish: The agent finish.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -210,8 +205,8 @@ class ToolManagerMixin:
 
         Args:
             output: The output of the tool.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -227,8 +222,8 @@ class ToolManagerMixin:
 
         Args:
             error: The error that occurred.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -257,8 +252,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized LLM.
             prompts: The prompts.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -284,8 +279,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized chat model.
             messages: The messages.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -306,13 +301,13 @@ 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 ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -334,8 +329,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized chain.
             inputs: The inputs.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -358,8 +353,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized chain.
             input_str: The input string.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             inputs: The inputs.
@@ -382,8 +377,8 @@ class RunManagerMixin:
 
         Args:
             text: The text.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -399,8 +394,8 @@ class RunManagerMixin:
 
         Args:
             retry_state: The retry state.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -418,12 +413,13 @@ 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).
+            tags: The tags associated with the custom event
+                (includes inherited tags).
+            metadata: The metadata associated with the custom event
+                (includes inherited metadata).
         """
 
 
@@ -435,7 +431,7 @@ class BaseCallbackHandler(
     CallbackManagerMixin,
     RunManagerMixin,
 ):
-    """Base callback handler."""
+    """Base callback handler for LangChain."""
 
     raise_error: bool = False
     """Whether to raise an error if an exception occurs."""
@@ -480,7 +476,7 @@ class BaseCallbackHandler(
 
 
 class AsyncCallbackHandler(BaseCallbackHandler):
-    """Base async callback handler."""
+    """Async callback handler for LangChain."""
 
     async def on_llm_start(
         self,
@@ -503,8 +499,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized LLM.
             prompts: The prompts.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -530,8 +526,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized chat model.
             messages: The messages.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -558,8 +554,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             token: The new token.
             chunk: The new generated chunk, containing content and other information.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -577,8 +573,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             response: The response which was generated.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -596,11 +592,10 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
-
                 - response (LLMResult): The response which was generated before
                     the error occurred.
         """
@@ -621,8 +616,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized chain.
             inputs: The inputs.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -641,8 +636,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             outputs: The outputs of the chain.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -660,8 +655,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -683,8 +678,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized tool.
             input_str: The input string.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             inputs: The inputs.
@@ -704,8 +699,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             output: The output of the tool.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -723,8 +718,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -742,8 +737,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             text: The text.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -760,8 +755,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             retry_state: The retry state.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             **kwargs: Additional keyword arguments.
         """
 
@@ -778,8 +773,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             action: The agent action.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -797,8 +792,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             finish: The agent finish.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -819,8 +814,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized retriever.
             query: The query.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -839,8 +834,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             documents: The documents retrieved.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -858,8 +853,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The ID of the current run.
-            parent_run_id: The ID of the parent run.
+            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.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -889,7 +884,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
 
 class BaseCallbackManager(CallbackManagerMixin):
-    """Base callback manager."""
+    """Base callback manager for LangChain."""
 
     def __init__(
         self,
@@ -938,9 +933,8 @@ 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.
@@ -967,29 +961,28 @@ class BaseCallbackManager(CallbackManagerMixin):
                 #    ['tag2', 'tag1']
             ```
         """  # noqa: E501
-        # 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__(
+        manager = self.__class__(
             parent_run_id=self.parent_run_id or other.parent_run_id,
-            handlers=combined_handlers,
-            inheritable_handlers=combined_inheritable,
+            handlers=[],
+            inheritable_handlers=[],
             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."""

libs/core/langchain_core/callbacks/manager.py

@@ -6,12 +6,14 @@ 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
@@ -37,13 +39,12 @@ 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
 
@@ -51,7 +52,6 @@ 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__)
 
@@ -229,24 +229,7 @@ def shielded(func: Func) -> Func:
 
     @functools.wraps(func)
     async def wrapped(*args: Any, **kwargs: Any) -> Any:
-        # 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 await asyncio.shield(func(*args, **kwargs))
 
     return cast("Func", wrapped)
 
@@ -504,7 +487,7 @@ class BaseRunManager(RunManagerMixin):
 
         """
         return cls(
-            run_id=uuid7(),
+            run_id=uuid.uuid4(),
             handlers=[],
             inheritable_handlers=[],
             tags=[],
@@ -1330,7 +1313,7 @@ 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 uuid7()
+            run_id_ = run_id if i == 0 and run_id is not None else uuid.uuid4()
             handle_event(
                 self.handlers,
                 "on_llm_start",
@@ -1384,7 +1367,7 @@ class CallbackManager(BaseCallbackManager):
                 run_id_ = run_id
                 run_id = None
             else:
-                run_id_ = uuid7()
+                run_id_ = uuid.uuid4()
             handle_event(
                 self.handlers,
                 "on_chat_model_start",
@@ -1433,7 +1416,7 @@ class CallbackManager(BaseCallbackManager):
 
         """
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
         handle_event(
             self.handlers,
             "on_chain_start",
@@ -1488,7 +1471,7 @@ class CallbackManager(BaseCallbackManager):
 
         """
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         handle_event(
             self.handlers,
@@ -1537,7 +1520,7 @@ class CallbackManager(BaseCallbackManager):
             The callback manager for the retriever run.
         """
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         handle_event(
             self.handlers,
@@ -1594,7 +1577,7 @@ class CallbackManager(BaseCallbackManager):
             )
             raise ValueError(msg)
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         handle_event(
             self.handlers,
@@ -1816,7 +1799,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 run_id_ = run_id
                 run_id = None
             else:
-                run_id_ = uuid7()
+                run_id_ = uuid.uuid4()
 
             if inline_handlers:
                 inline_tasks.append(
@@ -1900,7 +1883,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 run_id_ = run_id
                 run_id = None
             else:
-                run_id_ = uuid7()
+                run_id_ = uuid.uuid4()
 
             for handler in self.handlers:
                 task = ahandle_event(
@@ -1962,7 +1945,7 @@ class AsyncCallbackManager(BaseCallbackManager):
             The async callback manager for the chain run.
         """
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         await ahandle_event(
             self.handlers,
@@ -2010,7 +1993,7 @@ class AsyncCallbackManager(BaseCallbackManager):
             The async callback manager for the tool run.
         """
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         await ahandle_event(
             self.handlers,
@@ -2060,7 +2043,7 @@ class AsyncCallbackManager(BaseCallbackManager):
         if not self.handlers:
             return
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         if kwargs:
             msg = (
@@ -2102,7 +2085,7 @@ class AsyncCallbackManager(BaseCallbackManager):
             The async callback manager for the retriever run.
         """
         if run_id is None:
-            run_id = uuid7()
+            run_id = uuid.uuid4()
 
         await ahandle_event(
             self.handlers,

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-20241022")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
 
         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 `langchain-core` 0.3.49"
+    !!! version-added "Added in version 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`][langchain.messages.AIMessage.usage_metadata].
+    `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-20241022")
+        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-latest")
 
         with get_usage_metadata_callback() as cb:
             llm_1.invoke("Hello")
@@ -134,7 +134,7 @@ def get_usage_metadata_callback(
         }
         ```
 
-    !!! version-added "Added in `langchain-core` 0.3.49"
+    !!! version-added "Added in version 0.3.49"
 
     """
     usage_metadata_callback_var: ContextVar[UsageMetadataCallbackHandler | None] = (

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 `HumanMessage` to add to the store.
+            message: The human message 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 `AIMessage` string to the store.
+        """Convenience method for adding an AI message 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 `AIMessage` to add.
+            message: The AI message to add.
         """
         if isinstance(message, AIMessage):
             self.add_message(message)
@@ -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)
 

libs/core/langchain_core/document_loaders/langsmith.py

@@ -11,7 +11,6 @@ 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):
@@ -119,14 +118,14 @@ class LangSmithLoader(BaseLoader):
             for key in self.content_key:
                 content = content[key]
             content_str = self.format_content(content)
-            metadata = pydantic_to_dict(example)
+            metadata = example.dict()
             # 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, Any]) -> str:
+def _stringify(x: str | dict) -> str:
     if isinstance(x, str):
         return x
     try:

libs/core/langchain_core/documents/__init__.py

@@ -1,28 +1,7 @@
-"""Documents module for data retrieval and processing workflows.
+"""Documents module.
 
-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.
+**Document** module is a collection of classes that handle documents
+and their transformations.
 """
 
 from typing import TYPE_CHECKING
@@ -30,9 +9,9 @@ from typing import TYPE_CHECKING
 from langchain_core._import_utils import import_attr
 
 if TYPE_CHECKING:
-    from langchain_core.documents.base import Document
-    from langchain_core.documents.compressor import BaseDocumentCompressor
-    from langchain_core.documents.transformers import BaseDocumentTransformer
+    from .base import Document
+    from .compressor import BaseDocumentCompressor
+    from .transformers import BaseDocumentTransformer
 
 __all__ = ("BaseDocumentCompressor", "BaseDocumentTransformer", "Document")
 

libs/core/langchain_core/documents/base.py

@@ -1,16 +1,4 @@
-"""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.
-"""
+"""Base classes for media and documents."""
 
 from __future__ import annotations
 
@@ -31,18 +19,20 @@ PathLike = str | PurePath
 
 
 class BaseMedia(Serializable):
-    """Base class for content used in retrieval and data processing workflows.
+    """Use to represent media content.
 
-    Provides common fields for content that needs to be stored, indexed, or searched.
+    Media objects can be used to represent raw data, such as text or binary data.
 
-    !!! note
-        For multimodal content in **chat messages** (images, audio sent to/from LLMs),
-        use `langchain.messages` content blocks instead.
+    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.
     """
 
     # 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.
 
@@ -55,70 +45,71 @@ class BaseMedia(Serializable):
 
 
 class Blob(BaseMedia):
-    """Raw data abstraction for document loading and file processing.
+    """Blob represents raw data by either reference or value.
 
-    Represents raw bytes or text, either in-memory or by file reference. Used
-    primarily by document loaders to decouple data loading from parsing.
+    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.
 
     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
+    ```python
+    from langchain_core.documents import Blob
 
-        blob = Blob.from_data("Hello, world!")
+    blob = Blob.from_data("Hello, world!")
 
-        # Read the blob as a string
-        print(blob.as_string())
+    # Read the blob as a string
+    print(blob.as_string())
 
-        # Read the blob as bytes
-        print(blob.as_bytes())
+    # Read the blob as bytes
+    print(blob.as_bytes())
 
-        # Read the blob as a byte stream
-        with blob.as_bytes_io() as f:
-            print(f.read())
-        ```
+    # Read the blob as a byte stream
+    with blob.as_bytes_io() as f:
+        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
+    ```python
+    from langchain_core.documents import Blob
 
-        blob = Blob.from_data(
-            data="Hello, world!",
-            mime_type="text/plain",
-            metadata={"source": "https://example.com"},
-        )
-        ```
+    blob = Blob.from_data(
+        data="Hello, world!",
+        mime_type="text/plain",
+        metadata={"source": "https://example.com"},
+    )
+    ```
 
-    ??? example "Load the blob from a file"
+    Example: Load the blob from a file
 
-        ```python
-        from langchain_core.documents import Blob
+    ```python
+    from langchain_core.documents import Blob
 
-        blob = Blob.from_path("path/to/file.txt")
+    blob = Blob.from_path("path/to/file.txt")
 
-        # Read the blob as a string
-        print(blob.as_string())
+    # Read the blob as a string
+    print(blob.as_string())
 
-        # Read the blob as bytes
-        print(blob.as_bytes())
+    # Read the blob as bytes
+    print(blob.as_bytes())
 
-        # Read the blob as a byte stream
-        with blob.as_bytes_io() as f:
-            print(f.read())
-        ```
+    # Read the blob as a byte stream
+    with blob.as_bytes_io() as f:
+        print(f.read())
+    ```
     """
 
     data: bytes | str | None = None
     """Raw data associated with the `Blob`."""
     mimetype: str | None = None
-    """MIME type, not to be confused with a file extension."""
+    """MimeType not to be confused with a file extension."""
     encoding: str = "utf-8"
     """Encoding to use if decoding the bytes into a string.
 
-    Uses `utf-8` as default encoding if decoding to string.
+    Use `utf-8` as default encoding, if decoding to string.
     """
     path: PathLike | None = None
     """Location where the original content was found."""
@@ -134,7 +125,7 @@ class Blob(BaseMedia):
 
         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:
@@ -222,7 +213,7 @@ class Blob(BaseMedia):
             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 MIME type will be guessed from the file
-                extension, if a MIME type was not provided
+                extension, if a mime-type was not provided
             metadata: Metadata to associate with the `Blob`
 
         Returns:
@@ -283,10 +274,6 @@ 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
@@ -309,7 +296,7 @@ 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
@@ -322,10 +309,10 @@ 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__.
         #

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.
 
-    `Document` objects matching a given query are first retrieved.
+    Documents 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 `Document` objects.
+            documents: The retrieved documents.
             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 `Document` objects.
+            documents: The retrieved documents.
             query: The query context.
-            callbacks: Optional `Callbacks` to run during compression.
+            callbacks: Optional callbacks to run during compression.
 
         Returns:
             The compressed documents.

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

libs/core/langchain_core/embeddings/fake.py

@@ -18,7 +18,7 @@ class FakeEmbeddings(Embeddings, BaseModel):
 
     This embedding model creates embeddings by sampling from a normal distribution.
 
-    !!! danger "Toy model"
+    !!! warning
         Do not use this outside of testing, as it is not a real embedding model.
 
     Instantiate:
@@ -73,7 +73,7 @@ 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.
 
-    !!! danger "Toy model"
+    !!! warning
         Do not use this outside of testing, as it is not a real embedding model.
 
     Instantiate:

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(r"\n| ", text))
+    return len(re.split("\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)
+    example_text_lengths: list[int] = Field(default_factory=list)  # :meta private:
     """Length of each example."""
 
     def add_example(self, example: dict[str, str]) -> None:

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,
@@ -159,7 +159,7 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
                 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:
@@ -203,7 +203,7 @@ class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
                 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:
@@ -286,12 +286,12 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
             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.
-            fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
+            fetch_k: Number of Documents 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:
@@ -333,12 +333,12 @@ class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
             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.
-            fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
+            fetch_k: Number of Documents 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:

libs/core/langchain_core/exceptions.py

@@ -16,10 +16,9 @@ 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.
-
-    `OutputParserException` 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__(
@@ -33,19 +32,18 @@ class OutputParserException(ValueError, LangChainException):  # noqa: N818
 
         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.
-
                 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.
 
         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):
@@ -68,11 +66,11 @@ class ErrorCode(Enum):
     """Error codes."""
 
     INVALID_PROMPT_INPUT = "INVALID_PROMPT_INPUT"
-    INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"  # Used in JS; not Py (yet)
+    INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"
     MESSAGE_COERCION_FAILURE = "MESSAGE_COERCION_FAILURE"
-    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)
+    MODEL_AUTHENTICATION = "MODEL_AUTHENTICATION"
+    MODEL_NOT_FOUND = "MODEL_NOT_FOUND"
+    MODEL_RATE_LIMIT = "MODEL_RATE_LIMIT"
     OUTPUT_PARSING_FAILURE = "OUTPUT_PARSING_FAILURE"
 
 
@@ -88,6 +86,6 @@ def create_message(*, message: str, error_code: ErrorCode) -> str:
     """
     return (
         f"{message}\n"
-        "For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain"
-        f"/errors/{error_code.value} "
+        "For troubleshooting, visit: https://python.langchain.com/docs/"
+        f"troubleshooting/errors/{error_code.value} "
     )

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.
 """
 

libs/core/langchain_core/indexing/api.py

@@ -6,9 +6,16 @@ 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,
@@ -22,16 +29,6 @@ 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.
@@ -242,17 +239,6 @@ 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:
@@ -312,49 +298,48 @@ 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 `langchain-core` 0.3.25"
-
+    !!! warning "Behavior changed in 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.
+        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.
@@ -364,7 +349,7 @@ def index(
         key_encoder: Hashing algorithm to use for hashing the document content and
             metadata. Options include "blake2b", "sha256", and "sha512".
 
-            !!! version-added "Added in `langchain-core` 0.3.66"
+            !!! version-added "Added in version 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.
@@ -378,10 +363,10 @@ 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 `langchain-core` 0.3.10"
+            !!! version-added "Added in version 0.3.10"
 
     Returns:
         Indexing result which contains information about how many documents
@@ -390,10 +375,10 @@ 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).
     """
@@ -430,7 +415,7 @@ 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)
@@ -481,11 +466,11 @@ 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]} "
@@ -494,7 +479,7 @@ 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(
@@ -553,7 +538,7 @@ 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:
@@ -651,49 +636,48 @@ 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 `langchain-core` 0.3.25"
-
+    !!! warning "Behavior changed in 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.
+        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.
@@ -703,7 +687,7 @@ async def aindex(
         key_encoder: Hashing algorithm to use for hashing the document content and
             metadata. Options include "blake2b", "sha256", and "sha512".
 
-            !!! version-added "Added in `langchain-core` 0.3.66"
+            !!! version-added "Added in version 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.
@@ -717,10 +701,10 @@ 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 `langchain-core` 0.3.10"
+            !!! version-added "Added in version 0.3.10"
 
     Returns:
         Indexing result which contains information about how many documents
@@ -729,10 +713,10 @@ 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).
     """
@@ -773,7 +757,7 @@ 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)
@@ -831,11 +815,11 @@ 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]} "
@@ -844,7 +828,7 @@ 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(
@@ -904,7 +888,7 @@ 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:

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.
     """
 
@@ -518,7 +518,7 @@ 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`.
 
@@ -528,20 +528,20 @@ class DocumentIndex(BaseRetriever):
 
         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`.
 
@@ -551,7 +551,7 @@ class DocumentIndex(BaseRetriever):
 
         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(
@@ -568,7 +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.
@@ -586,7 +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.
 

libs/core/langchain_core/indexing/in_memory.py

@@ -62,10 +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

libs/core/langchain_core/language_models/__init__.py

@@ -1,26 +1,23 @@
-"""Core language model abstractions.
+"""Language models.
 
 LangChain has two main classes to work with language models: chat models and
-"old-fashioned" LLMs (string-in, string-out).
+"old-fashioned" LLMs.
 
 **Chat models**
 
 Language models that use a sequence of messages as inputs and return chat messages
-as outputs (as opposed to using plain text).
+as outputs (as opposed to using plain text). 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.
 
-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.
-
-The key abstraction for chat models is
-[`BaseChatModel`][langchain_core.language_models.BaseChatModel]. Implementations should
-inherit from this class.
+The key abstraction for chat models is `BaseChatModel`. Implementations
+should inherit from this class.
 
 See existing [chat model integrations](https://docs.langchain.com/oss/python/integrations/chat).
 
-**LLMs (legacy)**
+**LLMs**
 
 Language models that takes a string as input and returns a string.
-
 These are traditionally older models (newer models generally are chat models).
 
 Although the underlying models are string in, string out, the LangChain wrappers also
@@ -55,10 +52,6 @@ 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",
@@ -74,8 +67,6 @@ __all__ = (
     "LanguageModelInput",
     "LanguageModelLike",
     "LanguageModelOutput",
-    "ModelProfile",
-    "ModelProfileRegistry",
     "ParrotFakeChatModel",
     "SimpleChatModel",
     "get_tokenizer",
@@ -98,8 +89,6 @@ _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",
 }

libs/core/langchain_core/language_models/_utils.py

@@ -139,8 +139,7 @@ def _normalize_messages(
             directly; this may change in the future
     - LangChain v0 standard content blocks for backward compatibility
 
-    !!! warning "Behavior changed in `langchain-core` 1.0.0"
-
+    !!! warning "Behavior changed in 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

libs/core/langchain_core/language_models/base.py

@@ -12,14 +12,13 @@ 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  # noqa: TC001
-from langchain_core.callbacks import Callbacks  # noqa: TC001
+from langchain_core.caches import BaseCache
+from langchain_core.callbacks import Callbacks
 from langchain_core.globals import get_verbose
 from langchain_core.messages import (
     AIMessage,
@@ -87,28 +86,13 @@ 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 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
-
+    """Encode the text into token IDs."""
+    # get the cached tokenizer
     tokenizer = get_tokenizer()
 
-    # 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))
+    # tokenize the text using the GPT-2 tokenizer
+    return tokenizer.encode(text)
 
 
 LanguageModelInput = PromptValue | str | Sequence[MessageLikeRepresentation]
@@ -147,19 +131,14 @@ class BaseLanguageModel(
 
     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
     )
@@ -216,22 +195,15 @@ class BaseLanguageModel(
             type (e.g., pure text completion models vs chat models).
 
         Args:
-            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.
+            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 `Generation` objects for
@@ -260,22 +232,15 @@ class BaseLanguageModel(
             type (e.g., pure text completion models vs chat models).
 
         Args:
-            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.
+            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 `Generation` objects for
@@ -297,13 +262,13 @@ 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
+            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:
@@ -315,9 +280,6 @@ 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.
 
@@ -336,17 +298,9 @@ 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.
-
         !!! note
-
-            * 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.
+            The base implementation of `get_num_tokens_from_messages` ignores tool
+            schemas.
 
         Args:
             messages: The message inputs to tokenize.

libs/core/langchain_core/language_models/chat_models.py

@@ -5,6 +5,7 @@ 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
@@ -32,7 +33,6 @@ 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,
@@ -73,7 +73,6 @@ 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
@@ -89,10 +88,7 @@ def _generate_response_from_error(error: BaseException) -> list[ChatGeneration]:
             try:
                 metadata["body"] = response.json()
             except Exception:
-                try:
-                    metadata["body"] = getattr(response, "text", None)
-                except Exception:
-                    metadata["body"] = None
+                metadata["body"] = getattr(response, "text", None)
         if hasattr(response, "headers"):
             try:
                 metadata["headers"] = dict(response.headers)
@@ -333,36 +329,17 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     [`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 `langchain-core` 1.0.0"
+    !!! version-added "Added in version 1.0"
 
     """
 
-    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]:
-        # 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))
+        return dumpd(self)
 
     # --- Runnable methods ---
 
@@ -465,7 +442,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
         # Check if a runtime streaming flag has been passed in.
         if "stream" in kwargs:
-            return bool(kwargs["stream"])
+            return kwargs["stream"]
 
         if "streaming" in self.model_fields_set:
             streaming_value = getattr(self, "streaming", None)
@@ -551,7 +528,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                         ):
                             if block["type"] != index_type:
                                 index_type = block["type"]
-                                index += 1
+                                index = index + 1
                             if "index" not in block:
                                 block["index"] = index
                     run_manager.on_llm_new_token(
@@ -683,7 +660,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     ):
                         if block["type"] != index_type:
                             index_type = block["type"]
-                            index += 1
+                            index = index + 1
                         if "index" not in block:
                             block["index"] = index
                 await run_manager.on_llm_new_token(
@@ -734,7 +711,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     # --- Custom methods ---
 
-    def _combine_llm_outputs(self, _llm_outputs: list[dict | None], /) -> dict:
+    def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:  # noqa: ARG002
         return {}
 
     def _convert_cached_generations(self, cache_val: list) -> list[ChatGeneration]:
@@ -865,21 +842,16 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
         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
@@ -988,21 +960,16 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
         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
@@ -1148,15 +1115,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         if check_cache:
             if llm_cache:
                 llm_string = self._get_llm_string(stop=stop, **kwargs)
-                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)
+                prompt = dumps(messages)
                 cache_val = llm_cache.lookup(prompt, llm_string)
                 if isinstance(cache_val, list):
                     converted_generations = self._convert_cached_generations(cache_val)
@@ -1199,7 +1158,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     ):
                         if block["type"] != index_type:
                             index_type = block["type"]
-                            index += 1
+                            index = index + 1
                         if "index" not in block:
                             block["index"] = index
                 if run_manager:
@@ -1274,15 +1233,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         if check_cache:
             if llm_cache:
                 llm_string = self._get_llm_string(stop=stop, **kwargs)
-                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)
+                prompt = dumps(messages)
                 cache_val = await llm_cache.alookup(prompt, llm_string)
                 if isinstance(cache_val, list):
                     converted_generations = self._convert_cached_generations(cache_val)
@@ -1325,7 +1276,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     ):
                         if block["type"] != index_type:
                             index_type = block["type"]
-                            index += 1
+                            index = index + 1
                         if "index" not in block:
                             block["index"] = index
                 if run_manager:
@@ -1520,7 +1471,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     def bind_tools(
         self,
-        tools: Sequence[builtins.dict[str, Any] | type | Callable | BaseTool],
+        tools: Sequence[
+            typing.Dict[str, Any] | type | Callable | BaseTool  # noqa: UP006
+        ],
         *,
         tool_choice: str | None = None,
         **kwargs: Any,
@@ -1539,20 +1492,20 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     def with_structured_output(
         self,
-        schema: builtins.dict[str, Any] | type,
+        schema: typing.Dict | type,  # noqa: UP006
         *,
         include_raw: bool = False,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, builtins.dict[str, Any] | BaseModel]:
+    ) -> Runnable[LanguageModelInput, typing.Dict | BaseModel]:  # noqa: UP006
         """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
@@ -1564,15 +1517,11 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 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.
+                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'`.
@@ -1596,90 +1545,89 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     depends on the `schema` as described above.
                 - `'parsing_error'`: `BaseException | None`
 
-        ???+ example "Pydantic schema (`include_raw=False`)"
+        Example: Pydantic schema (`include_raw=False`):
 
-            ```python
-            from pydantic import BaseModel
+        ```python
+        from pydantic import BaseModel
 
 
-            class AnswerWithJustification(BaseModel):
-                '''An answer to the user question along with justification for the answer.'''
+        class AnswerWithJustification(BaseModel):
+            '''An answer to the user question along with justification for the answer.'''
 
-                answer: str
-                justification: str
+            answer: str
+            justification: str
 
 
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(AnswerWithJustification)
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(AnswerWithJustification)
 
-            structured_model.invoke(
-                "What weighs more a pound of bricks or a pound of feathers"
-            )
+        structured_model.invoke(
+            "What weighs more a pound of bricks or a pound of feathers"
+        )
 
-            # -> AnswerWithJustification(
-            #     answer='They weigh the same',
-            #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
-            # )
-            ```
+        # -> AnswerWithJustification(
+        #     answer='They weigh the same',
+        #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
+        # )
+        ```
 
-        ??? example "Pydantic schema (`include_raw=True`)"
+        Example: Pydantic schema (`include_raw=True`):
 
-            ```python
-            from pydantic import BaseModel
+        ```python
+        from pydantic import BaseModel
 
 
-            class AnswerWithJustification(BaseModel):
-                '''An answer to the user question along with justification for the answer.'''
+        class AnswerWithJustification(BaseModel):
+            '''An answer to the user question along with justification for the answer.'''
 
-                answer: str
-                justification: str
+            answer: str
+            justification: str
 
 
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(
-                AnswerWithJustification, include_raw=True
-            )
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(
+            AnswerWithJustification, include_raw=True
+        )
 
-            structured_model.invoke(
-                "What weighs more a pound of bricks or a pound of feathers"
-            )
-            # -> {
-            #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
-            #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
-            #     'parsing_error': None
-            # }
-            ```
+        structured_model.invoke(
+            "What weighs more a pound of bricks or a pound of feathers"
+        )
+        # -> {
+        #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
+        #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
+        #     'parsing_error': None
+        # }
+        ```
 
-        ??? example "Dictionary schema (`include_raw=False`)"
+        Example: `dict` schema (`include_raw=False`):
 
-            ```python
-            from pydantic import BaseModel
-            from langchain_core.utils.function_calling import convert_to_openai_tool
+        ```python
+        from pydantic import BaseModel
+        from langchain_core.utils.function_calling import convert_to_openai_tool
 
 
-            class AnswerWithJustification(BaseModel):
-                '''An answer to the user question along with justification for the answer.'''
+        class AnswerWithJustification(BaseModel):
+            '''An answer to the user question along with justification for the answer.'''
 
-                answer: str
-                justification: str
+            answer: str
+            justification: str
 
 
-            dict_schema = convert_to_openai_tool(AnswerWithJustification)
-            model = ChatModel(model="model-name", temperature=0)
-            structured_model = model.with_structured_output(dict_schema)
+        dict_schema = convert_to_openai_tool(AnswerWithJustification)
+        model = ChatModel(model="model-name", temperature=0)
+        structured_model = model.with_structured_output(dict_schema)
 
-            structured_model.invoke(
-                "What weighs more a pound of bricks or a pound of feathers"
-            )
-            # -> {
-            #     'answer': 'They weigh the same',
-            #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
-            # }
-            ```
+        structured_model.invoke(
+            "What weighs more a pound of bricks or a pound of feathers"
+        )
+        # -> {
+        #     'answer': 'They weigh the same',
+        #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
+        # }
+        ```
 
-        !!! warning "Behavior changed in `langchain-core` 0.2.26"
-
-            Added support for `TypedDict` class.
+        !!! warning "Behavior changed in 0.2.26"
+            Added support for TypedDict class.
 
         """  # noqa: E501
         _ = kwargs.pop("method", None)
@@ -1778,12 +1726,9 @@ 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 > _MAX_CLEANUP_DEPTH:  # Don't cooperate for pathological cases
+    if depth > 100:  # Don't cooperate for pathological cases
         return
 
     if not isinstance(serialized, dict):

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -1,4 +1,4 @@
-"""Fake chat models for testing purposes."""
+"""Fake chat model for testing purposes."""
 
 import asyncio
 import re

libs/core/langchain_core/language_models/llms.py

@@ -1,7 +1,4 @@
-"""Base interface for traditional large language models (LLMs) to expose.
-
-These are traditionally older models (newer models generally are chat models).
-"""
+"""Base interface for large language models to expose."""
 
 from __future__ import annotations
 
@@ -61,8 +58,6 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
-_background_tasks: set[asyncio.Task] = set()
-
 
 @functools.lru_cache
 def _log_error_once(msg: str) -> None:
@@ -102,9 +97,9 @@ def create_base_retry_decorator(
                         asyncio.run(coro)
                     else:
                         if loop.is_running():
-                            task = loop.create_task(coro)
-                            _background_tasks.add(task)
-                            task.add_done_callback(_background_tasks.discard)
+                            # TODO: Fix RUF006 - this task should have a reference
+                            #  and be awaited somewhere
+                            loop.create_task(coro)  # noqa: RUF006
                         else:
                             asyncio.run(coro)
                 except Exception as e:
@@ -301,10 +296,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
 
     @functools.cached_property
     def _serialized(self) -> dict[str, Any]:
-        # 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))
+        return dumpd(self)
 
     # --- Runnable methods ---
 
@@ -656,12 +648,9 @@ 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 these
-                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 the stop substrings.
+                If stop tokens are not supported consider raising NotImplementedError.
             run_manager: Callback manager for the run.
 
         Returns:
@@ -679,12 +668,9 @@ 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 these
-                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 the stop substrings.
+                If stop tokens are not supported consider raising NotImplementedError.
             run_manager: Callback manager for the run.
 
         Returns:
@@ -716,14 +702,11 @@ 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.
@@ -745,14 +728,11 @@ 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.
@@ -863,14 +843,10 @@ 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
@@ -880,9 +856,8 @@ 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.
@@ -1138,14 +1113,10 @@ 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
@@ -1155,9 +1126,8 @@ 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
@@ -1421,6 +1391,11 @@ 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
@@ -1437,16 +1412,12 @@ 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 these
-                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 the stop 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.
@@ -1467,16 +1438,12 @@ 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 these
-                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 the stop 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.

libs/core/langchain_core/language_models/model_profile.py

@@ -1,85 +0,0 @@
-"""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."""

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 InitValidator, loads
+    from langchain_core.load.load import loads
     from langchain_core.load.serializable import Serializable
 
 # Unfortunately, we have to eagerly import load from langchain_core/load/load.py
@@ -15,19 +15,11 @@ if TYPE_CHECKING:
 # the `from langchain_core.load.load import load` absolute import should also work.
 from langchain_core.load.load import load
 
-__all__ = (
-    "InitValidator",
-    "Serializable",
-    "dumpd",
-    "dumps",
-    "load",
-    "loads",
-)
+__all__ = ("Serializable", "dumpd", "dumps", "load", "loads")
 
 _dynamic_imports = {
     "dumpd": "dump",
     "dumps": "dump",
-    "InitValidator": "load",
     "loads": "load",
     "Serializable": "serializable",
 }

libs/core/langchain_core/load/_validation.py

@@ -1,174 +0,0 @@
-"""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

libs/core/langchain_core/load/dump.py

@@ -1,26 +1,10 @@
-"""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.
-"""
+"""Dump objects to json."""
 
 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
@@ -41,20 +25,6 @@ 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)
@@ -70,17 +40,10 @@ 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.
 
-    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 by either 2 spaces or the amount
-            provided in the `indent` kwarg.
+        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`).
         **kwargs: Additional arguments to pass to `json.dumps`
 
     Returns:
@@ -92,29 +55,28 @@ 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)
-
-    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)
+    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)
 
 
 def dumpd(obj: Any) -> Any:
     """Return a dict 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.
 
     Returns:
         Dictionary that can be serialized to json using `json.dumps`.
     """
-    obj = _dump_pydantic_models(obj)
-    return _serialize_value(obj)
+    # 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.
+    return json.loads(dumps(obj))

libs/core/langchain_core/load/load.py

@@ -1,83 +1,11 @@
-"""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,
-    ],
-)
-```
-"""
+"""Load LangChain objects from JSON strings or objects."""
 
 import importlib
 import json
 import os
-from collections.abc import Callable, Iterable
-from typing import Any, Literal, cast
+from typing import Any
 
 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,
@@ -116,209 +44,32 @@ 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.
-
-    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.
-    """
+    """Reviver for JSON objects."""
 
     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 = False,  # noqa: FBT001,FBT002
+        secrets_from_env: bool = True,  # 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:
-            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_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.
             secrets_from_env: Whether to load secrets from the environment.
-            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.
-            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 {}
@@ -337,26 +88,7 @@ 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.
@@ -407,20 +139,6 @@ 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.
@@ -428,11 +146,13 @@ class Reviver:
             ):
                 msg = f"Invalid namespace: {value}"
                 raise ValueError(msg)
-            # Determine explicit import path
+            # Has 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 "
@@ -440,16 +160,9 @@ class Reviver:
                     f"{mapping_key}."
                 )
                 raise ValueError(msg)
+            # Otherwise, treat namespace as path.
             else:
-                # 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))
+                mod = importlib.import_module(".".join(namespace))
 
             cls = getattr(mod, name)
 
@@ -461,10 +174,6 @@ 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
@@ -474,81 +183,40 @@ 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 = False,
+    secrets_from_env: bool = True,
     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.
-        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_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.
         secrets_from_env: Whether to load secrets from the environment.
-        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.
-        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.
     """
-    # 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,
+    return json.loads(
+        text,
+        object_hook=Reviver(
+            secrets_map,
+            valid_namespaces,
+            secrets_from_env,
+            additional_import_mappings,
+            ignore_unserializable_fields=ignore_unserializable_fields,
+        ),
     )
 
 
@@ -556,112 +224,43 @@ 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 = False,
+    secrets_from_env: bool = True,
     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`.
-
-    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.
+    Use this if you already have a parsed JSON object,
+    eg. from `json.load` or `orjson.loads`.
 
     Args:
         obj: The object to load.
-        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_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.
         secrets_from_env: Whether to load secrets from the environment.
-        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.
-        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):
-            # 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
+            # Need to revive leaf nodes before reviving this node
             loaded_obj = {k: _load(v) for k, v in obj.items()}
             return reviver(loaded_obj)
         if isinstance(obj, list):

libs/core/langchain_core/load/mapping.py

@@ -1,19 +1,21 @@
 """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, 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`.
+For example,
 
-The mapping allows us to deserialize an `AIMessage` created with an older
+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
 version of LangChain where the code was in a different location.
 """
 
@@ -273,11 +275,6 @@ 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",
@@ -532,6 +529,16 @@ 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",

libs/core/langchain_core/load/serializable.py

@@ -92,24 +92,20 @@ class Serializable(BaseModel, ABC):
 
     It relies on the following methods and properties:
 
-    - [`is_lc_serializable`][langchain_core.load.serializable.Serializable.is_lc_serializable]: Is this class 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.
-
+    - `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`][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
+    - `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.
-    """  # noqa: E501
+    """
 
     # Remove default BaseModel init docstring.
     def __init__(self, *args: Any, **kwargs: Any) -> None:
@@ -133,9 +129,8 @@ class Serializable(BaseModel, ABC):
     def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the LangChain object.
 
-        For example, if the class is
-        [`langchain.llms.openai.OpenAI`][langchain_openai.OpenAI], then the namespace is
-        `["langchain", "llms", "openai"]`
+        For example, if the class is `langchain.llms.openai.OpenAI`, then the
+        namespace is `["langchain", "llms", "openai"]`
 
         Returns:
             The namespace.

libs/core/langchain_core/messages/__init__.py

@@ -9,9 +9,6 @@ if TYPE_CHECKING:
     from langchain_core.messages.ai import (
         AIMessage,
         AIMessageChunk,
-        InputTokenDetails,
-        OutputTokenDetails,
-        UsageMetadata,
     )
     from langchain_core.messages.base import (
         BaseMessage,
@@ -90,12 +87,10 @@ __all__ = (
     "HumanMessage",
     "HumanMessageChunk",
     "ImageContentBlock",
-    "InputTokenDetails",
     "InvalidToolCall",
     "MessageLikeRepresentation",
     "NonStandardAnnotation",
     "NonStandardContentBlock",
-    "OutputTokenDetails",
     "PlainTextContentBlock",
     "ReasoningContentBlock",
     "RemoveMessage",
@@ -109,7 +104,6 @@ __all__ = (
     "ToolCallChunk",
     "ToolMessage",
     "ToolMessageChunk",
-    "UsageMetadata",
     "VideoContentBlock",
     "_message_from_dict",
     "convert_to_messages",
@@ -151,7 +145,6 @@ _dynamic_imports = {
     "HumanMessageChunk": "human",
     "NonStandardAnnotation": "content",
     "NonStandardContentBlock": "content",
-    "OutputTokenDetails": "ai",
     "PlainTextContentBlock": "content",
     "ReasoningContentBlock": "content",
     "RemoveMessage": "modifier",
@@ -161,14 +154,12 @@ _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",

libs/core/langchain_core/messages/ai.py

@@ -1,13 +1,12 @@
 """AI message."""
 
-import itertools
 import json
 import logging
 import operator
 from collections.abc import Sequence
 from typing import Any, Literal, cast, overload
 
-from pydantic import Field, model_validator
+from pydantic import model_validator
 from typing_extensions import NotRequired, Self, TypedDict, override
 
 from langchain_core.messages import content as types
@@ -49,25 +48,25 @@ class InputTokenDetails(TypedDict, total=False):
         }
         ```
 
+    !!! version-added "Added in version 0.3.9"
+
     May also hold extra provider-specific keys.
 
-    !!! version-added "Added in `langchain-core` 0.3.9"
     """
 
     audio: int
     """Audio input tokens."""
-
     cache_creation: int
     """Input tokens that were cached and there was a cache miss.
 
     Since there was a cache miss, the cache was created from these tokens.
     """
-
     cache_read: int
     """Input tokens that were cached and there was a cache hit.
 
     Since there was a cache hit, the tokens were read from the cache. More precisely,
     the model state given these tokens was read from the cache.
+
     """
 
 
@@ -84,20 +83,18 @@ class OutputTokenDetails(TypedDict, total=False):
         }
         ```
 
-    May also hold extra provider-specific keys.
-
-    !!! version-added "Added in `langchain-core` 0.3.9"
+    !!! version-added "Added in version 0.3.9"
 
     """
 
     audio: int
     """Audio output tokens."""
-
     reasoning: int
     """Reasoning output tokens.
 
     Tokens generated by the model in a chain of thought process (i.e. by OpenAI's o1
     models) that are not returned as part of model output.
+
     """
 
 
@@ -124,36 +121,27 @@ class UsageMetadata(TypedDict):
         }
         ```
 
-    !!! warning "Behavior changed in `langchain-core` 0.3.9"
-
+    !!! warning "Behavior changed in 0.3.9"
         Added `input_token_details` and `output_token_details`.
 
-    !!! note "LangSmith SDK"
-
-        The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,
-        LangSmith's `UsageMetadata` has additional fields to capture cost information
-        used by the LangSmith platform.
     """
 
     input_tokens: int
     """Count of input (or prompt) tokens. Sum of all input token types."""
-
     output_tokens: int
     """Count of output (or completion) tokens. Sum of all output token types."""
-
     total_tokens: int
-    """Total token count. Sum of `input_tokens` + `output_tokens`."""
-
+    """Total token count. Sum of input_tokens + output_tokens."""
     input_token_details: NotRequired[InputTokenDetails]
     """Breakdown of input token counts.
 
     Does *not* need to sum to full input token count. Does *not* need to have all keys.
     """
-
     output_token_details: NotRequired[OutputTokenDetails]
     """Breakdown of output token counts.
 
     Does *not* need to sum to full output token count. Does *not* need to have all keys.
+
     """
 
 
@@ -165,14 +153,13 @@ class AIMessage(BaseMessage):
     This message represents the output of the model and consists of both
     the raw output as returned by the model and standardized fields
     (e.g., tool calls, usage metadata) added by the LangChain framework.
+
     """
 
-    tool_calls: list[ToolCall] = Field(default_factory=list)
+    tool_calls: list[ToolCall] = []
     """If present, tool calls associated with the message."""
-
-    invalid_tool_calls: list[InvalidToolCall] = Field(default_factory=list)
+    invalid_tool_calls: list[InvalidToolCall] = []
     """If present, tool calls with parsing errors associated with the message."""
-
     usage_metadata: UsageMetadata | None = None
     """If present, usage metadata for a message, such as token counts.
 
@@ -327,7 +314,7 @@ class AIMessage(BaseMessage):
         if tool_calls := values.get("tool_calls"):
             values["tool_calls"] = [
                 create_tool_call(
-                    **{k: v for k, v in tc.items() if k not in {"type", "extras"}}
+                    **{k: v for k, v in tc.items() if k not in ("type", "extras")}
                 )
                 for tc in tool_calls
             ]
@@ -395,7 +382,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     type: Literal["AIMessageChunk"] = "AIMessageChunk"  # type: ignore[assignment]
     """The type of the message (used for deserialization)."""
 
-    tool_call_chunks: list[ToolCallChunk] = Field(default_factory=list)
+    tool_call_chunks: list[ToolCallChunk] = []
     """If provided, tool call chunks associated with the message."""
 
     chunk_position: Literal["last"] | None = None
@@ -406,8 +393,8 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     """
 
     @property
-    @override
     def lc_attributes(self) -> dict:
+        """Attributes to be serialized, even if they are derived from other initialization args."""  # noqa: E501
         return {
             "tool_calls": self.tool_calls,
             "invalid_tool_calls": self.invalid_tool_calls,
@@ -443,7 +430,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
             blocks = [
                 block
                 for block in blocks
-                if block["type"] not in {"tool_call", "invalid_tool_call"}
+                if block["type"] not in ("tool_call", "invalid_tool_call")
             ]
             for tool_call_chunk in self.tool_call_chunks:
                 tc: types.ToolCallChunk = {
@@ -564,11 +551,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 
     @model_validator(mode="after")
     def init_server_tool_calls(self) -> Self:
-        """Initialize server tool calls.
-
-        Parse `server_tool_call_chunks` from
-        [`ServerToolCallChunk`][langchain.messages.ServerToolCallChunk] objects.
-        """
+        """Parse `server_tool_call_chunks`."""
         if (
             self.chunk_position == "last"
             and self.response_metadata.get("output_version") == "v1"
@@ -578,7 +561,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
                 if (
                     isinstance(block, dict)
                     and block.get("type")
-                    in {"server_tool_call", "server_tool_call_chunk"}
+                    in ("server_tool_call", "server_tool_call_chunk")
                     and (args_str := block.get("args"))
                     and isinstance(args_str, str)
                 ):
@@ -656,28 +639,29 @@ def add_ai_message_chunks(
     else:
         usage_metadata = None
 
-    # Ranks are defined by the order of preference. Higher is better:
-    # 2. Provider-assigned IDs (non lc_* and non lc_run-*)
-    # 1. lc_run-* IDs
-    # 0. lc_* and other remaining IDs
-    best_rank = -1
     chunk_id = None
-    candidates = itertools.chain([left.id], (o.id for o in others))
-
+    candidates = [left.id] + [o.id for o in others]
+    # first pass: pick the first provider-assigned id (non-run-* and non-lc_*)
     for id_ in candidates:
-        if not id_:
-            continue
-
-        if not id_.startswith(LC_ID_PREFIX) and not id_.startswith(LC_AUTO_PREFIX):
+        if (
+            id_
+            and not id_.startswith(LC_ID_PREFIX)
+            and not id_.startswith(LC_AUTO_PREFIX)
+        ):
             chunk_id = id_
-            # Highest rank, return instantly
             break
-
-        rank = 1 if id_.startswith(LC_ID_PREFIX) else 0
-
-        if rank > best_rank:
-            best_rank = rank
-            chunk_id = id_
+    else:
+        # second pass: prefer lc_run-* ids over lc_* ids
+        for id_ in candidates:
+            if id_ and id_.startswith(LC_ID_PREFIX):
+                chunk_id = id_
+                break
+        else:
+            # third pass: take any remaining id (auto-generated lc_* ids)
+            for id_ in candidates:
+                if id_:
+                    chunk_id = id_
+                    break
 
     chunk_position: Literal["last"] | None = (
         "last" if any(x.chunk_position == "last" for x in [left, *others]) else None

libs/core/langchain_core/messages/base.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, cast, overload
 
 from pydantic import ConfigDict, Field
+from typing_extensions import Self
 
 from langchain_core._api.deprecation import warn_deprecated
 from langchain_core.load.serializable import Serializable
@@ -16,8 +17,6 @@ from langchain_core.utils.interactive_env import is_interactive_env
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
-    from typing_extensions import Self
-
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 
@@ -94,10 +93,6 @@ class BaseMessage(Serializable):
     """Base abstract message class.
 
     Messages are the inputs and outputs of a chat model.
-
-    Examples include [`HumanMessage`][langchain.messages.HumanMessage],
-    [`AIMessage`][langchain.messages.AIMessage], and
-    [`SystemMessage`][langchain.messages.SystemMessage].
     """
 
     content: str | list[str | dict]
@@ -200,10 +195,11 @@ class BaseMessage(Serializable):
     def content_blocks(self) -> list[types.ContentBlock]:
         r"""Load content blocks from the message content.
 
-        !!! version-added "Added in `langchain-core` 1.0.0"
+        !!! version-added "Added in version 1.0.0"
 
         """
         # Needed here to avoid circular import, as these classes import BaseMessages
+        from langchain_core.messages import content as types  # noqa: PLC0415
         from langchain_core.messages.block_translators.anthropic import (  # noqa: PLC0415
             _convert_to_v1_from_anthropic_input,
         )
@@ -265,9 +261,6 @@ class BaseMessage(Serializable):
 
         Can be used as both property (`message.text`) and method (`message.text()`).
 
-        Handles both string and list content types (e.g. for content blocks). Only
-        extracts blocks with `type: 'text'`; other block types are ignored.
-
         !!! deprecated
             As of `langchain-core` 1.0.0, calling `.text()` as a method is deprecated.
             Use `.text` as a property instead. This method will be removed in 2.0.0.
@@ -279,7 +272,7 @@ class BaseMessage(Serializable):
         if isinstance(self.content, str):
             text_value = self.content
         else:
-            # Must be a list
+            # must be a list
             blocks = [
                 block
                 for block in self.content
@@ -304,7 +297,7 @@ class BaseMessage(Serializable):
         from langchain_core.prompts.chat import ChatPromptTemplate  # noqa: PLC0415
 
         prompt = ChatPromptTemplate(messages=[self])
-        return prompt.__add__(other)
+        return prompt + other
 
     def pretty_repr(
         self,
@@ -393,12 +386,12 @@ class BaseMessageChunk(BaseMessage):
         Raises:
             TypeError: If the other object is not a message chunk.
 
-        Example:
-            ```txt
-              AIMessageChunk(content="Hello", ...)
-            + AIMessageChunk(content=" World", ...)
-            = AIMessageChunk(content="Hello World", ...)
-            ```
+        For example,
+
+        `AIMessageChunk(content="Hello") + AIMessageChunk(content=" World")`
+
+        will give `AIMessageChunk(content="Hello World")`
+
         """
         if isinstance(other, BaseMessageChunk):
             # If both are (subclasses of) BaseMessageChunk,

libs/core/langchain_core/messages/block_translators/__init__.py

@@ -12,11 +12,10 @@ the implementation in `BaseMessage`.
 
 from __future__ import annotations
 
+from collections.abc import Callable
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
-
     from langchain_core.messages import AIMessage, AIMessageChunk
     from langchain_core.messages import content as types
 

libs/core/langchain_core/messages/block_translators/anthropic.py

@@ -159,12 +159,12 @@ def _convert_citation_to_v1(citation: dict[str, Any]) -> types.Annotation:
 
         return url_citation
 
-    if citation_type in {
+    if citation_type in (
         "char_location",
         "content_block_location",
         "page_location",
         "search_result_location",
-    }:
+    ):
         document_citation: types.Citation = {
             "type": "citation",
             "cited_text": citation["cited_text"],
@@ -173,6 +173,8 @@ def _convert_citation_to_v1(citation: dict[str, Any]) -> types.Annotation:
             document_citation["title"] = citation["document_title"]
         elif title := citation.get("title"):
             document_citation["title"] = title
+        else:
+            pass
         known_fields = {
             "type",
             "cited_text",
@@ -243,20 +245,11 @@ def _convert_to_v1_from_anthropic(message: AIMessage) -> list[types.ContentBlock
                     and message.chunk_position != "last"
                 ):
                     # Isolated chunk
-                    chunk = message.tool_call_chunks[0]
-
-                    tool_call_chunk = types.ToolCallChunk(
-                        name=chunk.get("name"),
-                        id=chunk.get("id"),
-                        args=chunk.get("args"),
-                        type="tool_call_chunk",
+                    tool_call_chunk: types.ToolCallChunk = (
+                        message.tool_call_chunks[0].copy()  # type: ignore[assignment]
                     )
-                    if "caller" in block:
-                        tool_call_chunk["extras"] = {"caller": block["caller"]}
-
-                    index = chunk.get("index")
-                    if index is not None:
-                        tool_call_chunk["index"] = index
+                    if "type" not in tool_call_chunk:
+                        tool_call_chunk["type"] = "tool_call_chunk"
                     yield tool_call_chunk
                 else:
                     tool_call_block: types.ToolCall | None = None
@@ -278,6 +271,8 @@ def _convert_to_v1_from_anthropic(message: AIMessage) -> list[types.ContentBlock
                                     "id": tc.get("id"),
                                 }
                                 break
+                    else:
+                        pass
                     if not tool_call_block:
                         tool_call_block = {
                             "type": "tool_call",
@@ -287,27 +282,17 @@ def _convert_to_v1_from_anthropic(message: AIMessage) -> list[types.ContentBlock
                         }
                     if "index" in block:
                         tool_call_block["index"] = block["index"]
-                    if "caller" in block:
-                        if "extras" not in tool_call_block:
-                            tool_call_block["extras"] = {}
-                        tool_call_block["extras"]["caller"] = block["caller"]
-
                     yield tool_call_block
 
             elif block_type == "input_json_delta" and isinstance(
                 message, AIMessageChunk
             ):
                 if len(message.tool_call_chunks) == 1:
-                    chunk = message.tool_call_chunks[0]
-                    tool_call_chunk = types.ToolCallChunk(
-                        name=chunk.get("name"),
-                        id=chunk.get("id"),
-                        args=chunk.get("args"),
-                        type="tool_call_chunk",
+                    tool_call_chunk = (
+                        message.tool_call_chunks[0].copy()  # type: ignore[assignment]
                     )
-                    index = chunk.get("index")
-                    if index is not None:
-                        tool_call_chunk["index"] = index
+                    if "type" not in tool_call_chunk:
+                        tool_call_chunk["type"] = "tool_call_chunk"
                     yield tool_call_chunk
 
                 else:
@@ -461,26 +446,12 @@ def _convert_to_v1_from_anthropic(message: AIMessage) -> list[types.ContentBlock
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Anthropic content.
-
-    Args:
-        message: The message to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message with Anthropic content."""
     return _convert_to_v1_from_anthropic(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message chunk with Anthropic content.
-
-    Args:
-        message: The message chunk to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message chunk with Anthropic content."""
     return _convert_to_v1_from_anthropic(message)
 
 

libs/core/langchain_core/messages/block_translators/bedrock.py

@@ -65,28 +65,14 @@ def _convert_to_v1_from_bedrock_chunk(
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Bedrock content.
-
-    Args:
-        message: The message to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message with Bedrock content."""
     if "claude" not in message.response_metadata.get("model_name", "").lower():
         raise NotImplementedError  # fall back to best-effort parsing
     return _convert_to_v1_from_bedrock(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message chunk with Bedrock content.
-
-    Args:
-        message: The message chunk to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message chunk with Bedrock content."""
     # TODO: add model_name to all Bedrock chunks and update core merging logic
     # to not append during aggregation. Then raise NotImplementedError here if
     # not an Anthropic model to fall back to best-effort parsing.

libs/core/langchain_core/messages/block_translators/bedrock_converse.py

@@ -209,16 +209,11 @@ def _convert_to_v1_from_converse(message: AIMessage) -> list[types.ContentBlock]
                     and message.chunk_position != "last"
                 ):
                     # Isolated chunk
-                    chunk = message.tool_call_chunks[0]
-                    tool_call_chunk = types.ToolCallChunk(
-                        name=chunk.get("name"),
-                        id=chunk.get("id"),
-                        args=chunk.get("args"),
-                        type="tool_call_chunk",
+                    tool_call_chunk: types.ToolCallChunk = (
+                        message.tool_call_chunks[0].copy()  # type: ignore[assignment]
                     )
-                    index = chunk.get("index")
-                    if index is not None:
-                        tool_call_chunk["index"] = index
+                    if "type" not in tool_call_chunk:
+                        tool_call_chunk["type"] = "tool_call_chunk"
                     yield tool_call_chunk
                 else:
                     tool_call_block: types.ToolCall | None = None
@@ -240,6 +235,8 @@ def _convert_to_v1_from_converse(message: AIMessage) -> list[types.ContentBlock]
                                     "id": tc.get("id"),
                                 }
                                 break
+                    else:
+                        pass
                     if not tool_call_block:
                         tool_call_block = {
                             "type": "tool_call",
@@ -256,16 +253,11 @@ def _convert_to_v1_from_converse(message: AIMessage) -> list[types.ContentBlock]
                 and isinstance(message, AIMessageChunk)
                 and len(message.tool_call_chunks) == 1
             ):
-                chunk = message.tool_call_chunks[0]
-                tool_call_chunk = types.ToolCallChunk(
-                    name=chunk.get("name"),
-                    id=chunk.get("id"),
-                    args=chunk.get("args"),
-                    type="tool_call_chunk",
+                tool_call_chunk = (
+                    message.tool_call_chunks[0].copy()  # type: ignore[assignment]
                 )
-                index = chunk.get("index")
-                if index is not None:
-                    tool_call_chunk["index"] = index
+                if "type" not in tool_call_chunk:
+                    tool_call_chunk["type"] = "tool_call_chunk"
                 yield tool_call_chunk
 
             else:
@@ -281,26 +273,12 @@ def _convert_to_v1_from_converse(message: AIMessage) -> list[types.ContentBlock]
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Bedrock Converse content.
-
-    Args:
-        message: The message to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message with Bedrock Converse content."""
     return _convert_to_v1_from_converse(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a chunk with Bedrock Converse content.
-
-    Args:
-        message: The message chunk to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a chunk with Bedrock Converse content."""
     return _convert_to_v1_from_converse(message)
 
 

libs/core/langchain_core/messages/block_translators/google_genai.py

@@ -9,13 +9,6 @@ from langchain_core.messages import AIMessage, AIMessageChunk
 from langchain_core.messages import content as types
 from langchain_core.messages.content import Citation, create_citation
 
-try:
-    import filetype  # type: ignore[import-not-found]
-
-    _HAS_FILETYPE = True
-except ImportError:
-    _HAS_FILETYPE = False
-
 
 def _bytes_to_b64_str(bytes_: bytes) -> str:
     """Convert bytes to base64 encoded string."""
@@ -83,36 +76,21 @@ def translate_grounding_metadata_to_citations(
         for chunk_index in chunk_indices:
             if chunk_index < len(grounding_chunks):
                 chunk = grounding_chunks[chunk_index]
-
-                # Handle web and maps grounding
-                web_info = chunk.get("web") or {}
-                maps_info = chunk.get("maps") or {}
-
-                # Extract citation info depending on source
-                url = maps_info.get("uri") or web_info.get("uri")
-                title = maps_info.get("title") or web_info.get("title")
-
-                # Note: confidence_scores is a legacy field from Gemini 2.0 and earlier
-                # that indicated confidence (0.0-1.0) for each grounding chunk.
-                #
-                # In Gemini 2.5+, this field is always None/empty and should be ignored.
-                extras_metadata = {
-                    "web_search_queries": web_search_queries,
-                    "grounding_chunk_index": chunk_index,
-                    "confidence_scores": support.get("confidence_scores") or [],
-                }
-
-                # Add maps-specific metadata if present
-                if maps_info.get("placeId"):
-                    extras_metadata["place_id"] = maps_info["placeId"]
+                web_info = chunk.get("web", {})
 
                 citation = create_citation(
-                    url=url,
-                    title=title,
+                    url=web_info.get("uri"),
+                    title=web_info.get("title"),
                     start_index=start_index,
                     end_index=end_index,
                     cited_text=cited_text,
-                    google_ai_metadata=extras_metadata,
+                    extras={
+                        "google_ai_metadata": {
+                            "web_search_queries": web_search_queries,
+                            "grounding_chunk_index": chunk_index,
+                            "confidence_scores": support.get("confidence_scores", []),
+                        }
+                    },
                 )
                 citations.append(citation)
 
@@ -390,7 +368,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                     else:
                         # Assume it's raw base64 without data URI
                         try:
-                            # Validate base64 and decode for MIME type detection
+                            # Validate base64 and decode for mime type detection
                             decoded_bytes = base64.b64decode(url, validate=True)
 
                             image_url_b64_block = {
@@ -398,14 +376,19 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                                 "base64": url,
                             }
 
-                            if _HAS_FILETYPE:
-                                # Guess MIME type based on file bytes
+                            try:
+                                import filetype  # type: ignore[import-not-found] # noqa: PLC0415
+
+                                # Guess mime type based on file bytes
                                 mime_type = None
                                 kind = filetype.guess(decoded_bytes)
                                 if kind:
                                     mime_type = kind.mime
                                 if mime_type:
                                     image_url_b64_block["mime_type"] = mime_type
+                            except ImportError:
+                                # filetype library not available, skip type detection
+                                pass
 
                             converted_blocks.append(
                                 cast("types.ImageContentBlock", image_url_b64_block)
@@ -413,10 +396,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                         except Exception:
                             # Not valid base64, treat as non-standard
                             converted_blocks.append(
-                                {
-                                    "type": "non_standard",
-                                    "value": item,
-                                }
+                                {"type": "non_standard", "value": item}
                             )
                 else:
                     # This likely won't be reached according to previous implementations
@@ -478,8 +458,6 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                 if outcome is not None:
                     server_tool_result_block["extras"]["outcome"] = outcome
                 converted_blocks.append(server_tool_result_block)
-            elif item_type == "text":
-                converted_blocks.append(cast("types.TextContentBlock", item))
             else:
                 # Unknown type, preserve as non-standard
                 converted_blocks.append({"type": "non_standard", "value": item})
@@ -528,26 +506,12 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Google (GenAI) content.
-
-    Args:
-        message: The message to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message with Google (GenAI) content."""
     return _convert_to_v1_from_genai(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a chunk with Google (GenAI) content.
-
-    Args:
-        message: The message chunk to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a chunk with Google (GenAI) content."""
     return _convert_to_v1_from_genai(message)
 
 

libs/core/langchain_core/messages/block_translators/groq.py

@@ -105,40 +105,26 @@ def _convert_to_v1_from_groq(message: AIMessage) -> list[types.ContentBlock]:
     if isinstance(message.content, str) and message.content:
         content_blocks.append({"type": "text", "text": message.content})
 
-    content_blocks.extend(
-        {
-            "type": "tool_call",
-            "name": tool_call["name"],
-            "args": tool_call["args"],
-            "id": tool_call.get("id"),
-        }
-        for tool_call in message.tool_calls
-    )
+    for tool_call in message.tool_calls:
+        content_blocks.append(  # noqa: PERF401
+            {
+                "type": "tool_call",
+                "name": tool_call["name"],
+                "args": tool_call["args"],
+                "id": tool_call.get("id"),
+            }
+        )
 
     return content_blocks
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with groq content.
-
-    Args:
-        message: The message to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message with groq content."""
     return _convert_to_v1_from_groq(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message chunk with groq content.
-
-    Args:
-        message: The message chunk to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message chunk with groq content."""
     return _convert_to_v1_from_groq(message)
 
 

libs/core/langchain_core/messages/block_translators/openai.py

@@ -4,34 +4,21 @@ from __future__ import annotations
 
 import json
 import warnings
+from collections.abc import Iterable
 from typing import TYPE_CHECKING, Any, Literal, cast
 
 from langchain_core.language_models._utils import (
     _parse_data_uri,
     is_openai_data_block,
 )
-from langchain_core.messages import AIMessageChunk
 from langchain_core.messages import content as types
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable
-
-    from langchain_core.messages import AIMessage
+    from langchain_core.messages import AIMessage, AIMessageChunk
 
 
 def convert_to_openai_image_block(block: dict[str, Any]) -> dict:
-    """Convert `ImageContentBlock` to format expected by OpenAI Chat Completions.
-
-    Args:
-        block: The image content block to convert.
-
-    Raises:
-        ValueError: If required keys are missing.
-        ValueError: If source type is unsupported.
-
-    Returns:
-        The formatted image content block.
-    """
+    """Convert `ImageContentBlock` to format expected by OpenAI Chat Completions."""
     if "url" in block:
         return {
             "type": "image_url",
@@ -62,18 +49,6 @@ def convert_to_openai_data_block(
 
     "Standard data content block" can include old-style LangChain v0 blocks
     (URLContentBlock, Base64ContentBlock, IDContentBlock) or new ones.
-
-    Args:
-        block: The content block to convert.
-        api: The OpenAI API being targeted. Either "chat/completions" or "responses".
-
-    Raises:
-        ValueError: If required keys are missing.
-        ValueError: If file URLs are used with Chat Completions API.
-        ValueError: If block type is unsupported.
-
-    Returns:
-        The formatted content block.
     """
     if block["type"] == "image":
         chat_completions_block = convert_to_openai_image_block(block)
@@ -193,6 +168,8 @@ def _convert_to_v1_from_chat_completions_input(
     Returns:
         Updated list with OpenAI blocks converted to v1 format.
     """
+    from langchain_core.messages import content as types  # noqa: PLC0415
+
     converted_blocks = []
     unpacked_blocks: list[dict[str, Any]] = [
         cast("dict[str, Any]", block)
@@ -270,7 +247,7 @@ def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
                 if block_type == "text":
                     # Strip annotations
                     new_content.append({"type": "text", "text": block["text"]})
-                elif block_type in {"reasoning", "tool_call"}:
+                elif block_type in ("reasoning", "tool_call"):
                     pass
                 else:
                     new_content.append(block)
@@ -287,6 +264,8 @@ _FUNCTION_CALL_IDS_MAP_KEY = "__openai_function_call_ids__"
 
 def _convert_from_v03_ai_message(message: AIMessage) -> AIMessage:
     """Convert v0 AIMessage into `output_version="responses/v1"` format."""
+    from langchain_core.messages import AIMessageChunk  # noqa: PLC0415
+
     # Only update ChatOpenAI v0.3 AIMessages
     is_chatopenai_v03 = (
         isinstance(message.content, list)
@@ -703,6 +682,8 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                 ) = None
                 call_id = block.get("call_id", "")
 
+                from langchain_core.messages import AIMessageChunk  # noqa: PLC0415
+
                 if (
                     isinstance(message, AIMessageChunk)
                     and len(message.tool_call_chunks) == 1
@@ -724,6 +705,8 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                             if invalid_tool_call.get("id") == call_id:
                                 tool_call_block = invalid_tool_call.copy()
                                 break
+                else:
+                    pass
                 if tool_call_block:
                     if "id" in block:
                         if "extras" not in tool_call_block:
@@ -751,7 +734,7 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                         k: v for k, v in block["action"].items() if k != "sources"
                     }
                 for key in block:
-                    if key not in {"type", "id", "action", "status", "index"}:
+                    if key not in ("type", "id", "action", "status", "index"):
                         web_search_call[key] = block[key]
 
                 yield cast("types.ServerToolCall", web_search_call)
@@ -777,6 +760,8 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                         web_search_result["status"] = "success"
                     elif status:
                         web_search_result["extras"] = {"status": status}
+                    else:
+                        pass
                     if "index" in block and isinstance(block["index"], int):
                         web_search_result["index"] = f"lc_wsr_{block['index'] + 1}"
                     yield cast("types.ServerToolResult", web_search_result)
@@ -792,14 +777,14 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                     file_search_call["index"] = f"lc_fsc_{block['index']}"
 
                 for key in block:
-                    if key not in {
+                    if key not in (
                         "type",
                         "id",
                         "queries",
                         "results",
                         "status",
                         "index",
-                    }:
+                    ):
                         file_search_call[key] = block[key]
 
                 yield cast("types.ServerToolCall", file_search_call)
@@ -818,6 +803,8 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                     file_search_result["status"] = "success"
                 elif status:
                     file_search_result["extras"] = {"status": status}
+                else:
+                    pass
                 if "index" in block and isinstance(block["index"], int):
                     file_search_result["index"] = f"lc_fsr_{block['index'] + 1}"
                 yield cast("types.ServerToolResult", file_search_result)
@@ -861,6 +848,8 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                     code_interpreter_result["status"] = "success"
                 elif status:
                     code_interpreter_result["extras"] = {"status": status}
+                else:
+                    pass
                 if "index" in block and isinstance(block["index"], int):
                     code_interpreter_result["index"] = f"lc_cir_{block['index'] + 1}"
 
@@ -991,14 +980,7 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with OpenAI content.
-
-    Args:
-        message: The message to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message with OpenAI content."""
     if isinstance(message.content, str):
         return _convert_to_v1_from_chat_completions(message)
     message = _convert_from_v03_ai_message(message)
@@ -1006,14 +988,7 @@ def translate_content(message: AIMessage) -> list[types.ContentBlock]:
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message chunk with OpenAI content.
-
-    Args:
-        message: The message chunk to translate.
-
-    Returns:
-        The derived content blocks.
-    """
+    """Derive standard content blocks from a message chunk with OpenAI content."""
     if isinstance(message.content, str):
         return _convert_to_v1_from_chat_completions_chunk(message)
     message = _convert_from_v03_ai_message(message)  # type: ignore[assignment]

libs/core/langchain_core/messages/content.py

@@ -1,14 +1,18 @@
 """Standard, multimodal content blocks for Large Language Model I/O.
 
-This module provides standardized data structures for representing inputs to and outputs
-from LLMs. The core abstraction is the **Content Block**, a `TypedDict`.
+!!! warning
+    This module is under active development. The API is unstable and subject to
+    change in future releases.
+
+This module provides standardized data structures for representing inputs to and
+outputs from LLMs. The core abstraction is the **Content Block**, a `TypedDict`.
 
 **Rationale**
 
-Different LLM providers use distinct and incompatible API schemas. This module provides
-a unified, provider-agnostic format to facilitate these interactions. A message to or
-from a model is simply a list of content blocks, allowing for the natural interleaving
-of text, images, and other content in a single ordered sequence.
+Different LLM providers use distinct and incompatible API schemas. This module
+provides a unified, provider-agnostic format to facilitate these interactions. A
+message to or from a model is simply a list of content blocks, allowing for the natural
+interleaving of text, images, and other content in a single ordered sequence.
 
 An adapter for a specific provider is responsible for translating this standard list of
 blocks into the format required by its API.
@@ -21,27 +25,16 @@ without losing the benefits of type checking and validation.
 
 Furthermore, provider-specific fields **within** a standard block are fully supported
 by default in the `extras` field of each block. This allows for additional metadata
-to be included without breaking the standard structure. For example, Google's thought
-signature:
-
-```python
-AIMessage(
-    content=[
-        {
-            "type": "text",
-            "text": "J'adore la programmation.",
-            "extras": {"signature": "EpoWCpc..."},  # Thought signature
-        }
-    ], ...
-)
-```
+to be included without breaking the standard structure.
 
+!!! warning
+    Do not heavily rely on the `extras` field for provider-specific data! This field
+    is subject to deprecation in future releases as we move towards PEP 728.
 
 !!! note
-
     Following widespread adoption of [PEP 728](https://peps.python.org/pep-0728/), we
-    intend to add `extra_items=Any` as a param to Content Blocks. This will signify to
-    type checkers that additional provider-specific fields are allowed outside of the
+    will add `extra_items=Any` as a param to Content Blocks. This will signify to type
+    checkers that additional provider-specific fields are allowed outside of the
     `extras` field, and that will become the new standard approach to adding
     provider-specific metadata.
 
@@ -79,10 +72,30 @@ AIMessage(
         openai_data = my_block["openai_metadata"]  # Type: Any
         ```
 
+        PEP 728 is enabled with `# type: ignore[call-arg]` comments to suppress
+        warnings from type checkers that don't yet support it. The functionality works
+        correctly in Python 3.13+ and will be fully supported as the ecosystem catches
+        up.
+
+**Key Block Types**
+
+The module defines several types of content blocks, including:
+
+- `TextContentBlock`: Standard text output.
+- `Citation`: For annotations that link text output to a source document.
+- `ToolCall`: For function calling.
+- `ReasoningContentBlock`: To capture a model's thought process.
+- Multimodal data:
+    - `ImageContentBlock`
+    - `AudioContentBlock`
+    - `VideoContentBlock`
+    - `PlainTextContentBlock` (e.g. .txt or .md files)
+    - `FileContentBlock` (e.g. PDFs, etc.)
+
 **Example Usage**
 
 ```python
-# Direct construction
+# Direct construction:
 from langchain_core.messages.content import TextContentBlock, ImageContentBlock
 
 multimodal_message: AIMessage(
@@ -96,7 +109,7 @@ multimodal_message: AIMessage(
     ]
 )
 
-# Using factories
+# Using factories:
 from langchain_core.messages.content import create_text_block, create_image_block
 
 multimodal_message: AIMessage(
@@ -111,7 +124,6 @@ multimodal_message: AIMessage(
 ```
 
 Factory functions offer benefits such as:
-
 - Automatic ID generation (when not provided)
 - No need to manually specify the `type` field
 """
@@ -127,30 +139,30 @@ class Citation(TypedDict):
     """Annotation for citing data from a document.
 
     !!! note
-
         `start`/`end` indices refer to the **response text**,
         not the source text. This means that the indices are relative to the model's
         response, not the original document (as specified in the `url`).
 
     !!! note "Factory function"
-
         `create_citation` may also be used as a factory to create a `Citation`.
         Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["citation"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     url: NotRequired[str]
@@ -188,12 +200,13 @@ class NonStandardAnnotation(TypedDict):
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     value: dict[str, Any]
@@ -211,24 +224,25 @@ class TextContentBlock(TypedDict):
     from a language model or the text of a user message.
 
     !!! note "Factory function"
-
         `create_text_block` may also be used as a factory to create a
         `TextContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["text"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     text: str
@@ -256,12 +270,12 @@ class ToolCall(TypedDict):
         and an identifier of "123".
 
     !!! note "Factory function"
-
         `create_tool_call` may also be used as a factory to create a
         `ToolCall`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["tool_call"]
@@ -272,6 +286,7 @@ class ToolCall(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
+
     """
     # TODO: Consider making this NotRequired[str] in the future.
 
@@ -317,8 +332,8 @@ class ToolCallChunk(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
+
     """
-    # TODO: Consider making this NotRequired[str] in the future.
 
     name: str | None
     """The name of the tool to be called."""
@@ -338,6 +353,7 @@ class InvalidToolCall(TypedDict):
 
     Here we add an `error` key to surface errors made during generation
     (e.g., invalid JSON arguments.)
+
     """
 
     # TODO: Consider making fields NotRequired[str] in the future.
@@ -350,8 +366,8 @@ class InvalidToolCall(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
+
     """
-    # TODO: Consider making this NotRequired[str] in the future.
 
     name: str | None
     """The name of the tool to be called."""
@@ -407,13 +423,7 @@ class ServerToolCallChunk(TypedDict):
     """JSON substring of the arguments to the tool call."""
 
     id: NotRequired[str]
-    """Unique identifier for this server tool call chunk.
-
-    Either:
-
-    - Generated by the provider
-    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-    """
+    """An identifier associated with the tool call."""
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -429,13 +439,7 @@ class ServerToolResult(TypedDict):
     """Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this server tool result.
-
-    Either:
-
-    - Generated by the provider
-    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-    """
+    """An identifier associated with the server tool result."""
 
     tool_call_id: str
     """ID of the corresponding server tool call."""
@@ -457,24 +461,25 @@ class ReasoningContentBlock(TypedDict):
     """Reasoning output from a LLM.
 
     !!! note "Factory function"
-
         `create_reasoning_block` may also be used as a factory to create a
         `ReasoningContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["reasoning"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     reasoning: NotRequired[str]
@@ -482,6 +487,7 @@ class ReasoningContentBlock(TypedDict):
 
     Either the thought summary or the raw reasoning text itself. This is often parsed
     from `<think>` tags in the model's response.
+
     """
 
     index: NotRequired[int | str]
@@ -498,38 +504,35 @@ class ImageContentBlock(TypedDict):
     """Image data.
 
     !!! note "Factory function"
-
-        `create_image_block` may also be used as a factory to create an
+        `create_image_block` may also be used as a factory to create a
         `ImageContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["image"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     file_id: NotRequired[str]
-    """Reference to the image in an external file storage system.
-
-    For example, OpenAI or Anthropic's Files API.
-    """
+    """ID of the image file, e.g., from a file storage system."""
 
     mime_type: NotRequired[str]
-    """MIME type of the image.
-
-    Required for base64 data.
+    """MIME type of the image. Required for base64.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#image)
+
     """
 
     index: NotRequired[int | str]
@@ -549,38 +552,35 @@ class VideoContentBlock(TypedDict):
     """Video data.
 
     !!! note "Factory function"
-
         `create_video_block` may also be used as a factory to create a
         `VideoContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["video"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     file_id: NotRequired[str]
-    """Reference to the video in an external file storage system.
-
-    For example, OpenAI or Anthropic's Files API.
-    """
+    """ID of the video file, e.g., from a file storage system."""
 
     mime_type: NotRequired[str]
-    """MIME type of the video.
-
-    Required for base64 data.
+    """MIME type of the video. Required for base64.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#video)
+
     """
 
     index: NotRequired[int | str]
@@ -600,38 +600,34 @@ class AudioContentBlock(TypedDict):
     """Audio data.
 
     !!! note "Factory function"
-
         `create_audio_block` may also be used as a factory to create an
         `AudioContentBlock`. Benefits include:
-
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["audio"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     file_id: NotRequired[str]
-    """Reference to the audio file in an external file storage system.
-
-    For example, OpenAI or Anthropic's Files API.
-    """
+    """ID of the audio file, e.g., from a file storage system."""
 
     mime_type: NotRequired[str]
-    """MIME type of the audio.
-
-    Required for base64 data.
+    """MIME type of the audio. Required for base64.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#audio)
+
     """
 
     index: NotRequired[int | str]
@@ -648,52 +644,45 @@ class AudioContentBlock(TypedDict):
 
 
 class PlainTextContentBlock(TypedDict):
-    """Plaintext data (e.g., from a `.txt` or `.md` document).
+    """Plaintext data (e.g., from a document).
 
     !!! note
-
         A `PlainTextContentBlock` existed in `langchain-core<1.0.0`. Although the
         name has carried over, the structure has changed significantly. The only shared
         keys between the old and new versions are `type` and `text`, though the
         `type` value has changed from `'text'` to `'text-plain'`.
 
     !!! note
-
         Title and context are optional fields that may be passed to the model. See
-        Anthropic [example](https://platform.claude.com/docs/en/build-with-claude/citations#citable-vs-non-citable-content).
+        Anthropic [example](https://docs.claude.com/en/docs/build-with-claude/citations#citable-vs-non-citable-content).
 
     !!! note "Factory function"
-
         `create_plaintext_block` may also be used as a factory to create a
         `PlainTextContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["text-plain"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     file_id: NotRequired[str]
-    """Reference to the plaintext file in an external file storage system.
-
-    For example, OpenAI or Anthropic's Files API.
-    """
+    """ID of the plaintext file, e.g., from a file storage system."""
 
     mime_type: Literal["text/plain"]
-    """MIME type of the file.
-
-    Required for base64 data.
-    """
+    """MIME type of the file. Required for base64."""
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -728,44 +717,35 @@ class FileContentBlock(TypedDict):
     `PlainTextContentBlock`).
 
     !!! note "Factory function"
-
         `create_file_block` may also be used as a factory to create a
         `FileContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["file"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
-
-    Used for tracking and referencing specific blocks (e.g., during streaming).
-
-    Not to be confused with `file_id`, which references an external file in a
-    storage system.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     file_id: NotRequired[str]
-    """Reference to the file in an external file storage system.
-
-    For example, a file ID from OpenAI's Files API or another cloud storage provider.
-    This is distinct from `id`, which identifies the content block itself.
-    """
+    """ID of the file, e.g., from a file storage system."""
 
     mime_type: NotRequired[str]
-    """MIME type of the file.
-
-    Required for base64 data.
+    """MIME type of the file. Required for base64.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml)
+
     """
 
     index: NotRequired[int | str]
@@ -787,7 +767,7 @@ class FileContentBlock(TypedDict):
 
 
 class NonStandardContentBlock(TypedDict):
-    """Provider-specific content data.
+    """Provider-specific data.
 
     This block contains data for which there is not yet a standard type.
 
@@ -800,28 +780,29 @@ class NonStandardContentBlock(TypedDict):
     `value` field.
 
     !!! note "Factory function"
-
         `create_non_standard_block` may also be used as a factory to create a
         `NonStandardContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
+
     """
 
     type: Literal["non_standard"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Unique identifier for this content block.
+    """Content block identifier.
 
     Either:
 
-    - Generated by the provider
+    - Generated by the provider (e.g., OpenAI's file ID)
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+
     """
 
     value: dict[str, Any]
-    """Provider-specific content data."""
+    """Provider-specific data."""
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -874,7 +855,7 @@ KNOWN_BLOCK_TYPES = {
     "non_standard",
     # citation and non_standard_annotation intentionally omitted
 }
-"""These are block types known to `langchain-core >= 1.0.0`.
+"""These are block types known to `langchain-core>=1.0.0`.
 
 If a block has a type not in this set, it is considered to be provider-specific.
 """
@@ -886,7 +867,7 @@ def _get_data_content_block_types() -> tuple[str, ...]:
     Example: ("image", "video", "audio", "text-plain", "file")
 
     Note that old style multimodal blocks type literals with new style blocks.
-    Specifically, "image", "audio", and "file".
+    Speficially, "image", "audio", and "file".
 
     See the docstring of `_normalize_messages` in `language_models._utils` for details.
     """
@@ -914,6 +895,7 @@ def is_data_content_block(block: dict) -> bool:
 
     Returns:
         `True` if the content block is a data content block, `False` otherwise.
+
     """
     if block.get("type") not in _get_data_content_block_types():
         return False
@@ -924,7 +906,7 @@ def is_data_content_block(block: dict) -> bool:
 
         # 'text' is checked to support v0 PlainTextContentBlock types
         # We must guard against new style TextContentBlock which also has 'text' `type`
-        # by ensuring the presence of `source_type`
+        # by ensuring the presense of `source_type`
         if block["type"] == "text" and "source_type" not in block:  # noqa: SIM103  # This is more readable
             return False
 
@@ -958,21 +940,17 @@ def create_text_block(
 
     Args:
         text: The text content of the block.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
+        id: Content block identifier. Generated automatically if not provided.
         annotations: `Citation`s and other annotations for the text.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `TextContentBlock`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     block = TextContentBlock(
         type="text",
@@ -1007,15 +985,9 @@ def create_image_block(
         url: URL of the image.
         base64: Base64-encoded image data.
         file_id: ID of the image file from a file storage system.
-        mime_type: MIME type of the image.
-
-            Required for base64 data.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        mime_type: MIME type of the image. Required for base64 data.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `ImageContentBlock`.
@@ -1025,9 +997,9 @@ def create_image_block(
             `mime_type`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1069,15 +1041,9 @@ def create_video_block(
         url: URL of the video.
         base64: Base64-encoded video data.
         file_id: ID of the video file from a file storage system.
-        mime_type: MIME type of the video.
-
-            Required for base64 data.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        mime_type: MIME type of the video. Required for base64 data.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `VideoContentBlock`.
@@ -1087,9 +1053,9 @@ def create_video_block(
             `mime_type`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1135,15 +1101,9 @@ def create_audio_block(
         url: URL of the audio.
         base64: Base64-encoded audio data.
         file_id: ID of the audio file from a file storage system.
-        mime_type: MIME type of the audio.
-
-            Required for base64 data.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        mime_type: MIME type of the audio. Required for base64 data.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `AudioContentBlock`.
@@ -1153,9 +1113,9 @@ def create_audio_block(
             `mime_type`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1201,15 +1161,9 @@ def create_file_block(
         url: URL of the file.
         base64: Base64-encoded file data.
         file_id: ID of the file from a file storage system.
-        mime_type: MIME type of the file.
-
-            Required for base64 data.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        mime_type: MIME type of the file. Required for base64 data.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `FileContentBlock`.
@@ -1219,9 +1173,9 @@ def create_file_block(
             `mime_type`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1271,20 +1225,16 @@ def create_plaintext_block(
         file_id: ID of the plaintext file from a file storage system.
         title: Title of the text data.
         context: Context or description of the text content.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `PlainTextContentBlock`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     block = PlainTextContentBlock(
         type="text-plain",
@@ -1327,20 +1277,16 @@ def create_tool_call(
     Args:
         name: The name of the tool to be called.
         args: The arguments to the tool call.
-        id: An identifier for the tool call.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        id: An identifier for the tool call. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `ToolCall`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     block = ToolCall(
         type="tool_call",
@@ -1369,20 +1315,16 @@ def create_reasoning_block(
 
     Args:
         reasoning: The reasoning text or thought summary.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `ReasoningContentBlock`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     block = ReasoningContentBlock(
         type="reasoning",
@@ -1418,17 +1360,15 @@ def create_citation(
         start_index: Start index in the response text where citation applies.
         end_index: End index in the response text where citation applies.
         cited_text: Excerpt of source text being cited.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
+        id: Content block identifier. Generated automatically if not provided.
 
     Returns:
         A properly formatted `Citation`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     block = Citation(type="citation", id=ensure_id(id))
 
@@ -1459,21 +1399,17 @@ def create_non_standard_block(
     """Create a `NonStandardContentBlock`.
 
     Args:
-        value: Provider-specific content data.
-        id: Content block identifier.
-
-            Generated automatically if not provided.
-        index: Index of block in aggregate response.
-
-            Used during streaming.
+        value: Provider-specific data.
+        id: Content block identifier. Generated automatically if not provided.
+        index: Index of block in aggregate response. Used during streaming.
 
     Returns:
         A properly formatted `NonStandardContentBlock`.
 
     !!! note
-
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
+
     """
     block = NonStandardContentBlock(
         type="non_standard",

libs/core/langchain_core/messages/tool.py

@@ -29,39 +29,38 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     `ToolMessage` objects contain the result of a tool invocation. Typically, the result
     is encoded inside the `content` field.
 
-    `tool_call_id` is used to associate the tool call request with the tool call
-    response. Useful in situations where a chat model is able to request multiple tool
-    calls in parallel.
+    Example: A `ToolMessage` representing a result of `42` from a tool call with id
 
-    Example:
-        A `ToolMessage` representing a result of `42` from a tool call with id
+    ```python
+    from langchain_core.messages import ToolMessage
 
-        ```python
-        from langchain_core.messages import ToolMessage
+    ToolMessage(content="42", tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL")
+    ```
 
-        ToolMessage(content="42", tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL")
-        ```
+    Example: A `ToolMessage` where only part of the tool output is sent to the model
+    and the full output is passed in to artifact.
 
-    Example:
-        A `ToolMessage` where only part of the tool output is sent to the model
-        and the full output is passed in to artifact.
+    ```python
+    from langchain_core.messages import ToolMessage
 
-        ```python
-        from langchain_core.messages import ToolMessage
+    tool_output = {
+        "stdout": "From the graph we can see that the correlation between "
+        "x and y is ...",
+        "stderr": None,
+        "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."},
+    }
 
-        tool_output = {
-            "stdout": "From the graph we can see that the correlation between "
-            "x and y is ...",
-            "stderr": None,
-            "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."},
-        }
+    ToolMessage(
+        content=tool_output["stdout"],
+        artifact=tool_output,
+        tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL",
+    )
+    ```
+
+    The `tool_call_id` field is used to associate the tool call request with the
+    tool call response. Useful in situations where a chat model is able
+    to request multiple tool calls in parallel.
 
-        ToolMessage(
-            content=tool_output["stdout"],
-            artifact=tool_output,
-            tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL",
-        )
-        ```
     """
 
     tool_call_id: str
@@ -214,29 +213,20 @@ class ToolCall(TypedDict):
         This represents a request to call the tool named `'foo'` with arguments
         `{"a": 1}` and an identifier of `'123'`.
 
-    !!! note "Factory function"
-
-        `tool_call` may also be used as a factory to create a `ToolCall`. Benefits
-        include:
-
-        * Required arguments strictly validated at creation time
     """
 
     name: str
     """The name of the tool to be called."""
-
     args: dict[str, Any]
-    """The arguments to the tool call as a dictionary."""
-
+    """The arguments to the tool call."""
     id: str | None
     """An identifier associated with the tool call.
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
-    """
 
+    """
     type: NotRequired[Literal["tool_call"]]
-    """Used for discrimination."""
 
 
 def tool_call(
@@ -249,7 +239,7 @@ def tool_call(
 
     Args:
         name: The name of the tool to be called.
-        args: The arguments to the tool call as a dictionary.
+        args: The arguments to the tool call.
         id: An identifier associated with the tool call.
 
     Returns:
@@ -261,9 +251,9 @@ def tool_call(
 class ToolCallChunk(TypedDict):
     """A chunk of a tool call (yielded when streaming).
 
-    When merging `ToolCallChunk` objects (e.g., via `AIMessageChunk.__add__`), all
-    string attributes are concatenated. Chunks are only merged if their values of
-    `index` are equal and not `None`.
+    When merging `ToolCallChunk`s (e.g., via `AIMessageChunk.__add__`),
+    all string attributes are concatenated. Chunks are only merged if their
+    values of `index` are equal and not None.
 
     Example:
     ```python
@@ -279,25 +269,13 @@ class ToolCallChunk(TypedDict):
 
     name: str | None
     """The name of the tool to be called."""
-
     args: str | None
-    """The arguments to the tool call as a JSON-parseable string."""
-
+    """The arguments to the tool call."""
     id: str | None
-    """An identifier associated with the tool call.
-
-    An identifier is needed to associate a tool call request with a tool
-    call result in events when multiple concurrent tool calls are made.
-    """
-
+    """An identifier associated with the tool call."""
     index: int | None
-    """The index of the tool call in a sequence.
-
-    Used for merging chunks.
-    """
-
+    """The index of the tool call in a sequence."""
     type: NotRequired[Literal["tool_call_chunk"]]
-    """Used for discrimination."""
 
 
 def tool_call_chunk(
@@ -311,7 +289,7 @@ def tool_call_chunk(
 
     Args:
         name: The name of the tool to be called.
-        args: The arguments to the tool call as a JSON string.
+        args: The arguments to the tool call.
         id: An identifier associated with the tool call.
         index: The index of the tool call in a sequence.
 
@@ -334,7 +312,7 @@ def invalid_tool_call(
 
     Args:
         name: The name of the tool to be called.
-        args: The arguments to the tool call as a JSON string.
+        args: The arguments to the tool call.
         id: An identifier associated with the tool call.
         error: An error message associated with the tool call.
 

libs/core/langchain_core/messages/utils.py

@@ -15,16 +15,12 @@ import json
 import logging
 import math
 from collections.abc import Callable, Iterable, Sequence
-from functools import partial, wraps
+from functools import partial
 from typing import (
     TYPE_CHECKING,
     Annotated,
     Any,
-    Concatenate,
     Literal,
-    ParamSpec,
-    Protocol,
-    TypeVar,
     cast,
     overload,
 )
@@ -65,19 +61,14 @@ logger = logging.getLogger(__name__)
 def _get_type(v: Any) -> str:
     """Get the type associated with the object for serialization purposes."""
     if isinstance(v, dict) and "type" in v:
-        result = v["type"]
-    elif hasattr(v, "type"):
-        result = v.type
-    else:
-        msg = (
-            f"Expected either a dictionary with a 'type' key or an object "
-            f"with a 'type' attribute. Instead got type {type(v)}."
-        )
-        raise TypeError(msg)
-    if not isinstance(result, str):
-        msg = f"Expected 'type' to be a str, got {type(result).__name__}"
-        raise TypeError(msg)
-    return result
+        return v["type"]
+    if hasattr(v, "type"):
+        return v.type
+    msg = (
+        f"Expected either a dictionary with a 'type' key or an object "
+        f"with a 'type' attribute. Instead got type {type(v)}."
+    )
+    raise TypeError(msg)
 
 
 AnyMessage = Annotated[
@@ -95,14 +86,11 @@ AnyMessage = Annotated[
     | Annotated[ToolMessageChunk, Tag(tag="ToolMessageChunk")],
     Field(discriminator=Discriminator(_get_type)),
 ]
-"""A type representing any defined `Message` or `MessageChunk` type."""
+""""A type representing any defined `Message` or `MessageChunk` type."""
 
 
 def get_buffer_string(
-    messages: Sequence[BaseMessage],
-    human_prefix: str = "Human",
-    ai_prefix: str = "AI",
-    message_separator: str = "\n",
+    messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
 ) -> str:
     r"""Convert a sequence of messages to strings and concatenate them into one string.
 
@@ -110,7 +98,6 @@ def get_buffer_string(
         messages: Messages to be converted to strings.
         human_prefix: The prefix to prepend to contents of `HumanMessage`s.
         ai_prefix: The prefix to prepend to contents of `AIMessage`.
-        message_separator: The separator to use between messages.
 
     Returns:
         A single string concatenation of all input messages.
@@ -118,11 +105,6 @@ def get_buffer_string(
     Raises:
         ValueError: If an unsupported message type is encountered.
 
-    Note:
-        If a message is an `AIMessage` and contains both tool calls under `tool_calls`
-        and a function call under `additional_kwargs["function_call"]`, only the tool
-        calls will be appended to the string representation.
-
     Example:
         ```python
         from langchain_core import AIMessage, HumanMessage
@@ -152,19 +134,12 @@ def get_buffer_string(
         else:
             msg = f"Got unsupported message type: {m}"
             raise ValueError(msg)  # noqa: TRY004
-
         message = f"{role}: {m.text}"
-
-        if isinstance(m, AIMessage):
-            if m.tool_calls:
-                message += f"{m.tool_calls}"
-            elif "function_call" in m.additional_kwargs:
-                # Legacy behavior assumes only one function call per message
-                message += f"{m.additional_kwargs['function_call']}"
-
+        if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
+            message += f"{m.additional_kwargs['function_call']}"
         string_messages.append(message)
 
-    return message_separator.join(string_messages)
+    return "\n".join(string_messages)
 
 
 def _message_from_dict(message: dict) -> BaseMessage:
@@ -227,11 +202,8 @@ def message_chunk_to_message(chunk: BaseMessage) -> BaseMessage:
     ignore_keys = ["type"]
     if isinstance(chunk, AIMessageChunk):
         ignore_keys.extend(["tool_call_chunks", "chunk_position"])
-    return cast(
-        "BaseMessage",
-        chunk.__class__.__mro__[1](
-            **{k: v for k, v in chunk.__dict__.items() if k not in ignore_keys}
-        ),
+    return chunk.__class__.__mro__[1](
+        **{k: v for k, v in chunk.__dict__.items() if k not in ignore_keys}
     )
 
 
@@ -253,13 +225,13 @@ def _create_message_from_message_type(
     """Create a message from a `Message` type and content string.
 
     Args:
-        message_type: the type of the message (e.g., `'human'`, `'ai'`, etc.).
-        content: the content string.
-        name: the name of the message.
-        tool_call_id: the tool call id.
-        tool_calls: the tool calls.
-        id: the id of the message.
-        additional_kwargs: additional keyword arguments.
+        message_type: (str) the type of the message (e.g., `'human'`, `'ai'`, etc.).
+        content: (str) the content string.
+        name: (str) the name of the message.
+        tool_call_id: (str) the tool call id.
+        tool_calls: (list[dict[str, Any]]) the tool calls.
+        id: (str) the id of the message.
+        additional_kwargs: (dict[str, Any]) additional keyword arguments.
 
     Returns:
         a message of the appropriate type.
@@ -356,16 +328,12 @@ def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
     """
     if isinstance(message, BaseMessage):
         message_ = message
-    elif isinstance(message, Sequence):
-        if isinstance(message, str):
-            message_ = _create_message_from_message_type("human", message)
-        else:
-            try:
-                message_type_str, template = message
-            except ValueError as e:
-                msg = "Message as a sequence must be (role string, template)"
-                raise NotImplementedError(msg) from e
-            message_ = _create_message_from_message_type(message_type_str, template)
+    elif isinstance(message, str):
+        message_ = _create_message_from_message_type("human", message)
+    elif isinstance(message, Sequence) and len(message) == 2:
+        # mypy doesn't realise this can't be a string given the previous branch
+        message_type_str, template = message  # type: ignore[misc]
+        message_ = _create_message_from_message_type(message_type_str, template)
     elif isinstance(message, dict):
         msg_kwargs = message.copy()
         try:
@@ -412,54 +380,33 @@ def convert_to_messages(
     return [_convert_to_message(m) for m in messages]
 
 
-_P = ParamSpec("_P")
-_R_co = TypeVar("_R_co", covariant=True)
-
-
-class _RunnableSupportCallable(Protocol[_P, _R_co]):
+def _runnable_support(func: Callable) -> Callable:
     @overload
-    def __call__(
-        self,
-        messages: None = None,
-        *args: _P.args,
-        **kwargs: _P.kwargs,
-    ) -> Runnable[Sequence[MessageLikeRepresentation], _R_co]: ...
-
-    @overload
-    def __call__(
-        self,
-        messages: Sequence[MessageLikeRepresentation] | PromptValue,
-        *args: _P.args,
-        **kwargs: _P.kwargs,
-    ) -> _R_co: ...
-
-    def __call__(
-        self,
-        messages: Sequence[MessageLikeRepresentation] | PromptValue | None = None,
-        *args: _P.args,
-        **kwargs: _P.kwargs,
-    ) -> _R_co | Runnable[Sequence[MessageLikeRepresentation], _R_co]: ...
-
-
-def _runnable_support(
-    func: Callable[
-        Concatenate[Sequence[MessageLikeRepresentation] | PromptValue, _P], _R_co
-    ],
-) -> _RunnableSupportCallable[_P, _R_co]:
-    @wraps(func)
     def wrapped(
-        messages: Sequence[MessageLikeRepresentation] | PromptValue | None = None,
-        *args: _P.args,
-        **kwargs: _P.kwargs,
-    ) -> _R_co | Runnable[Sequence[MessageLikeRepresentation], _R_co]:
+        messages: None = None, **kwargs: Any
+    ) -> Runnable[Sequence[MessageLikeRepresentation], list[BaseMessage]]: ...
+
+    @overload
+    def wrapped(
+        messages: Sequence[MessageLikeRepresentation], **kwargs: Any
+    ) -> list[BaseMessage]: ...
+
+    def wrapped(
+        messages: Sequence[MessageLikeRepresentation] | None = None,
+        **kwargs: Any,
+    ) -> (
+        list[BaseMessage]
+        | Runnable[Sequence[MessageLikeRepresentation], list[BaseMessage]]
+    ):
         # Import locally to prevent circular import.
         from langchain_core.runnables.base import RunnableLambda  # noqa: PLC0415
 
         if messages is not None:
-            return func(messages, *args, **kwargs)
+            return func(messages, **kwargs)
         return RunnableLambda(partial(func, **kwargs), name=func.__name__)
 
-    return cast("_RunnableSupportCallable[_P, _R_co]", wrapped)
+    wrapped.__doc__ = func.__doc__
+    return wrapped
 
 
 @_runnable_support
@@ -563,7 +510,6 @@ def filter_messages(
         ):
             continue
 
-        new_msg = msg
         if isinstance(exclude_tool_calls, (list, tuple, set)):
             if isinstance(msg, AIMessage) and msg.tool_calls:
                 tool_calls = [
@@ -587,7 +533,7 @@ def filter_messages(
                         )
                     ]
 
-                new_msg = msg.model_copy(
+                msg = msg.model_copy(  # noqa: PLW2901
                     update={"tool_calls": tool_calls, "content": content}
                 )
             elif (
@@ -598,11 +544,11 @@ def filter_messages(
         # default to inclusion when no inclusion criteria given.
         if (
             not (include_types or include_ids or include_names)
-            or (include_names and new_msg.name in include_names)
-            or (include_types and _is_message_type(new_msg, include_types))
-            or (include_ids and new_msg.id in include_ids)
+            or (include_names and msg.name in include_names)
+            or (include_types and _is_message_type(msg, include_types))
+            or (include_ids and msg.id in include_ids)
         ):
-            filtered.append(new_msg)
+            filtered.append(msg)
 
     return filtered
 
@@ -745,8 +691,7 @@ def trim_messages(
     max_tokens: int,
     token_counter: Callable[[list[BaseMessage]], int]
     | Callable[[BaseMessage], int]
-    | BaseLanguageModel
-    | Literal["approximate"],
+    | BaseLanguageModel,
     strategy: Literal["first", "last"] = "last",
     allow_partial: bool = False,
     end_on: str | type[BaseMessage] | Sequence[str | type[BaseMessage]] | None = None,
@@ -784,65 +729,51 @@ def trim_messages(
         messages: Sequence of Message-like objects to trim.
         max_tokens: Max token count of trimmed messages.
         token_counter: Function or llm for counting tokens in a `BaseMessage` or a
-            list of `BaseMessage`.
-
-            If a `BaseLanguageModel` is passed in then
-            `BaseLanguageModel.get_num_tokens_from_messages()` will be used. Set to
-            `len` to count the number of **messages** in the chat history.
-
-            You can also use string shortcuts for convenience:
-
-            - `'approximate'`: Uses `count_tokens_approximately` for fast, approximate
-                token counts.
+            list of `BaseMessage`. If a `BaseLanguageModel` is passed in then
+            `BaseLanguageModel.get_num_tokens_from_messages()` will be used.
+            Set to `len` to count the number of **messages** in the chat history.
 
             !!! note
-
-                `count_tokens_approximately` (or the shortcut `'approximate'`) is
-                recommended for using `trim_messages` on the hot path, where exact token
-                counting is not necessary.
+                Use `count_tokens_approximately` to get fast, approximate token
+                counts.
+                This is recommended for using `trim_messages` on the hot path, where
+                exact token counting is not necessary.
 
         strategy: Strategy for trimming.
-
             - `'first'`: Keep the first `<= n_count` tokens of the messages.
             - `'last'`: Keep the last `<= n_count` tokens of the messages.
         allow_partial: Whether to split a message if only part of the message can be
-            included.
-
-            If `strategy='last'` then the last partial contents of a message are
-            included. If `strategy='first'` then the first partial contents of a
+            included. If `strategy='last'` then the last partial contents of a message
+            are included. If `strategy='first'` then the first partial contents of a
             message are included.
-        end_on: The message type to end on.
+        end_on: The message type to end on. If specified then every message after the
+            last occurrence of this type is ignored. If `strategy='last'` then this
+            is done before we attempt to get the last `max_tokens`. If
+            `strategy='first'` then this is done after we get the first
+            `max_tokens`. Can be specified as string names (e.g. `'system'`,
+            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g.
+            `SystemMessage`, `HumanMessage`, `AIMessage`, ...). Can be a single
+            type or a list of types.
 
-            If specified then every message after the last occurrence of this type is
-            ignored. If `strategy='last'` then this is done before we attempt to get the
-            last `max_tokens`. If `strategy='first'` then this is done after we get the
-            first `max_tokens`. Can be specified as string names (e.g. `'system'`,
-            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g. `SystemMessage`,
-            `HumanMessage`, `AIMessage`, ...). Can be a single type or a list of types.
-
-        start_on: The message type to start on.
-
-            Should only be specified if `strategy='last'`. If specified then every
-            message before the first occurrence of this type is ignored. This is done
-            after we trim the initial messages to the last `max_tokens`. Does not apply
-            to a `SystemMessage` at index 0 if `include_system=True`. Can be specified
-            as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or as
-            `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`, `AIMessage`,
-            ...). Can be a single type or a list of types.
+        start_on: The message type to start on. Should only be specified if
+            `strategy='last'`. If specified then every message before
+            the first occurrence of this type is ignored. This is done after we trim
+            the initial messages to the last `max_tokens`. Does not
+            apply to a `SystemMessage` at index 0 if `include_system=True`. Can be
+            specified as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or
+            as `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`,
+            `AIMessage`, ...). Can be a single type or a list of types.
 
         include_system: Whether to keep the `SystemMessage` if there is one at index
-            `0`.
-
-            Should only be specified if `strategy="last"`.
+            `0`. Should only be specified if `strategy="last"`.
         text_splitter: Function or `langchain_text_splitters.TextSplitter` for
-            splitting the string contents of a message.
-
-            Only used if `allow_partial=True`. If `strategy='last'` then the last split
-            tokens from a partial message will be included. if `strategy='first'` then
-            the first split tokens from a partial message will be included. Token
-            splitter assumes that separators are kept, so that split contents can be
-            directly concatenated to recreate the original text. Defaults to splitting
-            on newlines.
+            splitting the string contents of a message. Only used if
+            `allow_partial=True`. If `strategy='last'` then the last split tokens
+            from a partial message will be included. if `strategy='first'` then the
+            first split tokens from a partial message will be included. Token splitter
+            assumes that separators are kept, so that split contents can be directly
+            concatenated to recreate the original text. Defaults to splitting on
+            newlines.
 
     Returns:
         List of trimmed `BaseMessage`.
@@ -853,8 +784,8 @@ def trim_messages(
 
     Example:
         Trim chat history based on token count, keeping the `SystemMessage` if
-        present, and ensuring that the chat history starts with a `HumanMessage` (or a
-        `SystemMessage` followed by a `HumanMessage`).
+        present, and ensuring that the chat history starts with a `HumanMessage` (
+        or a `SystemMessage` followed by a `HumanMessage`).
 
         ```python
         from langchain_core.messages import (
@@ -907,34 +838,8 @@ def trim_messages(
         ]
         ```
 
-        Trim chat history using approximate token counting with `'approximate'`:
-
-        ```python
-        trim_messages(
-            messages,
-            max_tokens=45,
-            strategy="last",
-            # Using the "approximate" shortcut for fast token counting
-            token_counter="approximate",
-            start_on="human",
-            include_system=True,
-        )
-
-        # This is equivalent to using `count_tokens_approximately` directly
-        from langchain_core.messages.utils import count_tokens_approximately
-
-        trim_messages(
-            messages,
-            max_tokens=45,
-            strategy="last",
-            token_counter=count_tokens_approximately,
-            start_on="human",
-            include_system=True,
-        )
-        ```
-
         Trim chat history based on the message count, keeping the `SystemMessage` if
-        present, and ensuring that the chat history starts with a HumanMessage (
+        present, and ensuring that the chat history starts with a `HumanMessage` (
         or a `SystemMessage` followed by a `HumanMessage`).
 
             trim_messages(
@@ -1056,44 +961,24 @@ def trim_messages(
         raise ValueError(msg)
 
     messages = convert_to_messages(messages)
-
-    # Handle string shortcuts for token counter
-    if isinstance(token_counter, str):
-        if token_counter in _TOKEN_COUNTER_SHORTCUTS:
-            actual_token_counter = _TOKEN_COUNTER_SHORTCUTS[token_counter]
-        else:
-            available_shortcuts = ", ".join(
-                f"'{key}'" for key in _TOKEN_COUNTER_SHORTCUTS
-            )
-            msg = (
-                f"Invalid token_counter shortcut '{token_counter}'. "
-                f"Available shortcuts: {available_shortcuts}."
-            )
-            raise ValueError(msg)
-    else:
-        # Type narrowing: at this point token_counter is not a str
-        actual_token_counter = token_counter  # type: ignore[assignment]
-
-    if hasattr(actual_token_counter, "get_num_tokens_from_messages"):
-        list_token_counter = actual_token_counter.get_num_tokens_from_messages
-    elif callable(actual_token_counter):
+    if hasattr(token_counter, "get_num_tokens_from_messages"):
+        list_token_counter = token_counter.get_num_tokens_from_messages
+    elif callable(token_counter):
         if (
-            next(
-                iter(inspect.signature(actual_token_counter).parameters.values())
-            ).annotation
+            next(iter(inspect.signature(token_counter).parameters.values())).annotation
             is BaseMessage
         ):
 
             def list_token_counter(messages: Sequence[BaseMessage]) -> int:
-                return sum(actual_token_counter(msg) for msg in messages)  # type: ignore[arg-type, misc]
+                return sum(token_counter(msg) for msg in messages)  # type: ignore[arg-type, misc]
 
         else:
-            list_token_counter = actual_token_counter
+            list_token_counter = token_counter
     else:
         msg = (
             f"'token_counter' expected to be a model that implements "
             f"'get_num_tokens_from_messages()' or a function. Received object of type "
-            f"{type(actual_token_counter)}."
+            f"{type(token_counter)}."
         )
         raise ValueError(msg)
 
@@ -1128,38 +1013,11 @@ def trim_messages(
     raise ValueError(msg)
 
 
-_SingleMessage = BaseMessage | str | dict[str, Any]
-_T = TypeVar("_T", bound=_SingleMessage)
-# A sequence of _SingleMessage that is NOT a bare str
-_MultipleMessages = Sequence[_T]
-
-
-@overload
-def convert_to_openai_messages(
-    messages: _SingleMessage,
-    *,
-    text_format: Literal["string", "block"] = "string",
-    include_id: bool = False,
-    pass_through_unknown_blocks: bool = True,
-) -> dict: ...
-
-
-@overload
-def convert_to_openai_messages(
-    messages: _MultipleMessages,
-    *,
-    text_format: Literal["string", "block"] = "string",
-    include_id: bool = False,
-    pass_through_unknown_blocks: bool = True,
-) -> list[dict]: ...
-
-
 def convert_to_openai_messages(
     messages: MessageLikeRepresentation | Sequence[MessageLikeRepresentation],
     *,
     text_format: Literal["string", "block"] = "string",
     include_id: bool = False,
-    pass_through_unknown_blocks: bool = True,
 ) -> dict | list[dict]:
     """Convert LangChain messages into OpenAI message dicts.
 
@@ -1167,21 +1025,18 @@ def convert_to_openai_messages(
         messages: Message-like object or iterable of objects whose contents are
             in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
         text_format: How to format string or text block contents:
-            - `'string'`:
-                If a message has a string content, this is left as a string. If
-                a message has content blocks that are all of type `'text'`, these
-                are joined with a newline to make a single string. If a message has
-                content blocks and at least one isn't of type `'text'`, then
-                all blocks are left as dicts.
-            - `'block'`:
-                If a message has a string content, this is turned into a list
-                with a single content block of type `'text'`. If a message has
-                content blocks these are left as is.
-        include_id: Whether to include message IDs in the openai messages, if they
-            are present in the source messages.
-        pass_through_unknown_blocks: Whether to include content blocks with unknown
-            formats in the output. If `False`, an error is raised if an unknown
-            content block is encountered.
+                - `'string'`:
+                    If a message has a string content, this is left as a string. If
+                    a message has content blocks that are all of type `'text'`, these
+                    are joined with a newline to make a single string. If a message has
+                    content blocks and at least one isn't of type `'text'`, then
+                    all blocks are left as dicts.
+                - `'block'`:
+                    If a message has a string content, this is turned into a list
+                    with a single content block of type `'text'`. If a message has
+                    content blocks these are left as is.
+        include_id: Whether to include message ids in the openai messages, if they
+                    are present in the source messages.
 
     Raises:
         ValueError: if an unrecognized `text_format` is specified, or if a message
@@ -1242,14 +1097,14 @@ def convert_to_openai_messages(
         # ]
         ```
 
-    !!! version-added "Added in `langchain-core` 0.3.11"
+    !!! version-added "Added in version 0.3.11"
 
     """  # noqa: E501
     if text_format not in {"string", "block"}:
         err = f"Unrecognized {text_format=}, expected one of 'string' or 'block'."
         raise ValueError(err)
 
-    oai_messages: list[dict] = []
+    oai_messages: list = []
 
     if is_single := isinstance(messages, (BaseMessage, dict, str)):
         messages = [messages]
@@ -1431,36 +1286,6 @@ def convert_to_openai_messages(
                                 },
                             }
                         )
-                elif block.get("type") == "function_call":  # OpenAI Responses
-                    if not any(
-                        tool_call["id"] == block.get("call_id")
-                        for tool_call in cast("AIMessage", message).tool_calls
-                    ):
-                        if missing := [
-                            k
-                            for k in ("call_id", "name", "arguments")
-                            if k not in block
-                        ]:
-                            err = (
-                                f"Unrecognized content block at "
-                                f"messages[{i}].content[{j}] has 'type': "
-                                f"'tool_use', but is missing expected key(s) "
-                                f"{missing}. Full content block:\n\n{block}"
-                            )
-                            raise ValueError(err)
-                        oai_msg["tool_calls"] = oai_msg.get("tool_calls", [])
-                        oai_msg["tool_calls"].append(
-                            {
-                                "type": "function",
-                                "id": block.get("call_id"),
-                                "function": {
-                                    "name": block.get("name"),
-                                    "arguments": block.get("arguments"),
-                                },
-                            }
-                        )
-                    if pass_through_unknown_blocks:
-                        content.append(block)
                 elif block.get("type") == "tool_result":
                     if missing := [
                         k for k in ("content", "tool_use_id") if k not in block
@@ -1541,10 +1366,7 @@ def convert_to_openai_messages(
                             },
                         }
                     )
-                elif (
-                    block.get("type") in {"thinking", "reasoning"}
-                    or pass_through_unknown_blocks
-                ):
+                elif block.get("type") in ["thinking", "reasoning"]:
                     content.append(block)
                 else:
                     err = (
@@ -1816,11 +1638,7 @@ def _get_message_openai_role(message: BaseMessage) -> str:
     if isinstance(message, ToolMessage):
         return "tool"
     if isinstance(message, SystemMessage):
-        role = message.additional_kwargs.get("__openai_role__", "system")
-        if not isinstance(role, str):
-            msg = f"Expected '__openai_role__' to be a str, got {type(role).__name__}"
-            raise TypeError(msg)
-        return role
+        return message.additional_kwargs.get("__openai_role__", "system")
     if isinstance(message, FunctionMessage):
         return "function"
     if isinstance(message, ChatMessage):
@@ -1853,36 +1671,34 @@ def count_tokens_approximately(
     """Approximate the total number of tokens in messages.
 
     The token count includes stringified message content, role, and (optionally) name.
-
     - For AI messages, the token count also includes stringified tool calls.
     - For tool messages, the token count also includes the tool call ID.
 
     Args:
         messages: List of messages to count tokens for.
         chars_per_token: Number of characters per token to use for the approximation.
-
             One token corresponds to ~4 chars for common English text.
-
             You can also specify `float` values for more fine-grained control.
             [See more here](https://platform.openai.com/tokenizer).
         extra_tokens_per_message: Number of extra tokens to add per message, e.g.
             special tokens, including beginning/end of message.
-
             You can also specify `float` values for more fine-grained control.
             [See more here](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb).
         count_name: Whether to include message names in the count.
+            Enabled by default.
 
     Returns:
         Approximate number of tokens in the messages.
 
-    Note:
+    !!! note
         This is a simple approximation that may not match the exact token count used by
         specific models. For accurate counts, use model-specific tokenizers.
 
     Warning:
         This function does not currently support counting image tokens.
 
-    !!! version-added "Added in `langchain-core` 0.3.46"
+    !!! version-added "Added in version 0.3.46"
+
     """
     token_count = 0.0
     for message in convert_to_messages(messages):
@@ -1923,14 +1739,3 @@ def count_tokens_approximately(
 
     # round up once more time in case extra_tokens_per_message is a float
     return math.ceil(token_count)
-
-
-# Mapping from string shortcuts to token counter functions
-def _approximate_token_counter(messages: Sequence[BaseMessage]) -> int:
-    """Wrapper for `count_tokens_approximately` that matches expected signature."""
-    return count_tokens_approximately(messages)
-
-
-_TOKEN_COUNTER_SHORTCUTS = {
-    "approximate": _approximate_token_counter,
-}

libs/core/langchain_core/output_parsers/__init__.py

@@ -1,20 +1,4 @@
-"""`OutputParser` classes parse the output of an LLM call into structured data.
-
-!!! tip "Structured output"
-
-    Output parsers emerged as an early solution to the challenge of obtaining structured
-    output from LLMs.
-
-    Today, most LLMs support [structured output](https://docs.langchain.com/oss/python/langchain/models#structured-outputs)
-    natively. In such cases, using output parsers may be unnecessary, and you should
-    leverage the model's built-in capabilities for structured output. Refer to the
-    [documentation of your chosen model](https://docs.langchain.com/oss/python/integrations/providers/overview)
-    for guidance on how to achieve structured output directly.
-
-    Output parsers remain valuable when working with models that do not support
-    structured output natively, or when you require additional processing or validation
-    of the model's output beyond its inherent capabilities.
-"""
+"""**OutputParser** classes parse the output of an LLM call."""
 
 from typing import TYPE_CHECKING