typo

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -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: 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

@@ -1,18 +1,9 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: 📚 Documentation issue
-    url: https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml
+  - 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: 💬 LangChain Forum
-    url: https://forum.langchain.com/
-    about: Ask questions and get help from the community

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -13,11 +13,11 @@ body:
         Relevant links to check before filing a feature request to see if your request has already been made or
         if there's another way to achieve what you want:
 
-        * [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/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/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:
@@ -43,81 +39,6 @@ integration:
       - any-glob-to-any-file:
           - "libs/partners/**/*"
 
-anthropic:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/anthropic/**/*"
-
-chroma:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/chroma/**/*"
-
-deepseek:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/deepseek/**/*"
-
-exa:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/exa/**/*"
-
-fireworks:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/fireworks/**/*"
-
-groq:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/groq/**/*"
-
-huggingface:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/huggingface/**/*"
-
-mistralai:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/mistralai/**/*"
-
-nomic:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/nomic/**/*"
-
-ollama:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/ollama/**/*"
-
-openai:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/openai/**/*"
-
-perplexity:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/perplexity/**/*"
-
-prompty:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/prompty/**/*"
-
-qdrant:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/qdrant/**/*"
-
-xai:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "libs/partners/xai/**/*"
-
 # Infrastructure and DevOps
 infra:
   - changed-files:

.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/_release.yml

@@ -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
@@ -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 }}
 
@@ -410,7 +409,6 @@ jobs:
       AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
       AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
       AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-      LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
     steps:
       - uses: actions/checkout@v5
 
@@ -444,7 +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
           )"

.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@v6
-        with:
-          script: |
-            const body = context.payload.issue.body || "";
-
-            // Extract text under "### Package"
-            const match = body.match(/### Package\s+([\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/integration_tests.yml

@@ -155,7 +155,6 @@ jobs:
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
           XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
-          LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
         run: |
           cd langchain/${{ matrix.working-directory }}
           make integration_tests

.github/workflows/pr_lint.yml

@@ -26,14 +26,12 @@
 #   * revert     — reverts a previous commit
 #   * release    — prepare a new release
 #
-# Allowed Scope(s) (optional):
+# 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, deps
 #
-# Multiple scopes can be used by separating them with a comma.
-#
 # Rules:
 #   1. The 'Type' must start with a lowercase letter.
 #   2. Breaking changes: append "!" after type/scope (e.g., feat!: drop x support)
@@ -81,8 +79,8 @@ jobs:
             core
             cli
             langchain
+            langchain_v1
             langchain-classic
-            model-profiles
             standard-tests
             text-splitters
             docs

README.md

@@ -1,28 +1,40 @@
-<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://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>
-</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 agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
 
 ```bash
 pip install langchain
@@ -32,10 +44,7 @@ If you're looking for more advanced customization or agent orchestration, check
 
 ---
 
-**Documentation**:
-
-- [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
+**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview).
 
 **Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
 
@@ -48,12 +57,8 @@ 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
 
@@ -61,14 +66,12 @@ While the LangChain framework can be used standalone, it also integrates seamles
 
 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
+- [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.
+- [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).
 
 ## 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/blob/master/.github/CODE_OF_CONDUCT.md) – Our community guidelines and standards for participation.
+- [API Reference](https://reference.langchain.com/python): Detailed reference on navigating base packages and integrations for LangChain.
+- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview): List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
+- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues.

SECURITY.md

@@ -55,10 +55,10 @@ 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
+* **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`
+  * 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

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/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/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 .file import File
-    from .folder import Folder
+from .file import File
+from .folder import Folder
 
 
 @dataclass

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

@@ -1,12 +1,9 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from pathlib import Path
 
 from .file import File
 
-if TYPE_CHECKING:
-    from pathlib import Path
-
 
 class Folder:
     def __init__(self, name: str, *files: Folder | File) -> None:

libs/core/README.md

@@ -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/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

@@ -2,8 +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:
@@ -49,18 +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.
@@ -79,10 +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`
@@ -97,17 +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.
 
@@ -130,10 +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

libs/core/langchain_core/callbacks/manager.py

@@ -39,6 +39,7 @@ 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
 
@@ -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)
 

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"
 
     """
 
@@ -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/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

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
 
@@ -31,13 +19,15 @@ 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.
@@ -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

@@ -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/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"
 
 

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.
@@ -301,7 +298,7 @@ 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
@@ -352,7 +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.
@@ -369,7 +366,7 @@ def index(
             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
@@ -639,7 +636,7 @@ 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
@@ -690,7 +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.
@@ -707,7 +704,7 @@ async def aindex(
             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

libs/core/langchain_core/language_models/__init__.py

@@ -6,13 +6,12 @@ LangChain has two main classes to work with language models: chat models and
 **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`. 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).
 

libs/core/langchain_core/language_models/_utils.py

@@ -139,7 +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

@@ -131,19 +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
     )
@@ -200,22 +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
@@ -244,22 +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
@@ -299,9 +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.
 
@@ -320,17 +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

@@ -15,7 +15,6 @@ from typing import TYPE_CHECKING, Any, Literal, cast
 from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import override
 
-from langchain_core._api.beta_decorator import beta
 from langchain_core.caches import BaseCache
 from langchain_core.callbacks import (
     AsyncCallbackManager,
@@ -76,8 +75,6 @@ from langchain_core.utils.utils import LC_ID_PREFIX, from_env
 if TYPE_CHECKING:
     import uuid
 
-    from langchain_model_profiles import ModelProfile  # type: ignore[import-untyped]
-
     from langchain_core.output_parsers.base import OutputParserLike
     from langchain_core.runnables import Runnable, RunnableConfig
     from langchain_core.tools import BaseTool
@@ -91,10 +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)
@@ -316,6 +310,13 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     does not properly support streaming.
     """
 
+    model_provider: str | None = None
+    """The model provider name, e.g., 'openai', 'anthropic', etc.
+
+    Used to assign provenance on messages generated by the model, and to look up
+    model capabilities (e.g., context window sizes and feature support).
+    """
+
     output_version: str | None = Field(
         default_factory=from_env("LC_OUTPUT_VERSION", default=None)
     )
@@ -335,7 +336,7 @@ 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"
+    !!! version-added "Added in version 1.0"
 
     """
 
@@ -523,7 +524,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 for chunk in self._stream(input_messages, stop=stop, **kwargs):
                     if chunk.message.id is None:
                         chunk.message.id = run_id
-                    chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                    chunk.message.response_metadata = _gen_info_and_msg_metadata(
+                        chunk, model_provider=self.model_provider
+                    )
                     if self.output_version == "v1":
                         # Overwrite .content with .content_blocks
                         chunk.message = _update_message_content_to_blocks(
@@ -655,7 +658,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             ):
                 if chunk.message.id is None:
                     chunk.message.id = run_id
-                chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                chunk.message.response_metadata = _gen_info_and_msg_metadata(
+                    chunk, model_provider=self.model_provider
+                )
                 if self.output_version == "v1":
                     # Overwrite .content with .content_blocks
                     chunk.message = _update_message_content_to_blocks(
@@ -848,21 +853,16 @@ 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
@@ -971,21 +971,16 @@ 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
@@ -1163,7 +1158,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             index = -1
             index_type = ""
             for chunk in self._stream(messages, stop=stop, **kwargs):
-                chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                chunk.message.response_metadata = _gen_info_and_msg_metadata(
+                    chunk, model_provider=self.model_provider
+                )
                 if self.output_version == "v1":
                     # Overwrite .content with .content_blocks
                     chunk.message = _update_message_content_to_blocks(
@@ -1211,6 +1208,20 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         else:
             result = self._generate(messages, stop=stop, **kwargs)
 
+        # Add response metadata to each generation
+        for idx, generation in enumerate(result.generations):
+            if run_manager and generation.message.id is None:
+                generation.message.id = f"{LC_ID_PREFIX}-{run_manager.run_id}-{idx}"
+            generation.message.response_metadata = _gen_info_and_msg_metadata(
+                generation, model_provider=self.model_provider
+            )
+
+        if len(result.generations) == 1 and result.llm_output is not None:
+            result.generations[0].message.response_metadata = {
+                **result.llm_output,
+                **result.generations[0].message.response_metadata,
+            }
+
         if self.output_version == "v1":
             # Overwrite .content with .content_blocks
             for generation in result.generations:
@@ -1218,18 +1229,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     generation.message, "v1"
                 )
 
-        # Add response metadata to each generation
-        for idx, generation in enumerate(result.generations):
-            if run_manager and generation.message.id is None:
-                generation.message.id = f"{LC_ID_PREFIX}-{run_manager.run_id}-{idx}"
-            generation.message.response_metadata = _gen_info_and_msg_metadata(
-                generation
-            )
-        if len(result.generations) == 1 and result.llm_output is not None:
-            result.generations[0].message.response_metadata = {
-                **result.llm_output,
-                **result.generations[0].message.response_metadata,
-            }
         if check_cache and llm_cache:
             llm_cache.update(prompt, llm_string, result.generations)
         return result
@@ -1281,7 +1280,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             index = -1
             index_type = ""
             async for chunk in self._astream(messages, stop=stop, **kwargs):
-                chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
+                chunk.message.response_metadata = _gen_info_and_msg_metadata(
+                    chunk, model_provider=self.model_provider
+                )
                 if self.output_version == "v1":
                     # Overwrite .content with .content_blocks
                     chunk.message = _update_message_content_to_blocks(
@@ -1329,6 +1330,19 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         else:
             result = await self._agenerate(messages, stop=stop, **kwargs)
 
+        # Add response metadata to each generation
+        for idx, generation in enumerate(result.generations):
+            if run_manager and generation.message.id is None:
+                generation.message.id = f"{LC_ID_PREFIX}-{run_manager.run_id}-{idx}"
+            generation.message.response_metadata = _gen_info_and_msg_metadata(
+                generation, model_provider=self.model_provider
+            )
+        if len(result.generations) == 1 and result.llm_output is not None:
+            result.generations[0].message.response_metadata = {
+                **result.llm_output,
+                **result.generations[0].message.response_metadata,
+            }
+
         if self.output_version == "v1":
             # Overwrite .content with .content_blocks
             for generation in result.generations:
@@ -1336,18 +1350,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     generation.message, "v1"
                 )
 
-        # Add response metadata to each generation
-        for idx, generation in enumerate(result.generations):
-            if run_manager and generation.message.id is None:
-                generation.message.id = f"{LC_ID_PREFIX}-{run_manager.run_id}-{idx}"
-            generation.message.response_metadata = _gen_info_and_msg_metadata(
-                generation
-            )
-        if len(result.generations) == 1 and result.llm_output is not None:
-            result.generations[0].message.response_metadata = {
-                **result.llm_output,
-                **result.generations[0].message.response_metadata,
-            }
         if check_cache and llm_cache:
             await llm_cache.aupdate(prompt, llm_string, result.generations)
         return result
@@ -1518,10 +1520,10 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         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
@@ -1533,15 +1535,11 @@ 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'`.
@@ -1646,8 +1644,8 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         # }
         ```
 
-        !!! 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)
@@ -1688,40 +1686,6 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             return RunnableMap(raw=llm) | parser_with_fallback
         return llm | output_parser
 
-    @property
-    @beta()
-    def profile(self) -> ModelProfile:
-        """Return profiling information for the model.
-
-        This property relies on the `langchain-model-profiles` package to retrieve chat
-        model capabilities, such as context window sizes and supported features.
-
-        Raises:
-            ImportError: If `langchain-model-profiles` is not installed.
-
-        Returns:
-            A `ModelProfile` object containing profiling information for the model.
-        """
-        try:
-            from langchain_model_profiles import get_model_profile  # noqa: PLC0415
-        except ImportError as err:
-            informative_error_message = (
-                "To access model profiling information, please install the "
-                "`langchain-model-profiles` package: "
-                "`pip install langchain-model-profiles`."
-            )
-            raise ImportError(informative_error_message) from err
-
-        provider_id = self._llm_type
-        model_name = (
-            # Model name is not standardized across integrations. New integrations
-            # should prefer `model`.
-            getattr(self, "model", None)
-            or getattr(self, "model_name", None)
-            or getattr(self, "model_id", "")
-        )
-        return get_model_profile(provider_id, model_name) or {}
-
 
 class SimpleChatModel(BaseChatModel):
     """Simplified implementation for a chat model to inherit from.
@@ -1773,19 +1737,20 @@ class SimpleChatModel(BaseChatModel):
 
 def _gen_info_and_msg_metadata(
     generation: ChatGeneration | ChatGenerationChunk,
+    model_provider: str | None = None,
 ) -> dict:
-    return {
+    metadata = {
         **(generation.generation_info or {}),
         **generation.message.response_metadata,
     }
-
-
-_MAX_CLEANUP_DEPTH = 100
+    if model_provider and "model_provider" not in metadata:
+        metadata["model_provider"] = model_provider
+    return metadata
 
 
 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
 
@@ -651,12 +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:
@@ -674,12 +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:
@@ -711,14 +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.
@@ -740,14 +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.
@@ -858,14 +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
@@ -875,9 +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.
@@ -1133,14 +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
@@ -1150,9 +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
@@ -1432,16 +1407,12 @@ 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.
@@ -1462,16 +1433,12 @@ 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/load/load.py

@@ -61,15 +61,13 @@ class Reviver:
         """Initialize the reviver.
 
         Args:
-            secrets_map: A map of secrets to load.
-
-                If a secret is not found in the map, it will be loaded from the
-                environment if `secrets_from_env` is `True`.
+            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
-
                 You can use this to override default mappings or add new mappings.
             ignore_unserializable_fields: Whether to ignore unserializable fields.
         """
@@ -197,15 +195,13 @@ def loads(
 
     Args:
         text: The string to load.
-        secrets_map: A map of secrets to load.
-
-            If a secret is not found in the map, it will be loaded from the environment
-            if `secrets_from_env` is `True`.
+        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
-
             You can use this to override default mappings or add new mappings.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
 
@@ -241,15 +237,13 @@ def load(
 
     Args:
         obj: The object to load.
-        secrets_map: A map of secrets to load.
-
-            If a secret is not found in the map, it will be loaded from the environment
-            if `secrets_from_env` is `True`.
+        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
-
             You can use this to override default mappings or add new mappings.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
 

libs/core/langchain_core/load/serializable.py

@@ -97,14 +97,11 @@ class Serializable(BaseModel, ABC):
         by default. This is to prevent accidental serialization of objects that should
         not be serialized.
     - `get_lc_namespace`: Get the namespace of the LangChain object.
-
         During deserialization, this namespace is used to identify
         the correct class to instantiate.
-
         Please see the `Reviver` class in `langchain_core.load.load` for more details.
         During deserialization an additional mapping is handle classes that have moved
         or been renamed across package versions.
-
     - `lc_secrets`: A map of constructor argument names to secret ids.
     - `lc_attributes`: List of additional attribute names that should be included
         as part of the serialized representation.

libs/core/langchain_core/messages/ai.py

@@ -50,7 +50,7 @@ class InputTokenDetails(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"
 
     """
 
@@ -85,7 +85,7 @@ 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"
 
     """
 
@@ -123,7 +123,7 @@ 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"

libs/core/langchain_core/messages/base.py

@@ -5,9 +5,11 @@ 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
+from langchain_core.messages import content as types
 from langchain_core.utils import get_bolded_text
 from langchain_core.utils._merge import merge_dicts, merge_lists
 from langchain_core.utils.interactive_env import is_interactive_env
@@ -15,9 +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.messages import content as types
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 
@@ -200,7 +199,7 @@ 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

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/google_genai.py

@@ -368,7 +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 = {
@@ -379,7 +379,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                             try:
                                 import filetype  # type: ignore[import-not-found] # noqa: PLC0415
 
-                                # Guess MIME type based on file bytes
+                                # Guess mime type based on file bytes
                                 mime_type = None
                                 kind = filetype.guess(decoded_bytes)
                                 if kind:

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

@@ -4,6 +4,7 @@ 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 (
@@ -13,8 +14,6 @@ from langchain_core.language_models._utils import (
 from langchain_core.messages import content as types
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable
-
     from langchain_core.messages import AIMessage, AIMessageChunk
 
 

libs/core/langchain_core/messages/content.py

@@ -644,7 +644,7 @@ 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
@@ -767,7 +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.
 
@@ -802,7 +802,7 @@ class NonStandardContentBlock(TypedDict):
     """
 
     value: dict[str, Any]
-    """Provider-specific content data."""
+    """Provider-specific data."""
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -867,7 +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.
     """
@@ -906,7 +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
 
@@ -1399,7 +1399,7 @@ def create_non_standard_block(
     """Create a `NonStandardContentBlock`.
 
     Args:
-        value: Provider-specific content data.
+        value: Provider-specific data.
         id: Content block identifier. Generated automatically if not provided.
         index: Index of block in aggregate response. Used during streaming.
 

libs/core/langchain_core/messages/utils.py

@@ -86,7 +86,7 @@ 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(
@@ -328,16 +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:
@@ -1101,7 +1097,7 @@ 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"}:
@@ -1701,7 +1697,7 @@ def count_tokens_approximately(
     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

libs/core/langchain_core/output_parsers/format_instructions.py

@@ -1,16 +1,11 @@
 """Format instructions."""
 
-JSON_FORMAT_INSTRUCTIONS = """STRICT OUTPUT FORMAT:
-- Return only the JSON value that conforms to the schema. Do not include any additional text, explanations, headings, or separators.
-- Do not wrap the JSON in Markdown or code fences (no ``` or ```json).
-- Do not prepend or append any text (e.g., do not write "Here is the JSON:").
-- The response must be a single top-level JSON value exactly as required by the schema (object/array/etc.), with no trailing commas or comments.
+JSON_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
 
-The output should be formatted as a JSON instance that conforms to the JSON schema below.
+As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
+the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
 
-As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}} the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
-
-Here is the output schema (shown in a code block for readability only — do not include any backticks or Markdown in your output):
+Here is the output schema:
 ```
 {schema}
 ```"""  # noqa: E501

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -15,11 +15,7 @@ from langchain_core.messages.tool import tool_call as create_tool_call
 from langchain_core.output_parsers.transform import BaseCumulativeTransformOutputParser
 from langchain_core.outputs import ChatGeneration, Generation
 from langchain_core.utils.json import parse_partial_json
-from langchain_core.utils.pydantic import (
-    TypeBaseModel,
-    is_pydantic_v1_subclass,
-    is_pydantic_v2_subclass,
-)
+from langchain_core.utils.pydantic import TypeBaseModel
 
 logger = logging.getLogger(__name__)
 
@@ -327,15 +323,7 @@ class PydanticToolsParser(JsonOutputToolsParser):
             return None if self.first_tool_only else []
 
         json_results = [json_results] if self.first_tool_only else json_results
-        name_dict_v2: dict[str, TypeBaseModel] = {
-            tool.model_config.get("title") or tool.__name__: tool
-            for tool in self.tools
-            if is_pydantic_v2_subclass(tool)
-        }
-        name_dict_v1: dict[str, TypeBaseModel] = {
-            tool.__name__: tool for tool in self.tools if is_pydantic_v1_subclass(tool)
-        }
-        name_dict: dict[str, TypeBaseModel] = {**name_dict_v2, **name_dict_v1}
+        name_dict = {tool.__name__: tool for tool in self.tools}
         pydantic_objects = []
         for res in json_results:
             if not isinstance(res["args"], dict):

libs/core/langchain_core/outputs/chat_generation.py

@@ -2,17 +2,15 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Literal
+from typing import Literal
 
 from pydantic import model_validator
+from typing_extensions import Self
 
 from langchain_core.messages import BaseMessage, BaseMessageChunk
 from langchain_core.outputs.generation import Generation
 from langchain_core.utils._merge import merge_dicts
 
-if TYPE_CHECKING:
-    from typing_extensions import Self
-
 
 class ChatGeneration(Generation):
     """A single chat generation output.

libs/core/langchain_core/outputs/generation.py

@@ -20,7 +20,8 @@ class Generation(Serializable):
 
     LangChain users working with chat models will usually access information via
     `AIMessage` (returned from runnable interfaces) or `LLMResult` (available
-    via callbacks). Please refer to `AIMessage` and `LLMResult` for more information.
+    via callbacks). Please refer the `AIMessage` and `LLMResult` schema documentation
+    for more information.
     """
 
     text: str
@@ -33,13 +34,11 @@ class Generation(Serializable):
     """
     type: Literal["Generation"] = "Generation"
     """Type is used exclusively for serialization purposes.
-
-    Set to "Generation" for this class.
-    """
+    Set to "Generation" for this class."""
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -53,7 +52,7 @@ class Generation(Serializable):
 
 
 class GenerationChunk(Generation):
-    """`GenerationChunk`, which can be concatenated with other Generation chunks."""
+    """Generation chunk, which can be concatenated with other Generation chunks."""
 
     def __add__(self, other: GenerationChunk) -> GenerationChunk:
         """Concatenate two `GenerationChunk`s.

libs/core/langchain_core/prompt_values.py

@@ -30,13 +30,15 @@ class PromptValue(Serializable, ABC):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
     def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the LangChain object.
 
+        This is used to determine the namespace of the object when serializing.
+
         Returns:
             `["langchain", "schema", "prompt"]`
         """
@@ -48,7 +50,7 @@ class PromptValue(Serializable, ABC):
 
     @abstractmethod
     def to_messages(self) -> list[BaseMessage]:
-        """Return prompt as a list of messages."""
+        """Return prompt as a list of Messages."""
 
 
 class StringPromptValue(PromptValue):
@@ -62,6 +64,8 @@ class StringPromptValue(PromptValue):
     def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the LangChain object.
 
+        This is used to determine the namespace of the object when serializing.
+
         Returns:
             `["langchain", "prompts", "base"]`
         """
@@ -97,6 +101,8 @@ class ChatPromptValue(PromptValue):
     def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the LangChain object.
 
+        This is used to determine the namespace of the object when serializing.
+
         Returns:
             `["langchain", "prompts", "chat"]`
         """

libs/core/langchain_core/prompts/base.py

@@ -6,7 +6,7 @@ import contextlib
 import json
 import typing
 from abc import ABC, abstractmethod
-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from functools import cached_property
 from pathlib import Path
 from typing import (
@@ -33,8 +33,6 @@ from langchain_core.runnables.config import ensure_config
 from langchain_core.utils.pydantic import create_model_v2
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
-
     from langchain_core.documents import Document
 
 
@@ -48,27 +46,21 @@ class BasePromptTemplate(
 
     input_variables: list[str]
     """A list of the names of the variables whose values are required as inputs to the
-    prompt.
-    """
+    prompt."""
     optional_variables: list[str] = Field(default=[])
-    """A list of the names of the variables for placeholder or `MessagePlaceholder` that
-    are optional.
-
-    These variables are auto inferred from the prompt and user need not provide them.
-    """
+    """optional_variables: A list of the names of the variables for placeholder
+       or MessagePlaceholder that are optional. These variables are auto inferred
+       from the prompt and user need not provide them."""
     input_types: typing.Dict[str, Any] = Field(default_factory=dict, exclude=True)  # noqa: UP006
     """A dictionary of the types of the variables the prompt template expects.
-
-    If not provided, all variables are assumed to be strings.
-    """
+    If not provided, all variables are assumed to be strings."""
     output_parser: BaseOutputParser | None = None
     """How to parse the output of calling an LLM on this formatted prompt."""
     partial_variables: Mapping[str, Any] = Field(default_factory=dict)
     """A dictionary of the partial variables the prompt template carries.
 
-    Partial variables populate the template so that you don't need to pass them in every
-    time you call the prompt.
-    """
+    Partial variables populate the template so that you don't need to
+    pass them in every time you call the prompt."""
     metadata: typing.Dict[str, Any] | None = None  # noqa: UP006
     """Metadata to be used for tracing."""
     tags: list[str] | None = None
@@ -113,7 +105,7 @@ class BasePromptTemplate(
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     model_config = ConfigDict(
@@ -135,7 +127,7 @@ class BasePromptTemplate(
         """Get the input schema for the prompt.
 
         Args:
-            config: Configuration for the prompt.
+            config: configuration for the prompt.
 
         Returns:
             The input schema for the prompt.
@@ -203,8 +195,8 @@ class BasePromptTemplate(
         """Invoke the prompt.
 
         Args:
-            input: Input to the prompt.
-            config: Configuration for the prompt.
+            input: Dict, input to the prompt.
+            config: RunnableConfig, configuration for the prompt.
 
         Returns:
             The output of the prompt.
@@ -229,8 +221,8 @@ class BasePromptTemplate(
         """Async invoke the prompt.
 
         Args:
-            input: Input to the prompt.
-            config: Configuration for the prompt.
+            input: Dict, input to the prompt.
+            config: RunnableConfig, configuration for the prompt.
 
         Returns:
             The output of the prompt.
@@ -250,7 +242,7 @@ class BasePromptTemplate(
 
     @abstractmethod
     def format_prompt(self, **kwargs: Any) -> PromptValue:
-        """Create `PromptValue`.
+        """Create Prompt Value.
 
         Args:
             **kwargs: Any arguments to be passed to the prompt template.
@@ -260,7 +252,7 @@ class BasePromptTemplate(
         """
 
     async def aformat_prompt(self, **kwargs: Any) -> PromptValue:
-        """Async create `PromptValue`.
+        """Async create Prompt Value.
 
         Args:
             **kwargs: Any arguments to be passed to the prompt template.
@@ -274,7 +266,7 @@ class BasePromptTemplate(
         """Return a partial of the prompt template.
 
         Args:
-            **kwargs: Partial variables to set.
+            **kwargs: partial variables to set.
 
         Returns:
             A partial of the prompt template.
@@ -304,9 +296,9 @@ class BasePromptTemplate(
             A formatted string.
 
         Example:
-            ```python
-            prompt.format(variable1="foo")
-            ```
+        ```python
+        prompt.format(variable1="foo")
+        ```
         """
 
     async def aformat(self, **kwargs: Any) -> FormatOutputType:
@@ -319,9 +311,9 @@ class BasePromptTemplate(
             A formatted string.
 
         Example:
-            ```python
-            await prompt.aformat(variable1="foo")
-            ```
+        ```python
+        await prompt.aformat(variable1="foo")
+        ```
         """
         return self.format(**kwargs)
 
@@ -356,9 +348,9 @@ class BasePromptTemplate(
             NotImplementedError: If the prompt type is not implemented.
 
         Example:
-            ```python
-            prompt.save(file_path="path/prompt.yaml")
-            ```
+        ```python
+        prompt.save(file_path="path/prompt.yaml")
+        ```
         """
         if self.partial_variables:
             msg = "Cannot save prompt with partial variables."
@@ -410,23 +402,23 @@ def format_document(doc: Document, prompt: BasePromptTemplate[str]) -> str:
 
     First, this pulls information from the document from two sources:
 
-    1. `page_content`:
-        This takes the information from the `document.page_content` and assigns it to a
-        variable named `page_content`.
-    2. `metadata`:
-        This takes information from `document.metadata` and assigns it to variables of
-        the same name.
+    1. page_content:
+        This takes the information from the `document.page_content`
+        and assigns it to a variable named `page_content`.
+    2. metadata:
+        This takes information from `document.metadata` and assigns
+        it to variables of the same name.
 
     Those variables are then passed into the `prompt` to produce a formatted string.
 
     Args:
-        doc: `Document`, the `page_content` and `metadata` will be used to create
+        doc: Document, the page_content and metadata will be used to create
             the final string.
-        prompt: `BasePromptTemplate`, will be used to format the `page_content`
-            and `metadata` into the final string.
+        prompt: BasePromptTemplate, will be used to format the page_content
+            and metadata into the final string.
 
     Returns:
-        String of the document formatted.
+        string of the document formatted.
 
     Example:
         ```python
@@ -437,6 +429,7 @@ def format_document(doc: Document, prompt: BasePromptTemplate[str]) -> str:
         prompt = PromptTemplate.from_template("Page {page}: {page_content}")
         format_document(doc, prompt)
         >>> "Page 1: This is a joke"
+
         ```
     """
     return prompt.format(**_get_document_info(doc, prompt))
@@ -447,22 +440,22 @@ async def aformat_document(doc: Document, prompt: BasePromptTemplate[str]) -> st
 
     First, this pulls information from the document from two sources:
 
-    1. `page_content`:
-        This takes the information from the `document.page_content` and assigns it to a
-        variable named `page_content`.
-    2. `metadata`:
-        This takes information from `document.metadata` and assigns it to variables of
-        the same name.
+    1. page_content:
+        This takes the information from the `document.page_content`
+        and assigns it to a variable named `page_content`.
+    2. metadata:
+        This takes information from `document.metadata` and assigns
+        it to variables of the same name.
 
     Those variables are then passed into the `prompt` to produce a formatted string.
 
     Args:
-        doc: `Document`, the `page_content` and `metadata` will be used to create
+        doc: Document, the page_content and metadata will be used to create
             the final string.
-        prompt: `BasePromptTemplate`, will be used to format the `page_content`
-            and `metadata` into the final string.
+        prompt: BasePromptTemplate, will be used to format the page_content
+            and metadata into the final string.
 
     Returns:
-        String of the document formatted.
+        string of the document formatted.
     """
     return await prompt.aformat(**_get_document_info(doc, prompt))

libs/core/langchain_core/prompts/chat.py

@@ -587,15 +587,14 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
         for prompt in self.prompt:
             inputs = {var: kwargs[var] for var in prompt.input_variables}
             if isinstance(prompt, StringPromptTemplate):
-                formatted_text: str = prompt.format(**inputs)
-                if formatted_text != "":
-                    content.append({"type": "text", "text": formatted_text})
+                formatted: str | ImageURL | dict[str, Any] = prompt.format(**inputs)
+                content.append({"type": "text", "text": formatted})
             elif isinstance(prompt, ImagePromptTemplate):
-                formatted_image: ImageURL = prompt.format(**inputs)
-                content.append({"type": "image_url", "image_url": formatted_image})
+                formatted = prompt.format(**inputs)
+                content.append({"type": "image_url", "image_url": formatted})
             elif isinstance(prompt, DictPromptTemplate):
-                formatted_dict: dict[str, Any] = prompt.format(**inputs)
-                content.append(formatted_dict)
+                formatted = prompt.format(**inputs)
+                content.append(formatted)
         return self._msg_class(
             content=content, additional_kwargs=self.additional_kwargs
         )
@@ -618,15 +617,16 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
         for prompt in self.prompt:
             inputs = {var: kwargs[var] for var in prompt.input_variables}
             if isinstance(prompt, StringPromptTemplate):
-                formatted_text: str = await prompt.aformat(**inputs)
-                if formatted_text != "":
-                    content.append({"type": "text", "text": formatted_text})
+                formatted: str | ImageURL | dict[str, Any] = await prompt.aformat(
+                    **inputs
+                )
+                content.append({"type": "text", "text": formatted})
             elif isinstance(prompt, ImagePromptTemplate):
-                formatted_image: ImageURL = await prompt.aformat(**inputs)
-                content.append({"type": "image_url", "image_url": formatted_image})
+                formatted = await prompt.aformat(**inputs)
+                content.append({"type": "image_url", "image_url": formatted})
             elif isinstance(prompt, DictPromptTemplate):
-                formatted_dict: dict[str, Any] = prompt.format(**inputs)
-                content.append(formatted_dict)
+                formatted = prompt.format(**inputs)
+                content.append(formatted)
         return self._msg_class(
             content=content, additional_kwargs=self.additional_kwargs
         )
@@ -776,6 +776,11 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
     Use to create flexible templated prompts for chat models.
 
+    !!! warning "Behavior changed in 0.2.24"
+        You can pass any Message-like formats supported by
+        `ChatPromptTemplate.from_messages()` directly to `ChatPromptTemplate()`
+        init.
+
     ```python
     from langchain_core.prompts import ChatPromptTemplate
 
@@ -891,35 +896,25 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         """Create a chat prompt template from a variety of message formats.
 
         Args:
-            messages: Sequence of message representations.
-
+            messages: sequence of message representations.
                 A message can be represented using the following formats:
-
-                1. `BaseMessagePromptTemplate`
-                2. `BaseMessage`
-                3. 2-tuple of `(message type, template)`; e.g.,
-                    `("human", "{user_input}")`
-                4. 2-tuple of `(message class, template)`
-                5. A string which is shorthand for `("human", template)`; e.g.,
-                    `"{user_input}"`
-            template_format: Format of the template.
+                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
+                (message type, template); e.g., ("human", "{user_input}"),
+                (4) 2-tuple of (message class, template), (5) a string which is
+                shorthand for ("human", template); e.g., "{user_input}".
+            template_format: format of the template.
             input_variables: A list of the names of the variables whose values are
                 required as inputs to the prompt.
             optional_variables: A list of the names of the variables for placeholder
                 or MessagePlaceholder that are optional.
-
                 These variables are auto inferred from the prompt and user need not
                 provide them.
             partial_variables: A dictionary of the partial variables the prompt
-                template carries.
-
-                Partial variables populate the template so that you don't need to pass
-                them in every time you call the prompt.
+                template carries. Partial variables populate the template so that you
+                don't need to pass them in every time you call the prompt.
             validate_template: Whether to validate the template.
             input_types: A dictionary of the types of the variables the prompt template
-                expects.
-
-                If not provided, all variables are assumed to be strings.
+                expects. If not provided, all variables are assumed to be strings.
 
         Examples:
             Instantiation from a list of message templates:
@@ -1124,17 +1119,12 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
             )
             ```
         Args:
-            messages: Sequence of message representations.
-
+            messages: sequence of message representations.
                 A message can be represented using the following formats:
-
-                1. `BaseMessagePromptTemplate`
-                2. `BaseMessage`
-                3. 2-tuple of `(message type, template)`; e.g.,
-                    `("human", "{user_input}")`
-                4. 2-tuple of `(message class, template)`
-                5. A string which is shorthand for `("human", template)`; e.g.,
-                    `"{user_input}"`
+                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
+                (message type, template); e.g., ("human", "{user_input}"),
+                (4) 2-tuple of (message class, template), (5) a string which is
+                shorthand for ("human", template); e.g., "{user_input}".
             template_format: format of the template.
 
         Returns:
@@ -1246,7 +1236,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         """Extend the chat template with a sequence of messages.
 
         Args:
-            messages: Sequence of message representations to append.
+            messages: sequence of message representations to append.
         """
         self.messages.extend(
             [_convert_to_message_template(message) for message in messages]
@@ -1343,25 +1333,11 @@ def _create_template_from_message_type(
                 raise ValueError(msg)
             var_name = template[1:-1]
             message = MessagesPlaceholder(variable_name=var_name, optional=True)
-        else:
-            try:
-                var_name_wrapped, is_optional = template
-            except ValueError as e:
-                msg = (
-                    "Unexpected arguments for placeholder message type."
-                    " Expected either a single string variable name"
-                    " or a list of [variable_name: str, is_optional: bool]."
-                    f" Got: {template}"
-                )
-                raise ValueError(msg) from e
-
-            if not isinstance(is_optional, bool):
-                msg = f"Expected is_optional to be a boolean. Got: {is_optional}"
-                raise ValueError(msg)  # noqa: TRY004
-
+        elif len(template) == 2 and isinstance(template[1], bool):
+            var_name_wrapped, is_optional = template
             if not isinstance(var_name_wrapped, str):
                 msg = f"Expected variable name to be a string. Got: {var_name_wrapped}"
-                raise ValueError(msg)  # noqa: TRY004
+                raise ValueError(msg)  # noqa:TRY004
             if var_name_wrapped[0] != "{" or var_name_wrapped[-1] != "}":
                 msg = (
                     f"Invalid placeholder template: {var_name_wrapped}."
@@ -1371,6 +1347,14 @@ def _create_template_from_message_type(
             var_name = var_name_wrapped[1:-1]
 
             message = MessagesPlaceholder(variable_name=var_name, optional=is_optional)
+        else:
+            msg = (
+                "Unexpected arguments for placeholder message type."
+                " Expected either a single string variable name"
+                " or a list of [variable_name: str, is_optional: bool]."
+                f" Got: {template}"
+            )
+            raise ValueError(msg)
     else:
         msg = (
             f"Unexpected message type: {message_type}. Use one of 'human',"
@@ -1424,11 +1408,10 @@ def _convert_to_message_template(
                 )
                 raise ValueError(msg)
             message = (message["role"], message["content"])
-        try:
-            message_type_str, template = message
-        except ValueError as e:
+        if len(message) != 2:
             msg = f"Expected 2-tuple of (role, template), got {message}"
-            raise ValueError(msg) from e
+            raise ValueError(msg)
+        message_type_str, template = message
         if isinstance(message_type_str, str):
             message_ = _create_template_from_message_type(
                 message_type_str, template, template_format=template_format

libs/core/langchain_core/prompts/dict.py

@@ -69,7 +69,7 @@ class DictPromptTemplate(RunnableSerializable[dict, dict]):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod

libs/core/langchain_core/prompts/message.py

@@ -6,10 +6,10 @@ from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any
 
 from langchain_core.load import Serializable
+from langchain_core.messages import BaseMessage
 from langchain_core.utils.interactive_env import is_interactive_env
 
 if TYPE_CHECKING:
-    from langchain_core.messages import BaseMessage
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 
@@ -18,7 +18,7 @@ class BaseMessagePromptTemplate(Serializable, ABC):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -32,13 +32,13 @@ class BaseMessagePromptTemplate(Serializable, ABC):
 
     @abstractmethod
     def format_messages(self, **kwargs: Any) -> list[BaseMessage]:
-        """Format messages from kwargs. Should return a list of `BaseMessage` objects.
+        """Format messages from kwargs. Should return a list of BaseMessages.
 
         Args:
             **kwargs: Keyword arguments to use for formatting.
 
         Returns:
-            List of `BaseMessage` objects.
+            List of BaseMessages.
         """
 
     async def aformat_messages(self, **kwargs: Any) -> list[BaseMessage]:
@@ -48,7 +48,7 @@ class BaseMessagePromptTemplate(Serializable, ABC):
             **kwargs: Keyword arguments to use for formatting.
 
         Returns:
-            List of `BaseMessage` objects.
+            List of BaseMessages.
         """
         return self.format_messages(**kwargs)
 

libs/core/langchain_core/prompts/string.py

@@ -4,8 +4,9 @@ from __future__ import annotations
 
 import warnings
 from abc import ABC
+from collections.abc import Callable, Sequence
 from string import Formatter
-from typing import TYPE_CHECKING, Any, Literal
+from typing import Any, Literal
 
 from pydantic import BaseModel, create_model
 
@@ -15,9 +16,6 @@ from langchain_core.utils import get_colored_text, mustache
 from langchain_core.utils.formatting import formatter
 from langchain_core.utils.interactive_env import is_interactive_env
 
-if TYPE_CHECKING:
-    from collections.abc import Callable, Sequence
-
 try:
     from jinja2 import Environment, meta
     from jinja2.sandbox import SandboxedEnvironment

libs/core/langchain_core/prompts/structured.py

@@ -104,23 +104,19 @@ class StructuredPrompt(ChatPromptTemplate):
             )
             ```
         Args:
-            messages: Sequence of message representations.
-
+            messages: sequence of message representations.
                 A message can be represented using the following formats:
-
-                1. `BaseMessagePromptTemplate`
-                2. `BaseMessage`
-                3. 2-tuple of `(message type, template)`; e.g.,
-                    `("human", "{user_input}")`
-                4. 2-tuple of `(message class, template)`
-                5. A string which is shorthand for `("human", template)`; e.g.,
-                    `"{user_input}"`
-            schema: A dictionary representation of function call, or a Pydantic model.
+                (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of
+                (message type, template); e.g., ("human", "{user_input}"),
+                (4) 2-tuple of (message class, template), (5) a string which is
+                shorthand for ("human", template); e.g., "{user_input}"
+            schema: a dictionary representation of function call, or a Pydantic model.
             **kwargs: Any additional kwargs to pass through to
                 `ChatModel.with_structured_output(schema, **kwargs)`.
 
         Returns:
-            A structured prompt template
+            a structured prompt template
+
         """
         return cls(messages, schema, **kwargs)
 

libs/core/langchain_core/rate_limiters.py

@@ -105,9 +105,7 @@ class InMemoryRateLimiter(BaseRateLimiter):
 
         from langchain_anthropic import ChatAnthropic
 
-        model = ChatAnthropic(
-            model_name="claude-sonnet-4-5-20250929", rate_limiter=rate_limiter
-        )
+        model = ChatAnthropic(model_name="claude-sonnet-4-5", rate_limiter=rate_limiter)
 
         for _ in range(5):
             tic = time.time()

libs/core/langchain_core/retrievers.py

@@ -50,65 +50,65 @@ class LangSmithRetrieverParams(TypedDict, total=False):
 
 
 class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
-    """Abstract base class for a document retrieval system.
+    """Abstract base class for a Document retrieval system.
 
     A retrieval system is defined as something that can take string queries and return
-    the most 'relevant' documents from some source.
+    the most 'relevant' Documents from some source.
 
     Usage:
 
-    A retriever follows the standard `Runnable` interface, and should be used via the
-    standard `Runnable` methods of `invoke`, `ainvoke`, `batch`, `abatch`.
+    A retriever follows the standard Runnable interface, and should be used
+    via the standard Runnable methods of `invoke`, `ainvoke`, `batch`, `abatch`.
 
     Implementation:
 
-    When implementing a custom retriever, the class should implement the
-    `_get_relevant_documents` method to define the logic for retrieving documents.
+    When implementing a custom retriever, the class should implement
+    the `_get_relevant_documents` method to define the logic for retrieving documents.
 
     Optionally, an async native implementations can be provided by overriding the
     `_aget_relevant_documents` method.
 
-    !!! example "Retriever that returns the first 5 documents from a list of documents"
+    Example: A retriever that returns the first 5 documents from a list of documents
 
-        ```python
-        from langchain_core.documents import Document
-        from langchain_core.retrievers import BaseRetriever
+    ```python
+    from langchain_core.documents import Document
+    from langchain_core.retrievers import BaseRetriever
 
-        class SimpleRetriever(BaseRetriever):
-            docs: list[Document]
-            k: int = 5
+    class SimpleRetriever(BaseRetriever):
+        docs: list[Document]
+        k: int = 5
 
-            def _get_relevant_documents(self, query: str) -> list[Document]:
-                \"\"\"Return the first k documents from the list of documents\"\"\"
-                return self.docs[:self.k]
+        def _get_relevant_documents(self, query: str) -> list[Document]:
+            \"\"\"Return the first k documents from the list of documents\"\"\"
+            return self.docs[:self.k]
 
-            async def _aget_relevant_documents(self, query: str) -> list[Document]:
-                \"\"\"(Optional) async native implementation.\"\"\"
-                return self.docs[:self.k]
-        ```
+        async def _aget_relevant_documents(self, query: str) -> list[Document]:
+            \"\"\"(Optional) async native implementation.\"\"\"
+            return self.docs[:self.k]
+    ```
 
-    !!! example "Simple retriever based on a scikit-learn vectorizer"
+    Example: A simple retriever based on a scikit-learn vectorizer
 
-        ```python
-        from sklearn.metrics.pairwise import cosine_similarity
+    ```python
+    from sklearn.metrics.pairwise import cosine_similarity
 
 
-        class TFIDFRetriever(BaseRetriever, BaseModel):
-            vectorizer: Any
-            docs: list[Document]
-            tfidf_array: Any
-            k: int = 4
+    class TFIDFRetriever(BaseRetriever, BaseModel):
+        vectorizer: Any
+        docs: list[Document]
+        tfidf_array: Any
+        k: int = 4
 
-            class Config:
-                arbitrary_types_allowed = True
+        class Config:
+            arbitrary_types_allowed = True
 
-            def _get_relevant_documents(self, query: str) -> list[Document]:
-                # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
-                query_vec = self.vectorizer.transform([query])
-                # Op -- (n_docs,1) -- Cosine Sim with each doc
-                results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
-                return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
-        ```
+        def _get_relevant_documents(self, query: str) -> list[Document]:
+            # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
+            query_vec = self.vectorizer.transform([query])
+            # Op -- (n_docs,1) -- Cosine Sim with each doc
+            results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
+            return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
+    ```
     """
 
     model_config = ConfigDict(
@@ -119,19 +119,15 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
     _expects_other_args: bool = False
     tags: list[str] | None = None
     """Optional list of tags associated with the retriever.
-
     These tags will be associated with each call to this retriever,
     and passed as arguments to the handlers defined in `callbacks`.
-
     You can use these to eg identify a specific instance of a retriever with its
     use case.
     """
     metadata: dict[str, Any] | None = None
     """Optional metadata associated with the retriever.
-
     This metadata will be associated with each call to this retriever,
     and passed as arguments to the handlers defined in `callbacks`.
-
     You can use these to eg identify a specific instance of a retriever with its
     use case.
     """

libs/core/langchain_core/runnables/base.py

@@ -118,8 +118,6 @@ if TYPE_CHECKING:
 
 Other = TypeVar("Other")
 
-_RUNNABLE_GENERIC_NUM_ARGS = 2  # Input and Output
-
 
 class Runnable(ABC, Generic[Input, Output]):
     """A unit of work that can be invoked, batched, streamed, transformed and composed.
@@ -311,10 +309,7 @@ class Runnable(ABC, Generic[Input, Output]):
         for base in self.__class__.mro():
             if hasattr(base, "__pydantic_generic_metadata__"):
                 metadata = base.__pydantic_generic_metadata__
-                if (
-                    "args" in metadata
-                    and len(metadata["args"]) == _RUNNABLE_GENERIC_NUM_ARGS
-                ):
+                if "args" in metadata and len(metadata["args"]) == 2:
                     return metadata["args"][0]
 
         # If we didn't find a Pydantic model in the parent classes,
@@ -322,7 +317,7 @@ class Runnable(ABC, Generic[Input, Output]):
         # Runnables that are not pydantic models.
         for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
             type_args = get_args(cls)
-            if type_args and len(type_args) == _RUNNABLE_GENERIC_NUM_ARGS:
+            if type_args and len(type_args) == 2:
                 return type_args[0]
 
         msg = (
@@ -345,15 +340,12 @@ class Runnable(ABC, Generic[Input, Output]):
         for base in self.__class__.mro():
             if hasattr(base, "__pydantic_generic_metadata__"):
                 metadata = base.__pydantic_generic_metadata__
-                if (
-                    "args" in metadata
-                    and len(metadata["args"]) == _RUNNABLE_GENERIC_NUM_ARGS
-                ):
+                if "args" in metadata and len(metadata["args"]) == 2:
                     return metadata["args"][1]
 
         for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
             type_args = get_args(cls)
-            if type_args and len(type_args) == _RUNNABLE_GENERIC_NUM_ARGS:
+            if type_args and len(type_args) == 2:
                 return type_args[1]
 
         msg = (
@@ -432,7 +424,7 @@ class Runnable(ABC, Generic[Input, Output]):
             print(runnable.get_input_jsonschema())
             ```
 
-        !!! version-added "Added in `langchain-core` 0.3.0"
+        !!! version-added "Added in version 0.3.0"
 
         """
         return self.get_input_schema(config).model_json_schema()
@@ -510,7 +502,7 @@ class Runnable(ABC, Generic[Input, Output]):
             print(runnable.get_output_jsonschema())
             ```
 
-        !!! version-added "Added in `langchain-core` 0.3.0"
+        !!! version-added "Added in version 0.3.0"
 
         """
         return self.get_output_schema(config).model_json_schema()
@@ -574,7 +566,7 @@ class Runnable(ABC, Generic[Input, Output]):
         Returns:
             A JSON schema that represents the config of the `Runnable`.
 
-        !!! version-added "Added in `langchain-core` 0.3.0"
+        !!! version-added "Added in version 0.3.0"
 
         """
         return self.config_schema(include=include).model_json_schema()
@@ -774,7 +766,7 @@ class Runnable(ABC, Generic[Input, Output]):
         """Assigns new fields to the `dict` output of this `Runnable`.
 
         ```python
-        from langchain_core.language_models.fake import FakeStreamingListLLM
+        from langchain_community.llms.fake import FakeStreamingListLLM
         from langchain_core.output_parsers import StrOutputParser
         from langchain_core.prompts import SystemMessagePromptTemplate
         from langchain_core.runnables import Runnable
@@ -826,12 +818,10 @@ class Runnable(ABC, Generic[Input, Output]):
         Args:
             input: The input to the `Runnable`.
             config: A config to use when invoking the `Runnable`.
-
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys.
-
-                Please refer to `RunnableConfig` for more details.
+                do in parallel, and other keys. Please refer to the `RunnableConfig`
+                for more details.
 
         Returns:
             The output of the `Runnable`.
@@ -848,12 +838,10 @@ class Runnable(ABC, Generic[Input, Output]):
         Args:
             input: The input to the `Runnable`.
             config: A config to use when invoking the `Runnable`.
-
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys.
-
-                Please refer to `RunnableConfig` for more details.
+                do in parallel, and other keys. Please refer to the `RunnableConfig`
+                for more details.
 
         Returns:
             The output of the `Runnable`.
@@ -880,9 +868,8 @@ class Runnable(ABC, Generic[Input, Output]):
             config: A config to use when invoking the `Runnable`. The config supports
                 standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work
-                to do in parallel, and other keys.
-
-                Please refer to `RunnableConfig` for more details.
+                to do in parallel, and other keys. Please refer to the
+                `RunnableConfig` for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -945,12 +932,10 @@ class Runnable(ABC, Generic[Input, Output]):
         Args:
             inputs: A list of inputs to the `Runnable`.
             config: A config to use when invoking the `Runnable`.
-
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys.
-
-                Please refer to `RunnableConfig` for more details.
+                do in parallel, and other keys. Please refer to the `RunnableConfig`
+                for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -1013,12 +998,10 @@ class Runnable(ABC, Generic[Input, Output]):
         Args:
             inputs: A list of inputs to the `Runnable`.
             config: A config to use when invoking the `Runnable`.
-
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys.
-
-                Please refer to `RunnableConfig` for more details.
+                do in parallel, and other keys. Please refer to the `RunnableConfig`
+                for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -1078,12 +1061,10 @@ class Runnable(ABC, Generic[Input, Output]):
         Args:
             inputs: A list of inputs to the `Runnable`.
             config: A config to use when invoking the `Runnable`.
-
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys.
-
-                Please refer to `RunnableConfig` for more details.
+                do in parallel, and other keys. Please refer to the `RunnableConfig`
+                for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -1761,52 +1742,46 @@ class Runnable(ABC, Generic[Input, Output]):
             import time
             import asyncio
 
-
             def format_t(timestamp: float) -> str:
                 return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
 
-
             async def test_runnable(time_to_sleep: int):
                 print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
                 await asyncio.sleep(time_to_sleep)
                 print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")
 
-
             async def fn_start(run_obj: Runnable):
                 print(f"on start callback starts at {format_t(time.time())}")
                 await asyncio.sleep(3)
                 print(f"on start callback ends at {format_t(time.time())}")
 
-
             async def fn_end(run_obj: Runnable):
                 print(f"on end callback starts at {format_t(time.time())}")
                 await asyncio.sleep(2)
                 print(f"on end callback ends at {format_t(time.time())}")
 
-
             runnable = RunnableLambda(test_runnable).with_alisteners(
-                on_start=fn_start, on_end=fn_end
+                on_start=fn_start,
+                on_end=fn_end
             )
-
-
             async def concurrent_runs():
                 await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))
 
-
             asyncio.run(concurrent_runs())
-            # Result:
-            # on start callback starts at 2025-03-01T07:05:22.875378+00:00
-            # on start callback starts at 2025-03-01T07:05:22.875495+00:00
-            # on start callback ends at 2025-03-01T07:05:25.878862+00:00
-            # on start callback ends at 2025-03-01T07:05:25.878947+00:00
-            # Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
-            # Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
-            # Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
-            # on end callback starts at 2025-03-01T07:05:27.882360+00:00
-            # Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
-            # on end callback starts at 2025-03-01T07:05:28.882428+00:00
-            # on end callback ends at 2025-03-01T07:05:29.883893+00:00
-            # on end callback ends at 2025-03-01T07:05:30.884831+00:00
+            Result:
+            on start callback starts at 2025-03-01T07:05:22.875378+00:00
+            on start callback starts at 2025-03-01T07:05:22.875495+00:00
+            on start callback ends at 2025-03-01T07:05:25.878862+00:00
+            on start callback ends at 2025-03-01T07:05:25.878947+00:00
+            Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
+            Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
+            Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
+            on end callback starts at 2025-03-01T07:05:27.882360+00:00
+            Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
+            on end callback starts at 2025-03-01T07:05:28.882428+00:00
+            on end callback ends at 2025-03-01T07:05:29.883893+00:00
+            on end callback ends at 2025-03-01T07:05:30.884831+00:00
+
             ```
         """
         return RunnableBinding(
@@ -1868,7 +1843,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 `exp_base`, and `jitter` (all `float` values).
 
         Returns:
-            A new `Runnable` that retries the original `Runnable` on exceptions.
+            A new Runnable that retries the original Runnable on exceptions.
 
         Example:
             ```python
@@ -1952,9 +1927,7 @@ class Runnable(ABC, Generic[Input, Output]):
             exceptions_to_handle: A tuple of exception types to handle.
             exception_key: If `string` is specified then handled exceptions will be
                 passed to fallbacks as part of the input under the specified key.
-
                 If `None`, exceptions will not be passed to fallbacks.
-
                 If used, the base `Runnable` and its fallbacks must accept a
                 dictionary as input.
 
@@ -1990,9 +1963,7 @@ class Runnable(ABC, Generic[Input, Output]):
             exceptions_to_handle: A tuple of exception types to handle.
             exception_key: If `string` is specified then handled exceptions will be
                 passed to fallbacks as part of the input under the specified key.
-
                 If `None`, exceptions will not be passed to fallbacks.
-
                 If used, the base `Runnable` and its fallbacks must accept a
                 dictionary as input.
 
@@ -2458,14 +2429,10 @@ class Runnable(ABC, Generic[Input, Output]):
 
         `as_tool` will instantiate a `BaseTool` with a name, description, and
         `args_schema` from a `Runnable`. Where possible, schemas are inferred
-        from `runnable.get_input_schema`.
-
-        Alternatively (e.g., if the `Runnable` takes a dict as input and the specific
-        `dict` keys are not typed), the schema can be specified directly with
-        `args_schema`.
-
-        You can also pass `arg_types` to just specify the required arguments and their
-        types.
+        from `runnable.get_input_schema`. Alternatively (e.g., if the
+        `Runnable` takes a dict as input and the specific dict keys are not typed),
+        the schema can be specified directly with `args_schema`. You can also
+        pass `arg_types` to just specify the required arguments and their types.
 
         Args:
             args_schema: The schema for the tool.
@@ -2534,7 +2501,7 @@ class Runnable(ABC, Generic[Input, Output]):
         as_tool.invoke({"a": 3, "b": [1, 2]})
         ```
 
-        `str` input:
+        String input:
 
         ```python
         from langchain_core.runnables import RunnableLambda
@@ -2670,7 +2637,7 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
         from langchain_openai import ChatOpenAI
 
         model = ChatAnthropic(
-            model_name="claude-sonnet-4-5-20250929"
+            model_name="claude-3-7-sonnet-20250219"
         ).configurable_alternatives(
             ConfigurableField(id="llm"),
             default_key="anthropic",
@@ -2783,9 +2750,6 @@ def _seq_output_schema(
     return last.get_output_schema(config)
 
 
-_RUNNABLE_SEQUENCE_MIN_STEPS = 2
-
-
 class RunnableSequence(RunnableSerializable[Input, Output]):
     """Sequence of `Runnable` objects, where the output of one is the input of the next.
 
@@ -2895,7 +2859,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
             name: The name of the `Runnable`.
             first: The first `Runnable` in the sequence.
             middle: The middle `Runnable` objects in the sequence.
-            last: The last `Runnable` in the sequence.
+            last: The last Runnable in the sequence.
 
         Raises:
             ValueError: If the sequence has less than 2 steps.
@@ -2908,11 +2872,8 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
                 steps_flat.extend(step.steps)
             else:
                 steps_flat.append(coerce_to_runnable(step))
-        if len(steps_flat) < _RUNNABLE_SEQUENCE_MIN_STEPS:
-            msg = (
-                f"RunnableSequence must have at least {_RUNNABLE_SEQUENCE_MIN_STEPS} "
-                f"steps, got {len(steps_flat)}"
-            )
+        if len(steps_flat) < 2:
+            msg = f"RunnableSequence must have at least 2 steps, got {len(steps_flat)}"
             raise ValueError(msg)
         super().__init__(
             first=steps_flat[0],
@@ -2943,7 +2904,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     model_config = ConfigDict(
@@ -3649,7 +3610,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -3707,12 +3668,6 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
             == "object"
             for s in self.steps__.values()
         ):
-            for step in self.steps__.values():
-                fields = step.get_input_schema(config).model_fields
-                root_field = fields.get("root")
-                if root_field is not None and root_field.annotation != Any:
-                    return super().get_input_schema(config)
-
             # This is correct, but pydantic typings/mypy don't think so.
             return create_model_v2(
                 self.get_name("Input"),
@@ -4522,7 +4477,7 @@ class RunnableLambda(Runnable[Input, Output]):
             # on itemgetter objects, so we have to parse the repr
             items = str(func).replace("operator.itemgetter(", "")[:-1].split(", ")
             if all(
-                item[0] == "'" and item[-1] == "'" and item != "''" for item in items
+                item[0] == "'" and item[-1] == "'" and len(item) > 2 for item in items
             ):
                 fields = {item[1:-1]: (Any, ...) for item in items}
                 # It's a dict, lol
@@ -5184,7 +5139,7 @@ class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -5367,7 +5322,7 @@ class RunnableEach(RunnableEachBase[Input, Output]):
 
 
 class RunnableBindingBase(RunnableSerializable[Input, Output]):  # type: ignore[no-redef]
-    """`Runnable` that delegates calls to another `Runnable` with a set of `**kwargs`.
+    """`Runnable` that delegates calls to another `Runnable` with a set of kwargs.
 
     Use only if creating a new `RunnableBinding` subclass with different `__init__`
     args.
@@ -5507,7 +5462,7 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]):  # type: ignore[
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -5797,7 +5752,7 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):  # type: ignore[no-re
         ```python
         # Create a Runnable binding that invokes the chat model with the
         # additional kwarg `stop=['-']` when running it.
-        from langchain_openai import ChatOpenAI
+        from langchain_community.chat_models import ChatOpenAI
 
         model = ChatOpenAI()
         model.invoke('Say "Parrot-MAGIC"', stop=["-"])  # Should return `Parrot`

libs/core/langchain_core/runnables/branch.py

@@ -36,13 +36,11 @@ from langchain_core.runnables.utils import (
     get_unique_config_specs,
 )
 
-_MIN_BRANCHES = 2
-
 
 class RunnableBranch(RunnableSerializable[Input, Output]):
-    """`Runnable` that selects which branch to run based on a condition.
+    """Runnable that selects which branch to run based on a condition.
 
-    The `Runnable` is initialized with a list of `(condition, Runnable)` pairs and
+    The Runnable is initialized with a list of `(condition, Runnable)` pairs and
     a default branch.
 
     When operating on an input, the first condition that evaluates to True is
@@ -88,12 +86,12 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
                 Defaults a `Runnable` to run if no condition is met.
 
         Raises:
-            ValueError: If the number of branches is less than `2`.
+            ValueError: If the number of branches is less than 2.
             TypeError: If the default branch is not `Runnable`, `Callable` or `Mapping`.
-            TypeError: If a branch is not a `tuple` or `list`.
-            ValueError: If a branch is not of length `2`.
+            TypeError: If a branch is not a tuple or list.
+            ValueError: If a branch is not of length 2.
         """
-        if len(branches) < _MIN_BRANCHES:
+        if len(branches) < 2:
             msg = "RunnableBranch requires at least two branches"
             raise ValueError(msg)
 
@@ -120,7 +118,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
                 )
                 raise TypeError(msg)
 
-            if len(branch) != _MIN_BRANCHES:
+            if len(branch) != 2:
                 msg = (
                     f"RunnableBranch branches must be "
                     f"tuples or lists of length 2, not {len(branch)}"
@@ -142,7 +140,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -189,12 +187,12 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
     def invoke(
         self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
     ) -> Output:
-        """First evaluates the condition, then delegate to `True` or `False` branch.
+        """First evaluates the condition, then delegate to true or false branch.
 
         Args:
-            input: The input to the `Runnable`.
-            config: The configuration for the `Runnable`.
-            **kwargs: Additional keyword arguments to pass to the `Runnable`.
+            input: The input to the Runnable.
+            config: The configuration for the Runnable.
+            **kwargs: Additional keyword arguments to pass to the Runnable.
 
         Returns:
             The output of the branch that was run.
@@ -299,12 +297,12 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
         config: RunnableConfig | None = None,
         **kwargs: Any | None,
     ) -> Iterator[Output]:
-        """First evaluates the condition, then delegate to `True` or `False` branch.
+        """First evaluates the condition, then delegate to true or false branch.
 
         Args:
-            input: The input to the `Runnable`.
-            config: The configuration for the Runna`ble.
-            **kwargs: Additional keyword arguments to pass to the `Runnable`.
+            input: The input to the Runnable.
+            config: The configuration for the Runnable.
+            **kwargs: Additional keyword arguments to pass to the Runnable.
 
         Yields:
             The output of the branch that was run.
@@ -383,12 +381,12 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
         config: RunnableConfig | None = None,
         **kwargs: Any | None,
     ) -> AsyncIterator[Output]:
-        """First evaluates the condition, then delegate to `True` or `False` branch.
+        """First evaluates the condition, then delegate to true or false branch.
 
         Args:
-            input: The input to the `Runnable`.
-            config: The configuration for the `Runnable`.
-            **kwargs: Additional keyword arguments to pass to the `Runnable`.
+            input: The input to the Runnable.
+            config: The configuration for the Runnable.
+            **kwargs: Additional keyword arguments to pass to the Runnable.
 
         Yields:
             The output of the branch that was run.

libs/core/langchain_core/runnables/configurable.py

@@ -1,4 +1,4 @@
-"""`Runnable` objects that can be dynamically configured."""
+"""Runnables that can be dynamically configured."""
 
 from __future__ import annotations
 
@@ -47,14 +47,14 @@ if TYPE_CHECKING:
 
 
 class DynamicRunnable(RunnableSerializable[Input, Output]):
-    """Serializable `Runnable` that can be dynamically configured.
+    """Serializable Runnable that can be dynamically configured.
 
-    A `DynamicRunnable` should be initiated using the `configurable_fields` or
-    `configurable_alternatives` method of a `Runnable`.
+    A DynamicRunnable should be initiated using the `configurable_fields` or
+    `configurable_alternatives` method of a Runnable.
     """
 
     default: RunnableSerializable[Input, Output]
-    """The default `Runnable` to use."""
+    """The default Runnable to use."""
 
     config: RunnableConfig | None = None
     """The configuration to use."""
@@ -66,7 +66,7 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -120,13 +120,13 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
     def prepare(
         self, config: RunnableConfig | None = None
     ) -> tuple[Runnable[Input, Output], RunnableConfig]:
-        """Prepare the `Runnable` for invocation.
+        """Prepare the Runnable for invocation.
 
         Args:
             config: The configuration to use.
 
         Returns:
-            The prepared `Runnable` and configuration.
+            The prepared Runnable and configuration.
         """
         runnable: Runnable[Input, Output] = self
         while isinstance(runnable, DynamicRunnable):
@@ -316,12 +316,12 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
 
 
 class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
-    """`Runnable` that can be dynamically configured.
+    """Runnable that can be dynamically configured.
 
-    A `RunnableConfigurableFields` should be initiated using the
-    `configurable_fields` method of a `Runnable`.
+    A RunnableConfigurableFields should be initiated using the
+    `configurable_fields` method of a Runnable.
 
-    Here is an example of using a `RunnableConfigurableFields` with LLMs:
+    Here is an example of using a RunnableConfigurableFields with LLMs:
 
         ```python
         from langchain_core.prompts import PromptTemplate
@@ -348,7 +348,7 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
         chain.invoke({"x": 0}, config={"configurable": {"temperature": 0.9}})
         ```
 
-    Here is an example of using a `RunnableConfigurableFields` with `HubRunnables`:
+    Here is an example of using a RunnableConfigurableFields with HubRunnables:
 
         ```python
         from langchain_core.prompts import PromptTemplate
@@ -380,7 +380,7 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
 
     @property
     def config_specs(self) -> list[ConfigurableFieldSpec]:
-        """Get the configuration specs for the `RunnableConfigurableFields`.
+        """Get the configuration specs for the RunnableConfigurableFields.
 
         Returns:
             The configuration specs.
@@ -473,10 +473,10 @@ _enums_for_spec_lock = threading.Lock()
 
 
 class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
-    """`Runnable` that can be dynamically configured.
+    """Runnable that can be dynamically configured.
 
     A `RunnableConfigurableAlternatives` should be initiated using the
-    `configurable_alternatives` method of a `Runnable` or can be
+    `configurable_alternatives` method of a Runnable or can be
     initiated directly as well.
 
     Here is an example of using a `RunnableConfigurableAlternatives` that uses
@@ -531,7 +531,7 @@ class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
     """
 
     which: ConfigurableField
-    """The `ConfigurableField` to use to choose between alternatives."""
+    """The ConfigurableField to use to choose between alternatives."""
 
     alternatives: dict[
         str,
@@ -544,9 +544,8 @@ class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
 
     prefix_keys: bool
     """Whether to prefix configurable fields of each alternative with a namespace
-    of the form <which.id>==<alternative_key>, e.g. a key named "temperature" used by
-    the alternative named "gpt3" becomes "model==gpt3/temperature".
-    """
+    of the form <which.id>==<alternative_key>, eg. a key named "temperature" used by
+    the alternative named "gpt3" becomes "model==gpt3/temperature"."""
 
     @property
     @override
@@ -639,24 +638,24 @@ class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
 
 
 def _strremoveprefix(s: str, prefix: str) -> str:
-    """`str.removeprefix()` is only available in Python 3.9+."""
+    """str.removeprefix() is only available in Python 3.9+."""
     return s.replace(prefix, "", 1) if s.startswith(prefix) else s
 
 
 def prefix_config_spec(
     spec: ConfigurableFieldSpec, prefix: str
 ) -> ConfigurableFieldSpec:
-    """Prefix the id of a `ConfigurableFieldSpec`.
+    """Prefix the id of a ConfigurableFieldSpec.
 
-    This is useful when a `RunnableConfigurableAlternatives` is used as a
-    `ConfigurableField` of another `RunnableConfigurableAlternatives`.
+    This is useful when a RunnableConfigurableAlternatives is used as a
+    ConfigurableField of another RunnableConfigurableAlternatives.
 
     Args:
-        spec: The `ConfigurableFieldSpec` to prefix.
+        spec: The ConfigurableFieldSpec to prefix.
         prefix: The prefix to add.
 
     Returns:
-        The prefixed `ConfigurableFieldSpec`.
+        The prefixed ConfigurableFieldSpec.
     """
     return (
         ConfigurableFieldSpec(
@@ -678,15 +677,15 @@ def make_options_spec(
 ) -> ConfigurableFieldSpec:
     """Make options spec.
 
-    Make a `ConfigurableFieldSpec` for a `ConfigurableFieldSingleOption` or
-    `ConfigurableFieldMultiOption`.
+    Make a ConfigurableFieldSpec for a ConfigurableFieldSingleOption or
+    ConfigurableFieldMultiOption.
 
     Args:
-        spec: The `ConfigurableFieldSingleOption` or `ConfigurableFieldMultiOption`.
+        spec: The ConfigurableFieldSingleOption or ConfigurableFieldMultiOption.
         description: The description to use if the spec does not have one.
 
     Returns:
-        The `ConfigurableFieldSpec`.
+        The ConfigurableFieldSpec.
     """
     with _enums_for_spec_lock:
         if enum := _enums_for_spec.get(spec):

libs/core/langchain_core/runnables/fallbacks.py

@@ -35,20 +35,20 @@ if TYPE_CHECKING:
 
 
 class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
-    """`Runnable` that can fallback to other `Runnable`s if it fails.
+    """Runnable that can fallback to other Runnables if it fails.
 
     External APIs (e.g., APIs for a language model) may at times experience
     degraded performance or even downtime.
 
-    In these cases, it can be useful to have a fallback `Runnable` that can be
-    used in place of the original `Runnable` (e.g., fallback to another LLM provider).
+    In these cases, it can be useful to have a fallback Runnable that can be
+    used in place of the original Runnable (e.g., fallback to another LLM provider).
 
-    Fallbacks can be defined at the level of a single `Runnable`, or at the level
-    of a chain of `Runnable`s. Fallbacks are tried in order until one succeeds or
+    Fallbacks can be defined at the level of a single Runnable, or at the level
+    of a chain of Runnables. Fallbacks are tried in order until one succeeds or
     all fail.
 
     While you can instantiate a `RunnableWithFallbacks` directly, it is usually
-    more convenient to use the `with_fallbacks` method on a `Runnable`.
+    more convenient to use the `with_fallbacks` method on a Runnable.
 
     Example:
         ```python
@@ -87,7 +87,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
     """
 
     runnable: Runnable[Input, Output]
-    """The `Runnable` to run first."""
+    """The Runnable to run first."""
     fallbacks: Sequence[Runnable[Input, Output]]
     """A sequence of fallbacks to try."""
     exceptions_to_handle: tuple[type[BaseException], ...] = (Exception,)
@@ -97,12 +97,9 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
     """
     exception_key: str | None = None
     """If `string` is specified then handled exceptions will be passed to fallbacks as
-    part of the input under the specified key.
-
-    If `None`, exceptions will not be passed to fallbacks.
-
-    If used, the base `Runnable` and its fallbacks must accept a dictionary as input.
-    """
+        part of the input under the specified key. If `None`, exceptions
+        will not be passed to fallbacks. If used, the base Runnable and its fallbacks
+        must accept a dictionary as input."""
 
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
@@ -140,7 +137,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -155,10 +152,10 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
 
     @property
     def runnables(self) -> Iterator[Runnable[Input, Output]]:
-        """Iterator over the `Runnable` and its fallbacks.
+        """Iterator over the Runnable and its fallbacks.
 
         Yields:
-            The `Runnable` then its fallbacks.
+            The Runnable then its fallbacks.
         """
         yield self.runnable
         yield from self.fallbacks
@@ -592,14 +589,14 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
         await run_manager.on_chain_end(output)
 
     def __getattr__(self, name: str) -> Any:
-        """Get an attribute from the wrapped `Runnable` and its fallbacks.
+        """Get an attribute from the wrapped Runnable and its fallbacks.
 
         Returns:
-            If the attribute is anything other than a method that outputs a `Runnable`,
-            returns `getattr(self.runnable, name)`. If the attribute is a method that
-            does return a new `Runnable` (e.g. `model.bind_tools([...])` outputs a new
-            `RunnableBinding`) then `self.runnable` and each of the runnables in
-            `self.fallbacks` is replaced with `getattr(x, name)`.
+            If the attribute is anything other than a method that outputs a Runnable,
+            returns getattr(self.runnable, name). If the attribute is a method that
+            does return a new Runnable (e.g. model.bind_tools([...]) outputs a new
+            RunnableBinding) then self.runnable and each of the runnables in
+            self.fallbacks is replaced with getattr(x, name).
 
         Example:
             ```python
@@ -607,7 +604,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
             from langchain_anthropic import ChatAnthropic
 
             gpt_4o = ChatOpenAI(model="gpt-4o")
-            claude_3_sonnet = ChatAnthropic(model="claude-sonnet-4-5-20250929")
+            claude_3_sonnet = ChatAnthropic(model="claude-3-7-sonnet-20250219")
             model = gpt_4o.with_fallbacks([claude_3_sonnet])
 
             model.model_name
@@ -621,6 +618,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
                 runnable=RunnableBinding(bound=ChatOpenAI(...), kwargs={"tools": [...]}),
                 fallbacks=[RunnableBinding(bound=ChatAnthropic(...), kwargs={"tools": [...]})],
             )
+
             ```
         """  # noqa: E501
         attr = getattr(self.runnable, name)

libs/core/langchain_core/runnables/graph.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import inspect
 from collections import defaultdict
+from collections.abc import Callable
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import (
@@ -21,7 +22,7 @@ from langchain_core.runnables.base import Runnable, RunnableSerializable
 from langchain_core.utils.pydantic import _IgnoreUnserializable, is_basemodel_subclass
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Sequence
+    from collections.abc import Sequence
 
     from pydantic import BaseModel
 
@@ -641,7 +642,6 @@ class Graph:
         retry_delay: float = 1.0,
         frontmatter_config: dict[str, Any] | None = None,
         base_url: str | None = None,
-        proxies: dict[str, str] | None = None,
     ) -> bytes:
         """Draw the graph as a PNG image using Mermaid.
 
@@ -674,10 +674,11 @@ class Graph:
                 }
                 ```
             base_url: The base URL of the Mermaid server for rendering via API.
-            proxies: HTTP/HTTPS proxies for requests (e.g. `{"http": "http://127.0.0.1:7890"}`).
+
 
         Returns:
             The PNG image as bytes.
+
         """
         # Import locally to prevent circular import
         from langchain_core.runnables.graph_mermaid import (  # noqa: PLC0415
@@ -698,7 +699,6 @@ class Graph:
             padding=padding,
             max_retries=max_retries,
             retry_delay=retry_delay,
-            proxies=proxies,
             base_url=base_url,
         )
 

libs/core/langchain_core/runnables/graph_ascii.py

@@ -7,6 +7,7 @@ from __future__ import annotations
 
 import math
 import os
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 try:
@@ -19,8 +20,6 @@ except ImportError:
     _HAS_GRANDALF = False
 
 if TYPE_CHECKING:
-    from collections.abc import Mapping, Sequence
-
     from langchain_core.runnables.graph import Edge as LangEdge
 
 

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -281,7 +281,6 @@ def draw_mermaid_png(
     max_retries: int = 1,
     retry_delay: float = 1.0,
     base_url: str | None = None,
-    proxies: dict[str, str] | None = None,
 ) -> bytes:
     """Draws a Mermaid graph as PNG using provided syntax.
 
@@ -294,7 +293,6 @@ def draw_mermaid_png(
         max_retries: Maximum number of retries (MermaidDrawMethod.API).
         retry_delay: Delay between retries (MermaidDrawMethod.API).
         base_url: Base URL for the Mermaid.ink API.
-        proxies: HTTP/HTTPS proxies for requests (e.g. `{"http": "http://127.0.0.1:7890"}`).
 
     Returns:
         PNG image bytes.
@@ -316,7 +314,6 @@ def draw_mermaid_png(
             max_retries=max_retries,
             retry_delay=retry_delay,
             base_url=base_url,
-            proxies=proxies,
         )
     else:
         supported_methods = ", ".join([m.value for m in MermaidDrawMethod])
@@ -408,7 +405,6 @@ def _render_mermaid_using_api(
     file_type: Literal["jpeg", "png", "webp"] | None = "png",
     max_retries: int = 1,
     retry_delay: float = 1.0,
-    proxies: dict[str, str] | None = None,
     base_url: str | None = None,
 ) -> bytes:
     """Renders Mermaid graph using the Mermaid.INK API."""
@@ -449,7 +445,7 @@ def _render_mermaid_using_api(
 
     for attempt in range(max_retries + 1):
         try:
-            response = requests.get(image_url, timeout=10, proxies=proxies)
+            response = requests.get(image_url, timeout=10)
             if response.status_code == requests.codes.ok:
                 img_bytes = response.content
                 if output_file_path is not None:
@@ -458,10 +454,7 @@ def _render_mermaid_using_api(
                 return img_bytes
 
             # If we get a server error (5xx), retry
-            if (
-                requests.codes.internal_server_error <= response.status_code
-                and attempt < max_retries
-            ):
+            if 500 <= response.status_code < 600 and attempt < max_retries:
                 # Exponential backoff with jitter
                 sleep_time = retry_delay * (2**attempt) * (0.5 + 0.5 * random.random())  # noqa: S311 not used for crypto
                 time.sleep(sleep_time)

libs/core/langchain_core/runnables/graph_png.py

@@ -1,6 +1,5 @@
 """Helper class to draw a state graph into a PNG file."""
 
-from itertools import groupby
 from typing import Any
 
 from langchain_core.runnables.graph import Graph, LabelsDict
@@ -142,7 +141,6 @@ class PngDrawer:
         # Add nodes, conditional edges, and edges to the graph
         self.add_nodes(viz, graph)
         self.add_edges(viz, graph)
-        self.add_subgraph(viz, [node.split(":") for node in graph.nodes])
 
         # Update entrypoint and END styles
         self.update_styles(viz, graph)
@@ -163,32 +161,6 @@ class PngDrawer:
         for node in graph.nodes:
             self.add_node(viz, node)
 
-    def add_subgraph(
-        self,
-        viz: Any,
-        nodes: list[list[str]],
-        parent_prefix: list[str] | None = None,
-    ) -> None:
-        """Add subgraphs to the graph.
-
-        Args:
-            viz: The graphviz object.
-            nodes: The nodes to add.
-            parent_prefix: The prefix of the parent subgraph.
-        """
-        for prefix, grouped in groupby(
-            [node[:] for node in sorted(nodes)],
-            key=lambda x: x.pop(0),
-        ):
-            current_prefix = (parent_prefix or []) + [prefix]
-            grouped_nodes = list(grouped)
-            if len(grouped_nodes) > 1:
-                subgraph = viz.add_subgraph(
-                    [":".join(current_prefix + node) for node in grouped_nodes],
-                    name="cluster_" + ":".join(current_prefix),
-                )
-                self.add_subgraph(subgraph, grouped_nodes, current_prefix)
-
     def add_edges(self, viz: Any, graph: Graph) -> None:
         """Add edges to the graph.
 

libs/core/langchain_core/runnables/history.py

@@ -36,23 +36,23 @@ GetSessionHistoryCallable = Callable[..., BaseChatMessageHistory]
 
 
 class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
-    """`Runnable` that manages chat message history for another `Runnable`.
+    """Runnable that manages chat message history for another Runnable.
 
     A chat message history is a sequence of messages that represent a conversation.
 
-    `RunnableWithMessageHistory` wraps another `Runnable` and manages the chat message
+    RunnableWithMessageHistory wraps another Runnable and manages the chat message
     history for it; it is responsible for reading and updating the chat message
     history.
 
-    The formats supported for the inputs and outputs of the wrapped `Runnable`
+    The formats supported for the inputs and outputs of the wrapped Runnable
     are described below.
 
-    `RunnableWithMessageHistory` must always be called with a config that contains
+    RunnableWithMessageHistory must always be called with a config that contains
     the appropriate parameters for the chat message history factory.
 
-    By default, the `Runnable` is expected to take a single configuration parameter
+    By default, the Runnable is expected to take a single configuration parameter
     called `session_id` which is a string. This parameter is used to create a new
-    or look up an existing chat message history that matches the given `session_id`.
+    or look up an existing chat message history that matches the given session_id.
 
     In this case, the invocation would look like this:
 
@@ -117,12 +117,12 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
 
         ```
 
-    Example where the wrapped `Runnable` takes a dictionary input:
+    Example where the wrapped Runnable takes a dictionary input:
 
         ```python
         from typing import Optional
 
-        from langchain_anthropic import ChatAnthropic
+        from langchain_community.chat_models import ChatAnthropic
         from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
         from langchain_core.runnables.history import RunnableWithMessageHistory
 
@@ -166,7 +166,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
         print(store)  # noqa: T201
         ```
 
-    Example where the session factory takes two keys (`user_id` and `conversation_id`):
+    Example where the session factory takes two keys, user_id and conversation id):
 
         ```python
         store = {}
@@ -223,28 +223,21 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
     """
 
     get_session_history: GetSessionHistoryCallable
-    """Function that returns a new `BaseChatMessageHistory`.
-
+    """Function that returns a new BaseChatMessageHistory.
     This function should either take a single positional argument `session_id` of type
-    string and return a corresponding chat message history instance
-    """
+    string and return a corresponding chat message history instance"""
     input_messages_key: str | None = None
-    """Must be specified if the base `Runnable` accepts a `dict` as input.
-    The key in the input `dict` that contains the messages.
-    """
+    """Must be specified if the base runnable accepts a dict as input.
+    The key in the input dict that contains the messages."""
     output_messages_key: str | None = None
-    """Must be specified if the base `Runnable` returns a `dict` as output.
-    The key in the output `dict` that contains the messages.
-    """
+    """Must be specified if the base Runnable returns a dict as output.
+    The key in the output dict that contains the messages."""
     history_messages_key: str | None = None
-    """Must be specified if the base `Runnable` accepts a `dict` as input and expects a
-    separate key for historical messages.
-    """
+    """Must be specified if the base runnable accepts a dict as input and expects a
+    separate key for historical messages."""
     history_factory_config: Sequence[ConfigurableFieldSpec]
     """Configure fields that should be passed to the chat history factory.
-
-    See `ConfigurableFieldSpec` for more details.
-    """
+    See `ConfigurableFieldSpec` for more details."""
 
     def __init__(
         self,
@@ -261,16 +254,15 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
         history_factory_config: Sequence[ConfigurableFieldSpec] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Initialize `RunnableWithMessageHistory`.
+        """Initialize RunnableWithMessageHistory.
 
         Args:
-            runnable: The base `Runnable` to be wrapped.
-
+            runnable: The base Runnable to be wrapped.
                 Must take as input one of:
 
                 1. A list of `BaseMessage`
-                2. A `dict` with one key for all messages
-                3. A `dict` with one key for the current input string/message(s) and
+                2. A dict with one key for all messages
+                3. A dict with one key for the current input string/message(s) and
                     a separate key for historical messages. If the input key points
                     to a string, it will be treated as a `HumanMessage` in history.
 
@@ -278,15 +270,13 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
 
                 1. A string which can be treated as an `AIMessage`
                 2. A `BaseMessage` or sequence of `BaseMessage`
-                3. A `dict` with a key for a `BaseMessage` or sequence of
+                3. A dict with a key for a `BaseMessage` or sequence of
                     `BaseMessage`
 
-            get_session_history: Function that returns a new `BaseChatMessageHistory`.
-
+            get_session_history: Function that returns a new BaseChatMessageHistory.
                 This function should either take a single positional argument
                 `session_id` of type string and return a corresponding
                 chat message history instance.
-
                 ```python
                 def get_session_history(
                     session_id: str, *, user_id: str | None = None
@@ -305,17 +295,16 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
                 ) -> BaseChatMessageHistory: ...
                 ```
 
-            input_messages_key: Must be specified if the base runnable accepts a `dict`
+            input_messages_key: Must be specified if the base runnable accepts a dict
                 as input.
-            output_messages_key: Must be specified if the base runnable returns a `dict`
+            output_messages_key: Must be specified if the base runnable returns a dict
                 as output.
-            history_messages_key: Must be specified if the base runnable accepts a
-                `dict` as input and expects a separate key for historical messages.
+            history_messages_key: Must be specified if the base runnable accepts a dict
+                as input and expects a separate key for historical messages.
             history_factory_config: Configure fields that should be passed to the
                 chat history factory. See `ConfigurableFieldSpec` for more details.
-
-                Specifying these allows you to pass multiple config keys into the
-                `get_session_history` factory.
+                Specifying these allows you to pass multiple config keys
+                into the get_session_history factory.
             **kwargs: Arbitrary additional kwargs to pass to parent class
                 `RunnableBindingBase` init.
 
@@ -375,7 +364,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
     @property
     @override
     def config_specs(self) -> list[ConfigurableFieldSpec]:
-        """Get the configuration specs for the `RunnableWithMessageHistory`."""
+        """Get the configuration specs for the RunnableWithMessageHistory."""
         return get_unique_config_specs(
             super().config_specs + list(self.history_factory_config)
         )
@@ -617,6 +606,6 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
 
 
 def _get_parameter_names(callable_: GetSessionHistoryCallable) -> list[str]:
-    """Get the parameter names of the `Callable`."""
+    """Get the parameter names of the callable."""
     sig = inspect.signature(callable_)
     return list(sig.parameters.keys())

libs/core/langchain_core/runnables/passthrough.py

@@ -51,10 +51,10 @@ def identity(x: Other) -> Other:
     """Identity function.
 
     Args:
-        x: Input.
+        x: input.
 
     Returns:
-        Output.
+        output.
     """
     return x
 
@@ -63,10 +63,10 @@ async def aidentity(x: Other) -> Other:
     """Async identity function.
 
     Args:
-        x: Input.
+        x: input.
 
     Returns:
-        Output.
+        output.
     """
     return x
 
@@ -74,11 +74,11 @@ async def aidentity(x: Other) -> Other:
 class RunnablePassthrough(RunnableSerializable[Other, Other]):
     """Runnable to passthrough inputs unchanged or with additional keys.
 
-    This `Runnable` behaves almost like the identity function, except that it
+    This Runnable behaves almost like the identity function, except that it
     can be configured to add additional keys to the output, if the input is a
     dict.
 
-    The examples below demonstrate this `Runnable` works using a few simple
+    The examples below demonstrate this Runnable works using a few simple
     chains. The chains rely on simple lambdas to make the examples easy to execute
     and experiment with.
 
@@ -164,7 +164,7 @@ class RunnablePassthrough(RunnableSerializable[Other, Other]):
         input_type: type[Other] | None = None,
         **kwargs: Any,
     ) -> None:
-        """Create a `RunnablePassthrough`.
+        """Create e RunnablePassthrough.
 
         Args:
             func: Function to be called with the input.
@@ -180,7 +180,7 @@ class RunnablePassthrough(RunnableSerializable[Other, Other]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -213,11 +213,11 @@ class RunnablePassthrough(RunnableSerializable[Other, Other]):
         """Merge the Dict input with the output produced by the mapping argument.
 
         Args:
-            **kwargs: `Runnable`, `Callable` or a `Mapping` from keys to `Runnable`
-                objects or `Callable`s.
+            **kwargs: Runnable, Callable or a Mapping from keys to Runnables
+                or Callables.
 
         Returns:
-            A `Runnable` that merges the `dict` input with the output produced by the
+            A Runnable that merges the Dict input with the output produced by the
             mapping argument.
         """
         return RunnableAssign(RunnableParallel[dict[str, Any]](kwargs))
@@ -350,7 +350,7 @@ _graph_passthrough: RunnablePassthrough = RunnablePassthrough()
 
 
 class RunnableAssign(RunnableSerializable[dict[str, Any], dict[str, Any]]):
-    """Runnable that assigns key-value pairs to `dict[str, Any]` inputs.
+    """Runnable that assigns key-value pairs to dict[str, Any] inputs.
 
     The `RunnableAssign` class takes input dictionaries and, through a
     `RunnableParallel` instance, applies transformations, then combines
@@ -392,7 +392,7 @@ class RunnableAssign(RunnableSerializable[dict[str, Any], dict[str, Any]]):
     mapper: RunnableParallel
 
     def __init__(self, mapper: RunnableParallel[dict[str, Any]], **kwargs: Any) -> None:
-        """Create a `RunnableAssign`.
+        """Create a RunnableAssign.
 
         Args:
             mapper: A `RunnableParallel` instance that will be used to transform the
@@ -403,7 +403,7 @@ class RunnableAssign(RunnableSerializable[dict[str, Any], dict[str, Any]]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod
@@ -668,19 +668,13 @@ class RunnableAssign(RunnableSerializable[dict[str, Any], dict[str, Any]]):
             yield chunk
 
 
-class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
-    """`Runnable` that picks keys from `dict[str, Any]` inputs.
+class RunnablePick(RunnableSerializable[dict[str, Any], dict[str, Any]]):
+    """Runnable that picks keys from dict[str, Any] inputs.
 
-    `RunnablePick` class represents a `Runnable` that selectively picks keys from a
+    RunnablePick class represents a Runnable that selectively picks keys from a
     dictionary input. It allows you to specify one or more keys to extract
-    from the input dictionary.
-
-    !!! note "Return Type Behavior"
-        The return type depends on the `keys` parameter:
-
-        - When `keys` is a `str`: Returns the single value associated with that key
-        - When `keys` is a `list`: Returns a dictionary containing only the selected
-            keys
+    from the input dictionary. It returns a new dictionary containing only
+    the selected keys.
 
     Example:
         ```python
@@ -693,22 +687,18 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
             "country": "USA",
         }
 
-        # Single key - returns the value directly
-        runnable_single = RunnablePick(keys="name")
-        result_single = runnable_single.invoke(input_data)
-        print(result_single)  # Output: "John"
+        runnable = RunnablePick(keys=["name", "age"])
 
-        # Multiple keys - returns a dictionary
-        runnable_multiple = RunnablePick(keys=["name", "age"])
-        result_multiple = runnable_multiple.invoke(input_data)
-        print(result_multiple)  # Output: {'name': 'John', 'age': 30}
+        output_data = runnable.invoke(input_data)
+
+        print(output_data)  # Output: {'name': 'John', 'age': 30}
         ```
     """
 
     keys: str | list[str]
 
     def __init__(self, keys: str | list[str], **kwargs: Any) -> None:
-        """Create a `RunnablePick`.
+        """Create a RunnablePick.
 
         Args:
             keys: A single key or a list of keys to pick from the input dictionary.
@@ -718,7 +708,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod

libs/core/langchain_core/runnables/router.py

@@ -40,11 +40,11 @@ class RouterInput(TypedDict):
     key: str
     """The key to route on."""
     input: Any
-    """The input to pass to the selected `Runnable`."""
+    """The input to pass to the selected Runnable."""
 
 
 class RouterRunnable(RunnableSerializable[RouterInput, Output]):
-    """`Runnable` that routes to a set of `Runnable` based on `Input['key']`.
+    """Runnable that routes to a set of Runnables based on Input['key'].
 
     Returns the output of the selected Runnable.
 
@@ -74,10 +74,10 @@ class RouterRunnable(RunnableSerializable[RouterInput, Output]):
         self,
         runnables: Mapping[str, Runnable[Any, Output] | Callable[[Any], Output]],
     ) -> None:
-        """Create a `RouterRunnable`.
+        """Create a RouterRunnable.
 
         Args:
-            runnables: A mapping of keys to `Runnable` objects.
+            runnables: A mapping of keys to Runnables.
         """
         super().__init__(
             runnables={key: coerce_to_runnable(r) for key, r in runnables.items()}
@@ -90,7 +90,7 @@ class RouterRunnable(RunnableSerializable[RouterInput, Output]):
     @classmethod
     @override
     def is_lc_serializable(cls) -> bool:
-        """Return `True` as this class is serializable."""
+        """Return True as this class is serializable."""
         return True
 
     @classmethod

libs/core/langchain_core/runnables/schema.py

@@ -28,7 +28,7 @@ class EventData(TypedDict, total=False):
 
     This field is only available if the `Runnable` raised an exception.
 
-    !!! version-added "Added in `langchain-core` 1.0.0"
+    !!! version-added "Added in version 1.0.0"
     """
     output: Any
     """The output of the `Runnable` that generated the event.

libs/core/langchain_core/runnables/utils.py

@@ -7,7 +7,8 @@ import asyncio
 import inspect
 import sys
 import textwrap
-from collections.abc import Mapping, Sequence
+from collections.abc import Callable, Mapping, Sequence
+from contextvars import Context
 from functools import lru_cache
 from inspect import signature
 from itertools import groupby
@@ -30,11 +31,9 @@ if TYPE_CHECKING:
         AsyncIterable,
         AsyncIterator,
         Awaitable,
-        Callable,
         Coroutine,
         Iterable,
     )
-    from contextvars import Context
 
     from langchain_core.runnables.schema import StreamEvent
 

libs/core/langchain_core/sys_info.py

@@ -125,11 +125,9 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = ()) -> None:
         for dep in sub_dependencies:
             try:
                 dep_version = metadata.version(dep)
-            except Exception:
-                dep_version = None
-
-            if dep_version is not None:
                 print(f"> {dep}: {dep_version}")
+            except Exception:
+                print(f"> {dep}: Installed. No version info available.")
 
 
 if __name__ == "__main__":

libs/core/langchain_core/tools/base.py

@@ -386,14 +386,11 @@ class ToolException(Exception):  # noqa: N818
 
 ArgsSchema = TypeBaseModel | dict[str, Any]
 
-_EMPTY_SET: frozenset[str] = frozenset()
-
 
 class BaseTool(RunnableSerializable[str | dict | ToolCall, Any]):
     """Base class for all LangChain tools.
 
     This abstract class defines the interface that all LangChain tools must implement.
-
     Tools are components that can be called by agents to perform specific actions.
     """
 
@@ -404,7 +401,7 @@ class BaseTool(RunnableSerializable[str | dict | ToolCall, Any]):
             **kwargs: Additional keyword arguments passed to the parent class.
 
         Raises:
-            SchemaAnnotationError: If `args_schema` has incorrect type annotation.
+            SchemaAnnotationError: If args_schema has incorrect type annotation.
         """
         super().__init_subclass__(**kwargs)
 
@@ -445,15 +442,15 @@ class ChildTool(BaseTool):
 
     Args schema should be either:
 
-    - A subclass of `pydantic.BaseModel`.
-    - A subclass of `pydantic.v1.BaseModel` if accessing v1 namespace in pydantic 2
-    - A JSON schema dict
+    - A subclass of pydantic.BaseModel.
+    - A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2
+    - a JSON schema dict
     """
     return_direct: bool = False
     """Whether to return the tool's output directly.
 
-    Setting this to `True` means that after the tool is called, the `AgentExecutor` will
-    stop looping.
+    Setting this to True means
+    that after the tool is called, the AgentExecutor will stop looping.
     """
     verbose: bool = False
     """Whether to log the tool's progress."""
@@ -463,37 +460,31 @@ class ChildTool(BaseTool):
 
     tags: list[str] | None = None
     """Optional list of tags associated with the tool.
-
     These tags will be associated with each call to this tool,
     and passed as arguments to the handlers defined in `callbacks`.
-
-    You can use these to, e.g., identify a specific instance of a tool with its use
-    case.
+    You can use these to eg identify a specific instance of a tool with its use case.
     """
     metadata: dict[str, Any] | None = None
     """Optional metadata associated with the tool.
-
     This metadata will be associated with each call to this tool,
     and passed as arguments to the handlers defined in `callbacks`.
-
-    You can use these to, e.g., identify a specific instance of a tool with its use
-    case.
+    You can use these to eg identify a specific instance of a tool with its use case.
     """
 
     handle_tool_error: bool | str | Callable[[ToolException], str] | None = False
-    """Handle the content of the `ToolException` thrown."""
+    """Handle the content of the ToolException thrown."""
 
     handle_validation_error: (
         bool | str | Callable[[ValidationError | ValidationErrorV1], str] | None
     ) = False
-    """Handle the content of the `ValidationError` thrown."""
+    """Handle the content of the ValidationError thrown."""
 
     response_format: Literal["content", "content_and_artifact"] = "content"
     """The tool response format.
 
-    If `'content'` then the output of the tool is interpreted as the contents of a
-    `ToolMessage`. If `'content_and_artifact'` then the output is expected to be a
-    two-tuple corresponding to the `(content, artifact)` of a `ToolMessage`.
+    If `"content"` then the output of the tool is interpreted as the contents of a
+    `ToolMessage`. If `"content_and_artifact"` then the output is expected to be a
+    two-tuple corresponding to the (content, artifact) of a `ToolMessage`.
     """
 
     def __init__(self, **kwargs: Any) -> None:
@@ -501,7 +492,7 @@ class ChildTool(BaseTool):
 
         Raises:
             TypeError: If `args_schema` is not a subclass of pydantic `BaseModel` or
-                `dict`.
+                dict.
         """
         if (
             "args_schema" in kwargs
@@ -535,7 +526,7 @@ class ChildTool(BaseTool):
         """Get the tool's input arguments schema.
 
         Returns:
-            `dict` containing the tool's argument properties.
+            Dictionary containing the tool's argument properties.
         """
         if isinstance(self.args_schema, dict):
             json_schema = self.args_schema
@@ -571,11 +562,6 @@ class ChildTool(BaseTool):
             self.name, full_schema, fields, fn_description=self.description
         )
 
-    @functools.cached_property
-    def _injected_args_keys(self) -> frozenset[str]:
-        # base implementation doesn't manage injected args
-        return _EMPTY_SET
-
     # --- Runnable ---
 
     @override
@@ -630,9 +616,9 @@ class ChildTool(BaseTool):
 
         Raises:
             ValueError: If `string` input is provided with JSON schema `args_schema`.
-            ValueError: If `InjectedToolCallId` is required but `tool_call_id` is not
+            ValueError: If InjectedToolCallId is required but `tool_call_id` is not
                 provided.
-            TypeError: If `args_schema` is not a Pydantic `BaseModel` or dict.
+            TypeError: If args_schema is not a Pydantic `BaseModel` or dict.
         """
         input_args = self.args_schema
         if isinstance(tool_input, str):
@@ -656,7 +642,6 @@ class ChildTool(BaseTool):
             if isinstance(input_args, dict):
                 return tool_input
             if issubclass(input_args, BaseModel):
-                # Check args_schema for InjectedToolCallId
                 for k, v in get_all_basemodel_annotations(input_args).items():
                     if _is_injected_arg_type(v, injected_type=InjectedToolCallId):
                         if tool_call_id is None:
@@ -672,7 +657,6 @@ class ChildTool(BaseTool):
                 result = input_args.model_validate(tool_input)
                 result_dict = result.model_dump()
             elif issubclass(input_args, BaseModelV1):
-                # Check args_schema for InjectedToolCallId
                 for k, v in get_all_basemodel_annotations(input_args).items():
                     if _is_injected_arg_type(v, injected_type=InjectedToolCallId):
                         if tool_call_id is None:
@@ -692,25 +676,9 @@ class ChildTool(BaseTool):
                     f"args_schema must be a Pydantic BaseModel, got {self.args_schema}"
                 )
                 raise NotImplementedError(msg)
-            validated_input = {
-                k: getattr(result, k) for k in result_dict if k in tool_input
+            return {
+                k: getattr(result, k) for k, v in result_dict.items() if k in tool_input
             }
-            for k in self._injected_args_keys:
-                if k == "tool_call_id":
-                    if tool_call_id is None:
-                        msg = (
-                            "When tool includes an InjectedToolCallId "
-                            "argument, tool must always be invoked with a full "
-                            "model ToolCall of the form: {'args': {...}, "
-                            "'name': '...', 'type': 'tool_call', "
-                            "'tool_call_id': '...'}"
-                        )
-                        raise ValueError(msg)
-                    validated_input[k] = tool_call_id
-                if k in tool_input:
-                    injected_val = tool_input[k]
-                    validated_input[k] = injected_val
-            return validated_input
         return tool_input
 
     @abstractmethod
@@ -739,35 +707,6 @@ class ChildTool(BaseTool):
             kwargs["run_manager"] = kwargs["run_manager"].get_sync()
         return await run_in_executor(None, self._run, *args, **kwargs)
 
-    def _filter_injected_args(self, tool_input: dict) -> dict:
-        """Filter out injected tool arguments from the input dictionary.
-
-        Injected arguments are those annotated with `InjectedToolArg` or its
-        subclasses, or arguments in `FILTERED_ARGS` like `run_manager` and callbacks.
-
-        Args:
-            tool_input: The tool input dictionary to filter.
-
-        Returns:
-            A filtered dictionary with injected arguments removed.
-        """
-        # Start with filtered args from the constant
-        filtered_keys = set[str](FILTERED_ARGS)
-
-        # If we have an args_schema, use it to identify injected args
-        if self.args_schema is not None:
-            try:
-                annotations = get_all_basemodel_annotations(self.args_schema)
-                for field_name, field_type in annotations.items():
-                    if _is_injected_arg_type(field_type):
-                        filtered_keys.add(field_name)
-            except Exception:  # noqa: S110
-                # If we can't get annotations, just use FILTERED_ARGS
-                pass
-
-        # Filter out the injected keys from tool_input
-        return {k: v for k, v in tool_input.items() if k not in filtered_keys}
-
     def _to_args_and_kwargs(
         self, tool_input: str | dict, tool_call_id: str | None
     ) -> tuple[tuple, dict]:
@@ -778,7 +717,7 @@ class ChildTool(BaseTool):
             tool_call_id: The ID of the tool call, if available.
 
         Returns:
-            A tuple of `(positional_args, keyword_args)` for the tool.
+            A tuple of (positional_args, keyword_args) for the tool.
 
         Raises:
             TypeError: If the tool input type is invalid.
@@ -855,29 +794,17 @@ class ChildTool(BaseTool):
             self.metadata,
         )
 
-        # Filter out injected arguments from callback inputs
-        filtered_tool_input = (
-            self._filter_injected_args(tool_input)
-            if isinstance(tool_input, dict)
-            else None
-        )
-
-        # Use filtered inputs for the input_str parameter as well
-        tool_input_str = (
-            tool_input
-            if isinstance(tool_input, str)
-            else str(
-                filtered_tool_input if filtered_tool_input is not None else tool_input
-            )
-        )
-
         run_manager = callback_manager.on_tool_start(
             {"name": self.name, "description": self.description},
-            tool_input_str,
+            tool_input if isinstance(tool_input, str) else str(tool_input),
             color=start_color,
             name=run_name,
             run_id=run_id,
-            inputs=filtered_tool_input,
+            # Inputs by definition should always be dicts.
+            # For now, it's unclear whether this assumption is ever violated,
+            # but if it is we will send a `None` value to the callback instead
+            # TODO: will need to address issue via a patch.
+            inputs=tool_input if isinstance(tool_input, dict) else None,
             **kwargs,
         )
 
@@ -897,19 +824,16 @@ class ChildTool(BaseTool):
                     tool_kwargs |= {config_param: config}
                 response = context.run(self._run, *tool_args, **tool_kwargs)
             if self.response_format == "content_and_artifact":
-                msg = (
-                    "Since response_format='content_and_artifact' "
-                    "a two-tuple of the message content and raw tool output is "
-                    f"expected. Instead, generated response is of type: "
-                    f"{type(response)}."
-                )
-                if not isinstance(response, tuple):
+                if not isinstance(response, tuple) or len(response) != 2:
+                    msg = (
+                        "Since response_format='content_and_artifact' "
+                        "a two-tuple of the message content and raw tool output is "
+                        f"expected. Instead generated response of type: "
+                        f"{type(response)}."
+                    )
                     error_to_raise = ValueError(msg)
                 else:
-                    try:
-                        content, artifact = response
-                    except ValueError:
-                        error_to_raise = ValueError(msg)
+                    content, artifact = response
             else:
                 content = response
         except (ValidationError, ValidationErrorV1) as e:
@@ -981,30 +905,17 @@ class ChildTool(BaseTool):
             metadata,
             self.metadata,
         )
-
-        # Filter out injected arguments from callback inputs
-        filtered_tool_input = (
-            self._filter_injected_args(tool_input)
-            if isinstance(tool_input, dict)
-            else None
-        )
-
-        # Use filtered inputs for the input_str parameter as well
-        tool_input_str = (
-            tool_input
-            if isinstance(tool_input, str)
-            else str(
-                filtered_tool_input if filtered_tool_input is not None else tool_input
-            )
-        )
-
         run_manager = await callback_manager.on_tool_start(
             {"name": self.name, "description": self.description},
-            tool_input_str,
+            tool_input if isinstance(tool_input, str) else str(tool_input),
             color=start_color,
             name=run_name,
             run_id=run_id,
-            inputs=filtered_tool_input,
+            # Inputs by definition should always be dicts.
+            # For now, it's unclear whether this assumption is ever violated,
+            # but if it is we will send a `None` value to the callback instead
+            # TODO: will need to address issue via a patch.
+            inputs=tool_input if isinstance(tool_input, dict) else None,
             **kwargs,
         )
         content = None
@@ -1026,19 +937,16 @@ class ChildTool(BaseTool):
                 coro = self._arun(*tool_args, **tool_kwargs)
                 response = await coro_with_context(coro, context)
             if self.response_format == "content_and_artifact":
-                msg = (
-                    "Since response_format='content_and_artifact' "
-                    "a two-tuple of the message content and raw tool output is "
-                    f"expected. Instead, generated response is of type: "
-                    f"{type(response)}."
-                )
-                if not isinstance(response, tuple):
+                if not isinstance(response, tuple) or len(response) != 2:
+                    msg = (
+                        "Since response_format='content_and_artifact' "
+                        "a two-tuple of the message content and raw tool output is "
+                        f"expected. Instead generated response of type: "
+                        f"{type(response)}."
+                    )
                     error_to_raise = ValueError(msg)
                 else:
-                    try:
-                        content, artifact = response
-                    except ValueError:
-                        error_to_raise = ValueError(msg)
+                    content, artifact = response
             else:
                 content = response
         except ValidationError as e:
@@ -1086,7 +994,7 @@ def _handle_validation_error(
 
     Args:
         e: The validation error that occurred.
-        flag: How to handle the error (`bool`, `str`, or `Callable`).
+        flag: How to handle the error (bool, string, or callable).
 
     Returns:
         The error message to return.
@@ -1118,7 +1026,7 @@ def _handle_tool_error(
 
     Args:
         e: The tool exception that occurred.
-        flag: How to handle the error (`bool`, `str`, or `Callable`).
+        flag: How to handle the error (bool, string, or callable).
 
     Returns:
         The error message to return.
@@ -1149,12 +1057,12 @@ def _prep_run_args(
     """Prepare arguments for tool execution.
 
     Args:
-        value: The input value (`str`, `dict`, or `ToolCall`).
+        value: The input value (string, dict, or ToolCall).
         config: The runnable configuration.
         **kwargs: Additional keyword arguments.
 
     Returns:
-        A tuple of `(tool_input, run_kwargs)`.
+        A tuple of (tool_input, run_kwargs).
     """
     config = ensure_config(config)
     if _is_tool_call(value):
@@ -1185,7 +1093,7 @@ def _format_output(
     name: str,
     status: str,
 ) -> ToolOutputMixin | Any:
-    """Format tool output as a `ToolMessage` if appropriate.
+    """Format tool output as a ToolMessage if appropriate.
 
     Args:
         content: The main content of the tool output.
@@ -1195,7 +1103,7 @@ def _format_output(
         status: The execution status.
 
     Returns:
-        The formatted output, either as a `ToolMessage` or the original content.
+        The formatted output, either as a ToolMessage or the original content.
     """
     if isinstance(content, ToolOutputMixin) or tool_call_id is None:
         return content
@@ -1266,7 +1174,7 @@ def _get_type_hints(func: Callable) -> dict[str, type] | None:
         func: The function to get type hints from.
 
     Returns:
-        `dict` of type hints, or `None` if extraction fails.
+        Dictionary of type hints, or None if extraction fails.
     """
     if isinstance(func, functools.partial):
         func = func.func
@@ -1277,13 +1185,13 @@ def _get_type_hints(func: Callable) -> dict[str, type] | None:
 
 
 def _get_runnable_config_param(func: Callable) -> str | None:
-    """Find the parameter name for `RunnableConfig` in a function.
+    """Find the parameter name for RunnableConfig in a function.
 
     Args:
         func: The function to check.
 
     Returns:
-        The parameter name for `RunnableConfig`, or `None` if not found.
+        The parameter name for RunnableConfig, or None if not found.
     """
     type_hints = _get_type_hints(func)
     if not type_hints:
@@ -1307,11 +1215,9 @@ class _DirectlyInjectedToolArg:
 
     Injected via direct type annotation, rather than annotated metadata.
 
-    For example, `ToolRuntime` is a directly injected argument.
-
+    For example, ToolRuntime is a directly injected argument.
     Note the direct annotation rather than the verbose alternative:
-    `Annotated[ToolRuntime, InjectedRuntime]`
-
+    Annotated[ToolRuntime, InjectedRuntime]
     ```python
     from langchain_core.tools import tool, ToolRuntime
 
@@ -1354,11 +1260,11 @@ class InjectedToolCallId(InjectedToolArg):
 def _is_directly_injected_arg_type(type_: Any) -> bool:
     """Check if a type annotation indicates a directly injected argument.
 
-    This is currently only used for `ToolRuntime`.
-    Checks if either the annotation itself is a subclass of `_DirectlyInjectedToolArg`
-    or the origin of the annotation is a subclass of `_DirectlyInjectedToolArg`.
+    This is currently only used for ToolRuntime.
+    Checks if either the annotation itself is a subclass of _DirectlyInjectedToolArg
+    or the origin of the annotation is a subclass of _DirectlyInjectedToolArg.
 
-    Ex: `ToolRuntime` or `ToolRuntime[ContextT, StateT]` would both return `True`.
+    Ex: ToolRuntime or ToolRuntime[ContextT, StateT] would both return True.
     """
     return (
         isinstance(type_, type) and issubclass(type_, _DirectlyInjectedToolArg)
@@ -1400,14 +1306,14 @@ def _is_injected_arg_type(
 def get_all_basemodel_annotations(
     cls: TypeBaseModel | Any, *, default_to_bound: bool = True
 ) -> dict[str, type | TypeVar]:
-    """Get all annotations from a Pydantic `BaseModel` and its parents.
+    """Get all annotations from a Pydantic BaseModel and its parents.
 
     Args:
-        cls: The Pydantic `BaseModel` class.
-        default_to_bound: Whether to default to the bound of a `TypeVar` if it exists.
+        cls: The Pydantic BaseModel class.
+        default_to_bound: Whether to default to the bound of a TypeVar if it exists.
 
     Returns:
-        `dict` of field names to their type annotations.
+        A dictionary of field names to their type annotations.
     """
     # cls has no subscript: cls = FooBar
     if isinstance(cls, type):
@@ -1473,15 +1379,15 @@ def _replace_type_vars(
     *,
     default_to_bound: bool = True,
 ) -> type | TypeVar:
-    """Replace `TypeVar`s in a type annotation with concrete types.
+    """Replace TypeVars in a type annotation with concrete types.
 
     Args:
         type_: The type annotation to process.
-        generic_map: Mapping of `TypeVar`s to concrete types.
-        default_to_bound: Whether to use `TypeVar` bounds as defaults.
+        generic_map: Mapping of TypeVars to concrete types.
+        default_to_bound: Whether to use TypeVar bounds as defaults.
 
     Returns:
-        The type with `TypeVar`s replaced.
+        The type with TypeVars replaced.
     """
     generic_map = generic_map or {}
     if isinstance(type_, TypeVar):

libs/core/langchain_core/tools/convert.py

@@ -81,72 +81,57 @@ def tool(
     parse_docstring: bool = False,
     error_on_invalid_docstring: bool = True,
 ) -> BaseTool | Callable[[Callable | Runnable], BaseTool]:
-    """Convert Python functions and `Runnables` to LangChain tools.
-
-    Can be used as a decorator with or without arguments to create tools from functions.
-
-    Functions can have any signature - the tool will automatically infer input schemas
-    unless disabled.
-
-    !!! note "Requirements"
-        - Functions must have type hints for proper schema inference
-        - When `infer_schema=False`, functions must be `(str) -> str` and have
-            docstrings
-        - When using with `Runnable`, a string name must be provided
+    """Make tools out of Python functions, can be used with or without arguments.
 
     Args:
-        name_or_callable: Optional name of the tool or the `Callable` to be
-            converted to a tool. Overrides the function's name.
-
-            Must be provided as a positional argument.
-        runnable: Optional `Runnable` to convert to a tool.
-
-            Must be provided as a positional argument.
+        name_or_callable: Optional name of the tool or the callable to be
+            converted to a tool. Must be provided as a positional argument.
+        runnable: Optional runnable to convert to a tool. Must be provided as a
+            positional argument.
         description: Optional description for the tool.
-
             Precedence for the tool description value is as follows:
 
-            - This `description` argument
+            - `description` argument
                 (used even if docstring and/or `args_schema` are provided)
             - Tool function docstring
                 (used even if `args_schema` is provided)
             - `args_schema` description
-                (used only if `description` and docstring are not provided)
+                (used only if `description` / docstring are not provided)
         *args: Extra positional arguments. Must be empty.
-        return_direct: Whether to return directly from the tool rather than continuing
-            the agent loop.
+        return_direct: Whether to return directly from the tool rather
+            than continuing the agent loop.
         args_schema: Optional argument schema for user to specify.
-        infer_schema: Whether to infer the schema of the arguments from the function's
-            signature. This also makes the resultant tool accept a dictionary input to
-            its `run()` function.
-        response_format: The tool response format.
 
-            If `'content'`, then the output of the tool is interpreted as the contents
-            of a `ToolMessage`.
-
-            If `'content_and_artifact'`, then the output is expected to be a two-tuple
+        infer_schema: Whether to infer the schema of the arguments from
+            the function's signature. This also makes the resultant tool
+            accept a dictionary input to its `run()` function.
+        response_format: The tool response format. If `"content"` then the output of
+            the tool is interpreted as the contents of a `ToolMessage`. If
+            `"content_and_artifact"` then the output is expected to be a two-tuple
             corresponding to the `(content, artifact)` of a `ToolMessage`.
-        parse_docstring: If `infer_schema` and `parse_docstring`, will attempt to
+        parse_docstring: if `infer_schema` and `parse_docstring`, will attempt to
             parse parameter descriptions from Google Style function docstrings.
-        error_on_invalid_docstring: If `parse_docstring` is provided, configure
+        error_on_invalid_docstring: if `parse_docstring` is provided, configure
             whether to raise `ValueError` on invalid Google Style docstrings.
 
     Raises:
-        ValueError: If too many positional arguments are provided (e.g. violating the
-            `*args` constraint).
-        ValueError: If a `Runnable` is provided without a string name. When using `tool`
-            with a `Runnable`, a `str` name must be provided as the `name_or_callable`.
+        ValueError: If too many positional arguments are provided.
+        ValueError: If a runnable is provided without a string name.
         ValueError: If the first argument is not a string or callable with
             a `__name__` attribute.
         ValueError: If the function does not have a docstring and description
             is not provided and `infer_schema` is `False`.
         ValueError: If `parse_docstring` is `True` and the function has an invalid
             Google-style docstring and `error_on_invalid_docstring` is True.
-        ValueError: If a `Runnable` is provided that does not have an object schema.
+        ValueError: If a Runnable is provided that does not have an object schema.
 
     Returns:
         The tool.
 
+    Requires:
+        - Function must be of type `(str) -> str`
+        - Function must have a docstring
+
     Examples:
         ```python
         @tool

libs/core/langchain_core/tools/retriever.py

@@ -83,12 +83,11 @@ def create_retriever_tool(
             model, so should be descriptive.
         document_prompt: The prompt to use for the document.
         document_separator: The separator to use between documents.
-        response_format: The tool response format.
-
-            If `"content"` then the output of the tool is interpreted as the contents of
-            a `ToolMessage`. If `"content_and_artifact"` then the output is expected to
-            be a two-tuple corresponding to the `(content, artifact)` of a `ToolMessage`
-            (artifact being a list of documents in this case).
+        response_format: The tool response format. If `"content"` then the output of
+            the tool is interpreted as the contents of a `ToolMessage`. If
+            `"content_and_artifact"` then the output is expected to be a two-tuple
+            corresponding to the `(content, artifact)` of a `ToolMessage` (artifact
+            being a list of documents in this case).
 
     Returns:
         Tool class to pass to an agent.

libs/core/langchain_core/tools/structured.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import functools
 import textwrap
 from collections.abc import Awaitable, Callable
 from inspect import signature
@@ -22,12 +21,10 @@ from langchain_core.callbacks import (
 )
 from langchain_core.runnables import RunnableConfig, run_in_executor
 from langchain_core.tools.base import (
-    _EMPTY_SET,
     FILTERED_ARGS,
     ArgsSchema,
     BaseTool,
     _get_runnable_config_param,
-    _is_injected_arg_type,
     create_schema_from_function,
 )
 from langchain_core.utils.pydantic import is_basemodel_subclass
@@ -154,13 +151,11 @@ class StructuredTool(BaseTool):
             return_direct: Whether to return the result directly or as a callback.
             args_schema: The schema of the tool's input arguments.
             infer_schema: Whether to infer the schema from the function's signature.
-            response_format: The tool response format.
-
-                If `"content"` then the output of the tool is interpreted as the
-                contents of a `ToolMessage`. If `"content_and_artifact"` then the output
-                is expected to be a two-tuple corresponding to the `(content, artifact)`
-                of a `ToolMessage`.
-            parse_docstring: If `infer_schema` and `parse_docstring`, will attempt
+            response_format: The tool response format. If `"content"` then the output of
+                the tool is interpreted as the contents of a `ToolMessage`. If
+                `"content_and_artifact"` then the output is expected to be a two-tuple
+                corresponding to the `(content, artifact)` of a `ToolMessage`.
+            parse_docstring: if `infer_schema` and `parse_docstring`, will attempt
                 to parse parameter descriptions from Google Style function docstrings.
             error_on_invalid_docstring: if `parse_docstring` is provided, configure
                 whether to raise `ValueError` on invalid Google Style docstrings.
@@ -244,17 +239,6 @@ class StructuredTool(BaseTool):
             **kwargs,
         )
 
-    @functools.cached_property
-    def _injected_args_keys(self) -> frozenset[str]:
-        fn = self.func or self.coroutine
-        if fn is None:
-            return _EMPTY_SET
-        return frozenset(
-            k
-            for k, v in signature(fn).parameters.items()
-            if _is_injected_arg_type(v.annotation)
-        )
-
 
 def _filter_schema_args(func: Callable) -> list[str]:
     filter_args = list(FILTERED_ARGS)

libs/core/langchain_core/tracers/core.py

@@ -15,6 +15,12 @@ from typing import (
 
 from langchain_core.exceptions import TracerException
 from langchain_core.load import dumpd
+from langchain_core.outputs import (
+    ChatGeneration,
+    ChatGenerationChunk,
+    GenerationChunk,
+    LLMResult,
+)
 from langchain_core.tracers.schemas import Run
 
 if TYPE_CHECKING:
@@ -25,12 +31,6 @@ if TYPE_CHECKING:
 
     from langchain_core.documents import Document
     from langchain_core.messages import BaseMessage
-    from langchain_core.outputs import (
-        ChatGeneration,
-        ChatGenerationChunk,
-        GenerationChunk,
-        LLMResult,
-    )
 
 logger = logging.getLogger(__name__)
 

libs/core/langchain_core/utils/function_calling.py

@@ -8,6 +8,7 @@ import logging
 import types
 import typing
 import uuid
+from collections.abc import Callable
 from typing import (
     TYPE_CHECKING,
     Annotated,
@@ -32,8 +33,6 @@ from langchain_core.utils.json_schema import dereference_refs
 from langchain_core.utils.pydantic import is_basemodel_subclass
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
-
     from langchain_core.tools import BaseTool
 
 logger = logging.getLogger(__name__)
@@ -352,7 +351,7 @@ def convert_to_openai_function(
     Raises:
         ValueError: If function is not in a supported format.
 
-    !!! warning "Behavior changed in `langchain-core` 0.3.16"
+    !!! warning "Behavior changed in 0.3.16"
         `description` and `parameters` keys are now optional. Only `name` is
         required and guaranteed to be part of the output.
     """
@@ -413,7 +412,7 @@ def convert_to_openai_function(
     if strict is not None:
         if "strict" in oai_function and oai_function["strict"] != strict:
             msg = (
-                f"Tool/function already has a 'strict' key with value "
+                f"Tool/function already has a 'strict' key wth value "
                 f"{oai_function['strict']} which is different from the explicit "
                 f"`strict` arg received {strict=}."
             )
@@ -476,16 +475,16 @@ def convert_to_openai_tool(
         A dict version of the passed in tool which is compatible with the
         OpenAI tool-calling API.
 
-    !!! warning "Behavior changed in `langchain-core` 0.3.16"
+    !!! warning "Behavior changed in 0.3.16"
         `description` and `parameters` keys are now optional. Only `name` is
         required and guaranteed to be part of the output.
 
-    !!! warning "Behavior changed in `langchain-core` 0.3.44"
+    !!! warning "Behavior changed in 0.3.44"
         Return OpenAI Responses API-style tools unchanged. This includes
         any dict with `"type"` in `"file_search"`, `"function"`,
         `"computer_use_preview"`, `"web_search_preview"`.
 
-    !!! warning "Behavior changed in `langchain-core` 0.3.63"
+    !!! warning "Behavior changed in 0.3.63"
         Added support for OpenAI's image generation built-in tool.
     """
     # Import locally to prevent circular import
@@ -654,9 +653,6 @@ def tool_example_to_messages(
     return messages
 
 
-_MIN_DOCSTRING_BLOCKS = 2
-
-
 def _parse_google_docstring(
     docstring: str | None,
     args: list[str],
@@ -675,7 +671,7 @@ def _parse_google_docstring(
                 arg for arg in args if arg not in {"run_manager", "callbacks", "return"}
             }
             if filtered_annotations and (
-                len(docstring_blocks) < _MIN_DOCSTRING_BLOCKS
+                len(docstring_blocks) < 2
                 or not any(block.startswith("Args:") for block in docstring_blocks[1:])
             ):
                 msg = "Found invalid Google-Style docstring."

libs/core/langchain_core/utils/input.py

@@ -26,9 +26,6 @@ def get_color_mapping(
     colors = list(_TEXT_COLOR_MAPPING.keys())
     if excluded_colors is not None:
         colors = [c for c in colors if c not in excluded_colors]
-    if not colors:
-        msg = "No colors available after applying exclusions."
-        raise ValueError(msg)
     return {item: colors[i % len(colors)] for i, item in enumerate(items)}
 
 

libs/core/langchain_core/utils/json.py

@@ -4,13 +4,11 @@ from __future__ import annotations
 
 import json
 import re
-from typing import TYPE_CHECKING, Any
+from collections.abc import Callable
+from typing import Any
 
 from langchain_core.exceptions import OutputParserException
 
-if TYPE_CHECKING:
-    from collections.abc import Callable
-
 
 def _replace_new_line(match: re.Match[str]) -> str:
     value = match.group(2)

libs/core/langchain_core/utils/pydantic.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import inspect
 import textwrap
 import warnings
+from collections.abc import Callable
 from contextlib import nullcontext
 from functools import lru_cache, wraps
 from types import GenericAlias
@@ -40,12 +41,10 @@ from pydantic.json_schema import (
 )
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import create_model as create_model_v1
+from pydantic.v1.fields import ModelField
 from typing_extensions import deprecated, override
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
-
-    from pydantic.v1.fields import ModelField
     from pydantic_core import core_schema
 
 PYDANTIC_VERSION = version.parse(pydantic.__version__)
@@ -66,8 +65,8 @@ def get_pydantic_major_version() -> int:
 PYDANTIC_MAJOR_VERSION = PYDANTIC_VERSION.major
 PYDANTIC_MINOR_VERSION = PYDANTIC_VERSION.minor
 
-IS_PYDANTIC_V1 = False
-IS_PYDANTIC_V2 = True
+IS_PYDANTIC_V1 = PYDANTIC_VERSION.major == 1
+IS_PYDANTIC_V2 = PYDANTIC_VERSION.major == 2
 
 PydanticBaseModel = BaseModel
 TypeBaseModel = type[BaseModel]

libs/core/langchain_core/vectorstores/base.py

@@ -11,6 +11,7 @@ import logging
 import math
 import warnings
 from abc import ABC, abstractmethod
+from collections.abc import Callable
 from itertools import cycle
 from typing import (
     TYPE_CHECKING,
@@ -28,7 +29,7 @@ from langchain_core.retrievers import BaseRetriever, LangSmithRetrieverParams
 from langchain_core.runnables.config import run_in_executor
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Collection, Iterable, Iterator, Sequence
+    from collections.abc import Collection, Iterable, Iterator, Sequence
 
     from langchain_core.callbacks.manager import (
         AsyncCallbackManagerForRetrieverRun,
@@ -236,9 +237,8 @@ class VectorStore(ABC):
         Args:
             documents: Documents to add to the `VectorStore`.
             **kwargs: Additional keyword arguments.
-
-                If kwargs contains IDs and documents contain ids, the IDs in the kwargs
-                will receive precedence.
+                if kwargs contains IDs and documents contain ids,
+                the IDs in the kwargs will receive precedence.
 
         Returns:
             List of IDs of the added texts.
@@ -421,7 +421,7 @@ class VectorStore(ABC):
             **kwargs: Arguments to pass to the search method.
 
         Returns:
-            List of tuples of `(doc, similarity_score)`.
+            List of Tuples of `(doc, similarity_score)`.
         """
         raise NotImplementedError
 
@@ -435,7 +435,7 @@ class VectorStore(ABC):
             **kwargs: Arguments to pass to the search method.
 
         Returns:
-            List of tuples of `(doc, similarity_score)`.
+            List of Tuples of `(doc, similarity_score)`.
         """
         # This is a temporary workaround to make the similarity search
         # asynchronous. The proper solution is to make the similarity search
@@ -465,7 +465,7 @@ class VectorStore(ABC):
                 to filter the resulting set of retrieved docs
 
         Returns:
-            List of tuples of `(doc, similarity_score)`
+            List of Tuples of `(doc, similarity_score)`
         """
         relevance_score_fn = self._select_relevance_score_fn()
         docs_and_scores = self.similarity_search_with_score(query, k, **kwargs)
@@ -492,7 +492,7 @@ class VectorStore(ABC):
                 to filter the resulting set of retrieved docs
 
         Returns:
-            List of tuples of `(doc, similarity_score)`
+            List of Tuples of `(doc, similarity_score)`
         """
         relevance_score_fn = self._select_relevance_score_fn()
         docs_and_scores = await self.asimilarity_search_with_score(query, k, **kwargs)
@@ -516,7 +516,7 @@ class VectorStore(ABC):
                 to filter the resulting set of retrieved docs
 
         Returns:
-            List of tuples of `(doc, similarity_score)`.
+            List of Tuples of `(doc, similarity_score)`.
         """
         score_threshold = kwargs.pop("score_threshold", None)
 
@@ -565,7 +565,7 @@ class VectorStore(ABC):
                 to filter the resulting set of retrieved docs
 
         Returns:
-            List of tuples of `(doc, similarity_score)`
+            List of Tuples of `(doc, similarity_score)`
         """
         score_threshold = kwargs.pop("score_threshold", None)
 
@@ -667,7 +667,7 @@ class VectorStore(ABC):
             k: Number of `Document` objects to return.
             fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
             lambda_mult: Number between `0` and `1` that determines the degree
-                of diversity among the results with `0` corresponding
+                of diversity among the results with 0 corresponding
                 to maximum diversity and `1` to minimum diversity.
             **kwargs: Arguments to pass to the search method.
 
@@ -694,7 +694,7 @@ class VectorStore(ABC):
             k: Number of `Document` objects to return.
             fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
             lambda_mult: Number between `0` and `1` that determines the degree
-                of diversity among the results with `0` corresponding
+                of diversity among the results with 0 corresponding
                 to maximum diversity and `1` to minimum diversity.
             **kwargs: Arguments to pass to the search method.
 
@@ -732,7 +732,7 @@ class VectorStore(ABC):
             k: Number of `Document` objects to return.
             fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
             lambda_mult: Number between `0` and `1` that determines the degree
-                of diversity among the results with `0` corresponding
+                of diversity among the results with 0 corresponding
                 to maximum diversity and `1` to minimum diversity.
             **kwargs: Arguments to pass to the search method.
 
@@ -759,7 +759,7 @@ class VectorStore(ABC):
             k: Number of `Document` objects to return.
             fetch_k: Number of `Document` objects to fetch to pass to MMR algorithm.
             lambda_mult: Number between `0` and `1` that determines the degree
-                of diversity among the results with `0` corresponding
+                of diversity among the results with 0 corresponding
                 to maximum diversity and `1` to minimum diversity.
             **kwargs: Arguments to pass to the search method.
 

libs/core/langchain_core/vectorstores/in_memory.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import json
 import uuid
+from collections.abc import Callable
 from pathlib import Path
 from typing import (
     TYPE_CHECKING,
@@ -19,7 +20,7 @@ from langchain_core.vectorstores.utils import _cosine_similarity as cosine_simil
 from langchain_core.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Iterator, Sequence
+    from collections.abc import Iterator, Sequence
 
     from langchain_core.embeddings import Embeddings
 

libs/core/langchain_core/version.py

@@ -1,3 +1,3 @@
 """langchain-core version information and utilities."""
 
-VERSION = "1.0.6"
+VERSION = "1.0.1"

libs/core/pyproject.toml

@@ -3,13 +3,8 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [project]
-name = "langchain-core"
-description = "Building applications with LLMs through composability"
-license = {text = "MIT"}
-readme = "README.md"
 authors = []
-
-version = "1.0.6"
+license = {text = "MIT"}
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "langsmith>=0.3.45,<1.0.0",
@@ -20,6 +15,10 @@ dependencies = [
     "packaging>=23.2.0,<26.0.0",
     "pydantic>=2.7.4,<3.0.0",
 ]
+name = "langchain-core"
+version = "1.0.1"
+description = "Building applications with LLMs through composability"
+readme = "README.md"
 
 [project.urls]
 Homepage = "https://docs.langchain.com/"
@@ -36,7 +35,6 @@ typing = [
     "mypy>=1.18.1,<1.19.0",
     "types-pyyaml>=6.0.12.2,<7.0.0.0",
     "types-requests>=2.28.11.5,<3.0.0.0",
-    "langchain-model-profiles",
     "langchain-text-splitters",
 ]
 dev = [
@@ -58,7 +56,6 @@ test = [
     "blockbuster>=1.5.18,<1.6.0",
     "numpy>=1.26.4; python_version<'3.13'",
     "numpy>=2.1.0; python_version>='3.13'",
-    "langchain-model-profiles",
     "langchain-tests",
     "pytest-benchmark",
     "pytest-codspeed",
@@ -66,7 +63,6 @@ test = [
 test_integration = []
 
 [tool.uv.sources]
-langchain-model-profiles = { path = "../model-profiles" }
 langchain-tests = { path = "../standard-tests" }
 langchain-text-splitters = { path = "../text-splitters" }
 
@@ -105,6 +101,7 @@ ignore = [
     "ANN401",  # No Any types
     "BLE",     # Blind exceptions
     "ERA",     # No commented-out code
+    "PLR2004", # Comparison to magic number
 ]
 unfixable = [
     "B028",    # People should intentionally tune the stacklevel
@@ -125,7 +122,7 @@ ignore-var-parameters = true  # ignore missing documentation for *args and **kwa
 "langchain_core/utils/mustache.py" = [ "PLW0603",]
 "langchain_core/sys_info.py" = [ "T201",]
 "tests/unit_tests/test_tools.py" = [ "ARG",]
-"tests/**" = [ "D1", "PLR2004", "S", "SLF",]
+"tests/**" = [ "D1", "S", "SLF",]
 "scripts/**" = [ "INP", "S",]
 
 [tool.coverage.run]
@@ -133,10 +130,7 @@ omit = [ "tests/*",]
 
 [tool.pytest.ini_options]
 addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
-markers = [
-    "requires: mark tests as requiring a specific library",
-    "compile: mark placeholder test used to compile integration tests without running them",
-]
+markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them", ]
 asyncio_mode = "auto"
-asyncio_default_fixture_loop_scope = "function"
 filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",]
+asyncio_default_fixture_loop_scope = "function"

libs/core/tests/unit_tests/callbacks/test_async_callback_manager.py

@@ -148,65 +148,4 @@ async def test_inline_handlers_share_parent_context_multiple() -> None:
             2,
             3,
             3,
-        ]
-
-
-async def test_shielded_callback_context_preservation() -> None:
-    """Verify that shielded callbacks preserve context variables.
-
-    This test specifically addresses the issue where async callbacks decorated
-    with @shielded do not properly preserve context variables, breaking
-    instrumentation and other context-dependent functionality.
-
-    The issue manifests in callbacks that use the @shielded decorator:
-    * on_llm_end
-    * on_llm_error
-    * on_chain_end
-    * on_chain_error
-    * And other shielded callback methods
-    """
-    context_var: contextvars.ContextVar[str] = contextvars.ContextVar("test_context")
-
-    class ContextTestHandler(AsyncCallbackHandler):
-        """Handler that reads context variables in shielded callbacks."""
-
-        def __init__(self) -> None:
-            self.run_inline = False
-            self.context_values: list[str] = []
-
-        @override
-        async def on_llm_end(self, response: Any, **kwargs: Any) -> None:
-            """This method is decorated with @shielded in the run manager."""
-            # This should preserve the context variable value
-            self.context_values.append(context_var.get("not_found"))
-
-        @override
-        async def on_chain_end(self, outputs: Any, **kwargs: Any) -> None:
-            """This method is decorated with @shielded in the run manager."""
-            # This should preserve the context variable value
-            self.context_values.append(context_var.get("not_found"))
-
-    # Set up the test context
-    context_var.set("test_value")
-    handler = ContextTestHandler()
-    manager = AsyncCallbackManager(handlers=[handler])
-
-    # Create run managers that have the shielded methods
-    llm_managers = await manager.on_llm_start({}, ["test prompt"])
-    llm_run_manager = llm_managers[0]
-
-    chain_run_manager = await manager.on_chain_start({}, {"test": "input"})
-
-    # Test LLM end callback (which is shielded)
-    await llm_run_manager.on_llm_end({"response": "test"})  # type: ignore[arg-type]
-
-    # Test Chain end callback (which is shielded)
-    await chain_run_manager.on_chain_end({"output": "test"})
-
-    # The context should be preserved in shielded callbacks
-    # This was the main issue - shielded decorators were not preserving context
-    assert handler.context_values == ["test_value", "test_value"], (
-        f"Expected context values ['test_value', 'test_value'], "
-        f"but got {handler.context_values}. "
-        f"This indicates the shielded decorator is not preserving context variables."
-    )
+        ], f"Expected order of states was broken due to context loss. Got {states}"

libs/core/tests/unit_tests/indexing/test_hashed_document.py

@@ -33,7 +33,7 @@ def test_hashing() -> None:
     # hash should be deterministic
     assert hashed_document.id == "fd1dc827-051b-537d-a1fe-1fa043e8b276"
 
-    # Verify that hashing with sha1 is deterministic
+    # Verify that hashing with sha1 is determinstic
     another_hashed_document = _get_document_with_hash(document, key_encoder="sha1")
     assert another_hashed_document.id == hashed_document.id
 

libs/core/tests/unit_tests/language_models/chat_models/test_base.py

@@ -18,7 +18,6 @@ from langchain_core.language_models import (
     ParrotFakeChatModel,
 )
 from langchain_core.language_models._utils import _normalize_messages
-from langchain_core.language_models.chat_models import _generate_response_from_error
 from langchain_core.language_models.fake_chat_models import (
     FakeListChatModelError,
     GenericFakeChatModel,
@@ -1220,108 +1219,55 @@ def test_get_ls_params() -> None:
     assert ls_params["ls_stop"] == ["stop"]
 
 
-def test_model_profiles() -> None:
-    model = GenericFakeChatModel(messages=iter([]))
-    profile = model.profile
-    assert profile == {}
+@pytest.mark.parametrize("output_version", ["v0", "v1"])
+def test_model_provider_on_metadata(output_version: str) -> None:
+    """Test we assign model_provider to response metadata."""
+    messages = [AIMessage("hello")]
+    chunks = [AIMessageChunk(content="good"), AIMessageChunk(content="bye")]
 
-    class MyModel(GenericFakeChatModel):
-        model: str = "gpt-5"
-
-        @property
-        def _llm_type(self) -> str:
-            return "openai-chat"
-
-    model = MyModel(messages=iter([]))
-    profile = model.profile
-    assert profile
-
-
-class MockResponse:
-    """Mock response for testing _generate_response_from_error."""
-
-    def __init__(
-        self,
-        status_code: int = 400,
-        headers: dict[str, str] | None = None,
-        json_data: dict[str, Any] | None = None,
-        json_raises: type[Exception] | None = None,
-        text_raises: type[Exception] | None = None,
-    ):
-        self.status_code = status_code
-        self.headers = headers or {}
-        self._json_data = json_data
-        self._json_raises = json_raises
-        self._text_raises = text_raises
-
-    def json(self) -> dict[str, Any]:
-        if self._json_raises:
-            msg = "JSON parsing failed"
-            raise self._json_raises(msg)
-        return self._json_data or {}
-
-    @property
-    def text(self) -> str:
-        if self._text_raises:
-            msg = "Text access failed"
-            raise self._text_raises(msg)
-        return ""
-
-
-class MockAPIError(Exception):
-    """Mock API error with response attribute."""
-
-    def __init__(self, message: str, response: MockResponse | None = None):
-        super().__init__(message)
-        self.message = message
-        if response is not None:
-            self.response = response
-
-
-def test_generate_response_from_error_with_valid_json() -> None:
-    """Test `_generate_response_from_error` with valid JSON response."""
-    response = MockResponse(
-        status_code=400,
-        headers={"content-type": "application/json"},
-        json_data={"error": {"message": "Bad request", "type": "invalid_request"}},
+    model = _AnotherFakeChatModel(
+        responses=iter(messages),
+        chunks=iter(chunks),
+        output_version=output_version,
+        model_provider="provider_foo",
     )
-    error = MockAPIError("API Error", response=response)
 
-    generations = _generate_response_from_error(error)
+    response = model.invoke("hello")
+    assert response.response_metadata["model_provider"] == "provider_foo"
 
-    assert len(generations) == 1
-    generation = generations[0]
-    assert isinstance(generation, ChatGeneration)
-    assert isinstance(generation.message, AIMessage)
-    assert generation.message.content == ""
+    response = model.invoke("hello", stream=True)
+    assert response.response_metadata["model_provider"] == "provider_foo"
 
-    metadata = generation.message.response_metadata
-    assert metadata["body"] == {
-        "error": {"message": "Bad request", "type": "invalid_request"}
-    }
-    assert metadata["headers"] == {"content-type": "application/json"}
-    assert metadata["status_code"] == 400
+    model.chunks = iter([AIMessageChunk(content="good"), AIMessageChunk(content="bye")])
+    full: AIMessageChunk | None = None
+    for chunk in model.stream("hello"):
+        full = chunk if full is None else full + chunk
+    assert full is not None
+    assert full.response_metadata["model_provider"] == "provider_foo"
 
 
-def test_generate_response_from_error_handles_streaming_response_failure() -> None:
-    # Simulates scenario where accessing response.json() or response.text
-    # raises ResponseNotRead on streaming responses
-    response = MockResponse(
-        status_code=400,
-        headers={"content-type": "application/json"},
-        json_raises=Exception,  # Simulates ResponseNotRead or similar
-        text_raises=Exception,
+@pytest.mark.parametrize("output_version", ["v0", "v1"])
+async def test_model_provider_on_metadata_async(output_version: str) -> None:
+    """Test we assign model_provider to response metadata."""
+    messages = [AIMessage("hello")]
+    chunks = [AIMessageChunk(content="good"), AIMessageChunk(content="bye")]
+
+    model = _AnotherFakeChatModel(
+        responses=iter(messages),
+        chunks=iter(chunks),
+        output_version=output_version,
+        model_provider="provider_foo",
     )
-    error = MockAPIError("API Error", response=response)
 
-    # This should NOT raise an exception, but should handle it gracefully
-    generations = _generate_response_from_error(error)
+    response = await model.ainvoke("hello")
+    assert response.response_metadata["model_provider"] == "provider_foo"
 
-    assert len(generations) == 1
-    generation = generations[0]
-    metadata = generation.message.response_metadata
+    response = await model.ainvoke("hello", stream=True)
+    assert response.response_metadata["model_provider"] == "provider_foo"
 
-    # When both fail, body should be None instead of raising an exception
-    assert metadata["body"] is None
-    assert metadata["headers"] == {"content-type": "application/json"}
-    assert metadata["status_code"] == 400
+    model.chunks = iter([AIMessageChunk(content="good"), AIMessageChunk(content="bye")])
+    full: AIMessageChunk | None = None
+    async for chunk in model.astream("hello"):
+        full = chunk if full is None else full + chunk
+    assert full is not None
+    assert full.response_metadata["model_provider"] == "provider_foo"

libs/core/tests/unit_tests/messages/block_translators/test_groq.py

@@ -1,140 +0,0 @@
-"""Test groq block translator."""
-
-from typing import cast
-
-import pytest
-
-from langchain_core.messages import AIMessage
-from langchain_core.messages import content as types
-from langchain_core.messages.base import _extract_reasoning_from_additional_kwargs
-from langchain_core.messages.block_translators import PROVIDER_TRANSLATORS
-from langchain_core.messages.block_translators.groq import (
-    _parse_code_json,
-    translate_content,
-)
-
-
-def test_groq_translator_registered() -> None:
-    """Test that groq translator is properly registered."""
-    assert "groq" in PROVIDER_TRANSLATORS
-    assert "translate_content" in PROVIDER_TRANSLATORS["groq"]
-    assert "translate_content_chunk" in PROVIDER_TRANSLATORS["groq"]
-
-
-def test_extract_reasoning_from_additional_kwargs_exists() -> None:
-    """Test that _extract_reasoning_from_additional_kwargs can be imported."""
-    # Verify it's callable
-    assert callable(_extract_reasoning_from_additional_kwargs)
-
-
-def test_groq_translate_content_basic() -> None:
-    """Test basic groq content translation."""
-    # Test with simple text message
-    message = AIMessage(content="Hello world")
-    blocks = translate_content(message)
-
-    assert isinstance(blocks, list)
-    assert len(blocks) == 1
-    assert blocks[0]["type"] == "text"
-    assert blocks[0]["text"] == "Hello world"
-
-
-def test_groq_translate_content_with_reasoning() -> None:
-    """Test groq content translation with reasoning content."""
-    # Test with reasoning content in additional_kwargs
-    message = AIMessage(
-        content="Final answer",
-        additional_kwargs={"reasoning_content": "Let me think about this..."},
-    )
-    blocks = translate_content(message)
-
-    assert isinstance(blocks, list)
-    assert len(blocks) == 2
-
-    # First block should be reasoning
-    assert blocks[0]["type"] == "reasoning"
-    assert blocks[0]["reasoning"] == "Let me think about this..."
-
-    # Second block should be text
-    assert blocks[1]["type"] == "text"
-    assert blocks[1]["text"] == "Final answer"
-
-
-def test_groq_translate_content_with_tool_calls() -> None:
-    """Test groq content translation with tool calls."""
-    # Test with tool calls
-    message = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "name": "search",
-                "args": {"query": "test"},
-                "id": "call_123",
-            }
-        ],
-    )
-    blocks = translate_content(message)
-
-    assert isinstance(blocks, list)
-    assert len(blocks) == 1
-    assert blocks[0]["type"] == "tool_call"
-    assert blocks[0]["name"] == "search"
-    assert blocks[0]["args"] == {"query": "test"}
-    assert blocks[0]["id"] == "call_123"
-
-
-def test_groq_translate_content_with_executed_tools() -> None:
-    """Test groq content translation with executed tools (built-in tools)."""
-    # Test with executed_tools in additional_kwargs (Groq built-in tools)
-    message = AIMessage(
-        content="",
-        additional_kwargs={
-            "executed_tools": [
-                {
-                    "type": "python",
-                    "arguments": '{"code": "print(\\"hello\\")"}',
-                    "output": "hello\\n",
-                }
-            ]
-        },
-    )
-    blocks = translate_content(message)
-
-    assert isinstance(blocks, list)
-    # Should have server_tool_call and server_tool_result
-    assert len(blocks) >= 2
-
-    # Check for server_tool_call
-    tool_call_blocks = [
-        cast("types.ServerToolCall", b)
-        for b in blocks
-        if b.get("type") == "server_tool_call"
-    ]
-    assert len(tool_call_blocks) == 1
-    assert tool_call_blocks[0]["name"] == "code_interpreter"
-    assert "code" in tool_call_blocks[0]["args"]
-
-    # Check for server_tool_result
-    tool_result_blocks = [
-        cast("types.ServerToolResult", b)
-        for b in blocks
-        if b.get("type") == "server_tool_result"
-    ]
-    assert len(tool_result_blocks) == 1
-    assert tool_result_blocks[0]["output"] == "hello\\n"
-    assert tool_result_blocks[0]["status"] == "success"
-
-
-def test_parse_code_json() -> None:
-    """Test the _parse_code_json helper function."""
-    # Test valid code JSON
-    result = _parse_code_json('{"code": "print(\'hello\')"}')
-    assert result == {"code": "print('hello')"}
-
-    # Test code with unescaped quotes (Groq format)
-    result = _parse_code_json('{"code": "print("hello")"}')
-    assert result == {"code": 'print("hello")'}
-
-    # Test invalid format raises ValueError
-    with pytest.raises(ValueError, match="Could not extract Python code"):
-        _parse_code_json('{"invalid": "format"}')

libs/core/tests/unit_tests/output_parsers/test_openai_tools.py

@@ -1,4 +1,3 @@
-import sys
 from collections.abc import AsyncIterator, Iterator
 from typing import Any
 
@@ -887,461 +886,3 @@ def test_max_tokens_error(caplog: Any) -> None:
         "`max_tokens` stop reason" in msg and record.levelname == "ERROR"
         for record, msg in zip(caplog.records, caplog.messages, strict=False)
     )
-
-
-def test_pydantic_tools_parser_with_mixed_pydantic_versions() -> None:
-    """Test PydanticToolsParser with both Pydantic v1 and v2 models."""
-    # For Python 3.14+ compatibility, use create_model for Pydantic v1
-    if sys.version_info >= (3, 14):
-        WeatherV1 = pydantic.v1.create_model(  # noqa: N806
-            "WeatherV1",
-            __doc__="Weather information using Pydantic v1.",
-            temperature=(int, ...),
-            conditions=(str, ...),
-        )
-    else:
-
-        class WeatherV1(pydantic.v1.BaseModel):
-            """Weather information using Pydantic v1."""
-
-            temperature: int
-            conditions: str
-
-    class LocationV2(BaseModel):
-        """Location information using Pydantic v2."""
-
-        city: str
-        country: str
-
-    # Test with Pydantic v1 model
-    parser_v1 = PydanticToolsParser(tools=[WeatherV1])
-    message_v1 = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_weather",
-                "name": "WeatherV1",
-                "args": {"temperature": 25, "conditions": "sunny"},
-            }
-        ],
-    )
-    generation_v1 = ChatGeneration(message=message_v1)
-    result_v1 = parser_v1.parse_result([generation_v1])
-
-    assert len(result_v1) == 1
-    assert isinstance(result_v1[0], WeatherV1)
-    assert result_v1[0].temperature == 25  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1[0].conditions == "sunny"  # type: ignore[attr-defined,unused-ignore]
-
-    # Test with Pydantic v2 model
-    parser_v2 = PydanticToolsParser(tools=[LocationV2])
-    message_v2 = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_location",
-                "name": "LocationV2",
-                "args": {"city": "Paris", "country": "France"},
-            }
-        ],
-    )
-    generation_v2 = ChatGeneration(message=message_v2)
-    result_v2 = parser_v2.parse_result([generation_v2])
-
-    assert len(result_v2) == 1
-    assert isinstance(result_v2[0], LocationV2)
-    assert result_v2[0].city == "Paris"
-    assert result_v2[0].country == "France"
-
-    # Test with both v1 and v2 models
-    parser_mixed = PydanticToolsParser(tools=[WeatherV1, LocationV2])
-    message_mixed = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_weather",
-                "name": "WeatherV1",
-                "args": {"temperature": 20, "conditions": "cloudy"},
-            },
-            {
-                "id": "call_location",
-                "name": "LocationV2",
-                "args": {"city": "London", "country": "UK"},
-            },
-        ],
-    )
-    generation_mixed = ChatGeneration(message=message_mixed)
-    result_mixed = parser_mixed.parse_result([generation_mixed])
-
-    assert len(result_mixed) == 2
-    assert isinstance(result_mixed[0], WeatherV1)
-    assert result_mixed[0].temperature == 20  # type: ignore[attr-defined,unused-ignore]
-    assert isinstance(result_mixed[1], LocationV2)
-    assert result_mixed[1].city == "London"
-
-
-def test_pydantic_tools_parser_with_custom_title() -> None:
-    """Test PydanticToolsParser with Pydantic v2 model using custom title."""
-
-    class CustomTitleTool(BaseModel):
-        """Tool with custom title in model config."""
-
-        model_config = {"title": "MyCustomToolName"}
-
-        value: int
-        description: str
-
-    # Test with custom title - tool should be callable by custom name
-    parser = PydanticToolsParser(tools=[CustomTitleTool])
-    message = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_custom",
-                "name": "MyCustomToolName",
-                "args": {"value": 42, "description": "test"},
-            }
-        ],
-    )
-    generation = ChatGeneration(message=message)
-    result = parser.parse_result([generation])
-
-    assert len(result) == 1
-    assert isinstance(result[0], CustomTitleTool)
-    assert result[0].value == 42
-    assert result[0].description == "test"
-
-
-def test_pydantic_tools_parser_name_dict_fallback() -> None:
-    """Test that name_dict properly falls back to __name__ when title is None."""
-
-    class ToolWithoutTitle(BaseModel):
-        """Tool without explicit title."""
-
-        data: str
-
-    # Ensure model_config doesn't have a title or it's None
-    # (This is the default behavior)
-    parser = PydanticToolsParser(tools=[ToolWithoutTitle])
-    message = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_no_title",
-                "name": "ToolWithoutTitle",
-                "args": {"data": "test_data"},
-            }
-        ],
-    )
-    generation = ChatGeneration(message=message)
-    result = parser.parse_result([generation])
-
-    assert len(result) == 1
-    assert isinstance(result[0], ToolWithoutTitle)
-    assert result[0].data == "test_data"
-
-
-def test_pydantic_tools_parser_with_nested_models() -> None:
-    """Test PydanticToolsParser with nested Pydantic v1 and v2 models."""
-    # Nested v1 models
-    if sys.version_info >= (3, 14):
-        AddressV1 = pydantic.v1.create_model(  # noqa: N806
-            "AddressV1",
-            __doc__="Address using Pydantic v1.",
-            street=(str, ...),
-            city=(str, ...),
-            zip_code=(str, ...),
-        )
-        PersonV1 = pydantic.v1.create_model(  # noqa: N806
-            "PersonV1",
-            __doc__="Person with nested address using Pydantic v1.",
-            name=(str, ...),
-            age=(int, ...),
-            address=(AddressV1, ...),
-        )
-    else:
-
-        class AddressV1(pydantic.v1.BaseModel):
-            """Address using Pydantic v1."""
-
-            street: str
-            city: str
-            zip_code: str
-
-        class PersonV1(pydantic.v1.BaseModel):
-            """Person with nested address using Pydantic v1."""
-
-            name: str
-            age: int
-            address: AddressV1
-
-    # Nested v2 models
-    class CoordinatesV2(BaseModel):
-        """Coordinates using Pydantic v2."""
-
-        latitude: float
-        longitude: float
-
-    class LocationV2(BaseModel):
-        """Location with nested coordinates using Pydantic v2."""
-
-        name: str
-        coordinates: CoordinatesV2
-
-    # Test with nested Pydantic v1 model
-    parser_v1 = PydanticToolsParser(tools=[PersonV1])
-    message_v1 = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_person",
-                "name": "PersonV1",
-                "args": {
-                    "name": "Alice",
-                    "age": 30,
-                    "address": {
-                        "street": "123 Main St",
-                        "city": "Springfield",
-                        "zip_code": "12345",
-                    },
-                },
-            }
-        ],
-    )
-    generation_v1 = ChatGeneration(message=message_v1)
-    result_v1 = parser_v1.parse_result([generation_v1])
-
-    assert len(result_v1) == 1
-    assert isinstance(result_v1[0], PersonV1)
-    assert result_v1[0].name == "Alice"  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1[0].age == 30  # type: ignore[attr-defined,unused-ignore]
-    assert isinstance(result_v1[0].address, AddressV1)  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1[0].address.street == "123 Main St"  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1[0].address.city == "Springfield"  # type: ignore[attr-defined,unused-ignore]
-
-    # Test with nested Pydantic v2 model
-    parser_v2 = PydanticToolsParser(tools=[LocationV2])
-    message_v2 = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_location",
-                "name": "LocationV2",
-                "args": {
-                    "name": "Eiffel Tower",
-                    "coordinates": {"latitude": 48.8584, "longitude": 2.2945},
-                },
-            }
-        ],
-    )
-    generation_v2 = ChatGeneration(message=message_v2)
-    result_v2 = parser_v2.parse_result([generation_v2])
-
-    assert len(result_v2) == 1
-    assert isinstance(result_v2[0], LocationV2)
-    assert result_v2[0].name == "Eiffel Tower"
-    assert isinstance(result_v2[0].coordinates, CoordinatesV2)
-    assert result_v2[0].coordinates.latitude == 48.8584
-    assert result_v2[0].coordinates.longitude == 2.2945
-
-    # Test with both nested models in one message
-    parser_mixed = PydanticToolsParser(tools=[PersonV1, LocationV2])
-    message_mixed = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_person",
-                "name": "PersonV1",
-                "args": {
-                    "name": "Bob",
-                    "age": 25,
-                    "address": {
-                        "street": "456 Oak Ave",
-                        "city": "Portland",
-                        "zip_code": "97201",
-                    },
-                },
-            },
-            {
-                "id": "call_location",
-                "name": "LocationV2",
-                "args": {
-                    "name": "Golden Gate Bridge",
-                    "coordinates": {"latitude": 37.8199, "longitude": -122.4783},
-                },
-            },
-        ],
-    )
-    generation_mixed = ChatGeneration(message=message_mixed)
-    result_mixed = parser_mixed.parse_result([generation_mixed])
-
-    assert len(result_mixed) == 2
-    assert isinstance(result_mixed[0], PersonV1)
-    assert result_mixed[0].name == "Bob"  # type: ignore[attr-defined,unused-ignore]
-    assert result_mixed[0].address.city == "Portland"  # type: ignore[attr-defined,unused-ignore]
-    assert isinstance(result_mixed[1], LocationV2)
-    assert result_mixed[1].name == "Golden Gate Bridge"
-    assert result_mixed[1].coordinates.latitude == 37.8199
-
-
-def test_pydantic_tools_parser_with_optional_fields() -> None:
-    """Test PydanticToolsParser with optional fields in v1 and v2 models."""
-    if sys.version_info >= (3, 14):
-        ProductV1 = pydantic.v1.create_model(  # noqa: N806
-            "ProductV1",
-            __doc__="Product with optional fields using Pydantic v1.",
-            name=(str, ...),
-            price=(float, ...),
-            description=(str | None, None),
-            stock=(int, 0),
-        )
-    else:
-
-        class ProductV1(pydantic.v1.BaseModel):
-            """Product with optional fields using Pydantic v1."""
-
-            name: str
-            price: float
-            description: str | None = None
-            stock: int = 0
-
-    # v2 model with optional fields
-    class UserV2(BaseModel):
-        """User with optional fields using Pydantic v2."""
-
-        username: str
-        email: str
-        bio: str | None = None
-        age: int | None = None
-
-    # Test v1 with all fields provided
-    parser_v1_full = PydanticToolsParser(tools=[ProductV1])
-    message_v1_full = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_product_full",
-                "name": "ProductV1",
-                "args": {
-                    "name": "Laptop",
-                    "price": 999.99,
-                    "description": "High-end laptop",
-                    "stock": 50,
-                },
-            }
-        ],
-    )
-    generation_v1_full = ChatGeneration(message=message_v1_full)
-    result_v1_full = parser_v1_full.parse_result([generation_v1_full])
-
-    assert len(result_v1_full) == 1
-    assert isinstance(result_v1_full[0], ProductV1)
-    assert result_v1_full[0].name == "Laptop"  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1_full[0].price == 999.99  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1_full[0].description == "High-end laptop"  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1_full[0].stock == 50  # type: ignore[attr-defined,unused-ignore]
-
-    # Test v1 with only required fields
-    parser_v1_minimal = PydanticToolsParser(tools=[ProductV1])
-    message_v1_minimal = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_product_minimal",
-                "name": "ProductV1",
-                "args": {"name": "Mouse", "price": 29.99},
-            }
-        ],
-    )
-    generation_v1_minimal = ChatGeneration(message=message_v1_minimal)
-    result_v1_minimal = parser_v1_minimal.parse_result([generation_v1_minimal])
-
-    assert len(result_v1_minimal) == 1
-    assert isinstance(result_v1_minimal[0], ProductV1)
-    assert result_v1_minimal[0].name == "Mouse"  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1_minimal[0].price == 29.99  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1_minimal[0].description is None  # type: ignore[attr-defined,unused-ignore]
-    assert result_v1_minimal[0].stock == 0  # type: ignore[attr-defined,unused-ignore]
-
-    # Test v2 with all fields provided
-    parser_v2_full = PydanticToolsParser(tools=[UserV2])
-    message_v2_full = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_user_full",
-                "name": "UserV2",
-                "args": {
-                    "username": "john_doe",
-                    "email": "john@example.com",
-                    "bio": "Software developer",
-                    "age": 28,
-                },
-            }
-        ],
-    )
-    generation_v2_full = ChatGeneration(message=message_v2_full)
-    result_v2_full = parser_v2_full.parse_result([generation_v2_full])
-
-    assert len(result_v2_full) == 1
-    assert isinstance(result_v2_full[0], UserV2)
-    assert result_v2_full[0].username == "john_doe"
-    assert result_v2_full[0].email == "john@example.com"
-    assert result_v2_full[0].bio == "Software developer"
-    assert result_v2_full[0].age == 28
-
-    # Test v2 with only required fields
-    parser_v2_minimal = PydanticToolsParser(tools=[UserV2])
-    message_v2_minimal = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_user_minimal",
-                "name": "UserV2",
-                "args": {"username": "jane_smith", "email": "jane@example.com"},
-            }
-        ],
-    )
-    generation_v2_minimal = ChatGeneration(message=message_v2_minimal)
-    result_v2_minimal = parser_v2_minimal.parse_result([generation_v2_minimal])
-
-    assert len(result_v2_minimal) == 1
-    assert isinstance(result_v2_minimal[0], UserV2)
-    assert result_v2_minimal[0].username == "jane_smith"
-    assert result_v2_minimal[0].email == "jane@example.com"
-    assert result_v2_minimal[0].bio is None
-    assert result_v2_minimal[0].age is None
-
-    # Test mixed v1 and v2 with partial optional fields
-    parser_mixed = PydanticToolsParser(tools=[ProductV1, UserV2])
-    message_mixed = AIMessage(
-        content="",
-        tool_calls=[
-            {
-                "id": "call_product",
-                "name": "ProductV1",
-                "args": {"name": "Keyboard", "price": 79.99, "stock": 100},
-            },
-            {
-                "id": "call_user",
-                "name": "UserV2",
-                "args": {
-                    "username": "alice",
-                    "email": "alice@example.com",
-                    "age": 35,
-                },
-            },
-        ],
-    )
-    generation_mixed = ChatGeneration(message=message_mixed)
-    result_mixed = parser_mixed.parse_result([generation_mixed])
-
-    assert len(result_mixed) == 2
-    assert isinstance(result_mixed[0], ProductV1)
-    assert result_mixed[0].name == "Keyboard"  # type: ignore[attr-defined,unused-ignore]
-    assert result_mixed[0].description is None  # type: ignore[attr-defined,unused-ignore]
-    assert result_mixed[0].stock == 100  # type: ignore[attr-defined,unused-ignore]
-    assert isinstance(result_mixed[1], UserV2)
-    assert result_mixed[1].username == "alice"
-    assert result_mixed[1].bio is None
-    assert result_mixed[1].age == 35

libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr

@@ -682,7 +682,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -800,7 +800,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -1319,7 +1319,7 @@
               }
               ```
           
-          !!! 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"
@@ -2096,7 +2096,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -2214,7 +2214,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -2733,7 +2733,7 @@
               }
               ```
           
-          !!! 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"

libs/core/tests/unit_tests/prompts/test_chat.py

@@ -1193,350 +1193,3 @@ def test_dict_message_prompt_template_errors_on_jinja2() -> None:
         _ = ChatPromptTemplate.from_messages(
             [("human", [prompt])], template_format="jinja2"
         )
-
-
-def test_rendering_prompt_with_conditionals_no_empty_text_blocks() -> None:
-    manifest = {
-        "lc": 1,
-        "type": "constructor",
-        "id": ["langchain_core", "prompts", "chat", "ChatPromptTemplate"],
-        "kwargs": {
-            "messages": [
-                {
-                    "lc": 1,
-                    "type": "constructor",
-                    "id": [
-                        "langchain_core",
-                        "prompts",
-                        "chat",
-                        "SystemMessagePromptTemplate",
-                    ],
-                    "kwargs": {
-                        "prompt": {
-                            "lc": 1,
-                            "type": "constructor",
-                            "id": [
-                                "langchain_core",
-                                "prompts",
-                                "prompt",
-                                "PromptTemplate",
-                            ],
-                            "kwargs": {
-                                "input_variables": [],
-                                "template_format": "mustache",
-                                "template": "Always echo back whatever I send you.",
-                            },
-                        },
-                    },
-                },
-                {
-                    "lc": 1,
-                    "type": "constructor",
-                    "id": [
-                        "langchain_core",
-                        "prompts",
-                        "chat",
-                        "HumanMessagePromptTemplate",
-                    ],
-                    "kwargs": {
-                        "prompt": [
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": [],
-                                    "template_format": "mustache",
-                                    "template": "Here is the teacher's prompt:",
-                                    "additional_content_fields": {
-                                        "text": "Here is the teacher's prompt:",
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["promptDescription"],
-                                    "template_format": "mustache",
-                                    "template": '"{{promptDescription}}"\n',
-                                    "additional_content_fields": {
-                                        "text": '"{{promptDescription}}"\n',
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": [],
-                                    "template_format": "mustache",
-                                    "template": "Here is the expected answer or success criteria given by the teacher:",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "Here is the expected answer or success criteria given by the teacher:",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["expectedResponse"],
-                                    "template_format": "mustache",
-                                    "template": '"{{expectedResponse}}"\n',
-                                    "additional_content_fields": {
-                                        "text": '"{{expectedResponse}}"\n',
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": [],
-                                    "template_format": "mustache",
-                                    "template": "Note: This may be just one example of many possible correct ways for the student to respond.\n",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "Note: This may be just one example of many possible correct ways for the student to respond.\n",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": [],
-                                    "template_format": "mustache",
-                                    "template": "For your evaluation of the student's response:\n",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "For your evaluation of the student's response:\n",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": [],
-                                    "template_format": "mustache",
-                                    "template": "Here is a transcript of the student's explanation:",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "Here is a transcript of the student's explanation:",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["responseTranscript"],
-                                    "template_format": "mustache",
-                                    "template": '"{{responseTranscript}}"\n',
-                                    "additional_content_fields": {
-                                        "text": '"{{responseTranscript}}"\n',
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["readingFluencyAnalysis"],
-                                    "template_format": "mustache",
-                                    "template": "{{#readingFluencyAnalysis}} For this task, the student's reading pronunciation and fluency were important. Here is analysis of the student's oral response: \"{{readingFluencyAnalysis}}\" {{/readingFluencyAnalysis}}",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "{{#readingFluencyAnalysis}} For this task, the student's reading pronunciation and fluency were important. Here is analysis of the student's oral response: \"{{readingFluencyAnalysis}}\" {{/readingFluencyAnalysis}}",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["readingFluencyAnalysis"],
-                                    "template_format": "mustache",
-                                    "template": "{{#readingFluencyAnalysis}}Root analysis of the student's response (step 3) in this oral analysis rather than inconsistencies in the transcript.{{/readingFluencyAnalysis}}",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "{{#readingFluencyAnalysis}}Root analysis of the student's response (step 3) in this oral analysis rather than inconsistencies in the transcript.{{/readingFluencyAnalysis}}",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["readingFluencyAnalysis"],
-                                    "template_format": "mustache",
-                                    "template": "{{#readingFluencyAnalysis}}Remember this is a student, so we care about general fluency - not voice acting. {{/readingFluencyAnalysis}}\n",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "{{#readingFluencyAnalysis}}Remember this is a student, so we care about general fluency - not voice acting. {{/readingFluencyAnalysis}}\n",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": ["multipleChoiceAnalysis"],
-                                    "template_format": "mustache",
-                                    "template": "{{#multipleChoiceAnalysis}}Here is an analysis of the student's multiple choice response: {{multipleChoiceAnalysis}}{{/multipleChoiceAnalysis}}\n",  # noqa: E501
-                                    "additional_content_fields": {
-                                        "text": "{{#multipleChoiceAnalysis}}Here is an analysis of the student's multiple choice response: {{multipleChoiceAnalysis}}{{/multipleChoiceAnalysis}}\n",  # noqa: E501
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "prompt",
-                                    "PromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "input_variables": [],
-                                    "template_format": "mustache",
-                                    "template": "Here is the student's whiteboard:\n",
-                                    "additional_content_fields": {
-                                        "text": "Here is the student's whiteboard:\n",
-                                    },
-                                },
-                            },
-                            {
-                                "lc": 1,
-                                "type": "constructor",
-                                "id": [
-                                    "langchain_core",
-                                    "prompts",
-                                    "image",
-                                    "ImagePromptTemplate",
-                                ],
-                                "kwargs": {
-                                    "template": {
-                                        "url": "{{whiteboard}}",
-                                    },
-                                    "input_variables": ["whiteboard"],
-                                    "template_format": "mustache",
-                                    "additional_content_fields": {
-                                        "image_url": {
-                                            "url": "{{whiteboard}}",
-                                        },
-                                    },
-                                },
-                            },
-                        ],
-                        "additional_options": {},
-                    },
-                },
-            ],
-            "input_variables": [
-                "promptDescription",
-                "expectedResponse",
-                "responseTranscript",
-                "readingFluencyAnalysis",
-                "readingFluencyAnalysis",
-                "readingFluencyAnalysis",
-                "multipleChoiceAnalysis",
-                "whiteboard",
-            ],
-            "template_format": "mustache",
-            "metadata": {
-                "lc_hub_owner": "jacob",
-                "lc_hub_repo": "mustache-conditionals",
-                "lc_hub_commit_hash": "836ad82d512409ea6024fb760b76a27ba58fc68b1179656c0ba2789778686d46",  # noqa: E501
-            },
-        },
-    }
-
-    # Load the ChatPromptTemplate from the manifest
-    template = load(manifest)
-
-    # Format with conditional data - rules is empty, so mustache conditionals
-    # should not render
-    result = template.invoke(
-        {
-            "promptDescription": "What is the capital of the USA?",
-            "expectedResponse": "Washington, D.C.",
-            "responseTranscript": "Washington, D.C.",
-            "readingFluencyAnalysis": None,
-            "multipleChoiceAnalysis": "testing2",
-            "whiteboard": "https://foo.com/bar.png",
-        }
-    )
-    content = result.messages[1].content
-    assert isinstance(content, list)
-    assert not [
-        block for block in content if block["type"] == "text" and block["text"] == ""
-    ]

libs/core/tests/unit_tests/pydantic_utils.py

@@ -105,7 +105,7 @@ def _remove_additionalproperties(schema: dict) -> dict[str, Any]:
     generating JSON schemas for dict properties with `Any` or `object` values.
 
     Pydantic 2.12 and later versions include `"additionalProperties": True` when
-    generating JSON schemas for `TypedDict`.
+    generating JSON schemas for TypedDict.
     """
     if isinstance(schema, dict):
         if (

libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr

@@ -1106,7 +1106,7 @@
                 
                 May also hold extra provider-specific keys.
                 
-                !!! version-added "Added in `langchain-core` 0.3.9"
+                !!! version-added "Added in version 0.3.9"
               ''',
               'properties': dict({
                 'audio': dict({
@@ -1224,7 +1224,7 @@
                 
                 May also hold extra provider-specific keys.
                 
-                !!! version-added "Added in `langchain-core` 0.3.9"
+                !!! version-added "Added in version 0.3.9"
               ''',
               'properties': dict({
                 'audio': dict({
@@ -1743,7 +1743,7 @@
                     }
                     ```
                 
-                !!! 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"

libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr

@@ -2629,7 +2629,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -2746,7 +2746,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -3259,7 +3259,7 @@
               }
               ```
           
-          !!! 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"
@@ -4086,7 +4086,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -4203,7 +4203,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -4735,7 +4735,7 @@
               }
               ```
           
-          !!! 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"
@@ -5574,7 +5574,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -5691,7 +5691,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -6223,7 +6223,7 @@
               }
               ```
           
-          !!! 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"
@@ -6937,7 +6937,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -7054,7 +7054,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -7567,7 +7567,7 @@
               }
               ```
           
-          !!! 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"
@@ -8436,7 +8436,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -8553,7 +8553,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -9085,7 +9085,7 @@
               }
               ```
           
-          !!! 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"
@@ -9844,7 +9844,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -9961,7 +9961,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -10474,7 +10474,7 @@
               }
               ```
           
-          !!! 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"
@@ -11251,7 +11251,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -11368,7 +11368,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -11911,7 +11911,7 @@
               }
               ```
           
-          !!! 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"
@@ -12700,7 +12700,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -12817,7 +12817,7 @@
           
           May also hold extra provider-specific keys.
           
-          !!! version-added "Added in `langchain-core` 0.3.9"
+          !!! version-added "Added in version 0.3.9"
         ''',
         'properties': dict({
           'audio': dict({
@@ -13349,7 +13349,7 @@
               }
               ```
           
-          !!! 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"

libs/core/tests/unit_tests/runnables/test_concurrency.py

@@ -3,14 +3,12 @@
 import asyncio
 import time
 from threading import Lock
-from typing import TYPE_CHECKING, Any
+from typing import Any
 
 import pytest
 
 from langchain_core.runnables import RunnableConfig, RunnableLambda
-
-if TYPE_CHECKING:
-    from langchain_core.runnables.base import Runnable
+from langchain_core.runnables.base import Runnable
 
 
 @pytest.mark.asyncio