Merge branch 'master' into mdrxy/post-patch-fixes

## Summary

Add XML format option for `get_buffer_string()` to provide unambiguous
message serialization. This fixes role prefix ambiguity when message
content contains strings like "Human:" or "AI:".

  Fixes #34786

  ## Changes

- Add `format="xml"` parameter with proper XML escaping using
`quoteattr()` for attributes
- Add explicit validation for format parameter (raises `ValueError` for
invalid values)
  - Add comprehensive tests for XML format edge cases

<img width="1952" height="706" alt="image"
src="https://github.com/user-attachments/assets/1cd6f887-9365-43cf-a532-72d7addd8bad"
/>
<img width="2786" height="776" alt="image"
src="https://github.com/user-attachments/assets/a07b0db0-519c-46d7-b34b-b404237d812b"
/>

---------

Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>

.devcontainer/devcontainer.json

@@ -26,7 +26,7 @@
   // Use 'forwardPorts' to make a list of ports inside the container available locally.
   // "forwardPorts": [],
   // Run commands after the container is created
-  "postCreateCommand": "uv sync && echo 'LangChain (Python) dev environment ready!'",
+  "postCreateCommand": "cd libs/langchain_v1 && uv sync && echo 'LangChain (Python) dev environment ready!'",
   // Configure tool-specific properties.
   "customizations": {
     "vscode": {
@@ -42,7 +42,7 @@
         "GitHub.copilot-chat"
       ],
       "settings": {
-        "python.defaultInterpreterPath": ".venv/bin/python",
+        "python.defaultInterpreterPath": "libs/langchain_v1/.venv/bin/python",
         "python.formatting.provider": "none",
         "[python]": {
           "editor.formatOnSave": true,

.dockerignore

@@ -0,0 +1,34 @@
+# Git
+.git
+.github
+
+# Python
+__pycache__
+*.pyc
+*.pyo
+.venv
+.mypy_cache
+.pytest_cache
+.ruff_cache
+*.egg-info
+.tox
+
+# IDE
+.idea
+.vscode
+
+# Worktree
+worktree
+
+# Test artifacts
+.coverage
+htmlcov
+coverage.xml
+
+# Build artifacts
+dist
+build
+
+# Misc
+*.log
+.DS_Store

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the LangChain forum.
+description: Report a bug in LangChain. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
 labels: ["bug"]
 type: bug
 body:
@@ -76,7 +76,7 @@ body:
     validations:
       required: true
     attributes:
-      label: Example Code (Python)
+      label: Reproduction Steps / Example Code (Python)
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
 

.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +1,6 @@
 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
-    about: Report an issue related to the LangChain documentation
   - name: 💬 LangChain Forum
     url:  https://forum.langchain.com/
     about: General community discussions and support
@@ -13,3 +10,6 @@ contact_links:
   - name: 📚 API Reference Documentation
     url: https://reference.langchain.com/python/
     about: View the official LangChain API reference documentation
+  - name: 📚 Documentation issue
+    url: https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml
+    about: Report an issue related to the LangChain documentation

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,5 +1,5 @@
 name: "✨ Feature Request"
-description: Request a new feature or enhancement for LangChain. For questions, please use the LangChain forum.
+description: Request a new feature or enhancement for LangChain. For questions, please use the LangChain forum (below).
 labels: ["feature request"]
 type: feature
 body:

.github/PULL_REQUEST_TEMPLATE.md

@@ -17,7 +17,7 @@ Thank you for contributing to LangChain! Follow these steps to have your pull re
   - Write 1-2 sentences summarizing the change.
   - If this PR addresses a specific issue, please include "Fixes #ISSUE_NUMBER" in the description to automatically close the issue when the PR is merged.
   - If there are any breaking changes, please clearly describe them.
-  - If this PR depends on another PR being merged first, please include "Depends on #PR_NUMBER" inthe description.
+  - If this PR depends on another PR being merged first, please include "Depends on #PR_NUMBER" in the description.
 
 3. Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified.
 
@@ -27,4 +27,4 @@ Additional guidelines:
 
   - We ask that if you use generative AI for your contribution, you include a disclaimer.
   - PRs should not touch more than one package unless absolutely necessary.
-  - Do not update the `uv.lock` files unless or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer.
+  - Do not update the `uv.lock` files or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer.

.github/actions/uv_setup/action.yml

@@ -27,7 +27,7 @@ runs:
   using: composite
   steps:
     - name: Install uv and set the python version
-      uses: astral-sh/setup-uv@v6
+      uses: astral-sh/setup-uv@v7
       with:
         version: ${{ env.UV_VERSION }}
         python-version: ${{ inputs.python-version }}

.github/pr-file-labeler.yml

@@ -118,17 +118,6 @@ xai:
       - any-glob-to-any-file:
           - "libs/partners/xai/**/*"
 
-# Infrastructure and DevOps
-infra:
-  - changed-files:
-      - any-glob-to-any-file:
-          - ".github/**/*"
-          - "Makefile"
-          - ".pre-commit-config.yaml"
-          - "scripts/**/*"
-          - "docker/**/*"
-          - "Dockerfile*"
-
 github_actions:
   - changed-files:
       - any-glob-to-any-file:
@@ -142,11 +131,3 @@ dependencies:
           - "uv.lock"
           - "**/requirements*.txt"
           - "**/poetry.lock"
-
-# Documentation
-documentation:
-  - changed-files:
-      - any-glob-to-any-file:
-          - "**/*.md"
-          - "**/README*"
-

.github/workflows/_lint.yml

@@ -47,6 +47,12 @@ jobs:
           cache-suffix: lint-${{ inputs.working-directory }}
           working-directory: ${{ inputs.working-directory }}
 
+      # - name: "🔒 Verify Lockfile is Up-to-Date"
+      #   working-directory: ${{ inputs.working-directory }}
+      #   run: |
+      #     unset UV_FROZEN
+      #     uv lock --check
+
       - name: "📦 Install Lint & Typing Dependencies"
         working-directory: ${{ inputs.working-directory }}
         run: |

.github/workflows/_release.yml

@@ -77,7 +77,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v5
+        uses: actions/upload-artifact@v6
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -208,7 +208,7 @@ jobs:
     steps:
       - uses: actions/checkout@v6
 
-      - uses: actions/download-artifact@v6
+      - uses: actions/download-artifact@v7
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -258,7 +258,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v6
+      - uses: actions/download-artifact@v7
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -394,9 +394,10 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       contents: read
+    if: false # temporarily skip
     strategy:
       matrix:
-        partner: [anthropic]
+        partner: [openai, anthropic]
       fail-fast: false # Continue testing other partners if one fails
     env:
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -430,7 +431,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v6
+      - uses: actions/download-artifact@v7
         if: startsWith(inputs.working-directory, 'libs/core')
         with:
           name: dist
@@ -470,6 +471,67 @@ jobs:
           uv pip install ../../core/dist/*.whl
           make integration_tests
 
+  # Test external packages that depend on langchain-core/langchain against the new release
+  # Only runs for core and langchain_v1 releases to catch breaking changes before publish
+  test-dependents:
+    name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.package.path }}"
+    needs:
+      - build
+      - release-notes
+      - test-pypi-publish
+      - pre-release-checks
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    # Only run for core or langchain_v1 releases
+    if: startsWith(inputs.working-directory, 'libs/core') || startsWith(inputs.working-directory, 'libs/langchain_v1')
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11", "3.13"]
+        package:
+          - name: deepagents
+            repo: langchain-ai/deepagents
+            path: libs/deepagents
+    # No API keys needed for now - deepagents `make test` only runs unit tests
+
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          path: langchain
+
+      - uses: actions/checkout@v6
+        with:
+          repository: ${{ matrix.package.repo }}
+          path: ${{ matrix.package.name }}
+
+      - name: Set up Python + uv
+        uses: "./langchain/.github/actions/uv_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - uses: actions/download-artifact@v7
+        with:
+          name: dist
+          path: dist/
+
+      - name: Install ${{ matrix.package.name }} with local packages
+        # External dependents don't have [tool.uv.sources] pointing to this repo,
+        # so we install the package normally then override with the built wheel.
+        run: |
+          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
+
+          # Install the package with test dependencies
+          uv sync --group test
+
+          # Override with the built wheel from this release
+          uv pip install $GITHUB_WORKSPACE/dist/*.whl
+
+      - name: Run ${{ matrix.package.name }} tests
+        run: |
+          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
+          make test
+
   publish:
     # Publishes the package to PyPI
     needs:
@@ -477,7 +539,10 @@ jobs:
       - release-notes
       - test-pypi-publish
       - pre-release-checks
-      - test-prior-published-packages-against-new-core
+      - test-dependents
+      # - test-prior-published-packages-against-new-core
+    # Run if all needed jobs succeeded or were skipped (test-dependents only runs for core/langchain_v1)
+    if: ${{ !cancelled() && !failure() }}
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:
@@ -499,7 +564,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v6
+      - uses: actions/download-artifact@v7
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -539,7 +604,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - uses: actions/download-artifact@v6
+      - uses: actions/download-artifact@v7
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/

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

@@ -17,8 +17,8 @@ jobs:
           script: |
             const body = context.payload.issue.body || "";
 
-            // Extract text under "### Package"
-            const match = body.match(/### Package\s+([\s\S]*?)\n###/i);
+            // Extract text under "### Package" (handles " (Required)" suffix and being last section)
+            const match = body.match(/### Package[^\n]*\n([\s\S]*?)(?:\n###|$)/i);
             if (!match) return;
 
             const packageSection = match[1].trim();

.github/workflows/integration_tests.yml

@@ -1,8 +1,8 @@
 # Routine integration tests against partner libraries with live API credentials.
 #
-# Uses `make integration_tests` for each library in the matrix.
+# Uses `make integration_tests` within each library being tested.
 #
-# Runs daily. Can also be triggered manually for immediate updates.
+# Runs daily with the option to trigger manually.
 
 name: "⏰ Integration Tests"
 run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.10, 3.13' }})"
@@ -24,17 +24,29 @@ permissions:
 
 env:
   UV_FROZEN: "true"
-  DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/xai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
+  DEFAULT_LIBS: >-
+    ["libs/partners/openai",
+    "libs/partners/anthropic",
+    "libs/partners/fireworks",
+    "libs/partners/groq",
+    "libs/partners/mistralai",
+    "libs/partners/xai",
+    "libs/partners/google-vertexai",
+    "libs/partners/google-genai",
+    "libs/partners/aws"]
 
 jobs:
   # Generate dynamic test matrix based on input parameters or defaults
   # Only runs on the main repo (for scheduled runs) or when manually triggered
   compute-matrix:
+    # Defend against forks running scheduled jobs, but allow manual runs from forks
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
+
     runs-on: ubuntu-latest
     name: "📋 Compute Test Matrix"
     outputs:
       matrix: ${{ steps.set-matrix.outputs.matrix }}
+      python-version-min-3-11: ${{ steps.set-matrix.outputs.python-version-min-3-11 }}
     steps:
       - name: "🔢 Generate Python & Library Matrix"
         id: set-matrix
@@ -47,9 +59,16 @@ jobs:
           # python-version should default to 3.10 and 3.13, but is overridden to [PYTHON_VERSION_FORCE] if set
           # working-directory should default to DEFAULT_LIBS, but is overridden to [WORKING_DIRECTORY_FORCE] if set
           python_version='["3.10", "3.13"]'
+          python_version_min_3_11='["3.11", "3.13"]'
           working_directory="$DEFAULT_LIBS"
           if [ -n "$PYTHON_VERSION_FORCE" ]; then
             python_version="[\"$PYTHON_VERSION_FORCE\"]"
+            # Bound forced version to >= 3.11 for packages requiring it
+            if [ "$(echo "$PYTHON_VERSION_FORCE >= 3.11" | bc -l)" -eq 1 ]; then
+              python_version_min_3_11="[\"$PYTHON_VERSION_FORCE\"]"
+            else
+              python_version_min_3_11='["3.11"]'
+            fi
           fi
           if [ -n "$WORKING_DIRECTORY_FORCE" ]; then
             working_directory="[\"$WORKING_DIRECTORY_FORCE\"]"
@@ -57,8 +76,10 @@ jobs:
           matrix="{\"python-version\": $python_version, \"working-directory\": $working_directory}"
           echo $matrix
           echo "matrix=$matrix" >> $GITHUB_OUTPUT
+          echo "python-version-min-3-11=$python_version_min_3_11" >> $GITHUB_OUTPUT
+
   # Run integration tests against partner libraries with live API credentials
-  build:
+  integration-tests:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.working-directory }}"
     runs-on: ubuntu-latest
@@ -74,15 +95,27 @@ jobs:
       - uses: actions/checkout@v6
         with:
           path: langchain
+
+      # These libraries exist outside of the monorepo and need to be checked out separately
       - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langchain-google
           path: langchain-google
+      - name: "🔐 Authenticate to Google Cloud"
+        id: "auth"
+        uses: google-github-actions/auth@v3
+        with:
+          credentials_json: "${{ secrets.GOOGLE_CREDENTIALS }}"
       - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langchain-aws
           path: langchain-aws
-
+      - name: "🔐 Configure AWS Credentials"
+        uses: aws-actions/configure-aws-credentials@v5
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
       - name: "📦 Organize External Libraries"
         run: |
           rm -rf \
@@ -97,27 +130,27 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: "🔐 Authenticate to Google Cloud"
-        id: "auth"
-        uses: google-github-actions/auth@v3
-        with:
-          credentials_json: "${{ secrets.GOOGLE_CREDENTIALS }}"
-
-      - name: "🔐 Configure AWS Credentials"
-        uses: aws-actions/configure-aws-credentials@v5
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ secrets.AWS_REGION }}
-
       - name: "📦 Install Dependencies"
+        # Partner packages use [tool.uv.sources] in their pyproject.toml to resolve
+        # langchain-core/langchain to local editable installs, so `uv sync` automatically
+        # tests against the versions from the current branch (not published releases).
+
+        # TODO: external google/aws don't have local resolution since they live in
+        # separate repos, so they pull `core`/`langchain_v1` from PyPI. We should update
+        # their dev groups to use git source dependencies pointing to the current
+        # branch's latest commit SHA to fully test against local langchain changes.
         run: |
           echo "Running scheduled tests, installing dependencies with uv..."
           cd langchain/${{ matrix.working-directory }}
           uv sync --group test --group test_integration
 
       - name: "🚀 Run Integration Tests"
+        # WARNING: All secrets below are available to every matrix job regardless of
+        # which package is being tested. This is intentional for simplicity, but means
+        # any test file could technically access any key. Only use for trusted code.
         env:
+          LANGCHAIN_TESTS_USER_AGENT: ${{ secrets.LANGCHAIN_TESTS_USER_AGENT }}
+
           AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
@@ -155,7 +188,6 @@ 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
@@ -179,3 +211,59 @@ jobs:
           # grep will exit non-zero if the target message isn't found,
           # and `set -e` above will cause the step to fail.
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
+
+  # Test dependent packages against local packages to catch breaking changes
+  test-dependents:
+    # Defend against forks running scheduled jobs, but allow manual runs from forks
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
+
+    name: "🐍 Python ${{ matrix.python-version }}: ${{ matrix.package.path }}"
+    runs-on: ubuntu-latest
+    needs: [compute-matrix]
+    timeout-minutes: 30
+    strategy:
+      fail-fast: false
+      matrix:
+        # deepagents requires Python >= 3.11, use bounded version from compute-matrix
+        python-version: ${{ fromJSON(needs.compute-matrix.outputs.python-version-min-3-11) }}
+        package:
+          - name: deepagents
+            repo: langchain-ai/deepagents
+            path: libs/deepagents
+
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          path: langchain
+
+      - uses: actions/checkout@v6
+        with:
+          repository: ${{ matrix.package.repo }}
+          path: ${{ matrix.package.name }}
+
+      - name: "🐍 Set up Python ${{ matrix.python-version }} + UV"
+        uses: "./langchain/.github/actions/uv_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: "📦 Install ${{ matrix.package.name }} with Local"
+        # Unlike partner packages (which use [tool.uv.sources] for local resolution),
+        # external dependents live in separate repos and need explicit overrides to
+        # test against the langchain versions from the current branch, as their
+        # pyproject.toml files point to released versions.
+        run: |
+          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
+
+          # Install the package with test dependencies
+          uv sync --group test
+
+          # Override langchain packages with local versions
+          uv pip install \
+            -e $GITHUB_WORKSPACE/langchain/libs/core \
+            -e $GITHUB_WORKSPACE/langchain/libs/langchain_v1
+
+      # No API keys needed for now - deepagents `make test` only runs unit tests
+      - name: "🚀 Run ${{ matrix.package.name }} Tests"
+        run: |
+          cd ${{ matrix.package.name }}/${{ matrix.package.path }}
+          make test

.github/workflows/pr_labeler_file.yml

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

.github/workflows/pr_lint.yml

@@ -27,7 +27,7 @@
 #   * release    — prepare a new release
 #
 # Allowed Scope(s) (optional):
-#   core, cli, langchain, langchain_v1, langchain-classic, model-profiles,
+#   core, cli, langchain, langchain-classic, model-profiles,
 #   standard-tests, text-splitters, docs, anthropic, chroma, deepseek, exa,
 #   fireworks, groq, huggingface, mistralai, nomic, ollama, openai,
 #   perplexity, prompty, qdrant, xai, infra, deps

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

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

.pre-commit-config.yaml

@@ -1,4 +1,24 @@
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: no-commit-to-branch # prevent direct commits to protected branches
+        args: ["--branch", "master"]
+      - id: check-yaml # validate YAML syntax
+        args: ["--unsafe"] # allow custom tags
+      - id: check-toml # validate TOML syntax
+      - id: end-of-file-fixer # ensure files end with a newline
+      - id: trailing-whitespace # remove trailing whitespace from lines
+        exclude: \.ambr$
+
+  # Text normalization hooks for consistent formatting
+  - repo: https://github.com/sirosen/texthooks
+    rev: 0.6.8
+    hooks:
+      - id: fix-smartquotes # replace curly quotes with straight quotes
+      - id: fix-spaces # replace non-standard spaces (e.g., non-breaking) with regular spaces
+
+  # Per-package format and lint hooks for the monorepo
   - repo: local
     hooks:
       - id: core
@@ -97,3 +117,15 @@ repos:
         entry: make -C libs/partners/qdrant format lint
         files: ^libs/partners/qdrant/
         pass_filenames: false
+      - id: core-version
+        name: check core version consistency
+        language: system
+        entry: make -C libs/core check_version
+        files: ^libs/core/(pyproject\.toml|langchain_core/version\.py)$
+        pass_filenames: false
+      - id: langchain-v1-version
+        name: check langchain version consistency
+        language: system
+        entry: make -C libs/langchain_v1 check_version
+        files: ^libs/langchain_v1/(pyproject\.toml|langchain/__init__\.py)$
+        pass_filenames: false

.vscode/extensions.json

@@ -6,8 +6,6 @@
     "ms-toolsai.jupyter",
     "ms-toolsai.jupyter-keymap",
     "ms-toolsai.jupyter-renderers",
-    "ms-toolsai.vscode-jupyter-cell-tags",
-    "ms-toolsai.vscode-jupyter-slideshow",
     "yzhang.markdown-all-in-one",
     "davidanson.vscode-markdownlint",
     "bierner.markdown-mermaid",

AGENTS.md

@@ -85,6 +85,7 @@ Suggest PR titles that follow Conventional Commits format. Refer to .github/work
 ### Maintain stable public interfaces
 
 CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.
+You should warn the developer for any function signature changes, regardless of whether they look breaking or not.
 
 **Before making ANY changes to public APIs:**
 

CLAUDE.md

@@ -85,6 +85,7 @@ Suggest PR titles that follow Conventional Commits format. Refer to .github/work
 ### Maintain stable public interfaces
 
 CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.
+You should warn the developer for any function signature changes, regardless of whether they look breaking or not.
 
 **Before making ANY changes to public APIs:**
 

README.md

@@ -19,7 +19,7 @@
   <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>
+  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
 </div>
 
 LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development – all while future-proofing decisions as the underlying technology evolves.
@@ -71,4 +71,5 @@ To improve your LLM application development, pair LangChain with:
 
 - [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.
+- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – Our community guidelines and standards for participation.
+- [LangChain Academy](https://academy.langchain.com/) – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.

libs/Makefile

@@ -0,0 +1,20 @@
+# Makefile for libs/ directory
+# Contains targets that operate across multiple packages
+
+LANGCHAIN_DIRS = core text-splitters langchain langchain_v1 model-profiles
+
+.PHONY: lock check-lock
+
+# Regenerate lockfiles for all core packages
+lock:
+	@for dir in $(LANGCHAIN_DIRS); do \
+		echo "=== Locking $$dir ==="; \
+		(cd $$dir && uv lock); \
+	done
+
+# Verify all lockfiles are up-to-date
+check-lock:
+	@for dir in $(LANGCHAIN_DIRS); do \
+		echo "=== Checking $$dir ==="; \
+		(cd $$dir && uv lock --check) || exit 1; \
+	done

libs/cli/README.md

@@ -3,7 +3,7 @@
 [![PyPI - Version](https://img.shields.io/pypi/v/langchain-cli?label=%20)](https://pypi.org/project/langchain-cli/#history)
 [![PyPI - License](https://img.shields.io/pypi/l/langchain-cli)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-cli)](https://pypistats.org/packages/langchain-cli)
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)
 
 ## Quick Install
 

libs/cli/langchain_cli/integration_template/pyproject.toml

@@ -36,6 +36,9 @@ dev-dependencies = [
 [tool.ruff.lint]
 select = ["E", "F", "I", "T201"]
 
+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
 [tool.ruff.lint.per-file-ignores]
 "docs/**" = [ "ALL",]
 

libs/cli/pyproject.toml

@@ -24,7 +24,7 @@ Homepage = "https://docs.langchain.com/"
 Documentation = "https://docs.langchain.com/"
 Source = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
 Changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-cli%3D%3D1%22"
-Twitter = "https://x.com/LangChainAI"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 
@@ -38,14 +38,16 @@ dev = [
     "pytest-watcher>=0.3.4,<1.0.0"
 ]
 lint = [
-    "ruff>=0.13.1,<0.14",
-    "mypy>=1.18.1,<1.19"
+    "ruff>=0.14.11,<0.15.0"
 ]
 test = [
     "langchain-core",
     "langchain-classic"
 ]
-typing = ["langchain-classic"]
+typing = [
+    "mypy>=1.19.1,<1.20",
+    "langchain-classic"
+]
 test_integration = []
 
 [tool.uv.sources]
@@ -64,10 +66,6 @@ ignore = [
     "FIX002",  # Line contains TODO
     "PERF203", # Rarely useful
     "PLR09",   # Too many something (arg, statements, etc)
-    "RUF012",  # Doesn't play well with Pydantic
-    "TC001",   # Doesn't play well with Pydantic
-    "TC002",   # Doesn't play well with Pydantic
-    "TC003",   # Doesn't play well with Pydantic
     "TD002",   # Missing author in TODO
     "TD003",   # Missing issue link in TODO
 
@@ -76,7 +74,6 @@ ignore = [
 ]
 unfixable = [
     "B028",    # People should intentionally tune the stacklevel
-    "PLW1510", # People should intentionally set the check argument
 ]
 
 flake8-annotations.allow-star-arg-any = true
@@ -89,6 +86,9 @@ pyupgrade.keep-runtime-typing = true
 convention = "google"
 ignore-var-parameters = true  # ignore missing documentation for *args and **kwargs parameters
 
+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
 [tool.ruff.lint.per-file-ignores]
 "tests/**" = [ "D1", "S", "SLF",]
 "scripts/**" = [ "INP", "S",]

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

@@ -4,8 +4,8 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from .file import File
-    from .folder import Folder
+    from tests.unit_tests.migrate.cli_runner.file import File
+    from tests.unit_tests.migrate.cli_runner.folder import Folder
 
 
 @dataclass

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

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from .file import File
+from tests.unit_tests.migrate.cli_runner.file import File
 
 if TYPE_CHECKING:
     from pathlib import Path

libs/cli/uv.lock

@@ -2,6 +2,15 @@ version = 1
 revision = 3
 requires-python = ">=3.10.0, <4.0.0"
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -143,16 +152,17 @@ wheels = [
 
 [[package]]
 name = "fastapi"
-version = "0.118.0"
+version = "0.128.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
+    { name = "annotated-doc" },
     { name = "pydantic" },
     { name = "starlette" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/28/3c/2b9345a6504e4055eaa490e0b41c10e338ad61d9aeaae41d97807873cdf2/fastapi-0.118.0.tar.gz", hash = "sha256:5e81654d98c4d2f53790a7d32d25a7353b30c81441be7d0958a26b5d761fa1c8", size = 310536, upload-time = "2025-09-29T03:37:23.126Z" }
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+    { url = "https://files.pythonhosted.org/packages/5c/05/5cbb59154b093548acd0f4c7c474a118eda06da25aa75c616b72d8fcd92a/fastapi-0.128.0-py3-none-any.whl", hash = "sha256:aebd93f9716ee3b4f4fcfe13ffb7cf308d99c9f3ab5622d8877441072561582d", size = 103094, upload-time = "2025-12-27T15:21:12.154Z" },
 ]
 
 [[package]]
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+version = "1.0.1"
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     { name = "langchain-text-splitters", editable = "../text-splitters" },
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@@ -475,6 +493,7 @@ test = [
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 typing = [
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+    { name = "mypy" },
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 ]
 test-integration = []
-typing = [{ name = "langchain-classic", editable = "../langchain" }]
+typing = [
+    { name = "langchain-classic", editable = "../langchain" },
+    { name = "mypy", specifier = ">=1.19.1,<1.20" },
+]
 
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-version = "1.0.0"
+version = "1.2.7"
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@@ -515,6 +534,7 @@ dependencies = [
     { name = "pyyaml" },
     { name = "tenacity" },
     { name = "typing-extensions" },
+    { name = "uuid-utils" },
 ]
 
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@@ -526,6 +546,7 @@ requires-dist = [
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@@ -556,14 +577,14 @@ test = [
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-version = "1.0.0"
+version = "1.1.0"
 source = { editable = "../text-splitters" }
 dependencies = [
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+version = "1.0.6"
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 ]
 
 [[package]]

libs/core/Makefile

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

libs/core/README.md

@@ -3,7 +3,7 @@
 [![PyPI - Version](https://img.shields.io/pypi/v/langchain-core?label=%20)](https://pypi.org/project/langchain-core/#history)
 [![PyPI - License](https://img.shields.io/pypi/l/langchain-core)](https://opensource.org/licenses/MIT)
 [![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-core)](https://pypistats.org/packages/langchain-core)
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain)
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 

libs/core/langchain_core/_api/__init__.py

@@ -13,20 +13,20 @@ from typing import TYPE_CHECKING
 from langchain_core._import_utils import import_attr
 
 if TYPE_CHECKING:
-    from .beta_decorator import (
+    from langchain_core._api.beta_decorator import (
         LangChainBetaWarning,
         beta,
         suppress_langchain_beta_warning,
         surface_langchain_beta_warnings,
     )
-    from .deprecation import (
+    from langchain_core._api.deprecation import (
         LangChainDeprecationWarning,
         deprecated,
         suppress_langchain_deprecation_warning,
         surface_langchain_deprecation_warnings,
         warn_deprecated,
     )
-    from .path import as_import_path, get_relative_path
+    from langchain_core._api.path import as_import_path, get_relative_path
 
 __all__ = (
     "LangChainBetaWarning",
@@ -58,6 +58,20 @@ _dynamic_imports = {
 
 
 def __getattr__(attr_name: str) -> object:
+    """Dynamically import and return an attribute from a submodule.
+
+    This function enables lazy loading of API functions from submodules, reducing
+    initial import time and circular dependency issues.
+
+    Args:
+        attr_name: Name of the attribute to import.
+
+    Returns:
+        The imported attribute object.
+
+    Raises:
+        AttributeError: If the attribute is not a valid dynamic import.
+    """
     module_name = _dynamic_imports.get(attr_name)
     result = import_attr(attr_name, module_name, __spec__.parent)
     globals()[attr_name] = result
@@ -65,4 +79,9 @@ def __getattr__(attr_name: str) -> object:
 
 
 def __dir__() -> list[str]:
+    """Return a list of available attributes for this module.
+
+    Returns:
+        List of attribute names that can be imported from this module.
+    """
     return list(__all__)

libs/core/langchain_core/_api/beta_decorator.py

@@ -125,7 +125,7 @@ def beta(
             _name = _name or obj.__qualname__
             old_doc = obj.__doc__
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                 """Finalize the annotation of a class."""
                 # Can't set new_doc on some extension objects.
                 with contextlib.suppress(AttributeError):
@@ -168,7 +168,7 @@ def beta(
                     emit_warning()
                 obj.fdel(instance)
 
-            def finalize(_wrapper: Callable[..., Any], new_doc: str) -> Any:
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> Any:
                 """Finalize the property."""
                 return property(fget=_fget, fset=_fset, fdel=_fdel, doc=new_doc)
 
@@ -181,7 +181,7 @@ def beta(
             wrapped = obj
             old_doc = wrapped.__doc__
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str, /) -> T:
                 """Wrap the wrapped function using the wrapper and update the docstring.
 
                 Args:

libs/core/langchain_core/_api/deprecation.py

@@ -28,6 +28,27 @@ from pydantic.v1.fields import FieldInfo as FieldInfoV1
 from langchain_core._api.internal import is_caller_internal
 
 
+def _build_deprecation_message(
+    *,
+    alternative: str = "",
+    alternative_import: str = "",
+) -> str:
+    """Build a simple deprecation message for `__deprecated__` attribute.
+
+    Args:
+        alternative: An alternative API name.
+        alternative_import: A fully qualified import path for the alternative.
+
+    Returns:
+        A deprecation message string for IDE/type checker display.
+    """
+    if alternative_import:
+        return f"Use {alternative_import} instead."
+    if alternative:
+        return f"Use {alternative} instead."
+    return "Deprecated."
+
+
 class LangChainDeprecationWarning(DeprecationWarning):
     """A class for issuing deprecation warnings for LangChain users."""
 
@@ -81,60 +102,57 @@ def deprecated(
 ) -> Callable[[T], T]:
     """Decorator to mark a function, a class, or a property as deprecated.
 
-    When deprecating a classmethod, a staticmethod, or a property, the
-    `@deprecated` decorator should go *under* `@classmethod` and
-    `@staticmethod` (i.e., `deprecated` should directly decorate the
-    underlying callable), but *over* `@property`.
+    When deprecating a classmethod, a staticmethod, or a property, the `@deprecated`
+    decorator should go *under* `@classmethod` and `@staticmethod` (i.e., `deprecated`
+    should directly decorate the underlying callable), but *over* `@property`.
 
-    When deprecating a class `C` intended to be used as a base class in a
-    multiple inheritance hierarchy, `C` *must* define an `__init__` method
-    (if `C` instead inherited its `__init__` from its own base class, then
-    `@deprecated` would mess up `__init__` inheritance when installing its
-    own (deprecation-emitting) `C.__init__`).
+    When deprecating a class `C` intended to be used as a base class in a multiple
+    inheritance hierarchy, `C` *must* define an `__init__` method (if `C` instead
+    inherited its `__init__` from its own base class, then `@deprecated` would mess up
+    `__init__` inheritance when installing its own (deprecation-emitting) `C.__init__`).
 
-    Parameters are the same as for `warn_deprecated`, except that *obj_type*
-    defaults to 'class' if decorating a class, 'attribute' if decorating a
-    property, and 'function' otherwise.
+    Parameters are the same as for `warn_deprecated`, except that *obj_type* defaults to
+    'class' if decorating a class, 'attribute' if decorating a property, and 'function'
+    otherwise.
 
     Args:
-        since:
-            The release at which this API became deprecated.
-        message:
-            Override the default deprecation message. The %(since)s,
-            %(name)s, %(alternative)s, %(obj_type)s, %(addendum)s,
-            and %(removal)s format specifiers will be replaced by the
+        since: The release at which this API became deprecated.
+        message: Override the default deprecation message.
+
+            The `%(since)s`, `%(name)s`, `%(alternative)s`, `%(obj_type)s`,
+            `%(addendum)s`, and `%(removal)s` format specifiers will be replaced by the
             values of the respective arguments passed to this function.
-        name:
-            The name of the deprecated object.
-        alternative:
-            An alternative API that the user may use in place of the
-            deprecated API. The deprecation warning will tell the user
-            about this alternative if provided.
-        alternative_import:
-            An alternative import that the user may use instead.
-        pending:
-            If `True`, uses a `PendingDeprecationWarning` instead of a
-            DeprecationWarning. Cannot be used together with removal.
-        obj_type:
-            The object type being deprecated.
-        addendum:
-            Additional text appended directly to the final message.
-        removal:
-            The expected removal version. With the default (an empty
-            string), a removal version is automatically computed from
-            since. Set to other Falsy values to not schedule a removal
-            date. Cannot be used together with pending.
-        package:
-            The package of the deprecated object.
+        name: The name of the deprecated object.
+        alternative: An alternative API that the user may use in place of the deprecated
+            API.
+
+            The deprecation warning will tell the user about this alternative if
+            provided.
+        alternative_import: An alternative import that the user may use instead.
+        pending: If `True`, uses a `PendingDeprecationWarning` instead of a
+            `DeprecationWarning`.
+
+            Cannot be used together with removal.
+        obj_type: The object type being deprecated.
+        addendum: Additional text appended directly to the final message.
+        removal: The expected removal version.
+
+            With the default (an empty string), a removal version is automatically
+            computed from since. Set to other Falsy values to not schedule a removal
+            date.
+
+            Cannot be used together with pending.
+        package: The package of the deprecated object.
 
     Returns:
         A decorator to mark a function or class as deprecated.
 
-    ```python
-    @deprecated("1.4.0")
-    def the_function_to_deprecate():
-        pass
-    ```
+    Example:
+        ```python
+        @deprecated("1.4.0")
+        def the_function_to_deprecate():
+            pass
+        ```
     """
     _validate_deprecation_params(
         removal, alternative, alternative_import, pending=pending
@@ -204,7 +222,7 @@ def deprecated(
             _name = _name or obj.__qualname__
             old_doc = obj.__doc__
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                 """Finalize the deprecation of a class."""
                 # Can't set new_doc on some extension objects.
                 with contextlib.suppress(AttributeError):
@@ -223,6 +241,11 @@ def deprecated(
                 obj.__init__ = functools.wraps(obj.__init__)(  # type: ignore[misc]
                     warn_if_direct_instance
                 )
+                # Set __deprecated__ for PEP 702 (IDE/type checker support)
+                obj.__deprecated__ = _build_deprecation_message(  # type: ignore[attr-defined]
+                    alternative=alternative,
+                    alternative_import=alternative_import,
+                )
                 return obj
 
         elif isinstance(obj, FieldInfoV1):
@@ -234,7 +257,7 @@ def deprecated(
                 raise ValueError(msg)
             old_doc = obj.description
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                 return cast(
                     "T",
                     FieldInfoV1(
@@ -255,7 +278,7 @@ def deprecated(
                 raise ValueError(msg)
             old_doc = obj.description
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                 return cast(
                     "T",
                     FieldInfo(
@@ -313,14 +336,17 @@ def deprecated(
                     if _name == "<lambda>":
                         _name = set_name
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:  # noqa: ARG001
+            def finalize(_: Callable[..., Any], new_doc: str, /) -> T:
                 """Finalize the property."""
-                return cast(
-                    "T",
-                    _DeprecatedProperty(
-                        fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
-                    ),
+                prop = _DeprecatedProperty(
+                    fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc
                 )
+                # Set __deprecated__ for PEP 702 (IDE/type checker support)
+                prop.__deprecated__ = _build_deprecation_message(  # type: ignore[attr-defined]
+                    alternative=alternative,
+                    alternative_import=alternative_import,
+                )
+                return cast("T", prop)
 
         else:
             _name = _name or cast("type | Callable", obj).__qualname__
@@ -331,7 +357,7 @@ def deprecated(
             wrapped = obj
             old_doc = wrapped.__doc__
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
+            def finalize(wrapper: Callable[..., Any], new_doc: str, /) -> T:
                 """Wrap the wrapped function using the wrapper and update the docstring.
 
                 Args:
@@ -343,6 +369,11 @@ def deprecated(
                 """
                 wrapper = functools.wraps(wrapped)(wrapper)
                 wrapper.__doc__ = new_doc
+                # Set __deprecated__ for PEP 702 (IDE/type checker support)
+                wrapper.__deprecated__ = _build_deprecation_message(  # type: ignore[attr-defined]
+                    alternative=alternative,
+                    alternative_import=alternative_import,
+                )
                 return cast("T", wrapper)
 
         old_doc = inspect.cleandoc(old_doc or "").strip("\n")
@@ -398,7 +429,7 @@ def deprecated(
 
 @contextlib.contextmanager
 def suppress_langchain_deprecation_warning() -> Generator[None, None, None]:
-    """Context manager to suppress LangChainDeprecationWarning."""
+    """Context manager to suppress `LangChainDeprecationWarning`."""
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", LangChainDeprecationWarning)
         warnings.simplefilter("ignore", LangChainPendingDeprecationWarning)
@@ -421,35 +452,33 @@ def warn_deprecated(
     """Display a standardized deprecation.
 
     Args:
-        since:
-            The release at which this API became deprecated.
-        message:
-            Override the default deprecation message. The %(since)s,
-            %(name)s, %(alternative)s, %(obj_type)s, %(addendum)s,
-            and %(removal)s format specifiers will be replaced by the
+        since: The release at which this API became deprecated.
+        message: Override the default deprecation message.
+
+            The `%(since)s`, `%(name)s`, `%(alternative)s`, `%(obj_type)s`,
+            `%(addendum)s`, and `%(removal)s` format specifiers will be replaced by the
             values of the respective arguments passed to this function.
-        name:
-            The name of the deprecated object.
-        alternative:
-            An alternative API that the user may use in place of the
-            deprecated API. The deprecation warning will tell the user
-            about this alternative if provided.
-        alternative_import:
-            An alternative import that the user may use instead.
-        pending:
-            If `True`, uses a `PendingDeprecationWarning` instead of a
-            DeprecationWarning. Cannot be used together with removal.
-        obj_type:
-            The object type being deprecated.
-        addendum:
-            Additional text appended directly to the final message.
-        removal:
-            The expected removal version. With the default (an empty
-            string), a removal version is automatically computed from
-            since. Set to other Falsy values to not schedule a removal
-            date. Cannot be used together with pending.
-        package:
-            The package of the deprecated object.
+        name: The name of the deprecated object.
+        alternative: An alternative API that the user may use in place of the
+            deprecated API.
+
+            The deprecation warning will tell the user about this alternative if
+            provided.
+        alternative_import: An alternative import that the user may use instead.
+        pending: If `True`, uses a `PendingDeprecationWarning` instead of a
+            `DeprecationWarning`.
+
+            Cannot be used together with removal.
+        obj_type: The object type being deprecated.
+        addendum: Additional text appended directly to the final message.
+        removal: The expected removal version.
+
+            With the default (an empty string), a removal version is automatically
+            computed from since. Set to other Falsy values to not schedule a removal
+            date.
+
+            Cannot be used together with pending.
+        package: The package of the deprecated object.
     """
     if not pending:
         if not removal:
@@ -534,8 +563,8 @@ def rename_parameter(
     """Decorator indicating that parameter *old* of *func* is renamed to *new*.
 
     The actual implementation of *func* should use *new*, not *old*. If *old* is passed
-    to *func*, a DeprecationWarning is emitted, and its value is used, even if *new* is
-    also passed by keyword.
+    to *func*, a `DeprecationWarning` is emitted, and its value is used, even if *new*
+    is also passed by keyword.
 
     Args:
         since: The version in which the parameter was renamed.

libs/core/langchain_core/_api/internal.py

@@ -1,4 +1,5 @@
 import inspect
+from typing import cast
 
 
 def is_caller_internal(depth: int = 2) -> bool:
@@ -16,7 +17,7 @@ def is_caller_internal(depth: int = 2) -> bool:
                 return False
         # Directly access the module name from the frame's global variables
         module_globals = frame.f_globals
-        caller_module_name = module_globals.get("__name__", "")
+        caller_module_name = cast("str", module_globals.get("__name__", ""))
         return caller_module_name.startswith("langchain")
     finally:
         del frame

libs/core/langchain_core/caches.py

@@ -3,6 +3,7 @@
 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
     unless you've taken appropriate precautions.
 

libs/core/langchain_core/callbacks/base.py

@@ -21,7 +21,7 @@ _LOGGER = logging.getLogger(__name__)
 
 
 class RetrieverManagerMixin:
-    """Mixin for Retriever callbacks."""
+    """Mixin for `Retriever` callbacks."""
 
     def on_retriever_error(
         self,
@@ -31,12 +31,12 @@ class RetrieverManagerMixin:
         parent_run_id: UUID | None = None,
         **kwargs: Any,
     ) -> Any:
-        """Run when Retriever errors.
+        """Run when `Retriever` errors.
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -48,12 +48,12 @@ class RetrieverManagerMixin:
         parent_run_id: UUID | None = None,
         **kwargs: Any,
     ) -> Any:
-        """Run when Retriever ends running.
+        """Run when `Retriever` ends running.
 
         Args:
             documents: The documents retrieved.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -68,6 +68,7 @@ class LLMManagerMixin:
         chunk: GenerationChunk | ChatGenerationChunk | None = None,
         run_id: UUID,
         parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
         **kwargs: Any,
     ) -> Any:
         """Run on new output token. Only available when streaming is enabled.
@@ -77,8 +78,9 @@ class LLMManagerMixin:
         Args:
             token: The new token.
             chunk: The new generated chunk, containing content and other information.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
+            tags: The tags.
             **kwargs: Additional keyword arguments.
         """
 
@@ -88,14 +90,16 @@ class LLMManagerMixin:
         *,
         run_id: UUID,
         parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
         **kwargs: Any,
     ) -> Any:
         """Run when LLM ends running.
 
         Args:
             response: The response which was generated.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
+            tags: The tags.
             **kwargs: Additional keyword arguments.
         """
 
@@ -105,14 +109,16 @@ class LLMManagerMixin:
         *,
         run_id: UUID,
         parent_run_id: UUID | None = None,
+        tags: list[str] | None = None,
         **kwargs: Any,
     ) -> Any:
         """Run when LLM errors.
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
+            tags: The tags.
             **kwargs: Additional keyword arguments.
         """
 
@@ -132,8 +138,8 @@ class ChainManagerMixin:
 
         Args:
             outputs: The outputs of the chain.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -149,8 +155,8 @@ class ChainManagerMixin:
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -166,8 +172,8 @@ class ChainManagerMixin:
 
         Args:
             action: The agent action.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -183,8 +189,8 @@ class ChainManagerMixin:
 
         Args:
             finish: The agent finish.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -204,8 +210,8 @@ class ToolManagerMixin:
 
         Args:
             output: The output of the tool.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -221,8 +227,8 @@ class ToolManagerMixin:
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -251,8 +257,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized LLM.
             prompts: The prompts.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -278,8 +284,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized chat model.
             messages: The messages.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -300,13 +306,13 @@ class CallbackManagerMixin:
         metadata: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Any:
-        """Run when the Retriever starts running.
+        """Run when the `Retriever` starts running.
 
         Args:
-            serialized: The serialized Retriever.
+            serialized: The serialized `Retriever`.
             query: The query.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -328,8 +334,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized chain.
             inputs: The inputs.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -352,8 +358,8 @@ class CallbackManagerMixin:
         Args:
             serialized: The serialized chain.
             input_str: The input string.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             inputs: The inputs.
@@ -376,8 +382,8 @@ class RunManagerMixin:
 
         Args:
             text: The text.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -393,8 +399,8 @@ class RunManagerMixin:
 
         Args:
             retry_state: The retry state.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -412,13 +418,12 @@ class RunManagerMixin:
 
         Args:
             name: The name of the custom event.
-            data: The data for the custom event. Format will match
-                the format specified by the user.
+            data: The data for the custom event. Format will match the format specified
+                by the user.
             run_id: The ID of the run.
-            tags: The tags associated with the custom event
-                (includes inherited tags).
-            metadata: The metadata associated with the custom event
-                (includes inherited metadata).
+            tags: The tags associated with the custom event (includes inherited tags).
+            metadata: The metadata associated with the custom event (includes inherited
+                metadata).
         """
 
 
@@ -430,7 +435,7 @@ class BaseCallbackHandler(
     CallbackManagerMixin,
     RunManagerMixin,
 ):
-    """Base callback handler for LangChain."""
+    """Base callback handler."""
 
     raise_error: bool = False
     """Whether to raise an error if an exception occurs."""
@@ -475,7 +480,7 @@ class BaseCallbackHandler(
 
 
 class AsyncCallbackHandler(BaseCallbackHandler):
-    """Async callback handler for LangChain."""
+    """Base async callback handler."""
 
     async def on_llm_start(
         self,
@@ -498,8 +503,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized LLM.
             prompts: The prompts.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -525,8 +530,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized chat model.
             messages: The messages.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -553,8 +558,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             token: The new token.
             chunk: The new generated chunk, containing content and other information.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -572,8 +577,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             response: The response which was generated.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -591,10 +596,11 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
+
                 - response (LLMResult): The response which was generated before
                     the error occurred.
         """
@@ -615,8 +621,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized chain.
             inputs: The inputs.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -635,8 +641,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             outputs: The outputs of the chain.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -654,8 +660,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -677,8 +683,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized tool.
             input_str: The input string.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             inputs: The inputs.
@@ -698,8 +704,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             output: The output of the tool.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -717,8 +723,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -736,8 +742,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             text: The text.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -754,8 +760,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             retry_state: The retry state.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             **kwargs: Additional keyword arguments.
         """
 
@@ -772,8 +778,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             action: The agent action.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -791,8 +797,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             finish: The agent finish.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -813,8 +819,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
         Args:
             serialized: The serialized retriever.
             query: The query.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             metadata: The metadata.
             **kwargs: Additional keyword arguments.
@@ -833,8 +839,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             documents: The documents retrieved.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -852,8 +858,8 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
         Args:
             error: The error that occurred.
-            run_id: The run ID. This is the ID of the current run.
-            parent_run_id: The parent run ID. This is the ID of the parent run.
+            run_id: The ID of the current run.
+            parent_run_id: The ID of the parent run.
             tags: The tags.
             **kwargs: Additional keyword arguments.
         """
@@ -883,7 +889,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
 
 class BaseCallbackManager(CallbackManagerMixin):
-    """Base callback manager for LangChain."""
+    """Base callback manager."""
 
     def __init__(
         self,
@@ -932,8 +938,9 @@ class BaseCallbackManager(CallbackManagerMixin):
     def merge(self, other: BaseCallbackManager) -> Self:
         """Merge the callback manager with another callback manager.
 
-        May be overwritten in subclasses. Primarily used internally
-        within merge_configs.
+        May be overwritten in subclasses.
+
+        Primarily used internally within `merge_configs`.
 
         Returns:
             The merged callback manager of the same type as the current object.
@@ -960,28 +967,29 @@ class BaseCallbackManager(CallbackManagerMixin):
                 #    ['tag2', 'tag1']
             ```
         """  # noqa: E501
-        manager = self.__class__(
+        # Combine handlers and inheritable_handlers separately, using sets
+        # to deduplicate (order not preserved)
+        combined_handlers = list(set(self.handlers) | set(other.handlers))
+        combined_inheritable = list(
+            set(self.inheritable_handlers) | set(other.inheritable_handlers)
+        )
+
+        return self.__class__(
             parent_run_id=self.parent_run_id or other.parent_run_id,
-            handlers=[],
-            inheritable_handlers=[],
+            handlers=combined_handlers,
+            inheritable_handlers=combined_inheritable,
             tags=list(set(self.tags + other.tags)),
             inheritable_tags=list(set(self.inheritable_tags + other.inheritable_tags)),
             metadata={
                 **self.metadata,
                 **other.metadata,
             },
+            inheritable_metadata={
+                **self.inheritable_metadata,
+                **other.inheritable_metadata,
+            },
         )
 
-        handlers = self.handlers + other.handlers
-        inheritable_handlers = self.inheritable_handlers + other.inheritable_handlers
-
-        for handler in handlers:
-            manager.add_handler(handler)
-
-        for handler in inheritable_handlers:
-            manager.add_handler(handler, inherit=True)
-        return manager
-
     @property
     def is_async(self) -> bool:
         """Whether the callback manager is async."""

libs/core/langchain_core/callbacks/manager.py

@@ -12,7 +12,6 @@ from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager, contextmanager
 from contextvars import copy_context
 from typing import TYPE_CHECKING, Any, TypeVar, cast
-from uuid import UUID
 
 from langsmith.run_helpers import get_tracing_context
 from typing_extensions import Self, override
@@ -44,6 +43,7 @@ from langchain_core.utils.uuid import uuid7
 
 if TYPE_CHECKING:
     from collections.abc import AsyncGenerator, Coroutine, Generator, Sequence
+    from uuid import UUID
 
     from tenacity import RetryCallState
 

libs/core/langchain_core/document_loaders/langsmith.py

@@ -11,6 +11,7 @@ from typing_extensions import override
 
 from langchain_core.document_loaders.base import BaseLoader
 from langchain_core.documents import Document
+from langchain_core.tracers._compat import pydantic_to_dict
 
 
 class LangSmithLoader(BaseLoader):
@@ -118,14 +119,14 @@ class LangSmithLoader(BaseLoader):
             for key in self.content_key:
                 content = content[key]
             content_str = self.format_content(content)
-            metadata = example.dict()
+            metadata = pydantic_to_dict(example)
             # Stringify datetime and UUID types.
             for k in ("dataset_id", "created_at", "modified_at", "source_run_id", "id"):
                 metadata[k] = str(metadata[k]) if metadata[k] else metadata[k]
             yield Document(content_str, metadata=metadata)
 
 
-def _stringify(x: str | dict) -> str:
+def _stringify(x: str | dict[str, Any]) -> str:
     if isinstance(x, str):
         return x
     try:

libs/core/langchain_core/documents/__init__.py

@@ -30,9 +30,9 @@ from typing import TYPE_CHECKING
 from langchain_core._import_utils import import_attr
 
 if TYPE_CHECKING:
-    from .base import Document
-    from .compressor import BaseDocumentCompressor
-    from .transformers import BaseDocumentTransformer
+    from langchain_core.documents.base import Document
+    from langchain_core.documents.compressor import BaseDocumentCompressor
+    from langchain_core.documents.transformers import BaseDocumentTransformer
 
 __all__ = ("BaseDocumentCompressor", "BaseDocumentTransformer", "Document")
 

libs/core/langchain_core/example_selectors/length_based.py

@@ -11,7 +11,7 @@ from langchain_core.prompts.prompt import PromptTemplate
 
 
 def _get_length_based(text: str) -> int:
-    return len(re.split("\n| ", text))
+    return len(re.split(r"\n| ", text))
 
 
 class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):

libs/core/langchain_core/indexing/api.py

@@ -242,6 +242,17 @@ def _delete(
     vector_store: VectorStore | DocumentIndex,
     ids: list[str],
 ) -> None:
+    """Delete documents from a vector store or document index by their IDs.
+
+    Args:
+        vector_store: The vector store or document index to delete from.
+        ids: List of document IDs to delete.
+
+    Raises:
+        IndexingException: If the delete operation fails.
+        TypeError: If the `vector_store` is neither a `VectorStore` nor a
+            `DocumentIndex`.
+    """
     if isinstance(vector_store, VectorStore):
         delete_ok = vector_store.delete(ids)
         if delete_ok is not None and delete_ok is False:

libs/core/langchain_core/language_models/base.py

@@ -12,13 +12,14 @@ from typing import (
     Literal,
     TypeAlias,
     TypeVar,
+    cast,
 )
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 from typing_extensions import TypedDict, override
 
-from langchain_core.caches import BaseCache
-from langchain_core.callbacks import Callbacks
+from langchain_core.caches import BaseCache  # noqa: TC001
+from langchain_core.callbacks import Callbacks  # noqa: TC001
 from langchain_core.globals import get_verbose
 from langchain_core.messages import (
     AIMessage,
@@ -86,13 +87,28 @@ def get_tokenizer() -> Any:
     return GPT2TokenizerFast.from_pretrained("gpt2")
 
 
+_GPT2_TOKENIZER_WARNED = False
+
+
 def _get_token_ids_default_method(text: str) -> list[int]:
-    """Encode the text into token IDs."""
-    # get the cached tokenizer
+    """Encode the text into token IDs using the fallback GPT-2 tokenizer."""
+    global _GPT2_TOKENIZER_WARNED  # noqa: PLW0603
+    if not _GPT2_TOKENIZER_WARNED:
+        warnings.warn(
+            "Using fallback GPT-2 tokenizer for token counting. "
+            "Token counts may be inaccurate for non-GPT-2 models. "
+            "For accurate counts, use a model-specific method if available.",
+            stacklevel=3,
+        )
+        _GPT2_TOKENIZER_WARNED = True
+
     tokenizer = get_tokenizer()
 
-    # tokenize the text using the GPT-2 tokenizer
-    return tokenizer.encode(text)
+    # Pass verbose=False to suppress the "Token indices sequence length is longer than
+    # the specified maximum sequence length" warning from HuggingFace. This warning is
+    # about GPT-2's 1024 token context limit, but we're only using the tokenizer for
+    # counting, not for model input.
+    return cast("list[int]", tokenizer.encode(text, verbose=False))
 
 
 LanguageModelInput = PromptValue | str | Sequence[MessageLikeRepresentation]

libs/core/langchain_core/language_models/chat_models.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 import asyncio
 import inspect
 import json
-import typing
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator, Callable, Iterator, Sequence
 from functools import cached_property
@@ -74,6 +73,7 @@ from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
 from langchain_core.utils.utils import LC_ID_PREFIX, from_env
 
 if TYPE_CHECKING:
+    import builtins
     import uuid
 
     from langchain_core.output_parsers.base import OutputParserLike
@@ -341,6 +341,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
     """Profile detailing model capabilities.
 
     !!! warning "Beta feature"
+
         This is a beta feature. The format of model profiles is subject to change.
 
     If not specified, automatically loaded from the provider package on initialization
@@ -358,7 +359,10 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     @cached_property
     def _serialized(self) -> dict[str, Any]:
-        return dumpd(self)
+        # self is always a Serializable object in this case, thus the result is
+        # guaranteed to be a dict since dumps uses the default callback, which uses
+        # obj.to_json which always returns TypedDict subclasses
+        return cast("dict[str, Any]", dumpd(self))
 
     # --- Runnable methods ---
 
@@ -461,7 +465,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
         # Check if a runtime streaming flag has been passed in.
         if "stream" in kwargs:
-            return kwargs["stream"]
+            return bool(kwargs["stream"])
 
         if "streaming" in self.model_fields_set:
             streaming_value = getattr(self, "streaming", None)
@@ -547,7 +551,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                         ):
                             if block["type"] != index_type:
                                 index_type = block["type"]
-                                index = index + 1
+                                index += 1
                             if "index" not in block:
                                 block["index"] = index
                     run_manager.on_llm_new_token(
@@ -679,7 +683,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     ):
                         if block["type"] != index_type:
                             index_type = block["type"]
-                            index = index + 1
+                            index += 1
                         if "index" not in block:
                             block["index"] = index
                 await run_manager.on_llm_new_token(
@@ -730,7 +734,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     # --- Custom methods ---
 
-    def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:  # noqa: ARG002
+    def _combine_llm_outputs(self, _llm_outputs: list[dict | None], /) -> dict:
         return {}
 
     def _convert_cached_generations(self, cache_val: list) -> list[ChatGeneration]:
@@ -1144,7 +1148,15 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         if check_cache:
             if llm_cache:
                 llm_string = self._get_llm_string(stop=stop, **kwargs)
-                prompt = dumps(messages)
+                normalized_messages = [
+                    (
+                        msg.model_copy(update={"id": None})
+                        if getattr(msg, "id", None) is not None
+                        else msg
+                    )
+                    for msg in messages
+                ]
+                prompt = dumps(normalized_messages)
                 cache_val = llm_cache.lookup(prompt, llm_string)
                 if isinstance(cache_val, list):
                     converted_generations = self._convert_cached_generations(cache_val)
@@ -1187,7 +1199,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     ):
                         if block["type"] != index_type:
                             index_type = block["type"]
-                            index = index + 1
+                            index += 1
                         if "index" not in block:
                             block["index"] = index
                 if run_manager:
@@ -1262,7 +1274,15 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         if check_cache:
             if llm_cache:
                 llm_string = self._get_llm_string(stop=stop, **kwargs)
-                prompt = dumps(messages)
+                normalized_messages = [
+                    (
+                        msg.model_copy(update={"id": None})
+                        if getattr(msg, "id", None) is not None
+                        else msg
+                    )
+                    for msg in messages
+                ]
+                prompt = dumps(normalized_messages)
                 cache_val = await llm_cache.alookup(prompt, llm_string)
                 if isinstance(cache_val, list):
                     converted_generations = self._convert_cached_generations(cache_val)
@@ -1305,7 +1325,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                     ):
                         if block["type"] != index_type:
                             index_type = block["type"]
-                            index = index + 1
+                            index += 1
                         if "index" not in block:
                             block["index"] = index
                 if run_manager:
@@ -1500,9 +1520,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     def bind_tools(
         self,
-        tools: Sequence[
-            typing.Dict[str, Any] | type | Callable | BaseTool  # noqa: UP006
-        ],
+        tools: Sequence[builtins.dict[str, Any] | type | Callable | BaseTool],
         *,
         tool_choice: str | None = None,
         **kwargs: Any,
@@ -1521,11 +1539,11 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
 
     def with_structured_output(
         self,
-        schema: typing.Dict | type,  # noqa: UP006
+        schema: builtins.dict[str, Any] | type,
         *,
         include_raw: bool = False,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, typing.Dict | BaseModel]:  # noqa: UP006
+    ) -> Runnable[LanguageModelInput, builtins.dict[str, Any] | BaseModel]:
         """Model wrapper that returns outputs formatted to match the given schema.
 
         Args:

libs/core/langchain_core/language_models/llms.py

@@ -301,7 +301,10 @@ class BaseLLM(BaseLanguageModel[str], ABC):
 
     @functools.cached_property
     def _serialized(self) -> dict[str, Any]:
-        return dumpd(self)
+        # self is always a Serializable object in this case, thus the result is
+        # guaranteed to be a dict since dumps uses the default callback, which uses
+        # obj.to_json which always returns TypedDict subclasses
+        return cast("dict[str, Any]", dumpd(self))
 
     # --- Runnable methods ---
 

libs/core/langchain_core/language_models/model_profile.py

@@ -7,6 +7,7 @@ class ModelProfile(TypedDict, total=False):
     """Model profile.
 
     !!! warning "Beta feature"
+
         This is a beta feature. The format of model profiles is subject to change.
 
     Provides information about chat model capabilities, such as context window sizes

libs/core/langchain_core/load/__init__.py

@@ -6,7 +6,7 @@ from langchain_core._import_utils import import_attr
 
 if TYPE_CHECKING:
     from langchain_core.load.dump import dumpd, dumps
-    from langchain_core.load.load import loads
+    from langchain_core.load.load import InitValidator, loads
     from langchain_core.load.serializable import Serializable
 
 # Unfortunately, we have to eagerly import load from langchain_core/load/load.py
@@ -15,11 +15,19 @@ if TYPE_CHECKING:
 # the `from langchain_core.load.load import load` absolute import should also work.
 from langchain_core.load.load import load
 
-__all__ = ("Serializable", "dumpd", "dumps", "load", "loads")
+__all__ = (
+    "InitValidator",
+    "Serializable",
+    "dumpd",
+    "dumps",
+    "load",
+    "loads",
+)
 
 _dynamic_imports = {
     "dumpd": "dump",
     "dumps": "dump",
+    "InitValidator": "load",
     "loads": "load",
     "Serializable": "serializable",
 }

libs/core/langchain_core/load/_validation.py

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

libs/core/langchain_core/load/dump.py

@@ -1,10 +1,26 @@
-"""Dump objects to json."""
+"""Serialize LangChain objects to JSON.
+
+Provides `dumps` (to JSON string) and `dumpd` (to dict) for serializing
+`Serializable` objects.
+
+## Escaping
+
+During serialization, plain dicts (user data) that contain an `'lc'` key are escaped
+by wrapping them: `{"__lc_escaped__": {...original...}}`. This prevents injection
+attacks where malicious data could trick the deserializer into instantiating
+arbitrary classes. The escape marker is removed during deserialization.
+
+This is an allowlist approach: only dicts explicitly produced by
+`Serializable.to_json()` are treated as LC objects; everything else is escaped if it
+could be confused with the LC format.
+"""
 
 import json
 from typing import Any
 
 from pydantic import BaseModel
 
+from langchain_core.load._validation import _serialize_value
 from langchain_core.load.serializable import Serializable, to_json_not_implemented
 from langchain_core.messages import AIMessage
 from langchain_core.outputs import ChatGeneration
@@ -25,6 +41,20 @@ def default(obj: Any) -> Any:
 
 
 def _dump_pydantic_models(obj: Any) -> Any:
+    """Convert nested Pydantic models to dicts for JSON serialization.
+
+    Handles the special case where a `ChatGeneration` contains an `AIMessage`
+    with a parsed Pydantic model in `additional_kwargs["parsed"]`. Since
+    Pydantic models aren't directly JSON serializable, this converts them to
+    dicts.
+
+    Args:
+        obj: The object to process.
+
+    Returns:
+        A copy of the object with nested Pydantic models converted to dicts, or
+            the original object unchanged if no conversion was needed.
+    """
     if (
         isinstance(obj, ChatGeneration)
         and isinstance(obj.message, AIMessage)
@@ -40,10 +70,17 @@ def _dump_pydantic_models(obj: Any) -> Any:
 def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
     """Return a JSON string representation of an object.
 
+    Note:
+        Plain dicts containing an `'lc'` key are automatically escaped to prevent
+        confusion with LC serialization format. The escape marker is removed during
+        deserialization.
+
     Args:
         obj: The object to dump.
-        pretty: Whether to pretty print the json. If `True`, the json will be
-            indented with 2 spaces (if no indent is provided as part of `kwargs`).
+        pretty: Whether to pretty print the json.
+
+            If `True`, the json will be indented by either 2 spaces or the amount
+            provided in the `indent` kwarg.
         **kwargs: Additional arguments to pass to `json.dumps`
 
     Returns:
@@ -55,28 +92,29 @@ def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
     if "default" in kwargs:
         msg = "`default` should not be passed to dumps"
         raise ValueError(msg)
-    try:
-        obj = _dump_pydantic_models(obj)
-        if pretty:
-            indent = kwargs.pop("indent", 2)
-            return json.dumps(obj, default=default, indent=indent, **kwargs)
-        return json.dumps(obj, default=default, **kwargs)
-    except TypeError:
-        if pretty:
-            indent = kwargs.pop("indent", 2)
-            return json.dumps(to_json_not_implemented(obj), indent=indent, **kwargs)
-        return json.dumps(to_json_not_implemented(obj), **kwargs)
+
+    obj = _dump_pydantic_models(obj)
+    serialized = _serialize_value(obj)
+
+    if pretty:
+        indent = kwargs.pop("indent", 2)
+        return json.dumps(serialized, indent=indent, **kwargs)
+    return json.dumps(serialized, **kwargs)
 
 
 def dumpd(obj: Any) -> Any:
     """Return a dict representation of an object.
 
+    Note:
+        Plain dicts containing an `'lc'` key are automatically escaped to prevent
+        confusion with LC serialization format. The escape marker is removed during
+        deserialization.
+
     Args:
         obj: The object to dump.
 
     Returns:
         Dictionary that can be serialized to json using `json.dumps`.
     """
-    # Unfortunately this function is not as efficient as it could be because it first
-    # dumps the object to a json string and then loads it back into a dictionary.
-    return json.loads(dumps(obj))
+    obj = _dump_pydantic_models(obj)
+    return _serialize_value(obj)

libs/core/langchain_core/load/load.py

@@ -1,16 +1,83 @@
 """Load LangChain objects from JSON strings or objects.
 
-!!! warning
-    `load` and `loads` are vulnerable to remote code execution. Never use with untrusted
-    input.
+## How it works
+
+Each `Serializable` LangChain object has a unique identifier (its "class path"), which
+is a list of strings representing the module path and class name. For example:
+
+- `AIMessage` -> `["langchain_core", "messages", "ai", "AIMessage"]`
+- `ChatPromptTemplate` -> `["langchain_core", "prompts", "chat", "ChatPromptTemplate"]`
+
+When deserializing, the class path from the JSON `'id'` field is checked against an
+allowlist. If the class is not in the allowlist, deserialization raises a `ValueError`.
+
+## Security model
+
+The `allowed_objects` parameter controls which classes can be deserialized:
+
+- **`'core'` (default)**: Allow classes defined in the serialization mappings for
+    langchain_core.
+- **`'all'`**: Allow classes defined in the serialization mappings. This
+    includes core LangChain types (messages, prompts, documents, etc.) and trusted
+    partner integrations. See `langchain_core.load.mapping` for the full list.
+- **Explicit list of classes**: Only those specific classes are allowed.
+
+For simple data types like messages and documents, the default allowlist is safe to use.
+These classes do not perform side effects during initialization.
+
+!!! note "Side effects in allowed classes"
+
+    Deserialization calls `__init__` on allowed classes. If those classes perform side
+    effects during initialization (network calls, file operations, etc.), those side
+    effects will occur. The allowlist prevents instantiation of classes outside the
+    allowlist, but does not sandbox the allowed classes themselves.
+
+Import paths are also validated against trusted namespaces before any module is
+imported.
+
+### Injection protection (escape-based)
+
+During serialization, plain dicts that contain an `'lc'` key are escaped by wrapping
+them: `{"__lc_escaped__": {...}}`. During deserialization, escaped dicts are unwrapped
+and returned as plain dicts, NOT instantiated as LC objects.
+
+This is an allowlist approach: only dicts explicitly produced by
+`Serializable.to_json()` (which are NOT escaped) are treated as LC objects;
+everything else is user data.
+
+Even if an attacker's payload includes `__lc_escaped__` wrappers, it will be unwrapped
+to plain dicts and NOT instantiated as malicious objects.
+
+## Examples
+
+```python
+from langchain_core.load import load
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.messages import AIMessage, HumanMessage
+
+# Use default allowlist (classes from mappings) - recommended
+obj = load(data)
+
+# Allow only specific classes (most restrictive)
+obj = load(
+    data,
+    allowed_objects=[
+        ChatPromptTemplate,
+        AIMessage,
+        HumanMessage,
+    ],
+)
+```
 """
 
 import importlib
 import json
 import os
-from typing import Any
+from collections.abc import Callable, Iterable
+from typing import Any, Literal, cast
 
 from langchain_core._api import beta
+from langchain_core.load._validation import _is_escaped_dict, _unescape_value
 from langchain_core.load.mapping import (
     _JS_SERIALIZABLE_MAPPING,
     _OG_SERIALIZABLE_MAPPING,
@@ -49,34 +116,209 @@ ALL_SERIALIZABLE_MAPPINGS = {
     **_JS_SERIALIZABLE_MAPPING,
 }
 
+# Cache for the default allowed class paths computed from mappings
+# Maps mode ("all" or "core") to the cached set of paths
+_default_class_paths_cache: dict[str, set[tuple[str, ...]]] = {}
+
+
+def _get_default_allowed_class_paths(
+    allowed_object_mode: Literal["all", "core"],
+) -> set[tuple[str, ...]]:
+    """Get the default allowed class paths from the serialization mappings.
+
+    This uses the mappings as the source of truth for what classes are allowed
+    by default. Both the legacy paths (keys) and current paths (values) are included.
+
+    Args:
+        allowed_object_mode: either `'all'` or `'core'`.
+
+    Returns:
+        Set of class path tuples that are allowed by default.
+    """
+    if allowed_object_mode in _default_class_paths_cache:
+        return _default_class_paths_cache[allowed_object_mode]
+
+    allowed_paths: set[tuple[str, ...]] = set()
+    for key, value in ALL_SERIALIZABLE_MAPPINGS.items():
+        if allowed_object_mode == "core" and value[0] != "langchain_core":
+            continue
+        allowed_paths.add(key)
+        allowed_paths.add(value)
+
+    _default_class_paths_cache[allowed_object_mode] = allowed_paths
+    return _default_class_paths_cache[allowed_object_mode]
+
+
+def _block_jinja2_templates(
+    class_path: tuple[str, ...],
+    kwargs: dict[str, Any],
+) -> None:
+    """Block jinja2 templates during deserialization for security.
+
+    Jinja2 templates can execute arbitrary code, so they are blocked by default when
+    deserializing objects with `template_format='jinja2'`.
+
+    Note:
+        We intentionally do NOT check the `class_path` here to keep this simple and
+        future-proof. If any new class is added that accepts `template_format='jinja2'`,
+        it will be automatically blocked without needing to update this function.
+
+    Args:
+        class_path: The class path tuple being deserialized (unused).
+        kwargs: The kwargs dict for the class constructor.
+
+    Raises:
+        ValueError: If `template_format` is `'jinja2'`.
+    """
+    _ = class_path  # Unused - see docstring for rationale. Kept to satisfy signature.
+    if kwargs.get("template_format") == "jinja2":
+        msg = (
+            "Jinja2 templates are not allowed during deserialization for security "
+            "reasons. Use 'f-string' template format instead, or explicitly allow "
+            "jinja2 by providing a custom init_validator."
+        )
+        raise ValueError(msg)
+
+
+def default_init_validator(
+    class_path: tuple[str, ...],
+    kwargs: dict[str, Any],
+) -> None:
+    """Default init validator that blocks jinja2 templates.
+
+    This is the default validator used by `load()` and `loads()` when no custom
+    validator is provided.
+
+    Args:
+        class_path: The class path tuple being deserialized.
+        kwargs: The kwargs dict for the class constructor.
+
+    Raises:
+        ValueError: If template_format is `'jinja2'`.
+    """
+    _block_jinja2_templates(class_path, kwargs)
+
+
+AllowedObject = type[Serializable]
+"""Type alias for classes that can be included in the `allowed_objects` parameter.
+
+Must be a `Serializable` subclass (the class itself, not an instance).
+"""
+
+InitValidator = Callable[[tuple[str, ...], dict[str, Any]], None]
+"""Type alias for a callable that validates kwargs during deserialization.
+
+The callable receives:
+
+- `class_path`: A tuple of strings identifying the class being instantiated
+    (e.g., `('langchain', 'schema', 'messages', 'AIMessage')`).
+- `kwargs`: The kwargs dict that will be passed to the constructor.
+
+The validator should raise an exception if the object should not be deserialized.
+"""
+
+
+def _compute_allowed_class_paths(
+    allowed_objects: Iterable[AllowedObject],
+    import_mappings: dict[tuple[str, ...], tuple[str, ...]],
+) -> set[tuple[str, ...]]:
+    """Return allowed class paths from an explicit list of classes.
+
+    A class path is a tuple of strings identifying a serializable class, derived from
+    `Serializable.lc_id()`. For example: `('langchain_core', 'messages', 'AIMessage')`.
+
+    Args:
+        allowed_objects: Iterable of `Serializable` subclasses to allow.
+        import_mappings: Mapping of legacy class paths to current class paths.
+
+    Returns:
+        Set of allowed class paths.
+
+    Example:
+        ```python
+        # Allow a specific class
+        _compute_allowed_class_paths([MyPrompt], {}) ->
+            {("langchain_core", "prompts", "MyPrompt")}
+
+        # Include legacy paths that map to the same class
+        import_mappings = {("old", "Prompt"): ("langchain_core", "prompts", "MyPrompt")}
+        _compute_allowed_class_paths([MyPrompt], import_mappings) ->
+            {("langchain_core", "prompts", "MyPrompt"), ("old", "Prompt")}
+        ```
+    """
+    allowed_objects_list = list(allowed_objects)
+
+    allowed_class_paths: set[tuple[str, ...]] = set()
+    for allowed_obj in allowed_objects_list:
+        if not isinstance(allowed_obj, type) or not issubclass(
+            allowed_obj, Serializable
+        ):
+            msg = "allowed_objects must contain Serializable subclasses."
+            raise TypeError(msg)
+
+        class_path = tuple(allowed_obj.lc_id())
+        allowed_class_paths.add(class_path)
+        # Add legacy paths that map to the same class.
+        for mapping_key, mapping_value in import_mappings.items():
+            if tuple(mapping_value) == class_path:
+                allowed_class_paths.add(mapping_key)
+    return allowed_class_paths
+
 
 class Reviver:
-    """Reviver for JSON objects."""
+    """Reviver for JSON objects.
+
+    Used as the `object_hook` for `json.loads` to reconstruct LangChain objects from
+    their serialized JSON representation.
+
+    Only classes in the allowlist can be instantiated.
+    """
 
     def __init__(
         self,
+        allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
         secrets_map: dict[str, str] | None = None,
         valid_namespaces: list[str] | None = None,
-        secrets_from_env: bool = True,  # noqa: FBT001,FBT002
+        secrets_from_env: bool = False,  # noqa: FBT001,FBT002
         additional_import_mappings: dict[tuple[str, ...], tuple[str, ...]]
         | None = None,
         *,
         ignore_unserializable_fields: bool = False,
+        init_validator: InitValidator | None = default_init_validator,
     ) -> None:
         """Initialize the reviver.
 
         Args:
-            secrets_map: A map of secrets to load.
+            allowed_objects: Allowlist of classes that can be deserialized.
+                - `'core'` (default): Allow classes defined in the serialization
+                    mappings for `langchain_core`.
+                - `'all'`: Allow classes defined in the serialization mappings.
 
+                    This includes core LangChain types (messages, prompts, documents,
+                    etc.) and trusted partner integrations. See
+                    `langchain_core.load.mapping` for the full list.
+                - Explicit list of classes: Only those specific classes are allowed.
+            secrets_map: A map of secrets to load.
                 If a secret is not found in the map, it will be loaded from the
                 environment if `secrets_from_env` is `True`.
-            valid_namespaces: A list of additional namespaces (modules)
-                to allow to be deserialized.
+            valid_namespaces: Additional namespaces (modules) to allow during
+                deserialization, beyond the default trusted namespaces.
             secrets_from_env: Whether to load secrets from the environment.
-            additional_import_mappings: A dictionary of additional namespace mappings
+            additional_import_mappings: A dictionary of additional namespace mappings.
 
                 You can use this to override default mappings or add new mappings.
+
+                When `allowed_objects` is `None` (using defaults), paths from these
+                mappings are also added to the allowed class paths.
             ignore_unserializable_fields: Whether to ignore unserializable fields.
+            init_validator: Optional callable to validate kwargs before instantiation.
+
+                If provided, this function is called with `(class_path, kwargs)` where
+                `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+                The validator should raise an exception if the object should not be
+                deserialized, otherwise return `None`.
+
+                Defaults to `default_init_validator` which blocks jinja2 templates.
         """
         self.secrets_from_env = secrets_from_env
         self.secrets_map = secrets_map or {}
@@ -95,7 +337,26 @@ class Reviver:
             if self.additional_import_mappings
             else ALL_SERIALIZABLE_MAPPINGS
         )
+        # Compute allowed class paths:
+        # - "all" -> use default paths from mappings (+ additional_import_mappings)
+        # - Explicit list -> compute from those classes
+        if allowed_objects in ("all", "core"):
+            self.allowed_class_paths: set[tuple[str, ...]] | None = (
+                _get_default_allowed_class_paths(
+                    cast("Literal['all', 'core']", allowed_objects)
+                ).copy()
+            )
+            # Add paths from additional_import_mappings to the defaults
+            if self.additional_import_mappings:
+                for key, value in self.additional_import_mappings.items():
+                    self.allowed_class_paths.add(key)
+                    self.allowed_class_paths.add(value)
+        else:
+            self.allowed_class_paths = _compute_allowed_class_paths(
+                cast("Iterable[AllowedObject]", allowed_objects), self.import_mappings
+            )
         self.ignore_unserializable_fields = ignore_unserializable_fields
+        self.init_validator = init_validator
 
     def __call__(self, value: dict[str, Any]) -> Any:
         """Revive the value.
@@ -146,6 +407,20 @@ class Reviver:
             [*namespace, name] = value["id"]
             mapping_key = tuple(value["id"])
 
+            if (
+                self.allowed_class_paths is not None
+                and mapping_key not in self.allowed_class_paths
+            ):
+                msg = (
+                    f"Deserialization of {mapping_key!r} is not allowed. "
+                    "The default (allowed_objects='core') only permits core "
+                    "langchain-core classes. To allow trusted partner integrations, "
+                    "use allowed_objects='all'. Alternatively, pass an explicit list "
+                    "of allowed classes via allowed_objects=[...]. "
+                    "See langchain_core.load.mapping for the full allowlist."
+                )
+                raise ValueError(msg)
+
             if (
                 namespace[0] not in self.valid_namespaces
                 # The root namespace ["langchain"] is not a valid identifier.
@@ -153,13 +428,11 @@ class Reviver:
             ):
                 msg = f"Invalid namespace: {value}"
                 raise ValueError(msg)
-            # Has explicit import path.
+            # Determine explicit import path
             if mapping_key in self.import_mappings:
                 import_path = self.import_mappings[mapping_key]
                 # Split into module and name
                 import_dir, name = import_path[:-1], import_path[-1]
-                # Import module
-                mod = importlib.import_module(".".join(import_dir))
             elif namespace[0] in DISALLOW_LOAD_FROM_PATH:
                 msg = (
                     "Trying to deserialize something that cannot "
@@ -167,9 +440,16 @@ class Reviver:
                     f"{mapping_key}."
                 )
                 raise ValueError(msg)
-            # Otherwise, treat namespace as path.
             else:
-                mod = importlib.import_module(".".join(namespace))
+                # Otherwise, treat namespace as path.
+                import_dir = namespace
+
+            # Validate import path is in trusted namespaces before importing
+            if import_dir[0] not in self.valid_namespaces:
+                msg = f"Invalid namespace: {value}"
+                raise ValueError(msg)
+
+            mod = importlib.import_module(".".join(import_dir))
 
             cls = getattr(mod, name)
 
@@ -181,6 +461,10 @@ class Reviver:
             # We don't need to recurse on kwargs
             # as json.loads will do that for us.
             kwargs = value.get("kwargs", {})
+
+            if self.init_validator is not None:
+                self.init_validator(mapping_key, kwargs)
+
             return cls(**kwargs)
 
         return value
@@ -190,46 +474,81 @@ class Reviver:
 def loads(
     text: str,
     *,
+    allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
     secrets_map: dict[str, str] | None = None,
     valid_namespaces: list[str] | None = None,
-    secrets_from_env: bool = True,
+    secrets_from_env: bool = False,
     additional_import_mappings: dict[tuple[str, ...], tuple[str, ...]] | None = None,
     ignore_unserializable_fields: bool = False,
+    init_validator: InitValidator | None = default_init_validator,
 ) -> Any:
     """Revive a LangChain class from a JSON string.
 
-    !!! warning
-        This function is vulnerable to remote code execution. Never use with untrusted
-        input.
-
     Equivalent to `load(json.loads(text))`.
 
+    Only classes in the allowlist can be instantiated. The default allowlist includes
+    core LangChain types (messages, prompts, documents, etc.). See
+    `langchain_core.load.mapping` for the full list.
+
+    !!! warning "Beta feature"
+
+        This is a beta feature. Please be wary of deploying experimental code to
+        production unless you've taken appropriate precautions.
+
     Args:
         text: The string to load.
+        allowed_objects: Allowlist of classes that can be deserialized.
+
+            - `'core'` (default): Allow classes defined in the serialization mappings
+                for `langchain_core`.
+            - `'all'`: Allow classes defined in the serialization mappings.
+
+                This includes core LangChain types (messages, prompts, documents, etc.)
+                and trusted partner integrations. See `langchain_core.load.mapping` for
+                the full list.
+
+            - Explicit list of classes: Only those specific classes are allowed.
+            - `[]`: Disallow all deserialization (will raise on any object).
         secrets_map: A map of secrets to load.
 
             If a secret is not found in the map, it will be loaded from the environment
             if `secrets_from_env` is `True`.
-        valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized.
+        valid_namespaces: Additional namespaces (modules) to allow during
+            deserialization, beyond the default trusted namespaces.
         secrets_from_env: Whether to load secrets from the environment.
-        additional_import_mappings: A dictionary of additional namespace mappings
+        additional_import_mappings: A dictionary of additional namespace mappings.
 
             You can use this to override default mappings or add new mappings.
+
+            When `allowed_objects` is `None` (using defaults), paths from these
+            mappings are also added to the allowed class paths.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
+        init_validator: Optional callable to validate kwargs before instantiation.
+
+            If provided, this function is called with `(class_path, kwargs)` where
+            `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+            The validator should raise an exception if the object should not be
+            deserialized, otherwise return `None`.
+
+            Defaults to `default_init_validator` which blocks jinja2 templates.
 
     Returns:
         Revived LangChain objects.
+
+    Raises:
+        ValueError: If an object's class path is not in the `allowed_objects` allowlist.
     """
-    return json.loads(
-        text,
-        object_hook=Reviver(
-            secrets_map,
-            valid_namespaces,
-            secrets_from_env,
-            additional_import_mappings,
-            ignore_unserializable_fields=ignore_unserializable_fields,
-        ),
+    # Parse JSON and delegate to load() for proper escape handling
+    raw_obj = json.loads(text)
+    return load(
+        raw_obj,
+        allowed_objects=allowed_objects,
+        secrets_map=secrets_map,
+        valid_namespaces=valid_namespaces,
+        secrets_from_env=secrets_from_env,
+        additional_import_mappings=additional_import_mappings,
+        ignore_unserializable_fields=ignore_unserializable_fields,
+        init_validator=init_validator,
     )
 
 
@@ -237,49 +556,112 @@ def loads(
 def load(
     obj: Any,
     *,
+    allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
     secrets_map: dict[str, str] | None = None,
     valid_namespaces: list[str] | None = None,
-    secrets_from_env: bool = True,
+    secrets_from_env: bool = False,
     additional_import_mappings: dict[tuple[str, ...], tuple[str, ...]] | None = None,
     ignore_unserializable_fields: bool = False,
+    init_validator: InitValidator | None = default_init_validator,
 ) -> Any:
     """Revive a LangChain class from a JSON object.
 
-    !!! warning
-        This function is vulnerable to remote code execution. Never use with untrusted
-        input.
+    Use this if you already have a parsed JSON object, eg. from `json.load` or
+    `orjson.loads`.
 
-    Use this if you already have a parsed JSON object,
-    eg. from `json.load` or `orjson.loads`.
+    Only classes in the allowlist can be instantiated. The default allowlist includes
+    core LangChain types (messages, prompts, documents, etc.). See
+    `langchain_core.load.mapping` for the full list.
+
+    !!! warning "Beta feature"
+
+        This is a beta feature. Please be wary of deploying experimental code to
+        production unless you've taken appropriate precautions.
 
     Args:
         obj: The object to load.
+        allowed_objects: Allowlist of classes that can be deserialized.
+
+            - `'core'` (default): Allow classes defined in the serialization mappings
+                for `langchain_core`.
+            - `'all'`: Allow classes defined in the serialization mappings.
+
+                This includes core LangChain types (messages, prompts, documents, etc.)
+                and trusted partner integrations. See `langchain_core.load.mapping` for
+                the full list.
+
+            - Explicit list of classes: Only those specific classes are allowed.
+            - `[]`: Disallow all deserialization (will raise on any object).
         secrets_map: A map of secrets to load.
 
             If a secret is not found in the map, it will be loaded from the environment
             if `secrets_from_env` is `True`.
-        valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized.
+        valid_namespaces: Additional namespaces (modules) to allow during
+            deserialization, beyond the default trusted namespaces.
         secrets_from_env: Whether to load secrets from the environment.
-        additional_import_mappings: A dictionary of additional namespace mappings
+        additional_import_mappings: A dictionary of additional namespace mappings.
 
             You can use this to override default mappings or add new mappings.
+
+            When `allowed_objects` is `None` (using defaults), paths from these
+            mappings are also added to the allowed class paths.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
+        init_validator: Optional callable to validate kwargs before instantiation.
+
+            If provided, this function is called with `(class_path, kwargs)` where
+            `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+            The validator should raise an exception if the object should not be
+            deserialized, otherwise return `None`.
+
+            Defaults to `default_init_validator` which blocks jinja2 templates.
 
     Returns:
         Revived LangChain objects.
+
+    Raises:
+        ValueError: If an object's class path is not in the `allowed_objects` allowlist.
+
+    Example:
+        ```python
+        from langchain_core.load import load, dumpd
+        from langchain_core.messages import AIMessage
+
+        msg = AIMessage(content="Hello")
+        data = dumpd(msg)
+
+        # Deserialize using default allowlist
+        loaded = load(data)
+
+        # Or with explicit allowlist
+        loaded = load(data, allowed_objects=[AIMessage])
+
+        # Or extend defaults with additional mappings
+        loaded = load(
+            data,
+            additional_import_mappings={
+                ("my_pkg", "MyClass"): ("my_pkg", "module", "MyClass"),
+            },
+        )
+        ```
     """
     reviver = Reviver(
+        allowed_objects,
         secrets_map,
         valid_namespaces,
         secrets_from_env,
         additional_import_mappings,
         ignore_unserializable_fields=ignore_unserializable_fields,
+        init_validator=init_validator,
     )
 
     def _load(obj: Any) -> Any:
         if isinstance(obj, dict):
-            # Need to revive leaf nodes before reviving this node
+            # Check for escaped dict FIRST (before recursing).
+            # Escaped dicts are user data that should NOT be processed as LC objects.
+            if _is_escaped_dict(obj):
+                return _unescape_value(obj)
+
+            # Not escaped - recurse into children then apply reviver
             loaded_obj = {k: _load(v) for k, v in obj.items()}
             return reviver(loaded_obj)
         if isinstance(obj, list):

libs/core/langchain_core/load/mapping.py

@@ -1,21 +1,19 @@
 """Serialization mapping.
 
-This file contains a mapping between the lc_namespace path for a given
-subclass that implements from Serializable to the namespace
+This file contains a mapping between the `lc_namespace` path for a given
+subclass that implements from `Serializable` to the namespace
 where that class is actually located.
 
 This mapping helps maintain the ability to serialize and deserialize
 well-known LangChain objects even if they are moved around in the codebase
 across different LangChain versions.
 
-For example,
+For example, the code for the `AIMessage` class is located in
+`langchain_core.messages.ai.AIMessage`. This message is associated with the
+`lc_namespace` of `["langchain", "schema", "messages", "AIMessage"]`,
+because this code was originally in `langchain.schema.messages.AIMessage`.
 
-The code for AIMessage class is located in langchain_core.messages.ai.AIMessage,
-This message is associated with the lc_namespace
-["langchain", "schema", "messages", "AIMessage"],
-because this code was originally in langchain.schema.messages.AIMessage.
-
-The mapping allows us to deserialize an AIMessage created with an older
+The mapping allows us to deserialize an `AIMessage` created with an older
 version of LangChain where the code was in a different location.
 """
 
@@ -275,6 +273,11 @@ SERIALIZABLE_MAPPING: dict[tuple[str, ...], tuple[str, ...]] = {
         "chat_models",
         "ChatGroq",
     ),
+    ("langchain_xai", "chat_models", "ChatXAI"): (
+        "langchain_xai",
+        "chat_models",
+        "ChatXAI",
+    ),
     ("langchain", "chat_models", "fireworks", "ChatFireworks"): (
         "langchain_fireworks",
         "chat_models",
@@ -529,16 +532,6 @@ SERIALIZABLE_MAPPING: dict[tuple[str, ...], tuple[str, ...]] = {
         "structured",
         "StructuredPrompt",
     ),
-    ("langchain_sambanova", "chat_models", "ChatSambaNovaCloud"): (
-        "langchain_sambanova",
-        "chat_models",
-        "ChatSambaNovaCloud",
-    ),
-    ("langchain_sambanova", "chat_models", "ChatSambaStudio"): (
-        "langchain_sambanova",
-        "chat_models",
-        "ChatSambaStudio",
-    ),
     ("langchain_core", "prompts", "message", "_DictMessagePromptTemplate"): (
         "langchain_core",
         "prompts",

libs/core/langchain_core/load/serializable.py

@@ -92,11 +92,12 @@ class Serializable(BaseModel, ABC):
 
     It relies on the following methods and properties:
 
-    - `is_lc_serializable`: Is this class serializable?
+    - [`is_lc_serializable`][langchain_core.load.serializable.Serializable.is_lc_serializable]: Is this class serializable?
+
         By design, even if a class inherits from `Serializable`, it is not serializable
         by default. This is to prevent accidental serialization of objects that should
         not be serialized.
-    - `get_lc_namespace`: Get the namespace of the LangChain object.
+    - [`get_lc_namespace`][langchain_core.load.serializable.Serializable.get_lc_namespace]: Get the namespace of the LangChain object.
 
         During deserialization, this namespace is used to identify
         the correct class to instantiate.
@@ -105,10 +106,10 @@ class Serializable(BaseModel, ABC):
         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
+    - [`lc_secrets`][langchain_core.load.serializable.Serializable.lc_secrets]: A map of constructor argument names to secret ids.
+    - [`lc_attributes`][langchain_core.load.serializable.Serializable.lc_attributes]: List of additional attribute names that should be included
         as part of the serialized representation.
-    """
+    """  # noqa: E501
 
     # Remove default BaseModel init docstring.
     def __init__(self, *args: Any, **kwargs: Any) -> None:
@@ -132,8 +133,9 @@ class Serializable(BaseModel, ABC):
     def get_lc_namespace(cls) -> list[str]:
         """Get the namespace of the LangChain object.
 
-        For example, if the class is `langchain.llms.openai.OpenAI`, then the
-        namespace is `["langchain", "llms", "openai"]`
+        For example, if the class is
+        [`langchain.llms.openai.OpenAI`][langchain_openai.OpenAI], then the namespace is
+        `["langchain", "llms", "openai"]`
 
         Returns:
             The namespace.

libs/core/langchain_core/messages/ai.py

@@ -1,12 +1,13 @@
 """AI message."""
 
+import itertools
 import json
 import logging
 import operator
 from collections.abc import Sequence
 from typing import Any, Literal, cast, overload
 
-from pydantic import model_validator
+from pydantic import Field, model_validator
 from typing_extensions import NotRequired, Self, TypedDict, override
 
 from langchain_core.messages import content as types
@@ -166,10 +167,10 @@ class AIMessage(BaseMessage):
     (e.g., tool calls, usage metadata) added by the LangChain framework.
     """
 
-    tool_calls: list[ToolCall] = []
+    tool_calls: list[ToolCall] = Field(default_factory=list)
     """If present, tool calls associated with the message."""
 
-    invalid_tool_calls: list[InvalidToolCall] = []
+    invalid_tool_calls: list[InvalidToolCall] = Field(default_factory=list)
     """If present, tool calls with parsing errors associated with the message."""
 
     usage_metadata: UsageMetadata | None = None
@@ -326,7 +327,7 @@ class AIMessage(BaseMessage):
         if tool_calls := values.get("tool_calls"):
             values["tool_calls"] = [
                 create_tool_call(
-                    **{k: v for k, v in tc.items() if k not in ("type", "extras")}
+                    **{k: v for k, v in tc.items() if k not in {"type", "extras"}}
                 )
                 for tc in tool_calls
             ]
@@ -394,7 +395,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     type: Literal["AIMessageChunk"] = "AIMessageChunk"  # type: ignore[assignment]
     """The type of the message (used for deserialization)."""
 
-    tool_call_chunks: list[ToolCallChunk] = []
+    tool_call_chunks: list[ToolCallChunk] = Field(default_factory=list)
     """If provided, tool call chunks associated with the message."""
 
     chunk_position: Literal["last"] | None = None
@@ -405,8 +406,8 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     """
 
     @property
+    @override
     def lc_attributes(self) -> dict:
-        """Attributes to be serialized, even if they are derived from other initialization args."""  # noqa: E501
         return {
             "tool_calls": self.tool_calls,
             "invalid_tool_calls": self.invalid_tool_calls,
@@ -442,7 +443,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
             blocks = [
                 block
                 for block in blocks
-                if block["type"] not in ("tool_call", "invalid_tool_call")
+                if block["type"] not in {"tool_call", "invalid_tool_call"}
             ]
             for tool_call_chunk in self.tool_call_chunks:
                 tc: types.ToolCallChunk = {
@@ -563,7 +564,11 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
 
     @model_validator(mode="after")
     def init_server_tool_calls(self) -> Self:
-        """Parse `server_tool_call_chunks` from [`ServerToolCallChunk`][langchain.messages.ServerToolCallChunk] objects."""  # noqa: E501
+        """Initialize server tool calls.
+
+        Parse `server_tool_call_chunks` from
+        [`ServerToolCallChunk`][langchain.messages.ServerToolCallChunk] objects.
+        """
         if (
             self.chunk_position == "last"
             and self.response_metadata.get("output_version") == "v1"
@@ -573,7 +578,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
                 if (
                     isinstance(block, dict)
                     and block.get("type")
-                    in ("server_tool_call", "server_tool_call_chunk")
+                    in {"server_tool_call", "server_tool_call_chunk"}
                     and (args_str := block.get("args"))
                     and isinstance(args_str, str)
                 ):
@@ -651,29 +656,28 @@ def add_ai_message_chunks(
     else:
         usage_metadata = None
 
+    # Ranks are defined by the order of preference. Higher is better:
+    # 2. Provider-assigned IDs (non lc_* and non lc_run-*)
+    # 1. lc_run-* IDs
+    # 0. lc_* and other remaining IDs
+    best_rank = -1
     chunk_id = None
-    candidates = [left.id] + [o.id for o in others]
-    # first pass: pick the first provider-assigned id (non-run-* and non-lc_*)
+    candidates = itertools.chain([left.id], (o.id for o in others))
+
     for id_ in candidates:
-        if (
-            id_
-            and not id_.startswith(LC_ID_PREFIX)
-            and not id_.startswith(LC_AUTO_PREFIX)
-        ):
+        if not id_:
+            continue
+
+        if not id_.startswith(LC_ID_PREFIX) and not id_.startswith(LC_AUTO_PREFIX):
             chunk_id = id_
+            # Highest rank, return instantly
             break
-    else:
-        # second pass: prefer lc_run-* IDs over lc_* IDs
-        for id_ in candidates:
-            if id_ and id_.startswith(LC_ID_PREFIX):
-                chunk_id = id_
-                break
-        else:
-            # third pass: take any remaining ID (auto-generated lc_* IDs)
-            for id_ in candidates:
-                if id_:
-                    chunk_id = id_
-                    break
+
+        rank = 1 if id_.startswith(LC_ID_PREFIX) else 0
+
+        if rank > best_rank:
+            best_rank = rank
+            chunk_id = id_
 
     chunk_position: Literal["last"] | None = (
         "last" if any(x.chunk_position == "last" for x in [left, *others]) else None

libs/core/langchain_core/messages/base.py

@@ -8,6 +8,7 @@ from pydantic import ConfigDict, Field
 
 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
@@ -17,7 +18,6 @@ if TYPE_CHECKING:
 
     from typing_extensions import Self
 
-    from langchain_core.messages import content as types
     from langchain_core.prompts.chat import ChatPromptTemplate
 
 
@@ -204,7 +204,6 @@ class BaseMessage(Serializable):
 
         """
         # Needed here to avoid circular import, as these classes import BaseMessages
-        from langchain_core.messages import content as types  # noqa: PLC0415
         from langchain_core.messages.block_translators.anthropic import (  # noqa: PLC0415
             _convert_to_v1_from_anthropic_input,
         )
@@ -266,6 +265,9 @@ class BaseMessage(Serializable):
 
         Can be used as both property (`message.text`) and method (`message.text()`).
 
+        Handles both string and list content types (e.g. for content blocks). Only
+        extracts blocks with `type: 'text'`; other block types are ignored.
+
         !!! deprecated
             As of `langchain-core` 1.0.0, calling `.text()` as a method is deprecated.
             Use `.text` as a property instead. This method will be removed in 2.0.0.
@@ -277,7 +279,7 @@ class BaseMessage(Serializable):
         if isinstance(self.content, str):
             text_value = self.content
         else:
-            # must be a list
+            # Must be a list
             blocks = [
                 block
                 for block in self.content
@@ -302,7 +304,7 @@ class BaseMessage(Serializable):
         from langchain_core.prompts.chat import ChatPromptTemplate  # noqa: PLC0415
 
         prompt = ChatPromptTemplate(messages=[self])
-        return prompt + other
+        return prompt.__add__(other)
 
     def pretty_repr(
         self,

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

@@ -159,12 +159,12 @@ def _convert_citation_to_v1(citation: dict[str, Any]) -> types.Annotation:
 
         return url_citation
 
-    if citation_type in (
+    if citation_type in {
         "char_location",
         "content_block_location",
         "page_location",
         "search_result_location",
-    ):
+    }:
         document_citation: types.Citation = {
             "type": "citation",
             "cited_text": citation["cited_text"],
@@ -173,8 +173,6 @@ def _convert_citation_to_v1(citation: dict[str, Any]) -> types.Annotation:
             document_citation["title"] = citation["document_title"]
         elif title := citation.get("title"):
             document_citation["title"] = title
-        else:
-            pass
         known_fields = {
             "type",
             "cited_text",
@@ -280,8 +278,6 @@ def _convert_to_v1_from_anthropic(message: AIMessage) -> list[types.ContentBlock
                                     "id": tc.get("id"),
                                 }
                                 break
-                    else:
-                        pass
                     if not tool_call_block:
                         tool_call_block = {
                             "type": "tool_call",
@@ -465,12 +461,26 @@ def _convert_to_v1_from_anthropic(message: AIMessage) -> list[types.ContentBlock
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Anthropic content."""
+    """Derive standard content blocks from a message with Anthropic content.
+
+    Args:
+        message: The message to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     return _convert_to_v1_from_anthropic(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message chunk with Anthropic content."""
+    """Derive standard content blocks from a message chunk with Anthropic content.
+
+    Args:
+        message: The message chunk to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     return _convert_to_v1_from_anthropic(message)
 
 

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

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

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

@@ -240,8 +240,6 @@ def _convert_to_v1_from_converse(message: AIMessage) -> list[types.ContentBlock]
                                     "id": tc.get("id"),
                                 }
                                 break
-                    else:
-                        pass
                     if not tool_call_block:
                         tool_call_block = {
                             "type": "tool_call",
@@ -283,12 +281,26 @@ def _convert_to_v1_from_converse(message: AIMessage) -> list[types.ContentBlock]
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Bedrock Converse content."""
+    """Derive standard content blocks from a message with Bedrock Converse content.
+
+    Args:
+        message: The message to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     return _convert_to_v1_from_converse(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a chunk with Bedrock Converse content."""
+    """Derive standard content blocks from a chunk with Bedrock Converse content.
+
+    Args:
+        message: The message chunk to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     return _convert_to_v1_from_converse(message)
 
 

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

@@ -9,6 +9,13 @@ from langchain_core.messages import AIMessage, AIMessageChunk
 from langchain_core.messages import content as types
 from langchain_core.messages.content import Citation, create_citation
 
+try:
+    import filetype  # type: ignore[import-not-found]
+
+    _HAS_FILETYPE = True
+except ImportError:
+    _HAS_FILETYPE = False
+
 
 def _bytes_to_b64_str(bytes_: bytes) -> str:
     """Convert bytes to base64 encoded string."""
@@ -391,9 +398,7 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                                 "base64": url,
                             }
 
-                            try:
-                                import filetype  # type: ignore[import-not-found] # noqa: PLC0415
-
+                            if _HAS_FILETYPE:
                                 # Guess MIME type based on file bytes
                                 mime_type = None
                                 kind = filetype.guess(decoded_bytes)
@@ -401,9 +406,6 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                                     mime_type = kind.mime
                                 if mime_type:
                                     image_url_b64_block["mime_type"] = mime_type
-                            except ImportError:
-                                # filetype library not available, skip type detection
-                                pass
 
                             converted_blocks.append(
                                 cast("types.ImageContentBlock", image_url_b64_block)
@@ -411,7 +413,10 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
                         except Exception:
                             # Not valid base64, treat as non-standard
                             converted_blocks.append(
-                                {"type": "non_standard", "value": item}
+                                {
+                                    "type": "non_standard",
+                                    "value": item,
+                                }
                             )
                 else:
                     # This likely won't be reached according to previous implementations
@@ -523,12 +528,26 @@ def _convert_to_v1_from_genai(message: AIMessage) -> list[types.ContentBlock]:
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with Google (GenAI) content."""
+    """Derive standard content blocks from a message with Google (GenAI) content.
+
+    Args:
+        message: The message to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     return _convert_to_v1_from_genai(message)
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a chunk with Google (GenAI) content."""
+    """Derive standard content blocks from a chunk with Google (GenAI) content.
+
+    Args:
+        message: The message chunk to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     return _convert_to_v1_from_genai(message)
 
 

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

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

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

@@ -10,16 +10,28 @@ from langchain_core.language_models._utils import (
     _parse_data_uri,
     is_openai_data_block,
 )
+from langchain_core.messages import AIMessageChunk
 from langchain_core.messages import content as types
 
 if TYPE_CHECKING:
     from collections.abc import Iterable
 
-    from langchain_core.messages import AIMessage, AIMessageChunk
+    from langchain_core.messages import AIMessage
 
 
 def convert_to_openai_image_block(block: dict[str, Any]) -> dict:
-    """Convert `ImageContentBlock` to format expected by OpenAI Chat Completions."""
+    """Convert `ImageContentBlock` to format expected by OpenAI Chat Completions.
+
+    Args:
+        block: The image content block to convert.
+
+    Raises:
+        ValueError: If required keys are missing.
+        ValueError: If source type is unsupported.
+
+    Returns:
+        The formatted image content block.
+    """
     if "url" in block:
         return {
             "type": "image_url",
@@ -50,6 +62,18 @@ def convert_to_openai_data_block(
 
     "Standard data content block" can include old-style LangChain v0 blocks
     (URLContentBlock, Base64ContentBlock, IDContentBlock) or new ones.
+
+    Args:
+        block: The content block to convert.
+        api: The OpenAI API being targeted. Either "chat/completions" or "responses".
+
+    Raises:
+        ValueError: If required keys are missing.
+        ValueError: If file URLs are used with Chat Completions API.
+        ValueError: If block type is unsupported.
+
+    Returns:
+        The formatted content block.
     """
     if block["type"] == "image":
         chat_completions_block = convert_to_openai_image_block(block)
@@ -169,8 +193,6 @@ def _convert_to_v1_from_chat_completions_input(
     Returns:
         Updated list with OpenAI blocks converted to v1 format.
     """
-    from langchain_core.messages import content as types  # noqa: PLC0415
-
     converted_blocks = []
     unpacked_blocks: list[dict[str, Any]] = [
         cast("dict[str, Any]", block)
@@ -248,7 +270,7 @@ def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
                 if block_type == "text":
                     # Strip annotations
                     new_content.append({"type": "text", "text": block["text"]})
-                elif block_type in ("reasoning", "tool_call"):
+                elif block_type in {"reasoning", "tool_call"}:
                     pass
                 else:
                     new_content.append(block)
@@ -265,8 +287,6 @@ _FUNCTION_CALL_IDS_MAP_KEY = "__openai_function_call_ids__"
 
 def _convert_from_v03_ai_message(message: AIMessage) -> AIMessage:
     """Convert v0 AIMessage into `output_version="responses/v1"` format."""
-    from langchain_core.messages import AIMessageChunk  # noqa: PLC0415
-
     # Only update ChatOpenAI v0.3 AIMessages
     is_chatopenai_v03 = (
         isinstance(message.content, list)
@@ -683,8 +703,6 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                 ) = None
                 call_id = block.get("call_id", "")
 
-                from langchain_core.messages import AIMessageChunk  # noqa: PLC0415
-
                 if (
                     isinstance(message, AIMessageChunk)
                     and len(message.tool_call_chunks) == 1
@@ -706,8 +724,6 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                             if invalid_tool_call.get("id") == call_id:
                                 tool_call_block = invalid_tool_call.copy()
                                 break
-                else:
-                    pass
                 if tool_call_block:
                     if "id" in block:
                         if "extras" not in tool_call_block:
@@ -735,7 +751,7 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                         k: v for k, v in block["action"].items() if k != "sources"
                     }
                 for key in block:
-                    if key not in ("type", "id", "action", "status", "index"):
+                    if key not in {"type", "id", "action", "status", "index"}:
                         web_search_call[key] = block[key]
 
                 yield cast("types.ServerToolCall", web_search_call)
@@ -761,8 +777,6 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                         web_search_result["status"] = "success"
                     elif status:
                         web_search_result["extras"] = {"status": status}
-                    else:
-                        pass
                     if "index" in block and isinstance(block["index"], int):
                         web_search_result["index"] = f"lc_wsr_{block['index'] + 1}"
                     yield cast("types.ServerToolResult", web_search_result)
@@ -778,14 +792,14 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                     file_search_call["index"] = f"lc_fsc_{block['index']}"
 
                 for key in block:
-                    if key not in (
+                    if key not in {
                         "type",
                         "id",
                         "queries",
                         "results",
                         "status",
                         "index",
-                    ):
+                    }:
                         file_search_call[key] = block[key]
 
                 yield cast("types.ServerToolCall", file_search_call)
@@ -804,8 +818,6 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                     file_search_result["status"] = "success"
                 elif status:
                     file_search_result["extras"] = {"status": status}
-                else:
-                    pass
                 if "index" in block and isinstance(block["index"], int):
                     file_search_result["index"] = f"lc_fsr_{block['index'] + 1}"
                 yield cast("types.ServerToolResult", file_search_result)
@@ -849,8 +861,6 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
                     code_interpreter_result["status"] = "success"
                 elif status:
                     code_interpreter_result["extras"] = {"status": status}
-                else:
-                    pass
                 if "index" in block and isinstance(block["index"], int):
                     code_interpreter_result["index"] = f"lc_cir_{block['index'] + 1}"
 
@@ -981,7 +991,14 @@ def _convert_to_v1_from_responses(message: AIMessage) -> list[types.ContentBlock
 
 
 def translate_content(message: AIMessage) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message with OpenAI content."""
+    """Derive standard content blocks from a message with OpenAI content.
+
+    Args:
+        message: The message to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     if isinstance(message.content, str):
         return _convert_to_v1_from_chat_completions(message)
     message = _convert_from_v03_ai_message(message)
@@ -989,7 +1006,14 @@ def translate_content(message: AIMessage) -> list[types.ContentBlock]:
 
 
 def translate_content_chunk(message: AIMessageChunk) -> list[types.ContentBlock]:
-    """Derive standard content blocks from a message chunk with OpenAI content."""
+    """Derive standard content blocks from a message chunk with OpenAI content.
+
+    Args:
+        message: The message chunk to translate.
+
+    Returns:
+        The derived content blocks.
+    """
     if isinstance(message.content, str):
         return _convert_to_v1_from_chat_completions_chunk(message)
     message = _convert_from_v03_ai_message(message)  # type: ignore[assignment]

libs/core/langchain_core/messages/content.py

@@ -1,18 +1,14 @@
 """Standard, multimodal content blocks for Large Language Model I/O.
 
-!!! warning
-    This module is under active development. The API is unstable and subject to
-    change in future releases.
-
-This module provides standardized data structures for representing inputs to and
-outputs from LLMs. The core abstraction is the **Content Block**, a `TypedDict`.
+This module provides standardized data structures for representing inputs to and outputs
+from LLMs. The core abstraction is the **Content Block**, a `TypedDict`.
 
 **Rationale**
 
-Different LLM providers use distinct and incompatible API schemas. This module
-provides a unified, provider-agnostic format to facilitate these interactions. A
-message to or from a model is simply a list of content blocks, allowing for the natural
-interleaving of text, images, and other content in a single ordered sequence.
+Different LLM providers use distinct and incompatible API schemas. This module provides
+a unified, provider-agnostic format to facilitate these interactions. A message to or
+from a model is simply a list of content blocks, allowing for the natural interleaving
+of text, images, and other content in a single ordered sequence.
 
 An adapter for a specific provider is responsible for translating this standard list of
 blocks into the format required by its API.
@@ -25,16 +21,27 @@ without losing the benefits of type checking and validation.
 
 Furthermore, provider-specific fields **within** a standard block are fully supported
 by default in the `extras` field of each block. This allows for additional metadata
-to be included without breaking the standard structure.
+to be included without breaking the standard structure. For example, Google's thought
+signature:
+
+```python
+AIMessage(
+    content=[
+        {
+            "type": "text",
+            "text": "J'adore la programmation.",
+            "extras": {"signature": "EpoWCpc..."},  # Thought signature
+        }
+    ], ...
+)
+```
 
-!!! warning
-    Do not heavily rely on the `extras` field for provider-specific data! This field
-    is subject to deprecation in future releases as we move towards PEP 728.
 
 !!! note
+
     Following widespread adoption of [PEP 728](https://peps.python.org/pep-0728/), we
-    will add `extra_items=Any` as a param to Content Blocks. This will signify to type
-    checkers that additional provider-specific fields are allowed outside of the
+    intend to add `extra_items=Any` as a param to Content Blocks. This will signify to
+    type checkers that additional provider-specific fields are allowed outside of the
     `extras` field, and that will become the new standard approach to adding
     provider-specific metadata.
 
@@ -72,30 +79,10 @@ to be included without breaking the standard structure.
         openai_data = my_block["openai_metadata"]  # Type: Any
         ```
 
-        PEP 728 is enabled with `# type: ignore[call-arg]` comments to suppress
-        warnings from type checkers that don't yet support it. The functionality works
-        correctly in Python 3.13+ and will be fully supported as the ecosystem catches
-        up.
-
-**Key Block Types**
-
-The module defines several types of content blocks, including:
-
-- `TextContentBlock`: Standard text output.
-- `Citation`: For annotations that link text output to a source document.
-- `ToolCall`: For function calling.
-- `ReasoningContentBlock`: To capture a model's thought process.
-- Multimodal data:
-    - `ImageContentBlock`
-    - `AudioContentBlock`
-    - `VideoContentBlock`
-    - `PlainTextContentBlock` (e.g. .txt or .md files)
-    - `FileContentBlock` (e.g. PDFs, etc.)
-
 **Example Usage**
 
 ```python
-# Direct construction:
+# Direct construction
 from langchain_core.messages.content import TextContentBlock, ImageContentBlock
 
 multimodal_message: AIMessage(
@@ -109,7 +96,7 @@ multimodal_message: AIMessage(
     ]
 )
 
-# Using factories:
+# Using factories
 from langchain_core.messages.content import create_text_block, create_image_block
 
 multimodal_message: AIMessage(
@@ -124,6 +111,7 @@ multimodal_message: AIMessage(
 ```
 
 Factory functions offer benefits such as:
+
 - Automatic ID generation (when not provided)
 - No need to manually specify the `type` field
 """
@@ -139,30 +127,30 @@ class Citation(TypedDict):
     """Annotation for citing data from a document.
 
     !!! note
+
         `start`/`end` indices refer to the **response text**,
         not the source text. This means that the indices are relative to the model's
         response, not the original document (as specified in the `url`).
 
     !!! note "Factory function"
+
         `create_citation` may also be used as a factory to create a `Citation`.
         Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["citation"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     url: NotRequired[str]
@@ -200,13 +188,12 @@ class NonStandardAnnotation(TypedDict):
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     value: dict[str, Any]
@@ -224,25 +211,24 @@ class TextContentBlock(TypedDict):
     from a language model or the text of a user message.
 
     !!! note "Factory function"
+
         `create_text_block` may also be used as a factory to create a
         `TextContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["text"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     text: str
@@ -270,12 +256,12 @@ class ToolCall(TypedDict):
         and an identifier of "123".
 
     !!! note "Factory function"
+
         `create_tool_call` may also be used as a factory to create a
         `ToolCall`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["tool_call"]
@@ -286,7 +272,6 @@ class ToolCall(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
-
     """
     # TODO: Consider making this NotRequired[str] in the future.
 
@@ -332,8 +317,8 @@ class ToolCallChunk(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
-
     """
+    # TODO: Consider making this NotRequired[str] in the future.
 
     name: str | None
     """The name of the tool to be called."""
@@ -353,7 +338,6 @@ class InvalidToolCall(TypedDict):
 
     Here we add an `error` key to surface errors made during generation
     (e.g., invalid JSON arguments.)
-
     """
 
     # TODO: Consider making fields NotRequired[str] in the future.
@@ -366,8 +350,8 @@ class InvalidToolCall(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
-
     """
+    # TODO: Consider making this NotRequired[str] in the future.
 
     name: str | None
     """The name of the tool to be called."""
@@ -423,7 +407,13 @@ class ServerToolCallChunk(TypedDict):
     """JSON substring of the arguments to the tool call."""
 
     id: NotRequired[str]
-    """An identifier associated with the tool call."""
+    """Unique identifier for this server tool call chunk.
+
+    Either:
+
+    - Generated by the provider
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+    """
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -439,7 +429,13 @@ class ServerToolResult(TypedDict):
     """Used for discrimination."""
 
     id: NotRequired[str]
-    """An identifier associated with the server tool result."""
+    """Unique identifier for this server tool result.
+
+    Either:
+
+    - Generated by the provider
+    - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
+    """
 
     tool_call_id: str
     """ID of the corresponding server tool call."""
@@ -461,25 +457,24 @@ class ReasoningContentBlock(TypedDict):
     """Reasoning output from a LLM.
 
     !!! note "Factory function"
+
         `create_reasoning_block` may also be used as a factory to create a
         `ReasoningContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["reasoning"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     reasoning: NotRequired[str]
@@ -487,7 +482,6 @@ class ReasoningContentBlock(TypedDict):
 
     Either the thought summary or the raw reasoning text itself. This is often parsed
     from `<think>` tags in the model's response.
-
     """
 
     index: NotRequired[int | str]
@@ -504,35 +498,38 @@ class ImageContentBlock(TypedDict):
     """Image data.
 
     !!! note "Factory function"
-        `create_image_block` may also be used as a factory to create a
+
+        `create_image_block` may also be used as a factory to create an
         `ImageContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["image"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     file_id: NotRequired[str]
-    """ID of the image file, e.g., from a file storage system."""
+    """Reference to the image in an external file storage system.
+
+    For example, OpenAI or Anthropic's Files API.
+    """
 
     mime_type: NotRequired[str]
-    """MIME type of the image. Required for base64.
+    """MIME type of the image.
+
+    Required for base64 data.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#image)
-
     """
 
     index: NotRequired[int | str]
@@ -552,35 +549,38 @@ class VideoContentBlock(TypedDict):
     """Video data.
 
     !!! note "Factory function"
+
         `create_video_block` may also be used as a factory to create a
         `VideoContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["video"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     file_id: NotRequired[str]
-    """ID of the video file, e.g., from a file storage system."""
+    """Reference to the video in an external file storage system.
+
+    For example, OpenAI or Anthropic's Files API.
+    """
 
     mime_type: NotRequired[str]
-    """MIME type of the video. Required for base64.
+    """MIME type of the video.
+
+    Required for base64 data.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#video)
-
     """
 
     index: NotRequired[int | str]
@@ -600,34 +600,38 @@ class AudioContentBlock(TypedDict):
     """Audio data.
 
     !!! note "Factory function"
+
         `create_audio_block` may also be used as a factory to create an
         `AudioContentBlock`. Benefits include:
+
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["audio"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     file_id: NotRequired[str]
-    """ID of the audio file, e.g., from a file storage system."""
+    """Reference to the audio file in an external file storage system.
+
+    For example, OpenAI or Anthropic's Files API.
+    """
 
     mime_type: NotRequired[str]
-    """MIME type of the audio. Required for base64.
+    """MIME type of the audio.
+
+    Required for base64 data.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml#audio)
-
     """
 
     index: NotRequired[int | str]
@@ -647,42 +651,49 @@ class PlainTextContentBlock(TypedDict):
     """Plaintext data (e.g., from a `.txt` or `.md` document).
 
     !!! note
+
         A `PlainTextContentBlock` existed in `langchain-core<1.0.0`. Although the
         name has carried over, the structure has changed significantly. The only shared
         keys between the old and new versions are `type` and `text`, though the
         `type` value has changed from `'text'` to `'text-plain'`.
 
     !!! note
+
         Title and context are optional fields that may be passed to the model. See
         Anthropic [example](https://platform.claude.com/docs/en/build-with-claude/citations#citable-vs-non-citable-content).
 
     !!! note "Factory function"
+
         `create_plaintext_block` may also be used as a factory to create a
         `PlainTextContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["text-plain"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     file_id: NotRequired[str]
-    """ID of the plaintext file, e.g., from a file storage system."""
+    """Reference to the plaintext file in an external file storage system.
+
+    For example, OpenAI or Anthropic's Files API.
+    """
 
     mime_type: Literal["text/plain"]
-    """MIME type of the file. Required for base64."""
+    """MIME type of the file.
+
+    Required for base64 data.
+    """
 
     index: NotRequired[int | str]
     """Index of block in aggregate response. Used during streaming."""
@@ -717,35 +728,44 @@ class FileContentBlock(TypedDict):
     `PlainTextContentBlock`).
 
     !!! note "Factory function"
+
         `create_file_block` may also be used as a factory to create a
         `FileContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["file"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
+
+    Used for tracking and referencing specific blocks (e.g., during streaming).
+
+    Not to be confused with `file_id`, which references an external file in a
+    storage system.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     file_id: NotRequired[str]
-    """ID of the file, e.g., from a file storage system."""
+    """Reference to the file in an external file storage system.
+
+    For example, a file ID from OpenAI's Files API or another cloud storage provider.
+    This is distinct from `id`, which identifies the content block itself.
+    """
 
     mime_type: NotRequired[str]
-    """MIME type of the file. Required for base64.
+    """MIME type of the file.
+
+    Required for base64 data.
 
     [Examples from IANA](https://www.iana.org/assignments/media-types/media-types.xhtml)
-
     """
 
     index: NotRequired[int | str]
@@ -780,25 +800,24 @@ class NonStandardContentBlock(TypedDict):
     `value` field.
 
     !!! note "Factory function"
+
         `create_non_standard_block` may also be used as a factory to create a
         `NonStandardContentBlock`. Benefits include:
 
         * Automatic ID generation (when not provided)
         * Required arguments strictly validated at creation time
-
     """
 
     type: Literal["non_standard"]
     """Type of the content block. Used for discrimination."""
 
     id: NotRequired[str]
-    """Content block identifier.
+    """Unique identifier for this content block.
 
     Either:
 
-    - Generated by the provider (e.g., OpenAI's file ID)
+    - Generated by the provider
     - Generated by LangChain upon creation (`UUID4` prefixed with `'lc_'`))
-
     """
 
     value: dict[str, Any]
@@ -855,7 +874,7 @@ KNOWN_BLOCK_TYPES = {
     "non_standard",
     # citation and non_standard_annotation intentionally omitted
 }
-"""These are block types known to `langchain-core>=1.0.0`.
+"""These are block types known to `langchain-core >= 1.0.0`.
 
 If a block has a type not in this set, it is considered to be provider-specific.
 """
@@ -895,7 +914,6 @@ def is_data_content_block(block: dict) -> bool:
 
     Returns:
         `True` if the content block is a data content block, `False` otherwise.
-
     """
     if block.get("type") not in _get_data_content_block_types():
         return False
@@ -940,17 +958,21 @@ def create_text_block(
 
     Args:
         text: The text content of the block.
-        id: Content block identifier. Generated automatically if not provided.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
         annotations: `Citation`s and other annotations for the text.
-        index: Index of block in aggregate response. Used during streaming.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `TextContentBlock`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     block = TextContentBlock(
         type="text",
@@ -985,9 +1007,15 @@ def create_image_block(
         url: URL of the image.
         base64: Base64-encoded image data.
         file_id: ID of the image file from a file storage system.
-        mime_type: MIME type of the image. Required for base64 data.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        mime_type: MIME type of the image.
+
+            Required for base64 data.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `ImageContentBlock`.
@@ -997,9 +1025,9 @@ def create_image_block(
             `mime_type`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1041,9 +1069,15 @@ def create_video_block(
         url: URL of the video.
         base64: Base64-encoded video data.
         file_id: ID of the video file from a file storage system.
-        mime_type: MIME type of the video. Required for base64 data.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        mime_type: MIME type of the video.
+
+            Required for base64 data.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `VideoContentBlock`.
@@ -1053,9 +1087,9 @@ def create_video_block(
             `mime_type`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1101,9 +1135,15 @@ def create_audio_block(
         url: URL of the audio.
         base64: Base64-encoded audio data.
         file_id: ID of the audio file from a file storage system.
-        mime_type: MIME type of the audio. Required for base64 data.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        mime_type: MIME type of the audio.
+
+            Required for base64 data.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `AudioContentBlock`.
@@ -1113,9 +1153,9 @@ def create_audio_block(
             `mime_type`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1161,9 +1201,15 @@ def create_file_block(
         url: URL of the file.
         base64: Base64-encoded file data.
         file_id: ID of the file from a file storage system.
-        mime_type: MIME type of the file. Required for base64 data.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        mime_type: MIME type of the file.
+
+            Required for base64 data.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `FileContentBlock`.
@@ -1173,9 +1219,9 @@ def create_file_block(
             `mime_type`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     if not any([url, base64, file_id]):
         msg = "Must provide one of: url, base64, or file_id"
@@ -1225,16 +1271,20 @@ def create_plaintext_block(
         file_id: ID of the plaintext file from a file storage system.
         title: Title of the text data.
         context: Context or description of the text content.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `PlainTextContentBlock`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     block = PlainTextContentBlock(
         type="text-plain",
@@ -1277,16 +1327,20 @@ def create_tool_call(
     Args:
         name: The name of the tool to be called.
         args: The arguments to the tool call.
-        id: An identifier for the tool call. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        id: An identifier for the tool call.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `ToolCall`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     block = ToolCall(
         type="tool_call",
@@ -1315,16 +1369,20 @@ def create_reasoning_block(
 
     Args:
         reasoning: The reasoning text or thought summary.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `ReasoningContentBlock`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     block = ReasoningContentBlock(
         type="reasoning",
@@ -1360,15 +1418,17 @@ def create_citation(
         start_index: Start index in the response text where citation applies.
         end_index: End index in the response text where citation applies.
         cited_text: Excerpt of source text being cited.
-        id: Content block identifier. Generated automatically if not provided.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
 
     Returns:
         A properly formatted `Citation`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     block = Citation(type="citation", id=ensure_id(id))
 
@@ -1400,16 +1460,20 @@ def create_non_standard_block(
 
     Args:
         value: Provider-specific content data.
-        id: Content block identifier. Generated automatically if not provided.
-        index: Index of block in aggregate response. Used during streaming.
+        id: Content block identifier.
+
+            Generated automatically if not provided.
+        index: Index of block in aggregate response.
+
+            Used during streaming.
 
     Returns:
         A properly formatted `NonStandardContentBlock`.
 
     !!! note
+
         The `id` is generated automatically if not provided, using a UUID4 format
         prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
-
     """
     block = NonStandardContentBlock(
         type="non_standard",

libs/core/langchain_core/messages/tool.py

@@ -214,20 +214,29 @@ class ToolCall(TypedDict):
         This represents a request to call the tool named `'foo'` with arguments
         `{"a": 1}` and an identifier of `'123'`.
 
+    !!! note "Factory function"
+
+        `tool_call` may also be used as a factory to create a `ToolCall`. Benefits
+        include:
+
+        * Required arguments strictly validated at creation time
     """
 
     name: str
     """The name of the tool to be called."""
+
     args: dict[str, Any]
-    """The arguments to the tool call."""
+    """The arguments to the tool call as a dictionary."""
+
     id: str | None
     """An identifier associated with the tool call.
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
-
     """
+
     type: NotRequired[Literal["tool_call"]]
+    """Used for discrimination."""
 
 
 def tool_call(
@@ -240,7 +249,7 @@ def tool_call(
 
     Args:
         name: The name of the tool to be called.
-        args: The arguments to the tool call.
+        args: The arguments to the tool call as a dictionary.
         id: An identifier associated with the tool call.
 
     Returns:
@@ -252,9 +261,9 @@ def tool_call(
 class ToolCallChunk(TypedDict):
     """A chunk of a tool call (yielded when streaming).
 
-    When merging `ToolCallChunk`s (e.g., via `AIMessageChunk.__add__`),
-    all string attributes are concatenated. Chunks are only merged if their
-    values of `index` are equal and not None.
+    When merging `ToolCallChunk` objects (e.g., via `AIMessageChunk.__add__`), all
+    string attributes are concatenated. Chunks are only merged if their values of
+    `index` are equal and not `None`.
 
     Example:
     ```python
@@ -270,13 +279,25 @@ class ToolCallChunk(TypedDict):
 
     name: str | None
     """The name of the tool to be called."""
+
     args: str | None
-    """The arguments to the tool call."""
+    """The arguments to the tool call as a JSON-parseable string."""
+
     id: str | None
-    """An identifier associated with the tool call."""
+    """An identifier associated with the tool call.
+
+    An identifier is needed to associate a tool call request with a tool
+    call result in events when multiple concurrent tool calls are made.
+    """
+
     index: int | None
-    """The index of the tool call in a sequence."""
+    """The index of the tool call in a sequence.
+
+    Used for merging chunks.
+    """
+
     type: NotRequired[Literal["tool_call_chunk"]]
+    """Used for discrimination."""
 
 
 def tool_call_chunk(
@@ -290,7 +311,7 @@ def tool_call_chunk(
 
     Args:
         name: The name of the tool to be called.
-        args: The arguments to the tool call.
+        args: The arguments to the tool call as a JSON string.
         id: An identifier associated with the tool call.
         index: The index of the tool call in a sequence.
 
@@ -313,7 +334,7 @@ def invalid_tool_call(
 
     Args:
         name: The name of the tool to be called.
-        args: The arguments to the tool call.
+        args: The arguments to the tool call as a JSON string.
         id: An identifier associated with the tool call.
         error: An error message associated with the tool call.
 

libs/core/langchain_core/messages/utils.py

@@ -28,6 +28,7 @@ from typing import (
     cast,
     overload,
 )
+from xml.sax.saxutils import escape, quoteattr
 
 from pydantic import Discriminator, Field, Tag
 
@@ -65,14 +66,19 @@ logger = logging.getLogger(__name__)
 def _get_type(v: Any) -> str:
     """Get the type associated with the object for serialization purposes."""
     if isinstance(v, dict) and "type" in v:
-        return v["type"]
-    if hasattr(v, "type"):
-        return v.type
-    msg = (
-        f"Expected either a dictionary with a 'type' key or an object "
-        f"with a 'type' attribute. Instead got type {type(v)}."
-    )
-    raise TypeError(msg)
+        result = v["type"]
+    elif hasattr(v, "type"):
+        result = v.type
+    else:
+        msg = (
+            f"Expected either a dictionary with a 'type' key or an object "
+            f"with a 'type' attribute. Instead got type {type(v)}."
+        )
+        raise TypeError(msg)
+    if not isinstance(result, str):
+        msg = f"Expected 'type' to be a str, got {type(result).__name__}"
+        raise TypeError(msg)
+    return result
 
 
 AnyMessage = Annotated[
@@ -93,8 +99,199 @@ AnyMessage = Annotated[
 """A type representing any defined `Message` or `MessageChunk` type."""
 
 
+def _has_base64_data(block: dict) -> bool:
+    """Check if a content block contains base64 encoded data.
+
+    Args:
+        block: A content block dictionary.
+
+    Returns:
+        Whether the block contains base64 data.
+    """
+    # Check for explicit base64 field (standard content blocks)
+    if block.get("base64"):
+        return True
+
+    # Check for data: URL in url field
+    url = block.get("url", "")
+    if isinstance(url, str) and url.startswith("data:"):
+        return True
+
+    # Check for OpenAI-style image_url with data: URL
+    image_url = block.get("image_url", {})
+    if isinstance(image_url, dict):
+        url = image_url.get("url", "")
+        if isinstance(url, str) and url.startswith("data:"):
+            return True
+
+    return False
+
+
+_XML_CONTENT_BLOCK_MAX_LEN = 500
+
+
+def _truncate(text: str, max_len: int = _XML_CONTENT_BLOCK_MAX_LEN) -> str:
+    """Truncate text to `max_len` characters, adding ellipsis if truncated."""
+    if len(text) <= max_len:
+        return text
+    return text[:max_len] + "..."
+
+
+def _format_content_block_xml(block: dict) -> str | None:
+    """Format a content block as XML.
+
+    Args:
+        block: A LangChain content block.
+
+    Returns:
+        XML string representation of the block, or `None` if the block should be
+            skipped.
+
+    Note:
+        Plain text document content, server tool call arguments, and server tool
+        result outputs are truncated to 500 characters.
+    """
+    block_type = block.get("type", "")
+
+    # Skip blocks with base64 encoded data
+    if _has_base64_data(block):
+        return None
+
+    # Text blocks
+    if block_type == "text":
+        text = block.get("text", "")
+        return escape(text) if text else None
+
+    # Reasoning blocks
+    if block_type == "reasoning":
+        reasoning = block.get("reasoning", "")
+        if reasoning:
+            return f"<reasoning>{escape(reasoning)}</reasoning>"
+        return None
+
+    # Image blocks (URL only, base64 already filtered)
+    if block_type == "image":
+        url = block.get("url")
+        file_id = block.get("file_id")
+        if url:
+            return f"<image url={quoteattr(url)} />"
+        if file_id:
+            return f"<image file_id={quoteattr(file_id)} />"
+        return None
+
+    # OpenAI-style image_url blocks
+    if block_type == "image_url":
+        image_url = block.get("image_url", {})
+        if isinstance(image_url, dict):
+            url = image_url.get("url", "")
+            if url and not url.startswith("data:"):
+                return f"<image url={quoteattr(url)} />"
+        return None
+
+    # Audio blocks (URL only)
+    if block_type == "audio":
+        url = block.get("url")
+        file_id = block.get("file_id")
+        if url:
+            return f"<audio url={quoteattr(url)} />"
+        if file_id:
+            return f"<audio file_id={quoteattr(file_id)} />"
+        return None
+
+    # Video blocks (URL only)
+    if block_type == "video":
+        url = block.get("url")
+        file_id = block.get("file_id")
+        if url:
+            return f"<video url={quoteattr(url)} />"
+        if file_id:
+            return f"<video file_id={quoteattr(file_id)} />"
+        return None
+
+    # Plain text document blocks
+    if block_type == "text-plain":
+        text = block.get("text", "")
+        return escape(_truncate(text)) if text else None
+
+    # Server tool call blocks (from AI messages)
+    if block_type == "server_tool_call":
+        tc_id = quoteattr(str(block.get("id") or ""))
+        tc_name = quoteattr(str(block.get("name") or ""))
+        tc_args_json = json.dumps(block.get("args", {}), ensure_ascii=False)
+        tc_args = escape(_truncate(tc_args_json))
+        return (
+            f"<server_tool_call id={tc_id} name={tc_name}>{tc_args}</server_tool_call>"
+        )
+
+    # Server tool result blocks
+    if block_type == "server_tool_result":
+        tool_call_id = quoteattr(str(block.get("tool_call_id") or ""))
+        status = quoteattr(str(block.get("status") or ""))
+        output = block.get("output")
+        if output:
+            output_json = json.dumps(output, ensure_ascii=False)
+            output_str = escape(_truncate(output_json))
+        else:
+            output_str = ""
+        return (
+            f"<server_tool_result tool_call_id={tool_call_id} status={status}>"
+            f"{output_str}</server_tool_result>"
+        )
+
+    # Unknown block type - skip silently
+    return None
+
+
+def _get_message_type_str(
+    m: BaseMessage,
+    human_prefix: str,
+    ai_prefix: str,
+    system_prefix: str,
+    function_prefix: str,
+    tool_prefix: str,
+) -> str:
+    """Get the type string for XML message element.
+
+    Args:
+        m: The message to get the type string for.
+        human_prefix: The prefix to use for `HumanMessage`.
+        ai_prefix: The prefix to use for `AIMessage`.
+        system_prefix: The prefix to use for `SystemMessage`.
+        function_prefix: The prefix to use for `FunctionMessage`.
+        tool_prefix: The prefix to use for `ToolMessage`.
+
+    Returns:
+        The type string for the message element.
+
+    Raises:
+        ValueError: If an unsupported message type is encountered.
+    """
+    if isinstance(m, HumanMessage):
+        return human_prefix.lower()
+    if isinstance(m, AIMessage):
+        return ai_prefix.lower()
+    if isinstance(m, SystemMessage):
+        return system_prefix.lower()
+    if isinstance(m, FunctionMessage):
+        return function_prefix.lower()
+    if isinstance(m, ToolMessage):
+        return tool_prefix.lower()
+    if isinstance(m, ChatMessage):
+        return m.role
+    msg = f"Got unsupported message type: {m}"
+    raise ValueError(msg)
+
+
 def get_buffer_string(
-    messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
+    messages: Sequence[BaseMessage],
+    human_prefix: str = "Human",
+    ai_prefix: str = "AI",
+    *,
+    system_prefix: str = "System",
+    function_prefix: str = "Function",
+    tool_prefix: str = "Tool",
+    message_separator: str = "\n",
+    format: Literal["prefix", "xml"] = "prefix",  # noqa: A002
 ) -> str:
     r"""Convert a sequence of messages to strings and concatenate them into one string.
 
@@ -102,6 +299,15 @@ def get_buffer_string(
         messages: Messages to be converted to strings.
         human_prefix: The prefix to prepend to contents of `HumanMessage`s.
         ai_prefix: The prefix to prepend to contents of `AIMessage`.
+        system_prefix: The prefix to prepend to contents of `SystemMessage`s.
+        function_prefix: The prefix to prepend to contents of `FunctionMessage`s.
+        tool_prefix: The prefix to prepend to contents of `ToolMessage`s.
+        message_separator: The separator to use between messages.
+        format: The output format. `'prefix'` uses `Role: content` format (default).
+
+            `'xml'` uses XML-style `<message type='role'>` format with proper character
+            escaping, which is useful when message content may contain role-like
+            prefixes that could cause ambiguity.
 
     Returns:
         A single string concatenation of all input messages.
@@ -109,9 +315,38 @@ def get_buffer_string(
     Raises:
         ValueError: If an unsupported message type is encountered.
 
+    Note:
+        If a message is an `AIMessage` and contains both tool calls under `tool_calls`
+        and a function call under `additional_kwargs["function_call"]`, only the tool
+        calls will be appended to the string representation.
+
+        When using `format='xml'`:
+
+        - All messages use uniform `<message type="role">content</message>` format.
+        - The `type` attribute uses `human_prefix` (lowercased) for `HumanMessage`,
+            `ai_prefix` (lowercased) for `AIMessage`, `system_prefix` (lowercased)
+            for `SystemMessage`, `function_prefix` (lowercased) for `FunctionMessage`,
+            `tool_prefix` (lowercased) for `ToolMessage`, and the original role
+            (unchanged) for `ChatMessage`.
+        - Message content is escaped using `xml.sax.saxutils.escape()`.
+        - Attribute values are escaped using `xml.sax.saxutils.quoteattr()`.
+        - AI messages with tool calls use nested structure with `<content>` and
+            `<tool_call>` elements.
+        - For multi-modal content (list of content blocks), supported block types
+            are: `text`, `reasoning`, `image` (URL/file_id only), `image_url`
+            (OpenAI-style, URL only), `audio` (URL/file_id only), `video` (URL/file_id
+            only), `text-plain`, `server_tool_call`, and `server_tool_result`.
+        - Content blocks with base64-encoded data are skipped (including blocks
+            with `base64` field or `data:` URLs).
+        - Unknown block types are skipped.
+        - Plain text document content (`text-plain`), server tool call arguments,
+            and server tool result outputs are truncated to 500 characters.
+
     Example:
+        Default prefix format:
+
         ```python
-        from langchain_core import AIMessage, HumanMessage
+        from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string
 
         messages = [
             HumanMessage(content="Hi, how are you?"),
@@ -120,7 +355,54 @@ def get_buffer_string(
         get_buffer_string(messages)
         # -> "Human: Hi, how are you?\nAI: Good, how are you?"
         ```
+
+        XML format (useful when content contains role-like prefixes):
+
+        ```python
+        messages = [
+            HumanMessage(content="Example: Human: some text"),
+            AIMessage(content="I see the example."),
+        ]
+        get_buffer_string(messages, format="xml")
+        # -> '<message type="human">Example: Human: some text</message>\\n'
+        # -> '<message type="ai">I see the example.</message>'
+        ```
+
+        XML format with special characters (automatically escaped):
+
+        ```python
+        messages = [
+            HumanMessage(content="Is 5 < 10 & 10 > 5?"),
+        ]
+        get_buffer_string(messages, format="xml")
+        # -> '<message type="human">Is 5 &lt; 10 &amp; 10 &gt; 5?</message>'
+        ```
+
+        XML format with tool calls:
+
+        ```python
+        messages = [
+            AIMessage(
+                content="I'll search for that.",
+                tool_calls=[
+                    {"id": "call_123", "name": "search", "args": {"query": "weather"}}
+                ],
+            ),
+        ]
+        get_buffer_string(messages, format="xml")
+        # -> '<message type="ai">\\n'
+        # -> '  <content>I\\'ll search for that.</content>\\n'
+        # -> '  <tool_call id="call_123" name="search">'
+        # -> '{"query": "weather"}</tool_call>\\n'
+        # -> '</message>'
+        ```
     """
+    if format not in ("prefix", "xml"):
+        msg = (
+            f"Unrecognized format={format!r}. Supported formats are 'prefix' and 'xml'."
+        )
+        raise ValueError(msg)
+
     string_messages = []
     for m in messages:
         if isinstance(m, HumanMessage):
@@ -128,22 +410,96 @@ def get_buffer_string(
         elif isinstance(m, AIMessage):
             role = ai_prefix
         elif isinstance(m, SystemMessage):
-            role = "System"
+            role = system_prefix
         elif isinstance(m, FunctionMessage):
-            role = "Function"
+            role = function_prefix
         elif isinstance(m, ToolMessage):
-            role = "Tool"
+            role = tool_prefix
         elif isinstance(m, ChatMessage):
             role = m.role
         else:
             msg = f"Got unsupported message type: {m}"
             raise ValueError(msg)  # noqa: TRY004
-        message = f"{role}: {m.text}"
-        if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
-            message += f"{m.additional_kwargs['function_call']}"
+
+        if format == "xml":
+            msg_type = _get_message_type_str(
+                m, human_prefix, ai_prefix, system_prefix, function_prefix, tool_prefix
+            )
+
+            # Format content blocks
+            if isinstance(m.content, str):
+                content_parts = [escape(m.content)] if m.content else []
+            else:
+                # List of content blocks
+                content_parts = []
+                for block in m.content:
+                    if isinstance(block, str):
+                        if block:
+                            content_parts.append(escape(block))
+                    else:
+                        formatted = _format_content_block_xml(block)
+                        if formatted:
+                            content_parts.append(formatted)
+
+            # Check if this is an AIMessage with tool calls
+            has_tool_calls = isinstance(m, AIMessage) and m.tool_calls
+            has_function_call = (
+                isinstance(m, AIMessage)
+                and not m.tool_calls
+                and "function_call" in m.additional_kwargs
+            )
+
+            if has_tool_calls or has_function_call:
+                # Use nested structure for AI messages with tool calls
+                # Type narrowing: at this point m is AIMessage (verified above)
+                ai_msg = cast("AIMessage", m)
+                parts = [f"<message type={quoteattr(msg_type)}>"]
+                if content_parts:
+                    parts.append(f"  <content>{' '.join(content_parts)}</content>")
+
+                if has_tool_calls:
+                    for tc in ai_msg.tool_calls:
+                        tc_id = quoteattr(str(tc.get("id") or ""))
+                        tc_name = quoteattr(str(tc.get("name") or ""))
+                        tc_args = escape(
+                            json.dumps(tc.get("args", {}), ensure_ascii=False)
+                        )
+                        parts.append(
+                            f"  <tool_call id={tc_id} name={tc_name}>"
+                            f"{tc_args}</tool_call>"
+                        )
+                elif has_function_call:
+                    fc = ai_msg.additional_kwargs["function_call"]
+                    fc_name = quoteattr(str(fc.get("name") or ""))
+                    fc_args = escape(str(fc.get("arguments") or "{}"))
+                    parts.append(
+                        f"  <function_call name={fc_name}>{fc_args}</function_call>"
+                    )
+
+                parts.append("</message>")
+                message = "\n".join(parts)
+            else:
+                # Simple structure for messages without tool calls
+                joined_content = " ".join(content_parts)
+                message = (
+                    f"<message type={quoteattr(msg_type)}>{joined_content}</message>"
+                )
+        else:  # format == "prefix"
+            content = m.text
+            message = f"{role}: {content}"
+            tool_info = ""
+            if isinstance(m, AIMessage):
+                if m.tool_calls:
+                    tool_info = str(m.tool_calls)
+                elif "function_call" in m.additional_kwargs:
+                    # Legacy behavior assumes only one function call per message
+                    tool_info = str(m.additional_kwargs["function_call"])
+            if tool_info:
+                message += tool_info  # Preserve original behavior
+
         string_messages.append(message)
 
-    return "\n".join(string_messages)
+    return message_separator.join(string_messages)
 
 
 def _message_from_dict(message: dict) -> BaseMessage:
@@ -206,8 +562,11 @@ def message_chunk_to_message(chunk: BaseMessage) -> BaseMessage:
     ignore_keys = ["type"]
     if isinstance(chunk, AIMessageChunk):
         ignore_keys.extend(["tool_call_chunks", "chunk_position"])
-    return chunk.__class__.__mro__[1](
-        **{k: v for k, v in chunk.__dict__.items() if k not in ignore_keys}
+    return cast(
+        "BaseMessage",
+        chunk.__class__.__mro__[1](
+            **{k: v for k, v in chunk.__dict__.items() if k not in ignore_keys}
+        ),
     )
 
 
@@ -229,13 +588,13 @@ def _create_message_from_message_type(
     """Create a message from a `Message` type and content string.
 
     Args:
-        message_type: (str) the type of the message (e.g., `'human'`, `'ai'`, etc.).
-        content: (str) the content string.
-        name: (str) the name of the message.
-        tool_call_id: (str) the tool call id.
-        tool_calls: (list[dict[str, Any]]) the tool calls.
-        id: (str) the id of the message.
-        additional_kwargs: (dict[str, Any]) additional keyword arguments.
+        message_type: the type of the message (e.g., `'human'`, `'ai'`, etc.).
+        content: the content string.
+        name: the name of the message.
+        tool_call_id: the tool call id.
+        tool_calls: the tool calls.
+        id: the id of the message.
+        additional_kwargs: additional keyword arguments.
 
     Returns:
         a message of the appropriate type.
@@ -539,6 +898,7 @@ def filter_messages(
         ):
             continue
 
+        new_msg = msg
         if isinstance(exclude_tool_calls, (list, tuple, set)):
             if isinstance(msg, AIMessage) and msg.tool_calls:
                 tool_calls = [
@@ -562,7 +922,7 @@ def filter_messages(
                         )
                     ]
 
-                msg = msg.model_copy(  # noqa: PLW2901
+                new_msg = msg.model_copy(
                     update={"tool_calls": tool_calls, "content": content}
                 )
             elif (
@@ -573,11 +933,11 @@ def filter_messages(
         # default to inclusion when no inclusion criteria given.
         if (
             not (include_types or include_ids or include_names)
-            or (include_names and msg.name in include_names)
-            or (include_types and _is_message_type(msg, include_types))
-            or (include_ids and msg.id in include_ids)
+            or (include_names and new_msg.name in include_names)
+            or (include_types and _is_message_type(new_msg, include_types))
+            or (include_ids and new_msg.id in include_ids)
         ):
-            filtered.append(msg)
+            filtered.append(new_msg)
 
     return filtered
 
@@ -720,7 +1080,8 @@ def trim_messages(
     max_tokens: int,
     token_counter: Callable[[list[BaseMessage]], int]
     | Callable[[BaseMessage], int]
-    | BaseLanguageModel,
+    | BaseLanguageModel
+    | Literal["approximate"],
     strategy: Literal["first", "last"] = "last",
     allow_partial: bool = False,
     end_on: str | type[BaseMessage] | Sequence[str | type[BaseMessage]] | None = None,
@@ -758,53 +1119,65 @@ def trim_messages(
         messages: Sequence of Message-like objects to trim.
         max_tokens: Max token count of trimmed messages.
         token_counter: Function or llm for counting tokens in a `BaseMessage` or a
-            list of `BaseMessage`. If a `BaseLanguageModel` is passed in then
-            `BaseLanguageModel.get_num_tokens_from_messages()` will be used.
-            Set to `len` to count the number of **messages** in the chat history.
+            list of `BaseMessage`.
+
+            If a `BaseLanguageModel` is passed in then
+            `BaseLanguageModel.get_num_tokens_from_messages()` will be used. Set to
+            `len` to count the number of **messages** in the chat history.
+
+            You can also use string shortcuts for convenience:
+
+            - `'approximate'`: Uses `count_tokens_approximately` for fast, approximate
+                token counts.
 
             !!! note
 
-                Use `count_tokens_approximately` to get fast, approximate token
-                counts.
-
-                This is recommended for using `trim_messages` on the hot path, where
-                exact token counting is not necessary.
+                `count_tokens_approximately` (or the shortcut `'approximate'`) is
+                recommended for using `trim_messages` on the hot path, where exact token
+                counting is not necessary.
 
         strategy: Strategy for trimming.
+
             - `'first'`: Keep the first `<= n_count` tokens of the messages.
             - `'last'`: Keep the last `<= n_count` tokens of the messages.
         allow_partial: Whether to split a message if only part of the message can be
-            included. If `strategy='last'` then the last partial contents of a message
-            are included. If `strategy='first'` then the first partial contents of a
-            message are included.
-        end_on: The message type to end on. If specified then every message after the
-            last occurrence of this type is ignored. If `strategy='last'` then this
-            is done before we attempt to get the last `max_tokens`. If
-            `strategy='first'` then this is done after we get the first
-            `max_tokens`. Can be specified as string names (e.g. `'system'`,
-            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g.
-            `SystemMessage`, `HumanMessage`, `AIMessage`, ...). Can be a single
-            type or a list of types.
+            included.
 
-        start_on: The message type to start on. Should only be specified if
-            `strategy='last'`. If specified then every message before
-            the first occurrence of this type is ignored. This is done after we trim
-            the initial messages to the last `max_tokens`. Does not
-            apply to a `SystemMessage` at index 0 if `include_system=True`. Can be
-            specified as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or
-            as `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`,
-            `AIMessage`, ...). Can be a single type or a list of types.
+            If `strategy='last'` then the last partial contents of a message are
+            included. If `strategy='first'` then the first partial contents of a
+            message are included.
+        end_on: The message type to end on.
+
+            If specified then every message after the last occurrence of this type is
+            ignored. If `strategy='last'` then this is done before we attempt to get the
+            last `max_tokens`. If `strategy='first'` then this is done after we get the
+            first `max_tokens`. Can be specified as string names (e.g. `'system'`,
+            `'human'`, `'ai'`, ...) or as `BaseMessage` classes (e.g. `SystemMessage`,
+            `HumanMessage`, `AIMessage`, ...). Can be a single type or a list of types.
+
+        start_on: The message type to start on.
+
+            Should only be specified if `strategy='last'`. If specified then every
+            message before the first occurrence of this type is ignored. This is done
+            after we trim the initial messages to the last `max_tokens`. Does not apply
+            to a `SystemMessage` at index 0 if `include_system=True`. Can be specified
+            as string names (e.g. `'system'`, `'human'`, `'ai'`, ...) or as
+            `BaseMessage` classes (e.g. `SystemMessage`, `HumanMessage`, `AIMessage`,
+            ...). Can be a single type or a list of types.
 
         include_system: Whether to keep the `SystemMessage` if there is one at index
-            `0`. Should only be specified if `strategy="last"`.
+            `0`.
+
+            Should only be specified if `strategy="last"`.
         text_splitter: Function or `langchain_text_splitters.TextSplitter` for
-            splitting the string contents of a message. Only used if
-            `allow_partial=True`. If `strategy='last'` then the last split tokens
-            from a partial message will be included. if `strategy='first'` then the
-            first split tokens from a partial message will be included. Token splitter
-            assumes that separators are kept, so that split contents can be directly
-            concatenated to recreate the original text. Defaults to splitting on
-            newlines.
+            splitting the string contents of a message.
+
+            Only used if `allow_partial=True`. If `strategy='last'` then the last split
+            tokens from a partial message will be included. if `strategy='first'` then
+            the first split tokens from a partial message will be included. Token
+            splitter assumes that separators are kept, so that split contents can be
+            directly concatenated to recreate the original text. Defaults to splitting
+            on newlines.
 
     Returns:
         List of trimmed `BaseMessage`.
@@ -815,8 +1188,8 @@ def trim_messages(
 
     Example:
         Trim chat history based on token count, keeping the `SystemMessage` if
-        present, and ensuring that the chat history starts with a `HumanMessage` (
-        or a `SystemMessage` followed by a `HumanMessage`).
+        present, and ensuring that the chat history starts with a `HumanMessage` (or a
+        `SystemMessage` followed by a `HumanMessage`).
 
         ```python
         from langchain_core.messages import (
@@ -869,8 +1242,34 @@ def trim_messages(
         ]
         ```
 
+        Trim chat history using approximate token counting with `'approximate'`:
+
+        ```python
+        trim_messages(
+            messages,
+            max_tokens=45,
+            strategy="last",
+            # Using the "approximate" shortcut for fast token counting
+            token_counter="approximate",
+            start_on="human",
+            include_system=True,
+        )
+
+        # This is equivalent to using `count_tokens_approximately` directly
+        from langchain_core.messages.utils import count_tokens_approximately
+
+        trim_messages(
+            messages,
+            max_tokens=45,
+            strategy="last",
+            token_counter=count_tokens_approximately,
+            start_on="human",
+            include_system=True,
+        )
+        ```
+
         Trim chat history based on the message count, keeping the `SystemMessage` if
-        present, and ensuring that the chat history starts with a `HumanMessage` (
+        present, and ensuring that the chat history starts with a HumanMessage (
         or a `SystemMessage` followed by a `HumanMessage`).
 
             trim_messages(
@@ -992,24 +1391,44 @@ def trim_messages(
         raise ValueError(msg)
 
     messages = convert_to_messages(messages)
-    if hasattr(token_counter, "get_num_tokens_from_messages"):
-        list_token_counter = token_counter.get_num_tokens_from_messages
-    elif callable(token_counter):
+
+    # Handle string shortcuts for token counter
+    if isinstance(token_counter, str):
+        if token_counter in _TOKEN_COUNTER_SHORTCUTS:
+            actual_token_counter = _TOKEN_COUNTER_SHORTCUTS[token_counter]
+        else:
+            available_shortcuts = ", ".join(
+                f"'{key}'" for key in _TOKEN_COUNTER_SHORTCUTS
+            )
+            msg = (
+                f"Invalid token_counter shortcut '{token_counter}'. "
+                f"Available shortcuts: {available_shortcuts}."
+            )
+            raise ValueError(msg)
+    else:
+        # Type narrowing: at this point token_counter is not a str
+        actual_token_counter = token_counter  # type: ignore[assignment]
+
+    if hasattr(actual_token_counter, "get_num_tokens_from_messages"):
+        list_token_counter = actual_token_counter.get_num_tokens_from_messages
+    elif callable(actual_token_counter):
         if (
-            next(iter(inspect.signature(token_counter).parameters.values())).annotation
+            next(
+                iter(inspect.signature(actual_token_counter).parameters.values())
+            ).annotation
             is BaseMessage
         ):
 
             def list_token_counter(messages: Sequence[BaseMessage]) -> int:
-                return sum(token_counter(msg) for msg in messages)  # type: ignore[arg-type, misc]
+                return sum(actual_token_counter(msg) for msg in messages)  # type: ignore[arg-type, misc]
 
         else:
-            list_token_counter = token_counter
+            list_token_counter = actual_token_counter
     else:
         msg = (
             f"'token_counter' expected to be a model that implements "
             f"'get_num_tokens_from_messages()' or a function. Received object of type "
-            f"{type(token_counter)}."
+            f"{type(actual_token_counter)}."
         )
         raise ValueError(msg)
 
@@ -1044,11 +1463,38 @@ def trim_messages(
     raise ValueError(msg)
 
 
+_SingleMessage = BaseMessage | str | dict[str, Any]
+_T = TypeVar("_T", bound=_SingleMessage)
+# A sequence of _SingleMessage that is NOT a bare str
+_MultipleMessages = Sequence[_T]
+
+
+@overload
+def convert_to_openai_messages(
+    messages: _SingleMessage,
+    *,
+    text_format: Literal["string", "block"] = "string",
+    include_id: bool = False,
+    pass_through_unknown_blocks: bool = True,
+) -> dict: ...
+
+
+@overload
+def convert_to_openai_messages(
+    messages: _MultipleMessages,
+    *,
+    text_format: Literal["string", "block"] = "string",
+    include_id: bool = False,
+    pass_through_unknown_blocks: bool = True,
+) -> list[dict]: ...
+
+
 def convert_to_openai_messages(
     messages: MessageLikeRepresentation | Sequence[MessageLikeRepresentation],
     *,
     text_format: Literal["string", "block"] = "string",
     include_id: bool = False,
+    pass_through_unknown_blocks: bool = True,
 ) -> dict | list[dict]:
     """Convert LangChain messages into OpenAI message dicts.
 
@@ -1068,6 +1514,9 @@ def convert_to_openai_messages(
                 content blocks these are left as is.
         include_id: Whether to include message IDs in the openai messages, if they
             are present in the source messages.
+        pass_through_unknown_blocks: Whether to include content blocks with unknown
+            formats in the output. If `False`, an error is raised if an unknown
+            content block is encountered.
 
     Raises:
         ValueError: if an unrecognized `text_format` is specified, or if a message
@@ -1135,7 +1584,7 @@ def convert_to_openai_messages(
         err = f"Unrecognized {text_format=}, expected one of 'string' or 'block'."
         raise ValueError(err)
 
-    oai_messages: list = []
+    oai_messages: list[dict] = []
 
     if is_single := isinstance(messages, (BaseMessage, dict, str)):
         messages = [messages]
@@ -1317,6 +1766,36 @@ def convert_to_openai_messages(
                                 },
                             }
                         )
+                elif block.get("type") == "function_call":  # OpenAI Responses
+                    if not any(
+                        tool_call["id"] == block.get("call_id")
+                        for tool_call in cast("AIMessage", message).tool_calls
+                    ):
+                        if missing := [
+                            k
+                            for k in ("call_id", "name", "arguments")
+                            if k not in block
+                        ]:
+                            err = (
+                                f"Unrecognized content block at "
+                                f"messages[{i}].content[{j}] has 'type': "
+                                f"'tool_use', but is missing expected key(s) "
+                                f"{missing}. Full content block:\n\n{block}"
+                            )
+                            raise ValueError(err)
+                        oai_msg["tool_calls"] = oai_msg.get("tool_calls", [])
+                        oai_msg["tool_calls"].append(
+                            {
+                                "type": "function",
+                                "id": block.get("call_id"),
+                                "function": {
+                                    "name": block.get("name"),
+                                    "arguments": block.get("arguments"),
+                                },
+                            }
+                        )
+                    if pass_through_unknown_blocks:
+                        content.append(block)
                 elif block.get("type") == "tool_result":
                     if missing := [
                         k for k in ("content", "tool_use_id") if k not in block
@@ -1397,7 +1876,10 @@ def convert_to_openai_messages(
                             },
                         }
                     )
-                elif block.get("type") in ["thinking", "reasoning"]:
+                elif (
+                    block.get("type") in {"thinking", "reasoning"}
+                    or pass_through_unknown_blocks
+                ):
                     content.append(block)
                 else:
                     err = (
@@ -1669,7 +2151,11 @@ def _get_message_openai_role(message: BaseMessage) -> str:
     if isinstance(message, ToolMessage):
         return "tool"
     if isinstance(message, SystemMessage):
-        return message.additional_kwargs.get("__openai_role__", "system")
+        role = message.additional_kwargs.get("__openai_role__", "system")
+        if not isinstance(role, str):
+            msg = f"Expected '__openai_role__' to be a str, got {type(role).__name__}"
+            raise TypeError(msg)
+        return role
     if isinstance(message, FunctionMessage):
         return "function"
     if isinstance(message, ChatMessage):
@@ -1702,26 +2188,29 @@ def count_tokens_approximately(
     """Approximate the total number of tokens in messages.
 
     The token count includes stringified message content, role, and (optionally) name.
+
     - For AI messages, the token count also includes stringified tool calls.
     - For tool messages, the token count also includes the tool call ID.
 
     Args:
         messages: List of messages to count tokens for.
         chars_per_token: Number of characters per token to use for the approximation.
+
             One token corresponds to ~4 chars for common English text.
+
             You can also specify `float` values for more fine-grained control.
             [See more here](https://platform.openai.com/tokenizer).
         extra_tokens_per_message: Number of extra tokens to add per message, e.g.
             special tokens, including beginning/end of message.
+
             You can also specify `float` values for more fine-grained control.
             [See more here](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb).
         count_name: Whether to include message names in the count.
-            Enabled by default.
 
     Returns:
         Approximate number of tokens in the messages.
 
-    !!! note
+    Note:
         This is a simple approximation that may not match the exact token count used by
         specific models. For accurate counts, use model-specific tokenizers.
 
@@ -1729,7 +2218,6 @@ def count_tokens_approximately(
         This function does not currently support counting image tokens.
 
     !!! version-added "Added in `langchain-core` 0.3.46"
-
     """
     token_count = 0.0
     for message in convert_to_messages(messages):
@@ -1770,3 +2258,14 @@ def count_tokens_approximately(
 
     # round up once more time in case extra_tokens_per_message is a float
     return math.ceil(token_count)
+
+
+# Mapping from string shortcuts to token counter functions
+def _approximate_token_counter(messages: Sequence[BaseMessage]) -> int:
+    """Wrapper for `count_tokens_approximately` that matches expected signature."""
+    return count_tokens_approximately(messages)
+
+
+_TOKEN_COUNTER_SHORTCUTS = {
+    "approximate": _approximate_token_counter,
+}

libs/core/langchain_core/output_parsers/base.py

@@ -9,6 +9,7 @@ from typing import (
     Any,
     Generic,
     TypeVar,
+    cast,
 )
 
 from typing_extensions import override
@@ -46,7 +47,7 @@ class BaseLLMOutputParser(ABC, Generic[T]):
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model `Generation` objects into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
         Args:
             result: A list of `Generation` to be parsed. The Generations are assumed
@@ -56,7 +57,7 @@ class BaseLLMOutputParser(ABC, Generic[T]):
 
         Returns:
             Structured output.
-        """  # noqa: E501
+        """
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
 
@@ -77,7 +78,7 @@ class BaseGenerationOutputParser(
         """Return the output type for the parser."""
         # even though mypy complains this isn't valid,
         # it is good enough for pydantic to build the schema from
-        return T  # type: ignore[misc]
+        return cast("type[T]", T)  # type: ignore[misc]
 
     @override
     def invoke(
@@ -181,7 +182,7 @@ class BaseOutputParser(
             if hasattr(base, "__pydantic_generic_metadata__"):
                 metadata = base.__pydantic_generic_metadata__
                 if "args" in metadata and len(metadata["args"]) > 0:
-                    return metadata["args"][0]
+                    return cast("type[T]", metadata["args"][0])
 
         msg = (
             f"Runnable {self.__class__.__name__} doesn't have an inferable OutputType. "
@@ -267,7 +268,7 @@ class BaseOutputParser(
     async def aparse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> T:
-        """Async parse a list of candidate model `Generation` objects into a specific format.
+        """Parse a list of candidate model `Generation` objects into a specific format.
 
         The return value is parsed from only the first `Generation` in the result, which
             is assumed to be the highest-likelihood `Generation`.
@@ -280,7 +281,7 @@ class BaseOutputParser(
 
         Returns:
             Structured output.
-        """  # noqa: E501
+        """
         return await run_in_executor(None, self.parse_result, result, partial=partial)
 
     async def aparse(self, text: str) -> T:

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -37,7 +37,7 @@ class OutputFunctionsParser(BaseGenerationOutputParser[Any]):
             The parsed JSON object.
 
         Raises:
-            `OutputParserException`: If the output is not valid JSON.
+            OutputParserException: If the output is not valid JSON.
         """
         generation = result[0]
         if not isinstance(generation, ChatGeneration):
@@ -88,7 +88,7 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
             The parsed JSON object.
 
         Raises:
-            OutputParserExcept`ion: If the output is not valid JSON.
+            OutputParserException: If the output is not valid JSON.
         """
         if len(result) != 1:
             msg = f"Expected exactly one result, but got {len(result)}"
@@ -228,7 +228,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
 
     @model_validator(mode="before")
     @classmethod
-    def validate_schema(cls, values: dict) -> Any:
+    def validate_schema(cls, values: dict[str, Any]) -> Any:
         """Validate the Pydantic schema.
 
         Args:

libs/core/langchain_core/output_parsers/pydantic.py

@@ -1,7 +1,7 @@
 """Output parsers using Pydantic."""
 
 import json
-from typing import Annotated, Generic
+from typing import Annotated, Generic, Literal, overload
 
 import pydantic
 from pydantic import SkipValidation
@@ -42,6 +42,16 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
         msg = f"Failed to parse {name} from completion {json_string}. Got: {e}"
         return OutputParserException(msg, llm_output=json_string)
 
+    @overload
+    def parse_result(
+        self, result: list[Generation], *, partial: Literal[False] = False
+    ) -> TBaseModel: ...
+
+    @overload
+    def parse_result(
+        self, result: list[Generation], *, partial: bool = False
+    ) -> TBaseModel | None: ...
+
     def parse_result(
         self, result: list[Generation], *, partial: bool = False
     ) -> TBaseModel | None:
@@ -54,7 +64,7 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
                 all the keys that have been returned so far.
 
         Raises:
-            `OutputParserException`: If the result is not valid JSON
+            OutputParserException: If the result is not valid JSON
                 or does not conform to the Pydantic model.
 
         Returns:
@@ -77,7 +87,7 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
         Returns:
             The parsed Pydantic object.
         """
-        return super().parse(text)
+        return self.parse_result([Generation(text=text)])
 
     def get_format_instructions(self) -> str:
         """Return the format instructions for the JSON output.

libs/core/langchain_core/outputs/chat_generation.py

@@ -57,16 +57,18 @@ class ChatGeneration(Generation):
         text = ""
         if isinstance(self.message.content, str):
             text = self.message.content
-        # Assumes text in content blocks in OpenAI format.
-        # Uses first text block.
+        # Extracts first text block from content blocks.
+        # Skips blocks with explicit non-text type (e.g., thinking, reasoning).
         elif isinstance(self.message.content, list):
             for block in self.message.content:
                 if isinstance(block, str):
                     text = block
                     break
                 if isinstance(block, dict) and "text" in block:
-                    text = block["text"]
-                    break
+                    block_type = block.get("type")
+                    if block_type is None or block_type == "text":
+                        text = block["text"]
+                        break
         self.text = text
         return self
 

libs/core/langchain_core/prompt_values.py

@@ -104,18 +104,31 @@ class ChatPromptValue(PromptValue):
 
 
 class ImageURL(TypedDict, total=False):
-    """Image URL."""
+    """Image URL for multimodal model inputs (OpenAI format).
+
+    Represents the inner `image_url` object in OpenAI's Chat Completion API format. This
+    is used by `ImagePromptTemplate` and `ChatPromptTemplate`.
+
+    See Also:
+        `ImageContentBlock`: LangChain's provider-agnostic image format used in message
+        content blocks. Use `ImageContentBlock` when working with the standardized
+        message format across different providers.
+
+    Note:
+        The `detail` field values are not validated locally. Invalid values
+        will be rejected by the downstream API, allowing new valid values to
+        be used without requiring a LangChain update.
+    """
 
     detail: Literal["auto", "low", "high"]
     """Specifies the detail level of the image.
 
-    Can be `'auto'`, `'low'`, or `'high'`.
-
-    This follows OpenAI's Chat Completion API's image URL format.
+    Defaults to ``'auto'`` if not specified. Higher detail levels consume
+    more tokens but provide better image understanding.
     """
 
     url: str
-    """Either a URL of the image or the base64 encoded image data."""
+    """URL of the image or base64-encoded image data."""
 
 
 class ImagePromptValue(PromptValue):

libs/core/langchain_core/prompts/base.py

@@ -2,19 +2,14 @@
 
 from __future__ import annotations
 
+import builtins  # noqa: TC003
 import contextlib
 import json
-import typing
 from abc import ABC, abstractmethod
-from collections.abc import Mapping
+from collections.abc import Mapping  # noqa: TC003
 from functools import cached_property
 from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Generic,
-    TypeVar,
-)
+from typing import TYPE_CHECKING, Any, Generic, TypeVar, cast
 
 import yaml
 from pydantic import BaseModel, ConfigDict, Field, model_validator
@@ -22,7 +17,7 @@ from typing_extensions import Self, override
 
 from langchain_core.exceptions import ErrorCode, create_message
 from langchain_core.load import dumpd
-from langchain_core.output_parsers.base import BaseOutputParser
+from langchain_core.output_parsers.base import BaseOutputParser  # noqa: TC001
 from langchain_core.prompt_values import (
     ChatPromptValueConcrete,
     PromptValue,
@@ -56,7 +51,7 @@ class BasePromptTemplate(
 
     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
+    input_types: builtins.dict[str, Any] = Field(default_factory=dict, exclude=True)
     """A dictionary of the types of the variables the prompt template expects.
 
     If not provided, all variables are assumed to be strings.
@@ -69,7 +64,7 @@ class BasePromptTemplate(
     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: builtins.dict[str, Any] | None = None
     """Metadata to be used for tracing."""
     tags: list[str] | None = None
     """Tags to be used for tracing."""
@@ -122,7 +117,10 @@ class BasePromptTemplate(
 
     @cached_property
     def _serialized(self) -> dict[str, Any]:
-        return dumpd(self)
+        # self is always a Serializable object in this case, thus the result is
+        # guaranteed to be a dict since dumpd uses the default callback, which uses
+        # obj.to_json which always returns TypedDict subclasses
+        return cast("dict[str, Any]", dumpd(self))
 
     @property
     @override
@@ -156,7 +154,7 @@ class BasePromptTemplate(
         if not isinstance(inner_input, dict):
             if len(self.input_variables) == 1:
                 var_name = self.input_variables[0]
-                inner_input = {var_name: inner_input}
+                inner_input_ = {var_name: inner_input}
 
             else:
                 msg = (
@@ -168,12 +166,14 @@ class BasePromptTemplate(
                         message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT
                     )
                 )
-        missing = set(self.input_variables).difference(inner_input)
+        else:
+            inner_input_ = inner_input
+        missing = set(self.input_variables).difference(inner_input_)
         if missing:
             msg = (
                 f"Input to {self.__class__.__name__} is missing variables {missing}. "
                 f" Expected: {self.input_variables}"
-                f" Received: {list(inner_input.keys())}"
+                f" Received: {list(inner_input_.keys())}"
             )
             example_key = missing.pop()
             msg += (
@@ -184,7 +184,7 @@ class BasePromptTemplate(
             raise KeyError(
                 create_message(message=msg, error_code=ErrorCode.INVALID_PROMPT_INPUT)
             )
-        return inner_input
+        return inner_input_
 
     def _format_prompt_with_error_handling(self, inner_input: dict) -> PromptValue:
         inner_input_ = self._validate_input(inner_input)

libs/core/langchain_core/prompts/chat.py

@@ -3,9 +3,9 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Sequence
 from pathlib import Path
 from typing import (
-    TYPE_CHECKING,
     Annotated,
     Any,
     TypedDict,
@@ -48,9 +48,6 @@ from langchain_core.prompts.string import (
 from langchain_core.utils import get_colored_text
 from langchain_core.utils.interactive_env import is_interactive_env
 
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
 
 class MessagesPlaceholder(BaseMessagePromptTemplate):
     """Prompt template that assumes variable is already list of messages.
@@ -765,7 +762,7 @@ MessageLike = BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate
 
 MessageLikeRepresentation = (
     MessageLike
-    | tuple[str | type, str | list[dict] | list[object]]
+    | tuple[str | type, str | Sequence[dict] | Sequence[object]]
     | str
     | dict[str, Any]
 )
@@ -848,9 +845,9 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
     !!! note "Single-variable template"
 
-        If your prompt has only a single input variable (i.e., 1 instance of "{variable_nams}"),
-        and you invoke the template with a non-dict object, the prompt template will
-        inject the provided argument into that variable location.
+        If your prompt has only a single input variable (i.e., 1 instance of
+        "{variable_nams}"), and you invoke the template with a non-dict object, the
+        prompt template will inject the provided argument into that variable location.
 
         ```python
         from langchain_core.prompts import ChatPromptTemplate
@@ -874,7 +871,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
         #     ]
         # )
         ```
-    """  # noqa: E501
+    """
 
     messages: Annotated[list[MessageLike], SkipValidation()]
     """List of messages consisting of either message prompt templates or messages."""
@@ -1428,16 +1425,26 @@ def _convert_to_message_template(
                     f" Got: {message}"
                 )
                 raise ValueError(msg)
-            message = (message["role"], message["content"])
-        try:
+            message_type_str = message["role"]
+            template = message["content"]
+        else:
+            if len(message) != 2:  # noqa: PLR2004
+                msg = f"Expected 2-tuple of (role, template), got {message}"
+                raise ValueError(msg)
             message_type_str, template = message
-        except ValueError as e:
-            msg = f"Expected 2-tuple of (role, template), got {message}"
-            raise ValueError(msg) from e
+
         if isinstance(message_type_str, str):
             message_ = _create_template_from_message_type(
                 message_type_str, template, template_format=template_format
             )
+        elif (
+            hasattr(message_type_str, "model_fields")
+            and "type" in message_type_str.model_fields
+        ):
+            message_type = message_type_str.model_fields["type"].default
+            message_ = _create_template_from_message_type(
+                message_type, template, template_format=template_format
+            )
         else:
             message_ = message_type_str(
                 prompt=PromptTemplate.from_template(

libs/core/langchain_core/prompts/dict.py

@@ -2,7 +2,7 @@
 
 import warnings
 from functools import cached_property
-from typing import Any, Literal
+from typing import Any, Literal, cast
 
 from typing_extensions import override
 
@@ -65,7 +65,10 @@ class DictPromptTemplate(RunnableSerializable[dict, dict]):
 
     @cached_property
     def _serialized(self) -> dict[str, Any]:
-        return dumpd(self)
+        # self is always a Serializable object in this case, thus the result is
+        # guaranteed to be a dict since dumpd uses the default callback, which uses
+        # obj.to_json which always returns TypedDict subclasses
+        return cast("dict[str, Any]", dumpd(self))
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
@@ -116,7 +119,7 @@ def _insert_input_variables(
     inputs: dict[str, Any],
     template_format: Literal["f-string", "mustache"],
 ) -> dict[str, Any]:
-    formatted = {}
+    formatted: dict[str, Any] = {}
     formatter = DEFAULT_FORMATTER_MAPPING[template_format]
     for k, v in template.items():
         if isinstance(v, str):
@@ -132,7 +135,7 @@ def _insert_input_variables(
                 warnings.warn(msg, stacklevel=2)
             formatted[k] = _insert_input_variables(v, inputs, template_format)
         elif isinstance(v, (list, tuple)):
-            formatted_v = []
+            formatted_v: list[str | dict[str, Any]] = []
             for x in v:
                 if isinstance(x, str):
                     formatted_v.append(formatter(x, **inputs))

libs/core/langchain_core/prompts/few_shot_with_templates.py

@@ -6,6 +6,7 @@ from typing import Any
 from pydantic import ConfigDict, model_validator
 from typing_extensions import Self
 
+from langchain_core.example_selectors import BaseExampleSelector
 from langchain_core.prompts.prompt import PromptTemplate
 from langchain_core.prompts.string import (
     DEFAULT_FORMATTER_MAPPING,
@@ -21,7 +22,7 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
     """Examples to format into the prompt.
     Either this or example_selector should be provided."""
 
-    example_selector: Any = None
+    example_selector: BaseExampleSelector | None = None
     """ExampleSelector to choose the examples to format into the prompt.
     Either this or examples should be provided."""
 

libs/core/langchain_core/prompts/image.py

@@ -1,6 +1,6 @@
 """Image prompt template for a multimodal model."""
 
-from typing import Any
+from typing import Any, Literal, cast
 
 from pydantic import Field
 
@@ -125,7 +125,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         output: ImageURL = {"url": url}
         if detail:
             # Don't check literal values here: let the API check them
-            output["detail"] = detail
+            output["detail"] = cast("Literal['auto', 'low', 'high']", detail)
         return output
 
     async def aformat(self, **kwargs: Any) -> ImageURL:

libs/core/langchain_core/prompts/message.py

@@ -92,4 +92,4 @@ class BaseMessagePromptTemplate(Serializable, ABC):
         from langchain_core.prompts.chat import ChatPromptTemplate  # noqa: PLC0415
 
         prompt = ChatPromptTemplate(messages=[self])
-        return prompt + other
+        return prompt.__add__(other)

libs/core/langchain_core/prompts/string.py

@@ -3,11 +3,12 @@
 from __future__ import annotations
 
 import warnings
-from abc import ABC
+from abc import ABC, abstractmethod
 from string import Formatter
-from typing import TYPE_CHECKING, Any, Literal
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 from pydantic import BaseModel, create_model
+from typing_extensions import override
 
 from langchain_core.prompt_values import PromptValue, StringPromptValue
 from langchain_core.prompts.base import BasePromptTemplate
@@ -189,17 +190,20 @@ def mustache_schema(template: str) -> type[BaseModel]:
     return _create_model_recursive("PromptInput", defs)
 
 
-def _create_model_recursive(name: str, defs: Defs) -> type:
-    return create_model(  # type: ignore[call-overload]
-        name,
-        **{
-            k: (_create_model_recursive(k, v), None) if v else (type(v), None)
-            for k, v in defs.items()
-        },
+def _create_model_recursive(name: str, defs: Defs) -> type[BaseModel]:
+    return cast(
+        "type[BaseModel]",
+        create_model(  # type: ignore[call-overload]
+            name,
+            **{
+                k: (_create_model_recursive(k, v), None) if v else (type(v), None)
+                for k, v in defs.items()
+            },
+        ),
     )
 
 
-DEFAULT_FORMATTER_MAPPING: dict[str, Callable] = {
+DEFAULT_FORMATTER_MAPPING: dict[str, Callable[..., str]] = {
     "f-string": formatter.format,
     "mustache": mustache_formatter,
     "jinja2": jinja2_formatter,
@@ -330,6 +334,10 @@ class StringPromptTemplate(BasePromptTemplate, ABC):
         """
         return StringPromptValue(text=await self.aformat(**kwargs))
 
+    @override
+    @abstractmethod
+    def format(self, **kwargs: Any) -> str: ...
+
     def pretty_repr(
         self,
         html: bool = False,  # noqa: FBT001,FBT002

libs/core/langchain_core/prompts/structured.py

@@ -48,6 +48,9 @@ class StructuredPrompt(ChatPromptTemplate):
             schema_: schema for the structured prompt.
             structured_output_kwargs: additional kwargs for structured output.
             template_format: template format for the prompt.
+
+        Raises:
+            ValueError: if schema is not provided.
         """
         schema_ = schema_ or kwargs.pop("schema", None)
         if not schema_:

libs/core/langchain_core/runnables/base.py

@@ -315,7 +315,7 @@ class Runnable(ABC, Generic[Input, Output]):
                     "args" in metadata
                     and len(metadata["args"]) == _RUNNABLE_GENERIC_NUM_ARGS
                 ):
-                    return metadata["args"][0]
+                    return cast("type[Input]", metadata["args"][0])
 
         # If we didn't find a Pydantic model in the parent classes,
         # then loop through __orig_bases__. This corresponds to
@@ -323,7 +323,7 @@ class Runnable(ABC, Generic[Input, Output]):
         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:
-                return type_args[0]
+                return cast("type[Input]", type_args[0])
 
         msg = (
             f"Runnable {self.get_name()} doesn't have an inferable InputType. "
@@ -349,12 +349,12 @@ class Runnable(ABC, Generic[Input, Output]):
                     "args" in metadata
                     and len(metadata["args"]) == _RUNNABLE_GENERIC_NUM_ARGS
                 ):
-                    return metadata["args"][1]
+                    return cast("type[Output]", 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:
-                return type_args[1]
+                return cast("type[Output]", type_args[1])
 
         msg = (
             f"Runnable {self.get_name()} doesn't have an inferable OutputType. "
@@ -369,7 +369,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
     def get_input_schema(
         self,
-        config: RunnableConfig | None = None,  # noqa: ARG002
+        config: RunnableConfig | None = None,
     ) -> type[BaseModel]:
         """Get a Pydantic model that can be used to validate input to the `Runnable`.
 
@@ -385,6 +385,7 @@ class Runnable(ABC, Generic[Input, Output]):
         Returns:
             A Pydantic model that can be used to validate input.
         """
+        _ = config
         root_type = self.InputType
 
         if (
@@ -447,7 +448,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
     def get_output_schema(
         self,
-        config: RunnableConfig | None = None,  # noqa: ARG002
+        config: RunnableConfig | None = None,
     ) -> type[BaseModel]:
         """Get a Pydantic model that can be used to validate output to the `Runnable`.
 
@@ -463,6 +464,7 @@ class Runnable(ABC, Generic[Input, Output]):
         Returns:
             A Pydantic model that can be used to validate output.
         """
+        _ = config
         root_type = self.OutputType
 
         if (
@@ -2277,6 +2279,9 @@ class Runnable(ABC, Generic[Input, Output]):
         Use this to implement `stream` or `transform` in `Runnable` subclasses.
 
         """
+        # Extract defers_inputs from kwargs if present
+        defers_inputs = kwargs.pop("defers_inputs", False)
+
         # tee the input so we can iterate over it twice
         input_for_tracing, input_for_transform = tee(inputs, 2)
         # Start the input iterator to ensure the input Runnable starts before this one
@@ -2293,6 +2298,7 @@ class Runnable(ABC, Generic[Input, Output]):
             run_type=run_type,
             name=config.get("run_name") or self.get_name(),
             run_id=config.pop("run_id", None),
+            defers_inputs=defers_inputs,
         )
         try:
             child_config = patch_config(config, callbacks=run_manager.get_child())
@@ -2374,6 +2380,9 @@ class Runnable(ABC, Generic[Input, Output]):
         Use this to implement `astream` or `atransform` in `Runnable` subclasses.
 
         """
+        # Extract defers_inputs from kwargs if present
+        defers_inputs = kwargs.pop("defers_inputs", False)
+
         # tee the input so we can iterate over it twice
         input_for_tracing, input_for_transform = atee(inputs, 2)
         # Start the input iterator to ensure the input Runnable starts before this one
@@ -2390,6 +2399,7 @@ class Runnable(ABC, Generic[Input, Output]):
             run_type=run_type,
             name=config.get("run_name") or self.get_name(),
             run_id=config.pop("run_id", None),
+            defers_inputs=defers_inputs,
         )
         try:
             child_config = patch_config(config, callbacks=run_manager.get_child())
@@ -4323,6 +4333,7 @@ class RunnableGenerator(Runnable[Input, Output]):
             input,
             self._transform,  # type: ignore[arg-type]
             config,
+            defers_inputs=True,
             **kwargs,
         )
 
@@ -4356,7 +4367,7 @@ class RunnableGenerator(Runnable[Input, Output]):
             raise NotImplementedError(msg)
 
         return self._atransform_stream_with_config(
-            input, self._atransform, config, **kwargs
+            input, self._atransform, config, defers_inputs=True, **kwargs
         )
 
     @override
@@ -4429,6 +4440,138 @@ class RunnableLambda(Runnable[Input, Output]):
         ```
     """
 
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input, RunnableConfig], Awaitable[Output]],
+        afunc: None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input], Awaitable[Output]],
+        afunc: None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input], AsyncIterator[Output]],
+        afunc: None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]],
+        afunc: None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ],
+        afunc: None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input, RunnableConfig], Output],
+        afunc: Callable[[Input], Awaitable[Output]]
+        | Callable[[Input], AsyncIterator[Output]]
+        | Callable[[Input, RunnableConfig], Awaitable[Output]]
+        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
+        | Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ]
+        | None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input], Iterator[Output]],
+        afunc: Callable[[Input], Awaitable[Output]]
+        | Callable[[Input], AsyncIterator[Output]]
+        | Callable[[Input, RunnableConfig], Awaitable[Output]]
+        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
+        | Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ]
+        | None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input], Runnable[Input, Output]],
+        afunc: Callable[[Input], Awaitable[Output]]
+        | Callable[[Input], AsyncIterator[Output]]
+        | Callable[[Input, RunnableConfig], Awaitable[Output]]
+        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
+        | Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ]
+        | None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input, CallbackManagerForChainRun], Output],
+        afunc: Callable[[Input], Awaitable[Output]]
+        | Callable[[Input], AsyncIterator[Output]]
+        | Callable[[Input, RunnableConfig], Awaitable[Output]]
+        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
+        | Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ]
+        | None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output],
+        afunc: Callable[[Input], Awaitable[Output]]
+        | Callable[[Input], AsyncIterator[Output]]
+        | Callable[[Input, RunnableConfig], Awaitable[Output]]
+        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
+        | Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ]
+        | None = None,
+        name: str | None = None,
+    ) -> None: ...
+
+    @overload
+    def __init__(
+        self,
+        func: Callable[[Input], Output],
+        afunc: Callable[[Input], Awaitable[Output]]
+        | Callable[[Input], AsyncIterator[Output]]
+        | Callable[[Input, RunnableConfig], Awaitable[Output]]
+        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
+        | Callable[
+            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
+        ]
+        | None = None,
+        name: str | None = None,
+    ) -> None: ...
+
     def __init__(
         self,
         func: Callable[[Input], Iterator[Output]]

libs/core/langchain_core/runnables/config.py

@@ -3,7 +3,9 @@
 from __future__ import annotations
 
 import asyncio
-import uuid
+
+# Cannot move uuid to TYPE_CHECKING as RunnableConfig is used in Pydantic models
+import uuid  # noqa: TC003
 import warnings
 from collections.abc import Awaitable, Callable, Generator, Iterable, Iterator, Sequence
 from concurrent.futures import Executor, Future, ThreadPoolExecutor
@@ -49,8 +51,24 @@ class EmptyDict(TypedDict, total=False):
 class RunnableConfig(TypedDict, total=False):
     """Configuration for a `Runnable`.
 
-    See the [reference docs](https://reference.langchain.com/python/langchain_core/runnables/#langchain_core.runnables.RunnableConfig)
-    for more details.
+    !!! note Custom values
+
+        The `TypedDict` has `total=False` set intentionally to:
+
+        - Allow partial configs to be created and merged together via `merge_configs`
+        - Support config propagation from parent to child runnables via
+            `var_child_runnable_config` (a `ContextVar` that automatically passes
+            config down the call stack without explicit parameter passing), where
+            configs are merged rather than replaced
+
+        !!! example
+
+            ```python
+            # Parent sets tags
+            chain.invoke(input, config={"tags": ["parent"]})
+            # Child automatically inherits and can add:
+            # ensure_config({"tags": ["child"]}) -> {"tags": ["parent", "child"]}
+            ```
     """
 
     tags: list[str]
@@ -90,7 +108,8 @@ class RunnableConfig(TypedDict, total=False):
 
     configurable: dict[str, Any]
     """Runtime values for attributes previously made configurable on this `Runnable`,
-    or sub-Runnables, through `configurable_fields` or `configurable_alternatives`.
+    or sub-`Runnable` objects, through `configurable_fields` or
+    `configurable_alternatives`.
 
     Check `output_schema` for a description of the attributes that have been made
     configurable.

libs/core/langchain_core/runnables/graph_ascii.py

@@ -165,6 +165,9 @@ class AsciiCanvas:
             y0: y coordinate of the box corner.
             width: box width.
             height: box height.
+
+        Raises:
+            ValueError: if box dimensions are invalid.
         """
         if width <= 1 or height <= 1:
             msg = "Box dimensions should be > 1"

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -8,9 +8,10 @@ import random
 import re
 import string
 import time
+import urllib.parse
 from dataclasses import asdict
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Literal
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 import yaml
 
@@ -40,6 +41,8 @@ except ImportError:
 
 MARKDOWN_SPECIAL_CHARS = "*_`"
 
+_HEX_COLOR_PATTERN = re.compile(r"^#(?:[0-9a-fA-F]{3}){1,2}$")
+
 
 def draw_mermaid(
     nodes: dict[str, Node],
@@ -81,6 +84,7 @@ def draw_mermaid(
                 }
             }
             ```
+
     Returns:
         Mermaid graph syntax.
 
@@ -389,7 +393,7 @@ async def _render_mermaid_using_pyppeteer(
         }
     )
 
-    img_bytes = await page.screenshot({"fullPage": False})
+    img_bytes = cast("bytes", await page.screenshot({"fullPage": False}))
     await browser.close()
 
     if output_file_path is not None:
@@ -428,14 +432,14 @@ def _render_mermaid_using_api(
     )
 
     # Check if the background color is a hexadecimal color code using regex
-    if background_color is not None:
-        hex_color_pattern = re.compile(r"^#(?:[0-9a-fA-F]{3}){1,2}$")
-        if not hex_color_pattern.match(background_color):
-            background_color = f"!{background_color}"
+    if background_color is not None and not _HEX_COLOR_PATTERN.match(background_color):
+        background_color = f"!{background_color}"
 
+    # URL-encode the background_color to handle special characters like '!'
+    encoded_bg_color = urllib.parse.quote(str(background_color), safe="")
     image_url = (
         f"{base_url}/img/{mermaid_syntax_encoded}"
-        f"?type={file_type}&bgColor={background_color}"
+        f"?type={file_type}&bgColor={encoded_bg_color}"
     )
 
     error_msg_suffix = (

libs/core/langchain_core/runnables/graph_png.py

@@ -1,7 +1,7 @@
 """Helper class to draw a state graph into a PNG file."""
 
 from itertools import groupby
-from typing import Any
+from typing import Any, cast
 
 from langchain_core.runnables.graph import Graph, LabelsDict
 
@@ -149,7 +149,7 @@ class PngDrawer:
 
         # Save the graph as PNG
         try:
-            return viz.draw(output_path, format="png", prog="dot")
+            return cast("bytes | None", viz.draw(output_path, format="png", prog="dot"))
         finally:
             viz.close()
 
@@ -201,7 +201,8 @@ class PngDrawer:
                 viz, start, end, str(data) if data is not None else None, cond
             )
 
-    def update_styles(self, viz: Any, graph: Graph) -> None:
+    @staticmethod
+    def update_styles(viz: Any, graph: Graph) -> None:
         """Update the styles of the entrypoint and END nodes.
 
         Args:

libs/core/langchain_core/runnables/history.py

@@ -320,7 +320,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
                 `RunnableBindingBase` init.
 
         """
-        history_chain: Runnable = RunnableLambda(
+        history_chain: Runnable[Any, Any] = RunnableLambda(
             self._enter_history, self._aenter_history
         ).with_config(run_name="load_history")
         messages_key = history_messages_key or input_messages_key
@@ -329,16 +329,16 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
                 **{messages_key: history_chain}
             ).with_config(run_name="insert_history")
 
-        runnable_sync: Runnable = runnable.with_listeners(on_end=self._exit_history)
-        runnable_async: Runnable = runnable.with_alisteners(on_end=self._aexit_history)
+        runnable_sync = runnable.with_listeners(on_end=self._exit_history)
+        runnable_async = runnable.with_alisteners(on_end=self._aexit_history)
 
-        def _call_runnable_sync(_input: Any) -> Runnable:
+        def _call_runnable_sync(_input: Any) -> Runnable[Any, Any]:
             return runnable_sync
 
-        async def _call_runnable_async(_input: Any) -> Runnable:
+        async def _call_runnable_async(_input: Any) -> Runnable[Any, Any]:
             return runnable_async
 
-        bound: Runnable = (
+        bound = (
             history_chain
             | RunnableLambda(
                 _call_runnable_sync,
@@ -539,7 +539,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
         hist: BaseChatMessageHistory = config["configurable"]["message_history"]
 
         # Get the input messages
-        inputs = load(run.inputs)
+        inputs = load(run.inputs, allowed_objects="all")
         input_messages = self._get_input_messages(inputs)
         # If historic messages were prepended to the input messages, remove them to
         # avoid adding duplicate messages to history.
@@ -548,7 +548,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
             input_messages = input_messages[len(historic_messages) :]
 
         # Get the output messages
-        output_val = load(run.outputs)
+        output_val = load(run.outputs, allowed_objects="all")
         output_messages = self._get_output_messages(output_val)
         hist.add_messages(input_messages + output_messages)
 
@@ -556,7 +556,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
         hist: BaseChatMessageHistory = config["configurable"]["message_history"]
 
         # Get the input messages
-        inputs = load(run.inputs)
+        inputs = load(run.inputs, allowed_objects="all")
         input_messages = self._get_input_messages(inputs)
         # If historic messages were prepended to the input messages, remove them to
         # avoid adding duplicate messages to history.
@@ -565,7 +565,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):  # type: ignore[no-redef]
             input_messages = input_messages[len(historic_messages) :]
 
         # Get the output messages
-        output_val = load(run.outputs)
+        output_val = load(run.outputs, allowed_objects="all")
         output_messages = self._get_output_messages(output_val)
         await hist.aadd_messages(input_messages + output_messages)
 

libs/core/langchain_core/runnables/passthrough.py

@@ -753,25 +753,19 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
             return AddableDict(picked)
         return None
 
-    def _invoke(
-        self,
-        value: dict[str, Any],
-    ) -> dict[str, Any]:
-        return self._pick(value)
-
     @override
     def invoke(
         self,
         input: dict[str, Any],
         config: RunnableConfig | None = None,
         **kwargs: Any,
-    ) -> dict[str, Any]:
-        return self._call_with_config(self._invoke, input, config, **kwargs)
+    ) -> Any:
+        return self._call_with_config(self._pick, input, config, **kwargs)
 
     async def _ainvoke(
         self,
         value: dict[str, Any],
-    ) -> dict[str, Any]:
+    ) -> Any:
         return self._pick(value)
 
     @override
@@ -780,13 +774,13 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
         input: dict[str, Any],
         config: RunnableConfig | None = None,
         **kwargs: Any,
-    ) -> dict[str, Any]:
+    ) -> Any:
         return await self._acall_with_config(self._ainvoke, input, config, **kwargs)
 
     def _transform(
         self,
         chunks: Iterator[dict[str, Any]],
-    ) -> Iterator[dict[str, Any]]:
+    ) -> Iterator[Any]:
         for chunk in chunks:
             picked = self._pick(chunk)
             if picked is not None:
@@ -798,7 +792,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
         input: Iterator[dict[str, Any]],
         config: RunnableConfig | None = None,
         **kwargs: Any,
-    ) -> Iterator[dict[str, Any]]:
+    ) -> Iterator[Any]:
         yield from self._transform_stream_with_config(
             input, self._transform, config, **kwargs
         )
@@ -806,7 +800,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
     async def _atransform(
         self,
         chunks: AsyncIterator[dict[str, Any]],
-    ) -> AsyncIterator[dict[str, Any]]:
+    ) -> AsyncIterator[Any]:
         async for chunk in chunks:
             picked = self._pick(chunk)
             if picked is not None:
@@ -818,7 +812,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
         input: AsyncIterator[dict[str, Any]],
         config: RunnableConfig | None = None,
         **kwargs: Any,
-    ) -> AsyncIterator[dict[str, Any]]:
+    ) -> AsyncIterator[Any]:
         async for chunk in self._atransform_stream_with_config(
             input, self._atransform, config, **kwargs
         ):
@@ -830,7 +824,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
         input: dict[str, Any],
         config: RunnableConfig | None = None,
         **kwargs: Any,
-    ) -> Iterator[dict[str, Any]]:
+    ) -> Iterator[Any]:
         return self.transform(iter([input]), config, **kwargs)
 
     @override
@@ -839,7 +833,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], Any]):
         input: dict[str, Any],
         config: RunnableConfig | None = None,
         **kwargs: Any,
-    ) -> AsyncIterator[dict[str, Any]]:
+    ) -> AsyncIterator[Any]:
         async def input_aiter() -> AsyncIterator[dict[str, Any]]:
             yield input
 

libs/core/langchain_core/runnables/router.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from collections.abc import Callable, Mapping
+from collections.abc import Mapping
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -31,7 +31,7 @@ from langchain_core.runnables.utils import (
 )
 
 if TYPE_CHECKING:
-    from collections.abc import AsyncIterator, Iterator
+    from collections.abc import AsyncIterator, Callable, Iterator
 
 
 class RouterInput(TypedDict):
@@ -151,7 +151,7 @@ class RouterRunnable(RunnableSerializable[RouterInput, Output]):
             raise ValueError(msg)
 
         def invoke(
-            runnable: Runnable, input_: Input, config: RunnableConfig
+            runnable: Runnable[Input, Output], input_: Input, config: RunnableConfig
         ) -> Output | Exception:
             if return_exceptions:
                 try:
@@ -188,7 +188,7 @@ class RouterRunnable(RunnableSerializable[RouterInput, Output]):
             raise ValueError(msg)
 
         async def ainvoke(
-            runnable: Runnable, input_: Input, config: RunnableConfig
+            runnable: Runnable[Input, Output], input_: Input, config: RunnableConfig
         ) -> Output | Exception:
             if return_exceptions:
                 try:

libs/core/langchain_core/runnables/schema.py

@@ -45,6 +45,12 @@ class EventData(TypedDict, total=False):
     chunks support addition in general, and adding them up should result
     in the output of the `Runnable` that generated the event.
     """
+    tool_call_id: NotRequired[str | None]
+    """The tool call ID associated with the tool execution.
+
+    This field is available for the `on_tool_error` event and can be used to
+    link errors to specific tool calls in stateless agent implementations.
+    """
 
 
 class BaseStreamEvent(TypedDict):

libs/core/langchain_core/runnables/utils.py

@@ -7,7 +7,10 @@ import asyncio
 import inspect
 import sys
 import textwrap
-from collections.abc import Mapping, Sequence
+
+# Cannot move to TYPE_CHECKING as Mapping and Sequence are needed at runtime by
+# RunnableConfigurableFields.
+from collections.abc import Mapping, Sequence  # noqa: TC003
 from functools import lru_cache
 from inspect import signature
 from itertools import groupby
@@ -129,9 +132,12 @@ def asyncio_accepts_context() -> bool:
     return sys.version_info >= (3, 11)
 
 
+_T = TypeVar("_T")
+
+
 def coro_with_context(
-    coro: Awaitable[Any], context: Context, *, create_task: bool = False
-) -> Awaitable[Any]:
+    coro: Awaitable[_T], context: Context, *, create_task: bool = False
+) -> Awaitable[_T]:
     """Await a coroutine with a context.
 
     Args:

libs/core/langchain_core/stores.py

@@ -205,7 +205,7 @@ class InMemoryBaseStore(BaseStore[str, V], Generic[V]):
     async def amdelete(self, keys: Sequence[str]) -> None:
         self.mdelete(keys)
 
-    def yield_keys(self, prefix: str | None = None) -> Iterator[str]:
+    def yield_keys(self, *, prefix: str | None = None) -> Iterator[str]:
         """Get an iterator over keys that match the given prefix.
 
         Args:
@@ -221,7 +221,7 @@ class InMemoryBaseStore(BaseStore[str, V], Generic[V]):
                 if key.startswith(prefix):
                     yield key
 
-    async def ayield_keys(self, prefix: str | None = None) -> AsyncIterator[str]:
+    async def ayield_keys(self, *, prefix: str | None = None) -> AsyncIterator[str]:
         """Async get an async iterator over keys that match the given prefix.
 
         Args:

libs/core/langchain_core/tools/base.py

@@ -5,10 +5,11 @@ from __future__ import annotations
 import functools
 import inspect
 import json
+import logging
 import typing
 import warnings
 from abc import ABC, abstractmethod
-from collections.abc import Callable
+from collections.abc import Callable  # noqa: TC003
 from inspect import signature
 from typing import (
     TYPE_CHECKING,
@@ -22,6 +23,7 @@ from typing import (
     get_type_hints,
 )
 
+import typing_extensions
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -31,6 +33,7 @@ from pydantic import (
     ValidationError,
     validate_arguments,
 )
+from pydantic.fields import FieldInfo
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import ValidationError as ValidationErrorV1
 from pydantic.v1 import validate_arguments as validate_arguments_v1
@@ -80,6 +83,8 @@ TOOL_MESSAGE_BLOCK_TYPES = (
     "file",
 )
 
+_logger = logging.getLogger(__name__)
+
 
 class SchemaAnnotationError(TypeError):
     """Raised when args_schema is missing or has an incorrect type annotation."""
@@ -94,12 +99,14 @@ def _is_annotated_type(typ: type[Any]) -> bool:
     Returns:
         `True` if the type is an Annotated type, `False` otherwise.
     """
-    return get_origin(typ) is typing.Annotated
+    return get_origin(typ) in {typing.Annotated, typing_extensions.Annotated}
 
 
 def _get_annotation_description(arg_type: type) -> str | None:
     """Extract description from an Annotated type.
 
+    Checks for string annotations and `FieldInfo` objects with descriptions.
+
     Args:
         arg_type: The type to extract description from.
 
@@ -111,6 +118,8 @@ def _get_annotation_description(arg_type: type) -> str | None:
         for annotation in annotated_args[1:]:
             if isinstance(annotation, str):
                 return annotation
+            if isinstance(annotation, FieldInfo) and annotation.description:
+                return annotation.description
     return None
 
 
@@ -560,9 +569,12 @@ class ChildTool(BaseTool):
         elif self.args_schema and issubclass(self.args_schema, BaseModelV1):
             json_schema = self.args_schema.schema()
         else:
-            input_schema = self.get_input_schema()
-            json_schema = input_schema.model_json_schema()
-        return json_schema["properties"]
+            input_schema = self.tool_call_schema
+            if isinstance(input_schema, dict):
+                json_schema = input_schema
+            else:
+                json_schema = input_schema.model_json_schema()
+        return cast("dict", json_schema["properties"])
 
     @property
     def tool_call_schema(self) -> ArgsSchema:
@@ -653,6 +665,7 @@ class ChildTool(BaseTool):
             TypeError: If `args_schema` is not a Pydantic `BaseModel` or dict.
         """
         input_args = self.args_schema
+
         if isinstance(tool_input, str):
             if input_args is not None:
                 if isinstance(input_args, dict):
@@ -670,6 +683,7 @@ class ChildTool(BaseTool):
                     msg = f"args_schema must be a Pydantic BaseModel, got {input_args}"
                     raise TypeError(msg)
             return tool_input
+
         if input_args is not None:
             if isinstance(input_args, dict):
                 return tool_input
@@ -710,9 +724,30 @@ 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
-            }
+
+            # Include fields from tool_input, plus fields with explicit defaults.
+            # This applies Pydantic defaults (like Field(default=1)) while excluding
+            # synthetic "args"/"kwargs" fields that Pydantic creates for *args/**kwargs.
+            field_info = get_fields(input_args)
+            validated_input = {}
+            for k in result_dict:
+                if k in tool_input:
+                    # Field was provided in input - include it (validated)
+                    validated_input[k] = getattr(result, k)
+                elif k in field_info and k not in ("args", "kwargs"):
+                    # Check if field has an explicit default defined in the schema.
+                    # Exclude "args"/"kwargs" as these are synthetic fields for variadic
+                    # parameters that should not be passed as keyword arguments.
+                    fi = field_info[k]
+                    # Pydantic v2 uses is_required() method, v1 uses required attribute
+                    has_default = (
+                        not fi.is_required()
+                        if hasattr(fi, "is_required")
+                        else not getattr(fi, "required", True)
+                    )
+                    if has_default:
+                        validated_input[k] = getattr(result, k)
+
             for k in self._injected_args_keys:
                 if k in tool_input:
                     validated_input[k] = tool_input[k]
@@ -727,7 +762,9 @@ class ChildTool(BaseTool):
                         )
                         raise ValueError(msg)
                     validated_input[k] = tool_call_id
+
             return validated_input
+
         return tool_input
 
     @abstractmethod
@@ -771,6 +808,9 @@ class ChildTool(BaseTool):
         # Start with filtered args from the constant
         filtered_keys = set[str](FILTERED_ARGS)
 
+        # Add injected args from function signature (e.g., ToolRuntime parameters)
+        filtered_keys.update(self._injected_args_keys)
+
         # If we have an args_schema, use it to identify injected args
         if self.args_schema is not None:
             try:
@@ -778,9 +818,12 @@ class ChildTool(BaseTool):
                 for field_name, field_type in annotations.items():
                     if _is_injected_arg_type(field_type):
                         filtered_keys.add(field_name)
-            except Exception:  # noqa: S110
+            except Exception:
                 # If we can't get annotations, just use FILTERED_ARGS
-                pass
+                _logger.debug(
+                    "Failed to get args_schema annotations for filtering.",
+                    exc_info=True,
+                )
 
         # Filter out the injected keys from tool_input
         return {k: v for k, v in tool_input.items() if k not in filtered_keys}
@@ -946,7 +989,7 @@ class ChildTool(BaseTool):
             error_to_raise = e
 
         if error_to_raise:
-            run_manager.on_tool_error(error_to_raise)
+            run_manager.on_tool_error(error_to_raise, tool_call_id=tool_call_id)
             raise error_to_raise
         output = _format_output(content, artifact, tool_call_id, self.name, status)
         run_manager.on_tool_end(output, color=color, name=self.name, **kwargs)
@@ -1076,7 +1119,7 @@ class ChildTool(BaseTool):
             error_to_raise = e
 
         if error_to_raise:
-            await run_manager.on_tool_error(error_to_raise)
+            await run_manager.on_tool_error(error_to_raise, tool_call_id=tool_call_id)
             raise error_to_raise
 
         output = _format_output(content, artifact, tool_call_id, self.name, status)
@@ -1514,7 +1557,7 @@ def _replace_type_vars(
             _replace_type_vars(arg, generic_map, default_to_bound=default_to_bound)
             for arg in args
         )
-        return _py_38_safe_origin(origin)[new_args]  # type: ignore[index]
+        return cast("type", _py_38_safe_origin(origin)[new_args])  # type: ignore[index]
     return type_
 
 

libs/core/langchain_core/tools/convert.py

@@ -2,7 +2,7 @@
 
 import inspect
 from collections.abc import Callable
-from typing import Any, Literal, get_type_hints, overload
+from typing import Any, Literal, cast, get_type_hints, overload
 
 from pydantic import BaseModel, Field, create_model
 
@@ -407,7 +407,7 @@ def _get_schema_from_runnable_and_arg_types(
             )
             raise TypeError(msg) from e
     fields = {key: (key_type, Field(...)) for key, key_type in arg_types.items()}
-    return create_model(name, **fields)  # type: ignore[call-overload]
+    return cast("type[BaseModel]", create_model(name, **fields))  # type: ignore[call-overload]
 
 
 def convert_runnable_to_tool(

libs/core/langchain_core/tools/retriever.py

@@ -6,8 +6,10 @@ from typing import TYPE_CHECKING, Literal
 
 from pydantic import BaseModel, Field
 
-from langchain_core.callbacks import Callbacks
-from langchain_core.documents import Document
+# Cannot move Callbacks and Document to TYPE_CHECKING as StructuredTool's
+# func/coroutine parameter annotations are evaluated at runtime.
+from langchain_core.callbacks import Callbacks  # noqa: TC001
+from langchain_core.documents import Document  # noqa: TC001
 from langchain_core.prompts import (
     BasePromptTemplate,
     PromptTemplate,

libs/core/langchain_core/tools/simple.py

@@ -11,9 +11,10 @@ from typing import (
 
 from typing_extensions import override
 
+# Cannot move to TYPE_CHECKING as _run/_arun parameter annotations are needed at runtime
 from langchain_core.callbacks import (
-    AsyncCallbackManagerForToolRun,
-    CallbackManagerForToolRun,
+    AsyncCallbackManagerForToolRun,  # noqa: TC001
+    CallbackManagerForToolRun,  # noqa: TC001
 )
 from langchain_core.runnables import RunnableConfig, run_in_executor
 from langchain_core.tools.base import (

libs/core/langchain_core/tools/structured.py

@@ -16,9 +16,10 @@ from typing import (
 from pydantic import Field, SkipValidation
 from typing_extensions import override
 
+# Cannot move to TYPE_CHECKING as _run/_arun parameter annotations are needed at runtime
 from langchain_core.callbacks import (
-    AsyncCallbackManagerForToolRun,
-    CallbackManagerForToolRun,
+    AsyncCallbackManagerForToolRun,  # noqa: TC001
+    CallbackManagerForToolRun,  # noqa: TC001
 )
 from langchain_core.runnables import RunnableConfig, run_in_executor
 from langchain_core.tools.base import (

libs/core/langchain_core/tracers/_compat.py

@@ -0,0 +1,93 @@
+"""Compatibility helpers for Pydantic v1/v2 with langsmith Run objects.
+
+Note: The generic helpers (`pydantic_to_dict`, `pydantic_copy`) detect Pydantic
+version based on the langsmith `Run` model. They're intended for langsmith objects
+(`Run`, `Example`) which migrate together.
+
+For general Pydantic v1/v2 handling, see `langchain_core.utils.pydantic`.
+"""
+
+from __future__ import annotations
+
+from typing import Any, TypeVar
+
+from langchain_core.tracers.schemas import Run
+
+# Detect Pydantic version once at import time based on Run model
+_RUN_IS_PYDANTIC_V2 = hasattr(Run, "model_dump")
+
+T = TypeVar("T")
+
+
+def run_to_dict(run: Run, **kwargs: Any) -> dict[str, Any]:
+    """Convert run to dict, compatible with both Pydantic v1 and v2.
+
+    Args:
+        run: The run to convert.
+        **kwargs: Additional arguments passed to model_dump/dict.
+
+    Returns:
+        Dictionary representation of the run.
+    """
+    if _RUN_IS_PYDANTIC_V2:
+        return run.model_dump(**kwargs)
+    return run.dict(**kwargs)  # type: ignore[deprecated]
+
+
+def run_copy(run: Run, **kwargs: Any) -> Run:
+    """Copy run, compatible with both Pydantic v1 and v2.
+
+    Args:
+        run: The run to copy.
+        **kwargs: Additional arguments passed to model_copy/copy.
+
+    Returns:
+        A copy of the run.
+    """
+    if _RUN_IS_PYDANTIC_V2:
+        return run.model_copy(**kwargs)
+    return run.copy(**kwargs)  # type: ignore[deprecated]
+
+
+def run_construct(**kwargs: Any) -> Run:
+    """Construct run without validation, compatible with both Pydantic v1 and v2.
+
+    Args:
+        **kwargs: Fields to set on the run.
+
+    Returns:
+        A new Run instance constructed without validation.
+    """
+    if _RUN_IS_PYDANTIC_V2:
+        return Run.model_construct(**kwargs)
+    return Run.construct(**kwargs)  # type: ignore[deprecated]
+
+
+def pydantic_to_dict(obj: Any, **kwargs: Any) -> dict[str, Any]:
+    """Convert any Pydantic model to dict, compatible with both v1 and v2.
+
+    Args:
+        obj: The Pydantic model to convert.
+        **kwargs: Additional arguments passed to model_dump/dict.
+
+    Returns:
+        Dictionary representation of the model.
+    """
+    if _RUN_IS_PYDANTIC_V2:
+        return obj.model_dump(**kwargs)  # type: ignore[no-any-return]
+    return obj.dict(**kwargs)  # type: ignore[no-any-return]
+
+
+def pydantic_copy(obj: T, **kwargs: Any) -> T:
+    """Copy any Pydantic model, compatible with both v1 and v2.
+
+    Args:
+        obj: The Pydantic model to copy.
+        **kwargs: Additional arguments passed to model_copy/copy.
+
+    Returns:
+        A copy of the model.
+    """
+    if _RUN_IS_PYDANTIC_V2:
+        return obj.model_copy(**kwargs)  # type: ignore[attr-defined,no-any-return]
+    return obj.copy(**kwargs)  # type: ignore[attr-defined,no-any-return]

libs/core/langchain_core/tracers/context.py

@@ -137,7 +137,7 @@ def _get_tracer_project() -> str:
     tracing_context = ls_rh.get_tracing_context()
     run_tree = tracing_context["parent"]
     if run_tree is None and tracing_context["project_name"] is not None:
-        return tracing_context["project_name"]
+        return cast("str", tracing_context["project_name"])
     return getattr(
         run_tree,
         "session_name",

libs/core/langchain_core/tracers/core.py

@@ -184,7 +184,7 @@ class _TracerCore(ABC):
             # Changing this to "chat_model" may break triggering on_llm_start
             run_type="chat_model",
             tags=tags,
-            name=name,  # type: ignore[arg-type]
+            name=name,
         )
 
     def _create_llm_run(
@@ -213,7 +213,7 @@ class _TracerCore(ABC):
             start_time=start_time,
             run_type="llm",
             tags=tags or [],
-            name=name,  # type: ignore[arg-type]
+            name=name,
         )
 
     def _llm_run_with_token_event(
@@ -221,9 +221,10 @@ class _TracerCore(ABC):
         token: str,
         run_id: UUID,
         chunk: GenerationChunk | ChatGenerationChunk | None = None,
-        parent_run_id: UUID | None = None,  # noqa: ARG002
+        parent_run_id: UUID | None = None,
     ) -> Run:
         """Append token event to LLM run and return the run."""
+        _ = parent_run_id
         llm_run = self._get_run(run_id, run_type={"llm", "chat_model"})
         event_kwargs: dict[str, Any] = {"token": token}
         if chunk:
@@ -284,6 +285,16 @@ class _TracerCore(ABC):
         llm_run.end_time = datetime.now(timezone.utc)
         llm_run.events.append({"name": "end", "time": llm_run.end_time})
 
+        tool_call_count = 0
+        for generations in response.generations:
+            for generation in generations:
+                if hasattr(generation, "message"):
+                    msg = generation.message
+                    if hasattr(msg, "tool_calls") and msg.tool_calls:
+                        tool_call_count += len(msg.tool_calls)
+        if tool_call_count > 0:
+            llm_run.extra["tool_call_count"] = tool_call_count
+
         return llm_run
 
     def _errored_llm_run(
@@ -336,7 +347,7 @@ class _TracerCore(ABC):
             start_time=start_time,
             child_runs=[],
             run_type=run_type or "chain",
-            name=name,  # type: ignore[arg-type]
+            name=name,
             tags=tags or [],
         )
 
@@ -433,7 +444,7 @@ class _TracerCore(ABC):
             child_runs=[],
             run_type="tool",
             tags=tags or [],
-            name=name,  # type: ignore[arg-type]
+            name=name,
         )
 
     def _complete_tool_run(
@@ -528,43 +539,47 @@ class _TracerCore(ABC):
         """Return self copied."""
         return self
 
-    def _end_trace(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _end_trace(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """End a trace for a run.
 
         Args:
             run: The run.
         """
+        _ = run
         return None
 
-    def _on_run_create(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_run_create(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process a run upon creation.
 
         Args:
             run: The created run.
         """
+        _ = run
         return None
 
-    def _on_run_update(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_run_update(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process a run upon update.
 
         Args:
             run: The updated run.
         """
+        _ = run
         return None
 
-    def _on_llm_start(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_llm_start(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the LLM Run upon start.
 
         Args:
             run: The LLM run.
         """
+        _ = run
         return None
 
     def _on_llm_new_token(
         self,
-        run: Run,  # noqa: ARG002
-        token: str,  # noqa: ARG002
-        chunk: GenerationChunk | ChatGenerationChunk | None,  # noqa: ARG002
+        run: Run,
+        token: str,
+        chunk: GenerationChunk | ChatGenerationChunk | None,
     ) -> Coroutine[Any, Any, None] | None:
         """Process new LLM token.
 
@@ -573,100 +588,113 @@ class _TracerCore(ABC):
             token: The new token.
             chunk: Optional chunk.
         """
+        _ = (run, token, chunk)
         return None
 
-    def _on_llm_end(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_llm_end(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the LLM Run.
 
         Args:
             run: The LLM run.
         """
+        _ = run
         return None
 
-    def _on_llm_error(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_llm_error(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the LLM Run upon error.
 
         Args:
             run: The LLM run.
         """
+        _ = run
         return None
 
-    def _on_chain_start(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_chain_start(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Chain Run upon start.
 
         Args:
             run: The chain run.
         """
+        _ = run
         return None
 
-    def _on_chain_end(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_chain_end(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Chain Run.
 
         Args:
             run: The chain run.
         """
+        _ = run
         return None
 
-    def _on_chain_error(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_chain_error(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Chain Run upon error.
 
         Args:
             run: The chain run.
         """
+        _ = run
         return None
 
-    def _on_tool_start(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_tool_start(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Tool Run upon start.
 
         Args:
             run: The tool run.
         """
+        _ = run
         return None
 
-    def _on_tool_end(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_tool_end(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Tool Run.
 
         Args:
             run: The tool run.
         """
+        _ = run
         return None
 
-    def _on_tool_error(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_tool_error(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Tool Run upon error.
 
         Args:
             run: The tool run.
         """
+        _ = run
         return None
 
-    def _on_chat_model_start(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_chat_model_start(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Chat Model Run upon start.
 
         Args:
             run: The chat model run.
         """
+        _ = run
         return None
 
-    def _on_retriever_start(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_retriever_start(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Retriever Run upon start.
 
         Args:
             run: The retriever run.
         """
+        _ = run
         return None
 
-    def _on_retriever_end(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_retriever_end(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Retriever Run.
 
         Args:
             run: The retriever run.
         """
+        _ = run
         return None
 
-    def _on_retriever_error(self, run: Run) -> Coroutine[Any, Any, None] | None:  # noqa: ARG002
+    def _on_retriever_error(self, run: Run) -> Coroutine[Any, Any, None] | None:
         """Process the Retriever Run upon error.
 
         Args:
             run: The retriever run.
         """
+        _ = run
         return None

libs/core/langchain_core/tracers/evaluation.py

@@ -13,6 +13,7 @@ import langsmith
 from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults
 
 from langchain_core.tracers import langchain as langchain_tracer
+from langchain_core.tracers._compat import run_copy
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.context import tracing_v2_enabled
 from langchain_core.tracers.langchain import _get_executor
@@ -103,7 +104,7 @@ class EvaluatorCallbackHandler(BaseTracer):
             )
         else:
             self.executor = None
-        self.futures = weakref.WeakSet()
+        self.futures = weakref.WeakSet[Future[None]]()
         self.skip_unfinished = skip_unfinished
         self.project_name = project_name
         self.logged_eval_results = {}
@@ -154,8 +155,8 @@ class EvaluatorCallbackHandler(BaseTracer):
                     res
                 )
 
+    @staticmethod
     def _select_eval_results(
-        self,
         results: EvaluationResult | EvaluationResults,
     ) -> list[EvaluationResult]:
         if isinstance(results, EvaluationResult):
@@ -206,7 +207,7 @@ class EvaluatorCallbackHandler(BaseTracer):
         if self.skip_unfinished and not run.outputs:
             logger.debug("Skipping unfinished run %s", run.id)
             return
-        run_ = run.copy()
+        run_ = run_copy(run)
         run_.reference_example_id = self.example_id
         for evaluator in self.evaluators:
             if self.executor is None:

libs/core/langchain_core/tracers/event_stream.py

@@ -12,7 +12,6 @@ from typing import (
     TypeVar,
     cast,
 )
-from uuid import UUID
 
 from typing_extensions import NotRequired, override
 
@@ -47,6 +46,7 @@ from langchain_core.utils.uuid import uuid7
 
 if TYPE_CHECKING:
     from collections.abc import AsyncIterator, Iterator, Sequence
+    from uuid import UUID
 
     from langchain_core.documents import Document
     from langchain_core.runnables import Runnable, RunnableConfig
@@ -73,6 +73,8 @@ class RunInfo(TypedDict):
     """The inputs to the run."""
     parent_run_id: UUID | None
     """The ID of the parent run."""
+    tool_call_id: NotRequired[str | None]
+    """The tool call ID associated with the run."""
 
 
 def _assign_name(name: str | None, serialized: dict[str, Any] | None) -> str:
@@ -81,9 +83,9 @@ def _assign_name(name: str | None, serialized: dict[str, Any] | None) -> str:
         return name
     if serialized is not None:
         if "name" in serialized:
-            return serialized["name"]
+            return cast("str", serialized["name"])
         if "id" in serialized:
-            return serialized["id"][-1]
+            return cast("str", serialized["id"][-1])
     return "Unnamed"
 
 
@@ -301,6 +303,10 @@ class _AstreamEventsCallbackHandler(AsyncCallbackHandler, _StreamingCallbackHand
             # vs. None value.
             info["inputs"] = kwargs["inputs"]
 
+        if "tool_call_id" in kwargs:
+            # Store tool_call_id in run info for linking errors to tool calls
+            info["tool_call_id"] = kwargs["tool_call_id"]
+
         self.run_map[run_id] = info
         self.parent_map[run_id] = parent_run_id
 
@@ -426,6 +432,10 @@ class _AstreamEventsCallbackHandler(AsyncCallbackHandler, _StreamingCallbackHand
         """Run on new output token. Only available when streaming is enabled.
 
         For both chat models and non-chat models (legacy LLMs).
+
+        Raises:
+            ValueError: If the run type is not `llm` or `chat_model`.
+            AssertionError: If the run ID is not found in the run map.
         """
         run_info = self.run_map.get(run_id)
         chunk_: GenerationChunk | BaseMessageChunk
@@ -659,6 +669,7 @@ class _AstreamEventsCallbackHandler(AsyncCallbackHandler, _StreamingCallbackHand
             name_=name_,
             run_type="tool",
             inputs=inputs,
+            tool_call_id=kwargs.get("tool_call_id"),
         )
 
         self._send(
@@ -687,31 +698,31 @@ class _AstreamEventsCallbackHandler(AsyncCallbackHandler, _StreamingCallbackHand
         **kwargs: Any,
     ) -> None:
         """Run when tool errors."""
+        # Extract tool_call_id from kwargs if passed directly, or from run_info
+        # (which was stored during on_tool_start) as a fallback
+        tool_call_id = kwargs.get("tool_call_id")
         run_info, inputs = self._get_tool_run_info_with_inputs(run_id)
+        if tool_call_id is None:
+            tool_call_id = run_info.get("tool_call_id")
 
-        self._send(
-            {
-                "event": "on_tool_error",
-                "data": {
-                    "error": error,
-                    "input": inputs,
-                },
-                "run_id": str(run_id),
-                "name": run_info["name"],
-                "tags": run_info["tags"],
-                "metadata": run_info["metadata"],
-                "parent_ids": self._get_parent_ids(run_id),
+        event: StandardStreamEvent = {
+            "event": "on_tool_error",
+            "data": {
+                "error": error,
+                "input": inputs,
+                "tool_call_id": tool_call_id,
             },
-            "tool",
-        )
+            "run_id": str(run_id),
+            "name": run_info["name"],
+            "tags": run_info["tags"],
+            "metadata": run_info["metadata"],
+            "parent_ids": self._get_parent_ids(run_id),
+        }
+        self._send(event, "tool")
 
     @override
     async def on_tool_end(self, output: Any, *, run_id: UUID, **kwargs: Any) -> None:
-        """End a trace for a tool run.
-
-        Raises:
-            AssertionError: If the run ID is a tool call and does not have inputs
-        """
+        """End a trace for a tool run."""
         run_info, inputs = self._get_tool_run_info_with_inputs(run_id)
 
         self._send(

libs/core/langchain_core/tracers/langchain.py

@@ -21,6 +21,8 @@ from typing_extensions import override
 
 from langchain_core.env import get_runtime_environment
 from langchain_core.load import dumpd
+from langchain_core.messages.ai import UsageMetadata, add_usage
+from langchain_core.tracers._compat import run_construct, run_to_dict
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.schemas import Run
 
@@ -69,6 +71,32 @@ def _get_executor() -> ThreadPoolExecutor:
     return _EXECUTOR
 
 
+def _get_usage_metadata_from_generations(
+    generations: list[list[dict[str, Any]]],
+) -> UsageMetadata | None:
+    """Extract and aggregate `usage_metadata` from generations.
+
+    Iterates through generations to find and aggregate all `usage_metadata` found in
+    messages. This is typically present in chat model outputs.
+
+    Args:
+        generations: List of generation batches, where each batch is a list
+            of generation dicts that may contain a `'message'` key with
+            `'usage_metadata'`.
+
+    Returns:
+        The aggregated `usage_metadata` dict if found, otherwise `None`.
+    """
+    output: UsageMetadata | None = None
+    for generation_batch in generations:
+        for generation in generation_batch:
+            if isinstance(generation, dict) and "message" in generation:
+                message = generation["message"]
+                if isinstance(message, dict) and "usage_metadata" in message:
+                    output = add_usage(output, message["usage_metadata"])
+    return output
+
+
 class LangChainTracer(BaseTracer):
     """Implementation of the SharedTracer that POSTS to the LangChain endpoint."""
 
@@ -156,7 +184,7 @@ class LangChainTracer(BaseTracer):
             start_time=start_time,
             run_type="llm",
             tags=tags,
-            name=name,  # type: ignore[arg-type]
+            name=name,
         )
         self._start_trace(chat_model_run)
         self._on_chat_model_start(chat_model_run)
@@ -165,8 +193,9 @@ class LangChainTracer(BaseTracer):
     def _persist_run(self, run: Run) -> None:
         # We want to free up more memory by avoiding keeping a reference to the
         # whole nested run tree.
-        self.latest_run = Run.construct(
-            **run.dict(exclude={"child_runs", "inputs", "outputs"}),
+        run_data = run_to_dict(run, exclude={"child_runs", "inputs", "outputs"})
+        self.latest_run = run_construct(
+            **run_data,
             inputs=run.inputs,
             outputs=run.outputs,
         )
@@ -214,13 +243,20 @@ class LangChainTracer(BaseTracer):
             run.tags = self._get_tags(run)
             if run.ls_client is not self.client:
                 run.ls_client = self.client
+            # Mark whether inputs are real (not placeholder) so we can exclude them
+            # from PATCH for normal invocations.
+            #
+            # Streaming invocations use {"input": ""} as placeholder in POST and send
+            # real inputs in PATCH.
+            run.extra["inputs_is_truthy"] = run.inputs != {"input": ""}
             run.post()
         except Exception as e:
             # Errors are swallowed by the thread executor so we need to log them here
             log_error_once("post", e)
             raise
 
-    def _update_run_single(self, run: Run) -> None:
+    @staticmethod
+    def _update_run_single(run: Run) -> None:
         """Update a run."""
         if run.extra.get("__disabled"):
             return
@@ -266,6 +302,15 @@ class LangChainTracer(BaseTracer):
 
     def _on_llm_end(self, run: Run) -> None:
         """Process the LLM Run."""
+        # Extract usage_metadata from outputs and store in extra.metadata
+        if run.outputs and "generations" in run.outputs:
+            usage_metadata = _get_usage_metadata_from_generations(
+                run.outputs["generations"]
+            )
+            if usage_metadata is not None:
+                if "metadata" not in run.extra:
+                    run.extra["metadata"] = {}
+                run.extra["metadata"]["usage_metadata"] = usage_metadata
         self._update_run_single(run)
 
     def _on_llm_error(self, run: Run) -> None:
@@ -276,15 +321,28 @@ class LangChainTracer(BaseTracer):
         """Process the Chain Run upon start."""
         if run.parent_run_id is None:
             run.reference_example_id = self.example_id
-        self._persist_run_single(run)
+        # Skip persisting if inputs are deferred (e.g., iterator/generator inputs).
+        # The run will be posted when _on_chain_end is called with realized inputs.
+        if not run.extra.get("defers_inputs"):
+            self._persist_run_single(run)
 
     def _on_chain_end(self, run: Run) -> None:
         """Process the Chain Run."""
-        self._update_run_single(run)
+        # If inputs were deferred, persist (POST) the run now that inputs are realized.
+        # Otherwise, update (PATCH) the existing run.
+        if run.extra.get("defers_inputs"):
+            self._persist_run_single(run)
+        else:
+            self._update_run_single(run)
 
     def _on_chain_error(self, run: Run) -> None:
         """Process the Chain Run upon error."""
-        self._update_run_single(run)
+        # If inputs were deferred, persist (POST) the run now that inputs are realized.
+        # Otherwise, update (PATCH) the existing run.
+        if run.extra.get("defers_inputs"):
+            self._persist_run_single(run)
+        else:
+            self._update_run_single(run)
 
     def _on_tool_start(self, run: Run) -> None:
         """Process the Tool Run upon start."""

libs/core/langchain_core/tracers/log_stream.py

@@ -541,7 +541,7 @@ class LogStreamCallbackHandler(BaseTracer, _StreamingCallbackHandler):
 
 def _get_standardized_inputs(
     run: Run, schema_format: Literal["original", "streaming_events"]
-) -> dict[str, Any] | None:
+) -> Any:
     """Extract standardized inputs from a run.
 
     Standardizes the inputs based on the type of the runnable used.
@@ -563,14 +563,14 @@ def _get_standardized_inputs(
         )
         raise NotImplementedError(msg)
 
-    inputs = load(run.inputs)
+    inputs = load(run.inputs, allowed_objects="all")
 
     if run.run_type in {"retriever", "llm", "chat_model"}:
         return inputs
 
     # new style chains
     # These nest an additional 'input' key inside the 'inputs' to make sure
-    # the input is always a dict. We need to unpack and user the inner value.
+    # the input is always a dict. We need to unpack and use the inner value.
     inputs = inputs["input"]
     # We should try to fix this in Runnables and callbacks/tracers
     # Runnables should be using a None type here not a placeholder
@@ -595,7 +595,7 @@ def _get_standardized_outputs(
     Returns:
         An output if returned, otherwise a None
     """
-    outputs = load(run.outputs)
+    outputs = load(run.outputs, allowed_objects="all")
     if schema_format == "original":
         if run.run_type == "prompt" and "output" in outputs:
             # These were previously dumped before the tracer.

libs/core/langchain_core/tracers/run_collector.py

@@ -3,6 +3,7 @@
 from typing import Any
 from uuid import UUID
 
+from langchain_core.tracers._compat import run_copy
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.schemas import Run
 
@@ -35,6 +36,6 @@ class RunCollectorCallbackHandler(BaseTracer):
         Args:
             run: The run to be persisted.
         """
-        run_ = run.copy()
+        run_ = run_copy(run)
         run_.reference_example_id = self.example_id
         self.traced_runs.append(run_)

libs/core/langchain_core/utils/_merge.py

@@ -7,8 +7,8 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
     r"""Merge dictionaries.
 
     Merge many dicts, handling specific scenarios where a key exists in both
-    dictionaries but has a value of None in 'left'. In such cases, the method uses the
-    value from 'right' for that key in the merged dictionary.
+    dictionaries but has a value of `None` in `'left'`. In such cases, the method uses
+    the value from `'right'` for that key in the merged dictionary.
 
     Args:
         left: The first dictionary to merge.
@@ -22,11 +22,10 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
         TypeError: If the value has an unsupported type.
 
     Example:
-        If left = {"function_call": {"arguments": None}} and
-        right = {"function_call": {"arguments": "{\n"}}
-        then, after merging, for the key "function_call",
-        the value from 'right' is used,
-        resulting in merged = {"function_call": {"arguments": "{\n"}}.
+        If `left = {"function_call": {"arguments": None}}` and
+        `right = {"function_call": {"arguments": "{\n"}}`, then, after merging, for the
+        key `'function_call'`, the value from `'right'` is used, resulting in
+        `merged = {"function_call": {"arguments": "{\n"}}`.
     """
     merged = left.copy()
     for right in others:
@@ -58,7 +57,7 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
                 #             "all dicts."
                 #         )
                 if (right_k == "index" and merged[right_k].startswith("lc_")) or (
-                    right_k in ("id", "output_version", "model_provider")
+                    right_k in {"id", "output_version", "model_provider"}
                     and merged[right_k] == right_v
                 ):
                     continue
@@ -81,7 +80,7 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
 
 
 def merge_lists(left: list | None, *others: list | None) -> list | None:
-    """Add many lists, handling None.
+    """Add many lists, handling `None`.
 
     Args:
         left: The first list to merge.
@@ -156,9 +155,9 @@ def merge_lists(left: list | None, *others: list | None) -> list | None:
 def merge_obj(left: Any, right: Any) -> Any:
     """Merge two objects.
 
-    It handles specific scenarios where a key exists in both
-    dictionaries but has a value of None in 'left'. In such cases, the method uses the
-    value from 'right' for that key in the merged dictionary.
+    It handles specific scenarios where a key exists in both dictionaries but has a
+    value of `None` in `'left'`. In such cases, the method uses the value from `'right'`
+    for that key in the merged dictionary.
 
     Args:
         left: The first object to merge.

libs/core/langchain_core/utils/aiter.py

@@ -130,7 +130,7 @@ async def tee_peer(
                     if buffer:
                         continue
                     try:
-                        item = await iterator.__anext__()
+                        item = await anext(iterator)
                     except StopAsyncIteration:
                         break
                     else: