release(core): 0.3.83 (#34733)

Backports #34172

---------

Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -119,7 +119,3 @@ body:
         python -m langchain_core.sys_info
     validations:
       required: true
-
-
-
-

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -42,11 +42,11 @@ body:
       label: Feature Description
       description: |
         Please provide a clear and concise description of the feature you would like to see added to LangChain.
-        
+
         What specific functionality are you requesting? Be as detailed as possible.
       placeholder: |
         I would like LangChain to support...
-        
+
         This feature would allow users to...
   - type: textarea
     id: use-case
@@ -56,13 +56,13 @@ body:
       label: Use Case
       description: |
         Describe the specific use case or problem this feature would solve.
-        
+
         Why do you need this feature? What problem does it solve for you or other users?
       placeholder: |
         I'm trying to build an application that...
-        
+
         Currently, I have to work around this by...
-        
+
         This feature would help me/users to...
   - type: textarea
     id: proposed-solution
@@ -72,13 +72,13 @@ body:
       label: Proposed Solution
       description: |
         If you have ideas about how this feature could be implemented, please describe them here.
-        
+
         This is optional but can be helpful for maintainers to understand your vision.
       placeholder: |
         I think this could be implemented by...
-        
+
         The API could look like...
-        
+
         ```python
         # Example of how the feature might work
         ```
@@ -90,15 +90,15 @@ body:
       label: Alternatives Considered
       description: |
         Have you considered any alternative solutions or workarounds?
-        
+
         What other approaches have you tried or considered?
       placeholder: |
         I've tried using...
-        
+
         Alternative approaches I considered:
         1. ...
         2. ...
-        
+
         But these don't work because...
   - type: textarea
     id: additional-context
@@ -110,9 +110,9 @@ body:
         Add any other context, screenshots, examples, or references that would help explain your feature request.
       placeholder: |
         Related issues: #...
-        
+
         Similar features in other libraries:
         - ...
-        
+
         Additional context or examples:
         - ...

.github/actions/people/action.yml

@@ -1,4 +1,6 @@
 # Adapted from https://github.com/tiangolo/fastapi/blob/master/.github/actions/people/action.yml
+# TODO: fix this, migrate to new docs repo?
+
 name: "Generate LangChain People"
 description: "Generate the data for the LangChain People page"
 author: "Jacob Lee <jacob@langchain.dev>"

.github/actions/uv_setup/action.yml

@@ -1,3 +1,5 @@
+# Helper to set up Python and uv with caching
+
 name: uv-install
 description: Set up Python and uv with caching
 
@@ -8,15 +10,15 @@ inputs:
   enable-cache:
     description: Enable caching for uv dependencies
     required: false
-    default: 'true'
+    default: "true"
   cache-suffix:
     description: Custom cache key suffix for cache invalidation
     required: false
-    default: ''
+    default: ""
   working-directory:
     description: Working directory for cache glob scoping
     required: false
-    default: '**'
+    default: "**"
 
 env:
   UV_VERSION: "0.5.25"

.github/pr-file-labeler.yml

@@ -1,4 +1,4 @@
-# GitHub PR Labeler Configuration for LangChain
+# Label PRs (config)
 # Automatically applies labels based on changed files and branch patterns
 
 # Core packages

.github/pr-title-labeler.yml

@@ -1,27 +1,41 @@
-# PR Title Labeler Configuration
+# PR title labeler config
+#
 # Labels PRs based on conventional commit patterns in titles
+#
 # Format: type(scope): description or type!: description (breaking)
 
 add-missing-labels: true
+clear-prexisting: false
 include-commits: false
 include-title: true
 label-for-breaking-changes: breaking
 
 label-mapping:
-  # Features and enhancements
-  feature: ["feat"]
-
-  # Bug fixes
-  fix: ["fix"]
-
-  # Documentation
   documentation: ["docs"]
-
-  # Infrastructure and tooling
-  infra: ["chore", "ci", "build", "infra"]
-
-  # Integration partners - detected by scope
-  integration: ["anthropic", "chroma", "deepseek", "exa", "fireworks", "groq", "huggingface", "mistralai", "nomic", "ollama", "openai", "perplexity", "prompty", "qdrant", "xai"]
-
-  # Releases
+  feature: ["feat"]
+  fix: ["fix"]
+  infra: ["build", "ci", "chore"]
+  integration:
+    [
+      "anthropic",
+      "chroma",
+      "deepseek",
+      "exa",
+      "fireworks",
+      "groq",
+      "huggingface",
+      "mistralai",
+      "nomic",
+      "ollama",
+      "openai",
+      "perplexity",
+      "prompty",
+      "qdrant",
+      "xai",
+    ]
+  linting: ["style"]
+  performance: ["perf"]
+  refactor: ["refactor"]
   release: ["release"]
+  revert: ["revert"]
+  tests: ["test"]

.github/scripts/check_diff.py

@@ -1,3 +1,18 @@
+"""Analyze git diffs to determine which directories need to be tested.
+
+Intelligently determines which LangChain packages and directories need to be tested,
+linted, or built based on the changes. Handles dependency relationships between
+packages, maps file changes to appropriate CI job configurations, and outputs JSON
+configurations for GitHub Actions.
+
+- Maps changed files to affected package directories (libs/core, libs/partners/*, etc.)
+- Builds dependency graph to include dependent packages when core components change
+- Generates test matrix configurations with appropriate Python versions
+- Handles special cases for Pydantic version testing and performance benchmarks
+
+Used as part of the check_diffs workflow.
+"""
+
 import glob
 import json
 import os
@@ -17,7 +32,7 @@ LANGCHAIN_DIRS = [
     "libs/langchain_v1",
 ]
 
-# when set to True, we are ignoring core dependents
+# When set to True, we are ignoring core dependents
 # in order to be able to get CI to pass for each individual
 # package that depends on core
 # e.g. if you touch core, we don't then add textsplitters/etc to CI
@@ -49,9 +64,9 @@ def all_package_dirs() -> Set[str]:
 
 
 def dependents_graph() -> dict:
-    """
-    Construct a mapping of package -> dependents, such that we can
-    run tests on all dependents of a package when a change is made.
+    """Construct a mapping of package -> dependents
+
+    Done such that we can run tests on all dependents of a package when a change is made.
     """
     dependents = defaultdict(set)
 
@@ -123,9 +138,6 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ == "libs/core":
         py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13"]
     # custom logic for specific directories
-    elif dir_ == "libs/partners/milvus":
-        # milvus doesn't allow 3.12 because they declare deps in funny way
-        py_versions = ["3.9", "3.11"]
 
     elif dir_ in PY_312_MAX_PACKAGES:
         py_versions = ["3.9", "3.12"]

.github/scripts/check_prerelease_dependencies.py

@@ -1,3 +1,5 @@
+"""Check that no dependencies allow prereleases unless we're releasing a prerelease."""
+
 import sys
 
 import tomllib
@@ -6,15 +8,14 @@ if __name__ == "__main__":
     # Get the TOML file path from the command line argument
     toml_file = sys.argv[1]
 
-    # read toml file
     with open(toml_file, "rb") as file:
         toml_data = tomllib.load(file)
 
-    # see if we're releasing an rc
+    # See if we're releasing an rc or dev version
     version = toml_data["project"]["version"]
     releasing_rc = "rc" in version or "dev" in version
 
-    # if not, iterate through dependencies and make sure none allow prereleases
+    # If not, iterate through dependencies and make sure none allow prereleases
     if not releasing_rc:
         dependencies = toml_data["project"]["dependencies"]
         for dep_version in dependencies:

.github/scripts/get_min_versions.py

@@ -1,3 +1,5 @@
+"""Get minimum versions of dependencies from a pyproject.toml file."""
+
 import sys
 from collections import defaultdict
 from typing import Optional
@@ -5,7 +7,7 @@ from typing import Optional
 if sys.version_info >= (3, 11):
     import tomllib
 else:
-    # for python 3.10 and below, which doesnt have stdlib tomllib
+    # For Python 3.10 and below, which doesnt have stdlib tomllib
     import tomli as tomllib
 
 import re
@@ -34,14 +36,13 @@ SKIP_IF_PULL_REQUEST = [
 
 
 def get_pypi_versions(package_name: str) -> List[str]:
-    """
-    Fetch all available versions for a package from PyPI.
+    """Fetch all available versions for a package from PyPI.
 
     Args:
-        package_name (str): Name of the package
+        package_name: Name of the package
 
     Returns:
-        List[str]: List of all available versions
+        List of all available versions
 
     Raises:
         requests.exceptions.RequestException: If PyPI API request fails
@@ -54,24 +55,23 @@ def get_pypi_versions(package_name: str) -> List[str]:
 
 
 def get_minimum_version(package_name: str, spec_string: str) -> Optional[str]:
-    """
-    Find the minimum published version that satisfies the given constraints.
+    """Find the minimum published version that satisfies the given constraints.
 
     Args:
-        package_name (str): Name of the package
-        spec_string (str): Version specification string (e.g., ">=0.2.43,<0.4.0,!=0.3.0")
+        package_name: Name of the package
+        spec_string: Version specification string (e.g., ">=0.2.43,<0.4.0,!=0.3.0")
 
     Returns:
-        Optional[str]: Minimum compatible version or None if no compatible version found
+        Minimum compatible version or None if no compatible version found
     """
-    # rewrite occurrences of ^0.0.z to 0.0.z (can be anywhere in constraint string)
+    # Rewrite occurrences of ^0.0.z to 0.0.z (can be anywhere in constraint string)
     spec_string = re.sub(r"\^0\.0\.(\d+)", r"0.0.\1", spec_string)
-    # rewrite occurrences of ^0.y.z to >=0.y.z,<0.y+1 (can be anywhere in constraint string)
+    # Rewrite occurrences of ^0.y.z to >=0.y.z,<0.y+1 (can be anywhere in constraint string)
     for y in range(1, 10):
         spec_string = re.sub(
             rf"\^0\.{y}\.(\d+)", rf">=0.{y}.\1,<0.{y + 1}", spec_string
         )
-    # rewrite occurrences of ^x.y.z to >=x.y.z,<x+1.0.0 (can be anywhere in constraint string)
+    # Rewrite occurrences of ^x.y.z to >=x.y.z,<x+1.0.0 (can be anywhere in constraint string)
     for x in range(1, 10):
         spec_string = re.sub(
             rf"\^{x}\.(\d+)\.(\d+)", rf">={x}.\1.\2,<{x + 1}", spec_string
@@ -154,22 +154,25 @@ def get_min_version_from_toml(
 
 
 def check_python_version(version_string, constraint_string):
-    """
-    Check if the given Python version matches the given constraints.
+    """Check if the given Python version matches the given constraints.
 
-    :param version_string: A string representing the Python version (e.g. "3.8.5").
-    :param constraint_string: A string representing the package's Python version constraints (e.g. ">=3.6, <4.0").
-    :return: True if the version matches the constraints, False otherwise.
+    Args:
+        version_string: A string representing the Python version (e.g. "3.8.5").
+        constraint_string: A string representing the package's Python version
+            constraints (e.g. ">=3.6, <4.0").
+
+    Returns:
+        True if the version matches the constraints
     """
 
-    # rewrite occurrences of ^0.0.z to 0.0.z (can be anywhere in constraint string)
+    # Rewrite occurrences of ^0.0.z to 0.0.z (can be anywhere in constraint string)
     constraint_string = re.sub(r"\^0\.0\.(\d+)", r"0.0.\1", constraint_string)
-    # rewrite occurrences of ^0.y.z to >=0.y.z,<0.y+1.0 (can be anywhere in constraint string)
+    # Rewrite occurrences of ^0.y.z to >=0.y.z,<0.y+1.0 (can be anywhere in constraint string)
     for y in range(1, 10):
         constraint_string = re.sub(
             rf"\^0\.{y}\.(\d+)", rf">=0.{y}.\1,<0.{y + 1}.0", constraint_string
         )
-    # rewrite occurrences of ^x.y.z to >=x.y.z,<x+1.0.0 (can be anywhere in constraint string)
+    # Rewrite occurrences of ^x.y.z to >=x.y.z,<x+1.0.0 (can be anywhere in constraint string)
     for x in range(1, 10):
         constraint_string = re.sub(
             rf"\^{x}\.0\.(\d+)", rf">={x}.0.\1,<{x + 1}.0.0", constraint_string

.github/scripts/prep_api_docs_build.py

@@ -1,5 +1,8 @@
 #!/usr/bin/env python
-"""Script to sync libraries from various repositories into the main langchain repository."""
+"""Sync libraries from various repositories into this monorepo.
+
+Moves cloned partner packages into libs/partners structure.
+"""
 
 import os
 import shutil
@@ -10,7 +13,7 @@ import yaml
 
 
 def load_packages_yaml() -> Dict[str, Any]:
-    """Load and parse the packages.yml file."""
+    """Load and parse packages.yml."""
     with open("langchain/libs/packages.yml", "r") as f:
         return yaml.safe_load(f)
 
@@ -61,12 +64,15 @@ def move_libraries(packages: list) -> None:
 
 
 def main():
-    """Main function to orchestrate the library sync process."""
+    """Orchestrate the library sync process."""
     try:
         # Load packages configuration
         package_yaml = load_packages_yaml()
 
-        # Clean target directories
+        # Clean/empty target directories in preparation for moving new ones
+        #
+        # Only for packages in the langchain-ai org or explicitly included via
+        # include_in_api_ref, excluding 'langchain' itself and 'langchain-ai21'
         clean_target_directories(
             [
                 p
@@ -80,7 +86,9 @@ def main():
             ]
         )
 
-        # Move libraries to their new locations
+        # Move cloned libraries to their new locations, only for packages in the
+        # langchain-ai org or explicitly included via include_in_api_ref,
+        # excluding 'langchain' itself and 'langchain-ai21'
         move_libraries(
             [
                 p
@@ -95,7 +103,7 @@ def main():
             ]
         )
 
-        # Delete ones without a pyproject.toml
+        # Delete partner packages without a pyproject.toml
         for partner in Path("langchain/libs/partners").iterdir():
             if partner.is_dir() and not (partner / "pyproject.toml").exists():
                 print(f"Removing {partner} as it does not have a pyproject.toml")

.github/workflows/_compile_integration_test.yml

@@ -1,3 +1,11 @@
+# Validates that a package's integration tests compile without syntax or import errors.
+#
+# (If an integration test fails to compile, it won't run.)
+#
+# Called as part of check_diffs.yml workflow
+#
+# Runs pytest with compile marker to check syntax/imports.
+
 name: '🔗 Compile Integration Tests'
 
 on:

.github/workflows/_integration_test.yml

@@ -1,3 +1,10 @@
+
+# Runs `make integration_tests` on the specified package.
+#
+# Manually triggered via workflow_dispatch for testing with real APIs.
+#
+# Installs integration test dependencies and executes full test suite.
+
 name: '🚀 Integration Tests'
 run-name: 'Test ${{ inputs.working-directory }} on Python ${{ inputs.python-version }}'
 
@@ -83,7 +90,7 @@ jobs:
         run: |
           make integration_tests
 
-      - name: Ensure the tests did not create any additional files
+      - name: 'Ensure testing did not create/modify files'
         shell: bash
         run: |
           set -eu

.github/workflows/_lint.yml

@@ -1,6 +1,11 @@
-name: '🧹 Code Linting'
-# Runs code quality checks using ruff, mypy, and other linting tools
-# Checks both package code and test code for consistency
+# Runs linting.
+#
+# Uses the package's Makefile to run the checks, specifically the
+# `lint_package` and `lint_tests` targets.
+#
+# Called as part of check_diffs.yml workflow.
+
+name: '🧹 Linting'
 
 on:
   workflow_call:
@@ -43,14 +48,6 @@ jobs:
           working-directory: ${{ inputs.working-directory }}
 
       - name: '📦 Install Lint & Typing Dependencies'
-        # Also installs dev/lint/test/typing dependencies, to ensure we have
-        # type hints for as many of our libraries as possible.
-        # This helps catch errors that require dependencies to be spotted, for example:
-        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
-        #
-        # If you change this configuration, make sure to change the `cache-key`
-        # in the `poetry_setup` action above to stop using the old cache.
-        # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
         run: |
           uv sync --group lint --group typing
@@ -60,20 +57,13 @@ jobs:
         run: |
           make lint_package
 
-      - name: '📦 Install Unit Test Dependencies'
-        # Also installs dev/lint/test/typing dependencies, to ensure we have
-        # type hints for as many of our libraries as possible.
-        # This helps catch errors that require dependencies to be spotted, for example:
-        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
-        #
-        # If you change this configuration, make sure to change the `cache-key`
-        # in the `poetry_setup` action above to stop using the old cache.
-        # It doesn't matter how you change it, any change will cause a cache-bust.
+      - name: '📦 Install Test Dependencies (non-partners)'
+        # (For directories NOT starting with libs/partners/)
         if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
         working-directory: ${{ inputs.working-directory }}
         run: |
           uv sync --inexact --group test
-      - name: '📦 Install Unit + Integration Test Dependencies'
+      - name: '📦 Install Test Dependencies'
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         working-directory: ${{ inputs.working-directory }}
         run: |

.github/workflows/_release.yml

@@ -1,3 +1,9 @@
+# Builds and publishes LangChain packages to PyPI.
+#
+# Manually triggered, though can be used as a reusable workflow (workflow_call).
+#
+# Handles version bumping, building, and publishing to PyPI with authentication.
+
 name: '🚀 Package Release'
 run-name: 'Release ${{ inputs.working-directory }} ${{ inputs.release-version }}'
 on:
@@ -52,8 +58,8 @@ jobs:
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
-      # The release stage has trusted publishing and GitHub repo contents write access,
-      # and we want to keep the scope of that access limited just to the release job.
+      # (Release stage has trusted publishing and GitHub repo contents write access,
+      #
       # Otherwise, a malicious `build` step (e.g. via a compromised dependency)
       # could get access to our GitHub or PyPI credentials.
       #
@@ -288,16 +294,19 @@ jobs:
         run: |
           VIRTUAL_ENV=.venv uv pip install dist/*.whl
 
-      - name: Run unit tests
-        run: make tests
-        working-directory: ${{ inputs.working-directory }}
-
       - name: Check for prerelease versions
+        # Block release if any dependencies allow prerelease versions
+        # (unless this is itself a prerelease version)
         working-directory: ${{ inputs.working-directory }}
         run: |
           uv run python $GITHUB_WORKSPACE/.github/scripts/check_prerelease_dependencies.py pyproject.toml
 
+      - name: Run unit tests
+        run: make tests
+        working-directory: ${{ inputs.working-directory }}
+
       - name: Get minimum versions
+        # Find the minimum published versions that satisfies the given constraints
         working-directory: ${{ inputs.working-directory }}
         id: min-version
         run: |
@@ -322,6 +331,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
       - name: Run integration tests
+        # Uses the Makefile's `integration_tests` target for the specified package
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
           AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
@@ -362,7 +372,11 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
   # Test select published packages against new core
+  # Done when code changes are made to langchain-core
   test-prior-published-packages-against-new-core:
+    # Installs the new core with old partners: Installs the new unreleased core
+    # alongside the previously published partner packages and runs integration tests
+    if: github.ref != 'refs/heads/v0.3'
     needs:
       - build
       - release-notes
@@ -390,6 +404,7 @@ jobs:
 
       # We implement this conditional as Github Actions does not have good support
       # for conditionally needing steps. https://github.com/actions/runner/issues/491
+      # TODO: this seems to be resolved upstream, so we can probably remove this workaround
       - name: Check if libs/core
         run: |
           if [ "${{ startsWith(inputs.working-directory, 'libs/core') }}" != "true" ]; then
@@ -417,7 +432,7 @@ jobs:
             git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
             | awk '{print $2}' \
             | sed 's|refs/tags/||' \
-            | grep -E '[0-9]+\.[0-9]+\.[0-9]+$' \
+            | grep -E '==0\.3\.[0-9]+$' \
             | sort -Vr \
             | head -n 1
           )"
@@ -444,12 +459,12 @@ jobs:
           make integration_tests
 
   publish:
+    # Publishes the package to PyPI
     needs:
       - build
       - release-notes
       - test-pypi-publish
       - pre-release-checks
-      - test-prior-published-packages-against-new-core
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:
@@ -486,6 +501,7 @@ jobs:
           attestations: false
 
   mark-release:
+    # Marks the GitHub release with the new version tag
     needs:
       - build
       - release-notes
@@ -495,7 +511,7 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       # This permission is needed by `ncipollo/release-action` to
-      # create the GitHub release.
+      # create the GitHub release/tag
       contents: write
 
     defaults:

.github/workflows/_test.yml

@@ -1,6 +1,7 @@
-name: '🧪 Unit Testing'
 # Runs unit tests with both current and minimum supported dependency versions
-# to ensure compatibility across the supported range
+# to ensure compatibility across the supported range.
+
+name: '🧪 Unit Testing'
 
 on:
   workflow_call:

.github/workflows/_test_doc_imports.yml

@@ -1,3 +1,10 @@
+# Validates that all import statements in `.ipynb` notebooks are correct and functional.
+#
+# Called as part of check_diffs.yml.
+#
+# Installs test dependencies and LangChain packages in editable mode and
+# runs check_imports.py.
+
 name: '📑 Documentation Import Testing'
 
 on:

.github/workflows/_test_pydantic.yml

@@ -1,3 +1,5 @@
+# Facilitate unit testing against different Pydantic versions for a provided package.
+
 name: '🐍 Pydantic Version Testing'
 
 on:

.github/workflows/api_doc_build.yml

@@ -1,11 +1,19 @@
+# Build the API reference documentation.
+#
+# Runs daily. Can also be triggered manually for immediate updates.
+#
+# Built HTML pushed to langchain-ai/langchain-api-docs-html.
+#
+# Looks for langchain-ai org repos in packages.yml and checks them out.
+# Calls prep_api_docs_build.py.
+
 name: '📚 API Docs'
 run-name: 'Build & Deploy API Reference'
-# Runs daily or can be triggered manually for immediate updates
 
 on:
   workflow_dispatch:
   schedule:
-    - cron:  '0 13 * * *'  # Daily at 1PM UTC
+    - cron:  '0 13 * * *'  # Runs daily at 1PM UTC (9AM EDT/6AM PDT)
 env:
   PYTHON_VERSION: "3.11"
 
@@ -31,6 +39,8 @@ jobs:
         uses: mikefarah/yq@master
         with:
           cmd: |
+            # Extract repos from packages.yml that are in the langchain-ai org
+            # (excluding 'langchain' itself)
             yq '
               .packages[]
               | select(
@@ -77,24 +87,31 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 
-      - name: '📦 Install Initial Python Dependencies'
+      - name: '📦 Install Initial Python Dependencies using uv'
         working-directory: langchain
         run: |
           python -m pip install -U uv
           python -m uv pip install --upgrade --no-cache-dir pip setuptools pyyaml
 
       - name: '📦 Organize Library Directories'
+        # Places cloned partner packages into libs/partners structure
         run: python langchain/.github/scripts/prep_api_docs_build.py
 
-      - name: '🧹 Remove Old HTML Files'
+      - name: '🧹 Clear Prior Build'
         run:
+          # Remove artifacts from prior docs build
           rm -rf langchain-api-docs-html/api_reference_build/html
 
-      - name: '📦 Install Documentation Dependencies'
+      - name: '📦 Install Documentation Dependencies using uv'
         working-directory: langchain
         run: |
-          python -m uv pip install $(ls ./libs/partners | xargs -I {} echo "./libs/partners/{}") --overrides ./docs/vercel_overrides.txt
+          # Install all partner packages in editable mode with overrides
+          python -m uv pip install $(ls ./libs/partners | xargs -I {} echo "./libs/partners/{}") --overrides ./docs/vercel_overrides.txt --prerelease=allow
+
+          # Install core langchain and other main packages
           python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental libs/standard-tests
+
+          # Install Sphinx and related packages for building docs
           python -m uv pip install -r docs/api_reference/requirements.txt
 
       - name: '🔧 Configure Git Settings'
@@ -106,14 +123,29 @@ jobs:
       - name: '📚 Build API Documentation'
         working-directory: langchain
         run: |
+          # Generate the API reference RST files
           python docs/api_reference/create_api_rst.py
+
+          # Build the HTML documentation using Sphinx
+          # -T: show full traceback on exception
+          # -E: don't use cached environment (force rebuild, ignore cached doctrees)
+          # -b html: build HTML docs (vs PDS, etc.)
+          # -d: path for the cached environment (parsed document trees / doctrees)
+          #     - Separate from output dir for faster incremental builds
+          # -c: path to conf.py
+          # -j auto: parallel build using all available CPU cores
           python -m sphinx -T -E -b html -d ../langchain-api-docs-html/_build/doctrees -c docs/api_reference docs/api_reference ../langchain-api-docs-html/api_reference_build/html -j auto
+
+          # Post-process the generated HTML
           python docs/api_reference/scripts/custom_formatter.py ../langchain-api-docs-html/api_reference_build/html
+
           # Default index page is blank so we copy in the actual home page.
           cp ../langchain-api-docs-html/api_reference_build/html/{reference,index}.html
+
+          # Removes Sphinx's intermediate build artifacts after the build is complete.
           rm -rf ../langchain-api-docs-html/_build/
 
-      # https://github.com/marketplace/actions/add-commit
+      # Commit and push changes to langchain-api-docs-html repo
       - uses: EndBug/add-and-commit@v9
         with:
           cwd: langchain-api-docs-html

.github/workflows/check-broken-links.yml

@@ -1,9 +1,11 @@
+# Runs broken link checker in /docs on a daily schedule.
+
 name: '🔗 Check Broken Links'
 
 on:
   workflow_dispatch:
   schedule:
-    - cron:  '0 13 * * *'
+    - cron:  '0 13 * * *'  # Runs daily at 1PM UTC (9AM EDT/6AM PDT)
 
 permissions:
   contents: read
@@ -15,7 +17,7 @@ jobs:
     steps:
       - uses: actions/checkout@v5
       - name: '🟢 Setup Node.js 18.x'
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
           node-version: 18.x
           cache: "yarn"

.github/workflows/check_core_versions.yml

@@ -1,6 +1,8 @@
-name: '🔍 Check `core` Version Equality'
-# Ensures version numbers in pyproject.toml and version.py stay in sync
-# Prevents releases with mismatched version numbers
+# Ensures version numbers in pyproject.toml and version.py stay in sync.
+#
+# (Prevents releases with mismatched version numbers)
+
+name: '🔍 Check Version Equality'
 
 on:
   pull_request:

.github/workflows/check_diffs.yml

@@ -1,3 +1,18 @@
+# Primary CI workflow.
+#
+# Only runs against packages that have changed files.
+#
+# Runs:
+# - Linting (_lint.yml)
+# - Unit Tests (_test.yml)
+# - Pydantic compatibility tests (_test_pydantic.yml)
+# - Documentation import tests (_test_doc_imports.yml)
+# - Integration test compilation checks (_compile_integration_test.yml)
+# - Extended test suites that require additional dependencies
+# - Codspeed benchmarks (if not labeled 'codspeed-ignore')
+#
+# Reports status to GitHub checks and PR status.
+
 name: '🔧 CI'
 
 on:
@@ -11,8 +26,8 @@ on:
 # cancel the earlier run in favor of the next run.
 #
 # There's no point in testing an outdated version of the code. GitHub only allows
-# a limited number of job runners to be active at the same time, so it's better to cancel
-# pointless jobs early so that more useful jobs can run sooner.
+# a limited number of job runners to be active at the same time, so it's better to
+# cancel pointless jobs early so that more useful jobs can run sooner.
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/check_new_docs.yml

@@ -1,3 +1,6 @@
+# For integrations, we run check_templates.py to ensure that new docs use the correct
+# templates based on their type. See the script for more details.
+
 name: '📑 Integration Docs Lint'
 
 on:

.github/workflows/people.yml

@@ -1,9 +1,11 @@
+# Updates the LangChain People data by fetching the latest info from the LangChain Git.
+# TODO: broken/not used
+
 name: '👥 LangChain People'
 run-name: 'Update People Data'
-# This workflow updates the LangChain People data by fetching the latest information from the LangChain Git
 on:
   schedule:
-    - cron: "0 14 1 * *"
+    - cron: "0 14 1 * *"  # Runs at 14:00 UTC on the 1st of every month (10AM EDT/7AM PDT)
   push:
     branches: [jacob/people]
   workflow_dispatch:

.github/workflows/pr_labeler_file.yml

@@ -1,8 +1,14 @@
+# Label PRs based on changed files.
+#
+# See `.github/pr-file-labeler.yml` to see rules for each label/directory.
+
 name: "🏷️ Pull Request Labeler"
 
 on:
+  # Safe since we're not checking out or running the PR's code
+  # Never check out the PR's head in a pull_request_target job
   pull_request_target:
-    types: [opened, synchronize, reopened]
+    types: [opened, synchronize, reopened, edited]
 
 jobs:
   labeler:
@@ -18,5 +24,5 @@ jobs:
       uses: actions/labeler@v6
       with:
         repo-token: "${{ secrets.GITHUB_TOKEN }}"
-        configuration-path: .github/labeler.yml
+        configuration-path: .github/pr-file-labeler.yml
         sync-labels: false

.github/workflows/pr_labeler_title.yml

@@ -1,7 +1,13 @@
+# Label PRs based on their titles.
+#
+# See `.github/pr-title-labeler.yml` to see rules for each label/title pattern.
+
 name: "🏷️ PR Title Labeler"
 
 on:
-  pull_request:
+  # 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]
 
 jobs:
@@ -15,7 +21,8 @@ jobs:
 
     steps:
     - name: Label PR based on title
-      uses: grafana/pr-labeler-action@v0.1.0
+      # Archived repo; latest commit (v0.1.0)
+      uses: grafana/pr-labeler-action@f19222d3ef883d2ca5f04420fdfe8148003763f0
       with:
         token: ${{ secrets.GITHUB_TOKEN }}
         configuration-path: .github/pr-title-labeler.yml

.github/workflows/pr_lint.yml

@@ -1,29 +1,30 @@
-# -----------------------------------------------------------------------------
-# PR Title Lint Workflow
+# PR title linting.
 #
-# Purpose:
-#   Enforces Conventional Commits format for pull request titles to maintain a
-#   clear, consistent, and machine-readable change history across our repository.
-#   This helps with automated changelog generation and semantic versioning.
+# FORMAT (Conventional Commits 1.0.0):
 #
-# Enforced Commit Message Format (Conventional Commits 1.0.0):
 #   <type>[optional scope]: <description>
 #   [optional body]
 #   [optional footer(s)]
 #
+# Examples:
+#     feat(core): add multi‐tenant support
+#     fix(cli): resolve flag parsing error
+#     docs: update API usage examples
+#     docs(openai): update API usage examples
+#
 # Allowed Types:
-#   • feat       — a new feature (MINOR bump)
-#   • fix        — a bug fix (PATCH bump)
-#   • docs       — documentation only changes
-#   • style      — formatting, missing semi-colons, etc.; no code change
-#   • refactor   — code change that neither fixes a bug nor adds a feature
-#   • perf       — code change that improves performance
-#   • test       — adding missing tests or correcting existing tests
-#   • build      — changes that affect the build system or external dependencies
-#   • ci         — continuous integration/configuration changes
-#   • chore      — other changes that don't modify src or test files
-#   • revert     — reverts a previous commit
-#   • release    — prepare a new release
+#   * feat       — a new feature (MINOR)
+#   * fix        — a bug fix (PATCH)
+#   * docs       — documentation only changes (either in /docs or code comments)
+#   * style      — formatting, linting, etc.; no code change or typing refactors
+#   * refactor   — code change that neither fixes a bug nor adds a feature
+#   * perf       — code change that improves performance
+#   * test       — adding tests or correcting existing
+#   * build      — changes that affect the build system/external dependencies
+#   * ci         — continuous integration/configuration changes
+#   * chore      — other changes that don't modify source or test files
+#   * revert     — reverts a previous commit
+#   * release    — prepare a new release
 #
 # Allowed Scopes (optional):
 #   core, cli, langchain, langchain_v1, langchain_legacy, standard-tests,
@@ -31,21 +32,12 @@
 #   huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant,
 #   xai, infra
 #
-# Rules & Tips for New Committers:
-#   1. Subject (type) must start with a lowercase letter and, if possible, be
-#      followed by a scope wrapped in parenthesis `(scope)`
-#   2. Breaking changes:
-#        – Append "!" after type/scope (e.g., feat!: drop Node 12 support)
-#        – Or include a footer "BREAKING CHANGE: <details>"
-#   3. Example PR titles:
-#        feat(core): add multi‐tenant support
-#        fix(cli): resolve flag parsing error
-#        docs: update API usage examples
-#        docs(openai): update API usage examples
+# Rules:
+#   1. The 'Type' must start with a lowercase letter.
+#   2. Breaking changes: append "!" after type/scope (e.g., feat!: drop x support)
 #
-# Resources:
-#   • Conventional Commits spec: https://www.conventionalcommits.org/en/v1.0.0/
-# -----------------------------------------------------------------------------
+# Enforces Conventional Commits format for pull request titles to maintain a clear and
+# machine-readable change history.
 
 name: '🏷️ PR Title Lint'
 
@@ -57,7 +49,7 @@ on:
     types: [opened, edited, synchronize]
 
 jobs:
-  # Validates that PR title follows Conventional Commits specification
+  # Validates that PR title follows Conventional Commits 1.0.0 specification
   lint-pr-title:
     name: 'validate format'
     runs-on: ubuntu-latest

.github/workflows/run_notebooks.yml

@@ -1,3 +1,5 @@
+# Integration tests for documentation notebooks.
+
 name: '📓 Validate Documentation Notebooks'
 run-name: 'Test notebooks in ${{ inputs.working-directory  }}'
 on:

.github/workflows/scheduled_test.yml

@@ -1,8 +1,14 @@
+# Routine integration tests against partner libraries with live API credentials.
+#
+# Uses `make integration_tests` for each library in the matrix.
+#
+# Runs daily. Can also be triggered manually for immediate updates.
+
 name: '⏰ Scheduled Integration Tests'
 run-name: "Run Integration Tests - ${{ inputs.working-directory-force || 'all libs' }} (Python ${{ inputs.python-version-force || '3.9, 3.11' }})"
 
 on:
-  workflow_dispatch:  # Allows maintainers to trigger the workflow manually in GitHub UI
+  workflow_dispatch:
     inputs:
       working-directory-force:
         type: string
@@ -54,13 +60,13 @@ jobs:
           echo $matrix
           echo "matrix=$matrix" >> $GITHUB_OUTPUT
   # Run integration tests against partner libraries with live API credentials
-  # Tests are run with both Poetry and UV depending on the library's setup
+  # Tests are run with Poetry or UV depending on the library's setup
   build:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     name: '🐍 Python ${{ matrix.python-version }}: ${{ matrix.working-directory }}'
     runs-on: ubuntu-latest
     needs: [compute-matrix]
-    timeout-minutes: 20
+    timeout-minutes: 30
     strategy:
       fail-fast: false
       matrix:
@@ -161,7 +167,7 @@ jobs:
           make integration_tests
 
       - name: '🧹 Clean up External Libraries'
-        # Clean up external libraries to avoid affecting git status check
+        # Clean up external libraries to avoid affecting the following git status check
         run: |
           rm -rf \
             langchain/libs/partners/google-genai \

.github/workflows/v1_changes.md

@@ -0,0 +1,9 @@
+With the deprecation of v0 docs, the following files will need to be migrated/supported
+in the new docs repo:
+
+- run_notebooks.yml: New repo should run Integration tests on code snippets?
+- people.yml: Need to fix and somehow display on the new docs site
+  - Subsequently, `.github/actions/people/`
+- _test_doc_imports.yml
+- check_new_docs.yml
+- check-broken-links.yml

.readthedocs.yaml

@@ -1,25 +0,0 @@
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-version: 2
-
-# Set the version of Python and other tools you might need
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.11"
-  commands:
-    - mkdir -p $READTHEDOCS_OUTPUT
-    - cp -r api_reference_build/* $READTHEDOCS_OUTPUT
-
-# Build documentation in the docs/ directory with Sphinx
-sphinx:
-  configuration: docs/api_reference/conf.py
-
-# If using Sphinx, optionally build your docs in additional formats such as PDF
-formats:
-  - pdf
-
-# Optionally declare the Python requirements required to build your docs
-python:
-  install:
-    - requirements: docs/api_reference/requirements.txt

.vscode/settings.json

@@ -78,5 +78,10 @@
     "editor.insertSpaces": true
   },
   "python.terminal.activateEnvironment": false,
-  "python.defaultInterpreterPath": "./.venv/bin/python"
+  "python.defaultInterpreterPath": "./.venv/bin/python",
+  "github.copilot.chat.commitMessageGeneration.instructions": [
+    {
+      "file": ".github/workflows/pr_lint.yml"
+    }
+  ]
 }

AGENTS.md

@@ -0,0 +1,325 @@
+# Global Development Guidelines for LangChain Projects
+
+## Core Development Principles
+
+### 1. Maintain Stable Public Interfaces ⚠️ CRITICAL
+
+**Always attempt to preserve function signatures, argument positions, and names for exported/public methods.**
+
+❌ **Bad - Breaking Change:**
+
+```python
+def get_user(id, verbose=False):  # Changed from `user_id`
+    pass
+```
+
+✅ **Good - Stable Interface:**
+
+```python
+def get_user(user_id: str, verbose: bool = False) -> User:
+    """Retrieve user by ID with optional verbose output."""
+    pass
+```
+
+**Before making ANY changes to public APIs:**
+
+- Check if the function/class is exported in `__init__.py`
+- Look for existing usage patterns in tests and examples
+- Use keyword-only arguments for new parameters: `*, new_param: str = "default"`
+- Mark experimental features clearly with docstring warnings (using reStructuredText, like `.. warning::`)
+
+🧠 *Ask yourself:* "Would this change break someone's code if they used it last week?"
+
+### 2. Code Quality Standards
+
+**All Python code MUST include type hints and return types.**
+
+❌ **Bad:**
+
+```python
+def p(u, d):
+    return [x for x in u if x not in d]
+```
+
+✅ **Good:**
+
+```python
+def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
+    """Filter out users that are not in the known users set.
+
+    Args:
+        users: List of user identifiers to filter.
+        known_users: Set of known/valid user identifiers.
+
+    Returns:
+        List of users that are not in the known_users set.
+    """
+    return [user for user in users if user not in known_users]
+```
+
+**Style Requirements:**
+
+- Use descriptive, **self-explanatory variable names**. Avoid overly short or cryptic identifiers.
+- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense
+- Avoid unnecessary abstraction or premature optimization
+- Follow existing patterns in the codebase you're modifying
+
+### 3. Testing Requirements
+
+**Every new feature or bugfix MUST be covered by unit tests.**
+
+**Test Organization:**
+
+- Unit tests: `tests/unit_tests/` (no network calls allowed)
+- Integration tests: `tests/integration_tests/` (network calls permitted)
+- Use `pytest` as the testing framework
+
+**Test Quality Checklist:**
+
+- [ ] Tests fail when your new logic is broken
+- [ ] Happy path is covered
+- [ ] Edge cases and error conditions are tested
+- [ ] Use fixtures/mocks for external dependencies
+- [ ] Tests are deterministic (no flaky tests)
+
+Checklist questions:
+
+- [ ] Does the test suite fail if your new logic is broken?
+- [ ] Are all expected behaviors exercised (happy path, invalid input, etc)?
+- [ ] Do tests use fixtures or mocks where needed?
+
+```python
+def test_filter_unknown_users():
+    """Test filtering unknown users from a list."""
+    users = ["alice", "bob", "charlie"]
+    known_users = {"alice", "bob"}
+
+    result = filter_unknown_users(users, known_users)
+
+    assert result == ["charlie"]
+    assert len(result) == 1
+```
+
+### 4. Security and Risk Assessment
+
+**Security Checklist:**
+
+- No `eval()`, `exec()`, or `pickle` on user-controlled input
+- Proper exception handling (no bare `except:`) and use a `msg` variable for error messages
+- Remove unreachable/commented code before committing
+- Race conditions or resource leaks (file handles, sockets, threads).
+- Ensure proper resource cleanup (file handles, connections)
+
+❌ **Bad:**
+
+```python
+def load_config(path):
+    with open(path) as f:
+        return eval(f.read())  # ⚠️ Never eval config
+```
+
+✅ **Good:**
+
+```python
+import json
+
+def load_config(path: str) -> dict:
+    with open(path) as f:
+        return json.load(f)
+```
+
+### 5. Documentation Standards
+
+**Use Google-style docstrings with Args section for all public functions.**
+
+❌ **Insufficient Documentation:**
+
+```python
+def send_email(to, msg):
+    """Send an email to a recipient."""
+```
+
+✅ **Complete Documentation:**
+
+```python
+def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
+    """
+    Send an email to a recipient with specified priority.
+
+    Args:
+        to: The email address of the recipient.
+        msg: The message body to send.
+        priority: Email priority level (``'low'``, ``'normal'``, ``'high'``).
+
+    Returns:
+        True if email was sent successfully, False otherwise.
+
+    Raises:
+        InvalidEmailError: If the email address format is invalid.
+        SMTPConnectionError: If unable to connect to email server.
+    """
+```
+
+**Documentation Guidelines:**
+
+- Types go in function signatures, NOT in docstrings
+- Focus on "why" rather than "what" in descriptions
+- Document all parameters, return values, and exceptions
+- Keep descriptions concise but clear
+- Use reStructuredText for docstrings to enable rich formatting
+
+📌 *Tip:* Keep descriptions concise but clear. Only document return values if non-obvious.
+
+### 6. Architectural Improvements
+
+**When you encounter code that could be improved, suggest better designs:**
+
+❌ **Poor Design:**
+
+```python
+def process_data(data, db_conn, email_client, logger):
+    # Function doing too many things
+    validated = validate_data(data)
+    result = db_conn.save(validated)
+    email_client.send_notification(result)
+    logger.log(f"Processed {len(data)} items")
+    return result
+```
+
+✅ **Better Design:**
+
+```python
+@dataclass
+class ProcessingResult:
+    """Result of data processing operation."""
+    items_processed: int
+    success: bool
+    errors: List[str] = field(default_factory=list)
+
+class DataProcessor:
+    """Handles data validation, storage, and notification."""
+
+    def __init__(self, db_conn: Database, email_client: EmailClient):
+        self.db = db_conn
+        self.email = email_client
+
+    def process(self, data: List[dict]) -> ProcessingResult:
+        """Process and store data with notifications."""
+        validated = self._validate_data(data)
+        result = self.db.save(validated)
+        self._notify_completion(result)
+        return result
+```
+
+**Design Improvement Areas:**
+
+If there's a **cleaner**, **more scalable**, or **simpler** design, highlight it and suggest improvements that would:
+
+- Reduce code duplication through shared utilities
+- Make unit testing easier
+- Improve separation of concerns (single responsibility)
+- Make unit testing easier through dependency injection
+- Add clarity without adding complexity
+- Prefer dataclasses for structured data
+
+## Development Tools & Commands
+
+### Package Management
+
+```bash
+# Add package
+uv add package-name
+
+# Sync project dependencies
+uv sync
+uv lock
+```
+
+### Testing
+
+```bash
+# Run unit tests (no network)
+make test
+
+# Don't run integration tests, as API keys must be set
+
+# Run specific test file
+uv run --group test pytest tests/unit_tests/test_specific.py
+```
+
+### Code Quality
+
+```bash
+# Lint code
+make lint
+
+# Format code
+make format
+
+# Type checking
+uv run --group lint mypy .
+```
+
+### Dependency Management Patterns
+
+**Local Development Dependencies:**
+
+```toml
+[tool.uv.sources]
+langchain-core = { path = "../core", editable = true }
+langchain-tests = { path = "../standard-tests", editable = true }
+```
+
+**For tools, use the `@tool` decorator from `langchain_core.tools`:**
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def search_database(query: str) -> str:
+    """Search the database for relevant information.
+
+    Args:
+        query: The search query string.
+    """
+    # Implementation here
+    return results
+```
+
+## Commit Standards
+
+**Use Conventional Commits format for PR titles:**
+
+- `feat(core): add multi-tenant support`
+- `fix(cli): resolve flag parsing error`
+- `docs: update API usage examples`
+- `docs(openai): update API usage examples`
+
+## Framework-Specific Guidelines
+
+- Follow the existing patterns in `langchain-core` for base abstractions
+- Use `langchain_core.callbacks` for execution tracking
+- Implement proper streaming support where applicable
+- Avoid deprecated components like legacy `LLMChain`
+
+### Partner Integrations
+
+- Follow the established patterns in existing partner libraries
+- Implement standard interfaces (`BaseChatModel`, `BaseEmbeddings`, etc.)
+- Include comprehensive integration tests
+- Document API key requirements and authentication
+
+---
+
+## Quick Reference Checklist
+
+Before submitting code changes:
+
+- [ ] **Breaking Changes**: Verified no public API changes
+- [ ] **Type Hints**: All functions have complete type annotations
+- [ ] **Tests**: New functionality is fully tested
+- [ ] **Security**: No dangerous patterns (eval, silent failures, etc.)
+- [ ] **Documentation**: Google-style docstrings for public functions
+- [ ] **Code Quality**: `make lint` and `make format` pass
+- [ ] **Architecture**: Suggested improvements where applicable
+- [ ] **Commit Message**: Follows Conventional Commits format

README.md

@@ -1,83 +1,75 @@
-<picture>
-  <source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="docs/static/img/logo-light.svg">
-  <img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
-</picture>
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
+    <source media="(prefers-color-scheme: dark)" srcset="docs/static/img/logo-light.svg">
+    <img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
+  </picture>
+</p>
 
-<div>
-<br>
-</div>
+<p align="center">
+    The platform for reliable agents.
+</p>
 
-[![PyPI - License](https://img.shields.io/pypi/l/langchain-core?style=flat-square)](https://opensource.org/licenses/MIT)
-[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain)](https://pypistats.org/packages/langchain-core)
-[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
-[<img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchain)
-[![CodSpeed Badge](https://img.shields.io/endpoint?url=https://codspeed.io/badge.json)](https://codspeed.io/langchain-ai/langchain)
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
+<p align="center">
+  <a href="https://opensource.org/licenses/MIT" target="_blank">
+      <img src="https://img.shields.io/pypi/l/langchain-core?style=flat-square" alt="PyPI - License">
+  </a>
+  <a href="https://pypistats.org/packages/langchain-core" target="_blank">
+      <img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads">
+  </a>
+  <a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank">
+      <img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square" alt="Open in Dev Containers">
+  </a>
+  <a href="https://codespaces.new/langchain-ai/langchain" target="_blank">
+      <img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">
+  </a>
+  <a href="https://codspeed.io/langchain-ai/langchain" target="_blank">
+      <img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed Badge">
+  </a>
+  <a href="https://twitter.com/langchainai" target="_blank">
+      <img src="https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI" alt="Twitter / X">
+  </a>
+</p>
 
-> [!NOTE]
-> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
-
-LangChain is a framework for building LLM-powered applications. It helps you chain
-together interoperable components and third-party integrations to simplify AI
-application development —  all while future-proofing decisions as the underlying
-technology evolves.
+LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development —  all while future-proofing decisions as the underlying technology evolves.
 
 ```bash
 pip install -U langchain
 ```
 
-To learn more about LangChain, check out
-[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
-advanced customization or agent orchestration, check out
-[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
-controllable agent workflows.
+---
+
+**Documentation**: To learn more about LangChain, check out [the docs](https://python.langchain.com/docs/introduction/).
+
+If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building controllable agent workflows.
+
+> [!NOTE]
+> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 
 ## Why use LangChain?
 
-LangChain helps developers build applications powered by LLMs through a standard
-interface for models, embeddings, vector stores, and more.
+LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
 
 Use LangChain for:
 
-- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
-external/internal systems, drawing from LangChain’s vast library of integrations with
-model providers, tools, vector stores, retrievers, and more.
-- **Model interoperability**. Swap models in and out as your engineering team
-experiments to find the best choice for your application’s needs. As the industry
-frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
-losing momentum.
+- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
+- **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
 
 ## LangChain’s ecosystem
 
-While the LangChain framework can be used standalone, it also integrates seamlessly
-with any LangChain product, giving developers a full suite of tools when building LLM
-applications.
+While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
 
 To improve your LLM application development, pair LangChain with:
 
-- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and
-observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
-visibility in production, and improve performance over time.
-- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
-reliably handle complex tasks with LangGraph, our low-level agent orchestration
-framework. LangGraph offers customizable architecture, long-term memory, and
-human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
-Uber, Klarna, and GitLab.
-- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy
-and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across
-teams — and iterate quickly with visual prototyping in
-[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
+- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
+- [LangGraph Platform](https://docs.langchain.com/langgraph-platform) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 
 ## Additional resources
 
-- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
-guided examples on getting started with LangChain.
-- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
-snippets for topics such as tool calling, RAG use cases, and more.
-- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
-concepts behind the LangChain framework.
+- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with guided examples on getting started with LangChain.
+- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
+- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key concepts behind the LangChain framework.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
-- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
-navigating base packages and integrations for LangChain.
+- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on navigating base packages and integrations for LangChain.
 - [Chat LangChain](https://chat.langchain.com/): Ask questions & chat with our documentation.

SECURITY.md

@@ -35,7 +35,7 @@ open source projects at [huntr](https://huntr.com/bounties/disclose/?target=http
 Before reporting a vulnerability, please review:
 
 1) In-Scope Targets and Out-of-Scope Targets below.
-2) The [langchain-ai/langchain](https://python.langchain.com/docs/contributing/repo_structure) monorepo structure.
+2) The [langchain-ai/langchain](https://docs.langchain.com/oss/python/contributing/code#supporting-packages) monorepo structure.
 3) The [Best Practices](#best-practices) above to understand what we consider to be a security vulnerability vs. developer responsibility.
 
 ### In-Scope Targets

docs/README.md

@@ -1,3 +1,154 @@
 # LangChain Documentation
 
-For more information on contributing to our documentation, see the [Documentation Contributing Guide](https://python.langchain.com/docs/contributing/how_to/documentation)
+For more information on contributing to our documentation, see the [Documentation Contributing Guide](https://python.langchain.com/docs/contributing/how_to/documentation).
+
+## Structure
+
+The primary documentation is located in the `docs/` directory. This directory contains
+both the source files for the main documentation as well as the API reference doc
+build process.
+
+### API Reference
+
+API reference documentation is located in `docs/api_reference/` and is generated from
+the codebase using Sphinx.
+
+The API reference have additional build steps that differ from the main documentation.
+
+#### Deployment Process
+
+Currently, the build process roughly follows these steps:
+
+1. Using the `api_doc_build.yml` GitHub workflow, the API reference docs are
+    [built](#build-technical-details) and copied to the `langchain-api-docs-html`
+    repository. This workflow is triggered either (1) on a cron routine interval or (2)
+    triggered manually.
+
+    In short, the workflow extracts all `langchain-ai`-org-owned repos defined in
+    `langchain/libs/packages.yml`, clones them locally (in the workflow runner's file
+    system), and then builds the API reference RST files (using `create_api_rst.py`).
+    Following post-processing, the HTML files are pushed to the
+    `langchain-api-docs-html` repository.
+2. After the HTML files are in the `langchain-api-docs-html` repository, they are **not**
+    automatically published to the [live docs site](https://python.langchain.com/api_reference/).
+
+    The docs site is served by Vercel. The Vercel deployment process copies the HTML
+    files from the `langchain-api-docs-html` repository and deploys them to the live
+    site. Deployments are triggered on each new commit pushed to `v0.3`.
+
+#### Build Technical Details
+
+The build process creates a virtual monorepo by syncing multiple repositories, then generates comprehensive API documentation:
+
+1. **Repository Sync Phase:**
+   - `.github/scripts/prep_api_docs_build.py` - Clones external partner repos and organizes them into the `libs/partners/` structure to create a virtual monorepo for documentation building
+
+2. **RST Generation Phase:**
+   - `docs/api_reference/create_api_rst.py` - Main script that **generates RST files** from Python source code
+     - Scans `libs/` directories and extracts classes/functions from each module (using `inspect`)
+     - Creates `.rst` files using specialized templates for different object types
+     - Templates in `docs/api_reference/templates/` (`pydantic.rst`, `runnable_pydantic.rst`, etc.)
+
+3. **HTML Build Phase:**
+   - Sphinx-based, uses `sphinx.ext.autodoc` (auto-extracts docstrings from the codebase)
+     - `docs/api_reference/conf.py` (sphinx config) configures `autodoc` and other extensions
+   - `sphinx-build` processes the generated `.rst` files into HTML using autodoc
+   - `docs/api_reference/scripts/custom_formatter.py` - Post-processes the generated HTML
+   - Copies `reference.html` to `index.html` to create the default landing page (artifact? might not need to do this - just put everyhing in index directly?)
+
+4. **Deployment:**
+   - `.github/workflows/api_doc_build.yml` - Workflow responsible for orchestrating the entire build and deployment process
+   - Built HTML files are committed and pushed to the `langchain-api-docs-html` repository
+
+#### Local Build
+
+For local development and testing of API documentation, use the Makefile targets in the repository root:
+
+```bash
+# Full build
+make api_docs_build
+```
+
+Like the CI process, this target:
+
+- Installs the CLI package in editable mode
+- Generates RST files for all packages using `create_api_rst.py`
+- Builds HTML documentation with Sphinx
+- Post-processes the HTML with `custom_formatter.py`
+- Opens the built documentation (`reference.html`) in your browser
+
+**Quick Preview:**
+
+```bash
+make api_docs_quick_preview API_PKG=openai
+```
+
+- Generates RST files for only the specified package (default: `text-splitters`)
+- Builds and post-processes HTML documentation
+- Opens the preview in your browser
+
+Both targets automatically clean previous builds and handle the complete build pipeline locally, mirroring the CI process but for faster iteration during development.
+
+#### Documentation Standards
+
+**Docstring Format:**
+The API reference uses **Google-style docstrings** with reStructuredText markup. Sphinx processes these through the `sphinx.ext.napoleon` extension to generate documentation.
+
+**Required format:**
+
+```python
+def example_function(param1: str, param2: int = 5) -> bool:
+    """Brief description of the function.
+
+    Longer description can go here. Use reStructuredText syntax for
+    rich formatting like **bold** and *italic*.
+
+    TODO: code: figure out what works?
+
+    Args:
+        param1: Description of the first parameter.
+        param2: Description of the second parameter with default value.
+
+    Returns:
+        Description of the return value.
+
+    Raises:
+        ValueError: When param1 is empty.
+        TypeError: When param2 is not an integer.
+
+    .. warning::
+        This function is experimental and may change.
+    """
+```
+
+**Special Markers:**
+
+- `:private:` in docstrings excludes members from documentation
+- `.. warning::` adds warning admonitions
+
+#### Site Styling and Assets
+
+**Theme and Styling:**
+
+- Uses [**PyData Sphinx Theme**](https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html) (`pydata_sphinx_theme`)
+- Custom CSS in `docs/api_reference/_static/css/custom.css` with LangChain-specific:
+  - Color palette
+  - Inter font family
+  - Custom navbar height and sidebar formatting
+  - Deprecated/beta feature styling
+
+**Static Assets:**
+
+- Logos: `_static/wordmark-api.svg` (light) and `_static/wordmark-api-dark.svg` (dark mode)
+- Favicon: `_static/img/brand/favicon.png`
+- Custom CSS: `_static/css/custom.css`
+
+**Post-Processing:**
+
+- `scripts/custom_formatter.py` cleans up generated HTML:
+  - Shortens TOC entries from `ClassName.method()` to `method()`
+
+**Analytics and Integration:**
+
+- GitHub integration (source links, edit buttons)
+- Example backlinking through custom `ExampleLinksDirective`

docs/api_reference/_extensions/gallery_directive.py

@@ -50,7 +50,7 @@ class GalleryGridDirective(SphinxDirective):
     individual cards + ["image", "header", "content", "title"].
 
     Danger:
-        This directive can only be used in the context of a Myst documentation page as
+        This directive can only be used in the context of a MyST documentation page as
         the templates use Markdown flavored formatting.
     """
 

docs/api_reference/_static/css/custom.css

@@ -394,3 +394,21 @@ p {
   font-size: 0.9rem;
   margin-bottom: 0.5rem;
 }
+
+/* Deprecation announcement banner styling */
+.bd-header-announcement {
+  background-color: #790000 !important;
+  color: white !important;
+  font-weight: 600;
+  padding: 0.75rem 1rem;
+  text-align: center;
+}
+
+.bd-header-announcement a {
+  color: white !important;
+  text-decoration: underline !important;
+}
+
+.bd-header-announcement a:hover {
+  color: #f0f0f0 !important;
+}

docs/api_reference/conf.py

@@ -1,7 +1,5 @@
 """Configuration file for the Sphinx documentation builder."""
 
-# Configuration file for the Sphinx documentation builder.
-#
 # This file only contains a selection of the most common options. For a full
 # list see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
@@ -20,16 +18,18 @@ from docutils.parsers.rst.directives.admonitions import BaseAdmonition
 from docutils.statemachine import StringList
 from sphinx.util.docutils import SphinxDirective
 
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-
+# Add paths to Python import system so Sphinx can import LangChain modules
+# This allows autodoc to introspect and document the actual code
 _DIR = Path(__file__).parent.absolute()
-sys.path.insert(0, os.path.abspath("."))
-sys.path.insert(0, os.path.abspath("../../libs/langchain"))
+sys.path.insert(0, os.path.abspath("."))  # Current directory
+sys.path.insert(0, os.path.abspath("../../libs/langchain"))  # LangChain main package
 
+# Load package metadata from pyproject.toml (for version info, etc.)
 with (_DIR.parents[1] / "libs" / "langchain" / "pyproject.toml").open("r") as f:
     data = toml.load(f)
+
+# Load mapping of classes to example notebooks for backlinking
+# This file is generated by scripts that scan our tutorial/example notebooks
 with (_DIR / "guide_imports.json").open("r") as f:
     imported_classes = json.load(f)
 
@@ -86,6 +86,7 @@ class Beta(BaseAdmonition):
 
 
 def setup(app):
+    """Register custom directives and hooks with Sphinx."""
     app.add_directive("example_links", ExampleLinksDirective)
     app.add_directive("beta", Beta)
     app.connect("autodoc-skip-member", skip_private_members)
@@ -125,7 +126,7 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
     "IPython.sphinxext.ipython_console_highlighting",
-    "myst_parser",
+    "myst_parser",  # For generated index.md and reference.md
     "_extensions.gallery_directive",
     "sphinx_design",
     "sphinx_copybutton",
@@ -217,7 +218,7 @@ html_theme_options = {
     #     # Use :html_theme.sidebar_secondary.remove: for file-wide removal
     #     "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]},
     #     "show_version_warning_banner": True,
-    #     "announcement": None,
+    "announcement": "⚠️ THESE DOCS ARE OUTDATED. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank' style='color: white; text-decoration: underline;'>Visit the new v1.0 docs</a> and new <a href='https://reference.langchain.com/python' target='_blank' style='color: white; text-decoration: underline;'>reference docs</a>",
     "icon_links": [
         {
             # Label for this link
@@ -258,6 +259,7 @@ html_static_path = ["_static"]
 html_css_files = ["css/custom.css"]
 html_use_index = False
 
+# Only used on the generated index.md and reference.md files
 myst_enable_extensions = ["colon_fence"]
 
 # generate autosummary even if no references
@@ -268,11 +270,11 @@ autosummary_ignore_module_all = False
 html_copy_source = False
 html_show_sourcelink = False
 
+googleanalytics_id = "G-9B66JQQH2F"
+
 # Set canonical URL from the Read the Docs Domain
 html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "")
 
-googleanalytics_id = "G-9B66JQQH2F"
-
 # Tell Jinja2 templates the build is running on Read the Docs
 if os.environ.get("READTHEDOCS", "") == "True":
     html_context["READTHEDOCS"] = True

docs/api_reference/create_api_rst.py

@@ -1,4 +1,41 @@
-"""Script for auto-generating api_reference.rst."""
+"""Auto-generate API reference documentation (RST files) for LangChain packages.
+
+* Automatically discovers all packages in `libs/` and `libs/partners/`
+* For each package, recursively walks the filesystem to:
+    * Load Python modules using importlib
+    * Extract classes and functions using Python's inspect module
+    * Classify objects by type (Pydantic models, Runnables, TypedDicts, etc.)
+    * Filter out private members (names starting with '_') and deprecated items
+* Creates structured RST files with:
+    * Module-level documentation pages with autosummary tables
+    * Different Sphinx templates based on object type (see templates/ directory)
+    * Proper cross-references and navigation structure
+    * Separation of current vs deprecated APIs
+* Generates a directory tree like:
+    ```
+    docs/api_reference/
+    ├── index.md              # Main landing page with package gallery
+    ├── reference.md          # Package overview and navigation
+    ├── core/                 # langchain-core documentation
+    │   ├── index.rst
+    │   ├── callbacks.rst
+    │   └── ...
+    ├── langchain/            # langchain documentation
+    │   ├── index.rst
+    │   └── ...
+    └── partners/             # Integration packages
+        ├── openai/
+        ├── anthropic/
+        └── ...
+    ```
+
+## Key Features
+
+* Respects privacy markers:
+    * Modules with `:private:` in docstring are excluded entirely
+    * Objects with `:private:` in docstring are filtered out
+    * Names starting with '_' are treated as private
+"""
 
 import importlib
 import inspect
@@ -177,12 +214,13 @@ def _load_package_modules(
     Traversal based on the file system makes it easy to determine which
     of the modules/packages are part of the package vs. 3rd party or built-in.
 
-    Parameters:
-        package_directory (Union[str, Path]): Path to the package directory.
-        submodule (Optional[str]): Optional name of submodule to load.
+    Args:
+        package_directory: Path to the package directory.
+        submodule: Optional name of submodule to load.
 
     Returns:
-        Dict[str, ModuleMembers]: A dictionary where keys are module names and values are ModuleMembers objects.
+        A dictionary where keys are module names and values are `ModuleMembers`
+        objects.
     """
     package_path = (
         Path(package_directory)
@@ -199,12 +237,13 @@ def _load_package_modules(
         package_path = package_path / submodule
 
     for file_path in package_path.rglob("*.py"):
+        # Skip private modules
         if file_path.name.startswith("_"):
             continue
 
+        # Skip integration_template and project_template directories (for libs/cli)
         if "integration_template" in file_path.parts:
             continue
-
         if "project_template" in file_path.parts:
             continue
 
@@ -215,8 +254,13 @@ def _load_package_modules(
             continue
 
         # Get the full namespace of the module
+        # Example: langchain_core/schema/output_parsers.py ->
+        #          langchain_core.schema.output_parsers
         namespace = str(relative_module_name).replace(".py", "").replace("/", ".")
+
         # Keep only the top level namespace
+        # Example: langchain_core.schema.output_parsers ->
+        #          langchain_core
         top_namespace = namespace.split(".")[0]
 
         try:
@@ -253,16 +297,16 @@ def _construct_doc(
     members_by_namespace: Dict[str, ModuleMembers],
     package_version: str,
 ) -> List[typing.Tuple[str, str]]:
-    """Construct the contents of the reference.rst file for the given package.
+    """Construct the contents of the `reference.rst` for the given package.
 
     Args:
         package_namespace: The package top level namespace
-        members_by_namespace: The members of the package, dict organized by top level
-                              module contains a list of classes and functions
-                              inside of the top level namespace.
+        members_by_namespace: The members of the package dict organized by top level.
+            Module contains a list of classes and functions inside of the top level
+            namespace.
 
     Returns:
-        The contents of the reference.rst file.
+        The string contents of the reference.rst file.
     """
     docs = []
     index_doc = f"""\
@@ -465,10 +509,13 @@ def _construct_doc(
 
 
 def _build_rst_file(package_name: str = "langchain") -> None:
-    """Create a rst file for building of documentation.
+    """Create a rst file for a given package.
 
     Args:
-        package_name: Can be either "langchain" or "core"
+        package_name: Name of the package to create the rst file for.
+
+    Returns:
+        The rst file is created in the same directory as this script.
     """
     package_dir = _package_dir(package_name)
     package_members = _load_package_modules(package_dir)
@@ -500,7 +547,10 @@ def _package_namespace(package_name: str) -> str:
 
 
 def _package_dir(package_name: str = "langchain") -> Path:
-    """Return the path to the directory containing the documentation."""
+    """Return the path to the directory containing the documentation.
+
+    Attempts to find the package in `libs/` first, then `libs/partners/`.
+    """
     if (ROOT_DIR / "libs" / package_name).exists():
         return ROOT_DIR / "libs" / package_name / _package_namespace(package_name)
     else:
@@ -514,7 +564,7 @@ def _package_dir(package_name: str = "langchain") -> Path:
 
 
 def _get_package_version(package_dir: Path) -> str:
-    """Return the version of the package."""
+    """Return the version of the package by reading the `pyproject.toml`."""
     try:
         with open(package_dir.parent / "pyproject.toml", "r") as f:
             pyproject = toml.load(f)
@@ -540,6 +590,15 @@ def _out_file_path(package_name: str) -> Path:
 
 
 def _build_index(dirs: List[str]) -> None:
+    """Build the index.md file for the API reference.
+
+    Args:
+        dirs: List of package directories to include in the index.
+
+    Returns:
+        The index.md file is created in the same directory as this script.
+    """
+
     custom_names = {
         "aws": "AWS",
         "ai21": "AI21",
@@ -556,12 +615,17 @@ def _build_index(dirs: List[str]) -> None:
     integrations = sorted(dir_ for dir_ in dirs if dir_ not in main_)
     doc = """# LangChain Python API Reference
 
-Welcome to the LangChain Python API reference. This is a reference for all
+Welcome to the LangChain v0.3 Python API reference. This is a reference for all
 `langchain-x` packages.
 
-For user guides see [https://python.langchain.com](https://python.langchain.com).
+```{danger}
+These pages refer to the the v0.3 versions of LangChain packages and integrations. To
+visit the documentation for the latest versions of LangChain, visit [https://docs.langchain.com](https://docs.langchain.com)
+and [https://reference.langchain.com/python/](https://reference.langchain.com/python/) (for references.)
+
+For the legacy API reference (<v0.3) hosted on ReadTheDocs see [https://api.python.langchain.com/](https://api.python.langchain.com/).
+```
 
-For the legacy API reference hosted on ReadTheDocs see [https://api.python.langchain.com/](https://api.python.langchain.com/).
 """
 
     if main_:
@@ -647,9 +711,14 @@ See the full list of integrations in the Section Navigation.
 {integration_tree}
 ```
 """
+    # Write the reference.md file
     with open(HERE / "reference.md", "w") as f:
         f.write(doc)
 
+    # Write a dummy index.md file that points to reference.md
+    # Sphinx requires an index file to exist in each doc directory
+    # TODO: investigate why we don't just put everything in index.md directly?
+    # if it works it works I guess
     dummy_index = """\
 # API reference
 
@@ -665,8 +734,11 @@ Reference<reference>
 
 
 def main(dirs: Optional[list] = None) -> None:
-    """Generate the api_reference.rst file for each package."""
-    print("Starting to build API reference files.")
+    """Generate the `api_reference.rst` file for each package.
+
+    If dirs is None, generate for all packages in `libs/` and `libs/partners/`.
+    Otherwise generate only for the specified package(s).
+    """
     if not dirs:
         dirs = [
             p.parent.name
@@ -675,18 +747,17 @@ def main(dirs: Optional[list] = None) -> None:
             if p.parent.parent.name in ("libs", "partners")
         ]
     for dir_ in sorted(dirs):
-        # Skip any hidden directories
+        # Skip any hidden directories prefixed with a dot
         # Some of these could be present by mistake in the code base
-        # e.g., .pytest_cache from running tests from the wrong location.
+        # (e.g., .pytest_cache from running tests from the wrong location)
         if dir_.startswith("."):
             print("Skipping dir:", dir_)
             continue
         else:
-            print("Building package:", dir_)
+            print("Building:", dir_)
             _build_rst_file(package_name=dir_)
 
     _build_index(sorted(dirs))
-    print("API reference files built.")
 
 
 if __name__ == "__main__":

docs/api_reference/requirements.txt

@@ -1,12 +1,12 @@
 autodoc_pydantic>=2,<3
 sphinx>=8,<9
-myst-parser>=3
 sphinx-autobuild>=2024
-pydata-sphinx-theme>=0.15
-toml>=0.10.2
-myst-nb>=1.1.1
-pyyaml
 sphinx-design
 sphinx-copybutton
-beautifulsoup4
 sphinxcontrib-googleanalytics
+pydata-sphinx-theme>=0.15
+myst-parser>=3
+myst-nb>=1.1.1
+toml>=0.10.2
+pyyaml
+beautifulsoup4

docs/api_reference/scripts/custom_formatter.py

@@ -1,3 +1,10 @@
+"""Post-process generated HTML files to clean up table-of-contents headers.
+
+Runs after Sphinx generates the API reference HTML. It finds TOC entries like
+"ClassName.method_name()" and shortens them to just "method_name()" for better
+readability in the sidebar navigation.
+"""
+
 import sys
 from glob import glob
 from pathlib import Path

docs/docs/concepts/index.mdx

@@ -1,3 +1,7 @@
+:::danger
+⚠️ THESE DOCS ARE OUTDATED. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the new v1.0 docs</a>
+:::
+
 # Conceptual guide
 
 This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly.

docs/docs/how_to/index.mdx

@@ -3,6 +3,10 @@ sidebar_position: 0
 sidebar_class_name: hidden
 ---
 
+:::danger
+⚠️ THESE DOCS ARE OUTDATED. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the new v1.0 docs</a>
+:::
+
 # How-to guides
 
 Here you’ll find answers to "How do I….?" types of questions.
@@ -72,7 +76,7 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
 
 ### Example selectors
 
-[Example Selectors](/docs/concepts/example_selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
+[Example Selectors](/docs/concepts/example_selectors) are responsible for selecting the correct few-shot examples to pass to the prompt.
 
 - [How to: use example selectors](/docs/how_to/example_selectors)
 - [How to: select examples by length](/docs/how_to/example_selectors_length_based)
@@ -168,7 +172,7 @@ See [supported integrations](/docs/integrations/vectorstores/) for details on ge
 
 Indexing is the process of keeping your vectorstore in-sync with the underlying data source.
 
-- [How to: reindex data to keep your vectorstore in sync with the underlying data source](/docs/how_to/indexing)
+- [How to: reindex data to keep your vectorstore in-sync with the underlying data source](/docs/how_to/indexing)
 
 ### Tools
 

docs/docs/integrations/chat/anthropic.ipynb

@@ -1191,6 +1191,40 @@
     "response.content"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "74247a07-b153-444f-9c56-77659aeefc88",
+   "metadata": {},
+   "source": [
+    "## Context management\n",
+    "\n",
+    "Anthropic supports a context editing feature that will automatically manage the model's context window (e.g., by clearing tool results).\n",
+    "\n",
+    "See [Anthropic documentation](https://docs.claude.com/en/docs/build-with-claude/context-editing) for details and configuration options.\n",
+    "\n",
+    ":::info\n",
+    "Requires ``langchain-anthropic>=0.3.21``\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbb79c5d-37b5-4212-b36f-f27366192cf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-sonnet-4-5-20250929\",\n",
+    "    betas=[\"context-management-2025-06-27\"],\n",
+    "    context_management={\"edits\": [{\"type\": \"clear_tool_uses_20250919\"}]},\n",
+    ")\n",
+    "llm_with_tools = llm.bind_tools([{\"type\": \"web_search_20250305\", \"name\": \"web_search\"}])\n",
+    "response = llm_with_tools.invoke(\"Search for recent developments in AI\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cbfec7a9-d9df-4d12-844e-d922456dd9bf",
@@ -1457,6 +1491,38 @@
     "</details>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "29405da2-d2ef-415c-b674-6e29073cd05e",
+   "metadata": {},
+   "source": [
+    "### Memory tool\n",
+    "\n",
+    "Claude supports a memory tool for client-side storage and retrieval of context across conversational threads. See docs [here](https://docs.claude.com/en/docs/agents-and-tools/tool-use/memory-tool) for details.\n",
+    "\n",
+    ":::info\n",
+    "Requires ``langchain-anthropic>=0.3.21``\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbd76eaa-041f-4fb8-8346-ca8fe0001c01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-sonnet-4-5-20250929\",\n",
+    "    betas=[\"context-management-2025-06-27\"],\n",
+    ")\n",
+    "llm_with_tools = llm.bind_tools([{\"type\": \"memory_20250818\", \"name\": \"memory\"}])\n",
+    "\n",
+    "response = llm_with_tools.invoke(\"What are my interests?\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "040f381a-1768-479a-9a5e-aa2d7d77e0d5",

docs/docs/introduction.mdx

@@ -3,6 +3,10 @@ sidebar_position: 0
 sidebar_class_name: hidden
 ---
 
+:::danger
+⚠️ THESE DOCS ARE OUTDATED. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the new v1.0 docs</a>
+:::
+
 # Introduction
 
 **LangChain** is a framework for developing applications powered by large language models (LLMs).

docs/docs/tutorials/index.mdx

@@ -2,6 +2,11 @@
 sidebar_position: 0
 sidebar_class_name: hidden
 ---
+
+:::danger
+⚠️ THESE DOCS ARE OUTDATED. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the new v1.0 docs</a>
+:::
+
 # Tutorials
 
 New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications.

docs/docs/versions/v0_3/index.mdx

@@ -1,6 +1,6 @@
 # LangChain v0.3
 
-*Last updated: 09.16.24*
+*Last updated: 09.16.2024*
 
 ## What's changed
 

docs/docusaurus.config.js

@@ -87,7 +87,7 @@ const config = {
       ({
         docs: {
           editUrl:
-            "https://github.com/langchain-ai/langchain/edit/master/docs/",
+            "https://github.com/langchain-ai/langchain/edit/v0.3/docs/",
           sidebarPath: require.resolve("./sidebars.js"),
           remarkPlugins: [
             [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],
@@ -142,8 +142,8 @@ const config = {
         respectPrefersColorScheme: true,
       },
       announcementBar: {
-        content: "These docs will be deprecated and no longer maintained with the release of LangChain v1.0 in October 2025. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the v1.0 alpha docs</a>",
-        backgroundColor: "#FFAE42",
+        content: "⚠️ THESE DOCS ARE OUTDATED. <a href='https://docs.langchain.com/oss/python/langchain/overview' target='_blank'>Visit the new v1.0 docs</a>",
+        backgroundColor: "#790000ff",
       },
       prism: {
         theme: {

docs/ignore-step.sh

@@ -16,14 +16,13 @@ fi
 
 if  { \
         [ "$VERCEL_ENV" == "production" ] || \
-        [ "$VERCEL_GIT_COMMIT_REF" == "master" ] || \
         [ "$VERCEL_GIT_COMMIT_REF" == "v0.1" ] || \
         [ "$VERCEL_GIT_COMMIT_REF" == "v0.2" ] || \
         [ "$VERCEL_GIT_COMMIT_REF" == "v0.3rc" ]; \
     } && [ "$VERCEL_GIT_REPO_OWNER" == "langchain-ai" ]
 then
-     echo "✅ Production build - proceeding with build"
-     exit 1
+    echo "✅ Production build - proceeding with build"
+    exit 1
 fi
 
 

docs/scripts/check_imports.py

@@ -1,4 +1,20 @@
-"""This script checks documentation for broken import statements."""
+"""Check documentation for broken import statements.
+
+Validates that all import statements in Jupyter notebooks within the documentation
+directory are functional and can be successfully imported.
+
+- Scans all `.ipynb` files in `docs/`
+- Extracts import statements from code cells
+- Tests each import to ensure it works
+- Reports any broken imports that would fail for users
+
+Usage:
+    python docs/scripts/check_imports.py
+
+Exit codes:
+    0: All imports are valid
+    1: Found broken imports (ImportError raised)
+"""
 
 import importlib
 import json

docs/scripts/vectorstore_feat_table.py

@@ -204,7 +204,7 @@ def get_vectorstore_table():
             "similarity_search_with_score": True,
             "asearch": True,
             "Passes Standard Tests": True,
-            "Multi Tenancy": False,
+            "Multi Tenancy": True,
             "Local/Cloud": "Local",
             "IDs in add Documents": True,
         },

docs/src/theme/FeatureTables.js

@@ -40,15 +40,15 @@ const FEATURE_TABLES = {
                 "apiLink": "https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html"
             },
             {
-              "name": "ChatAIMLAPI",
-              "package": "langchain-aimlapi",
-              "link": "aimlapi/",
-              "structured_output": true,
-              "tool_calling": true,
-              "json_mode": true,
-              "multimodal": true,
-              "local": false,
-              "apiLink": "https://python.langchain.com/api_reference/aimlapi/chat_models/langchain_aimlapi.chat_models.ChatAIMLAPI.html"
+                "name": "ChatAIMLAPI",
+                "package": "langchain-aimlapi",
+                "link": "aimlapi/",
+                "structured_output": true,
+                "tool_calling": true,
+                "json_mode": true,
+                "multimodal": true,
+                "local": false,
+                "apiLink": "https://python.langchain.com/api_reference/aimlapi/chat_models/langchain_aimlapi.chat_models.ChatAIMLAPI.html"
             },
             {
                 "name": "ChatFireworks",
@@ -1199,7 +1199,7 @@ const FEATURE_TABLES = {
                 searchWithScore: true,
                 async: true,
                 passesStandardTests: false,
-                multiTenancy: false,
+                multiTenancy: true,
                 local: true,
                 idsInAddDocuments: true,
             },
@@ -1230,17 +1230,17 @@ const FEATURE_TABLES = {
                 idsInAddDocuments: true,
             },
             {
-              name: "PGVectorStore",
-              link: "pgvectorstore",
-              deleteById: true,
-              filtering: true,
-              searchByVector: true,
-              searchWithScore: true,
-              async: true,
-              passesStandardTests: true,
-              multiTenancy: false,
-              local: true,
-              idsInAddDocuments: true,
+                name: "PGVectorStore",
+                link: "pgvectorstore",
+                deleteById: true,
+                filtering: true,
+                searchByVector: true,
+                searchWithScore: true,
+                async: true,
+                passesStandardTests: true,
+                multiTenancy: false,
+                local: true,
+                idsInAddDocuments: true,
             },
             {
                 name: "PineconeVectorStore",

docs/src/theme/NotFound/Content/index.js

@@ -2910,4 +2910,4 @@ const suggestedLinks = {
       "/v0.1/docs/integrations/toolkits/"
     ]
   }
-}
+}

docs/vercel.json

@@ -7,160 +7,682 @@
     {
       "source": "/docs/integrations(/?)",
       "destination": "/docs/integrations/platforms/"
-    },
-    {
-      "source": "/v0.1",
-      "destination": "https://langchain-v01.vercel.app/v0.1"
-    },
-    {
-      "source": "/v0.1/:path(.*/?)*",
-      "destination": "https://langchain-v01.vercel.app/v0.1/:path*"
-    },
-    {
-      "source": "/v0.2",
-      "destination": "https://langchain-v02.vercel.app/v0.2"
-    },
-    {
-      "source": "/v0.2/:path(.*/?)*",
-      "destination": "https://langchain-v02.vercel.app/v0.2/:path*"
     }
   ],
   "redirects": [
     {
       "source": "/v0.3/docs/:path(.*/?)*",
-      "destination": "/docs/:path*"
+      "destination": "https://docs.langchain.com/oss/python/langchain/overview"
     },
     {
-      "source": "/docs/modules/agents/tools/custom_tools(/?)",
-      "destination": "/docs/how_to/custom_tools/"
+      "source": "/docs/integrations/platforms/:path(.*)",
+      "destination": "https://docs.langchain.com/oss/python/integrations/providers/:path"
     },
     {
-      "source": "/docs/expression_language(/?)",
-      "destination": "/docs/concepts/lcel"
+      "source": "/docs/troubleshooting/errors/GRAPH_RECURSION_LIMIT",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/GRAPH_RECURSION_LIMIT"
     },
     {
-      "source": "/docs/expression_language/interface(/?)",
-      "destination": "/docs/concepts/runnables"
+      "source": "/docs/troubleshooting/errors/GRAPH_RECURSION_LIMIT/",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/GRAPH_RECURSION_LIMIT"
+    },
+
+    {
+      "source": "/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/INVALID_CONCURRENT_GRAPH_UPDATE"
     },
     {
-      "source": "/docs/versions/overview(/?)",
-      "destination": "/docs/versions/v0_2/overview/"
+      "source": "/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE/",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/INVALID_CONCURRENT_GRAPH_UPDATE"
     },
     {
-      "source": "/docs/how_to/tool_calls_multi_modal(/?)",
-      "destination": "/docs/how_to/multimodal_inputs/"
+      "source": "/docs/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/INVALID_GRAPH_NODE_RETURN_VALUE"
     },
     {
-      "source": "/docs/tutorials/pdf_qa",
-      "destination": "/docs/tutorials/retrievers/"
+      "source": "/docs/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE/",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/INVALID_GRAPH_NODE_RETURN_VALUE"
     },
     {
-      "source": "/docs/tutorials/query_analysis",
-      "destination": "/docs/tutorials/rag#query-analysis"
+      "source": "/docs/troubleshooting/errors/MULTIPLE_SUBGRAPHS",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/MULTIPLE_SUBGRAPHS"
     },
     {
-      "source": "/docs/tutorials/local_rag",
-      "destination": "/docs/tutorials/rag"
+      "source": "/docs/troubleshooting/errors/MULTIPLE_SUBGRAPHS/",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/MULTIPLE_SUBGRAPHS"
     },
     {
-      "source": "/docs/how_to/graph_mapping(/?)",
-      "destination": "/docs/tutorials/graph#query-validation"
+      "source": "/docs/troubleshooting/errors/INVALID_CHAT_HISTORY",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/INVALID_CHAT_HISTORY"
     },
     {
-      "source": "/docs/how_to/graph_prompting(/?)",
-      "destination": "/docs/tutorials/graph#few-shot-prompting"
-    },
-    {
-      "source": "/docs/how_to/HTML_header_metadata_splitter(/?)",
-      "destination": "/docs/how_to/split_html#using-htmlheadertextsplitter"
-    },
-    {
-      "source": "/docs/how_to/HTML_section_aware_splitter(/?)",
-      "destination": "/docs/how_to/split_html#using-htmlsectionsplitter"
-    },
-    {
-      "source": "/docs/tutorials/data_generation",
-      "destination": "https://python.langchain.com/v0.2/docs/tutorials/data_generation/"
-    },
-    {
-      "source": "/docs/langsmith(/?)",
-      "destination": "https://docs.smith.langchain.com/"
-    },
-    {
-      "source": "/docs/langgraph(/?)",
-      "destination": "https://langchain-ai.github.io/langgraph"
-    },
-    {
-      "source": "/",
-      "destination": "/docs/introduction/"
-    },
-    {
-      "source": "/docs(/?)",
-      "destination": "/docs/introduction/"
-    },
-    {
-      "source": "/docs/get_started/introduction(/?)",
-      "destination": "/docs/introduction/"
-    },
-    {
-      "source": "/docs/how_to/migrate_chains(/?)",
-      "destination": "/docs/versions/migrating_chains"
-    },
-    {
-      "source": "/v0.2/docs/templates/:path(.*/?)*",
-      "destination": "https://github.com/langchain-ai/langchain/tree/v0.2/templates/:path*"
-    },
-    {
-      "source": "/docs/integrations/providers/mlflow_ai_gateway(/?)",
-      "destination": "/docs/integrations/providers/mlflow/"
-    },
-    {
-      "source": "/docs/integrations/platforms/:path((?:anthropic|aws|google|huggingface|microsoft|openai)?/?)*",
-      "destination": "/docs/integrations/providers/:path*"
-    },
-    {
-      "source": "/docs/troubleshooting/errors/:path((?:GRAPH_RECURSION_LIMIT|INVALID_CONCURRENT_GRAPH_UPDATE|INVALID_GRAPH_NODE_RETURN_VALUE|MULTIPLE_SUBGRAPHS|INVALID_CHAT_HISTORY)/?)*",
-      "destination": "https://langchain-ai.github.io/langgraph/troubleshooting/errors/:path*"
+      "source": "/docs/troubleshooting/errors/INVALID_CHAT_HISTORY/",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/errors/INVALID_CHAT_HISTORY"
     },
     {
       "source": "/docs/contributing/:path((?:code|documentation|integrations|testing)(?:/|/.*/?)?)",
-      "destination": "/docs/contributing/how_to/:path"
+      "destination": "https://docs.langchain.com/oss/python/langchain/overview"
     },
     {
       "source": "/docs/contributing/:path((?:faq|repo_structure|review_process)/?)",
-      "destination": "/docs/contributing/reference/:path"
-    },
-    {
-      "source": "/docs/integrations/retrievers/weaviate-hybrid(/?)",
-      "destination": "/docs/integrations/vectorstores/weaviate/#search-mechanism"
-    },
-    {
-      "source": "/docs/integrations/vectorstores/singlestoredb(/?)",
-      "destination": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/singlestoredb/"
-    },
-    {
-      "source": "/docs/integrations/providers/singlestoredb(/?)",
-      "destination": "/docs/integrations/providers/singlestore/"
-    },
-    {
-      "source": "/docs/integrations/retrievers/singlestoredb(/?)",
-      "destination": "https://python.langchain.com/v0.2/docs/integrations/retrievers/singlestoredb/"
-    },
-    {
-      "source": "/docs/integrations/providers/dspy(/?)",
-      "destination": "https://python.langchain.com/v0.2/docs/integrations/providers/dspy/"
+      "destination": "https://docs.langchain.com/oss/python/langchain/overview"
     },
     {
       "source": "/api_reference/mongodb/:path(.*/?)*",
-      "destination": "https://langchain-mongodb.readthedocs.io/en/latest/langchain_mongodb/api_docs.html"
+      "destination": "https://docs.langchain.com/oss/python/integrations/providers/overview"
     },
     {
       "source": "/api_reference/tests/:path(.*/?)*",
-      "destination": "/api_reference/standard_tests/:path"
+      "destination": "https://reference.langchain.com/python/"
     },
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+      "source": "docs/introduction/%23%EF%B8%8F-langgraph",
+      "destination": "https://docs.langchain.com/oss/python/langgraph/overview"
+    },
+    {
+      "source": "docs/introduction/%23%EF%B8%8F-langsmith",
+      "destination": "https://docs.langchain.com/langsmith/home"
+    },
+    {
+      "source": "/docs/get_started/introduction",
+      "destination": "https://docs.langchain.com/oss/python/langchain/quickstart"
+    },
+
+    {
+      "source": "/docs/troubleshooting/errors/INVALID_PROMPT_INPUT/",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/INVALID_PROMPT_INPUT"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/INVALID_TOOL_RESULTS/",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/INVALID_TOOL_RESULTS"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/MESSAGE_COERCION_FAILURE"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/MODEL_AUTHENTICATION",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/MODEL_AUTHENTICATION"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/MODEL_NOT_FOUND",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/MODEL_NOT_FOUND"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/MODEL_RATE_LIMIT",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/MODEL_RATE_LIMIT"
+    },
+    {
+      "source": "/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE",
+      "destination": "https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE"
+    },
+    {
+      "source": "/:path((?!api_reference).*)",
+      "destination": "https://docs.langchain.com/oss/python/langchain/overview"
     }
   ]
 }

libs/cli/CONTRIBUTING.md

@@ -1,3 +0,0 @@
-# Contributing to langchain-cli
-
-Update CLI versions with `poe bump` to ensure that version commands display correctly.

libs/cli/langchain_cli/dev_scripts.py

@@ -18,7 +18,7 @@ def create_demo_server(
 
     Args:
         config_keys: Optional sequence of config keys to expose in the playground.
-        playground_type: The type of playground to use. Can be `'default'` or `'chat'`.
+        playground_type: The type of playground to use.
 
     Returns:
         The demo server.

libs/cli/langchain_cli/integration_template/integration_template/retrievers.py

@@ -46,7 +46,7 @@ class __ModuleName__Retriever(BaseRetriever):
 
             retriever.invoke(query)
 
-        .. code-block:: none
+        .. code-block::
 
             # TODO: Example output.
 
@@ -80,7 +80,7 @@ class __ModuleName__Retriever(BaseRetriever):
 
             chain.invoke("...")
 
-        .. code-block:: none
+        .. code-block::
 
              # TODO: Example output.
 

libs/cli/langchain_cli/integration_template/integration_template/toolkits.py

@@ -42,7 +42,7 @@ class __ModuleName__Toolkit(BaseToolkit):
 
             toolkit.get_tools()
 
-        .. code-block:: none
+        .. code-block::
 
             # TODO: Example output.
 
@@ -62,7 +62,7 @@ class __ModuleName__Toolkit(BaseToolkit):
             for event in events:
                 event["messages"][-1].pretty_print()
 
-        .. code-block:: none
+        .. code-block::
 
              # TODO: Example output.
 

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_retrievers.py

@@ -18,7 +18,5 @@ class Test__ModuleName__Retriever(RetrieversIntegrationTests):
 
     @property
     def retriever_query_example(self) -> str:
-        """
-        Returns a str representing the "query" of an example retriever call.
-        """
+        """Returns a str representing the "query" of an example retriever call."""
         return "example query"

libs/cli/langchain_cli/integration_template/tests/integration_tests/test_tools.py

@@ -21,7 +21,7 @@ class TestParrotMultiplyToolIntegration(ToolsIntegrationTests):
         """
         Returns a dictionary representing the "args" of an example tool call.
 
-        This should NOT be a ToolCall dict - i.e. it should not
-        have {"name", "id", "args"} keys.
+        This should NOT be a ToolCall dict - i.e. it should not have
+        `{"name", "id", "args"}` keys.
         """
         return {"a": 2, "b": 3}

libs/cli/langchain_cli/integration_template/tests/unit_tests/test_tools.py

@@ -11,7 +11,7 @@ class TestParrotMultiplyToolUnit(ToolsUnitTests):
 
     @property
     def tool_constructor_params(self) -> dict:
-        # if your tool constructor instead required initialization arguments like
+        # If your tool constructor instead required initialization arguments like
         # `def __init__(self, some_arg: int):`, you would return those here
         # as a dictionary, e.g.: `return {'some_arg': 42}`
         return {}
@@ -21,7 +21,7 @@ class TestParrotMultiplyToolUnit(ToolsUnitTests):
         """
         Returns a dictionary representing the "args" of an example tool call.
 
-        This should NOT be a ToolCall dict - i.e. it should not
-        have {"name", "id", "args"} keys.
+        This should NOT be a ToolCall dict - i.e. it should not have
+        `{"name", "id", "args"}` keys.
         """
         return {"a": 2, "b": 3}

libs/cli/langchain_cli/namespaces/app.py

@@ -159,8 +159,8 @@ def add(
     """Add the specified template to the current LangServe app.
 
     e.g.:
-    langchain app add extraction-openai-functions
-    langchain app add git+ssh://git@github.com/efriis/simple-pirate.git
+    `langchain app add extraction-openai-functions`
+    `langchain app add git+ssh://git@github.com/efriis/simple-pirate.git`
     """
     if branch is None:
         branch = []

libs/cli/langchain_cli/namespaces/integration.py

@@ -116,17 +116,17 @@ def new(
             typer.echo(f"Folder {destination_dir} exists.")
             raise typer.Exit(code=1)
 
-        # copy over template from ../integration_template
+        # Copy over template from ../integration_template
         shutil.copytree(project_template_dir, destination_dir, dirs_exist_ok=False)
 
-        # folder movement
+        # Folder movement
         package_dir = destination_dir / replacements["__module_name__"]
         shutil.move(destination_dir / "integration_template", package_dir)
 
-        # replacements in files
+        # Replacements in files
         replace_glob(destination_dir, "**/*", cast("dict[str, str]", replacements))
 
-        # dependency install
+        # Dependency install
         try:
             # Use --no-progress to avoid tty issues in CI/test environments
             env = os.environ.copy()
@@ -149,7 +149,7 @@ def new(
                 "`uv sync --dev` manually in the package directory.",
             )
     else:
-        # confirm src and dst are the same length
+        # Confirm src and dst are the same length
         if not src:
             typer.echo("Cannot provide --dst without --src.")
             raise typer.Exit(code=1)
@@ -158,7 +158,7 @@ def new(
             typer.echo("Number of --src and --dst arguments must match.")
             raise typer.Exit(code=1)
         if not dst:
-            # assume we're in a package dir, copy to equivalent path
+            # Assume we're in a package dir, copy to equivalent path
             dst_paths = [destination_dir / p for p in src]
         else:
             dst_paths = [Path.cwd() / p for p in dst]
@@ -169,7 +169,7 @@ def new(
                 for p in dst_paths
             ]
 
-        # confirm no duplicate dst_paths
+        # Confirm no duplicate dst_paths
         if len(dst_paths) != len(set(dst_paths)):
             typer.echo(
                 "Duplicate destination paths provided or computed - please "
@@ -177,7 +177,7 @@ def new(
             )
             raise typer.Exit(code=1)
 
-        # confirm no files exist at dst_paths
+        # Confirm no files exist at dst_paths
         for dst_path in dst_paths:
             if dst_path.exists():
                 typer.echo(f"File {dst_path} exists.")

libs/cli/langchain_cli/namespaces/migrate/generate/generic.py

@@ -75,7 +75,7 @@ def generate_raw_migrations(
 def generate_top_level_imports(pkg: str) -> list[tuple[str, str]]:
     """Look at all the top level modules in langchain_community.
 
-    Attempt to import everything from each ``__init__`` file. For example,
+    Attempt to import everything from each `__init__` file. For example,
 
     langchain_community/
         chat_models/
@@ -83,16 +83,15 @@ def generate_top_level_imports(pkg: str) -> list[tuple[str, str]]:
         llm/
             __init__.py # <-- import everything from here
 
-
     It'll collect all the imports, import the classes / functions it can find
     there. It'll return a list of 2-tuples
 
     Each tuple will contain the fully qualified path of the class / function to where
-    its logic is defined
-    (e.g., ``langchain_community.chat_models.xyz_implementation.ver2.XYZ``)
+    its logic is defined.
+    (e.g., `langchain_community.chat_models.xyz_implementation.ver2.XYZ`)
     and the second tuple will contain the path
     to importing it from the top level namespaces
-    (e.g., ``langchain_community.chat_models.XYZ``)
+    (e.g., `langchain_community.chat_models.XYZ`)
 
     Args:
         pkg: The package to scan.

libs/cli/langchain_cli/namespaces/migrate/generate/partner.py

@@ -28,7 +28,6 @@ def get_migrations_for_partner_package(pkg_name: str) -> list[tuple[str, str]]:
 
     Returns:
         List of 2-tuples containing old and new import paths.
-
     """
     package = importlib.import_module(pkg_name)
     classes_ = find_subclasses_in_module(

libs/cli/langchain_cli/utils/git.py

@@ -38,19 +38,19 @@ def parse_dependency_string(
     branch: str | None,
     api_path: str | None,
 ) -> DependencySource:
-    """Parse a dependency string into a DependencySource.
+    """Parse a dependency string into a `DependencySource`.
 
     Args:
-        dep: the dependency string.
-        repo: optional repository.
-        branch: optional branch.
-        api_path: optional API path.
+        dep: The dependency string
+        repo: Optional repository
+        branch: Optional branch
+        api_path: Optional API path
 
     Returns:
-        The parsed dependency source information.
+        The parsed dependency source information
 
     Raises:
-        ValueError: if the dependency string is invalid.
+        ValueError: If the dependency string is invalid
     """
     if dep is not None and dep.startswith("git+"):
         if repo is not None or branch is not None:
@@ -147,8 +147,8 @@ def parse_dependencies(
     """Parse dependencies.
 
     Args:
-        dependencies: the dependencies to parse
-        repo: the repositories to use
+        dependencies: The dependencies to parse
+        repo: The repositories to use
         branch: the branches to use
         api_path: the api paths to use
 
@@ -244,7 +244,7 @@ def copy_repo(
 ) -> None:
     """Copiy a repo, ignoring git folders.
 
-    Raises FileNotFound error if it can't find source
+    Raises `FileNotFound` if it can't find source
     """
 
     def ignore_func(_: str, files: list[str]) -> list[str]:

libs/cli/langchain_cli/utils/packages.py

@@ -37,13 +37,12 @@ def get_package_root(cwd: Path | None = None) -> Path:
 
 
 class LangServeExport(TypedDict):
-    """Fields from pyproject.toml that are relevant to LangServe.
+    """Fields from `pyproject.toml` that are relevant to LangServe.
 
     Attributes:
-        module: The module to import from, tool.langserve.export_module
-        attr: The attribute to import from the module, tool.langserve.export_attr
-        package_name: The name of the package, tool.poetry.name
-
+        module: The module to import from, `tool.langserve.export_module`
+        attr: The attribute to import from the module, `tool.langserve.export_attr`
+        package_name: The name of the package, `tool.poetry.name`
     """
 
     module: str

libs/cli/langchain_cli/utils/pyproject.py

@@ -19,7 +19,7 @@ def add_dependencies_to_pyproject_toml(
     pyproject_toml: Path,
     local_editable_dependencies: Iterable[tuple[str, Path]],
 ) -> None:
-    """Add dependencies to pyproject.toml."""
+    """Add dependencies to `pyproject.toml`."""
     with pyproject_toml.open(encoding="utf-8") as f:
         # tomlkit types aren't amazing - treat as Dict instead
         pyproject: dict[str, Any] = load(f)
@@ -37,7 +37,7 @@ def remove_dependencies_from_pyproject_toml(
     pyproject_toml: Path,
     local_editable_dependencies: Iterable[str],
 ) -> None:
-    """Remove dependencies from pyproject.toml."""
+    """Remove dependencies from `pyproject.toml`."""
     with pyproject_toml.open(encoding="utf-8") as f:
         pyproject: dict[str, Any] = load(f)
         # tomlkit types aren't amazing - treat as Dict instead

libs/cli/pyproject.toml

@@ -8,7 +8,7 @@ license = { text = "MIT" }
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "typer>=0.17.0,<1.0.0",
-    "gitpython>=3,<4.0.0",
+    "gitpython>=3.0.0,<4.0.0",
     "langserve[all]>=0.0.51,<1.0.0",
     "uvicorn>=0.23.0,<1.0.0",
     "tomlkit>=0.12.0,<1.0.0",
@@ -29,9 +29,18 @@ langchain = "langchain_cli.cli:app"
 langchain-cli = "langchain_cli.cli:app"
 
 [dependency-groups]
-dev = ["pytest>=7.4.2,<9.0.0", "pytest-watcher>=0.3.4,<1.0.0"]
-lint = ["ruff>=0.12.2,<0.13", "mypy>=1.18.1,<1.19"]
-test = ["langchain-core", "langchain"]
+dev = [
+    "pytest>=7.4.2,<9.0.0",
+    "pytest-watcher>=0.3.4,<1.0.0"
+]
+lint = [
+    "ruff>=0.13.1,<0.14",
+    "mypy>=1.18.1,<1.19"
+]
+test = [
+    "langchain-core",
+    "langchain"
+]
 typing = ["langchain"]
 test_integration = []
 
@@ -70,11 +79,14 @@ flake8-annotations.allow-star-arg-any = true
 flake8-annotations.mypy-init-return = true
 flake8-type-checking.runtime-evaluated-base-classes = ["pydantic.BaseModel","langchain_core.load.serializable.Serializable","langchain_core.runnables.base.RunnableSerializable"]
 pep8-naming.classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_init", "pydantic.field_validator", "pydantic.v1.root_validator",]
-pydocstyle.convention = "google"
 pyupgrade.keep-runtime-typing = true
 
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+ignore-var-parameters = true  # ignore missing documentation for *args and **kwargs parameters
+
 [tool.ruff.lint.per-file-ignores]
-"tests/**" = [ "D1", "DOC", "S", "SLF",]
+"tests/**" = [ "D1", "S", "SLF",]
 "scripts/**" = [ "INP", "S",]
 
 [tool.pytest.ini_options]

libs/cli/scripts/__init__.py

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

libs/cli/tests/unit_tests/migrate/generate/test_langchain_migration.py

@@ -9,7 +9,7 @@ from langchain_cli.namespaces.migrate.generate.generic import (
 
 @pytest.mark.xfail(reason="Unknown reason")
 def test_create_json_agent_migration() -> None:
-    """Test the migration of create_json_agent from langchain to langchain_community."""
+    """Test migration of `create_json_agent` from langchain to `langchain_community`."""
     with sup1(), sup2():
         raw_migrations = generate_simplified_migrations(
             from_package="langchain",
@@ -40,7 +40,7 @@ def test_create_json_agent_migration() -> None:
 
 @pytest.mark.xfail(reason="Unknown reason")
 def test_create_single_store_retriever_db() -> None:
-    """Test migration from langchain to langchain_core."""
+    """Test migration from `langchain` to `langchain_core`."""
     with sup1(), sup2():
         raw_migrations = generate_simplified_migrations(
             from_package="langchain",

libs/cli/uv.lock

@@ -13,7 +13,7 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
@@ -21,9 +21,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
+    { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
 ]
 
 [[package]]
@@ -110,14 +110,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.2.1"
+version = "8.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202", size = 286342, upload-time = "2025-05-20T23:19:49.832Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/46/61/de6cd827efad202d7057d93e0fed9294b96952e188f7384832791c7b2254/click-8.3.0.tar.gz", hash = "sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4", size = 276943, upload-time = "2025-09-18T17:32:23.696Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/85/32/10bb5764d90a8eee674e9dc6f4db6a0ab47c8c4d0d83c27f7c39ac415a4d/click-8.2.1-py3-none-any.whl", hash = "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b", size = 102215, upload-time = "2025-05-20T23:19:47.796Z" },
+    { url = "https://files.pythonhosted.org/packages/db/d3/9dcc0f5797f070ec8edf30fbadfb200e71d9db6b84d211e3b2085a7589a0/click-8.3.0-py3-none-any.whl", hash = "sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc", size = 107295, upload-time = "2025-09-18T17:32:22.42Z" },
 ]
 
 [[package]]
@@ -143,16 +143,16 @@ wheels = [
 
 [[package]]
 name = "fastapi"
-version = "0.116.2"
+version = "0.117.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pydantic" },
     { name = "starlette" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/01/64/1296f46d6b9e3b23fb22e5d01af3f104ef411425531376212f1eefa2794d/fastapi-0.116.2.tar.gz", hash = "sha256:231a6af2fe21cfa2c32730170ad8514985fc250bec16c9b242d3b94c835ef529", size = 298595, upload-time = "2025-09-16T18:29:23.058Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7e/7e/d9788300deaf416178f61fb3c2ceb16b7d0dc9f82a08fdb87a5e64ee3cc7/fastapi-0.117.1.tar.gz", hash = "sha256:fb2d42082d22b185f904ca0ecad2e195b851030bd6c5e4c032d1c981240c631a", size = 307155, upload-time = "2025-09-20T20:16:56.663Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/32/e4/c543271a8018874b7f682bf6156863c416e1334b8ed3e51a69495c5d4360/fastapi-0.116.2-py3-none-any.whl", hash = "sha256:c3a7a8fb830b05f7e087d920e0d786ca1fc9892eb4e9a84b227be4c1bc7569db", size = 95670, upload-time = "2025-09-16T18:29:21.329Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/45/d9d3e8eeefbe93be1c50060a9d9a9f366dba66f288bb518a9566a23a8631/fastapi-0.117.1-py3-none-any.whl", hash = "sha256:33c51a0d21cab2b9722d4e56dbb9316f3687155be6b276191790d8da03507552", size = 95959, upload-time = "2025-09-20T20:16:53.661Z" },
 ]
 
 [[package]]
@@ -372,7 +372,7 @@ dev = [
 lint = [
     { name = "cffi", marker = "python_full_version < '3.10'", specifier = "<1.17.1" },
     { name = "cffi", marker = "python_full_version >= '3.10'" },
-    { name = "ruff", specifier = ">=0.12.2,<0.13.0" },
+    { name = "ruff", specifier = ">=0.13.1,<0.14.0" },
 ]
 test = [
     { name = "blockbuster", specifier = ">=1.5.18,<1.6.0" },
@@ -459,7 +459,7 @@ typing = [
 
 [package.metadata]
 requires-dist = [
-    { name = "gitpython", specifier = ">=3,<4.0.0" },
+    { name = "gitpython", specifier = ">=3.0.0,<4.0.0" },
     { name = "gritql", specifier = ">=0.2.0,<1.0.0" },
     { name = "langserve", extras = ["all"], specifier = ">=0.0.51,<1.0.0" },
     { name = "tomlkit", specifier = ">=0.12.0,<1.0.0" },
@@ -474,7 +474,7 @@ dev = [
 ]
 lint = [
     { name = "mypy", specifier = ">=1.18.1,<1.19" },
-    { name = "ruff", specifier = ">=0.12.2,<0.13" },
+    { name = "ruff", specifier = ">=0.13.1,<0.14" },
 ]
 test = [
     { name = "langchain", editable = "../langchain" },
@@ -485,7 +485,7 @@ typing = [{ name = "langchain", editable = "../langchain" }]
 
 [[package]]
 name = "langchain-core"
-version = "0.3.76"
+version = "0.3.78"
 source = { editable = "../core" }
 dependencies = [
     { name = "jsonpatch" },
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@@ -1217,7 +1219,7 @@ wheels = [
 
 [[package]]
 name = "typer"
-version = "0.17.4"
+version = "0.19.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
@@ -1225,9 +1227,9 @@ dependencies = [
     { name = "shellingham" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/92/e8/2a73ccf9874ec4c7638f172efc8972ceab13a0e3480b389d6ed822f7a822/typer-0.17.4.tar.gz", hash = "sha256:b77dc07d849312fd2bb5e7f20a7af8985c7ec360c45b051ed5412f64d8dc1580", size = 103734, upload-time = "2025-09-05T18:14:40.746Z" }
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@@ -1262,16 +1264,16 @@ wheels = [
 
 [[package]]
 name = "uvicorn"
-version = "0.35.0"
+version = "0.37.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 [[package]]

libs/core/README.md

@@ -27,16 +27,6 @@ The LangChain ecosystem is built on top of `langchain-core`. Some of the benefit
 - **Stability**: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
 - **Battle-tested**: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
 
-## 1️⃣ Core Interface: Runnables
-
-The concept of a `Runnable` is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them
-
-- A common invocation interface (`invoke()`, `batch()`, `stream()`, etc.)
-- Built-in utilities for retries, fallbacks, schemas and runtime configurability
-- Easy deployment with [LangGraph](https://github.com/langchain-ai/langgraph)
-
-For more check out the [`Runnable` docs](https://python.langchain.com/docs/concepts/runnables/). Examples of components that implement the interface include: Chat Models, Tools, Retrievers, and Output Parsers.
-
 ## 📕 Releases & Versioning
 
 As `langchain-core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `langchain_core.beta`. The reason for `langchain_core.beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.

libs/core/langchain_core/_api/beta_decorator.py

@@ -174,6 +174,7 @@ def beta(
             def finalize(_wrapper: Callable[..., Any], new_doc: str) -> Any:
                 """Finalize the property."""
                 return property(fget=_fget, fset=_fset, fdel=_fdel, doc=new_doc)
+
         else:
             _name = _name or obj.__qualname__
             if not _obj_type:
@@ -226,17 +227,17 @@ def warn_beta(
 ) -> None:
     """Display a standardized beta annotation.
 
-    Arguments:
-        message : str, optional
+    Args:
+        message:
             Override the default beta message. The
             %(name)s, %(obj_type)s, %(addendum)s
             format specifiers will be replaced by the
             values of the respective arguments passed to this function.
-        name : str, optional
+        name:
             The name of the annotated object.
-        obj_type : str, optional
+        obj_type:
             The object type being annotated.
-        addendum : str, optional
+        addendum:
             Additional text appended directly to the final message.
     """
     if not message:

libs/core/langchain_core/_api/deprecation.py

@@ -431,35 +431,35 @@ def warn_deprecated(
 ) -> None:
     """Display a standardized deprecation.
 
-    Arguments:
-        since : str
+    Args:
+        since:
             The release at which this API became deprecated.
-        message : str, optional
+        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 : str, optional
+        name:
             The name of the deprecated object.
-        alternative : str, optional
+        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: str, optional
+        alternative_import:
             An alternative import that the user may use instead.
-        pending : bool, optional
+        pending:
             If True, uses a PendingDeprecationWarning instead of a
             DeprecationWarning. Cannot be used together with removal.
-        obj_type : str, optional
+        obj_type:
             The object type being deprecated.
-        addendum : str, optional
+        addendum:
             Additional text appended directly to the final message.
-        removal : str, optional
+        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: str, optional
+        package:
             The package of the deprecated object.
     """
     if not pending:

libs/core/langchain_core/callbacks/manager.py

@@ -6,7 +6,6 @@ import asyncio
 import atexit
 import functools
 import logging
-import uuid
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager, contextmanager
@@ -41,6 +40,7 @@ from langchain_core.tracers.langchain import LangChainTracer
 from langchain_core.tracers.schemas import Run
 from langchain_core.tracers.stdout import ConsoleCallbackHandler
 from langchain_core.utils.env import env_var_is_set
+from langchain_core.utils.uuid import uuid7
 
 if TYPE_CHECKING:
     from collections.abc import AsyncGenerator, Coroutine, Generator, Sequence
@@ -92,7 +92,7 @@ def trace_as_chain_group(
         metadata (dict[str, Any], optional): The metadata to apply to all runs.
             Defaults to None.
 
-    .. note:
+    .. note::
         Must have ``LANGCHAIN_TRACING_V2`` env var set to true to see the trace in
         LangSmith.
 
@@ -179,7 +179,7 @@ async def atrace_as_chain_group(
     Yields:
         The async callback manager for the chain group.
 
-    .. note:
+    .. note::
         Must have ``LANGCHAIN_TRACING_V2`` env var set to true to see the trace in
         LangSmith.
 
@@ -506,7 +506,7 @@ class BaseRunManager(RunManagerMixin):
 
         """
         return cls(
-            run_id=uuid.uuid4(),
+            run_id=uuid7(),
             handlers=[],
             inheritable_handlers=[],
             tags=[],
@@ -1339,7 +1339,7 @@ class CallbackManager(BaseCallbackManager):
         managers = []
         for i, prompt in enumerate(prompts):
             # Can't have duplicate runs with the same run ID (if provided)
-            run_id_ = run_id if i == 0 and run_id is not None else uuid.uuid4()
+            run_id_ = run_id if i == 0 and run_id is not None else uuid7()
             handle_event(
                 self.handlers,
                 "on_llm_start",
@@ -1394,7 +1394,7 @@ class CallbackManager(BaseCallbackManager):
                 run_id_ = run_id
                 run_id = None
             else:
-                run_id_ = uuid.uuid4()
+                run_id_ = uuid7()
             handle_event(
                 self.handlers,
                 "on_chat_model_start",
@@ -1443,7 +1443,7 @@ class CallbackManager(BaseCallbackManager):
 
         """
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
         handle_event(
             self.handlers,
             "on_chain_start",
@@ -1498,7 +1498,7 @@ class CallbackManager(BaseCallbackManager):
 
         """
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         handle_event(
             self.handlers,
@@ -1547,7 +1547,7 @@ class CallbackManager(BaseCallbackManager):
             The callback manager for the retriever run.
         """
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         handle_event(
             self.handlers,
@@ -1607,7 +1607,7 @@ class CallbackManager(BaseCallbackManager):
             )
             raise ValueError(msg)
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         handle_event(
             self.handlers,
@@ -1843,7 +1843,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 run_id_ = run_id
                 run_id = None
             else:
-                run_id_ = uuid.uuid4()
+                run_id_ = uuid7()
 
             if inline_handlers:
                 inline_tasks.append(
@@ -1928,7 +1928,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 run_id_ = run_id
                 run_id = None
             else:
-                run_id_ = uuid.uuid4()
+                run_id_ = uuid7()
 
             for handler in self.handlers:
                 task = ahandle_event(
@@ -1991,7 +1991,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 for the chain run.
         """
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         await ahandle_event(
             self.handlers,
@@ -2041,7 +2041,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 for the tool run.
         """
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         await ahandle_event(
             self.handlers,
@@ -2093,7 +2093,7 @@ class AsyncCallbackManager(BaseCallbackManager):
         if not self.handlers:
             return
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         if kwargs:
             msg = (
@@ -2136,7 +2136,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                 for the retriever run.
         """
         if run_id is None:
-            run_id = uuid.uuid4()
+            run_id = uuid7()
 
         await ahandle_event(
             self.handlers,

libs/core/langchain_core/callbacks/usage.py

@@ -32,7 +32,7 @@ class UsageMetadataCallbackHandler(BaseCallbackHandler):
             result_2 = llm_2.invoke("Hello", config={"callbacks": [callback]})
             callback.usage_metadata
 
-        .. code-block:: none
+        .. code-block::
 
             {'gpt-4o-mini-2024-07-18': {'input_tokens': 8,
               'output_tokens': 10,
@@ -119,7 +119,7 @@ def get_usage_metadata_callback(
                 llm_2.invoke("Hello")
                 print(cb.usage_metadata)
 
-        .. code-block:: none
+        .. code-block::
 
             {'gpt-4o-mini-2024-07-18': {'input_tokens': 8,
               'output_tokens': 10,

libs/core/langchain_core/document_loaders/langsmith.py

@@ -31,7 +31,7 @@ class LangSmithLoader(BaseLoader):
             for doc in loader.lazy_load():
                 docs.append(doc)
 
-        .. code-block:: pycon
+        .. code-block:: python
 
             # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
 

libs/core/langchain_core/indexing/api.py

@@ -296,7 +296,11 @@ def index(
     For the time being, documents are indexed using their hashes, and users
     are not able to specify the uid of the document.
 
-    Important:
+    .. versionchanged:: 0.3.25
+        Added ``scoped_full`` cleanup mode.
+
+    .. important::
+
         * In full mode, the loader should be returning
           the entire dataset, and not just a subset of the dataset.
           Otherwise, the auto_cleanup will remove documents that it is not
@@ -309,7 +313,7 @@ def index(
           chunks, and we index them using a batch size of 5, we'll have 3 batches
           all with the same source id. In general, to avoid doing too much
           redundant work select as big a batch size as possible.
-        * The `scoped_full` mode is suitable if determining an appropriate batch size
+        * The ``scoped_full`` mode is suitable if determining an appropriate batch size
           is challenging or if your data loader cannot return the entire dataset at
           once. This mode keeps track of source IDs in memory, which should be fine
           for most use cases. If your dataset is large (10M+ docs), you will likely
@@ -378,10 +382,6 @@ def index(
         TypeError: If ``vectorstore`` is not a VectorStore or a DocumentIndex.
         AssertionError: If ``source_id`` is None when cleanup mode is incremental.
             (should be unreachable code).
-
-    .. version_modified:: 0.3.25
-
-        * Added `scoped_full` cleanup mode.
     """
     # Behavior is deprecated, but we keep it for backwards compatibility.
     # # Warn only once per process.
@@ -636,26 +636,30 @@ async def aindex(
     documents were deleted, which documents should be skipped.
 
     For the time being, documents are indexed using their hashes, and users
-     are not able to specify the uid of the document.
+    are not able to specify the uid of the document.
 
-    Important:
-       * In full mode, the loader should be returning
-         the entire dataset, and not just a subset of the dataset.
-         Otherwise, the auto_cleanup will remove documents that it is not
-         supposed to.
-       * In incremental mode, if documents associated with a particular
-         source id appear across different batches, the indexing API
-         will do some redundant work. This will still result in the
-         correct end state of the index, but will unfortunately not be
-         100% efficient. For example, if a given document is split into 15
-         chunks, and we index them using a batch size of 5, we'll have 3 batches
-         all with the same source id. In general, to avoid doing too much
-         redundant work select as big a batch size as possible.
-       * The `scoped_full` mode is suitable if determining an appropriate batch size
-         is challenging or if your data loader cannot return the entire dataset at
-         once. This mode keeps track of source IDs in memory, which should be fine
-         for most use cases. If your dataset is large (10M+ docs), you will likely
-         need to parallelize the indexing process regardless.
+    .. versionchanged:: 0.3.25
+        Added ``scoped_full`` cleanup mode.
+
+    .. important::
+
+        * In full mode, the loader should be returning
+          the entire dataset, and not just a subset of the dataset.
+          Otherwise, the auto_cleanup will remove documents that it is not
+          supposed to.
+        * In incremental mode, if documents associated with a particular
+          source id appear across different batches, the indexing API
+          will do some redundant work. This will still result in the
+          correct end state of the index, but will unfortunately not be
+          100% efficient. For example, if a given document is split into 15
+          chunks, and we index them using a batch size of 5, we'll have 3 batches
+          all with the same source id. In general, to avoid doing too much
+          redundant work select as big a batch size as possible.
+        * The ``scoped_full`` mode is suitable if determining an appropriate batch size
+          is challenging or if your data loader cannot return the entire dataset at
+          once. This mode keeps track of source IDs in memory, which should be fine
+          for most use cases. If your dataset is large (10M+ docs), you will likely
+          need to parallelize the indexing process regardless.
 
     Args:
         docs_source: Data loader or iterable of documents to index.
@@ -720,10 +724,6 @@ async def aindex(
         TypeError: If ``vector_store`` is not a VectorStore or DocumentIndex.
         AssertionError: If ``source_id_key`` is None when cleanup mode is
             incremental or ``scoped_full`` (should be unreachable).
-
-    .. version_modified:: 0.3.25
-
-        * Added `scoped_full` cleanup mode.
     """
     # Behavior is deprecated, but we keep it for backwards compatibility.
     # # Warn only once per process.

libs/core/langchain_core/language_models/chat_models.py

@@ -471,7 +471,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         **kwargs: Any,
     ) -> Iterator[BaseMessageChunk]:
         if not self._should_stream(async_api=False, **{**kwargs, "stream": True}):
-            # model doesn't implement streaming, so use default implementation
+            # Model doesn't implement streaming, so use default implementation
             yield cast(
                 "BaseMessageChunk",
                 self.invoke(input, config=config, stop=stop, **kwargs),

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -19,7 +19,7 @@ from langchain_core.runnables import RunnableConfig
 
 
 class FakeMessagesListChatModel(BaseChatModel):
-    """Fake ChatModel for testing purposes."""
+    """Fake ``ChatModel`` for testing purposes."""
 
     responses: list[BaseMessage]
     """List of responses to **cycle** through in order."""
@@ -212,10 +212,11 @@ class GenericFakeChatModel(BaseChatModel):
     """Generic fake chat model that can be used to test the chat model interface.
 
     * Chat model should be usable in both sync and async tests
-    * Invokes on_llm_new_token to allow for testing of callback related code for new
+    * Invokes ``on_llm_new_token`` to allow for testing of callback related code for new
       tokens.
     * Includes logic to break messages into message chunk to facilitate testing of
       streaming.
+
     """
 
     messages: Iterator[Union[AIMessage, str]]
@@ -230,6 +231,7 @@ class GenericFakeChatModel(BaseChatModel):
     .. warning::
         Streaming is not implemented yet. We should try to implement it in the future by
         delegating to invoke and then breaking the resulting output into message chunks.
+
     """
 
     @override
@@ -351,6 +353,7 @@ class ParrotFakeChatModel(BaseChatModel):
     """Generic fake chat model that can be used to test the chat model interface.
 
     * Chat model should be usable in both sync and async tests
+
     """
 
     @override

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 +1,176 @@
+"""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
+
+_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.
+    """
+    from langchain_core.load.serializable import (  # noqa: PLC0415
+        Serializable,
+        to_json_not_implemented,
+    )
+
+    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.
+    """
+    from langchain_core.load.serializable import Serializable  # noqa: PLC0415
+
+    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)
@@ -40,12 +70,18 @@ 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).
-            Default is False.
-        kwargs: Additional arguments to pass to json.dumps
+        pretty: Whether to pretty print the json.
+
+            If `True`, the json will be indented by either 2 spaces or the amount
+            provided in the `indent` kwarg.
+        **kwargs: Additional arguments to pass to `json.dumps`
 
     Returns:
         A json string representation of the object.
@@ -56,25 +92,23 @@ def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
     if "default" in kwargs:
         msg = "`default` should not be passed to dumps"
         raise ValueError(msg)
-    try:
-        obj = _dump_pydantic_models(obj)
-        if pretty:
-            indent = kwargs.pop("indent", 2)
-            return json.dumps(obj, default=default, indent=indent, **kwargs)
-        return json.dumps(obj, default=default, **kwargs)
-    except TypeError:
-        if pretty:
-            indent = kwargs.pop("indent", 2)
-            return json.dumps(to_json_not_implemented(obj), indent=indent, **kwargs)
-        return json.dumps(to_json_not_implemented(obj), **kwargs)
+
+    obj = _dump_pydantic_models(obj)
+    serialized = _serialize_value(obj)
+
+    if pretty:
+        indent = kwargs.pop("indent", 2)
+        return json.dumps(serialized, indent=indent, **kwargs)
+    return json.dumps(serialized, **kwargs)
 
 
 def dumpd(obj: Any) -> Any:
     """Return a dict representation of an object.
 
-    .. note::
-        Unfortunately this function is not as efficient as it could be because it first
-        dumps the object to a json string and then loads it back into a dictionary.
+    Note:
+        Plain dicts containing an `'lc'` key are automatically escaped to prevent
+        confusion with LC serialization format. The escape marker is removed during
+        deserialization.
 
     Args:
         obj: The object to dump.
@@ -82,4 +116,5 @@ def dumpd(obj: Any) -> Any:
     Returns:
         dictionary that can be serialized to json using json.dumps
     """
-    return json.loads(dumps(obj))
+    obj = _dump_pydantic_models(obj)
+    return _serialize_value(obj)

libs/core/langchain_core/load/load.py

@@ -1,11 +1,85 @@
-"""Load LangChain objects from JSON strings or objects."""
+"""Load LangChain objects from JSON strings or objects.
+
+## How it works
+
+Each `Serializable` LangChain object has a unique identifier (its "class path"), which
+is a list of strings representing the module path and class name. For example:
+
+- `AIMessage` -> `["langchain_core", "messages", "ai", "AIMessage"]`
+- `ChatPromptTemplate` -> `["langchain_core", "prompts", "chat", "ChatPromptTemplate"]`
+
+When deserializing, the class path from the JSON `'id'` field is checked against an
+allowlist. If the class is not in the allowlist, deserialization raises a `ValueError`.
+
+## Security model
+
+The `allowed_objects` parameter controls which classes can be deserialized:
+
+- **`'core'` (default)**: Allow classes defined in the serialization mappings for
+    langchain_core.
+- **`'all'`**: Allow classes defined in the serialization mappings. This
+    includes core LangChain types (messages, prompts, documents, etc.) and trusted
+    partner integrations. See `langchain_core.load.mapping` for the full list.
+- **Explicit list of classes**: Only those specific classes are allowed.
+
+For simple data types like messages and documents, the default allowlist is safe to use.
+These classes do not perform side effects during initialization.
+
+!!! note "Side effects in allowed classes"
+
+    Deserialization calls `__init__` on allowed classes. If those classes perform side
+    effects during initialization (network calls, file operations, etc.), those side
+    effects will occur. The allowlist prevents instantiation of classes outside the
+    allowlist, but does not sandbox the allowed classes themselves.
+
+Import paths are also validated against trusted namespaces before any module is
+imported.
+
+### Injection protection (escape-based)
+
+During serialization, plain dicts that contain an `'lc'` key are escaped by wrapping
+them: `{"__lc_escaped__": {...}}`. During deserialization, escaped dicts are unwrapped
+and returned as plain dicts, NOT instantiated as LC objects.
+
+This is an allowlist approach: only dicts explicitly produced by
+`Serializable.to_json()` (which are NOT escaped) are treated as LC objects;
+everything else is user data.
+
+Even if an attacker's payload includes `__lc_escaped__` wrappers, it will be unwrapped
+to plain dicts and NOT instantiated as malicious objects.
+
+## Examples
+
+```python
+from langchain_core.load import load
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.messages import AIMessage, HumanMessage
+
+# Use default allowlist (classes from mappings) - recommended
+obj = load(data)
+
+# Allow only specific classes (most restrictive)
+obj = load(
+    data,
+    allowed_objects=[
+        ChatPromptTemplate,
+        AIMessage,
+        HumanMessage,
+    ],
+)
+```
+"""
+
+from __future__ import annotations
 
 import importlib
 import json
 import os
-from typing import Any, Optional
+from collections.abc import Callable, Iterable
+from typing import Any, Literal, Optional, cast
 
 from langchain_core._api import beta
+from langchain_core.load._validation import _is_escaped_dict, _unescape_value
 from langchain_core.load.mapping import (
     _JS_SERIALIZABLE_MAPPING,
     _OG_SERIALIZABLE_MAPPING,
@@ -44,12 +118,167 @@ 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: Optional[dict[str, str]] = None,
         valid_namespaces: Optional[list[str]] = None,
         secrets_from_env: bool = True,  # noqa: FBT001,FBT002
@@ -58,22 +287,51 @@ class Reviver:
         ] = None,
         *,
         ignore_unserializable_fields: bool = False,
+        init_validator: InitValidator | None = default_init_validator,
     ) -> None:
         """Initialize the reviver.
 
         Args:
-            secrets_map: A map of secrets to load. If a secret is not found in
-                the map, it will be loaded from the environment if `secrets_from_env`
-                is True. Defaults to None.
-            valid_namespaces: A list of additional namespaces (modules)
-                to allow to be deserialized. Defaults to None.
+            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`.
+
+                Defaults to `None`.
+            valid_namespaces: Additional namespaces (modules) to allow during
+                deserialization, beyond the default trusted namespaces.
+
+                Defaults to `None`.
             secrets_from_env: Whether to load secrets from the environment.
-                Defaults to True.
-            additional_import_mappings: A dictionary of additional namespace mappings
+
+                Defaults to `True`.
+            additional_import_mappings: A dictionary of additional namespace mappings.
+
                 You can use this to override default mappings or add new mappings.
-                Defaults to None.
+
+                When `allowed_objects` is `None` (using defaults), paths from these
+                mappings are also added to the allowed class paths.
+
+                Defaults to `None`.
             ignore_unserializable_fields: Whether to ignore unserializable fields.
-                Defaults to False.
+
+                Defaults to `False`.
+            init_validator: Optional callable to validate kwargs before instantiation.
+
+                If provided, this function is called with `(class_path, kwargs)` where
+                `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+                The validator should raise an exception if the object should not be
+                deserialized, otherwise return `None`.
+
+                Defaults to `default_init_validator` which blocks jinja2 templates.
         """
         self.secrets_from_env = secrets_from_env
         self.secrets_map = secrets_map or {}
@@ -92,7 +350,26 @@ 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.
@@ -143,6 +420,20 @@ 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.
@@ -150,13 +441,11 @@ 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 "
@@ -164,9 +453,16 @@ 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)
 
@@ -178,6 +474,10 @@ 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
@@ -187,43 +487,76 @@ class Reviver:
 def loads(
     text: str,
     *,
+    allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
     secrets_map: Optional[dict[str, str]] = None,
     valid_namespaces: Optional[list[str]] = None,
     secrets_from_env: bool = True,
     additional_import_mappings: Optional[dict[tuple[str, ...], tuple[str, ...]]] = None,
     ignore_unserializable_fields: bool = False,
+    init_validator: InitValidator | None = default_init_validator,
 ) -> Any:
     """Revive a LangChain class from a JSON string.
 
     Equivalent to `load(json.loads(text))`.
 
+    Only classes in the allowlist can be instantiated. The default allowlist includes
+    core LangChain types (messages, prompts, documents, etc.). See
+    `langchain_core.load.mapping` for the full list.
+
     Args:
         text: The string to load.
-        secrets_map: A map of secrets to load. If a secret is not found in
-            the map, it will be loaded from the environment if `secrets_from_env`
-            is True. Defaults to None.
-        valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized. Defaults to None.
+        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`. Defaults to None.
+        valid_namespaces: Additional namespaces (modules) to allow during
+            deserialization, beyond the default trusted namespaces. Defaults to None.
         secrets_from_env: Whether to load secrets from the environment.
             Defaults to True.
-        additional_import_mappings: A dictionary of additional namespace mappings
+        additional_import_mappings: A dictionary of additional namespace mappings.
+
             You can use this to override default mappings or add new mappings.
-            Defaults to None.
+
+            When `allowed_objects` is `None` (using defaults), paths from these
+            mappings are also added to the allowed class paths. Defaults to None.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
             Defaults to False.
+        init_validator: Optional callable to validate kwargs before instantiation.
+
+            If provided, this function is called with `(class_path, kwargs)` where
+            `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+            The validator should raise an exception if the object should not be
+            deserialized, otherwise return `None`. Defaults to
+            `default_init_validator` which blocks jinja2 templates.
 
     Returns:
         Revived LangChain objects.
+
+    Raises:
+        ValueError: If an object's class path is not in the `allowed_objects` allowlist.
     """
-    return json.loads(
-        text,
-        object_hook=Reviver(
-            secrets_map,
-            valid_namespaces,
-            secrets_from_env,
-            additional_import_mappings,
-            ignore_unserializable_fields=ignore_unserializable_fields,
-        ),
+    # Parse JSON and delegate to load() for proper escape handling
+    raw_obj = json.loads(text)
+    return load(
+        raw_obj,
+        allowed_objects=allowed_objects,
+        secrets_map=secrets_map,
+        valid_namespaces=valid_namespaces,
+        secrets_from_env=secrets_from_env,
+        additional_import_mappings=additional_import_mappings,
+        ignore_unserializable_fields=ignore_unserializable_fields,
+        init_validator=init_validator,
     )
 
 
@@ -231,46 +564,107 @@ def loads(
 def load(
     obj: Any,
     *,
+    allowed_objects: Iterable[AllowedObject] | Literal["all", "core"] = "core",
     secrets_map: Optional[dict[str, str]] = None,
     valid_namespaces: Optional[list[str]] = None,
     secrets_from_env: bool = True,
     additional_import_mappings: Optional[dict[tuple[str, ...], tuple[str, ...]]] = None,
     ignore_unserializable_fields: bool = False,
+    init_validator: InitValidator | None = default_init_validator,
 ) -> Any:
     """Revive a LangChain class from a JSON object.
 
-    Use this if you already have a parsed JSON object,
-    eg. from `json.load` or `orjson.loads`.
+    Use this if you already have a parsed JSON object, eg. from `json.load` or
+    `orjson.loads`.
+
+    Only classes in the allowlist can be instantiated. The default allowlist includes
+    core LangChain types (messages, prompts, documents, etc.). See
+    `langchain_core.load.mapping` for the full list.
 
     Args:
         obj: The object to load.
-        secrets_map: A map of secrets to load. If a secret is not found in
-            the map, it will be loaded from the environment if `secrets_from_env`
-            is True. Defaults to None.
-        valid_namespaces: A list of additional namespaces (modules)
-            to allow to be deserialized. Defaults to None.
+        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`. Defaults to None.
+        valid_namespaces: Additional namespaces (modules) to allow during
+            deserialization, beyond the default trusted namespaces. Defaults to None.
         secrets_from_env: Whether to load secrets from the environment.
             Defaults to True.
-        additional_import_mappings: A dictionary of additional namespace mappings
+        additional_import_mappings: A dictionary of additional namespace mappings.
+
             You can use this to override default mappings or add new mappings.
-            Defaults to None.
+
+            When `allowed_objects` is `None` (using defaults), paths from these
+            mappings are also added to the allowed class paths. Defaults to None.
         ignore_unserializable_fields: Whether to ignore unserializable fields.
             Defaults to False.
+        init_validator: Optional callable to validate kwargs before instantiation.
+
+            If provided, this function is called with `(class_path, kwargs)` where
+            `class_path` is the class path tuple and `kwargs` is the kwargs dict.
+            The validator should raise an exception if the object should not be
+            deserialized, otherwise return `None`. Defaults to
+            `default_init_validator` which blocks jinja2 templates.
 
     Returns:
         Revived LangChain objects.
+
+    Raises:
+        ValueError: If an object's class path is not in the `allowed_objects` allowlist.
+
+    Example:
+        ```python
+        from langchain_core.load import load, dumpd
+        from langchain_core.messages import AIMessage
+
+        msg = AIMessage(content="Hello")
+        data = dumpd(msg)
+
+        # Deserialize using default allowlist
+        loaded = load(data)
+
+        # Or with explicit allowlist
+        loaded = load(data, allowed_objects=[AIMessage])
+
+        # Or extend defaults with additional mappings
+        loaded = load(
+            data,
+            additional_import_mappings={
+                ("my_pkg", "MyClass"): ("my_pkg", "module", "MyClass"),
+            },
+        )
+        ```
     """
     reviver = Reviver(
+        allowed_objects,
         secrets_map,
         valid_namespaces,
         secrets_from_env,
         additional_import_mappings,
         ignore_unserializable_fields=ignore_unserializable_fields,
+        init_validator=init_validator,
     )
 
     def _load(obj: Any) -> Any:
         if isinstance(obj, dict):
-            # Need to revive leaf nodes before reviving this node
+            # Check for escaped dict FIRST (before recursing).
+            # Escaped dicts are user data that should NOT be processed as LC objects.
+            if _is_escaped_dict(obj):
+                return _unescape_value(obj)
+
+            # Not escaped - recurse into children then apply reviver
             loaded_obj = {k: _load(v) for k, v in obj.items()}
             return reviver(loaded_obj)
         if isinstance(obj, list):

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",
@@ -530,16 +533,6 @@ 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

@@ -111,7 +111,7 @@ class Serializable(BaseModel, ABC):
 
     # Remove default BaseModel init docstring.
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        """"""  # noqa: D419
+        """"""  # noqa: D419  # Intentional blank docstring
         super().__init__(*args, **kwargs)
 
     @classmethod

libs/core/langchain_core/messages/ai.py

@@ -45,7 +45,6 @@ class InputTokenDetails(TypedDict, total=False):
     Does *not* need to sum to full input token count. Does *not* need to have all keys.
 
     Example:
-
         .. code-block:: python
 
             {
@@ -72,6 +71,7 @@ class InputTokenDetails(TypedDict, total=False):
 
     Since there was a cache hit, the tokens were read from the cache. More precisely,
     the model state given these tokens was read from the cache.
+
     """
 
 
@@ -81,7 +81,6 @@ class OutputTokenDetails(TypedDict, total=False):
     Does *not* need to sum to full output token count. Does *not* need to have all keys.
 
     Example:
-
         .. code-block:: python
 
             {
@@ -100,6 +99,7 @@ class OutputTokenDetails(TypedDict, total=False):
 
     Tokens generated by the model in a chain of thought process (i.e. by OpenAI's o1
     models) that are not returned as part of model output.
+
     """
 
 
@@ -109,7 +109,6 @@ class UsageMetadata(TypedDict):
     This is a standard representation of token usage that is consistent across models.
 
     Example:
-
         .. code-block:: python
 
             {
@@ -148,6 +147,7 @@ class UsageMetadata(TypedDict):
     """Breakdown of output token counts.
 
     Does *not* need to sum to full output token count. Does *not* need to have all keys.
+
     """
 
 
@@ -159,12 +159,14 @@ class AIMessage(BaseMessage):
     This message represents the output of the model and consists of both
     the raw output as returned by the model together standardized fields
     (e.g., tool calls, usage metadata) added by the LangChain framework.
+
     """
 
     example: bool = False
     """Use to denote that a message is part of an example conversation.
 
     At the moment, this is ignored by most models. Usage is discouraged.
+
     """
 
     tool_calls: list[ToolCall] = []
@@ -175,15 +177,18 @@ class AIMessage(BaseMessage):
     """If provided, usage metadata for a message, such as token counts.
 
     This is a standard representation of token usage that is consistent across models.
+
     """
 
     type: Literal["ai"] = "ai"
-    """The type of the message (used for deserialization). Defaults to "ai"."""
+    """The type of the message (used for deserialization). Defaults to ``'ai'``."""
 
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
     ) -> None:
-        """Pass in content as positional arg.
+        """Initialize ``AIMessage``.
 
         Args:
             content: The content of the message.
@@ -254,6 +259,7 @@ class AIMessage(BaseMessage):
 
         Returns:
             A pretty representation of the message.
+
         """
         base = super().pretty_repr(html=html)
         lines = []
@@ -293,7 +299,10 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     # non-chunk variant.
     type: Literal["AIMessageChunk"] = "AIMessageChunk"  # type: ignore[assignment]
     """The type of the message (used for deserialization).
-    Defaults to "AIMessageChunk"."""
+
+    Defaults to ``AIMessageChunk``.
+
+    """
 
     tool_call_chunks: list[ToolCallChunk] = []
     """If provided, tool call chunks associated with the message."""
@@ -311,7 +320,10 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
         """Initialize tool calls from tool call chunks.
 
         Returns:
-            This ``AIMessageChunk``.
+            The values with tool calls initialized.
+
+        Raises:
+            ValueError: If the tool call chunks are malformed.
         """
         if not self.tool_call_chunks:
             if self.tool_calls:
@@ -522,9 +534,9 @@ def add_usage(
 def subtract_usage(
     left: Optional[UsageMetadata], right: Optional[UsageMetadata]
 ) -> UsageMetadata:
-    """Recursively subtract two UsageMetadata objects.
+    """Recursively subtract two ``UsageMetadata`` objects.
 
-    Token counts cannot be negative so the actual operation is max(left - right, 0).
+    Token counts cannot be negative so the actual operation is ``max(left - right, 0)``.
 
     Example:
         .. code-block:: python

libs/core/langchain_core/messages/base.py

@@ -20,7 +20,7 @@ if TYPE_CHECKING:
 class BaseMessage(Serializable):
     """Base abstract message class.
 
-    Messages are the inputs and outputs of ChatModels.
+    Messages are the inputs and outputs of ``ChatModel``s.
     """
 
     content: Union[str, list[Union[str, dict]]]
@@ -31,17 +31,18 @@ class BaseMessage(Serializable):
 
     For example, for a message from an AI, this could include tool calls as
     encoded by the model provider.
+
     """
 
     response_metadata: dict = Field(default_factory=dict)
-    """Response metadata. For example: response headers, logprobs, token counts, model
-    name."""
+    """Examples: response headers, logprobs, token counts, model name."""
 
     type: str
     """The type of the message. Must be a string that is unique to the message type.
 
     The purpose of this field is to allow for easy identification of the message type
     when deserializing messages.
+
     """
 
     name: Optional[str] = None
@@ -51,20 +52,26 @@ class BaseMessage(Serializable):
 
     Usage of this field is optional, and whether it's used or not is up to the
     model implementation.
+
     """
 
     id: Optional[str] = Field(default=None, coerce_numbers_to_str=True)
-    """An optional unique identifier for the message. This should ideally be
-    provided by the provider/model which created the message."""
+    """An optional unique identifier for the message.
+
+    This should ideally be provided by the provider/model which created the message.
+
+    """
 
     model_config = ConfigDict(
         extra="allow",
     )
 
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
     ) -> None:
-        """Pass in content as positional arg.
+        """Initialize ``BaseMessage``.
 
         Args:
             content: The string contents of the message.
@@ -73,7 +80,7 @@ class BaseMessage(Serializable):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
-        """BaseMessage is serializable.
+        """``BaseMessage`` is serializable.
 
         Returns:
             True
@@ -90,10 +97,11 @@ class BaseMessage(Serializable):
         return ["langchain", "schema", "messages"]
 
     def text(self) -> str:
-        """Get the text content of the message.
+        """Get the text ``content`` of the message.
 
         Returns:
             The text content of the message.
+
         """
         if isinstance(self.content, str):
             return self.content
@@ -136,6 +144,7 @@ class BaseMessage(Serializable):
 
         Returns:
             A pretty representation of the message.
+
         """
         title = get_msg_title_repr(self.type.title() + " Message", bold=html)
         # TODO: handle non-string content.
@@ -155,11 +164,12 @@ def merge_content(
     """Merge multiple message contents.
 
     Args:
-        first_content: The first content. Can be a string or a list.
-        contents: The other contents. Can be a string or a list.
+        first_content: The first ``content``. Can be a string or a list.
+        contents: The other ``content``s. Can be a string or a list.
 
     Returns:
         The merged content.
+
     """
     merged = first_content
     for content in contents:
@@ -207,9 +217,10 @@ class BaseMessageChunk(BaseMessage):
 
         For example,
 
-        `AIMessageChunk(content="Hello") + AIMessageChunk(content=" World")`
+        ``AIMessageChunk(content="Hello") + AIMessageChunk(content=" World")``
+
+        will give ``AIMessageChunk(content="Hello World")``
 
-        will give `AIMessageChunk(content="Hello World")`
         """
         if isinstance(other, BaseMessageChunk):
             # If both are (subclasses of) BaseMessageChunk,
@@ -257,8 +268,9 @@ def message_to_dict(message: BaseMessage) -> dict:
         message: Message to convert.
 
     Returns:
-        Message as a dict. The dict will have a "type" key with the message type
-        and a "data" key with the message data as a dict.
+        Message as a dict. The dict will have a ``type`` key with the message type
+        and a ``data`` key with the message data as a dict.
+
     """
     return {"type": message.type, "data": message.model_dump()}
 
@@ -267,10 +279,11 @@ def messages_to_dict(messages: Sequence[BaseMessage]) -> list[dict]:
     """Convert a sequence of Messages to a list of dictionaries.
 
     Args:
-        messages: Sequence of messages (as BaseMessages) to convert.
+        messages: Sequence of messages (as ``BaseMessage``s) to convert.
 
     Returns:
         List of messages as dicts.
+
     """
     return [message_to_dict(m) for m in messages]
 
@@ -284,6 +297,7 @@ def get_msg_title_repr(title: str, *, bold: bool = False) -> str:
 
     Returns:
         The title representation.
+
     """
     padded = " " + title + " "
     sep_len = (80 - len(padded)) // 2

libs/core/langchain_core/messages/chat.py

@@ -30,7 +30,10 @@ class ChatMessageChunk(ChatMessage, BaseMessageChunk):
     # non-chunk variant.
     type: Literal["ChatMessageChunk"] = "ChatMessageChunk"  # type: ignore[assignment]
     """The type of the message (used during serialization).
-    Defaults to "ChatMessageChunk"."""
+
+    Defaults to ``'ChatMessageChunk'``.
+
+    """
 
     @override
     def __add__(self, other: Any) -> BaseMessageChunk:  # type: ignore[override]

libs/core/langchain_core/messages/function.py

@@ -15,19 +15,20 @@ from langchain_core.utils._merge import merge_dicts
 class FunctionMessage(BaseMessage):
     """Message for passing the result of executing a tool back to a model.
 
-    FunctionMessage are an older version of the ToolMessage schema, and
-    do not contain the tool_call_id field.
+    ``FunctionMessage`` are an older version of the ``ToolMessage`` schema, and
+    do not contain the ``tool_call_id`` field.
 
-    The tool_call_id field is used to associate the tool call request with the
+    The ``tool_call_id`` field is used to associate the tool call request with the
     tool call response. This is useful in situations where a chat model is able
     to request multiple tool calls in parallel.
+
     """
 
     name: str
     """The name of the function that was executed."""
 
     type: Literal["function"] = "function"
-    """The type of the message (used for serialization). Defaults to "function"."""
+    """The type of the message (used for serialization). Defaults to ``'function'``."""
 
 
 class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
@@ -38,7 +39,10 @@ class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
     # non-chunk variant.
     type: Literal["FunctionMessageChunk"] = "FunctionMessageChunk"  # type: ignore[assignment]
     """The type of the message (used for serialization).
-    Defaults to "FunctionMessageChunk"."""
+
+    Defaults to ``'FunctionMessageChunk'``.
+
+    """
 
     @override
     def __add__(self, other: Any) -> BaseMessageChunk:  # type: ignore[override]

libs/core/langchain_core/messages/human.py

@@ -8,7 +8,7 @@ from langchain_core.messages.base import BaseMessage, BaseMessageChunk
 class HumanMessage(BaseMessage):
     """Message from a human.
 
-    HumanMessages are messages that are passed in from a human to the model.
+    ``HumanMessage``s are messages that are passed in from a human to the model.
 
     Example:
 
@@ -32,15 +32,22 @@ class HumanMessage(BaseMessage):
 
     At the moment, this is ignored by most models. Usage is discouraged.
     Defaults to False.
+
     """
 
     type: Literal["human"] = "human"
-    """The type of the message (used for serialization). Defaults to "human"."""
+    """The type of the message (used for serialization).
+
+    Defaults to ``'human'``.
+
+    """
 
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
     ) -> None:
-        """Pass in content as positional arg.
+        """Initialize ``HumanMessage``.
 
         Args:
             content: The string contents of the message.

libs/core/langchain_core/messages/modifier.py

@@ -24,6 +24,7 @@ class RemoveMessage(BaseMessage):
 
         Raises:
             ValueError: If the 'content' field is passed in kwargs.
+
         """
         if kwargs.pop("content", None):
             msg = "RemoveMessage does not support 'content' field."

libs/core/langchain_core/messages/system.py

@@ -28,7 +28,11 @@ class SystemMessage(BaseMessage):
     """
 
     type: Literal["system"] = "system"
-    """The type of the message (used for serialization). Defaults to "system"."""
+    """The type of the message (used for serialization).
+
+    Defaults to ``'system'``.
+
+    """
 
     def __init__(
         self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
@@ -50,4 +54,7 @@ class SystemMessageChunk(SystemMessage, BaseMessageChunk):
     # non-chunk variant.
     type: Literal["SystemMessageChunk"] = "SystemMessageChunk"  # type: ignore[assignment]
     """The type of the message (used for serialization).
-    Defaults to "SystemMessageChunk"."""
+
+    Defaults to ``'SystemMessageChunk'``.
+
+    """

libs/core/langchain_core/messages/tool.py

@@ -14,19 +14,20 @@ from langchain_core.utils._merge import merge_dicts, merge_obj
 class ToolOutputMixin:
     """Mixin for objects that tools can return directly.
 
-    If a custom BaseTool is invoked with a ToolCall and the output of custom code is
-    not an instance of ToolOutputMixin, the output will automatically be coerced to a
-    string and wrapped in a ToolMessage.
+    If a custom BaseTool is invoked with a ``ToolCall`` and the output of custom code is
+    not an instance of ``ToolOutputMixin``, the output will automatically be coerced to
+    a string and wrapped in a ``ToolMessage``.
+
     """
 
 
 class ToolMessage(BaseMessage, ToolOutputMixin):
     """Message for passing the result of executing a tool back to a model.
 
-    ToolMessages contain the result of a tool invocation. Typically, the result
-    is encoded inside the `content` field.
+    ``ToolMessage``s contain the result of a tool invocation. Typically, the result
+    is encoded inside the ``content`` field.
 
-    Example: A ToolMessage representing a result of 42 from a tool call with id
+    Example: A ``ToolMessage`` representing a result of ``42`` from a tool call with id
 
         .. code-block:: python
 
@@ -35,7 +36,7 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
             ToolMessage(content="42", tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL")
 
 
-    Example: A ToolMessage where only part of the tool output is sent to the model
+    Example: A ``ToolMessage`` where only part of the tool output is sent to the model
         and the full output is passed in to artifact.
 
         .. versionadded:: 0.2.17
@@ -57,7 +58,7 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
                 tool_call_id="call_Jja7J89XsjrOLA5r!MEOW!SL",
             )
 
-    The tool_call_id field is used to associate the tool call request with the
+    The ``tool_call_id`` field is used to associate the tool call request with the
     tool call response. This is useful in situations where a chat model is able
     to request multiple tool calls in parallel.
 
@@ -67,7 +68,11 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     """Tool call that this message is responding to."""
 
     type: Literal["tool"] = "tool"
-    """The type of the message (used for serialization). Defaults to "tool"."""
+    """The type of the message (used for serialization).
+
+    Defaults to ``'tool'``.
+
+    """
 
     artifact: Any = None
     """Artifact of the Tool execution which is not meant to be sent to the model.
@@ -77,12 +82,14 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     output is needed in other parts of the code.
 
     .. versionadded:: 0.2.17
+
     """
 
     status: Literal["success", "error"] = "success"
     """Status of the tool invocation.
 
     .. versionadded:: 0.2.24
+
     """
 
     additional_kwargs: dict = Field(default_factory=dict, repr=False)
@@ -97,6 +104,7 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
 
         Args:
             values: The model arguments.
+
         """
         content = values["content"]
         if isinstance(content, tuple):
@@ -135,9 +143,11 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
         return values
 
     def __init__(
-        self, content: Union[str, list[Union[str, dict]]], **kwargs: Any
+        self,
+        content: Union[str, list[Union[str, dict]]],
+        **kwargs: Any,
     ) -> None:
-        """Create a ToolMessage.
+        """Initialize ``ToolMessage``.
 
         Args:
             content: The string contents of the message.
@@ -187,8 +197,8 @@ class ToolCall(TypedDict):
 
             {"name": "foo", "args": {"a": 1}, "id": "123"}
 
-        This represents a request to call the tool named "foo" with arguments {"a": 1}
-        and an identifier of "123".
+        This represents a request to call the tool named ``'foo'`` with arguments
+        ``{"a": 1}`` and an identifier of ``'123'``.
 
     """
 
@@ -201,6 +211,7 @@ class ToolCall(TypedDict):
 
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
+
     """
     type: NotRequired[Literal["tool_call"]]
 
@@ -227,9 +238,9 @@ def tool_call(
 class ToolCallChunk(TypedDict):
     """A chunk of a tool call (e.g., as part of a stream).
 
-    When merging ToolCallChunks (e.g., via AIMessageChunk.__add__),
+    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.
+    values of ``index`` are equal and not None.
 
     Example:
 
@@ -282,7 +293,7 @@ def tool_call_chunk(
 class InvalidToolCall(TypedDict):
     """Allowance for errors made by LLM.
 
-    Here we add an `error` key to surface errors made during generation
+    Here we add an ``error`` key to surface errors made during generation
     (e.g., invalid JSON arguments.)
     """
 

libs/core/langchain_core/messages/utils.py

@@ -5,6 +5,7 @@ Some examples of what you can do with these functions include:
 * Convert messages to strings (serialization)
 * Convert messages from dicts to Message objects (deserialization)
 * Filter messages from a list of messages based on name, type or id etc.
+
 """
 
 from __future__ import annotations
@@ -91,13 +92,14 @@ AnyMessage = Annotated[
 def get_buffer_string(
     messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
 ) -> str:
-    r"""Convert a sequence of Messages to strings and concatenate them into one string.
+    r"""Convert a sequence of messages to strings and concatenate them into one string.
 
     Args:
         messages: Messages to be converted to strings.
-        human_prefix: The prefix to prepend to contents of HumanMessages.
-            Default is "Human".
-        ai_prefix: The prefix to prepend to contents of AIMessages. Default is "AI".
+        human_prefix: The prefix to prepend to contents of ``HumanMessage``s.
+            Default is ``'Human'``.
+        ai_prefix: The prefix to prepend to contents of ``AIMessage``. Default is
+            ``'AI'``.
 
     Returns:
         A single string concatenation of all input messages.
@@ -176,19 +178,20 @@ def _message_from_dict(message: dict) -> BaseMessage:
 
 
 def messages_from_dict(messages: Sequence[dict]) -> list[BaseMessage]:
-    """Convert a sequence of messages from dicts to Message objects.
+    """Convert a sequence of messages from dicts to ``Message`` objects.
 
     Args:
         messages: Sequence of messages (as dicts) to convert.
 
     Returns:
         list of messages (BaseMessages).
+
     """
     return [_message_from_dict(m) for m in messages]
 
 
 def message_chunk_to_message(chunk: BaseMessage) -> BaseMessage:
-    """Convert a message chunk to a message.
+    """Convert a message chunk to a ``Message``.
 
     Args:
         chunk: Message chunk to convert.
@@ -221,10 +224,10 @@ def _create_message_from_message_type(
     id: Optional[str] = None,
     **additional_kwargs: Any,
 ) -> BaseMessage:
-    """Create a message from a message type and content string.
+    """Create a message from a ``Message`` type and content string.
 
     Args:
-        message_type: (str) the type of the message (e.g., "human", "ai", etc.).
+        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. Default is None.
         tool_call_id: (str) the tool call id. Default is None.
@@ -236,8 +239,9 @@ def _create_message_from_message_type(
         a message of the appropriate type.
 
     Raises:
-        ValueError: if the message type is not one of "human", "user", "ai",
-            "assistant", "function", "tool", "system", or "developer".
+        ValueError: if the message type is not one of ``'human'``, ``'user'``, ``'ai'``,
+            ``'assistant'``, ``'function'``, ``'tool'``, ``'system'``, or
+            ``'developer'``.
     """
     kwargs: dict[str, Any] = {}
     if name is not None:
@@ -303,15 +307,15 @@ def _create_message_from_message_type(
 
 
 def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
-    """Instantiate a message from a variety of message formats.
+    """Instantiate a ``Message`` from a variety of message formats.
 
     The message format can be one of the following:
 
-    - BaseMessagePromptTemplate
-    - BaseMessage
-    - 2-tuple of (role string, template); e.g., ("human", "{user_input}")
+    - ``BaseMessagePromptTemplate``
+    - ``BaseMessage``
+    - 2-tuple of (role string, template); e.g., (``'human'``, ``'{user_input}'``)
     - dict: a message dict with role and content keys
-    - string: shorthand for ("human", template); e.g., "{user_input}"
+    - string: shorthand for (``'human'``, template); e.g., ``'{user_input}'``
 
     Args:
         message: a representation of a message in one of the supported formats.
@@ -322,6 +326,7 @@ def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
     Raises:
         NotImplementedError: if the message type is not supported.
         ValueError: if the message dict does not contain the required keys.
+
     """
     if isinstance(message, BaseMessage):
         message_ = message
@@ -367,6 +372,7 @@ def convert_to_messages(
 
     Returns:
         list of messages (BaseMessages).
+
     """
     # Import here to avoid circular imports
     from langchain_core.prompt_values import PromptValue  # noqa: PLC0415
@@ -417,36 +423,36 @@ def filter_messages(
     exclude_ids: Optional[Sequence[str]] = None,
     exclude_tool_calls: Optional[Sequence[str] | bool] = None,
 ) -> list[BaseMessage]:
-    """Filter messages based on name, type or id.
+    """Filter messages based on ``name``, ``type`` or ``id``.
 
     Args:
         messages: Sequence Message-like objects to filter.
         include_names: Message names to include. Default is None.
         exclude_names: Messages names to exclude. Default is None.
-        include_types: Message types to include. Can be specified as string names (e.g.
-            "system", "human", "ai", ...) or as BaseMessage classes (e.g.
-            SystemMessage, HumanMessage, AIMessage, ...). Default is None.
-        exclude_types: Message types to exclude. Can be specified as string names (e.g.
-            "system", "human", "ai", ...) or as BaseMessage classes (e.g.
-            SystemMessage, HumanMessage, AIMessage, ...). Default is None.
+        include_types: Message types to include. Can be specified as string names
+            (e.g. ``'system'``, ``'human'``, ``'ai'``, ...) or as ``BaseMessage``
+            classes (e.g. ``SystemMessage``, ``HumanMessage``, ``AIMessage``, ...).
+            Default is None.
+        exclude_types: Message types to exclude. Can be specified as string names
+            (e.g. ``'system'``, ``'human'``, ``'ai'``, ...) or as ``BaseMessage``
+            classes (e.g. ``SystemMessage``, ``HumanMessage``, ``AIMessage``, ...).
+            Default is None.
         include_ids: Message IDs to include. Default is None.
         exclude_ids: Message IDs to exclude. Default is None.
         exclude_tool_calls: Tool call IDs to exclude. Default is None.
             Can be one of the following:
-
-            - ``True``: Each ``AIMessages`` with tool calls and all ``ToolMessages``
-              will be excluded.
+            - ``True``: all ``AIMessage``s with tool calls and all
+              ``ToolMessage``s will be excluded.
             - a sequence of tool call IDs to exclude:
-
-              - ToolMessages with the corresponding tool call ID will be excluded.
-              - The ``tool_calls`` in the AIMessage will be updated to exclude matching
-                tool calls.
-                If all tool_calls are filtered from an AIMessage,
-                the whole message is excluded.
+              - ``ToolMessage``s with the corresponding tool call ID will be
+                excluded.
+              - The ``tool_calls`` in the AIMessage will be updated to exclude
+                matching tool calls. If all ``tool_calls`` are filtered from an
+                AIMessage, the whole message is excluded.
 
     Returns:
-        A list of Messages that meets at least one of the incl_* conditions and none
-        of the excl_* conditions. If not incl_* conditions are specified then
+        A list of Messages that meets at least one of the ``incl_*`` conditions and none
+        of the ``excl_*`` conditions. If not ``incl_*`` conditions are specified then
         anything that is not explicitly excluded will be included.
 
     Raises:
@@ -558,13 +564,14 @@ def merge_message_runs(
 ) -> list[BaseMessage]:
     r"""Merge consecutive Messages of the same type.
 
-    **NOTE**: ToolMessages are not merged, as each has a distinct tool call id that
-    can't be merged.
+    .. note::
+        ToolMessages are not merged, as each has a distinct tool call id that can't be
+        merged.
 
     Args:
         messages: Sequence Message-like objects to merge.
         chunk_separator: Specify the string to be inserted between message chunks.
-        Default is "\n".
+        Default is ``'\n'``.
 
     Returns:
         list of BaseMessages with consecutive runs of message types merged into single
@@ -705,8 +712,8 @@ def trim_messages(
 ) -> list[BaseMessage]:
     r"""Trim messages to be below a token count.
 
-    trim_messages can be used to reduce the size of a chat history to a specified token
-    count or specified message count.
+    ``trim_messages`` can be used to reduce the size of a chat history to a specified
+    token count or specified message count.
 
     In either case, if passing the trimmed chat history back into a chat model
     directly, the resulting chat history should usually satisfy the following
@@ -714,13 +721,13 @@ def trim_messages(
 
     1. The resulting chat history should be valid. Most chat models expect that chat
        history starts with either (1) a ``HumanMessage`` or (2) a ``SystemMessage``
-       followed by a ``HumanMessage``. To achieve this, set ``start_on="human"``.
+       followed by a ``HumanMessage``. To achieve this, set ``start_on='human'``.
        In addition, generally a ``ToolMessage`` can only appear after an ``AIMessage``
        that involved a tool call.
        Please see the following link for more information about messages:
        https://python.langchain.com/docs/concepts/#messages
     2. It includes recent messages and drops old messages in the chat history.
-       To achieve this set the ``strategy="last"``.
+       To achieve this set the ``strategy='last'``.
     3. Usually, the new chat history should include the ``SystemMessage`` if it
        was present in the original chat history since the ``SystemMessage`` includes
        special instructions to the chat model. The ``SystemMessage`` is almost always
@@ -734,67 +741,67 @@ def trim_messages(
     Args:
         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.
+        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.
 
             .. note::
-                Use `count_tokens_approximately` to get fast, approximate token counts.
-                This is recommended for using `trim_messages` on the hot path, where
+                Use ``count_tokens_approximately`` to get fast, approximate token
+                counts.
+                This is recommended for using ``trim_messages`` on the hot path, where
                 exact token counting is not necessary.
 
         strategy: Strategy for trimming.
-
-            - "first": Keep the first <= n_count tokens of the messages.
-            - "last": Keep the last <= n_count tokens of the messages.
-
+            - ``'first'``: Keep the first ``<= n_count`` tokens of the messages.
+            - ``'last'``: Keep the last ``<= n_count`` tokens of the messages.
             Default is ``'last'``.
         allow_partial: Whether to split a message if only part of the message can be
-            included. If ``strategy="last"`` then the last partial contents of a message
-            are included. If ``strategy="first"`` then the first partial contents of a
+            included. If ``strategy='last'`` then the last partial contents of a message
+            are included. If ``strategy='first'`` then the first partial contents of a
             message are included.
             Default is False.
         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
+            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.
+            ``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.
             Default is None.
         start_on: The message type to start on. Should only be specified if
-            ``strategy="last"``. If specified then every message before
+            ``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.
+            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.
             Default is None.
         include_system: Whether to keep the SystemMessage if there is one at index 0.
             Should only be specified if ``strategy="last"``.
             Default is False.
         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
+            ``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 BaseMessages.
+        list of trimmed ``BaseMessage``.
 
     Raises:
         ValueError: if two incompatible arguments are specified or an unrecognized
             ``strategy`` is specified.
 
     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).
+        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``).
 
         .. code-block:: python
 
@@ -849,9 +856,9 @@ def trim_messages(
                 HumanMessage(content="what do you call a speechless parrot"),
             ]
 
-        Trim chat history based on the message count, keeping the SystemMessage if
-        present, and ensuring that the chat history starts with a HumanMessage (
-        or a SystemMessage followed by a HumanMessage).
+        Trim chat history based on the message count, keeping the ``SystemMessage`` if
+        present, and ensuring that the chat history starts with a ``HumanMessage`` (
+        or a ``SystemMessage`` followed by a ``HumanMessage``).
 
             trim_messages(
                 messages,
@@ -1033,6 +1040,7 @@ def convert_to_openai_messages(
     messages: Union[MessageLikeRepresentation, Sequence[MessageLikeRepresentation]],
     *,
     text_format: Literal["string", "block"] = "string",
+    include_id: bool = False,
 ) -> Union[dict, list[dict]]:
     """Convert LangChain messages into OpenAI message dicts.
 
@@ -1040,17 +1048,18 @@ def convert_to_openai_messages(
         messages: Message-like object or iterable of objects whose contents are
             in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
         text_format: How to format string or text block contents:
-
-            - ``'string'``:
-              If a message has a string content, this is left as a string. If
-              a message has content blocks that are all of type 'text', these are
-              joined with a newline to make a single string. If a message has
-              content blocks and at least one isn't of type 'text', then
-              all blocks are left as dicts.
-            - ``'block'``:
-              If a message has a string content, this is turned into a list
-              with a single content block of type 'text'. If a message has content
-              blocks these are left as is.
+                - ``'string'``:
+                    If a message has a string content, this is left as a string. If
+                    a message has content blocks that are all of type ``'text'``, these
+                    are joined with a newline to make a single string. If a message has
+                    content blocks and at least one isn't of type ``'text'``, then
+                    all blocks are left as dicts.
+                - ``'block'``:
+                    If a message has a string content, this is turned into a list
+                    with a single content block of type ``'text'``. If a message has
+                    content blocks these are left as is.
+        include_id: Whether to include message ids in the openai messages, if they
+                    are present in the source messages.
 
     Raises:
         ValueError: if an unrecognized ``text_format`` is specified, or if a message
@@ -1139,6 +1148,8 @@ def convert_to_openai_messages(
             oai_msg["refusal"] = message.additional_kwargs["refusal"]
         if isinstance(message, ToolMessage):
             oai_msg["tool_call_id"] = message.tool_call_id
+        if include_id and message.id:
+            oai_msg["id"] = message.id
 
         if not message.content:
             content = "" if text_format == "string" else []
@@ -1379,7 +1390,7 @@ def convert_to_openai_messages(
                             },
                         }
                     )
-                elif block.get("type") == "thinking":
+                elif block.get("type") in ["thinking", "reasoning"]:
                     content.append(block)
                 else:
                     err = (

libs/core/langchain_core/outputs/chat_generation.py

@@ -15,14 +15,14 @@ from langchain_core.utils._merge import merge_dicts
 class ChatGeneration(Generation):
     """A single chat generation output.
 
-    A subclass of Generation that represents the response from a chat model
+    A subclass of ``Generation`` that represents the response from a chat model
     that generates chat messages.
 
-    The `message` attribute is a structured representation of the chat message.
-    Most of the time, the message will be of type `AIMessage`.
+    The ``message`` attribute is a structured representation of the chat message.
+    Most of the time, the message will be of type ``AIMessage``.
 
     Users working with chat models will usually access information via either
-    `AIMessage` (returned from runnable interfaces) or `LLMResult` (available
+    ``AIMessage`` (returned from runnable interfaces) or ``LLMResult`` (available
     via callbacks).
     """
 
@@ -31,6 +31,7 @@ class ChatGeneration(Generation):
 
     .. warning::
         SHOULD NOT BE SET DIRECTLY!
+
     """
     message: BaseMessage
     """The message output by the chat model."""
@@ -47,6 +48,9 @@ class ChatGeneration(Generation):
 
         Returns:
             The values of the object with the text attribute set.
+
+        Raises:
+            ValueError: If the message is not a string or a list.
         """
         text = ""
         if isinstance(self.message.content, str):
@@ -66,9 +70,9 @@ class ChatGeneration(Generation):
 
 
 class ChatGenerationChunk(ChatGeneration):
-    """ChatGeneration chunk.
+    """``ChatGeneration`` chunk.
 
-    ChatGeneration chunks can be concatenated with other ChatGeneration chunks.
+    ``ChatGeneration`` chunks can be concatenated with other ``ChatGeneration`` chunks.
     """
 
     message: BaseMessageChunk

libs/core/langchain_core/prompt_values.py

@@ -113,8 +113,12 @@ class ImageURL(TypedDict, total=False):
     """Image URL."""
 
     detail: Literal["auto", "low", "high"]
-    """Specifies the detail level of the image. Defaults to "auto".
-    Can be "auto", "low", or "high"."""
+    """Specifies the detail level of the image. Defaults to ``'auto'``.
+    Can be ``'auto'``, ``'low'``, or ``'high'``.
+
+    This follows OpenAI's Chat Completion API's image URL format.
+
+    """
 
     url: str
     """Either a URL of the image or the base64 encoded image data."""

libs/core/langchain_core/prompts/chat.py

@@ -262,7 +262,7 @@ class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
     def from_template_file(
         cls,
         template_file: Union[str, Path],
-        input_variables: list[str],
+        input_variables: list[str],  # noqa: ARG003  # Deprecated
         **kwargs: Any,
     ) -> Self:
         """Create a class from a template file.
@@ -275,7 +275,7 @@ class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
         Returns:
             A new instance of this class.
         """
-        prompt = PromptTemplate.from_file(template_file, input_variables)
+        prompt = PromptTemplate.from_file(template_file)
         return cls(prompt=prompt, **kwargs)
 
     @abstractmethod
@@ -813,7 +813,10 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
             )
 
             prompt_value = template.invoke(
-                {"name": "Bob", "user_input": "What is your name?"}
+                {
+                    "name": "Bob",
+                    "user_input": "What is your name?",
+                }
             )
             # Output:
             # ChatPromptValue(

libs/core/langchain_core/prompts/few_shot.py

@@ -281,7 +281,10 @@ class FewShotChatMessagePromptTemplate(
             ]
 
             example_prompt = ChatPromptTemplate.from_messages(
-                [("human", "What is {input}?"), ("ai", "{output}")]
+                [
+                    ("human", "What is {input}?"),
+                    ("ai", "{output}"),
+                ]
             )
 
             few_shot_prompt = FewShotChatMessagePromptTemplate(