langchain[patch]: Release 0.3.11 (#28657)

Resolves https://github.com/langchain-ai/langchain/issues/28543

Ollama recently
[released](https://github.com/ollama/ollama/releases/tag/v0.4.6) support
for streaming tool calls. Previously we would override the `stream`
parameter if tools were passed in.

Covered in standard tests here:
c1d348e95d/libs/standard-tests/langchain_tests/integration_tests/chat_models.py (L893-L897)

Before, the test generates one message chunk:
```python
[
    AIMessageChunk(
        content='',
        additional_kwargs={},
        response_metadata={
            'model': 'llama3.1',
            'created_at': '2024-12-10T17:49:04.468487Z',
            'done': True,
            'done_reason': 'stop',
            'total_duration': 525471208,
            'load_duration': 19701000,
            'prompt_eval_count': 170,
            'prompt_eval_duration': 31000000,
            'eval_count': 17,
            'eval_duration': 473000000,
            'message': Message(
                role='assistant',
                content='',
                images=None,
                tool_calls=[
                    ToolCall(
                        function=Function(name='magic_function', arguments={'input': 3})
                    )
                ]
            )
        },
        id='run-552bbe0f-8fb2-4105-ada1-fa38c1db444d',
        tool_calls=[
            {
                'name': 'magic_function',
                'args': {'input': 3},
                'id': 'b0a4dc07-7d7a-487b-bd7b-ad062c2363a2',
                'type': 'tool_call',
            },
        ],
        usage_metadata={
            'input_tokens': 170, 'output_tokens': 17, 'total_tokens': 187
        },
        tool_call_chunks=[
            {
                'name': 'magic_function',
                'args': '{"input": 3}',
                'id': 'b0a4dc07-7d7a-487b-bd7b-ad062c2363a2',
                'index': None,
                'type': 'tool_call_chunk',
            }
        ]
    )
]
```

After, it generates two (tool call in one, response metadata in
another):
```python
[
    AIMessageChunk(
        content='',
        additional_kwargs={},
        response_metadata={},
        id='run-9a3f0860-baa1-4bae-9562-13a61702de70',
        tool_calls=[
            {
                'name': 'magic_function',
                'args': {'input': 3},
                'id': '5bbaee2d-c335-4709-8d67-0783c74bd2e0',
                'type': 'tool_call',
            },
        ],
        tool_call_chunks=[
            {
                'name': 'magic_function',
                'args': '{"input": 3}',
                'id': '5bbaee2d-c335-4709-8d67-0783c74bd2e0',
                'index': None,
                'type': 'tool_call_chunk',
            },
        ],
    ),
    AIMessageChunk(
        content='',
        additional_kwargs={},
        response_metadata={
            'model': 'llama3.1',
            'created_at': '2024-12-10T17:46:43.278436Z',
            'done': True,
            'done_reason': 'stop',
            'total_duration': 514282750,
            'load_duration': 16894458,
            'prompt_eval_count': 170,
            'prompt_eval_duration': 31000000,
            'eval_count': 17,
            'eval_duration': 464000000,
            'message': Message(
                role='assistant', content='', images=None, tool_calls=None
            ),
        },
        id='run-9a3f0860-baa1-4bae-9562-13a61702de70',
        usage_metadata={
            'input_tokens': 170, 'output_tokens': 17, 'total_tokens': 187
        }
    ),
]
```

.github/DISCUSSION_TEMPLATE/q-a.yml

@@ -22,7 +22,7 @@ body:
         if there's another way to solve your problem:
         
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        [API Reference](https://api.python.langchain.com/en/stable/),
+        [API Reference](https://python.langchain.com/api_reference/),
         [GitHub search](https://github.com/langchain-ai/langchain),
         [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
         [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
         if there's another way to solve your problem:
         
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        [API Reference](https://api.python.langchain.com/en/stable/),
+        [API Reference](https://python.langchain.com/api_reference/),
         [GitHub search](https://github.com/langchain-ai/langchain),
         [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
         [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),

.github/ISSUE_TEMPLATE/documentation.yml

@@ -21,7 +21,7 @@ body:
       place to ask your question:
       
       [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-      [API Reference](https://api.python.langchain.com/en/stable/),
+      [API Reference](https://python.langchain.com/api_reference/),
       [GitHub search](https://github.com/langchain-ai/langchain),
       [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
       [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),

.github/scripts/check_diff.py

@@ -32,18 +32,8 @@ IGNORED_PARTNERS = [
     "huggingface",
 ]
 
-# Cap python version at 3.12 for some packages with dependencies that are not yet
-# compatible with python 3.13 (mostly hf tokenizers).
 PY_312_MAX_PACKAGES = [
-    f"libs/partners/{integration}"
-    for integration in [
-        "chroma",
-        "couchbase",
-        "huggingface",
-        "mistralai",
-        "nomic",
-        "qdrant",
-    ]
+    "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
 ]
 
 
@@ -134,10 +124,11 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ in PY_312_MAX_PACKAGES:
         py_versions = ["3.9", "3.12"]
 
-    elif dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
-        # community extended test resolution in 3.12 is slow
-        # even in uv
-        py_versions = ["3.9", "3.11"]
+    elif dir_ == "libs/langchain" and job == "extended-tests":
+        py_versions = ["3.9", "3.13"]
+
+    elif dir_ == "libs/community" and job == "extended-tests":
+        py_versions = ["3.9", "3.12"]
 
     elif dir_ == "libs/community" and job == "compile-integration-tests":
         # community integration deps are slow in 3.12
@@ -281,6 +272,9 @@ if __name__ == "__main__":
             # TODO: update to include all packages that rely on standard-tests (all partner packages)
             # note: won't run on external repo partners
             dirs_to_run["lint"].add("libs/standard-tests")
+            dirs_to_run["test"].add("libs/standard-tests")
+            dirs_to_run["lint"].add("libs/cli")
+            dirs_to_run["test"].add("libs/cli")
             dirs_to_run["test"].add("libs/partners/mistralai")
             dirs_to_run["test"].add("libs/partners/openai")
             dirs_to_run["test"].add("libs/partners/anthropic")
@@ -288,8 +282,9 @@ if __name__ == "__main__":
             dirs_to_run["test"].add("libs/partners/groq")
 
         elif file.startswith("libs/cli"):
-            # todo: add cli makefile
-            pass
+            dirs_to_run["lint"].add("libs/cli")
+            dirs_to_run["test"].add("libs/cli")
+            
         elif file.startswith("libs/partners"):
             partner_dir = file.split("/")[2]
             if os.path.isdir(f"libs/partners/{partner_dir}") and [

.github/workflows/_compile_integration_test.yml

@@ -13,7 +13,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:
@@ -21,6 +21,7 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     name: "poetry run pytest -m compile tests/integration_tests #${{ inputs.python-version }}"
     steps:
       - uses: actions/checkout@v4

.github/workflows/_integration_test.yml

@@ -12,7 +12,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:

.github/workflows/_lint.yml

@@ -13,7 +13,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
   WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
 
   # This env var allows us to get inline annotations when ruff has complaints.
@@ -23,6 +23,7 @@ jobs:
   build:
     name: "make lint #${{ inputs.python-version }}"
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     steps:
       - uses: actions/checkout@v4
 

.github/workflows/_release.yml

@@ -21,7 +21,7 @@ on:
 
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:
@@ -167,6 +167,7 @@ jobs:
       - release-notes
       - test-pypi-publish
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     steps:
       - uses: actions/checkout@v4
 
@@ -191,7 +192,12 @@ jobs:
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
 
-      - name: Import published package
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: ${{ inputs.working-directory }}/dist/
+
+      - name: Import dist package
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         env:
@@ -207,15 +213,7 @@ jobs:
         # - attempt install again after 5 seconds if it fails because there is
         #   sometimes a delay in availability on test pypi
         run: |
-          poetry run pip install \
-            --extra-index-url https://test.pypi.org/simple/ \
-            "$PKG_NAME==$VERSION" || \
-          ( \
-            sleep 15 && \
-            poetry run pip install \
-              --extra-index-url https://test.pypi.org/simple/ \
-              "$PKG_NAME==$VERSION" \
-          )
+          poetry run pip install dist/*.whl
 
           # Replace all dashes in the package name with underscores,
           # since that's how Python imports packages with dashes in the name.
@@ -224,10 +222,10 @@ jobs:
           poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with test
+        run: poetry install --with test --no-root
         working-directory: ${{ inputs.working-directory }}
 
-      # Overwrite the local version of the package with the test PyPI version.
+      # Overwrite the local version of the package with the built version
       - name: Import published package (again)
         working-directory: ${{ inputs.working-directory }}
         shell: bash
@@ -235,9 +233,7 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          poetry run pip install \
-            --extra-index-url https://test.pypi.org/simple/ \
-            "$PKG_NAME==$VERSION"
+          poetry run pip install dist/*.whl
 
       - name: Run unit tests
         run: make tests

.github/workflows/_test.yml

@@ -13,7 +13,7 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:
@@ -21,6 +21,7 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     name: "make test #${{ inputs.python-version }}"
     steps:
       - uses: actions/checkout@v4

.github/workflows/_test_doc_imports.yml

@@ -9,11 +9,12 @@ on:
         description: "Python version to use"
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     name: "check doc imports #${{ inputs.python-version }}"
     steps:
       - uses: actions/checkout@v4

.github/workflows/_test_pydantic.yml

@@ -18,7 +18,7 @@ on:
         description: "Pydantic version to test."
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:
@@ -26,6 +26,7 @@ jobs:
       run:
         working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     name: "make test # pydantic: ~=${{ inputs.pydantic-version }}, python: ${{ inputs.python-version }}, "
     steps:
       - uses: actions/checkout@v4

.github/workflows/_test_release.yml

@@ -14,7 +14,7 @@ on:
         description: "Release from a non-master branch (danger!)"
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
   PYTHON_VERSION: "3.10"
 
 jobs:

.github/workflows/api_doc_build.yml

@@ -5,7 +5,7 @@ on:
   schedule:
     - cron:  '0 13 * * *'
 env:
-  POETRY_VERSION: "1.8.1"
+  POETRY_VERSION: "1.8.4"
   PYTHON_VERSION: "3.11"
 
 jobs:
@@ -73,7 +73,7 @@ jobs:
         working-directory: langchain
         run: |
           python -m uv pip install $(ls ./libs/partners | xargs -I {} echo "./libs/partners/{}")
-          python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental
+          python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental libs/standard-tests
           python -m uv pip install -r docs/api_reference/requirements.txt
 
       - name: Set Git config

.github/workflows/check_diffs.yml

@@ -5,6 +5,7 @@ on:
   push:
     branches: [master]
   pull_request:
+  merge_group:
 
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
@@ -17,7 +18,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:
@@ -119,6 +120,7 @@ jobs:
         job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
       fail-fast: false
     runs-on: ubuntu-latest
+    timeout-minutes: 20
     defaults:
       run:
         working-directory: ${{ matrix.job-configs.working-directory }}

.github/workflows/run_notebooks.yml

@@ -15,7 +15,7 @@ on:
     - cron: '0 13 * * *'
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
 
 jobs:
   build:

.github/workflows/scheduled_test.yml

@@ -2,32 +2,60 @@ name: Scheduled tests
 
 on:
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+    inputs:
+      working-directory-force:
+        type: string
+        description: "From which folder this pipeline executes - defaults to all in matrix - example value: libs/partners/anthropic"
+      python-version-force:
+        type: string
+        description: "Python version to use - defaults to 3.9 and 3.11 in matrix - example value: 3.9"
   schedule:
     - cron:  '0 13 * * *'
 
 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "1.8.4"
+  DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
 
 jobs:
+  compute-matrix:
+    if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
+    runs-on: ubuntu-latest
+    name: Compute matrix
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - name: Set matrix
+        id: set-matrix
+        env:
+          DEFAULT_LIBS: ${{ env.DEFAULT_LIBS }}
+          WORKING_DIRECTORY_FORCE: ${{ github.event.inputs.working-directory-force || '' }}
+          PYTHON_VERSION_FORCE: ${{ github.event.inputs.python-version-force || '' }}
+        run: |
+          # echo "matrix=..." where matrix is a json formatted str with keys python-version and working-directory
+          # python-version should default to 3.9 and 3.11, but is overridden to [PYTHON_VERSION_FORCE] if set
+          # working-directory should default to DEFAULT_LIBS, but is overridden to [WORKING_DIRECTORY_FORCE] if set
+          python_version='["3.9", "3.11"]'
+          working_directory="$DEFAULT_LIBS"
+          if [ -n "$PYTHON_VERSION_FORCE" ]; then
+            python_version="[\"$PYTHON_VERSION_FORCE\"]"
+          fi
+          if [ -n "$WORKING_DIRECTORY_FORCE" ]; then
+            working_directory="[\"$WORKING_DIRECTORY_FORCE\"]"
+          fi
+          matrix="{\"python-version\": $python_version, \"working-directory\": $working_directory}"
+          echo $matrix
+          echo "matrix=$matrix" >> $GITHUB_OUTPUT
   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
     strategy:
       fail-fast: false
       matrix:
-        python-version:
-          - "3.9"
-          - "3.11"
-        working-directory:
-          - "libs/partners/openai"
-          - "libs/partners/anthropic"
-          - "libs/partners/fireworks"
-          - "libs/partners/groq"
-          - "libs/partners/mistralai"
-          - "libs/partners/google-vertexai"
-          - "libs/partners/google-genai"
-          - "libs/partners/aws"
+        python-version: ${{ fromJSON(needs.compute-matrix.outputs.matrix).python-version }}
+        working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}
 
     steps:
       - uses: actions/checkout@v4

Makefile

@@ -69,7 +69,11 @@ lint lint_package lint_tests:
 	poetry run ruff check docs cookbook
 	poetry run ruff format docs cookbook cookbook --diff
 	poetry run ruff check --select I docs cookbook
-	git grep 'from langchain import' docs/docs cookbook | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
+	git --no-pager grep 'from langchain import' docs cookbook | grep -vE 'from langchain import (hub)' && echo "Error: no importing langchain from root in docs, except for hub" && exit 1 || exit 0
+	
+	git --no-pager grep 'api.python.langchain.com' -- docs/docs ':!docs/docs/additional_resources/arxiv_references.mdx' ':!docs/docs/integrations/document_loaders/sitemap.ipynb' || exit 0 && \
+	echo "Error: you should link python.langchain.com/api_reference, not api.python.langchain.com in the docs" && \
+	exit 1
 
 ## format: Format the project files.
 format format_diff:

README.md

@@ -38,18 +38,21 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/providers/).
+
+- **Open-source libraries**: Build your applications using LangChain's open-source
+[components](https://python.langchain.com/docs/concepts/) and
+[third-party integrations](https://python.langchain.com/docs/integrations/providers/).
   Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
-- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
+- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).
 
 ### Open-source libraries
 
-- **`langchain-core`**: Base abstractions and LangChain Expression Language.
-- **`langchain-community`**: Third party integrations.
-  - Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
+- **`langchain-core`**: Base abstractions.
+- **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
+- **`langchain-community`**: Third-party integrations that are community maintained.
+- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### Productionization:
 
@@ -57,7 +60,7 @@ For these applications, LangChain simplifies the entire application lifecycle:
 
 ### Deployment:
 
-- **[LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
+- **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
 
 ![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_112024.svg#gh-light-mode-only "LangChain Architecture Overview")
 ![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_112024_dark.svg#gh-dark-mode-only "LangChain Architecture Overview")
@@ -85,19 +88,12 @@ And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutoria
 
 The main value props of the LangChain libraries are:
 
-1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
-2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
-
-Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
-
-## LangChain Expression Language (LCEL)
-
-LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.
-
-- **[Overview](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
-- **[Interface](https://python.langchain.com/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
-- **[Primitives](https://python.langchain.com/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
-- **[Cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
+1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
+2. **Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
+built on top of `langchain-core`, has built-in support for [messages](https://python.langchain.com/docs/concepts/messages/), [tools](https://python.langchain.com/docs/concepts/tools/),
+and other LangChain abstractions. This makes it easy to combine components into
+production-ready applications with persistence, streaming, and other key features.
+Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
 
 ## Components
 
@@ -105,15 +101,19 @@ Components fall into the following **modules**:
 
 **📃 Model I/O**
 
-This includes [prompt management](https://python.langchain.com/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/docs/concepts/#output-parsers).
+This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
+and a generic interface for [chat models](https://python.langchain.com/docs/concepts/chat_models/), including a consistent interface for [tool-calling](https://python.langchain.com/docs/concepts/tool_calling/) and [structured output](https://python.langchain.com/docs/concepts/structured_outputs/) across model providers.
 
 **📚 Retrieval**
 
-Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/#retrievers) it for use in the generation step.
+Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/text_splitters/), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/retrievers/) it for use in the generation step.
 
 **🤖 Agents**
 
-Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
+Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
+LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
+and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
+LLM agents.
 
 ## 📖 Documentation
 
@@ -123,7 +123,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 - [Tutorials](https://python.langchain.com/docs/tutorials/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
 - [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
 - [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
-- [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method.
+- [API Reference](https://python.langchain.com/api_reference/): Thorough documentation of every class and method.
 
 ## 🌐 Ecosystem
 

SECURITY.md

@@ -1,5 +1,30 @@
 # Security Policy
 
+LangChain has a large ecosystem of integrations with various external resources like local and remote file systems, APIs and databases. These integrations allow developers to create versatile applications that combine the power of LLMs with the ability to access, interact with and manipulate external resources.
+
+## Best practices
+
+When building such applications developers should remember to follow good security practices:
+
+* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
+* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it’s safest to assume that any LLM able to use those credentials may in fact delete data.
+* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It’s best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
+
+Risks of not doing so include, but are not limited to:
+* Data corruption or loss.
+* Unauthorized access to confidential information.
+* Compromised performance or availability of critical resources.
+
+Example scenarios with mitigation strategies:
+
+* A user may ask an agent with access to the file system to delete files that should not be deleted or read the content of files that contain sensitive information. To mitigate, limit the agent to only use a specific directory and only allow it to read or write files that are safe to read or write. Consider further sandboxing the agent by running it in a container.
+* A user may ask an agent with write access to an external API to write malicious data to the API, or delete data from that API. To mitigate, give the agent read-only API keys, or limit it to only use endpoints that are already resistant to such misuse.
+* A user may ask an agent with access to a database to drop a table or mutate the schema. To mitigate, scope the credentials to only the tables that the agent needs to access and consider issuing READ-ONLY credentials.
+
+If you're building applications that access external resources like file systems, APIs
+or databases, consider speaking with your company's security team to determine how to best
+design and secure your applications.
+
 ## Reporting OSS Vulnerabilities
 
 LangChain is partnered with [huntr by Protect AI](https://huntr.com/) to provide 
@@ -14,7 +39,7 @@ 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.
-3) LangChain [security guidelines](https://python.langchain.com/docs/security) to
+3) The [Best practicies](#best-practices) above to
    understand what we consider to be a security vulnerability vs. developer
    responsibility.
 
@@ -33,13 +58,13 @@ The following packages and repositories are eligible for bug bounties:
 All out of scope targets defined by huntr as well as:
 
 - **langchain-experimental**: This repository is for experimental code and is not
-  eligible for bug bounties, bug reports to it will be marked as interesting or waste of
+  eligible for bug bounties (see [package warning](https://pypi.org/project/langchain-experimental/)), bug reports to it will be marked as interesting or waste of
   time and published with no bounty attached.
 - **tools**: Tools in either langchain or langchain-community are not eligible for bug
   bounties. This includes the following directories
-  - langchain/tools
-  - langchain-community/tools
-  - Please review our [security guidelines](https://python.langchain.com/docs/security)
+  - libs/langchain/langchain/tools
+  - libs/community/langchain_community/tools
+  - Please review the [best practices](#best-practices)
     for more details, but generally tools interact with the real world. Developers are
     expected to understand the security implications of their code and are responsible
     for the security of their tools.
@@ -47,7 +72,7 @@ All out of scope targets defined by huntr as well as:
   case basis, but likely will not be eligible for a bounty as the code is already
   documented with guidelines for developers that should be followed for making their
   application secure.
-- Any LangSmith related repositories or APIs see below.
+- Any LangSmith related repositories or APIs (see [Reporting LangSmith Vulnerabilities](#reporting-langsmith-vulnerabilities)).
 
 ## Reporting LangSmith Vulnerabilities
 

cookbook/README.md

@@ -63,4 +63,5 @@ Notebook | Description
 [oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS.
 [rag-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag-locally-on-intel-cpu.ipynb) | Perform Retrieval-Augmented-Generation (RAG) on locally downloaded open-source models using langchain and open source tools and execute it on Intel Xeon CPU. We showed an example of how to apply RAG on Llama 2 model and enable it to answer the queries related to Intel Q1 2024 earnings release.
 [visual_RAG_vdms.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/visual_RAG_vdms.ipynb) | Performs Visual Retrieval-Augmented-Generation (RAG) using videos and scene descriptions generated by open source models.
-[contextual_rag.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/contextual_rag.ipynb) | Performs contextual retrieval-augmented generation (RAG) prepending chunk-specific explanatory context to each chunk before embedding.
+[contextual_rag.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/contextual_rag.ipynb) | Performs contextual retrieval-augmented generation (RAG) prepending chunk-specific explanatory context to each chunk before embedding.
+[rag-agents-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/local_rag_agents_intel_cpu.ipynb) | Build a RAG agent locally with open source models that routes questions through one of two paths to find answers. The agent generates answers based on documents retrieved from either the vector database or retrieved from web search. If the vector database lacks relevant information, the agent opts for web search. Open-source models for LLM and embeddings are used locally on an Intel Xeon CPU to execute this pipeline.

docs/Makefile

@@ -47,6 +47,7 @@ generate-files:
 	$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
 
 	curl https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md | sed 's/<=/\&lt;=/g' > $(INTERMEDIATE_DIR)/langserve.md
+	cp ../SECURITY.md $(INTERMEDIATE_DIR)/security.md
 	$(PYTHON) scripts/resolve_local_links.py $(INTERMEDIATE_DIR)/langserve.md https://github.com/langchain-ai/langserve/tree/main/
 
 copy-infra:
@@ -59,6 +60,7 @@ copy-infra:
 	cp package.json $(OUTPUT_NEW_DIR)
 	cp sidebars.js $(OUTPUT_NEW_DIR)
 	cp -r static $(OUTPUT_NEW_DIR)
+	cp -r ../libs/cli/langchain_cli/integration_template $(OUTPUT_NEW_DIR)/src/theme
 	cp yarn.lock $(OUTPUT_NEW_DIR)
 
 render:
@@ -80,6 +82,7 @@ build: install-py-deps generate-files copy-infra render md-sync append-related
 vercel-build: install-vercel-deps build generate-references
 	rm -rf docs
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
+	cp -r ../libs/cli/langchain_cli/integration_template src/theme
 	rm -rf build
 	mkdir static/api_reference
 	git clone --depth=1 https://github.com/langchain-ai/langchain-api-docs-html.git

docs/api_reference/_static/css/custom.css

@@ -80,6 +80,8 @@
 html {
     --pst-font-family-base: 'Inter';
     --pst-font-family-heading: 'Inter Tight', sans-serif;
+
+    --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-triangle);
 }
 
 /*******************************************************************************
@@ -92,7 +94,7 @@ html {
  * https://sass-lang.com/documentation/interpolation
  */
 /* Defaults to light mode if data-theme is not set */
-html:not([data-theme]) {
+html:not([data-theme]), html[data-theme=light] {
   --pst-color-primary: #287977;
   --pst-color-primary-bg: #80D6D3;
   --pst-color-secondary: #6F3AED;
@@ -122,58 +124,8 @@ html:not([data-theme]) {
   --pst-color-on-background: #F4F9F8;
   --pst-color-surface: #F4F9F8;
   --pst-color-on-surface: #222832;
-}
-html:not([data-theme]) {
-  --pst-color-link: var(--pst-color-primary);
-  --pst-color-link-hover: var(--pst-color-secondary);
-}
-html:not([data-theme]) .only-dark,
-html:not([data-theme]) .only-dark ~ figcaption {
-  display: none !important;
-}
-
-/* NOTE: @each {...} is like a for-loop
- * https://sass-lang.com/documentation/at-rules/control/each
- */
-html[data-theme=light] {
-  --pst-color-primary: #287977;
-  --pst-color-primary-bg: #80D6D3;
-  --pst-color-secondary: #6F3AED;
-  --pst-color-secondary-bg: #DAD6FE;
-  --pst-color-accent: #c132af;
-  --pst-color-accent-bg: #f8dff5;
-  --pst-color-info: #276be9;
-  --pst-color-info-bg: #dce7fc;
-  --pst-color-warning: #f66a0a;
-  --pst-color-warning-bg: #f8e3d0;
-  --pst-color-success: #00843f;
-  --pst-color-success-bg: #d6ece1;
-  --pst-color-attention: var(--pst-color-warning);
-  --pst-color-attention-bg: var(--pst-color-warning-bg);
-  --pst-color-danger: #d72d47;
-  --pst-color-danger-bg: #f9e1e4;
-  --pst-color-text-base: #222832;
-  --pst-color-text-muted: #48566b;
-  --pst-color-heading-color: #ffffff;
-  --pst-color-shadow: rgba(0, 0, 0, 0.1);
-  --pst-color-border: #d1d5da;
-  --pst-color-border-muted: rgba(23, 23, 26, 0.2);
-  --pst-color-inline-code: #912583;
-  --pst-color-inline-code-links: #246161;
-  --pst-color-target: #f3cf95;
-  --pst-color-background: #ffffff;
-  --pst-color-on-background: #F4F9F8;
-  --pst-color-surface: #F4F9F8;
-  --pst-color-on-surface: #222832;
-  color-scheme: light;
-}
-html[data-theme=light] {
-  --pst-color-link: var(--pst-color-primary);
-  --pst-color-link-hover: var(--pst-color-secondary);
-}
-html[data-theme=light] .only-dark,
-html[data-theme=light] .only-dark ~ figcaption {
-  display: none !important;
+  --pst-color-deprecated: #f47d2e;
+  --pst-color-deprecated-bg: #fff3e8;
 }
 
 html[data-theme=dark] {
@@ -206,6 +158,8 @@ html[data-theme=dark] {
   --pst-color-on-background: #222832;
   --pst-color-surface: #29313d;
   --pst-color-on-surface: #f3f4f5;
+  --pst-color-deprecated: #b46f3e;
+  --pst-color-deprecated-bg: #341906;
   /* Adjust images in dark mode (unless they have class .only-dark or
    * .dark-light, in which case assume they're already optimized for dark
    * mode).
@@ -216,6 +170,30 @@ html[data-theme=dark] {
   */
   color-scheme: dark;
 }
+
+html:not([data-theme]) {
+  --pst-color-link: var(--pst-color-primary);
+  --pst-color-link-hover: var(--pst-color-secondary);
+}
+html:not([data-theme]) .only-dark,
+html:not([data-theme]) .only-dark ~ figcaption {
+  display: none !important;
+}
+
+/* NOTE: @each {...} is like a for-loop
+ * https://sass-lang.com/documentation/at-rules/control/each
+ */
+html[data-theme=light] {
+  color-scheme: light;
+}
+html[data-theme=light] {
+  --pst-color-link: var(--pst-color-primary);
+  --pst-color-link-hover: var(--pst-color-secondary);
+}
+html[data-theme=light] .only-dark,
+html[data-theme=light] .only-dark ~ figcaption {
+  display: none !important;
+}
 html[data-theme=dark] {
   --pst-color-link: var(--pst-color-primary);
   --pst-color-link-hover: var(--pst-color-secondary);
@@ -389,6 +367,13 @@ html[data-theme=dark] .MathJax_SVG * {
 div.deprecated {
   margin-top: 0.5em;
   margin-bottom: 2em;
+
+  background-color: var(--pst-color-deprecated-bg);
+  border-color: var(--pst-color-deprecated);
+}
+
+span.versionmodified.deprecated:before {
+  color: var(--pst-color-deprecated);
 }
 
 .admonition-beta.admonition, div.admonition-beta.admonition {
@@ -408,4 +393,4 @@ dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.
 p {
   font-size: 0.9rem;
   margin-bottom: 0.5rem;
-}
+}

docs/api_reference/conf.py

@@ -87,6 +87,18 @@ class Beta(BaseAdmonition):
 def setup(app):
     app.add_directive("example_links", ExampleLinksDirective)
     app.add_directive("beta", Beta)
+    app.connect("autodoc-skip-member", skip_private_members)
+
+
+def skip_private_members(app, what, name, obj, skip, options):
+    if skip:
+        return True
+    if hasattr(obj, "__doc__") and obj.__doc__ and ":private:" in obj.__doc__:
+        return True
+    if name == "__init__" and obj.__objclass__ is object:
+        # dont document default init
+        return True
+    return None
 
 
 # -- Project information -----------------------------------------------------

docs/api_reference/create_api_rst.py

@@ -72,14 +72,21 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
     Returns:
         list: A list of loaded module objects.
     """
+
     classes_: List[ClassInfo] = []
     functions: List[FunctionInfo] = []
     module = importlib.import_module(module_path)
+
+    if ":private:" in (module.__doc__ or ""):
+        return ModuleMembers(classes_=[], functions=[])
+
     for name, type_ in inspect.getmembers(module):
         if not hasattr(type_, "__module__"):
             continue
         if type_.__module__ != module_path:
             continue
+        if ":private:" in (type_.__doc__ or ""):
+            continue
 
         if inspect.isclass(type_):
             # The type of the class is used to select a template
@@ -479,11 +486,11 @@ def _package_namespace(package_name: str) -> str:
     Returns:
         modified package_name: Can be either "langchain" or "langchain_{package_name}"
     """
-    return (
-        package_name
-        if package_name == "langchain"
-        else f"langchain_{package_name.replace('-', '_')}"
-    )
+    if package_name == "langchain":
+        return "langchain"
+    if package_name == "standard-tests":
+        return "langchain_tests"
+    return f"langchain_{package_name.replace('-', '_')}"
 
 
 def _package_dir(package_name: str = "langchain") -> Path:

docs/cassettes/extraction_0bb138d7-116e-4542-aa5f-bebf0c301ec6.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/extraction_13165ac8-a1dc-44ce-a6ed-f52b577473e4.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/extraction_dd42a935-022f-4860-b9e0-84268f55b22a.msgpack.zlib

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docs/cassettes/llm_chain_0abb0863-bee7-448d-b013-79d8db01e330.msgpack.zlib

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docs/cassettes/llm_chain_1b2481f0.msgpack.zlib

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docs/cassettes/llm_chain_3e45595a.msgpack.zlib

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docs/cassettes/qa_chat_history_4fbca953-970d-4271-be30-6c7799893dd1.msgpack.zlib

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