LangChain Python Reference Documentation

This directory contains the source code and build process for the Python reference documentation site, hosted at reference.langchain.com/python. This site serves references for LangChain, LangGraph, LangSmith, and LangChain integration packages (such as langchain-anthropic, langchain-openai, etc.).

The site is built using MkDocs with the Material for MkDocs theme and the mkdocstrings plugin for generating API reference documentation from docstrings. See all config options in the mkdocs.yml file.

The docs/ directory contains the markdown files for the site, with the main entry point being index.md. At build time, the stubs provided in each file are substituted with the generated API reference documentation by mkdocstrings. This allows us to architect content ordering, layout, etc. in markdown, while still generating the API reference documentation automatically from the source code. Consequently, to make content changes to the API references themselves, you need to make changes in the source code (e.g., docstrings, class/method names, etc.) and then rebuild the site.

Contributing

As these docs are built from the source code, the best way to contribute is to make changes in the source code itself. This can include:

Improving docstrings
Adding missing docstrings
Fixing typos
etc.

You will notice that at the top of each page are two icons: one to view the page source, and the other to edit the page. The "view source" icon takes you to the markdown file for that page, while the "edit page" icon takes you to the relevant source code file in GitHub. Use these links to help you navigate to the right place to make your contributions.

Cross-reference in your project

If you maintain a project that depends on LangChain or LangGraph and would like to reference classes, methods, functions, and more from these docs, you can do so! These pages include an objects.inv file that certain docs platforms, such as MkDocs, can use to automatically create links to these docs.

To reference these docs in your project, add the following to your mkdocs.yml file:

mkdocstrings:
handlers:
    python:
    import:
        - https://reference.langchain.com/python/objects.inv
        - ... # any other inventories you want to include

TODO

This site is currently being migrated from a previous Sphinx-based implementation, so there are still some rough edges to be smoothed out. Here are some known issues and potential improvements:

For methods that are from base classes, indicate it is inherited from such and link to the base class
Exclude langchain-classic pages from search results?
Backlinks
More xref
Modernize annotations
Inheritance diagrams
Consider using inherited docstrings
Fix TOC shadow overflow (started in reference/python/docs/stylesheets/toc.css) but was funky
Post-processing step to link out to imports from code blocks
- Maybe there's a plugin?
Fix navigation.path feature/plugin in mkdocs.yml not working
Set up CI to fail on unexpected Griffe warnings
"Module last updated" auto-generation for module pages using source file commit timestamps or the MkDocs plugin git-revision-date-localized
Fix search magnifying glass icon color in dark mode
Copy page support (need to add a post-processing step to generate markdown files to serve alongside the API reference docs)
Language switcher (JS/TS)
Social cards
Google Analytics
Versioning?
Show keyboard shortcut in search window - also add cmd + k to match Mintlify

Paths

For packages that live in the langchain-ai/langchain monorepo, the path to the package should exist at https://reference.langchain.com/python/{PACKAGE}/ where PACKAGE is the package name as defined in the pyproject.toml file, with hyphens replaced by underscores. For example, the langchain-openai package should be documented at https://reference.langchain.com/python/langchain_openai/.

Local Development

Setup

This project supports two installation modes:

Development mode (pyproject.dev.toml) - Uses local editable installs from cloned repositories
Production mode (pyproject.toml) - Uses git sources directly

Development Workflow

For local development with live source code:

# 1. Ensure repos are cloned in the expected structure (see below)

# 2. Switch to development mode and install
make dev-install

# 3. Serve the docs locally
make serve-docs

# Check current configuration anytime
make config-status

When you edit source code in the local repositories, changes will be reflected immediately since packages are installed as editable.

How it works: The make dev-install command:

Switches pyproject.toml to use local editable installs (via switch-config.sh)
Backs up the production config to pyproject.prod.toml
Installs all packages from local repos with uv sync

Production/CI Workflow

For production builds or CI:

# Switch to production mode and install
make prod-install

# Build the documentation
make build

How it works: The make prod-install command:

Restores pyproject.toml to use git sources
Installs all packages from git with uv sync

Manual Configuration Switching

You can also use the script directly:

# Switch to development mode
./switch-config.sh dev

# Switch to production mode
./switch-config.sh prod

# Check current mode
./switch-config.sh status

Required Repository Structure

The pyproject.dev.toml file expects repositories to be cloned in this structure:

/some-parent-folder/
  ├── docs/                  # This repository
  │   └── reference/python/
  ├── langchain/             # Main LangChain monorepo
  ├── langgraph/             # Main LangGraph monorepo
  ├── langchain-community/
  ├── langchain-mcp-adapters/
  ├── langchain-datastax/
  ├── langchain-ai21/
  ├── langchain-aws/
  ├── langchain-azure/
  ├── langchain-cerebras/
  ├── langchain-cohere/
  ├── langchain-ibm/
  ├── langchain-elastic/
  ├── langchain-google/
  ├── langchain-milvus/
  ├── langchain-neo4j/
  ├── langchain-nvidia/
  ├── langchain-pinecone/
  ├── langchain-postgres/
  ├── langchain-redis/
  ├── langchain-sema4/
  ├── langchain-snowflake/
  ├── langchain-tavily/      # (External org)
  ├── langchain-together/
  ├── langchain-unstructured/
  ├── langchain-upstage/
  ├── langchain-weaviate/
  ├── langgraph-supervisor-py/
  └── langgraph-swarm-py/

langchain-mongodb is not included as it is maintained and hosted separately by the MongoDB team.

If you only need to work on specific packages, you can comment out the others in pyproject.dev.toml.

MkDocs/mkdocstrings Python Cross-Reference Linking Syntax

Basic Syntax

The general format for cross-references in mkdocstrings is:

[display text][python.path.to.object]

If you want the object name as the display text, use backticks:

[`object_name`][python.path.to.object]

Linking to Different Python Objects

Modules

[`langchain.agents`][langchain.agents]

# or

[agents module][langchain.agents]

Classes

[`ChatOpenAI`][langchain_openai.ChatOpenAI]

# or

[the ChatOpenAI class][langchain_openai.ChatOpenAI]

Functions

[`init_chat_model`][langchain.chat_models.init_chat_model]

# or

[initialization function][langchain.chat_models.init_chat_model]

Methods

[`invoke`][langchain_openai.ChatOpenAI.invoke]

# or

[the invoke method][langchain_openai.ChatOpenAI.invoke]

Class Attributes

[`temperature`][langchain_openai.ChatOpenAI.temperature]

# or

[the temperature attribute][langchain_openai.ChatOpenAI.temperature]

Function/Method Parameters

Note: Parameter linking requires the parameter_headings option to be enabled in the mkdocstrings config (in mkdocs.yml). This generates permalinks and TOC entries for each parameter, so don't disable it.

Use (parameter_name) syntax to link to specific parameters:

[`model_provider`][langchain.chat_models.init_chat_model(model_provider)]

# or

[the model_provider parameter][langchain.chat_models.init_chat_model(model_provider)]

For method parameters:

[`max_tokens`][langchain_openai.ChatOpenAI.invoke(max_tokens)]

For class __init__ parameters (when using merge_init_into_class):

[`temperature`][langchain_openai.ChatOpenAI(temperature)]

For variadic parameters:

[`*args`][package.module.function(*args)]
[`**kwargs`][package.module.function(**kwargs)]

Return Values

Not directly linkable, but you can link to the return type class:

Returns a [`ChatResult`][langchain_core.outputs.ChatResult] object.

Nested Classes

[`Config`][langchain_core.runnables.Runnable.Config]

Advanced Patterns

Linking Within Same Module

If you're documenting within the same module, you can use relative paths:

See also [`.other_method`][.other_method]

Linking to Exceptions

Raises [`ValueError`][ValueError] if input is invalid.
Raises [`CustomError`][my_package.exceptions.CustomError]

Linking to Type Aliases

[`RunnableConfig`][langchain_core.runnables.config.RunnableConfig]

Multiple Links in Args Documentation

def create_agent(
    model: BaseChatModel,
    tools: Sequence[BaseTool],
) -> AgentExecutor:
    """
    Create an agent executor.

    Args:
        model: A [`BaseChatModel`][langchain_core.language_models.BaseChatModel]
            instance. You can use [`init_chat_model`][langchain.chat_models.init_chat_model]
            to initialize from a string identifier (see the
            [`model_provider`][langchain.chat_models.init_chat_model(model_provider)]
            parameter for available providers).
        tools: A sequence of [`BaseTool`][langchain_core.tools.BaseTool] instances.
            Use the [`@tool`][langchain_core.tools.tool] decorator to create tools.

    Returns:
        An [`AgentExecutor`][langchain.agents.AgentExecutor] instance.
    """

Best Practices

1. Use Backticks for Code Identifiers

✅ [`init_chat_model`][langchain.chat_models.init_chat_model]
❌ [init_chat_model][langchain.chat_models.init_chat_model]

2. Use Full Paths for Clarity

✅ [`BaseChatModel`][langchain_core.language_models.BaseChatModel]
❌ [`BaseChatModel`][BaseChatModel]  # May not resolve correctly

3. Link to Public APIs Only

Only link to public, exported APIs that users should interact with. Avoid linking to internal implementation details (e.g., objects prefixed with _).

4. Use Descriptive Text for Complex References

✅ See the [`model_provider`][langchain.chat_models.init_chat_model(model_provider)]
   parameter for available providers.
❌ See [`model_provider`][langchain.chat_models.init_chat_model(model_provider)].

5. Verify Links Build Correctly

Build and manually check the generated HTML to ensure links resolve correctly.

Quick Reference Table

Object Type	Syntax	Example
Module	`[text][module.path]`	[`agents`][langchain.agents]
Class	`[text][module.Class]`	[`ChatOpenAI`][langchain_openai.ChatOpenAI]
Function	`[text][module.function]`	[`init_chat_model`][langchain.chat_models.init_chat_model]
Method	`[text][module.Class.method]`	[`invoke`][langchain_openai.ChatOpenAI.invoke]
Attribute	`[text][module.Class.attr]`	[`temperature`][langchain_openai.ChatOpenAI.temperature]
Function Param	`[text][module.function(param)]`	[`model_provider`][langchain.chat_models.init_chat_model(model_provider)]
Method Param	`[text][module.Class.method(param)]`	[`max_tokens`][langchain_openai.ChatOpenAI.invoke(max_tokens)]
Class Param	`[text][module.Class(param)]`	[`temperature`][langchain_openai.ChatOpenAI(temperature)]

Testing Links

To test if a link will work:

Check the object is in __init__.py exports
Verify the import path: from module.path import Object
Build docs with --strict mode
Check the generated HTML for broken links

mkdocs build --strict
mkdocs serve  # Preview at http://127.0.0.1:8000/

This syntax works with the mkdocstrings plugin for MkDocs using the Python handler. Adjust paths according to your package structure and exports.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!