langchain-ai/langchain-community

# Global development guidelines for the LangChain monorepo

This document provides context to understand the LangChain Python project and assist with development.

## Project architecture and context

This repo contains LangChain community integrations, as published by `langchain-community` in `libs/community`. It strongly depends on the `langchain-ai/langchain` monorepo, which contains the core LangChain functionality. This document provides context to understand the LangChain Python project and assist with development.

If needed, the langchain monorepo is usually cloned at the same level as this repo, so you can refer to its code directly by navigating to `../langchain/` from this repo.

### `langchain-ai/langchain` Monorepo structure

```txt
langchain/
├── libs/
│   ├── core/             # `langchain-core` primitives and base abstractions
│   ├── langchain/        # `langchain-classic` (legacy, no new features)
│   ├── langchain_v1/     # Actively maintained `langchain` package
│   ├── partners/         # Third-party integrations
│   │   ├── openai/       # OpenAI models and embeddings
│   │   ├── anthropic/    # Anthropic (Claude) integration
│   │   ├── ollama/       # Local model support
│   │   └── ... (other integrations maintained by the LangChain team)
│   ├── text-splitters/   # Document chunking utilities
│   ├── standard-tests/   # Shared test suite for integrations
│   ├── model-profiles/   # Model configuration profiles
│   └── cli/              # Command-line interface tools
├── .github/              # CI/CD workflows and templates
├── .vscode/              # VSCode IDE standard settings and recommended extensions
└── README.md             # Information about LangChain
```

- **Core layer** (`langchain-core`): Base abstractions, interfaces, and protocols. Users should not need to know about this layer directly.
- **Implementation layer** (`langchain`): Concrete implementations and high-level public utilities
- **Integration layer** (`partners/`): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such as `langchain-ai/langchain-google` and `langchain-ai/langchain-aws`. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to `../langchain-google/` from this monorepo.
- **Testing layer** (`standard-tests/`): Standardized integration tests for partner integrations

### Development tools & commands**

- `uv` – Fast Python package installer and resolver (replaces pip/poetry)
- `make` – Task runner for common development commands. Feel free to look at the `Makefile` for available commands and usage patterns.
- `ruff` – Fast Python linter and formatter
- `mypy` – Static type checking
- `pytest` – Testing framework

This monorepo uses `uv` for dependency management. Local development uses editable installs: `[tool.uv.sources]`

Each package in `libs/` has its own `pyproject.toml` and `uv.lock`.

```bash
# Run unit tests (no network)
make test

# Run specific test file
uv run --group test pytest tests/unit_tests/test_specific.py
```

```bash
# Lint code
make lint

# Format code
make format

# Type checking
uv run --group lint mypy .
```

#### Key config files

- pyproject.toml: Main workspace configuration with dependency groups
- uv.lock: Locked dependencies for reproducible builds
- Makefile: Development tasks

#### Commit standards

Suggest PR titles that follow Conventional Commits format. Refer to .github/workflows/pr_lint for allowed types and scopes.

#### Pull request guidelines

- Always add a disclaimer to the PR description mentioning how AI agents are involved with the contribution.
- Describe the "why" of the changes, why the proposed solution is the right one. Limit prose.
- Highlight areas of the proposed changes that require careful review.

## Core development principles

### Maintain stable public interfaces

CRITICAL: Always attempt to preserve function signatures, argument positions, and names for exported/public methods. Do not make breaking changes.

**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 MkDocs Material admonitions, like `!!! warning`)

Ask: "Would this change break someone's code if they used it last week?"

### Code quality standards

All Python code MUST include type hints and return types.

```python title="Example"
def filter_unknown_users(users: list[str], known_users: set[str]) -> list[str]:
    """Single line description of the function.

    Any additional context about the function can go here.

    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.
    """
```

- Use descriptive, self-explanatory variable names.
- Follow existing patterns in the codebase you're modifying
- Attempt to break up complex functions (>20 lines) into smaller, focused functions where it makes sense

### Testing requirements

Every new feature or bugfix MUST be covered by unit tests.

- Unit tests: `tests/unit_tests/` (no network calls allowed)
- Integration tests: `tests/integration_tests/` (network calls permitted)
- We use `pytest` as the testing framework; if in doubt, check other existing tests for examples.
- The testing file structure should mirror the source code structure.

**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)
- [ ] Does the test suite fail if your new logic is broken?

### Security and risk assessment

- 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)

### Documentation standards

Use Google-style docstrings with Args section for all public functions.

```python title="Example"
def send_email(to: str, msg: str, *, priority: str = "normal") -> bool:
    """Send an email to a recipient with specified priority.

    Any additional context about the function can go here.

    Args:
        to: The email address of the recipient.
        msg: The message body to send.
        priority: Email priority level.

    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.
    """
```

- Types go in function signatures, NOT in docstrings
  - If a default is present, DO NOT repeat it in the docstring unless there is post-processing or it is set conditionally.
- Focus on "why" rather than "what" in descriptions
- Document all parameters, return values, and exceptions
- Keep descriptions concise but clear
- Ensure American English spelling (e.g., "behavior", not "behaviour")

## Additional resources

- **Documentation:** <https://docs.langchain.com/oss/python/langchain/overview> and source at <https://github.com/langchain-ai/docs> or `../docs/`. Prefer the local install and use file search tools for best results. If needed, use the docs MCP server as defined in `.mcp.json` for programmatic access.
- **Contributing Guide:** [`.github/CONTRIBUTING.md`](https://docs.langchain.com/oss/python/contributing/overview)