langchain/libs/partners/anthropic/README.md

langchain-anthropic

Looking for the JS/TS version? Check out LangChain.js.

Quick Install

uv add langchain-anthropic

🤔 What is this?

This package contains the LangChain integration for Anthropic's generative models.

📖 Documentation

For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using these classes, see the LangChain Docs.

📕 Releases & Versioning

See our Releases and Versioning policies.

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the Contributing Guide.

Migrating to Claude Sonnet 5.5

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-5-5",
    max_tokens=16000,
    output_config={"effort": "medium"},
)

• Use with_structured_output(schema, method="json_schema") for native structured output. Sonnet 5.5 rejects forced tool choice ("any" or a tool name). Function-calling structured output does not force a call and raises a parsing error if the model answers without one.
• Thinking is adaptive by default. For no up-front thinking, use thinking={"type": "between_tools"} at high effort or below, with no additional thinking fields. disabled and budgeted enabled thinking are unsupported.
• Omit sampling settings; non-default temperature, top_p, and top_k are rejected. Budget output tokens for both thinking and text.
• Preserve signed thinking blocks, including empty ones, and keep history append-only. Use mid-conversation system messages to change instructions or tools rather than editing earlier turns.
• Progress updates can arrive as thinking blocks. Use adaptive thinking with display="summarized" or display="updates" to display them; the latter's beta header is added automatically.
• Computer use on the direct Claude API requires computer_toolset_20260801. Preserve the returned tool-use content: its toolset_name is retained on replay and copied to matching tool results. Remove the old fine-grained streaming beta when using toolsets.

See the migration guide for platform-specific restrictions, advisor pairings, and refusal/fallback behavior.

Resources

• LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
• Code of Conduct — community guidelines and standards