Changelog

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deepagents v0.4

• New integration packages for pluggable sandboxes: langchain-modal, langchain-daytona, and langchain-runloop. See sandboxes guide and example data analysis tutorial.
• Changes to conversation history summarization:
  • Summarization now happens in the model node via wrap_model_call events. Due to this we retain the full message history in the graph state.
  • More accurate token counting.
  • Summarization will now automatically trigger if a chat model raises a ContextOverflowError (defined in langchain-core). Currently langchain-anthropic and langchain-openai support this.
• We now default to the Responses API for model strings prefixed with "openai:".
  Note: to disable data retention with the Responses API, use

  Copy

  from langchain.chat_models import init_chat_model
  
  agent = create_deep_agent(
      model=init_chat_model(
          "openai:...",
          use_responses_api=True,
          store=False,
          include=["reasoning.encrypted_content"],
      )
  )
  

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langchain v1.2.0

• create_agent: Simplified support for provider-specific tool parameters and definitions via a new extras attribute on tools. Examples:
  • Provider-specific configuration such as Anthropic’s programmatic tool calling and tool search.
  • Built-in tools that are executed client-side, as supported by Anthropic, OpenAI, and other providers.
• Support for strict schema-adherence in agent response_format (see ProviderStrategy docs).

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langchain-google-genai v4.0.0

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langchain v1.1.0

• Model profiles: Chat models now expose supported features and capabilities through a .profile attribute. These data are derived from models.dev, an open source project providing model capability data.
• Summarization middleware: Updated to support flexible trigger points using model profiles for context-aware summarization.
• Structured output: ProviderStrategy support (native structured output) can now be inferred from model profiles.
• SystemMessage for create_agent: Support for passing SystemMessage instances directly to create_agent’s system_prompt parameter, enabling advanced features like cache control and structured content blocks.
• Model retry middleware: New middleware for automatically retrying failed model calls with configurable exponential backoff.
• Content moderation middleware: OpenAI content moderation middleware for detecting and handling unsafe content in agent interactions. Supports checking user input, model output, and tool results.

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v1.0.0

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langchain

• Release notes
• Migration guide

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langgraph

• Release notes
• Migration guide