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⚡ Building applications with LLMs through composability ⚡
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**langchain_openai: Support of reasoning summary streaming** **Description:** OpenAI API now supports streaming reasoning summaries for reasoning models (o1, o3, o3-mini, o4-mini). More info about it: https://platform.openai.com/docs/guides/reasoning#reasoning-summaries It is supported only in Responses API (not Completion API), so you need to create LangChain Open AI model as follows to support reasoning summaries streaming: ``` llm = ChatOpenAI( model="o4-mini", # also o1, o3, o3-mini support reasoning streaming use_responses_api=True, # reasoning streaming works only with responses api, not completion api model_kwargs={ "reasoning": { "effort": "high", # also "low" and "medium" supported "summary": "auto" # some models support "concise" summary, some "detailed", but auto will always work } } ) ``` Now, if you stream events from llm: ``` async for event in llm.astream_events(prompt, version="v2"): print(event) ``` or ``` for chunk in llm.stream(prompt): print (chunk) ``` OpenAI API will send you new types of events: `response.reasoning_summary_text.added` `response.reasoning_summary_text.delta` `response.reasoning_summary_text.done` These events are new, so they were ignored. So I have added support of these events in function `_convert_responses_chunk_to_generation_chunk`, so reasoning chunks or full reasoning added to the chunk additional_kwargs. Example of how this reasoning summary may be printed: ``` async for event in llm.astream_events(prompt, version="v2"): if event["event"] == "on_chat_model_stream": chunk: AIMessageChunk = event["data"]["chunk"] if "reasoning_summary_chunk" in chunk.additional_kwargs: print(chunk.additional_kwargs["reasoning_summary_chunk"], end="") elif "reasoning_summary" in chunk.additional_kwargs: print("\n\nFull reasoning step summary:", chunk.additional_kwargs["reasoning_summary"]) elif chunk.content and chunk.content[0]["type"] == "text": print(chunk.content[0]["text"], end="") ``` or ``` for chunk in llm.stream(prompt): if "reasoning_summary_chunk" in chunk.additional_kwargs: print(chunk.additional_kwargs["reasoning_summary_chunk"], end="") elif "reasoning_summary" in chunk.additional_kwargs: print("\n\nFull reasoning step summary:", chunk.additional_kwargs["reasoning_summary"]) elif chunk.content and chunk.content[0]["type"] == "text": print(chunk.content[0]["text"], end="") ``` --------- Co-authored-by: Chester Curme <chester.curme@gmail.com> |
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Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
pip install -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external / internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.