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## Overview Adding new `AgentMiddleware` primitive that supports `before_model`, `after_model`, and `prepare_model_request` hooks. This is very exciting! It makes our `create_agent` prebuilt much more extensible + capable. Still in alpha and subject to change. This is different than the initial [implementation](https://github.com/langchain-ai/langgraph/tree/nc/25aug/agent) in that it: * Fills in gaps w/ missing features, for ex -- new structured output, optionality of tools + system prompt, sync and async model requests, provider builtin tools * Exposes private state extensions for middleware, enabling things like model call tracking, etc * Middleware can register tools * Uses a `TypedDict` for `AgentState` -- dataclass subclassing is tricky w/ required values + required decorators * Addition of `model_settings` to `ModelRequest` so that we can pass through things to bind (like cache kwargs for anthropic middleware) ## TODOs ### top prio - [x] add middleware support to existing agent - [x] top prio middlewares - [x] summarization node - [x] HITL - [x] prompt caching other ones - [x] model call limits - [x] tool calling limits - [ ] usage (requires output state) ### secondary prio - [x] improve typing for state updates from middleware (not working right now w/ simple `AgentUpdate` and `AgentJump`, at least in Python) - [ ] add support for public state (input / output modifications via pregel channel mods) -- to be tackled in another PR - [x] testing! ### docs See https://github.com/langchain-ai/docs/pull/390 - [x] high level docs about middleware - [x] summarization node - [x] HITL - [x] prompt caching ## open questions Lots of open questions right now, many of them inlined as comments for the short term, will catalog some more significant ones here. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.
- Chat LangChain: Ask questions & chat with our documentation.