6e2f46d04c
## 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>
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS version? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
Quick Install
pip install langchain
🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
❓ Question answering with RAG
- Documentation
- End-to-end Example: Chat LangChain and repo
🧱 Extracting structured output
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
📖 Documentation
Please see our full documentation on:
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
🚀 What can this help with?
There are five main areas that LangChain is designed to help with. These are, in increasing order of complexity:
📃 Models and Prompts:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with chat models and LLMs.
🔗 Chains:
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
📚 Retrieval Augmented Generation:
Retrieval Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
🧐 Evaluation:
Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our full documentation.
💁 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.