hwchase17/langchain
Langchain
LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors.
The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the performance and reliability of deployed applications.
The framework covers a broad capability surface including retrieval augmented generation, workflow orchestration, and the creation of specialized agents. It further supports the deployment of stateful workflows and the monitoring of agent performance to debug operational issues.
Features
- AI Component Chaining - Provides a toolkit for chaining interoperable AI components and third-party tools into complex sequences.
- Autonomous Agent Orchestrators - Serves as a runtime environment that decomposes complex goals into multi-step plans by invoking external tools.
- Stateful Execution Contexts - Implements persistent memory mechanisms to track agent progress and intermediate data across multi-step workflows.
- Autonomous Agents - Integrates LLMs with memory and tool usage to create systems that autonomously execute multi-step tasks.
- Model Provider Integrations - Provides unified interfaces for connecting and configuring multiple language model providers to prevent vendor lock-in.
- LLM Application Orchestration - Implements tools for chaining model calls and coordinating complex agentic workflows.
- Autonomous Agents - Build agents that plan tasks, manage subagents, and interact with file systems to solve complex problems.
- LLM Application Development - Provides a comprehensive toolkit for building complex software that integrates large language models with external data and tools.
- LLM Application Frameworks - Provides a fundamental framework for building applications that chain large language models with external tools.
- Model Abstractions - Provides unified interfaces to standardize interactions across diverse AI model providers.
- Model Integration Layers - Implements a standardized interface that abstracts access to various LLM providers to avoid vendor lock-in.
- Modular AI Components - Provides reusable and swappable building blocks for constructing scalable artificial intelligence applications.
- Multi-Agent Orchestrators - Coordinates teams of specialized AI agents to solve complex, multi-step tasks through a low-level framework.
- Prompt Templates - Ships systems for defining and managing reusable prompt structures with dynamic variables.
- Retrieval-Augmented Generation - Provides a robust architecture for grounding language model outputs in external private data sources.
- Tool Calling - Enables language models to request and execute external functions to perform actions and retrieve data.
- Autonomous AI Agents - Provides a framework for building self-directed agents that plan and execute complex tasks.
- Directed Acyclic Graph Engines - Implements execution logic by chaining modular AI components into directed acyclic graphs.
- AI Application Monitoring - Tracks the production health and behavior of AI-driven applications to ensure reliability.
- AI Model Integrations - Connects chat models, embedding models, and specialized toolkits to extend application capabilities.
- Model Switching Interfaces - Provides utilities for dynamically swapping underlying model providers via a standard interface.
- LLM Observability and Evaluation - Provides tools for monitoring, tracing, and evaluating LLM performance to fix operational issues.
- External Data Integrations - Integrates language models with external data sources to augment prompts with real-time information.
- Context Injection Adapters - Provides mechanisms to transform unstructured external data into formatted context for model prompts.
- AI Component Chaining - Offers a toolkit for linking interoperable AI components into sequences to prototype complex behaviors.
- Agent Frameworks - Provides modular components for building complex LLM-powered applications.
- Application Development - Library for chaining sequences of language model prompts.
- Application Frameworks - Tool for chaining sequences of language model prompts.
- Infrastructure and Utilities - Framework for building applications with LLMs and external data.
- LLM Frameworks - Framework for building applications by chaining LLMs with external data.
- LLM Frameworks and Libraries - Framework for building applications through LLM composability.
- Prompt Engineering - Framework for building applications with LLMs through composability.
- Prompting Frameworks - Framework for building LLM applications through modular composability.
- Frameworks and Ports - The original Python implementation of the framework.
Star history
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