← All Tools ← 全部工具
⚙️ Skill Framework 技能框架 ★ 93k+ GitHub Stars llm framework rag

LangChain – LangChain 链式框架

Framework for building LLM-powered applications

View on GitHub ↗ 在 GitHub 查看 ↗ Official Website ↗ 官方网站 ↗
Category分类
Skill Framework 技能框架
skill
GitHub StarsGitHub 星数
93k+
Community adoption社区认可度
License许可证
MIT
Check repository 查看仓库
Tags标签
llm, framework, rag
4 tags total个标签

What Is LangChain? LangChain 是什么?

LangChain is an open-source developer framework for building AI applications with 93k+ GitHub stars. Framework for building LLM-powered applications

As a developer framework for building AI applications, LangChain is designed to help developers and teams build production-ready AI applications with reliable, tested abstractions. It handles the complexity of connecting LLMs to external data and tools, so engineers can focus on business logic instead of plumbing.

The project is maintained on GitHub at github.com/langchain-ai/langchain and is actively developed with a strong open-source community. With 93k+ stars, it is one of the most widely adopted tools in its category.

Key Features 核心功能

  • 🤖
    LLM Integration — Seamless integration with major LLMs including GPT-4o, Claude 4, Llama 3, and Mistral for text generation and reasoning.
  • ⚙️
    Modular Framework — Extensible architecture with plugin support; customize and extend for your specific use case.
  • 🧠
    RAG Pipeline — Retrieval-Augmented Generation that grounds LLM responses in your own documents and real-time data sources.
  • 🔓
    Open Source — MIT/Apache licensed—inspect, fork, modify, and self-host with no vendor lock-in.

Pros & Cons 优缺点

Pros优点

  • Most widely adopted LLM framework with the largest ecosystem
  • Modular design: swap LLM providers, vector stores, and tools freely
  • Built-in RAG pipeline with 50+ document loaders and vector store integrations
  • LangSmith platform for tracing, debugging, and evaluating chains

Cons缺点

  • Heavy abstraction can make debugging difficult for complex chains
  • Rapid API changes require frequent dependency updates

Use Cases 应用场景

LangChain is used across a wide range of applications in the AI development ecosystem. Here are the most common scenarios where teams choose LangChain:

🏗️ LLM Application Development

Build production-grade apps powered by language models with structured pipelines, retry logic, and observability.

📚 RAG & Knowledge Systems

Create document Q&A and knowledge base systems that ground LLM responses in proprietary data.

🤖 Agent Orchestration

Compose multi-step AI workflows where models plan, use tools, and iterate autonomously toward goals.

🔌 Model Provider Abstraction

Write once, run with any LLM provider—switch between OpenAI, Anthropic, and local models without code changes.

Getting Started with LangChain LangChain 快速开始

To get started with LangChain, visit the GitHub repository and follow the installation instructions in the README. Most Python frameworks can be installed via pip: pip install langchain

💡 Tip: Check the GitHub repository's Issues and Discussions pages for community support, and the Releases page for the latest stable version.
Get Started with LangChain 立即开始使用 LangChain
Visit the official site for documentation, downloads, and cloud plans. 访问官方网站获取文档、下载和云端方案。
Visit Official Site ↗ 访问官方网站 ↗

Similar Skill Frameworks 相似 技能框架

If LangChain doesn't fit your needs, here are other popular Skill Frameworks you might consider:

Frequently Asked Questions 常见问题

What is LangChain?
LangChain is an open-source framework for building applications powered by language models. It provides composable building blocks for chaining LLM calls, adding memory, integrating tools, and building RAG pipelines.
Is LangChain free?
Yes, LangChain the library is MIT-licensed and completely free. LangSmith (the observability platform) has a free tier and paid plans starting at $39/month for teams.
LangChain vs LlamaIndex: which should I use?
LangChain excels at building general-purpose LLM applications, agents, and conversational systems. LlamaIndex specializes in document ingestion, indexing, and retrieval (RAG). For pure document Q&A, LlamaIndex is often simpler; for complex agent workflows, LangChain has more flexibility.
Does LangChain work with local LLMs?
Yes. LangChain has a ChatOllama integration that connects to any Ollama-hosted model (Llama 3, Mistral, Gemma 2, etc.) with a single line: ChatOllama(model='llama3'). No API key required for fully local usage.