M4A

LangChain

Framework for developing applications powered by language models

freemiumproductionpythonjavascriptopen-sourceragagents
Visit WebsiteGitHub

Memory Types

episodic, semantic, conversation

Integrations

openai, anthropic, cohere, huggingface, pinecone, weaviate, qdrant


Overview


LangChain is the most popular open-source framework for building applications with large language models. Founded by Harrison Chase and having raised $25 million, LangChain has become the de facto standard for LLM application development. It provides a comprehensive set of tools for building everything from simple chatbots to complex multi-agent systems.


The framework offers abstractions for prompts, chains, agents, memory, and retrieval, making it easier to build sophisticated LLM applications. LangChain's modular design allows developers to swap components easily, and its extensive integrations ecosystem connects to virtually every LLM provider, vector database, and tool.


Key Features


  • **Chains**: Compose LLM calls and processing steps
  • **Agents**: Build autonomous agents with tool usage
  • **Memory**: Built-in conversation and context memory
  • **RAG Support**: Complete toolkit for retrieval-augmented generation
  • **LangSmith**: Observability and debugging platform
  • **LangServe**: Deploy LangChain applications as APIs
  • **Extensive Integrations**: 700+ integrations with tools and services
  • **Multi-Language**: Python and JavaScript/TypeScript SDKs

    • When to Use LangChain


    LangChain is ideal for:

  • Building RAG applications with LLMs
  • Creating AI agents with tool usage
  • Complex workflows combining multiple LLM calls
  • Applications requiring extensive integrations
  • Teams wanting a proven, well-documented framework
  • Projects needing both Python and JavaScript support

    • Pros


  • Largest ecosystem and community in LLM development
  • Comprehensive documentation and tutorials
  • Extensive integration library
  • Active development with frequent updates
  • Both open-source and commercial offerings
  • LangSmith for production monitoring
  • Strong backing and funding
  • Multi-language support

    • Cons


  • Can be overly complex for simple use cases
  • Abstraction layers can hide important details
  • Frequent API changes and deprecations
  • Performance overhead from abstractions
  • Learning curve for advanced features
  • Can encourage over-engineering
  • Some integrations are poorly maintained

    • Pricing


  • **Open Source**: Free framework
  • **LangSmith**: Free tier, then $39/month per user
  • **LangChain Enterprise**: Custom pricing

    • ← Back to Frameworks