langchain-ai/langchain

The agent engineering platform.

What it solves

LangChain simplifies the development of AI agents and LLM-powered applications by providing a standard interface for models, embeddings, and vector stores. It removes the need to rebuild workflows from scratch when swapping models or integrating new data sources, allowing developers to move from rapid prototyping to production-ready applications.

How it works

It uses a modular, component-based architecture that allows developers to "chain" together interoperable components and third-party integrations. This includes a vast library of integrations for model providers, tools, and retrievers, providing flexible abstraction layers that range from high-level chains for quick starts to low-level components for fine-grained control.

Who it’s for

Developers building LLM-powered applications and AI agents who need a scalable, future-proof framework that can easily integrate with various AI tools and data sources.

Highlights

  • Model Interoperability: Swap LLMs easily as the technology evolves without losing development momentum.
  • Real-time Data Augmentation: Connect LLMs to diverse internal and external data sources via extensive integrations.
  • Rapid Prototyping: Modular architecture enables fast iteration and testing of different workflows.
  • Production-ready Features: Built-in support for monitoring, evaluation, and debugging through integrations like LangSmith.
  • Extensive Ecosystem: Integrates with LangGraph for complex agent orchestration and Deep Agents for specialized agent capabilities.

Related

  • Project
    langchain-ai/langchainjs

    LangChain.js is a TypeScript framework for building applications that use large language models. It offers modular components (agents, models, retrievers, etc.), many integrations, and tools for rapid prototyping and production monitoring, with support for Node, browsers, edge runtimes, and more.

  • Project
    langroid/langroid

    A lightweight Python framework for building multi-agent LLM applications, enabling agents to collaborate via message exchange and tool integration.

  • Project
    Abraxas-365/langchain-rust

    A Rust implementation of the LangChain framework for building composable LLM applications with support for various models, vector stores, and agents.

  • Project
    tryAGI/LangChain

    A C# implementation of the LangChain framework that enables .NET developers to build composable AI applications using LLMs, vector databases, and document loaders.

  • Project
    davidmigloz/langchain_dart

    An unofficial Dart port of the LangChain framework that provides a set of ready-to-use components for building LLM-powered Dart and Flutter applications.