LangChain Go

LangChain Go (langchaingo) is the Go implementation of the LangChain framework, giving Go developers the same building blocks for LLM applications that Python and JavaScript users enjoy. It is one of the most-starred projects in its niche on GitHub, with more than 9,700 stars across 50 trending appearances, and it is actively maintained through 2026.

Overview

The library offers a provider-agnostic interface to large language models, so the same code can talk to OpenAI, Anthropic, Google, local Ollama models and many other backends. On top of that interface it layers the standard LangChain concepts: prompt templates, chains that compose steps, agents that choose tools, memory for conversations, document loaders and text splitters for RAG pipelines, and integrations with a wide range of vector stores. Everything is idiomatic Go, with contexts, interfaces and error handling that fit naturally into existing services.

Key Features

• One consistent API across major LLM providers, including streaming response support
• Chains and agents for composing multi-step reasoning and tool use
• RAG toolkit: document loaders, text splitters, embeddings and dozens of vector store integrations
• Conversation memory and output parsers for structured results
• Native Go performance and static binaries, ideal for backend services and CLI tools
• Extensive examples and documentation to shorten the path from idea to prototype

💳 Pricing

LangChain Go is free and open source under the MIT License. There is no commercial edition; costs are limited to whatever your chosen LLM or vector database provider charges.

Who Is It For

This library is for Go teams who want to add LLM features, chatbots, retrieval-augmented generation or agents to existing Go backends without introducing a Python sidecar. It is equally handy for infrastructure engineers building internal AI tooling in a language they already operate in production.