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Welcome to LangChainGo

LangChainGo is the Go Programming Language port/fork of LangChain.

LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:

  • Be data-aware: connect a language model to other sources of data
  • Be agentic: allow a language model to interact with its environment

The LangChain framework is designed with the above principles in mind.

Documentation Structure​

Note: These docs are for LangChainGo.

Our documentation follows a structured approach to help you learn and use LangChainGo effectively:

📚 Tutorials​

Step-by-step guides to build complete applications. Perfect for learning LangChainGo from the ground up.

  • Getting Started: Quick setup with Ollama • Quick setup with OpenAI
  • Basic Applications: Simple chat apps, Q&A systems, document summarization
  • Advanced Applications: RAG systems, agents with tools, multi-modal apps
  • Production: Deployment, optimization, monitoring

🛠️ How-to Guides​

Practical solutions for specific problems. Find answers to "How do I...?" questions.

  • LLM Integration: Configure providers, handle rate limits, implement streaming
  • Document Processing: Load documents, implement search, optimize retrieval
  • Agent Development: Create custom tools, multi-step reasoning, error handling
  • Production: Project structure, logging, deployment, scaling

🧠 Concepts​

Deep explanations of LangChainGo's architecture and design principles.

  • Core Architecture: Framework design, interfaces, Go-specific patterns
  • Language Models: Model abstraction, communication patterns, optimization
  • Agents & Memory: Agent patterns, memory management, state persistence
  • Production: Performance, reliability, security considerations

🔧 Components​

Technical reference for all LangChainGo modules and their capabilities.

  • Model I/O: LLMs, Chat Models, Embeddings, and Prompts
  • Data Connection: Document loaders, vector stores, text splitters, retrievers
  • Chains: Sequences of calls and end-to-end applications
  • Memory: State persistence and conversation management
  • Agents: Decision-making and autonomous behavior

API Reference​

Here you can find the API reference for all of the modules in LangChain, as well as full documentation for all exported classes and functions.

Get Involved​