LangChainGo is a Go-based implementation of the LangChain framework, designed to help developers build applications powered by large language models using the Go programming language. It provides a modular architecture that allows developers to combine components such as language models, chains, agents, memory systems, and vector stores into flexible workflows. The framework emphasizes composability, making it easy to create complex pipelines that integrate LLMs with external data sources, APIs, and tools. It supports multiple providers including OpenAI, Anthropic, Google, and local models, offering a unified interface for interacting with different backends. LangChainGo also includes support for embeddings, semantic search, and retrieval-augmented generation, enabling developers to build data-aware applications. With built-in abstractions for agents and tool usage, it enables the creation of systems that can reason, take actions, and interact with their environment.
Features
- Unified interface for multiple LLM providers
- Composable chains for building workflows
- Memory management for conversational context
- Agent framework with tool integration
- Vector store integration for semantic search
- Support for embeddings and RAG systems