Langgraphgo

LangGraphGo is an open-source Go library built on top of LangChainGo, enabling developers to create production-grade LLM applications with complex, stateful workflows. It models applications as graphs where nodes represent processing steps and edges define transitions, including loops and branching. Designed for Go developers building AI agents, multi-agent systems, and conversational AI requiring persistent memory and human-in-the-loop interaction. The library uses a graph-based state machine to manage conversations and agent actions. Each node receives the current state and returns an updated state, supporting cycles, subgraphs, and conditional routing. Built-in persistence enables checkpointing for time travel (pausing, rewinding, replaying agents) and includes 9 memory strategies (buffer, sliding window, summary, hierarchical, graph, etc.) for long-term context management. LangGraphGo is a true Go-native implementation, leveraging goroutines and channels for high-concurrency execution and offering strong type safety via generics. It features Programmatic Tool Calling (PTC) for 10x latency and token reduction, MCP protocol support, RAG (including GraphRAG), file-based checkpoints without external dependencies, and integrations with search tools like Tavily, Exa, and Brave. With 17+ pre-built agent architectures (ReAct, Supervisor, Tree-of-Thoughts, etc.) and subgraph orchestration, it provides a comprehensive yet lightweight framework for building advanced AI agents entirely in Go. Compared to Python-based LangGraph, LangGraphGo offers better performance and type safety for Go shops but has a smaller ecosystem and documentation base. It's a strong choice for teams already invested in Go who need stateful, multi-agent workflows without leaving the language.