AgenticGoKitThe Go Framework for Building Multi-Agent AI Systems

Python has LangChain. Go has AgenticGoKit. Build intelligent agent workflows with dynamic tool integration, multi-provider LLM support, and enterprise-grade orchestration.

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Production Ready

Modern, stable API with streamlined builder pattern. Built for production with full test coverage and battle-tested reliability.

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Go-Native Performance

Compiled binaries, efficient memory usage, and true parallel agent execution with goroutines.

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Multi-Agent Workflows

Sequential, Parallel, DAG, and Loop workflows with subworkflow composition and state sharing.

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Memory & RAG

Built-in vector databases (pgvector, Weaviate) and knowledge management capabilities.

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Tool Integration

MCP protocol support for dynamic tool discovery and external system integration.

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Streaming & Error Handling

Real-time streaming with 13 chunk types, typed errors, and comprehensive retry logic.

Why AgenticGoKit?

AgenticGoKit provides a production-ready, modern API for building intelligent multi-agent systems in Go.

Key Benefits

For Developers

Streamlined API: 8 core builder methods (down from 30+)
Type Safety: Compile-time error checking prevents runtime issues
Simple Deployment: Single binary, no complex Python environments
Native Concurrency: Goroutines for true parallel agent execution

For AI Systems

4 Workflow Types: Sequential, Parallel, DAG, Loop + Subworkflows
Memory & RAG: Built-in vector databases and knowledge management
Tool Integration: MCP protocol for dynamic tool discovery
Production Ready: Error handling, monitoring, streaming, and scaling patterns

📢 Notice: Legacy APIs (core/vnext) are deprecated. See the Migration Guide to upgrade to v1beta.

What You Can Build

🔍 Research Assistants

Multi-agent research teams with web search, analysis, and synthesis capabilities.

💬 Conversational Systems

Chat agents with persistent memory and context understanding.

📊 Data Processing Pipelines

Sequential workflows with error handling and monitoring.

📚 Knowledge Bases

RAG-powered Q&A with document ingestion and vector search.