agentgo-pkg

Build AI agents, RAG pipelines, and multi-agent workflows in Go.

6|Updated Aug 7, 2025

Search Tags:#ai #rag #agent #go #multi-agent #mcp #memory

Ready to run

npx skills add https://github.com/liliang-cn/agent-go --skill agentgo-pkg

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentgo-pkg
Source: https://github.com/liliang-cn/agent-go/tree/main/.skills/agentgo-pkg
Command: npx skills add https://github.com/liliang-cn/agent-go --skill agentgo-pkg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Go developers require a cohesive library to build AI agents and team-based workflows, including RAG pipelines and memory tools.

Core Features & Use Cases

End-to-end support for agents, RAG, memory, and multi-agent coordination in Go.
Modular components and tooling to assemble autonomous agents, document ingestion, semantic search, and task orchestration.
Real-world scenarios include building AI agents that ingest documents, perform reasoning across teams, and coordinate actions.

Quick Start

Install AgentGo in your Go project and start building AI agents, RAG pipelines, and multi-agent teams.

Frequently Asked Questions about agentgo-pkg

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build multi-agent workflows in Go?▼

Build multi-agent workflows in Go by utilizing a modular library that provides structured APIs for autonomous agents, team-based coordination, and task orchestration across your applications.

How does RAG document ingestion work for Go applications?▼

RAG document ingestion works through modular pipelines that enable document ingestion, semantic search, and memory tools to provide context for autonomous agent reasoning within your Go project.

Can I add custom tools to an AI agent built in Go?▼

Yes, you can add custom tools to an AI agent built in Go because the library features pluggable tools and a modular API design, allowing developers to assemble and extend agent capabilities.

What is the best way to implement semantic search in a Go AI agent?▼

The best way to implement semantic search in a Go AI agent is using a cohesive library that combines document ingestion pipelines with memory tools to deliver structured search results.

Does this Go agent library support MCP and memory coordination?▼

Yes, the Go agent library supports MCP and memory coordination, providing integrated memory tools and pluggable components to manage state and context across multi-agent teams.

When should I not use a Go-based multi-agent framework?▼

You should not use a Go-based multi-agent framework if your project lacks Go module compatibility or requires non-modular API design, as this library relies on structured workflows and Go-specific environments.