Agenticgokit is an open-source Agentic AI framework built in Go, designed for developing, orchestrating, and deploying intelligent agents. It provides an ecosystem for production-grade multi-agent AI systems. The platform connects to Model Context Protocol (MCP) servers, enabling tool discovery and integration. Go developers gain fine-grained control over AI agents, extending capabilities through both internal Go functions and external MCP tools. This framework supports various LLM providers, making it adaptable for diverse AI applications.

What does Agenticgokit connect to?

Agenticgokit connects to Model Context Protocol (MCP) servers, which are central for tool discovery and integration within its ecosystem. This connection allows Agenticgokit to extend the capabilities of intelligent agents by integrating external MCP tools. It also supports direct integration of Internal tools, which are in-process Go functions. The framework is LLM-agnostic, ensuring compatibility with many AI clients and providers. This includes OpenAI, Anthropic, Azure OpenAI, Ollama, HuggingFace, and OpenRouter, connecting agents to diverse language models.

Core features and tools

Agenticgokit acts as a framework for building agents and integrating capabilities, not a fixed set of callable tools. It enables agents to extend their functions using Go functions as Internal tools. It also integrates MCP tools discovered from external MCP servers. Examples of these external tools include web search, Docker management, file I/O, and databases. The framework supports various transport protocols for MCP, including stdio, Server-Sent Events (SSE), and streamable HTTP, with the latter recommended for full-duplex communication.

Who is it for?

Agenticgokit targets Go developers focused on building intelligent, multi-agent AI systems. It suits those who require fine-grained control over their AI agents and wish to deploy production-grade solutions. The framework supports complex workflows like multi-agent orchestration, tool integration, and memory management with Retrieval-Augmented Generation (RAG). It is ideal for developers needing to extend agent capabilities with Go functions or external services. Skip this if you are not a Go developer or do not need to build custom AI agents.

How to get started

When interacting with Large Language Model providers, setup involves configuring API keys. For example, an "OPENAI_API_KEY" environment variable is used for OpenAI. Streamable HTTP is the recommended transport protocol for full-duplex communication.

Use cases

• Multi-agent orchestration using patterns such as Sequential, Parallel, Directed Acyclic Graph (DAG), Loop, and SubWorkflows.
• Tool Integration to extend agents with external capabilities like search, file I/O, and databases via standard Go functions or the MCP.
• Memory and Retrieval-Augmented Generation (RAG), providing agents with built-in memory for chat history, semantic search, and document ingestion, with configurable backends.

Agenticgokit offers a Go-based framework for developing and deploying intelligent multi-agent AI systems. It supports integration with various LLM providers and external tools via MCP, alongside internal Go functions. Developers can orchestrate complex agent behaviors and manage memory for enhanced AI interactions. The platform is designed for those needing precise control over their AI agent deployments.