peakxy

peakxy / ai-agent-scaffold-go

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AI Agent Scaffold Go 是一个面向 Agent 应用开发的 Go 脚手架，围绕 Gin、Eino、Google ADK Go、GORM、MySQL、Redis 构建，重点提供清晰的 DDD 分层、配置驱动的 Agent 装配、多 Agent 工作流编排和 HTTP 运行时接口。

10
0
85% credibility
Found Jun 01, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

An open-source framework for building AI agents that can chat with users, execute workflows, and generate visual diagrams. The project includes a Go backend for agent orchestration and a Next.js frontend for interactive diagram creation.

How It Works

1
🔍 Discover the AI Agent Builder

You hear about a tool that lets you create AI assistants capable of drawing diagrams just by chatting with them.

2
📦 Get the Project Ready

You download the project files and set up everything on your computer using Docker containers.

3
🔗 Connect Your AI Service

You provide your AI provider's address and password so the assistant can think and respond.

4
🚀 Launch Everything

With one click, your backend server and web interface start running together.

5
🔐 Sign In

You enter your login details on the beautiful web page to access the assistant.

6
💬 Describe What You Want

You type a request like 'draw a login flow for my app' and watch the AI understand your needs.

7
🎨 See Your Diagram Appear

The AI creates a professional Draw.io diagram right in your browser, ready for you to review.

✅ Download and Use

You export your perfect diagram with one click, ready to share or include in your documentation.

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Star Growth

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AI-Generated Review

What is ai-agent-scaffold-go?

ai-agent-scaffold-go is a Go-based scaffolding framework for building AI agent applications. It wraps around web frameworks, AI model providers, and databases to let you assemble agents through YAML configuration rather than hard-coded logic. The framework handles multi-agent workflows, tool routing, and HTTP endpoints out of the box, giving you a running agent service with minimal boilerplate.

The stack includes Gin for HTTP routing, Eino for AI model integration, Google ADK patterns for agent orchestration, and optional MySQL/Redis backing. It ships with a Next.js frontend that embeds draw.io for interactive diagram generation through conversational agents.

Why is it gaining traction?

The config-driven approach is the differentiator. Instead of writing Go code to define agents, you edit YAML files that declare agent instructions, model settings, available tools, and workflow relationships. This lowers the barrier for non-Go developers to tweak agent behavior without touching the codebase.

Multi-agent orchestration handles sequential, parallel, and loop patterns, letting you compose agents that pass results between stages. Tool support includes MCP servers (both SSE and stdio transports) plus filesystem-based skills with markdown manifests. The 5-minute LLM request timeout accommodates longer multi-step agent flows.

Who should use this?

Backend engineers building internal agent services who want a structured starting point with DDD layering and working HTTP endpoints. Teams evaluating multi-agent architectures will find the workflow patterns immediately useful. Devs who need an agent that can call external tools or orchestrate drawing tasks via draw.io integration will get the most value. It's less suited for those wanting a managed SaaS or minimal prototype with no infrastructure overhead.

Verdict

This is an early-stage project with 10 stars and limited community evidence, giving it a credibility score around 0.85%. The code structure and config-driven design are thoughtful, but adoption requires tolerance for bleeding-edge tooling in a rapidly evolving space. Worth exploring for greenfield agent backends, but factor in maintenance risk given the project's maturity level.

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