by baby-llm · Agent Tool · ★ 396
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
BabyAgent - 后端工程师的 AI Agent 教学项目 (Go 语言版) 🏠 项目地址: https://github.com/baby-llm/baby-agent 🚀 从后端视角出发,用 Go 语言手写一遍 Agent 框架的关键原理。 本项目专为没有 LLM 背景但具备基础 Golang 经验的后端工程师设计。我们将跳过复杂的数学推导,优先从工程实现理解核心概念:你会看到每一步为什么这么设计、替代方案是什么、以及它们在真实系统里会遇到哪些坑。 重要说明(请先读) - 本项目是教学仓库:目标是用可运行的代码把 Agent、Tool Calling、Agent Loop、MCP、Memory、RAG 等机制讲清楚。 - 本项目不是“开箱即用的生产框架”,也不建议直接用于生产环境的二次开发。 - 如果你希望落地到真实业务:这里更适合作为原理参考 + 原型验证的起点,你需要根据自己的场景补齐工程化能力与安全边界。 🏗 核心技术栈 Language: Go 1.24+ LLM Concepts: Chat Completions API, SSE 流式传输, Function Calling, ReAct Agent Loop Advanced AI: 上下文工程, Memory 系统, Agentic RAG, 技能系统(Skills) System Design: MCP 协议, Guardrails 安全防护, Web 服务化 Engineering: LLM 评测, 可观测性(Trace/Metrics/Log) 🗺 学习路径图 (按知识循序渐进) 我们将按照"从基础调用到复杂 Agent"的顺序逐步深入,带你完成从零到复杂 Agent 的蜕变。每一阶段都包含可运行的代码示例。 第一章:初识 LLM(Raw HTTP 与 OpenAI SDK) 目标:拨开 SDK 的迷雾,直视大模型调用的本质。 协议本质:掌握 接口的最小化请求和响应结构 流式输出解析:深入了解 SSE(Server-Sent Events)协议,实现"打字机"效果 工程实践:对比 Raw HTTP 和 OpenAI Go SDK 的使用方式 LLM 原理(可选):Transformer、训练过程、Token 机制、生成原理 第二章:赋予 AI "手脚"(Tool Calling 和 Agent) 目标:让 LLM 从"只会聊天"升级为"能动手做事"的 Agent。 Function Calling 协议:如何向模型声明工具、如何解析工具调用 Agent Loop:工具调用 → 反馈 → 再推理的闭环流程 ReAct/Tool-Loop 原理:Reason + Act 的思维-行动交替模式 本地工具封装:读取文件、写入文件、编辑文件、执行 shell 命令 Chat...
| Stars | 396 |
| Forks | 55 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 69.1479321280787/100 |
| Open Issues | 6 |
| Last Updated | 2026-06-08 |
| Created | 2026-01-04 |
| Platforms | go |
| Est. Tokens | ~31k |
Looking for a baby-agent alternative? If you're comparing baby-agent with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Open-source agentic data engineering harness for dbt, SQL, and cloud warehouses. 100+ tools, 10 warehouses, AI
A desktop MCP client designed as a tool unitary utility integration, accelerating AI adoption through the Mode
MCP server for semantic code research and context generation on real-time using LLM patterns | Search naturall
The open context engine for AI agents support 15+ data sources. Built on Rust and Apache DataFusion.
The missing linter and lsp for AI coding assistants. Validate CLAUDE.md, AGENTS.md, SKILL.md, hooks, MCP. Plug
Open Python agent harness for production AI apps: tools, MCP, memory, workspace, telemetry, subagents, backgro
Explore other popular agent tool tools:
baby-agent is AI agent tutorials for backend developers without AI background. 适合后端工程师的零基础 AI Agent 教程. It is categorized as a Agent Tool with 396 GitHub stars.
baby-agent is primarily written in Go. It covers topics such as agent, ai, harness-engineering.
You can find installation instructions and usage details in the baby-agent GitHub repository at github.com/baby-llm/baby-agent. The project has 396 stars and 55 forks, indicating an active community.
baby-agent is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to baby-agent on Agent Skills Hub include altimate-code, tuui, octocode-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.