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0.1.4

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ai-collab

AI Collaboration System / 智能多 Agent 协作编排框架

Published2 Mar 2026

Direct dependencies10

Transitive dependenciesNot calculated

README

Version 0.1.4 documentation

AI Collaboration System / 智能协作系统

面向 Codex / Claude / Gemini 的终端多 Agent 协作编排框架。

本框架将真实开发流程中的规划、实现、审查与交付环节拆分至不同模型，并提供可观测、可回放、可降级的协作机制。

English Documentation

核心特性

主控优先：先规划再执行，避免单 Agent 盲目推进
配置驱动：模型与角色通过配置文件管理，无硬编码依赖
过程可见：基于 tmux 的协作模式支持实时交互、日志记录与会话回放
故障可控：针对命令不可用、权限受限、子 Agent 失败等场景提供显式降级策略

统一命令接口

项目采用单一命令入口：ai-collab

执行任务：ai-collab "<task>"
管理操作：ai-collab <subcommand>（如 init、status、config）

查看完整执行参数：

ai-collab run --help

安装方式

从源码安装

git clone https://github.com/skyhua0224/ai-collab.git
cd ai-collab
python3 -m pip install -e .

从 PyPI 安装

pip install ai-collab

快速开始

1. 初始化配置

ai-collab init
ai-collab status

2. 执行协作任务

ai-collab "设计并实现一个带鉴权的 REST API，并给出测试与发布建议"

3. 启用 tmux 可视化协作

ai-collab \
  --provider codex \
  --execution-mode tmux \
  --tmux-target inline \
  --tmux-prewarm-subagents \
  "实现一个包含前端与后端的最小业务功能，并完成审查"

常用子命令

命令	功能说明
`ai-collab init`	初始化配置文件模板
`ai-collab status`	查看当前配置与 Agent 可用性状态
`ai-collab detect`	检测协作需求并生成编排建议
`ai-collab monitor`	手动启动 tmux 协作工作区
`ai-collab config`	管理配置项
`ai-collab select`	根据任务复杂度选择模型策略

可观测性与故障排查

日志目录结构

.ai-collab/logs/<session>/

常用排查命令

# 实时查看日志
tail -f .ai-collab/logs/<session>/*.log

# 检测任务编排建议（JSON 格式）
ai-collab detect "<task>" --output json

# 模拟执行（不实际运行）
ai-collab --dry-run --output json "<task>"

文档导航

开发与验证

# 运行测试
pytest -q

# 构建分发包
python3 -m build

# 验证分发包
python3 -m twine check dist/*

联系方式

📧 Email: dev@sky-hua.xyz
💬 Telegram: @skyhua
🐧 QQ: 2110591491
🔗 GitHub Issues: 提交 Issue

开源协议

MIT License

Downloads & popularity

Awaiting observations

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Download counts and popularity will appear here as the collector receives them.

Declared dependencies 10

Native requirements

10 direct targetsNot calculated transitive count

Package / target	Requirement	Relationship
click Environment { "extras": "", "marker": null, "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`click>=8.0.0`	runtime
rich Environment { "extras": "", "marker": null, "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`rich>=13.0.0`	runtime
questionary Environment { "extras": "", "marker": null, "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`questionary>=2.0.1`	runtime
pydantic Environment { "extras": "", "marker": null, "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`pydantic>=2.0.0`	runtime
pyyaml Environment { "extras": "", "marker": null, "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`pyyaml>=6.0`	runtime
pytest Environment { "extras": "", "marker": "extra == \"dev\"", "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`pytest>=7.0.0; extra == "dev"`	runtime
pytest-cov Environment { "extras": "", "marker": "extra == \"dev\"", "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`pytest-cov>=4.0.0; extra == "dev"`	runtime
black Environment { "extras": "", "marker": "extra == \"dev\"", "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`black>=23.0.0; extra == "dev"`	runtime
ruff Environment { "extras": "", "marker": "extra == \"dev\"", "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`ruff>=0.1.0; extra == "dev"`	runtime
mypy Environment { "extras": "", "marker": "extra == \"dev\"", "requires_python": ">=3.9", "evaluation": "NOT_EVALUATED" }	`mypy>=1.0.0; extra == "dev"`	runtime