智能代理工作流
经 AI Skill Hub 精选评估，智能代理工作流 获评「强烈推荐」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。
📚 深度解析
智能代理工作流 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.0 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
智能代理工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
📖 中文文档
智能代理工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：pip 安装（推荐）
pip install roboco
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install roboco
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/rennf93/roboco
cd roboco
pip install -e .
# 验证安装
python -c "import roboco; print('安装成功')"
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 命令行使用
roboco --help
# 基本用法
roboco input_file -o output_file
# Python 代码中调用
import roboco
# 示例
result = roboco.process("input")
print(result)
# roboco 配置文件示例（config.yml） app: name: "roboco" debug: false log_level: "INFO" # 运行时指定配置文件 roboco --config config.yml # 或通过环境变量配置 export ROBOCO_API_KEY="your-key" export ROBOCO_OUTPUT_DIR="./output"
RoboCo
AI Agents Company - A virtual organization of 25 AI agents + 1 human CEO, designed to operate as a complete software development workforce.
<p align="center"> <a href="https://www.youtube.com/watch?v=qB5fXv5LeHc"> <img src="https://img.youtube.com/vi/qB5fXv5LeHc/maxresdefault.jpg" alt="Watch the Quick Guide to RoboCo on YouTube - how to set up a 25-agent autonomous AI workforce" width="70%"> </a> <br> <sub>▶ <b><a href="https://www.youtube.com/watch?v=qB5fXv5LeHc">Quick Guide to RoboCo: Setup a 25-Agent Autonomous AI Workforce</a></b></sub> </p>
<p align="center"> <img src="https://docs.roboco.tech/docs/videos/panel-teaser.gif" alt="Twelve-second looping preview of the RoboCo control panel — the org tree, a task in progress, and an approval queue." width="80%"> <br> <sub><a href="https://docs.roboco.tech/docs/videos/panel-full-walkthrough.mp4">Watch the full 2:33 walkthrough (.mp4) →</a></sub> </p>
[!WARNING] RoboCo is early-stage, work-in-progress software (v0). It's under active development, runs in a homelab, and will have rough edges, breaking changes, and bugs. It is not production-ready and the API/database schema are not stable yet. Treat it as a working prototype to explore and build on — please don't expose it to the public internet as-is. Issues and PRs very welcome.
[!TIP] 📚 Full documentation: docs.roboco.tech — install & first run, the company model, a page-by-page panel reference, model providers, the optional subsystems, deployment, and the API.
Overview
RoboCo implements a structured organizational hierarchy with formal communication protocols, task management, and quality controls. The system enables a single human (CEO) to orchestrate complex multi-project development at scale.
CEO (You, the human)
│
├── Intake (on-demand interviewer: chats only with you to draft a task)
├── Secretary (on-demand chief-of-staff: reads company state, runs gated directives)
├── PR Reviewer (read-only main reviewer: inbound external/fork + internal PRs, and the root→master in-path gate)
│
└── Board (3 agents)
├── Product Owner
├── Head of Marketing
└── Auditor (silent observer, reports to you)
│
└── Main PM (coordinates all cells)
│
├── Backend Cell (6 agents: 2 Devs, 1 QA, 1 PM, 1 Documenter, 1 PR Reviewer)
├── Frontend Cell (6 agents: 2 Devs, 1 QA, 1 PM, 1 Documenter, 1 PR Reviewer)
└── UX/UI Cell (6 agents: 2 Devs, 1 QA, 1 PM, 1 Documenter, 1 PR Reviewer)The 25 agents = Intake + Secretary + PR Reviewer + the Board (3) + Main PM + the three 6-agent cells (18). Agents run on Anthropic Claude by default, or on xAI Grok, OpenAI Codex, Google Gemini, or Moonshot Kimi K3 (each on its own official CLI and subscription, no metered API key) — see the provider note under Configuration.
Install dev dependencies
uv sync --all-extras
Option 2 — Build from source
The same full stack, built locally from the Dockerfiles instead of pulled:
git clone https://github.com/rennf93/roboco.git && cd roboco
cp .env.example .env # then edit in your secrets
docker compose up -d # builds images on first run, then starts everythingOption 1 — Run the pre-built images (quickest)
Every release publishes all RoboCo images to both the GitHub Container Registry and Docker Hub, so you can run the full stack without building anything. One command brings it up:
git clone https://github.com/rennf93/roboco.git && cd roboco
make quickstart # no make? run ./scripts/bootstrap.sh directlymake quickstart (scripts/bootstrap.sh) is idempotent — safe to re-run any time. What it does:
```bash cp .env.example .env # only if missing — an existing .env is never touched
Option 3 — Local development (no full stack)
For hacking on the code itself, run only the backing services in Docker and the API on your host. RoboCo's own code requires Python 3.13+ (uv will fetch it if needed):
```bash uv sync docker compose up -d postgres redis ollama # backing services only uv run alembic upgrade head # migrate the database uv run python -m roboco.cli # API + orchestrator
Configuration
Key environment variables (see roboco/config.py for all options). The panel's Settings → Feature Flags card is the authoritative source for the full set of 36 feature flags — toggle them there rather than editing env by hand. The bash block below shows the env-var equivalents of a few representative flags plus settings that have no panel toggle:
```bash
Feature flags (default-off unless noted; toggle from Settings → Feature Flags)
ROBOCO_CONVENTIONS_ENABLED=false # per-project architectural conventions standard ROBOCO_TOOLCHAIN_MATCH_ENABLED=false # build each target project under its own Python ROBOCO_OVERLOAD_BREAK_ENABLED=true # park a provider on a persistent model-API overload ROBOCO_DOCS_SYNC_ENABLED=false # docs-divergence sync (release → docs-update task). Default-off; when on, a successful release publish originates one bounded, deduped docs-update task against the roboco-website project. ROBOCO_DOCS_SYNC_MAX_OPEN_TASKS=3 # rolling cap on concurrently-open docs-sync tasks ROBOCO_DOCS_SYNC_MAX_PER_CYCLE=1 # max docs-sync tasks originated per publish invocation
Or just the API without the orchestrator:
uv run uvicorn roboco.api.app:app --reload --host 0.0.0.0 --port 8000 ```
API Server
ROBOCO_HOST=0.0.0.0 ROBOCO_PORT=8000
API Endpoints
Domain routes are mounted under /api:
| Route Group | Description |
|---|---|
/api/tasks | Task CRUD, lifecycle, claiming |
/api/agents | Agent management |
/api/projects | Project (repo) management |
/api/work-sessions | Git work session tracking |
/api/git | Git operations (status, commit, push, PR) |
/api/orchestrator | Orchestrator / dispatcher status |
/api/kanban | Kanban board views per team |
/api/dashboard | Dashboard, metrics, and analytics |
/api/notifications | Formal notifications (ack-required) |
/api/journals | Agent journals/reflections |
/api/a2a | Agent-to-agent direct messaging |
/api/optimal | RAG/Knowledge base queries |
/api/stream | Stream processing and permissions |
/api/settings | Feature flags and settings store |
/api/company-goals | Company goals, scorecard, brand voice |
/api/research | Web research (external search/fetch) |
/api/cockpit | CEO read-only business summary |
/api/release | Release manager proposals and approval |
/api/secretary | Secretary chief-of-staff reads and directives |
/api/prompter | Intake live chat (SSE relay) |
/api/pitches | Board proposals and CEO approve/provision |
/api/playbooks | Playbook library curation (approve/reject/archive) |
/api/x | X (Twitter) post queue and credentials |
/api/video | Video engine request, pipeline, and approval |
/api/tiktok | TikTok OAuth credentials (write-only) |
/api/telegram | Telegram notifications bridge |
/api/roadmap | Board roadmap cycle approval |
/api/board-programs | Board Programs registry status and run-now |
/api/pest-control | Pest Control bug-hunt cycle |
/api/periscope | Periscope market-research briefs |
/api/coroner | Coroner postmortem reports |
/api/sentinel | Sentinel quality-drift reports |
/api/spackle | Spackle gap-fill cycle |
/api/scales | Scales portfolio-rebalance cycle |
/api/mirror | Mirror messaging-fix cycle |
/api/dogfood | Dogfood friction-fix cycle |
/api/github-app | GitHub App credentials and repo listing |
/api/products | Product CRUD |
/api/providers | AI provider model routing |
/api/docs | Project documentation management |
/api/usage | Token usage analytics |
/api/system | System monitoring (rate limits, etc.) |
/api/auth | Cloud auth login/logout (mounted only when ROBOCO_CLOUD_AUTH_ENABLED) |
The agent gateway verbs are served separately under /api/v1/flow/{role}/{verb} (intent verbs) and /api/v1/do (content tools) — see the Agent Gateway.
高质量的智能代理工作流开源项目
- 构建多智能体协作系统的 Agent 开发者
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- Python 依赖冲突：建议用 venv / uv 隔离环境
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 构建多智能体协作系统的 Agent 开发者
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- Python 依赖冲突：建议用 venv / uv 隔离环境
👥 适合人群
🎯 使用场景
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
该工具使用 AGPL-3.0 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
⚠️ AGPL 3.0 — 最严格的 Copyleft，网络服务端使用也需开源，SaaS 使用受限。
🔗 相关工具推荐
❓ 常见问题 FAQ
AI Skill Hub 点评：智能代理工作流 的核心功能完整，质量优秀。对于自动化工程师和运维人员来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | roboco |
| 原始描述 | 开源AI工作流：Not a loop. Not a harness. Not a worflow. Not a framework. An AI Agents Organiza。⭐41 · Python |
| Topics | agentic-aistate-machinepython |
| GitHub | https://github.com/rennf93/roboco |
| License | AGPL-3.0 |
| 语言 | Python |
收录时间：2026-06-20 · 更新时间：2026-06-22 · License：AGPL-3.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。