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Agent工作流

芬奇机器人

基于 Python · 无代码搭建完整 AI 自动化流程

英文名：FinchBot

⭐ 63 Stars 🍴 4 Forks 💻 Python 📄 MIT 🏷 AI 8.0分

8.0AI 综合评分

AIagent工作流

⬇ 下载源码 ZIP ⚙️ 配置说明

✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐：芬奇机器人是一款优质的Agent工作流。AI 综合评分 8.0 分，在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案，这是一个值得深入了解的选择。

📚 深度解析

芬奇机器人是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升，基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化（模拟鼠标键盘操作），AI Agent 工作流通过理解任务意图、动态规划执行路径，能够处理更复杂的非结构化任务。

芬奇机器人工作流的设计遵循"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

芬奇机器人是一套完整的 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。

GitHub Stars

⭐ 63

开发语言

Python

支持平台

Windows / macOS / Linux

维护状态

轻量级项目，按需更新

开源协议

MIT

AI 综合评分

8.0 分

工具类型

Agent工作流

Forks

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

📌 核心特色

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

🎯 主要使用场景

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

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 方式一：pip 安装（推荐）
pip install finchbot

# 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 finchbot

# 方式三：从源码安装（获取最新功能）
git clone https://github.com/xt765/FinchBot
cd FinchBot
pip install -e .

# 验证安装
python -c "import finchbot; print('安装成功')"

📋 安装步骤说明

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

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 命令行使用
finchbot --help

# 基本用法
finchbot input_file -o output_file

# Python 代码中调用
import finchbot

# 示例
result = finchbot.process("input")
print(result)

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

# finchbot 配置文件示例（config.yml）
app:
  name: "finchbot"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
finchbot --config config.yml

# 或通过环境变量配置
export FINCHBOT_API_KEY="your-key"
export FINCHBOT_OUTPUT_DIR="./output"

📑 README 深度解析真实文档完整度 58/100 含工作流图查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

FinchBot — A Lightweight, Flexible, Self-Extending AI Agent Framework

Built on LangChain v1.2 & LangGraph v1.0 with persistent memory, dynamic prompts, autonomous capability extension

🌐 Language: <a href="README.md">English</a> | <a href="README_CN.md">中文</a>

FinchBot is an AI Agent framework that empowers agents with true autonomy, built on LangChain v1.2 and LangGraph v1.0. With fully async architecture, agents gain the ability to self-decide, self-extend, and self-evolve:

Capability Self-Extension — Agent can use built-in tools to configure MCP and create skills when hitting capability boundaries
Task Self-Scheduling — Agent can self-set background tasks and scheduled execution without blocking conversations
Memory Self-Management — Agent can self-remember, self-retrieve, and self-forget with Agentic RAG + Weighted RRF hybrid retrieval
Behavior Self-Evolution — Both Agent and users can self-modify prompts, continuously iterating and optimizing behavior

---

Prerequisites

Item	Requirement
OS	Windows / Linux / macOS
Python	3.13+
Package Manager	uv (Recommended)

Install dependencies

uv sync


> **Note**: The embedding model (~95MB) will be automatically downloaded to the local cache when you run the application for the first time.

<details>
<summary>Development Installation</summary>

bash uv sync --extra dev ```

This includes: pytest, ruff, basedpyright

</details>

Installation

```bash

Docker Deployment

```bash

Quick Start

Basic Usage

```bash

Extension Guide

1. Capability Self-Extension: Built-in Tools + MCP Config + Skill Creation

FinchBot provides a three-layer capability extension mechanism, allowing agents to self-extend when hitting capability boundaries.

Three-Layer Extension Mechanism

flowchart LR classDef layer1 fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px,color:#1b5e20; classDef layer2 fill:#e3f2fd,stroke:#1565c0,stroke-width:2px,color:#0d47a1; classDef layer3 fill:#fff9c4,stroke:#f9a825,stroke-width:2px,color:#f57f17; L1[Layer 1
Built-in Tools
Ready to Use]:::layer1 --> L2[Layer 2
MCP Config
Agent Self-Config]:::layer2 --> L3[Layer 3
Skill Creation
Agent Self-Create]:::layer3

Layer	Method	Autonomy	Description
Layer 1	Built-in Tools	Ready to use	19 built-in tools, no configuration needed
Layer 2	MCP Config	Agent self-config	Dynamically add external capabilities via `configure_mcp`
Layer 3	Skill Creation	Agent self-create	Create new skills via `skill-creator`

Built-in Tools

Category	Tool	Function
File Ops	`read_file`	Read local files
	`write_file`	Write local files
	`edit_file`	Edit file content
	`list_dir`	List directory contents
Web	`web_search`	Web search (Tavily/Brave/DDG)
	`web_extract`	Extract web content
Memory	`remember`	Store memory
	`recall`	Retrieve memory
	`forget`	Delete/archive memory
System	`exec`	Execute shell commands safely
Config	`configure_mcp`	Configure MCP servers dynamically
	`refresh_capabilities`	Refresh capability description
Background	`start_background_task`	Start background task
	`check_task_status`	Check task status
	`get_task_result`	Get task result
	`cancel_task`	Cancel task
Schedule	`create_cron`	Create scheduled task
	`list_crons`	List all scheduled tasks
	`delete_cron`	Delete scheduled task

Web Search

web_search tool uses a three-engine fallback design, ensuring it always works:

flowchart TD classDef check fill:#fff9c4,stroke:#fbc02d,stroke-width:2px,color:#f57f17; classDef engine fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px,color:#1b5e20; classDef fallback fill:#e3f2fd,stroke:#1565c0,stroke-width:2px,color:#0d47a1; Start[Web Search Request]:::check Check1{TAVILY_API_KEY
Set?}:::check Tavily[Tavily
Best Quality
AI-Optimized]:::engine Check2{BRAVE_API_KEY
Set?}:::check Brave[Brave Search
Privacy Friendly
Large Free Tier]:::engine DDG[DuckDuckGo
Zero Config
Always Available]:::fallback Start --> Check1 Check1 -->|Yes| Tavily Check1 -->|No| Check2 Check2 -->|Yes| Brave Check2 -->|No| DDG

Priority	Engine	API Key	Features
1	Tavily	Required	Best quality, AI-optimized, deep search
2	Brave Search	Required	Large free tier, privacy-friendly
3	DuckDuckGo	Not required	Always available, zero config

Session Management

session_title tool makes session naming smart:

Method	Description	Example
Auto Generate	After 2-3 turns, AI automatically generates title based on content	"Python Async Programming Discussion"
Agent Modify	Tell Agent "Change session title to XXX"	Agent calls tool to modify automatically
Manual Rename	Press `r` key in session manager to rename	User manually enters new title

MCP Configuration

Agents can autonomously manage MCP servers through the configure_mcp tool:

Operation	Description
`add`	Add new MCP server
`update`	Update existing server configuration
`remove`	Delete MCP server
`enable`	Enable disabled MCP server
`disable`	Temporarily disable MCP server
`list`	List all configured servers

Dynamic Capability Updates:

flowchart LR classDef config fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px,color:#1b5e20; classDef system fill:#e3f2fd,stroke:#1565c0,stroke-width:2px,color:#0d47a1; classDef prompt fill:#fff9c4,stroke:#fbc02d,stroke-width:2px,color:#f57f17; MCP[MCP Config
configure_mcp]:::config --> Refresh[refresh_capabilities]:::system --> Builder[CapabilitiesBuilder
Regenerate]:::system --> Write[CAPABILITIES.md]:::prompt --> Load[Next Session
Auto-Load]:::prompt

Skill Creation

FinchBot includes a built-in skill-creator skill, allowing agents to autonomously create new skills:

User: Help me create a translation skill that can translate Chinese to English

Agent: Okay, I'll create a translation skill for you...
       [Invokes skill-creator skill]
       ✅ Created skills/translator/SKILL.md
       You can now use the translation feature directly!

No manual file creation, no coding—extend Agent capabilities with just one sentence!

Skill File Structure

skills/
├── skill-creator/        # Skill creator (Built-in) - Core of out-of-the-box
│   └── SKILL.md
├── summarize/            # Intelligent summarization (Built-in)
│   └── SKILL.md
├── weather/              # Weather query (Built-in)
│   └── SKILL.md
└── my-custom-skill/      # Agent auto-created or user-defined
    └── SKILL.md

Design Highlights

Feature	Description
Agent Auto-Create	Tell Agent your needs, auto-generates skill files
Dual Skill Source	Workspace skills first, built-in skills fallback
Dependency Check	Auto-check CLI tools and environment variables
Cache Invalidation	Smart caching based on file modification time
Progressive Loading	Always-on skills first, others on demand

Configure your platform and set webhook URL:

Step 1: Configure API keys

uv run finchbot config

2. Configure environment

cp .env.example .env

Edit .env and add your API keys

Environment Variables

```bash

Method 2: Use .env file

cp .env.example .env

Edit .env and add your API keys

```

Core Components

Step 5: Start webhook server (for LangBot integration)

uv run finchbot webhook --port 8000 ```

Command	Function
`finchbot config`	Interactive configuration for LLM providers, API keys
`finchbot chat`	Start or continue an interactive conversation
`finchbot sessions`	Full-screen session manager
`finchbot cron`	Scheduled task manager
`finchbot webhook`	Start webhook server for LangBot integration