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Agent工作流

Plano

基于 Rust · 无代码搭建完整 AI 自动化流程

英文名：plano

⭐ 6.6k Stars 🍴 431 Forks 💻 Rust 📄 Apache-2.0 🏷 AI 8.0分

8.0AI 综合评分

ai-gatewayenvoyrust

⬇ 下载源码 ZIP ⚙️ 配置说明 📺 TG 频道

✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估，Plano 获评「强烈推荐」。已获得 6.6k 颗 GitHub Star，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

Plano 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。

Plano 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

Plano 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

⭐ 6.6k

开发语言

Rust

支持平台

Windows / macOS / Linux

维护状态

持续维护，定期更新

开源协议

Apache-2.0

AI 综合评分

8.0 分

工具类型

Agent工作流

Forks

431

📖 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：cargo install（推荐）
cargo install plano

# 方式二：从源码编译
git clone https://github.com/katanemo/plano
cd plano
cargo build --release
# 二进制在 ./target/release/plano

📋 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

# 查看帮助
plano --help

# 基本运行
plano [options] <input>

# 详细使用说明请查阅文档
# https://github.com/katanemo/plano

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

# plano 配置说明
# 查看配置选项
plano --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量（覆盖配置文件）
export PLANO_CONFIG="/path/to/config.yml"

📑 README 深度解析真实文档完整度 32/100 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。

简介

The AI-native proxy server and data plane for agentic apps.<br><br> Plano pulls out the rote plumbing work and decouples you from brittle framework abstractions, centralizing what shouldn’t be bespoke in every codebase - like agent routing and orchestration, rich agentic signals and traces for continuous improvement, guardrail filters for safety and moderation, and smart LLM routing APIs for model agility. Use any language or AI framework, and deliver agents faster to production.

Quickstart Guide • Build Agentic Apps with Plano • Documentation • Contact

Star ⭐️ the repo if you found Plano useful — new releases and updates land here first. </div>

Overview

Building agentic demos is easy. Shipping agentic applications safely, reliably, and repeatably to production is hard. After the thrill of a quick hack, you end up building the “hidden middleware” to reach production: routing logic to reach the right agent, guardrail hooks for safety and moderation, evaluation and observability glue for continuous learning, and model/provider quirks scattered across frameworks and application code.

Plano solves this by moving core delivery concerns into a unified, out-of-process dataplane.

🚦 Orchestration: Low-latency orchestration between agents; add new agents without modifying app code.
🔗 Model Agility: Route by model name, alias (semantic names) or automatically via preferences.
🕵 Agentic Signals™: Zero-code capture of Signals plus OTEL traces/metrics across every agent.
🛡️ Moderation & Memory Hooks: Build jailbreak protection, add moderation policies and memory consistently via Filter Chains.

Plano pulls rote plumbing out of your framework so you can stay focused on what matters most: the core product logic of your agentic applications. Plano is backed by industry-leading LLM research and built on Envoy by its core contributors, who built critical infrastructure at scale for modern worklaods.

High-Level Network Sequence Diagram: high-level network plano architecture for Plano

Jump to our docs to learn how you can use Plano to improve the speed, safety and obervability of your agentic applications.

[!IMPORTANT] Plano and the Plano family of LLMs (like Plano-Orchestrator) are hosted free of charge in the US-central region to give you a great first-run developer experience of Plano. To scale and run in production, you can either run these LLMs locally or contact us on Discord for API keys.

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What you declare: Agent URLs and natural language descriptions

Build Agentic Apps with Plano

Plano handles orchestration, model management, and observability as modular building blocks - letting you configure only what you need (edge proxying for agentic orchestration and guardrails, or LLM routing from your services, or both together) to fit cleanly into existing architectures. Below is a simple multi-agent travel agent built with Plano that showcases all three core capabilities

📁 Full working code: See demos/agent_orchestration/travel_agents/ for complete weather and flight agents you can run locally.

What You Didn't Have to Build

Infrastructure Concern	Without Plano	With Plano
Agent Orchestration	Write intent classifier + routing logic	Declare agent descriptions in YAML
Model Management	Handle each provider's API quirks	Unified LLM APIs with state management
Rich Tracing	Instrument every service with OTEL	Automatic end-to-end traces and logs
Learning Signals	Build pipeline to capture/export spans	Zero-code agentic signals
Adding Agents	Update routing code, test, redeploy	Add to config, restart

Why it's efficient: Plano uses purpose-built, lightweight LLMs (like our 4B-parameter orchestrator) instead of heavyweight frameworks or GPT-4 for routing - giving you production-grade routing at a fraction of the cost and latency.

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