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Agent工作流

Hatchet

基于 Go · 无代码搭建完整 AI 自动化流程

英文名：hatchet

⭐ 7.3k Stars 🍴 405 Forks 💻 Go 📄 MIT 🏷 AI 8.0分

8.0AI 综合评分

工作流并发DAG分布式系统可靠执行

⬇ 下载源码 ZIP ⚙️ 配置说明

✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐：Hatchet 是一款优质的Agent工作流。已获得 7.3k 颗 GitHub Star，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

Hatchet 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。

Hatchet 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

Hatchet 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

⭐ 7.3k

开发语言

支持平台

Windows / macOS / Linux（跨平台）

维护状态

持续维护，定期更新

开源协议

MIT

AI 综合评分

8.0 分

工具类型

Agent工作流

Forks

405

📖 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：go install（推荐）
go install github.com/hatchet-dev/hatchet@latest

# 方式二：从源码编译
git clone https://github.com/hatchet-dev/hatchet
cd hatchet
go build -o hatchet .

# 方式三：下载预编译二进制
# 访问 Releases 页面下载对应平台二进制文件
# https://github.com/hatchet-dev/hatchet/releases

📋 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
hatchet --help

# 基本运行
hatchet [options] <input>

# 详细使用说明请查阅文档
# https://github.com/hatchet-dev/hatchet

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

# hatchet 配置说明
# 查看配置选项
hatchet --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 HATCHET_CONFIG="/path/to/config.yml"

📑 README 深度解析真实文档完整度 33/100 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

Hatchet Features

Background Tasks

Background tasks: Hatchet supports one-off background tasks defined as simple functions. It supports both fire-and-forget and fire-and-wait tasks with subscriptions
Retries: flexible and configurable retry policies for tasks, with optional exponential backoff
Cron jobs and scheduled runs for scheduling tasks at some point in the future
Task routing based on strict conditions, like worker labels, or more complex, weighted scheduling rules using worker affinity
Event-based triggering and listeners to build event-driven, highly distributed systems
Webhook-based triggering for easily triggering Hatchet tasks from upstream data sources

Task orchestration and workflows

Durable tasks for building fault-tolerant, long-running workflows which can easily recover from failure
DAGs (directed acyclic graphs) for building data pipelines and simple workflows. See our guide on choosing between durable tasks and DAGs
Complex pause/resume conditions using durable sleep, event waits, or a combination of both

Scale

Priority so that critical tasks can run before tasks which aren't latency sensitive, like backfill jobs
Rate limiting to deal with third-party APIs, or even to enforce per-user rate limits using dynamic rate limits
Fair scheduling using Hatchet's concurrency policies, which can set a concurrency limit for tasks based on dynamic keys
Worker slots for ensuring that workers cannot take on more work than they can handle

Monitoring, observability, and management

Real-time web UI with alerting, monitoring, and logging
OpenTelemetry (using Hatchet's built-in collector or external destinations)
Prometheus metrics
Multi-tenant by default, so a single Hatchet instance can support multiple teams
Users and roles

Hatchet Cloud features

Autoscaling and pay-as-you-go plans
Multi-region deployments
SSO
Improved performance for monitoring, logging, and observability

An orchestration engine for background tasks, AI agents, and durable workflows

<p align="center"> <a href="https://cloud.hatchet.run">Hatchet Cloud</a> · <a href="https://docs.hatchet.run">Documentation</a> · <a href="https://hatchet.run">Website</a> · <a href="https://github.com/hatchet-dev/hatchet/issues">Issues</a> </p>

</div>

Hatchet vs...

<details> <summary>Hatchet vs Durable Execution Platforms (Temporal, DBOS)</summary>

####

Hatchet's durable tasks feature is a drop-in replacement for Temporal or DBOS workflows. You also get:

End-to-end observability of durable tasks using OpenTelemetry, monitoring and logging
Features built for running workflows at scale, such as rate limiting, complex routing, and worker-level slot control
Multi-tenancy, users and roles supported out of the box

In addition to making durable execution easier to use, Hatchet can also be used as a general-purpose queue, a DAG-based orchestrator, a durable execution engine, or all three, allowing teams to centralize their async and background processing in a single platform.

</details>

<summary>Hatchet vs Task Queues (Celery, BullMQ)</summary>

####

Traditional task queues like BullMQ and Celery trade off durability for throughput. Tasks persist on the broker (typically Redis or RabbitMQ) while the task is executing, but are not persisted afterwards. This makes it difficult to build complex workflows, as there is no persistent intermediate state. It also makes it difficult to recover and replay tasks which failed and were removed from the queue, resulting in custom admin tooling to work with these libraries at scale.

On the other hand, Hatchet is a durable task queue, meaning it persists the history of all executions (up to a defined retention period), which allows for easy monitoring, debugging and durable task features. Hatchet's durability features add some overhead: while Hatchet has been load-tested up to 10k tasks/second, it consumes more resources than a system built on Redis or RabbitMQ, which can reach much higher throughput.

</details>

<summary>Hatchet vs DAG-based platforms (Airflow, Prefect, Dagster)</summary>

####

These tools are usually built with data engineers in mind, and aren’t designed to run as part of a high-volume application. They’re usually higher latency and higher cost, with their primary selling point being integrations with common datastores and connectors.

When to use Hatchet: when you'd like to use a DAG-based framework, write your own integrations and functions, and require higher throughput (>100/s)

When to use other DAG-based platforms: when you'd like to use other data stores and connectors that work out of the box

</details>

🎯 aiskill88 AI 点评 A 级 2026-06-03

Hatchet是一个高质量的开源AI工作流引擎，支持多种类型的工作流