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Hatchet

Orchestration engine for background tasks, AI agents, and durable workflows

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About

Hatchet is an open-source (MIT) orchestration engine for background tasks, durable workflows, and AI agents. You define tasks and DAG workflows as code in Python, TypeScript, Go, or Ruby; workers pull work from a durable queue with automatic retries, checkpointing, and replay.

It deploys as a service (engine + API + dashboard) on top of Postgres — no Kafka/ZooKeeper cluster to operate — and offers a managed cloud with the same application code. A strong fit for teams that want Temporal-style durability without operating a Temporal cluster.

What it can do

  • • Durable tasks with automatic retries, checkpointing, and replay from a durable event log
  • • DAG workflows with step dependencies, fan-out/fan-in, and conditional execution
  • • Event-driven triggers plus cron/scheduled runs
  • • Fair-share scheduling, priorities, and worker slot control for multi-tenant workloads
  • • Real-time dashboard: run visualization, logs, alerting
  • • SDKs for Python, TypeScript, Go, Ruby; workers authenticate over gRPC with API tokens
  • • AI-agent oriented: automatic checkpointing of long-running agent invocations

Setup

  1. Install the Hatchet CLI: curl -fsSL https://install.hatchet.run/install.sh | bash (macOS/Linux/WSL) or brew install hatchet-dev/hatchet/hatchet --cask (macOS). Verify with hatchet --version.
  2. Start a local engine (requires Docker): hatchet server start — this spins up Postgres and the Hatchet server, dashboard on http://localhost:8888.
  3. Install an SDK in your app (e.g. pip install hatchet-sdk or npm install @hatchet-dev/typescript) and write a worker.
  4. Create an API token for the worker in the dashboard under Settings → API Tokens, then run your worker against the local instance.
  5. Optional: silence CLI telemetry with export HATCHET_CLI_TELEMETRY_ENABLED=false (see docs.hatchet.run CLI reference).

Prerequisites

Docker (for `hatchet server start`); Hatchet Lite needs nothing else — it uses Postgres as both DB and queue. Full production self-host uses Postgres + RabbitMQ via the provided docker-compose.

Use cases

automationcodedata

Works with

Generic APIAny agent (.md)

Trust score — 9/10

/10 trustVerified

Scored 9/10 (verified tier, metrics-v1). Evidence: 8,105 GitHub stars (log-scaled vs catalog peers); last push 0d ago; 100 releases/12mo; 142 open issues. Manual review: CLI telemetry on by default (opt-out); --disable-auth dev image has fixed worker token. License: MIT.

★ 8,105 GitHub stars

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
7.4/10
Maintenance · recency, releases, issue load
9.3/10
Openness · license + docs
10.0/10
Manual review · sanity read + red-flag check
9.0/10
  • · 8,105 GitHub stars (log-scaled vs catalog peers)
  • · last push 0d ago; 100 releases/12mo; 142 open issues
  • · manual review: CLI telemetry on by default (opt-out); --disable-auth dev image has fixed worker token

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.