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Hatchet

Also known as: Hatchet Cloud

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Entry priceDeveloper free plus $10 per 1M runs · Team $500/mo plus usageFull pricing detail

Open source, MIT-licensed task queue and durable workflow engine on Postgres for AI agents and background tasks, with DAGs, durable waits, broad triggers and observability, self-hosted or on Hatchet Cloud.

Hatchet is an open source, MIT licensed distributed task queue and workflow engine built on Postgres. Tasks written in Python, TypeScript, Go or Ruby compose into DAG workflows and durable tasks with child spawning, retries, timeouts, cancellation, durable sleeps and event waits, concurrency control, rate limits, priority and batch tasks, and every task, DAG, event and agent invocation is stored in a durable event log.

Runs start from cron schedules, scheduled runs, events and webhooks from GitHub, Stripe, Slack or any sender. The dashboard shows run history with logs and alerting, OpenTelemetry exports traces, and Prometheus exposes worker metrics. A CLI bundles an MCP server so coding agents can trigger and inspect runs, and cookbooks show agents built on the Claude Agent SDK and OpenAI Agents SDK running as Hatchet tasks.

Hatchet self hosts with Docker Compose or Kubernetes, or runs on Hatchet Cloud, with a free Developer plan covering the first 100,000 task runs and $10 per million after, Team at $500 a month, Scale at $1,000 a month with audit logs and HIPAA, and custom Enterprise with SSO and bring your own cloud. SOC 2 Type II applies from the free plan up.

Vendor details

Canonical URL

https://hatchet.run

Category

Agent infrastructure

Funding status

Private, pre-seed (a February 2024 round, per third-party funding data). Founded by Alexander Belanger and Gabe Ruttner, Y Combinator Winter 2024, backed by Y Combinator and Liquid 2 Ventures. Based in San Francisco.

Company status

independent

Use cases & customers

Primary use cases

AI agent orchestration and durabilityBackground task and job queuesDurable long-running workflowsMassively parallel workloads

Target customers

DevelopersAI application teamsScale-ups and enterprisesData-intensive engineering teams

Deployment options

SaaSself-hostedon-prem

Integrations

Provides SDKs for Python, TypeScript, Go, and Ruby, webhook-based triggering from upstream data sources, OpenTelemetry-based observability, and runs on PostgreSQL as its only required dependency; workers run on the user's own infrastructure.

In practice

Your support agent, built with the OpenAI Agents SDK, keeps failing halfway through long tool chains. You run each step as a durable Hatchet task, so after a crash it replays the completed steps and carries on instead of starting over.

A refund over a set amount needs a manager's sign off before it goes out. Your workflow suspends on a durable event wait until the approval event arrives or a timeout passes, then continues or stops.

Every new Stripe payment should start document generation and a welcome email. A Hatchet webhook endpoint starts the workflow, and the dashboard shows each task's logs so you can replay a run that went wrong.

Agentic Index coverage score

8.5 / 14 capabilities · 61%

Integrations & Tool Calling Partial

The customer's agents and tools run here, but Hatchet does not supply them. Cookbooks show agents built with the Claude Agent SDK and the OpenAI Agents SDK running as Hatchet tasks, and any task can call an outside API. There is no connector catalog, custom tool route or framework for authenticated actions of Hatchet's own, so the tools and credentials are the customer's code. Webhook integrations with GitHub, Stripe and Slack start runs.

Sourcedocs.hatchet.run/cookbooks/hatchet-claude-agent-sdk-trusted-envread 2026-09-23

Workflow Orchestration Full

Tasks compose into DAG workflows and durable tasks that can spawn child tasks, so Hatchet works as a workflow engine. They carry retry policies, timeouts, cancellation, durable sleeps and event waits, along with concurrency control, rate limits, priority, batch tasks and bulk triggering. Workers run them in Python, TypeScript, Go and Ruby.

Sourcedocs.hatchet.run/v1/directed-acyclic-graphsread 2026-09-23

Knowledge Grounding & RAG Not documented

There is no retrieval structure that Hatchet maintains over the customer's corpus. Its cookbooks run document pipelines as tasks, but the parsing, indexing and retrieval are the customer's own code.

Sourcedocs.hatchet.run/cookbooks/pdf-pipelineread 2026-09-23

Human Oversight & Guardrails Partial

Holding for a person runs on durable waits that the customer's code calls. A durable event wait suspends a task until a matching user event arrives, filtered with CEL expressions, or until a timeout passes, and workflows can be paused and resumed. This is a general wait put to use for approval. There is no approval step, approver identity, review screen or policy guardrail of Hatchet's own.

Sourcedocs.hatchet.run/v1/durable-event-waitsread 2026-09-23

Security, Identity & Governance Full

SOC 2 Type II applies from the free Developer plan, and HIPAA with audit logs from the Scale plan. Tenants separate environments, each with its own users. Hatchet Cloud signs users in with Google or GitHub OAuth, enterprises can set up custom SSO, and Enterprise includes SSO and audit logging.

Sourcehatchet.run/pricingread 2026-09-23

Observability & Auditability Full

Every task, DAG, event and agent invocation is recorded in a durable event log shown in the dashboard, with real time monitoring, alerting and task logs. Traces export through OpenTelemetry, Prometheus metrics are exposed, audit logs are kept from the Scale plan, and data retention is set by plan, at 3 days on Team and 7 on Scale.

Sourcedocs.hatchet.run/v1/opentelemetryread 2026-09-23

Memory & State Persistence Partial

State lasts within one run. Hatchet is durable by default and stores every task, DAG, event and agent invocation in a durable event log, so a durable task replays its completed steps and carries on after a failure. But the checkpointed state only lets the same run finish and carries nothing into the next run. Sticky worker assignment keeps local memory on one worker for a workflow's tasks, which is placement, not a store. There is no state store that persists across runs, keyed per user, conversation or entity, for agent memory.

Sourcedocs.hatchet.run/v1/durable-executionread 2026-09-23

Deployment & Data Residency Full

Customers can self host in their own infrastructure on Docker Compose or Kubernetes under an MIT license, and there is an embedded mode. Hatchet Cloud is the managed service, and Enterprise offers custom deployment and bring your own cloud.

Sourcegithub.com/hatchet-dev/hatchetread 2026-09-23

Prebuilt Agents / Templates / Packs Partial

Starting points come as cookbooks for webhook handlers for GitHub, Stripe and Slack, document pipelines, email flows and agents built on the Claude Agent SDK and OpenAI Agents SDK, plus skills that coding agents can install. These are examples a developer builds from, not a catalog of agents a customer adopts.

Sourcedocs.hatchet.run/cookbooksread 2026-09-23

Triggers & Channel Coverage Full

Work wakes on its own through cron runs, scheduled runs, events that trigger workflows, and webhook endpoints that start runs from GitHub, Stripe, Slack or any sender, and one service can trigger work in another.

Sourcedocs.hatchet.run/v1/webhooksread 2026-09-23

Model Flexibility & Routing Not documented

There is no model in Hatchet for the customer to choose. The model lives in the customer's own agent code, and the cookbooks use the OpenAI Agents SDK and the Claude Agent SDK, while Hatchet schedules and records the tasks whatever the model is. It works with any model only because the model belongs to someone else, and it offers no model selection of its own.

Sourcedocs.hatchet.run/cookbooks/hatchet-openai-agents-sdk-trusted-envread 2026-09-23

APIs / SDKs / MCP Extensibility Full

Official SDKs for Python, TypeScript, Go and Ruby make the platform callable from outside, alongside a REST and gRPC API for triggering and inspecting runs and the hatchet CLI. A local MCP server bundled with the CLI lets coding agents trigger runs, inspect status and events, and list workers.

Sourcedocs.hatchet.run/v1/using-coding-agentsread 2026-09-23

Testing, Debugging & Optimization Partial

When a run goes wrong, it can be replayed from the durable event log, retried or canceled in bulk, and inspected by a coding agent through the MCP server. That helps with debugging, not testing. There is no evaluation harness, scored test cases or quality gate of Hatchet's own.

Sourcedocs.hatchet.run/v1/durable-executionread 2026-09-23

Browser / Computer-use Not documented

The agent operates no browser or desktop, and Hatchet has no browser automation environment of its own. It schedules and runs the customer's tasks on the customer's workers.

Sourcedocs.hatchet.run/v1/using-coding-agentsread 2026-09-23

The Agentic Index coverage score grades every vendor Full, Partial or Not documented against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded

Recent platform changes

2026-09-29·Pricing / packagingVerified

Pay as you go pricing replaced Hatchet Cloud's self serve plans for new signups. The first million task runs and million events each month are free, then $30 per million runs and $5 per million events, with retries not billed, and the old Team, Starter and Growth plans are being retired.

Bears on: Pricing / packaging

View source
2026-09-15·MCP / tool calling / APIPartially Verified

Hatchet v0.107.0 bundles a local MCP server into its CLI, letting coding agents trigger runs, inspect run status and events, and list workers.

Bears on: MCP / tool calling / API

View source
2026-08-10·Workflow orchestrationVerified

Hatchet introduced idempotency keys to ensure workflows execute exactly once, and batch tasks to automatically aggregate multiple tasks into a single run. The release also includes hatchet-embedded for running a sidecar engine during local development, and a Slot Cost API to manage worker resource allocations.

Bears on: Memory / state

View source
View all 3 changes for Hatchet →Tracked since Aug 2026 · Verified from public vendor sources

Pricing

Developer free plus $10 per 1M runs · Team $500/mo plus usage

Monthly plan plus usage at $10 per million task runs; the Developer plan includes the first 100,000 runs

Free tier

Cost watchouts

Every task run counts toward usage, so fan-out and retries multiply runs; data retention is short (3 days on Team, 7 on Scale), and HIPAA, audit logs and SSO sit on Scale or Enterprise.

Variable cost rationale

Usage is billed per task run on top of the plan fee, so fan-out, retries and fine-grained task splitting raise cost.

Overage / add-ons

Self-host scales with your own infrastructure; Cloud usage scales with task throughput.

Sales call required

No, self serve available

Free / trial

Developer plan free with the first 100,000 task runs, no credit card; self-hosting free under MIT

Lowest paid plan

Team $500 a month plus usage

Key ambiguities

The jump from the free Developer plan to Team at $500 a month is steep, and usage is metered per task run on every plan, so cost depends on how finely work is split into tasks.

Agentic Index verified 2026-09-23

Alternatives to Hatchet

The closest documented capability profiles to Hatchet among agent infrastructure platforms tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.

  • DBOS8.5 / 14Matches Hatchet across all 14 documented capabilitiesHatchet vs DBOS →
  • Temporal8.5 / 14Matches Hatchet across all 14 documented capabilities
  • Restate8.5 / 14Fuller documented coverage on Memory & State Persistence
  • Inngest10.0 / 14Adds documented Model Flexibility & Routing
  • C TWO8.5 / 14Fuller documented coverage on Integrations & Tool Calling and Human Oversight & Guardrails
  • Netlify10.5 / 14Adds documented Knowledge Grounding & RAG and Model Flexibility & Routing

Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded

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