hatchet-sdk
This is the official Python SDK for Hatchet, a distributed, fault-tolerant task queue. The SDK allows you to easily integrate Hatchet's task scheduling and workflow orchestration capabilities into your Python applications.
Decision gist · record as of 2026-08-14
Yes, if you need distributed task orchestration in Python. The SDK is actively maintained (1 day old release), has low install friction, no known vulnerabilities, and a permissive MIT license. It's well-suited for workflows, scheduling, and event-driven task execution. Install it if you're already running a Hatchet server or planning to; otherwise, verify that Hatchet's architecture fits your deployment model.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports current versions, <4).
- A running Hatchet server instance is needed to execute workflows.
- Low friction install with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this SDK with minimal restrictions, including in commercial projects.
last release 2026-08-13 (1 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,432,129 downloads/mo, #3,911 on PyPI
Alternatives
Verify before relying
pip install hatchet-sdk
from hatchet_sdk import Hatchet
hatchet = Hatchet()
# Define and run workflows via the SDK- Whether the SDK requires network connectivity to a Hatchet server at import time or only at workflow execution.
- Performance characteristics and throughput limits for typical workflow loads.
- Whether gRPC dependencies (grpcio, grpcio-tools) are optional or always required.
What it is and what it does
Hatchet SDK is the official Python client for integrating Hatchet, a distributed fault-tolerant task queue, into your applications. It provides APIs for defining workflows with dependencies, parallel execution, automatic retry policies, and event-driven triggers. The SDK handles communication with a Hatchet server via gRPC and includes observability hooks (prometheus-client) for monitoring workflow execution.
You use it to register task handlers in your Python code, define workflow orchestration logic, and schedule or trigger those workflows from your application. It abstracts away the complexity of distributed task coordination—retries, fault tolerance, and scheduling are handled by the framework. The SDK requires Python 3.10+ and depends on aiohttp for async HTTP, pydantic for configuration, and gRPC for server communication.
Use it for
- Build a multi-step data pipeline where tasks depend on prior results and failed steps automatically retry.
- Schedule recurring reports or batch jobs to run at specific times without managing cron jobs.
- Trigger long-running operations (file processing, API calls) from a web request without blocking the response.
- Coordinate parallel tasks across multiple workers and aggregate their results.
- Monitor and observe task execution metrics and workflow progress in real time.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need distributed task orchestration in Python.
The SDK is actively maintained (1 day old release), has low install friction, no known vulnerabilities, and a permissive MIT license. It's well-suited for workflows, scheduling, and event-driven task execution. Install it if you're already running a Hatchet server or planning to; otherwise, verify that Hatchet's architecture fits your deployment model.
Install
hatchet-sdk on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance (released 2026-08-13, 1 day old). Depends on 12 runtime packages including aiohttp, grpcio, pydantic, and prometheus-client—a moderate but standard set for a distributed systems client.
Requires Python 3.10 or later (supports current versions, <4). A running Hatchet server instance is needed to execute workflows.
License in practice
MIT license is permissive; you can use, modify, and distribute this SDK with minimal restrictions, including in commercial projects.
Quickstart
pip install hatchet-sdk
from hatchet_sdk import Hatchet
hatchet = Hatchet()
# Define and run workflows via the SDKVerify before relying
- Whether the SDK requires network connectivity to a Hatchet server at import time or only at workflow execution.
- Performance characteristics and throughput limits for typical workflow loads.
- Whether gRPC dependencies (grpcio, grpcio-tools) are optional or always required.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagesaiohttpgrpcio-statusgrpcio-toolsgrpcioprometheus-clientprotobufpydantic-settingspydanticpython-dateutiltenacitytyping-inspectionurllib3 |
| Maintenance | Actively maintained 1 days since the last release |
| First released | |
| Downloads | 1,432,129 / month, #3,911 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: hatchet_sdk-1.37.2-py3-none-any.whl
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