taskowl¶

Modern Celery task monitoring with MCP integration. No UI, just data.

taskowl watches your Celery cluster's event stream, stores every event in PostgreSQL as an append-only audit log, and exposes that data — plus task, worker, and queue operations — through a REST API and a set of MCP tools for LLM-driven monitoring and management.

Features¶

MCP-first: Query and manage tasks, workers, and queues via the Model Context Protocol
Event sourcing: Append-only event log for a complete audit trail and state reconstruction
Real-time monitoring: Capture Celery events as they happen
Task actions: Revoke, retry, recover orphaned tasks, and execute tasks by name
Worker management: List, inspect, scale, restart, and shut down workers
Queue monitoring: Per-queue message and consumer counts for any kombu broker
Workflow automations: Declarative trigger → conditions → actions engine with webhooks, retry orchestration, cooldowns, rate limits, and circuit breakers
Prometheus metrics: Scrape task, worker, and automation telemetry via /metrics
PostgreSQL backend: Production-ready, async throughout
Broker-agnostic: RabbitMQ, LavinMQ, Redis, or any Celery/kombu broker

Architecture¶

Celery workers ──events──▶ Broker ──▶ taskowl consumer ──▶ PostgreSQL
                                                              │
                         REST API ◀───────────────────────────┘
                              ▲
                              │ HTTP
                         MCP server ──▶ LLM / MCP client

Consumer (separate process) captures Celery events and appends them to PostgreSQL (task_events, worker_events, automation_runs).
REST API serves queries and actions over the event-sourcing tables.
MCP server is a thin wrapper that calls the REST API for LLM access.

Get started¶

Jump into the Installation guide to run taskowl, or head straight to the Usage Guide for the MCP tools and REST API.

Note

taskowl only sees what your Celery workers emit. Make sure events are enabled — otherwise taskowl sees nothing.