Celery Performance Monitoring
Per-task timing, queue metrics, memory bloat detection, and error tracking for your Celery workers. Scout's Python agent instruments Celery automatically with no configuration. Connects task failures to the full trace and surrounding log lines.
Errors, Logs, and Traces
Errors, Logs, and Traces for Every Celery Task
Scout connects Celery task failures to the full transaction trace and surrounding log lines in a single view. When a worker raises an exception, you see the error, the task trace with timing for every operation, and the log output together.
Error Monitoring
Automatic exception capture for every Celery task. Errors are grouped by task name, linked to the originating trace, and enriched with task context so you know which tasks are failing and why.
Log Management
Log output from Celery workers is enriched with trace context. Filter logs by trace ID to see exactly what a specific task execution did, including any database queries and HTTP requests it made.
App Traces
Every Celery task execution appears as its own transaction in Scout with per-operation timing. See time spent in SQLAlchemy queries, Django ORM calls, Redis operations, and external HTTP requests broken out by task name.
Query Analysis
N+1 Detection Inside Celery Tasks
Scout automatically detects N+1 query patterns in SQLAlchemy and the Django ORM within Celery task executions. If a background task triggers repeated queries in a loop, Scout identifies the pattern, shows the code location, and reports the total performance impact.
Auto-Instrumentation
What Scout Instruments in Your Celery Workers
Scout's Python agent auto-instruments Celery tasks with per-execution timing and error tracking. SQLAlchemy queries, Django ORM calls, Redis operations, ElasticSearch calls, and UrlLib3/Requests HTTP calls made inside tasks are all captured automatically. No task decorators or manual spans required.
Supported Frameworks
Works with the Celery frameworks you already use
Scout auto-instruments the frameworks and libraries in your stack. No manual configuration required.
Task Queues
- Celery
- Dramatiq
- Huey
- RQ
Databases
- SQLAlchemy
- Django ORM
- PyMongo
- Redis
- ElasticSearch
Web Frameworks
- Django
- Flask
- FastAPI
Memory Profiling
Memory Bloat Detection for Celery Workers
Celery workers can accumulate memory over time, especially when processing large payloads or holding object references across task executions. Scout detects memory bloat at the task level, showing which tasks and code paths are responsible for memory growth.
AI Native
Query Your Celery App Data from AI Assistants and the Terminal
Scout offers hosted and local MCP servers with 17 tools, a Go CLI available via Homebrew with TOON format for LLM output, and a public API. Query your Celery task performance, error rates, and N+1 insights from Claude Code, Cursor, or the terminal.
MCP Server
Hosted or local MCP server with 17 tools covering apps, endpoints, traces, errors, insights, background jobs, and usage data. Works with Claude Code, Cursor, VS Code Copilot, and any MCP-enabled assistant.
Scout CLI
Go binary available via Homebrew. Query app metrics, endpoint performance, traces, error groups, and insights from the terminal. Outputs human-friendly tables or TOON format for LLM consumption.
Public API
Full programmatic access to your monitoring data for building custom integrations, dashboards, or automation workflows.
Get Started in Minutes
Add Scout to Your Celery App
Get Scout running on your Celery workers in under 3 minutes.
Install the package:
pip install scout-apmConfigure in your Django settings or Flask app, including your Scout key.
Deploy and see data in 5 minutes.
FAQ
Celery Monitoring FAQ
What is the best monitoring tool for Celery?
Scout Monitoring instruments Celery tasks automatically. Every task execution appears as its own transaction with per-operation timing, SQLAlchemy and Django ORM N+1 detection, memory bloat tracking, and error capture linked to the full task trace.
How do I monitor Celery task performance with Scout?
Install the scout-apm package, configure it in your Django settings or Flask app, and deploy. Celery tasks appear automatically in Scout as their own transactions alongside your web request transactions. No task decorators or manual instrumentation required.
Does Scout detect N+1 queries inside Celery tasks?
Yes. Scout automatically detects N+1 query patterns in SQLAlchemy and the Django ORM within Celery task executions. The agent identifies repeated query patterns, shows the exact code location, and reports the total time wasted.
How do I detect memory leaks in Celery workers?
Scout detects memory bloat at the task level in your Celery workers. The agent tracks which specific task types and code paths are responsible for memory growth, so you can fix the root cause rather than just restarting workers.
Does Scout support Dramatiq, Huey, and RQ as well as Celery?
Yes. Scout auto-instruments Celery, Dramatiq, Huey, and RQ. Each task queue framework appears with per-task timing and error tracking in Scout.
Can I use Scout with Celery and Django together?
Yes. Scout instruments both Django web requests and Celery tasks in the same agent. You get unified monitoring of your web layer and background tasks, with N+1 detection, memory bloat analysis, and error tracking applying to both.