Locust
Python-based load testing framework using task-decorated user classes to define behavior, with a real-time web UI and distributed worker mode for scaling.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Credentials for load-tested services should be passed via environment variables; avoid hardcoding auth tokens in locustfile.py committed to version control.
⚡ Reliability
Best When
Simulating complex user flows with Python business logic, conditional task weighting, or custom protocol support beyond plain HTTP.
Avoid When
You need a lightweight, config-file-driven load test with CI pass/fail thresholds — k6 or Artillery are better fits.
Use Cases
- • Write load test scenarios as Python classes with @task decorators to model weighted, realistic user behavior against HTTP services
- • Run distributed load tests by spawning multiple worker processes from a single master node to generate traffic beyond a single machine's capacity
- • Use the built-in web UI to monitor requests/sec, response times, and failure rates in real time during a load test run
- • Integrate custom metrics and events via Locust's event hooks (request_success, request_failure) for application-specific observability
- • Parametrize user behavior with on_start() / on_stop() methods and shared test data to simulate diverse user populations
Not For
- • JavaScript-native projects where k6's ES6 scripting aligns better with the team's skill set
- • Unit or functional correctness testing — Locust is purpose-built for throughput and latency measurement
- • Ultra-high VU counts on a single machine — Locust's Python GIL limits concurrency; use distributed mode or k6 for extreme scale
Interface
Authentication
Local tool — no auth needed. Locust web UI can be secured with --web-auth user:password for shared environments.
Pricing
Open source (MIT). No managed cloud service.
Agent Metadata
Known Gotchas
- ⚠ Locust uses gevent for concurrency — all network I/O must use gevent-compatible libraries (requests is patched automatically, but httpx or aiohttp require explicit gevent integration or will block the event loop)
- ⚠ Task weight in @task(N) is relative among tasks in the same TaskSet — agents generating multi-class user files must understand that weights are per-class, not global across the entire locustfile
- ⚠ The spawn_rate parameter controls how quickly users are added per second, not total users — agents must specify both --users and --spawn-rate to control ramp-up correctly
- ⚠ Locust's self.client is a session-based HTTP client that does NOT raise exceptions on 4xx/5xx by default — agents must call response.raise_for_status() or check response.status_code explicitly to count failures
- ⚠ In distributed mode, each worker runs an independent copy of the locustfile — shared state (databases, counters) cannot be accessed across workers without an external store like Redis
Alternatives
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Scores are editorial opinions as of 2026-03-07.