Locust
Locust is an open source user Load Testing Tool (LTT) (performance testing) that lets teams define test scenarios in Python and run distributed load against web applications, APIs, and other systems.
- Developer-centric load testing framework written in Python for Hypertext Transfer Protocol (HTTP) and other protocols
- Scriptable user behavior models using plain Python code for realistic traffic patterns
- Distributed load generation across multiple worker nodes for high-concurrency tests
- Web-based user interface for starting, stopping, and monitoring test runs and metrics
- Extensible plugin-style architecture for custom clients, metrics, and integrations
More About Locust
Locust is a Python-based LTT (performance engineering) used by software teams to simulate concurrent users against HTTP services, APIs, and other back-end systems. It is positioned as a scriptable alternative to GUI-driven load testing suites and is used in continuous delivery pipelines, pre-release validation, and performance regression testing. Engineering teams adopt Locust to model realistic traffic patterns as Python code and to run tests locally, in on-premises (on-prem) environments, or on cloud infrastructure.
Locust centers on the concept of user behavior classes written in Python, which define tasks representing user actions such as HTTP requests, waits, and flows between endpoints. These classes are executed by a load generator that can spawn many user instances, allowing performance tests to emulate business workflows instead of single endpoint benchmarks. Locust uses an event-driven architecture built on gevent (concurrency framework) for efficient handling of large numbers of concurrent users with relatively low resource usage compared with thread-based designs.
The tool exposes a web-based user interface where operators can start and stop tests, specify user counts and spawn rates, and track metrics such as requests per second, response times, failures, and percentiles. The UI also provides real-time test statistics and simple charts. For automation and non-interactive workflows, Locust can run in headless mode, which is used in Continuous Integration and Continuous Deployment (CI/CD) pipelines and scripted performance suites.
Locust supports distributed testing, where a master node coordinates multiple worker nodes to scale out load generation. This approach allows enterprises to distribute test load across containers, virtual machines, or physical servers to reach target concurrency levels. The communication between master and workers uses TCP-based messaging maintained by the framework. Organizations commonly deploy Locust with container orchestration platforms such as Kubernetes, although the project itself remains platform-agnostic.
Because tests are written in Python, Locust integrates with the broader Python ecosystem, including HTTP client libraries, authentication helpers, and custom protocol implementations. Users can write custom client code for non-HTTP protocols or wrap existing Python SDKs to exercise services such as message queues, WebSocket endpoints, or proprietary APIs. The extensibility model also allows for custom metrics, result exporters, and hooks into observability stacks, placing Locust in the performance testing and quality engineering category within enterprise toolchains.
In marketplace and tooling directories, Locust is categorized under load and performance testing, developer tools, and quality assurance automation. It is used as part of pre-production validation, capacity planning exercises, and ongoing performance monitoring through scheduled test runs. Its focus on code-defined tests aligns with Infrastructure-as-Code (IaC) and test-as-code practices, which enables source-controlled performance scenarios and repeatable test executions across environments.