{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"locust","slug":"locust","name":"Locust","type":"framework","url":"https://github.com/locustio/locust","page_url":"https://unfragile.ai/locust","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"locust__cap_0","uri":"capability://code.generation.editing.python.based.user.behavior.definition.with.decorator.driven.task.scheduling","name":"python-based user behavior definition with decorator-driven task scheduling","description":"Enables developers to define load test scenarios as Python classes inheriting from User or HttpUser, with @task decorators specifying which methods execute and their relative weights. The framework uses Python's full expressiveness for conditional logic, loops, and state management within task definitions, avoiding XML or GUI-based test design. Task execution is scheduled by the framework's event loop, which randomly selects weighted tasks and executes them sequentially per simulated user.","intents":["Define realistic user journeys with complex branching logic and state transitions","Write load tests that mirror actual application workflows without learning a DSL","Reuse Python libraries and utilities within test scenarios for data generation and validation"],"best_for":["Python developers building load tests for APIs, web services, and AI inference endpoints","Teams wanting version-controlled, code-reviewed test scenarios","Projects requiring dynamic test logic based on response data or external state"],"limitations":["Non-Python developers must learn Python syntax; no visual test builder alternative","Task weight distribution is static per class definition; dynamic weight adjustment requires code changes","Synchronous task execution per user means blocking I/O in tasks can reduce concurrency efficiency"],"requires":["Python 3.9+","locust package installed via pip","Basic understanding of Python classes and decorators"],"input_types":["Python code (locustfile)","Configuration parameters (command-line or environment variables)"],"output_types":["Executable test scenario","Task execution trace (via events)"],"categories":["code-generation-editing","testing-frameworks"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_1","uri":"capability://automation.workflow.gevent.based.greenlet.concurrency.for.lightweight.user.simulation","name":"gevent-based greenlet concurrency for lightweight user simulation","description":"Locust uses gevent's greenlet-based concurrency model to simulate thousands of concurrent users within a single process with minimal memory overhead. Each simulated user runs in its own greenlet (lightweight pseudo-thread), and the framework uses gevent's event loop to manage I/O-bound operations (HTTP requests, network calls) without blocking. This allows a single machine to generate load equivalent to tools requiring multiple processes or machines, by avoiding OS thread overhead and context-switching costs.","intents":["Simulate high concurrency (thousands of users) on a single machine without spawning thousands of OS threads","Reduce infrastructure costs for load testing by consolidating load generation onto fewer machines","Test AI API endpoints and LLM inference servers under realistic concurrent request patterns"],"best_for":["Teams with limited load testing infrastructure wanting to maximize concurrency per machine","Testing I/O-bound services (APIs, inference servers) where greenlet overhead is negligible","Cost-conscious organizations testing at scale without dedicated load testing clusters"],"limitations":["CPU-bound tasks within user code block the entire greenlet pool; true parallelism requires distributed mode","Greenlet context switching adds ~1-5ms latency per I/O operation compared to native async/await in some scenarios","Memory per greenlet (~50-100KB) means 10,000 users consume ~500MB-1GB base memory before request overhead"],"requires":["Python 3.9+","gevent library (installed as locust dependency)","I/O-bound workloads (HTTP, network calls) for efficiency; CPU-bound tasks should use distributed mode"],"input_types":["User class definitions with I/O operations"],"output_types":["Concurrent request stream","Performance metrics (response times, throughput)"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_10","uri":"capability://data.processing.analysis.csv.and.html.result.export.with.per.endpoint.metrics.and.failure.summaries","name":"csv and html result export with per-endpoint metrics and failure summaries","description":"Locust exports test results to CSV and HTML formats via the stats system. CSV exports include per-endpoint metrics (request count, response times, failure count) and a summary row. HTML exports include charts (response time distribution, requests over time), tables with detailed metrics, and failure summaries. Exports are triggered via the web UI or command-line arguments (--csv, --html), and can be customized via event listeners to include additional data.","intents":["Generate reports for stakeholders showing test results and performance metrics","Archive test results for trend analysis and historical comparison","Integrate test results into CI/CD pipelines for automated pass/fail decisions"],"best_for":["Teams generating reports for management or stakeholders","Organizations tracking performance trends over time","CI/CD pipelines requiring test result artifacts for archival or analysis"],"limitations":["HTML exports are static; no interactive exploration of metrics (requires external tools like Grafana)","CSV exports are flat; no support for hierarchical data or nested metrics","Exports are generated at test end; no streaming or incremental export during test execution","Custom metrics require modifying event listeners; no built-in extensibility for additional columns"],"requires":["Python 3.9+","locust.stats module (built-in)","File system write permissions for output directory"],"input_types":["RequestStats objects","Test metadata (duration, user count, etc.)"],"output_types":["CSV file (per-endpoint metrics)","HTML file (charts, tables, summary)","JSON export (programmatic access)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_11","uri":"capability://planning.reasoning.user.distribution.optimization.across.distributed.workers.using.kl.divergence.algorithm","name":"user distribution optimization across distributed workers using kl-divergence algorithm","description":"The UsersDispatcher component in distributed mode calculates optimal user distribution across workers using a KL-divergence (Kullback-Leibler divergence) algorithm. Given a target user count and worker capacity estimates, the dispatcher minimizes the divergence between target and actual user distribution, ensuring balanced load generation. This is more sophisticated than round-robin allocation because it accounts for worker heterogeneity (different CPU, network capacity) and adjusts distribution dynamically as workers join/leave.","intents":["Distribute load evenly across heterogeneous workers with different capacity","Optimize user allocation to minimize divergence from target distribution","Support dynamic worker scaling without manual rebalancing"],"best_for":["Teams running distributed tests across heterogeneous infrastructure (different machine types, cloud regions)","Organizations wanting optimal load distribution without manual configuration","Projects requiring fair load balancing across workers with varying capacity"],"limitations":["KL-divergence calculation assumes stable worker capacity; rapid worker changes may cause suboptimal distribution","Algorithm requires capacity estimates; if estimates are inaccurate, distribution may be suboptimal","No support for weighted user allocation (e.g., 70% of users from region A, 30% from region B)","Distribution is recalculated at discrete intervals; sub-second rebalancing is not supported"],"requires":["Python 3.9+","Distributed mode enabled (--master flag)","Multiple worker processes or machines","Worker capacity estimates (automatic or manual)"],"input_types":["Target user count","Worker capacity estimates","Current user distribution"],"output_types":["Optimized user count per worker","Distribution adjustment commands"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_12","uri":"capability://tool.use.integration.custom.protocol.support.via.user.subclassing.and.manual.request.event.firing","name":"custom protocol support via user subclassing and manual request event firing","description":"Locust's User base class is protocol-agnostic; developers can subclass User and implement custom client logic for non-HTTP protocols (gRPC, WebSocket, MQTT, custom binary). Custom implementations must manually fire request_success or request_failed events to integrate with Locust's metrics system. This allows any protocol to benefit from Locust's concurrency model, statistics collection, and distributed testing infrastructure without modifying the framework.","intents":["Test non-HTTP services (gRPC, WebSocket, message queues) using Locust's concurrency and metrics infrastructure","Implement custom protocol clients with full control over request/response handling","Reuse Locust's distributed testing and load shaping for non-HTTP workloads"],"best_for":["Teams testing gRPC, WebSocket, or message-based services","Projects requiring custom protocol implementations with Locust's metrics collection","Organizations testing microservices with heterogeneous protocols (HTTP + gRPC + message queues)"],"limitations":["Custom implementations must manually fire request events; no automatic metric collection","No built-in support for protocol-specific features (e.g., gRPC streaming, WebSocket frames); requires custom implementation","Debugging custom protocols is harder than HTTP because Locust provides less built-in tooling","Performance optimization is protocol-specific; no generic tuning advice applies"],"requires":["Python 3.9+","Protocol-specific client library (grpcio, websocket-client, pika, etc.)","Understanding of custom User subclassing and event firing"],"input_types":["Custom protocol requests (gRPC messages, WebSocket frames, etc.)","Protocol-specific configuration (endpoints, credentials, etc.)"],"output_types":["Custom protocol responses","Request/response metrics (via manual event firing)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_2","uri":"capability://automation.workflow.distributed.load.testing.with.master.worker.zmq.architecture","name":"distributed load testing with master-worker zmq architecture","description":"Locust implements a master-worker distributed testing pattern using ZMQ (ZeroMQ) for inter-process communication. The master runner coordinates test execution, collects statistics from multiple worker processes/machines, and exposes a unified web UI. Workers spawn user greenlets and report request metrics back to the master via ZMQ publish-subscribe channels. The UsersDispatcher component uses a KL-divergence algorithm to calculate optimal user distribution across workers, ensuring balanced load generation even with heterogeneous worker capacity.","intents":["Scale load generation across multiple machines to test large-scale systems","Distribute user simulation across a cluster while maintaining centralized control and monitoring","Generate realistic geographic or multi-region load patterns by deploying workers in different locations"],"best_for":["Teams testing high-scale systems requiring load from multiple geographic regions or data centers","Organizations with distributed infrastructure wanting to generate load from edge locations","Projects needing to exceed single-machine concurrency limits (10K+ concurrent users)"],"limitations":["ZMQ communication adds ~50-200ms latency for master-worker coordination; not suitable for sub-second test adjustments","Worker failure handling is basic; lost workers are not automatically replaced or rebalanced","KL-divergence user distribution algorithm assumes stable worker capacity; dynamic scaling requires manual rebalancing","Requires network connectivity and firewall rules between master and workers; not suitable for air-gapped environments"],"requires":["Python 3.9+","ZMQ library (zmq, installed as locust dependency)","Network connectivity between master and worker machines","Firewall rules allowing ZMQ port (default 5557) between master and workers"],"input_types":["Locustfile (same on all workers)","Master configuration (worker addresses, user count distribution)"],"output_types":["Aggregated statistics from all workers","Unified web UI showing cluster-wide metrics","CSV/HTML reports with combined results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_3","uri":"capability://automation.workflow.real.time.web.ui.with.live.metrics.control.and.test.execution.management","name":"real-time web ui with live metrics, control, and test execution management","description":"Locust provides a Flask-based web UI (accessible at http://localhost:8089 by default) that displays real-time request statistics, response time distributions, and failure rates. The UI allows operators to start/stop tests, adjust user count and spawn rate without restarting, and download results as CSV/HTML. The backend exposes a REST API that the React frontend consumes; the UI updates via WebSocket or polling to reflect live metrics from the runner and stats system.","intents":["Monitor load test execution in real-time without command-line tools","Adjust test parameters (user count, spawn rate) on-the-fly without stopping the test","Share test results and metrics with non-technical stakeholders via browser interface"],"best_for":["Teams running interactive load tests requiring real-time parameter adjustment","Organizations needing to demo load testing to stakeholders or management","Operators managing distributed tests across multiple workers from a single dashboard"],"limitations":["Web UI adds ~5-10% overhead to test execution due to Flask/WebSocket processing","Real-time updates via polling/WebSocket can lag by 1-5 seconds on high-concurrency tests (10K+ users)","No built-in authentication or RBAC; UI is accessible to anyone with network access to the master","Metrics are in-memory only; restarting the master loses historical data unless explicitly exported"],"requires":["Python 3.9+","Flask library (installed as locust dependency)","Web browser with JavaScript support","Network access to master machine on port 8089 (configurable)"],"input_types":["Test execution state (running/stopped)","User count and spawn rate adjustments","Test result export requests"],"output_types":["HTML web interface","JSON REST API responses","CSV/HTML result exports","Real-time WebSocket metric updates"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_4","uri":"capability://data.processing.analysis.comprehensive.request.statistics.collection.with.response.time.percentiles.and.failure.tracking","name":"comprehensive request statistics collection with response time percentiles and failure tracking","description":"Locust's RequestStats system collects detailed metrics for every request: response time, status code, request/response size, and failure reason. Statistics are aggregated per endpoint and globally, with percentile calculations (p50, p95, p99) computed incrementally to avoid storing all response times. The stats system supports custom aggregation via events (request_success, request_failed) and exports results to CSV, HTML, or JSON formats. Failure tracking includes categorization by error type (timeout, connection error, HTTP 5xx, etc.) for root-cause analysis.","intents":["Identify performance bottlenecks by analyzing response time percentiles per endpoint","Track failure rates and error types to detect system degradation under load","Export detailed metrics for post-test analysis, reporting, and trend tracking"],"best_for":["Performance engineers analyzing API response time distributions and SLA compliance","DevOps teams monitoring system behavior under load and identifying capacity limits","Teams building CI/CD pipelines that fail tests if p95 response time exceeds thresholds"],"limitations":["Percentile calculations use approximate algorithms (T-Digest or histogram binning); exact p99 may vary by ±5-10% on large datasets","Per-request metadata (headers, body) is not captured; only aggregated metrics are stored","Statistics are in-memory; tests with millions of requests may consume significant RAM for histogram storage","No built-in time-series database; historical data is lost unless explicitly exported between test runs"],"requires":["Python 3.9+","locust.stats module (built-in)","Requests to be made through Locust's HTTP client or custom client with event firing"],"input_types":["Request events (request_success, request_failed)","Response metadata (status code, response time, size)"],"output_types":["In-memory RequestStats objects","CSV export (per-endpoint metrics)","HTML report (charts, tables)","JSON export (programmatic access)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_5","uri":"capability://tool.use.integration.event.driven.hook.system.for.test.customization.and.extensibility","name":"event-driven hook system for test customization and extensibility","description":"Locust exposes an event system (EventHook pattern) that fires events at key lifecycle points: test_start, test_stop, request_success, request_failed, user_add, user_remove, etc. Developers can register listeners to these events to inject custom logic: logging, metrics collection, dynamic test adjustments, or integration with external systems. Events are fired synchronously from the runner/user code, allowing listeners to modify test behavior or collect supplementary data without modifying core framework code.","intents":["Integrate Locust with external monitoring systems (Prometheus, DataDog, CloudWatch) by exporting metrics via event listeners","Implement custom test logic that reacts to test state changes (e.g., pause test if error rate exceeds threshold)","Collect supplementary data (custom metrics, logs) during test execution for post-test analysis"],"best_for":["Teams integrating Locust into CI/CD pipelines with custom pass/fail criteria","Organizations sending Locust metrics to centralized monitoring platforms","Projects requiring test behavior modifications without forking the framework"],"limitations":["Event listeners are synchronous; blocking operations in listeners slow down the test execution loop","Event firing is not guaranteed to be thread-safe in distributed mode; listeners must handle concurrent calls from multiple workers","No built-in event filtering or priority system; all listeners execute in registration order","Event payload is fixed by framework; custom data requires storing in external state or modifying listener code"],"requires":["Python 3.9+","locust.events module (built-in)","Understanding of Python decorators and function registration"],"input_types":["Event type (test_start, request_success, etc.)","Event payload (request metadata, user info, etc.)"],"output_types":["Custom side effects (logging, metric export, test adjustment)","Modified test state or behavior"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_6","uri":"capability://planning.reasoning.load.shaping.with.custom.user.ramp.up.ramp.down.curves.and.dynamic.load.profiles","name":"load shaping with custom user ramp-up/ramp-down curves and dynamic load profiles","description":"Locust supports load shaping via the LoadShape class, allowing developers to define custom user count and spawn rate curves over time. Built-in shapes include StagesShape (step-wise increases) and LinearShape (linear ramp). Custom shapes implement a tick() method that returns (user_count, spawn_rate) at each time step, enabling complex load profiles: gradual ramp-up, plateau, spike tests, or realistic traffic patterns. The runner polls the shape at regular intervals and adjusts user count dynamically without stopping the test.","intents":["Simulate realistic traffic patterns (gradual ramp-up, peak hours, ramp-down) instead of constant load","Test system behavior under sudden traffic spikes or gradual degradation","Implement multi-stage load tests (warm-up, sustained load, stress test) in a single test run"],"best_for":["Performance engineers testing system behavior under realistic traffic patterns","Teams validating auto-scaling policies by simulating gradual load increases","Projects requiring complex load profiles that cannot be expressed as constant user count"],"limitations":["Shape adjustments are applied at discrete intervals (default 1 second); sub-second load changes are not supported","User ramp-down (removing users) is slower than ramp-up because users must complete current tasks before being stopped","Shape logic runs on the master; distributed tests may experience slight delays in worker synchronization","No built-in support for load patterns based on external data (e.g., historical traffic logs); requires custom implementation"],"requires":["Python 3.9+","locust.shape module (built-in)","Understanding of load profile design and time-based logic"],"input_types":["Time elapsed (seconds since test start)","Current test state (running, stopping)"],"output_types":["Tuple of (user_count, spawn_rate)","Dynamic user adjustment commands to runner"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_7","uri":"capability://tool.use.integration.http.client.abstraction.with.standard.and.fast.variants.for.protocol.flexibility","name":"http client abstraction with standard and fast variants for protocol flexibility","description":"Locust provides HttpUser base class that wraps the requests library, and FastHttpUser that uses a custom HTTP client (locust.contrib.fasthttp) optimized for high-throughput scenarios. Both clients support cookies, authentication, custom headers, and request/response inspection. The HTTP client is abstracted to allow custom protocol implementations: developers can subclass User and implement custom client logic (gRPC, WebSocket, custom binary protocols) by overriding the client attribute and firing request events manually.","intents":["Test HTTP/REST APIs with realistic client behavior (cookies, redirects, authentication)","Test high-throughput scenarios (10K+ RPS) using FastHttpUser's optimized client","Test non-HTTP protocols (gRPC, WebSocket, custom binary) by implementing custom User subclasses"],"best_for":["Teams testing REST APIs and web services with standard HTTP client","Performance engineers testing high-throughput APIs (LLM inference servers, real-time APIs) using FastHttpUser","Projects requiring custom protocol support (gRPC, WebSocket, MQTT) with Locust's metrics collection"],"limitations":["HttpUser uses requests library which is slower than FastHttpUser; suitable for <5K RPS per machine","FastHttpUser does not support all requests library features (e.g., proxies, some authentication methods)","Custom protocol implementations must manually fire request events; no automatic metric collection","HTTP/2 and HTTP/3 support is limited; primarily HTTP/1.1 focused"],"requires":["Python 3.9+","requests library (for HttpUser, installed as locust dependency)","locust.contrib.fasthttp module (for FastHttpUser, optional)","Target service URL and authentication credentials if required"],"input_types":["HTTP method (GET, POST, etc.)","URL, headers, body","Authentication credentials"],"output_types":["HTTP response (status code, headers, body)","Request/response metrics (time, size)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_8","uri":"capability://code.generation.editing.locustfile.loading.and.dynamic.test.scenario.discovery.from.python.modules","name":"locustfile loading and dynamic test scenario discovery from python modules","description":"Locust's main.py and argument_parser.py implement locustfile loading: the framework accepts a Python file path, imports it dynamically, and discovers User subclasses via introspection. The locustfile can define multiple User classes, each representing a different user type or scenario. The framework supports command-line options to select which User classes to run, or runs all discovered classes by default. Locustfiles can also define custom LoadShape classes and event listeners, making them self-contained test definitions.","intents":["Define complete load test scenarios in a single Python file without configuration files","Support multiple user types (e.g., authenticated users, anonymous users) in a single test run","Dynamically discover and run test scenarios without explicit registration or configuration"],"best_for":["Teams using version control for test scenarios and preferring code-based test definition","Projects with multiple user types or scenarios that need to run simultaneously","Organizations integrating Locust into CI/CD pipelines with dynamic test discovery"],"limitations":["Locustfile must be valid Python; syntax errors prevent test execution without helpful error messages","Dynamic import can execute arbitrary code in the locustfile; security risk if locustfiles come from untrusted sources","No built-in test parameterization; multiple test variations require multiple locustfiles or conditional logic","Locustfile discovery is file-based; no support for test discovery from packages or remote sources"],"requires":["Python 3.9+","Valid Python file with User subclass definitions","Locustfile path provided via command-line argument or LOCUST_LOCUSTFILE environment variable"],"input_types":["Python file path (locustfile)","Command-line arguments (--classes, --tags)"],"output_types":["Discovered User classes","Loaded LoadShape and event listeners","Executable test scenario"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__cap_9","uri":"capability://automation.workflow.command.line.interface.with.argument.parsing.for.test.configuration.and.execution.control","name":"command-line interface with argument parsing for test configuration and execution control","description":"Locust's argument_parser.py provides a comprehensive CLI with options for locustfile path, target URL, user count, spawn rate, test duration, distributed mode configuration, and output formats. The CLI supports both short and long option names, environment variable overrides, and configuration file loading. Arguments are parsed into an Options object that is passed to the Environment and runners, controlling test execution without code changes.","intents":["Configure and run load tests from command-line without modifying code","Integrate Locust into CI/CD pipelines with parameterized test execution","Support different test configurations (staging vs. production) via command-line arguments"],"best_for":["DevOps engineers and CI/CD pipelines automating load test execution","Teams running tests with different configurations without code changes","Organizations integrating Locust into monitoring and alerting systems"],"limitations":["CLI argument parsing is verbose; complex configurations require many arguments or config files","No built-in support for test parameterization (e.g., different user counts for different scenarios)","Environment variable overrides are limited to specific options; not all CLI arguments support env vars","Config file format is not standardized; YAML/JSON support requires custom parsing"],"requires":["Python 3.9+","locust command-line tool installed (via pip install locust)","Valid locustfile path and target URL"],"input_types":["Command-line arguments (--host, --users, --spawn-rate, etc.)","Environment variables (LOCUST_HOST, LOCUST_USERS, etc.)","Configuration file (if supported)"],"output_types":["Parsed Options object","Test execution with specified parameters"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"locust__headline","uri":"capability://testing.quality.open.source.load.testing.framework","name":"open-source load testing framework","description":"Locust is an open-source load testing framework that allows developers to define user behavior in Python code, enabling scalable and distributed testing of web applications and APIs under realistic traffic conditions.","intents":["best load testing framework","load testing for APIs","open-source load testing tool","scalable load testing solution","distributed load testing framework"],"best_for":["testing web applications","API performance testing"],"limitations":["requires Python knowledge"],"requires":["Python environment"],"input_types":["Python scripts"],"output_types":["performance metrics"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","locust package installed via pip","Basic understanding of Python classes and decorators","gevent library (installed as locust dependency)","I/O-bound workloads (HTTP, network calls) for efficiency; CPU-bound tasks should use distributed mode","locust.stats module (built-in)","File system write permissions for output directory","Distributed mode enabled (--master flag)","Multiple worker processes or machines","Worker capacity estimates (automatic or manual)"],"failure_modes":["Non-Python developers must learn Python syntax; no visual test builder alternative","Task weight distribution is static per class definition; dynamic weight adjustment requires code changes","Synchronous task execution per user means blocking I/O in tasks can reduce concurrency efficiency","CPU-bound tasks within user code block the entire greenlet pool; true parallelism requires distributed mode","Greenlet context switching adds ~1-5ms latency per I/O operation compared to native async/await in some scenarios","Memory per greenlet (~50-100KB) means 10,000 users consume ~500MB-1GB base memory before request overhead","HTML exports are static; no interactive exploration of metrics (requires external tools like Grafana)","CSV exports are flat; no support for hierarchical data or nested metrics","Exports are generated at test end; no streaming or incremental export during test execution","Custom metrics require modifying event listeners; no built-in extensibility for additional columns","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=locust","compare_url":"https://unfragile.ai/compare?artifact=locust"}},"signature":"yx/tdpjhpx4yFwIpXy7N/nuZhfGeBLP6b3NicxgyFNj6pfzC+pXPFwGAGvScdlvpupSptxpkA1lppPpX/hnQCw==","signedAt":"2026-06-15T06:52:09.925Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/locust","artifact":"https://unfragile.ai/locust","verify":"https://unfragile.ai/api/v1/verify?slug=locust","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}