Keploy
CLI ToolOpen source Tool for converting user traffic to Test Cases and Data Stubs.
Capabilities8 decomposed
http traffic interception and recording
Medium confidenceKeploy intercepts live HTTP/HTTPS traffic at the network layer using eBPF (extended Berkeley Packet Filter) on Linux or syscall hooking on other platforms, capturing request/response pairs with full headers, bodies, and timing metadata without requiring code instrumentation. This approach enables zero-modification traffic capture directly from running applications, recording both inbound client requests and outbound service calls in real-time.
Uses eBPF kernel-level packet capture instead of application-level instrumentation or proxy middleware, eliminating code changes and reducing latency overhead to <1ms per request
Captures traffic without code modification unlike VCR.py or Betamax, and with lower overhead than proxy-based tools like mitmproxy or Fiddler
automatic test case generation from traffic
Medium confidenceKeploy analyzes captured HTTP traffic and automatically generates executable test cases by extracting request parameters, response assertions, and dependency chains. It uses pattern matching and heuristics to identify test boundaries (request start/end), deduplicate similar requests, and create parameterized test templates that can be executed against different versions of the application.
Generates language-specific executable tests directly from traffic (not just test data), with built-in parameterization templates for common patterns like timestamps and UUIDs
Faster than manual test writing and more realistic than synthetic test generators; differs from Postman collections by producing runnable code rather than API definitions
dependency mocking and stub generation
Medium confidenceKeploy extracts outbound API calls from captured traffic and automatically generates mock stubs (recorded responses) that can be replayed during test execution. These stubs are stored as YAML or JSON files and injected into the application via a local mock server, allowing tests to run in isolation without hitting real external services. The system maintains request-response mappings with fuzzy matching to handle minor variations in requests.
Generates stubs automatically from real traffic rather than requiring manual mock definition, with fuzzy request matching to handle variations without exact duplication
More maintainable than hand-written mocks (like Sinon or Mockito) because stubs auto-update from traffic; simpler than VCR cassettes because matching is built-in
test replay and assertion validation
Medium confidenceKeploy executes generated test cases by replaying recorded requests against the application and comparing actual responses against captured baseline responses. It uses byte-level or semantic comparison (depending on content type) to validate that responses match, with configurable assertion strategies for handling non-deterministic fields like timestamps or request IDs. Test results are reported with detailed diffs showing where responses diverged.
Compares actual responses against recorded baselines with configurable field-level filtering for non-deterministic values, rather than requiring manual assertion code
Faster feedback than manual testing and more maintainable than hand-written assertions; differs from traditional unit test frameworks by validating entire API responses rather than individual functions
multi-language test code generation
Medium confidenceKeploy generates executable test code in multiple programming languages (Go, Java, Python, Node.js) from captured traffic, using language-specific idioms and testing frameworks (Go's testing package, JUnit, pytest, Jest). The code generator maintains a template system for each language, inserting captured request/response data into framework-appropriate structures, and produces code that can be immediately run without additional configuration.
Generates language-native test code using framework-specific patterns (Go's table-driven tests, JUnit annotations, pytest fixtures) rather than generic test definitions
More maintainable than polyglot test frameworks because tests use native idioms; faster to integrate than writing tests manually in each language
application state and database mocking
Medium confidenceKeploy captures database queries and state changes that occur during traffic recording, then replays those state changes during test execution to ensure the application operates with the same data context. It intercepts database calls (SQL, NoSQL) and records the queries and results, allowing tests to run against a consistent, reproducible data state without requiring manual database setup or teardown scripts.
Automatically captures and replays database state from production traffic rather than requiring manual database fixtures or seed scripts, maintaining exact data context across test runs
More maintainable than hand-written database fixtures because state auto-updates from traffic; more complete than schema-based generators because it captures actual data values
bidirectional test synchronization and versioning
Medium confidenceKeploy maintains version control for captured traffic, test cases, and stubs, allowing teams to track changes over time and synchronize test definitions across environments. When traffic is re-recorded, Keploy diffs new traffic against previous recordings and updates test cases incrementally, preserving manual edits while incorporating new observations. This enables collaborative test maintenance where multiple team members can contribute to test suites without conflicts.
Integrates test case versioning directly with Git, allowing incremental updates from traffic while preserving manual edits through intelligent diffing and merge strategies
More collaborative than static test suites because tests auto-update from traffic; simpler than manual Git workflows because Keploy handles diff and merge logic
ci/cd pipeline integration and reporting
Medium confidenceKeploy integrates with CI/CD systems (GitHub Actions, GitLab CI, Jenkins, CircleCI) via CLI commands and webhooks, executing test suites automatically on code changes and reporting results back to the pipeline. It generates structured test reports (JSON, HTML, JUnit XML) that integrate with standard CI/CD dashboards, and can block deployments if tests fail or coverage thresholds aren't met.
Provides native integrations with major CI/CD platforms via CLI and webhook support, with structured report generation that feeds into existing dashboards and quality gates
Simpler to integrate than custom test frameworks because Keploy handles report formatting; more flexible than platform-specific solutions because it supports multiple CI/CD systems
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Backend teams testing microservices without access to source code
- ✓DevOps engineers building test infrastructure for legacy applications
- ✓Integration testing teams needing realistic traffic replay
- ✓Teams with high API churn looking to maintain test coverage automatically
- ✓QA engineers building regression test suites from production data
- ✓Startups needing rapid test coverage without dedicated QA resources
- ✓Teams with heavy external service dependencies (payment gateways, analytics, third-party APIs)
- ✓CI/CD pipelines that need deterministic, fast test execution
Known Limitations
- ⚠eBPF-based capture requires Linux kernel 4.4+ with BPF support; macOS and Windows use less efficient syscall hooking with higher overhead
- ⚠Encrypted TLS traffic requires certificate pinning bypass or MITM proxy setup, adding complexity
- ⚠High-volume traffic (>10k req/s) may experience packet loss or buffering delays depending on system resources
- ⚠Generated tests may include environment-specific values (timestamps, UUIDs, API keys) requiring manual sanitization or regex-based parameterization
- ⚠Cannot infer business logic assertions — generates only response matching, not semantic correctness checks
- ⚠Deduplication heuristics may miss subtle variations in requests, creating redundant test cases
Requirements
Input / Output
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Open source Tool for converting user traffic to Test Cases and Data Stubs.
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