Flakestorm vs LangSmith
Comparing proactive testing with reactive observability
Quick Comparison
| Feature | Flakestorm | LangSmith |
|---|---|---|
| Primary Purpose | Proactive testing before deployment | Reactive observability and monitoring in production |
| Pricing | Open-source (free) + Cloud plans ($49-$299/month) | Paid SaaS service based on usage |
| Best For | Finding vulnerabilities before production, robustness validation | Understanding what happened in production, debugging issues |
| Timing | Tests agents in development and CI/CD pipelines | Monitors agents after deployment |
| Method | Actively attacks agents with adversarial inputs | Tracks traces, logs, and metrics from production usage |
| Output | Robustness score and pass/fail matrix before production | Traces, analytics, and debugging information |
| Focus | Finding vulnerabilities and proving reliability | Understanding what happened in production |
| Cost Model | Open-source version is free, runs locally | Paid SaaS service based on usage |
| Integration | CLI tool, CI/CD integration, local testing | SDK integration, cloud-based platform |
| Data Collection | No data sent (open-source), optional cloud analytics | Collects all traces and logs from production |
| Question Answered | "Will my agent break?" (proactive) | "Why did my agent break?" (reactive) |
Complementary Tools
Flakestorm and LangSmith serve different purposes and work best together. They are not competitors but complementary tools in your AI agent development workflow.
Use Flakestorm For:
- Testing agents before deployment
- Finding vulnerabilities in development
- Validating robustness in CI/CD
- Getting mathematical proof of reliability
- Blocking PRs when scores drop
Use LangSmith For:
- Monitoring agents in production
- Debugging production issues
- Analyzing trace data
- Understanding user interactions
- Performance optimization
Flakestorm Advantages
- Prevents issues before they reach production
- Open-source version is completely free
- Runs locally without sending data to external services
- Provides mathematical robustness scores
- Automatic adversarial mutation generation
- CI/CD integration to block problematic code
LangSmith Advantages
- Real-time production monitoring
- Detailed trace analysis
- Team collaboration features
- Integration with LangChain ecosystem
- Performance analytics and dashboards
- Production debugging tools
Recommended Workflow
1
Development Phase
Use Flakestorm to test your agent with adversarial inputs and validate robustness before committing code.
2
CI/CD Pipeline
Integrate Flakestorm to block PRs when robustness scores drop below threshold.
3
Production Deployment
Deploy with confidence knowing Flakestorm validated your agent's robustness.
4
Production Monitoring
Use LangSmith to monitor traces, debug issues, and optimize performance in production.