Make your agents self‑improve
from experience
Kayba learns from your agent's traces to recursively make your agent better.
Your agent makes the same mistakes every day.
Failures pile up silently across conversations, and your agent never learns from any of them.
Every failure makes your agent smarter
Kayba analyzes past agent traces, detects failures, and turns them into agent improvements. Every cycle, your agent gets better.
18
failures detected
Detect & catch failures
Spot wrong parameters, skipped policies, and bad routing before they reach your users.
Learn & deploy improvements
Every failure becomes an insight that recursively improves your agent.
Track reliability over time
Monitor how your agent improves across iterations. Measure consistency, not just accuracy.
From traces to self-improving agents
Three steps from your terminal
Call Kayba from your coding agent
Upload your traces or pull them directly from MLflow, LangSmith, and other observability tools. Kayba analyzes them and generates insights automatically.
See what your agent gets wrong
Kayba surfaces failure patterns, recurring issues, and blind spots across your traces. It builds deep context about your agent to understand not just what went wrong, but why.
Total Insights
0
Active Categories
—
Most Prevalent
—
Critical
—
Pick improvements and ship them
Kayba extracts insights from failures. Your coding agent turns them into concrete edits. Apply what you want, run your agent again, and feed the new traces back into Kayba.
Dynamic Evals
Kayba generates evaluation suites tailored to your agent's actual behavior. Kayba's Recursive Reflector builds deep context about your agent, then generates the right evaluations automatically.
- Auto-generated from your traces
- Built-in detectors for common failure modes
- Baseline scoring and regression tracking
Double your agent's consistency
Measured on τ2-bench, a benchmark that challenges agents to coordinate with users across complex enterprise domains. Kayba learns from every run and makes your agent more consistent each time.
| Baseline | Kayba | Improvement | |
|---|---|---|---|
| pass^1 | 41.2% | 55.3% | +34.2% |
| pass^2 | 28.3% | 44.2% | +56.2% |
| pass^3 | 22.5% | 41.2% | +83.1% |
| pass^4 | 20.0% | 40.0% | +100.0% |
Claude Haiku 4.5 · τ2-bench, a real-world agent benchmark by Sierra Research
Pricing
Start free, scale when you need to
- Kayba framework (pip install)
- Recursive Reflector
- Skillbook generation
- LiteLLM integration
- Community support (Discord)
- MIT Licensed
7-day free trial (no credit card required)
- Automated agent self-improvement
- CLI for Claude Code, Codex & more
- Hosted dashboard & analytics
- Import traces from observability tools (MLflow, LangSmith & more)
- Team collaboration
- Email support
- Everything in Pro
- SSO & audit logs
- Custom integrations
- Dedicated support
- SLA guarantees
- On-premise deployment