Agentic Context Engine
Free🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai
FreeFree tier
#Python
About Agentic Context Engine
Kayba is an agent debugging and verification tool that turns real agent failures into reproducible evals. It connects to existing observability stacks (Sentry, PostHog, OpenTelemetry), automatically extracts failure context, proposes fixes via pull requests, and tracks pass rates to prove that fixes actually work. Users can query failures in plain English and receive cited trace evidence. Kayba catches unknown unknowns—semantic failures and edge cases that predefined evals miss—and provides a complete loop from error to verified fix in under eight minutes.
Key Features
Connects to existing observability tools (Sentry, PostHog, OpenTelemetry, Langfuse, LangSmith, MLflow)
Turns real agent failures into custom, reproducible evals
Proposes fixes as pull requests with attached traces and error context
Verifies fixes by tracking eval pass rate over time
Answers queries about failures in plain English with cited traces
Catches semantic failures and edge cases that predefined evals miss
Pros & Cons
Pros
- Catches failures you wouldn't have thought to write evals for
- Provides cited traces to explain root cause without guessing
- Automates fix proposal and verification, reducing manual effort
- Integrates seamlessly with popular observability and tracing tools
- Shows concrete pass rate improvement to prove fixes worked
Cons
- Requires existing trace/error storage infrastructure (e.g., Sentry, PostHog)
- Fix proposals are AI-generated and still require human review before merging
- Effectiveness depends on quality of tracing and error data available
Best For
Debugging agent failures in productionVerifying that a fix actually resolves the root causeCatching unknown unknowns in agent behaviorAccelerating the loop from error detection to verified fixMonitoring agent performance and regression after deployments
FAQ
What is Kayba?
Kayba is a tool that turns agent failures into reproducible evals, proposes fixes, and verifies them against your real failure patterns — all from your existing traces and errors.
Which error sources and frameworks does Kayba support?
Kayba works with popular observability and tracing tools such as Langfuse, LangSmith, MLflow, OpenTelemetry, Sentry, and PostHog.
Where do the evals come from?
When an error lands, Kayba pulls out all context (trace, error, code) and turns the failure into a custom eval — a reproducible test grounded in your real failure patterns.
How accurate are the proposed fixes?
Kayba proposes fixes based on the failure trace and context. Every fix lands as a PR with the trace and error attached. You can merge, change, or write your own — the eval keeps checking whether the failure comes back.