Build self-improving agents
LLM judges that calibrate over time. Feedback loops that optimize your prompts automatically.
How it works
Four steps to agents that improve themselves
Install with two lines
Get started in seconds
Install the SDK and initialize it. Automatic instrumentation captures every LLM call from OpenAI, Anthropic, LangChain, LangGraph, and more.
- Zero configuration required
- Automatic tracing for all major LLM providers
- Works with existing codebases instantly
Create judges for any need
Binary, scored, filtered
Define what good looks like. Create judges with binary pass/fail, score-based rubrics, sample rates, and smart filters to evaluate exactly what matters.
- Binary judges for clear pass/fail decisions
- Scored rubrics with custom criteria and thresholds
- Filter by span type, tags, or custom properties
Calibrate with feedback
Teach your judges
When a judge gets it wrong, tell it why. Your feedback trains the judge to align with your standards. The more feedback you provide, the smarter it becomes.
- Thumbs up/down with optional reasoning
- Expected output for comparison
- Track alignment score over time
Optimize your agents
Close the loop
Turn feedback into action. We automatically rewrite your prompts based on patterns in user feedback. Your agents improve without manual tuning.
- Automatic prompt optimization from feedback
- Version control for all prompt changes
- Deploy optimized versions with one click
Feedback in
one API call.
Flag bad outputs with a thumbs down and why it failed. We extract constraints from complaints and evolve your prompts to address them.
“You are a helpful assistant.”
“You are a technical support specialist. Keep answers under 3 sentences. Be direct and specific.”
Prompts that improve
themselves.
ZeroEval analyzes feedback patterns and automatically generates optimized prompt versions. Deploy improvements without touching your code.
Make your AI agents smarter.
Join teams using production data to continuously improve their AI agents