Judgeval provides open-source tooling for tracing, evaluating, and monitoring autonomous, stateful agents, enabling continuous learning and self-improvement. It captures runtime data from agent-environment interactions, targeting developers and researchers building and deploying AI agents.

How It Works

Judgeval integrates via a Python SDK to automatically trace agent execution, capturing inputs, outputs, tool calls, latency, and custom metadata. This data can be exported for analysis, used to build custom evaluators (including LLM-as-a-judge), and trigger alerts for production monitoring. The approach facilitates debugging, performance bottleneck identification, and data-driven agent optimization.

Quick Start & Requirements

• Install: pip install judgeval
• Prerequisites: JUDGMENT_API_KEY and JUDGMENT_ORG_ID environment variables (or JUDGMENT_API_URL for self-hosted).
• Docs: https://docs.judgmentlabs.ai/
• Demo: https://www.youtube.com/watch?v=1S4LixpVbcc

Highlighted Details

• Automatic tracing for OpenAI, Anthropic, and LangGraph.
• Supports LLM-as-a-judge, manual labeling, and code-based evaluators.
• Production monitoring with Slack alerts and custom hooks.
• Data export to Parquet/S3 for scaled analysis and A/B testing.

Maintenance & Community

Maintained by Judgment Labs. Community channels include Discord (https://discord.gg/tGVFf8UBUY) and X (https://x.com/JudgmentLabs).

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. This requires clarification for commercial use or closed-source linking.

Limitations & Caveats

The license is not specified, which is a significant blocker for determining commercial usability. The core functionality relies on connecting to the Judgment Platform, requiring API keys or a self-hosted instance.