Report Abuse

Basic Information

Judgeval is an open-source SDK and toolkit designed to capture, evaluate, and manage runtime data from autonomous, stateful agents. It instruments agent-environment interactions and tool calls to produce traces that support monitoring, post-training workflows, and continuous improvement. The repo provides a pip-installable client and documentation for both cloud-hosted and self-hosted deployments, along with cookbooks and examples showing how to capture full environment responses with minimal code. Its primary purpose is to supply the data and evaluation signals needed to run evaluators, export datasets, track metrics, and integrate with developer workflows so teams can analyze agent behavior, run regressions, and iterate on agent policies and configurations.

Links

Categorization

App Details

Features
Judgeval includes three main capabilities: Evals, Monitoring, and Datasets. Evals support LLM-as-a-judge, manual labeling, and code-based evaluators tied to metric tracking for unit tests, A/B tests, and online guardrails. Monitoring offers failure alerts, Slack notifications, and custom hooks to respond to production regressions while visualizing performance trends across agent versions and time. Dataset tooling exports captured environment interactions and test cases to formats like Parquet and destinations like S3, enabling scaled analysis and retraining. The project supports self-hosting (deploy on your cloud, use Supabase, configure a custom domain), provides a CLI for deployment, and integrates development guidance such as Cursor rules and cookbooks for multi-agent observability.
Use Cases
Judgeval helps teams close the loop between production agent behavior and model improvement by turning runtime traces into actionable datasets and metrics. Captured interactions can be exported and used as training data for post-training methods such as supervised fine-tuning and reinforcement learning or as test suites for regression checks. Evals automate quality checks and A/B comparisons while monitoring surfaces degradations early through alerts and dashboards. Self-hosting options let organizations retain control over telemetry and data storage. Overall, the tooling reduces manual instrumentation work, centralizes evaluation and monitoring, and speeds iteration on agent policies and tooling across multi-agent systems.

Please fill the required fields*