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JudgmentLabs/judgeval

⭐ 1073 Python repository created 2024-10-25

Judgeval is an open-source Python SDK for observing and evaluating LLM-powered agents in development and in production. Instrumentation is decorator-based: a call to Tracer.init names the project, the wrap helper patches an LLM client, and the Tracer.observe decorator marks any function as a span with a type such as tool or agent. Inputs, outputs and token usage are captured automatically. The tracing layer is built on OpenTelemetry, so traces can flow into existing observability stacks alongside the vendor backend. On top of tracing the SDK provides agent judges: prompt-based scorers that grade agent behavior at scale and emit structured, labeled results rather than a single numeric score. Those results accumulate into a searchable record, so recurring failure modes can be found across many runs. Judges run against live production traffic server-side, which the project states adds no latency to the application path, and the same judges can be replayed over historical traces to check whether a code change actually fixed a regression before it ships. Detected behaviors can be routed to Slack alerts. A query language, JQL, is exposed through a Judgeval client for filtering traces by session or other fields from Python. Auto-instrumentation covers OpenAI, Anthropic, Google GenAI and Together AI clients, with framework-level support for LangGraph, OpenLit and the Claude Agent SDK. A companion CLI manages agents, traces, judges, behaviors and evaluations from the terminal, and an MCP server exposes the same data to MCP-compatible assistants and IDEs so failures can be inspected without leaving the editor. The SDK is installed from PyPI and requires an API key and organization id. It targets engineers running agents in production who need failure detection, root-cause triage and regression testing backed by real usage data.

https://github.com/JudgmentLabs/judgeval

observabilitytracingevaluationopentelemetryllm-judgepythonmcpmonitoring

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