{"id":"confident-ai-deepeval","name":"deepeval","af_score":58.8,"security_score":50.8,"reliability_score":37.5,"what_it_does":"deepeval is an open-source Python framework for evaluating LLM apps (e.g., chatbots, agents, RAG pipelines). It provides a test/pytest-like workflow and many ready-to-use evaluation metrics (including LLM-as-a-judge metrics such as G-Eval, RAG metrics, agent/tool metrics, multimodal metrics). Metrics can run locally (using the chosen models) and it can integrate with CI/CD and common LLM app frameworks (OpenAI, LangChain, LangGraph, LlamaIndex, CrewAI, etc.). It also offers a hosted “Confident AI” platform option with CLI login and a stated MCP server integration for persisting data/traces.","best_when":"You want to systematically evaluate LLM applications with reusable metrics and integrate those evaluations into an automated workflow (pytest/CI), optionally with a hosted platform for reports/tracing.","avoid_when":"You need strict offline-only operation while still using LLM-based metrics, or you require a pure API service interface rather than a Python library/CLI.","last_evaluated":"2026-03-29T13:19:24.629149+00:00","has_mcp":true,"has_api":false,"auth_methods":["CLI login (deepeval login) with API key"],"has_free_tier":true,"known_gotchas":["Many metrics rely on LLM-as-a-judge; results can vary run-to-run depending on model settings and nondeterminism","Authentication/reporting differs between purely local usage and hosted platform usage (CLI login vs env vars for judge/model providers)"],"error_quality":0.0}