DeepEval
DeepEval is an open-source LLM evaluation framework — built and maintained by Confident AI — for testing and benchmarking large language model applications. It is structured like Pytest but specialized for LLM systems, providing 40+ research-backed metrics (G-Eval, DAG, RAG metrics, agent metrics, multi-turn conversation metrics, multimodal metrics, MCP metrics, hallucination, bias, toxicity, summarization, JSON correctness) that run locally against any LLM provider (OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, Ollama, OpenRouter, vLLM, LM Studio, LiteLLM, Azure OpenAI, DeepSeek, Grok, Moonshot, Portkey). DeepEval supports end-to-end and component-level evaluation via the `@observe()` decorator, synthetic dataset generation, multi-turn conversation simulation, CI/CD integration, automatic prompt optimization, and one-line LLM benchmarking (MMLU, HellaSwag, DROP, BIG-Bench Hard, TruthfulQA, HumanEval, GSM8K). DeepEval ships as the `deepeval` Python package on PyPI together with a `deepeval` command-line tool. The framework integrates natively with pytest, LangChain, LangGraph, LlamaIndex, OpenAI Agents, CrewAI, Pydantic AI, AWS AgentCore, Google ADK, and Strands. DeepEval is open source under Apache 2.0 and is the engine that powers Confident AI's commercial LLM evaluation, observability, and red-teaming platform; `deepeval login` connects local test runs to the Confident AI cloud for shared regression reports, dataset management, production tracing, and prompt versioning. A sibling open-source framework, DeepTeam (`deepteam`), targets adversarial / red-team testing of LLM apps.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles DeepEval the way a machine reads it — 1 machine-readable artifact, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — DeepEval scores 27.1/100 (emerging), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile DeepEval
Each block below is one kind of artifact we hold for DeepEval. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for DeepEval — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 4
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Build 8
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 8 ·
Access & Security 1
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 4
Status, limits, changes, and where to get help
Commercial 4
Pricing, plans, and the legal terms of use
Company 5
The organization behind the API
Other 6
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/deepeval · machine-readable index on apis.io