confident-ai/deepeval

DeepEval is an LLM evaluation framework, according to its repository description.

github.com/confident-ai/deepeval
confident-ai/deepeval homepage screenshot

About confident-ai/deepeval

DeepEval is an open-source framework for evaluating large-language model systems, described as similar to Pytest but for unit testing LLM applications. It supports testing of agents, RAG pipelines, and chatbots at the level of end-to-end behavior, full agent trajectories, and individual steps such as LLM calls, tool use, and retrieval.

It includes ready-to-use metrics for custom, agentic, RAG, and multi-turn evaluation that can use a selected LLM or NLP models that run locally. Comparing iterations, sharing reports, and production monitoring are linked to Confident AI, a separate enterprise platform.

Key features

  • Evaluate LLM apps end-to-end as black boxes
  • Evaluate complete agent trajectories across decisions and actions
  • Evaluate individual steps such as LLM calls, tool use, retrieval
  • RAG metrics for answer relevancy, faithfulness, and retrieval context
  • Agentic metrics for task completion, tool correctness, and plan adherence
  • Custom metrics including G-Eval, DAG builder, and JevEval
Best for: Developers building and testing LLM agents, RAG pipelines, or chatbots, including those using LangChain or OpenAI.
Good for
  • Open-source framework for unit testing LLM applications
  • Evaluates end-to-end behavior, full trajectories, and individual agent steps
  • Metrics can use a chosen LLM or NLP models that run locally
Watch out for
  • Comparing iterations, sharing reports, and production monitoring require the separate Confident AI platform

confident-ai/deepeval at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Open source
Yes · Source code
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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confident-ai/deepeval FAQ

What can DeepEval evaluate?
End-to-end LLM apps as black boxes, complete agent trajectories, and individual steps such as LLM calls, tool use, retrieval, and sub-agent handoffs.
What kinds of metrics are included?
Custom metrics such as G-Eval, DAG, and JevEval, plus agentic, RAG, and multi-turn metrics such as task completion, faithfulness, and knowledge retention.
Does it work with LangChain or OpenAI implementations?
The repository states it supports agents, RAG pipelines, and chatbots implemented via LangChain or OpenAI.
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