Tool overview
DeepEval is listed under AI Governance Safety & Evaluation AI tools.
What is DeepEval?
DeepEval is an Apache 2.0 open-source Python framework for evaluating LLM applications, agents, RAG systems, and prompts. It runs locally, integrates with testing and agent frameworks, supports custom models and metrics, and optionally connects to the Confident AI platform for collaboration and observability.
Best for
Developers automating local and CI-based evaluation of LLM applications
Who is it for?
Decision note
Best for developers who need programmable, local-first LLM evaluation and want an optional path to a managed team platform.
Key features
Local-first Python evaluation framework
Metrics for agents, RAG, conversations, and prompts
Pytest, CI, OpenTelemetry, and framework integrations
Optional Confident AI collaboration and observability
Use cases
Test LLM outputs in CI pipelines
Evaluate RAG retrieval and answer quality
Red-team and score AI agents
Build custom domain-specific evaluation metrics
Pros
- Open source under Apache 2.0
- Evaluations run in the user’s environment
- Broad integrations and custom-model support
Cons
- Hosted collaboration belongs to a separate platform
- Metric reliability depends on configuration and judges
- Teams must design representative evaluation datasets
Limitations
DeepEval does not replace human review or production monitoring by itself. Results depend on metric choice, judge models, thresholds, test data, and repeatability controls.
Pricing details
Billing options
Pricing note
The DeepEval framework is free and open source. Confident AI is a separate enterprise platform with managed collaboration, observability, security, and custom deployment; public DeepEval framework pricing is therefore $0.
Supported languages
- English
Integrations
Pytest
OpenTelemetry
LangChain and LlamaIndex
OpenAI and Anthropic
Agent frameworks
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