DeepEval vs LangSmith
Detailed side-by-side comparison to help you choose the right tool
DeepEval
🔴DeveloperTesting & Quality
Open-source LLM evaluation framework for testing AI agents with 14+ metrics including hallucination detection, tool use correctness, and conversational quality.
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FreeLangSmith
🔴DeveloperAnalytics & Monitoring
Tracing, evaluation, and observability for LLM apps and agents.
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FreeFeature Comparison
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DeepEval - Pros & Cons
Pros
- ✓Most comprehensive LLM evaluation metric suite available
- ✓Pytest integration feels natural for Python developers
- ✓Tool correctness metric specifically designed for agent testing
- ✓Active development with frequent new metrics and features
- ✓Both open-source and managed cloud options
Cons
- ✗Metrics require LLM API calls adding cost
- ✗Some metrics can be slow for large evaluation datasets
- ✗Confident AI cloud required for team features
- ✗Documentation could be more comprehensive for advanced use cases
LangSmith - Pros & Cons
Pros
- ✓Best-in-class LLM tracing and debugging platform
- ✓Deep integration with LangChain ecosystem
- ✓Powerful evaluation and testing workflows for prompt development
- ✓Dataset management for building evaluation harnesses
- ✓Visual trace viewer makes debugging complex chains intuitive
Cons
- ✗Most valuable when used with LangChain — less useful standalone
- ✗Paid plans required for team features and higher volume
- ✗Data sent to LangSmith's servers — privacy considerations
- ✗Can add overhead to development workflow
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