DeepEval vs LangSmith
Updated March 10, 2026
Overview
DeepEval is an open-source LLM evaluation framework built for unit testing AI outputs. It provides 14+ evaluation metrics including hallucination detection, answer relevancy, and contextual recall. Integrates with pytest, supports custom metrics, and works with any LLM provider for automated quality assurance in CI/CD pipelines.
LangSmith is LangChain's observability and evaluation platform for LLM applications. It provides detailed tracing of every LLM call, chain execution, and agent step—showing inputs, outputs, latency, token usage, and cost. LangSmith includes annotation queues for human feedback, dataset management for evaluation, and regression testing for prompt changes. It's the most comprehensive debugging tool for LangChain-based applications.
Key features
Core capabilities each platform advertises.
- Trace visualization for LLM chains
- Prompt versioning and management
- Evaluation and testing suite
- Dataset management
- Tight LangChain integration
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Open-source
- Comprehensive metrics
Cons
- Manual setup
Pros
- Deep integration with LangChain framework provides unmatched observability for LangChain applications
- Comprehensive evaluation capabilities with custom metrics and automated regression testing
- Excellent tracing UI with detailed step-by-step execution visualization
- Strong dataset management and annotation workflows for human feedback
Cons
- Tight coupling to LangChain ecosystem limits flexibility for non-LangChain applications
- UI scalability issues reported when working with very large datasets
- Production testing limitations - relies primarily on production logs for test datasets
- SaaS-only deployment with no self-hosting option may not suit all enterprise requirements
DeepEval or LangSmith — which should you choose?
Choose DeepEval if you wantChoose if you want
Choose LangSmith if you wantChoose if you want
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
- Team collaboration on prompt engineering
- Regression testing for LLM apps
Compare DeepEval and LangSmith on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.