LangSmith vs Respan
Updated March 9, 2026
Overview
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.
Respan provides comprehensive LLM observability with real-time monitoring, tracing, and debugging for AI applications in production. It tracks prompts, completions, latency, cost, and quality metrics across all LLM providers, with built-in evaluation tools, prompt management, and alerting. Respan gives engineering teams full visibility into their AI stack from a single dashboard.
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
- 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
Pros
- Unified observability across all LLM providers in one dashboard
- Real-time alerting for cost spikes, latency, and quality issues
- Built-in evaluation tools for automated quality scoring
- Prompt management with versioning and A/B testing
- Easy integration with popular AI frameworks
Cons
- Newer platform building out integrations with more frameworks
- Advanced evaluation features require configuration effort
- Smaller community compared to established observability tools
LangSmith or Respan — which should you choose?
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
Choose Respan if you wantChoose if you want
Compare LangSmith and Respan 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.