Helicone vs LangSmith for evals

Short answer:
If your main goal is LLM evaluation workflows, LangSmith is generally the stronger choice. If you want observability + request logging + cost/latency tracking across LLM apps with a lighter-weight setup, Helicone is often better.

Quick comparison

LangSmith

Best for:

• Model/prompt evaluation
• Dataset-based testing
• Tracing complex agent chains
• Regression testing and prompt iteration

Strengths:

• Built specifically around the LangChain ecosystem, but usable beyond it
• Strong eval tooling: datasets, labeled examples, comparisons, experiment tracking
• Good for measuring quality over time
• Great for debugging multi-step chains/agents

Tradeoffs:

• More “platform” than simple proxy
• Can feel heavier if you only need request logging
• Best experience if you’re already in LangChain/LangGraph

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Helicone

Best for:

• LLM observability
• Gateway/proxy-based logging
• Cost, latency, token usage monitoring
• Quick integration across many providers

Strengths:

• Very easy to drop in as an API gateway/proxy
• Good centralized view of requests, errors, usage, and costs
• Provider-agnostic and simple for teams using multiple LLM APIs
• Useful for production monitoring and debugging

Tradeoffs:

• Evaluation workflows are not as mature/deep as LangSmith’s
• Less focused on dataset-driven benchmarking and experiment comparison
• Better for visibility than rigorous eval pipelines

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For evals specifically

Choose LangSmith if you need:

• Offline evals on a test set
• Human annotation + ground-truth comparison
• A/B prompt comparisons
• Regression testing before deployment
• Agent trace analysis with scoring

Choose Helicone if you need:

• Production monitoring plus some lightweight quality checks
• Centralized logging of prompts/responses
• Cost attribution by user/team/app
• Easy adoption without changing much code

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Practical recommendation

• For serious evaluation work: LangSmith
• For observability first, evals second: Helicone
• For many teams: use both
  • Helicone for production logging/cost/latency
  • LangSmith for dedicated eval workflows and experiments

Rule of thumb

• If you’re asking “How do I know my prompt is better?” → LangSmith
• If you’re asking “What are my requests doing in production?” → Helicone

If you want, I can also give you:

1. a feature-by-feature table, or
2. a recommendation based on your stack (LangChain, OpenAI SDK, agents, production volume, etc.).