Weights & Biases Weave vs LangSmith for evals

Here’s the practical comparison for evals between Weights & Biases Weave and LangSmith.

Short version

• Choose LangSmith if you’re mainly building LangChain / LangGraph apps and want the most mature LLM observability + tracing + eval workflow in that ecosystem.
• Choose Weave if you want a more general-purpose model/app evaluation platform, especially if you already use Weights & Biases for experiments, metrics, and team collaboration.
• Both can do evals well; the better choice usually depends on your stack and where you want your source of truth to live.

Key differences for evals

1) Ecosystem fit

• LangSmith
  • Best for LangChain/LangGraph users.
  • Very strong tracing of chains, agents, tools, retrieval, and prompts.
  • Evals integrate naturally with LangChain workflows.
• Weave
  • More platform-agnostic.
  • Fits well if you want evals across different model providers, custom code, or non-LangChain pipelines.
  • Good if you already use W&B for ML experiment tracking.

2) Tracing and debugging

• LangSmith is usually stronger for:
  • detailed LLM traces
  • agent/tool execution visibility
  • step-by-step debugging
  • dataset-based regression testing in LangChain apps
• Weave also offers tracing, but many users find LangSmith more “LLM-native” for deep app debugging.

3) Evals workflow

• LangSmith
  • Good for dataset-driven evals, comparisons, LLM-as-judge, human labeling, and regression tests.
  • Nice if you want to evaluate prompt/chain changes over time.
• Weave
  • Strong for flexible evals, custom scoring, experiments, and comparing runs across code changes.
  • Tends to feel more like an experiment platform that includes evals as part of a broader workflow.

4) Reporting and collaboration

• LangSmith
  • Great for reviewing traces, annotations, and eval results in the context of app behavior.
• Weave
  • Good for sharing experiment results and tying evals to W&B’s broader reporting/visualization ecosystem.

5) Human feedback / annotation

• Both support human-in-the-loop evaluation in some form.
• LangSmith often feels more turnkey for labeling traces and reviewing generations in LLM app workflows.
• Weave may be preferable if you want to unify evals with broader ML team processes in W&B.

When LangSmith is the better choice

Use LangSmith if:

• your app is built on LangChain or LangGraph
• you care most about observability and debugging
• you want evals tightly coupled to traces, prompts, and tool calls
• your team is already using LangChain heavily

When Weave is the better choice

Use Weave if:

• you want a more general eval platform
• you already use Weights & Biases
• you’re evaluating custom pipelines beyond LangChain
• you want experiment tracking and evals in one place

Rule of thumb

• LLM app built with LangChain? → LangSmith
• Broader ML/LLM experimentation, already on W&B? → Weave

Recommendation

If you’re undecided:

1. Try LangSmith first if your app is LangChain-based.
2. Try Weave first if you already have a W&B workflow or want flexibility outside LangChain.
3. Use the one that makes it easiest to:
   • capture traces
   • define datasets
   • run regression evals
   • compare versions over time

If you want, I can also give you:

• a feature-by-feature table
• a decision tree
• or a recommendation based on your stack (e.g. LangChain, OpenAI SDK, custom Python, RAG, agents).