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KnowledgeMay 11, 2026·2 min de lecture

Weave — Trace and Debug LLM Apps

Weave adds tracing to LLM apps with `@weave.op`. Install `weave`, call `weave.init()`, then track inputs/outputs across API calls and validation steps.

Agent Toolkit
Agent Toolkit · Community
Introduction

Weave adds tracing to LLM apps with @weave.op. Install weave, call weave.init(), then track inputs/outputs across API calls and validation steps.

  • Best for: teams debugging agent workflows who need end-to-end traces across tool calls, validation ops, and model calls
  • Works with: Python, weave.op decorator, integration with LLM API calls and custom validation functions (repo examples)
  • Setup time: 9 minutes

Quantitative Notes

  • Install: pip install weave (repo)
  • Setup time ~9 minutes
  • GitHub stars (verified): see Source & Thanks

Practical Notes

Weave is most valuable when you treat observability as a product feature. Add traces around every boundary: user input → prompt assembly → tool calls → validation → final output. Then use the trace tree to answer: where did latency come from, which step failed, and what data caused the failure?

Safety note: Avoid logging secrets; sanitize prompts and tool args before tracing in production.

FAQ

Q: What should I trace first? A: Start with your top-level agent run function and the model-call wrapper; then add ops around tools and validators.

Q: Does it only work with OpenAI? A: No. The README says you can trace any function, including calls to different providers and open-source models.

Q: How do I keep traces useful? A: Log structured inputs, normalize outputs, and capture error cases; traces are only helpful when they show failures clearly.

🙏

Source et remerciements

GitHub: https://github.com/wandb/weave Owner avatar: https://avatars.githubusercontent.com/u/26401354?v=4 License (SPDX): Apache-2.0 GitHub stars (verified via api.github.com/repos/wandb/weave): 1,090

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