
Weave
W&B's toolkit for tracing and evaluating LLM apps.
About
Weave is Weights & Biases' toolkit for LLM development: trace every model call, score outputs with evaluations, and iterate on prompts and models — integrated with the W&B platform teams already use for ML experiment tracking. 817k PyPI downloads/month, Apache-2.0. The GitHub star count (1.1k) understates its reach — it ships as a library, not a destination repo. Choose it if you're already in the W&B ecosystem; otherwise Langfuse or Opik have larger standalone communities.
What it can do
- **Tracing**: automatic capture of LLM calls and costs
- **Evaluations**: score outputs, compare runs
- **Prompt versioning**: track what changed
- **W&B integration**: same platform as your training runs
- **Apache-2.0**: open toolkit
Setup
```bash
pip install weave
```
```python
import weave
weave.init("my-project")
@weave.op()
def call_llm(prompt: str) -> str: ...
```
Docs: weave-docs.wandb.ai.
Prerequisites
Python 3.9+. W&B account for the hosted dashboard (or self-hosted).
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: 1,140 GitHub stars (log-scaled vs catalog peers); blended with ~817,064 monthly package downloads; last push 0d ago; 33 releases/12mo; 198 open issues. Manual review: W&B's LLM tracing/eval toolkit, 817k PyPI/mo; only 1.1k stars — mindshare trails Langfuse/Opik; W&B funnel shapes it. License: Apache-2.0.
★ 1,140 GitHub stars
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 6.8/10
- Maintenance · recency, releases, issue load
- 8.2/10
- Openness · license + docs
- 10.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · 1,140 GitHub stars (log-scaled vs catalog peers)
- · blended with ~817,064 monthly package downloads
- · last push 0d ago; 33 releases/12mo; 198 open issues
- · manual review: W&B's LLM tracing/eval toolkit, 817k PyPI/mo; only 1.1k stars — mindshare trails Langfuse/Opik; W&B funnel shapes it
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.