Weights & Biases Weave: Agent Evaluation & Tracing

Weave is W&B's toolkit for building and evaluating LLM applications and agents. It brings experiment tracking, prompt versioning, tracing, and scoring into the same workspace where ML engineers already log models and runs.

• You already use W&B for model training and want the same workflow for agents.
• You need to compare prompt versions, model choices, and agent strategies side by side.
• You want evaluation datasets, automated scoring, and human feedback loops.
• Your team thinks in experiments, not just logs.

• Unified ML + LLM workspace. Traces live next to model training runs and artifacts.
• Prompt and model comparison. A/B test prompts, models, and tool configurations.
• Custom evaluations. Define metrics, run them at scale, and track regressions.
• Trace structure. See the chain of LLM calls, tool executions, and retries.
• Collaboration. Share runs, reports, and dashboards with your team.

• Learning curve for non-ML teams. The W&B paradigm is powerful if you already know it.
• Not a standalone agent framework. It observes and evaluates; you still build the agent elsewhere.
• Pricing at scale. Heavy tracing and evaluation volume moves you beyond the free tier quickly.
• Setup overhead. You need to instrument your agent code to get value.

• Free tier for individuals and small projects.
• Paid plans start around $20/month and scale with tracked traces and storage.
• Enterprise pricing for teams with security, SSO, and support requirements.

ML teams who want the same rigor they apply to model training extended to agent development. Strong for systematic iteration: prompt engineering, model selection, and regression testing.

• OpenAI / Anthropic / Google through the Weave SDK.
• LangChain / LlamaIndex automatic instrumentation.
• OpenClaw / Hermes agents via manual tracing calls.