W&B Weave
✓ Editorially verifiedProduction observability, tracing, and evaluation for LLM and agent systems from the Weights & Biases stack.
Pick W&B Weave if you are shipping multi-turn agents to production and need tracing, evals, and guardrails wired into the same stack as your ML experiments.
Skip it if you just need lightweight prompt logging for a single-call LLM app or want a fully open-source self-hosted observability tool.
W&B Weave is the LLMOps arm of Weights & Biases, built specifically for teams running LLM apps and multi-agent systems in production. It captures sessions, turns, tool calls, and sub-agent steps as first-class traces, then layers on a flexible evaluation framework, prebuilt guardrails (toxicity, bias, PII, hallucination scorers), and a Playground for replaying production traces against new prompts or models.
Where Weave separates from generic OTEL-style tracing tools is its agent-native data model and tight coupling to the broader W&B platform that ML teams already use for experiment tracking and model management. It is aimed at engineering teams who have moved past prototype and need to debug multi-turn failures, prevent regressions across model swaps, and run continuous evals on live traffic. Pricing is not posted on the LLMOps landing page itself; W&B offers a free tier on its core platform with paid and enterprise tiers for larger teams.
Weave integrates with most major LLM providers and frameworks (OpenAI, Anthropic, LangChain, LlamaIndex, DSPy, and others) via a lightweight SDK, and connects to coding agents like Claude Code for autonomous improvement loops. The main caveat is that it sits inside the W&B ecosystem, so adoption is easiest for teams already comfortable with that stack rather than those wanting a standalone observability point tool.
Weave is one of the more credible LLMOps platforms because W&B already understands how engineering teams instrument ML systems. The agent-native trace model and built-in scorers make it a serious contender against Langfuse, Arize, and Braintrust, especially for teams already invested in the W&B ecosystem.
— The AI Tool Bible editorial team
Pros
- ✅ Agent-native trace model with sessions, turns, tools, and sub-agents
- ✅ Built-in scorers for toxicity, bias, PII, and hallucinations
- ✅ Playground replays production traces against new prompts/models
- ✅ Inherits the maturity of the W&B experiment-tracking platform
- ✅ Broad SDK coverage across OpenAI, Anthropic, LangChain, LlamaIndex, DSPy
Cons
- ⚠️ Pricing not transparent on the LLMOps landing page
- ⚠️ Best value if you are already a W&B customer
- ⚠️ Heavier than minimalist tracing tools for simple single-prompt apps
Use cases
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