--- name: weave-a10a8775a5f6 description: >- Weave is Weights & Biases' toolkit for tracing and evaluating LLM applications and AI agents. Use when a user wants to trace OpenAI or Anthropic calls, log agent runs, score outputs with evaluations, compare prompt or model versions, or debug an AI pipeline from a dashboard. license: Apache-2.0 compatibility: "Python 3.10+, a Weights & Biases account and WANDB_API_KEY" metadata: author: terminal-skills version: 1.1.0 repository: https://github.com/wandb/weave category: data-ai tags: "observability, evaluation, tracing, llm, weights-biases" --- # Weave — AI Application Tracking by Weights & Biases ## Overview Weave traces LLM calls and functions, runs evaluations, and versions models and prompts, with a dashboard in your W&B account for traces, cost, latency and scores. Current releases (0.53.x, Python 3.10+) also trace whole agents: with the OpenAI Agents SDK, Claude Agent SDK or Google ADK, `weave.init()` alone is enough and runs appear on the **Agents** page. Plain `@weave.op` functions and auto-patched LLM clients appear on the **Traces** page; the two kinds of trace are shown separately. ## Instructions Install and authenticate first (see Installation below), then initialise with `weave.init("/")`; a bare project name uses your default W&B entity. Use the sections below for tracing, evaluations and model versioning. ### Tracing agents ```bash pip install weave openai-agents ``` ```python import weave weave.init("acme-ml/support-agent") # then run the agent as usual; traces land on the Agents page ``` For a custom agent loop use `weave.start_conversation(agent_name=...)`, `weave.start_turn(...)` and `weave.start_llm(...)` as context managers (the older `start_session` is deprecated). ### Automatic Tracing ```python import json import weave import openai weave.init("acme-ml/support-agent") # team/project; created on first use client = openai.OpenAI() # OpenAI calls are automatically traced response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Explain transformers"}], ) # Weave captures: model, tokens, latency, cost, input/output — viewable in dashboard # Custom function tracing @weave.op() def extract_entities(text: str) -> list[str]: """Extract named entities from text.""" response = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": f"Extract entities from: {text}\nReturn JSON list."}], ) return json.loads(response.choices[0].message.content) @weave.op() def retrieve_documents(query: str) -> list[str]: """Stand-in retriever; replace with your vector search.""" return ["Python is a programming language created by Guido van Rossum."] @weave.op() def rag_pipeline(query: str) -> str: """Full RAG pipeline — each step traced as child span.""" docs = retrieve_documents(query) # traced as a child call because it is an op context = "\n".join(docs) response = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": f"Answer using:\n{context}"}, {"role": "user", "content": query}, ], ) return response.choices[0].message.content ``` ### Evaluations ```python # Define evaluation dataset eval_dataset = [ {"query": "What is Python?", "expected": "programming language"}, {"query": "Who created Linux?", "expected": "Linus Torvalds"}, {"query": "What is Docker?", "expected": "containerization platform"}, ] # Define scoring functions @weave.op() def relevance_scorer(output: str, expected: str) -> dict: """Score if output contains expected information.""" contains = expected.lower() in output.lower() return {"relevance": 1.0 if contains else 0.0} @weave.op() def length_scorer(output: str) -> dict: """Score response length (prefer concise).""" words = len(output.split()) return {"conciseness": min(1.0, 50 / max(words, 1))} # Run evaluation evaluation = weave.Evaluation( dataset=eval_dataset, scorers=[relevance_scorer, length_scorer], ) import asyncio results = asyncio.run(evaluation.evaluate(rag_pipeline)) # or await it inside async code # Results visible in Weave dashboard with per-example scores # Compare across model versions, prompts, parameters ``` ### Model Versioning ```python # Track model/prompt versions class SupportAgent(weave.Model): model_name: str = "gpt-4o" system_prompt: str = "You are a helpful support agent." temperature: float = 0.7 @weave.op() def predict(self, query: str) -> str: response = client.chat.completions.create( model=self.model_name, temperature=self.temperature, messages=[ {"role": "system", "content": self.system_prompt}, {"role": "user", "content": query}, ], ) return response.choices[0].message.content # Version 1 agent_v1 = SupportAgent(system_prompt="Be concise and helpful.") # Version 2 — compare in dashboard agent_v2 = SupportAgent(model_name="gpt-4o-mini", system_prompt="Be detailed and empathetic.") # Evaluate both versions for agent in [agent_v1, agent_v2]: asyncio.run(evaluation.evaluate(agent)) # Dashboard shows side-by-side comparison: quality, cost, latency ``` ## Installation ```bash pip install weave export WANDB_API_KEY=... # key from https://wandb.ai/authorize, or run `wandb login` ``` ## Examples **Trace a RAG function.** User: "Log every call to my answer_question function with its OpenAI usage." Add `weave.init("acme-ml/support-agent")` at startup and `@weave.op()` on the function; each call shows inputs, output, token counts, latency and cost as a trace with child spans on the Traces page. **Compare two prompts.** User: "Which of my two system prompts answers better?" Wrap each in a `weave.Model` subclass (`agent_v1`, `agent_v2` above), run the same `weave.Evaluation` on both, and open the Evaluations comparison view to see scores side by side. ## Guidelines 1. **@weave.op() decorator** — Add to any function to trace it; creates hierarchical spans for nested calls 2. **Auto-instrumentation** — OpenAI, Anthropic, LangChain calls traced automatically after `weave.init()` 3. **Evaluations** — Define datasets + scorers; run systematically; compare versions in dashboard 4. **weave.Model** — Subclass for versioned models; parameters tracked, comparable across evaluations 5. **W&B integration** — Weave data appears in your W&B workspace; share with team, add to reports 6. **Cost tracking** — Automatic per-call cost calculation; aggregate by function, model, or user 7. **Production monitoring** — Use in production for continuous quality tracking; alert on regressions 8. **Lightweight** — Single `@weave.op()` decorator; no complex setup, no separate infrastructure --- Source attribution: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/weave/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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