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Weave for Agents is in public preview. Features, APIs, and the Agents view UI may change before general availability.
Try in Colab · GitHub source The Weave SDK lets you trace agents built with popular SDKs or custom harnesses. This quickstart shows you how to manually integrate Weave into a custom-built multi-turn agent to emit and capture OpenTelemetry spans. For conceptual understanding about Weave for agents, see Trace your agents. If you’re looking to integrate Weave with SDKs or harnesses such as the Claude Agent SDK or Codex, see Trace agent integrations. Weave autopatches into several agent-building SDKs and agent harnesses for quick integration.

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What you’ll learn

By the end of this quickstart, you’ll have a working multi-turn agent that emits Weave-compatible OTel spans. You’ll also understand how Weave maps sessions, turns, LLM calls, and tool calls onto your agent code so you can apply the same pattern to your own custom agents. The code in this guide sets up a small research agent that can look things up on Wikipedia. It asks three questions (three turns) and uses the LLM to choose when to search Wikipedia for an answer. Weave records every step (the conversation, each question, each AI response, and each Wikipedia lookup) so you can see what happened in the Weave Agents view. This guide shows you how to:
  • Initialize Weave for agent tracing with weave.init().
  • Open a session and a turn with start_session / startSession and start_turn / startTurn.
  • Wrap LLM calls with start_llm / startLLM and record usage.
  • Wrap tool executions with start_tool / startTool and record results.
  • View the resulting session, turns, and tool calls in the Agents view.

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How the Weave SDK works with agents

The Weave SDK includes a generic OTel ingest system for agents, meaning that Weave can capture information from any OTel span in your agent’s code. However, Weave requires special handling of the following spans to render your agent’s traces in the Agents view of the Weave UI.
ConceptPythonTypeScriptOTel span
A conversationweave.start_session(...)weave.startSession(...)(no span, groups turns)
One user or agent exchangeweave.start_turn(...)weave.startTurn(...)invoke_agent
One LLM API callweave.start_llm(...)weave.startLLM(...)chat
One tool executionweave.start_tool(...)weave.startTool(...)execute_tool
In Python, all four functions work as context managers (with weave.start_*(...) as obj:). On exit, they end the span and flush attributes, including on exceptions. In TypeScript, call .end() on each returned object. Use try { ... } finally { obj.end(); } to guarantee cleanup on exceptions. Other GenAI semantic-convention attributes, such as gen_ai.usage.* and gen_ai.agent.name, enable additional rendering, but they’re optional.

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Prerequisites

  • A W&B account and API key.
  • An OpenAI API key.
  • Python 3.10+ (for the Python examples).
  • Node.js 18+ (the TypeScript examples require built-in fetch).

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Install packages

Install the following packages into your developer environment: 
pip install weave openai requests

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Initialize Weave

weave.init() authenticates with W&B and configures the OTel exporter that sends agent spans to the Agents view. If the project doesn’t exist on your team, Weave creates it the first time you write to it. 
import getpass
import os

os.environ["WANDB_API_KEY"] = getpass.getpass("Enter your W&B API key: ")
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

TEAM = input("Enter your W&B team name: ")
PROJECT = input("Enter your W&B project name: ")

import weave
weave.init(f"{TEAM}/{PROJECT}")

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Define a tool

The following code defines the agent’s Wikipedia search tool along with an OpenAI tool schema that specifies when and how to use the tool. 
import json
import requests

def wikipedia_search(query: str) -> str:
    r = requests.get(
        "https://en.wikipedia.org/w/api.php",
        params={
            "action": "query", "generator": "search", "gsrsearch": query, "gsrlimit": 1,
            "prop": "extracts", "exintro": True, "explaintext": True, "format": "json",
        },
        headers={"User-Agent": "weave-demo"},
    ).json()
    return next(iter(r["query"]["pages"].values()))["extract"]

wikipedia_tool_schema = {
    "type": "function",
    "function": {
        "name": "wikipedia_search",
        "description": "Search Wikipedia for a topic and return its intro paragraph.",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"],
        },
    },
}

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Run a traced multi-turn agent

With the tool and Weave initialization in place, the next step combines them into a complete agent loop. This loop shows how sessions, turns, LLM calls, and tool calls nest together. The following example runs three turns in a single session. Each turn:
  1. Opens a chat span and lets the LLM choose whether to call the tool.
  2. If the LLM requests a tool, opens an execute_tool span around the call and feeds the result back to the LLM.
  3. Opens a second chat span to produce the final answer.
from openai import OpenAI

openai_client = OpenAI()
MODEL = "gpt-4o-mini"

def run_turn(history, user_message):
    history.append({"role": "user", "content": user_message})

    with weave.start_turn(user_message=user_message, model=MODEL):
        # LLM call 1: the model might decide to use a tool.
        with weave.start_llm(model=MODEL, provider_name="openai") as llm:
            resp = openai_client.chat.completions.create(
                model=MODEL, messages=history, tools=[wikipedia_tool_schema],
            )
            msg = resp.choices[0].message
            llm.output(msg.content or "")
            llm.usage = weave.Usage(
                input_tokens=resp.usage.prompt_tokens,
                output_tokens=resp.usage.completion_tokens,
            )
            history.append(msg.model_dump(exclude_none=True))

        # If no tool was requested, the first LLM response is the answer.
        if not msg.tool_calls:
            return msg.content

        # Execute each requested tool call.
        for tc in msg.tool_calls:
            with weave.start_tool(
                name=tc.function.name,
                arguments=tc.function.arguments,
                tool_call_id=tc.id,
            ) as tool:
                tool.result = wikipedia_search(**json.loads(tc.function.arguments))
                history.append({
                    "role": "tool",
                    "tool_call_id": tc.id,
                    "content": tool.result,
                })

        # LLM call 2: synthesize the final answer.
        with weave.start_llm(model=MODEL, provider_name="openai") as llm:
            resp = openai_client.chat.completions.create(model=MODEL, messages=history)
            msg = resp.choices[0].message
            llm.output(msg.content)
            llm.usage = weave.Usage(
                input_tokens=resp.usage.prompt_tokens,
                output_tokens=resp.usage.completion_tokens,
            )
            history.append({"role": "assistant", "content": msg.content})
            return msg.content

with weave.start_session(agent_name="research-bot") as session:
    history = []
    for question in [
        "Who founded Anthropic?",
        "What is Claude (the AI assistant)?",
        "Summarize what we discussed in one sentence.",
    ]:
        print(f"USER: {question}")
        print(f"AGENT: {run_turn(history, question)}\n")

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See your agent traces in the Agents view

When weave.init() runs, it prints a link to your project where you can see:
  • A row in the Agents tab for research-bot.
  • One session containing three turns.
  • Each turn (invoke_agent) with two chat spans and an execute_tool span nested inside.
  • Token counts, latency, model, and the full message exchange on each chat.
Click any turn to inspect the inputs, outputs, tool arguments, and tool results.

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Next steps