OpenAI Agents SDK

Trace an agent built with the OpenAI Agents SDK using Weave.

The OpenAI Agents SDK is a lightweight framework for building agents and multi-agent workflows on top of OpenAI’s API. Weave automatically traces agents built with the OpenAI Agents SDK, including each agent invocation, sub-agent handoff, model call, and tool call. Weave displays the captured data in the Agents view of your project.

Trace OpenAI Agents SDK agents with Weave

Python

The Weave SDK autopatches with the OpenAI Agents SDK for Python so you can capture traces from your agents with minimal setup. This guide explains how to initialize Weave and then run a multi-turn research agent built with the OpenAI Agents SDK so that Weave captures every agent invocation, model call, and tool call across the session.

Prerequisites

Before you begin, make sure you have the following:

• A W&B account and API key set as a WANDB_API_KEY environment variable.
• An OpenAI API key.
• Python 3.10+.

TypeScript

Weave integrates with the OpenAI Agents Node SDK (@openai/agents) to automatically trace your agent runs. Requires @openai/agents version 0.4.15 or later.

Prerequisites

Before you begin, make sure you have the following:

• A W&B account and API key set as a WANDB_API_KEY environment variable.
• An OpenAI API key.
• Node.js 18+.

Install packages

Install the following packages in your developer environment so that Weave and the SDK are available to your script.

Python

pip install weave openai-agents requests

npm install weave @openai/agents zod

Initialize Weave in your code

Python

Add weave.init to the project, along with your W&B team and project names, and then build your agent as usual. The following code defines a wikipedia_search function tool and a Research assistant agent, then runs three questions through the OpenAI Agents SDK Runner while Weave captures the trace.

import asyncio
import requests
import weave
from agents import Agent, Runner, function_tool

weave.init("<your-team>/<your-project-name>")

@function_tool
def wikipedia_search(query: str) -> str:
    """Search Wikipedia for a topic and return its title and intro paragraph."""
    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()
    page = next(iter(r["query"]["pages"].values()))
    return f"{page['title']}: {page['extract']}"

agent = Agent(
    name="Research assistant",
    instructions=(
        "You are a research assistant. Use the wikipedia_search tool to look up "
        "topics when needed, and cite the article titles you used."
    ),
    tools=[wikipedia_search],
)

async def main():
    history = []
    for question in [
        "Who founded Anthropic?",
        "What is Claude (the AI assistant)?",
        "Summarize what we discussed in one sentence.",
    ]:
        history.append({"role": "user", "content": question})
        print(f"USER: {question}")
        result = await Runner.run(agent, input=history)
        print(f"AGENT: {result.final_output}\n")
        history = result.to_input_list()

asyncio.run(main())

The example runs three turns in a single conversation. The first two turns trigger Wikipedia lookups, and the third uses the prior conversation context to produce a summary without a tool call. Each call to Runner.run continues the conversation by passing the previous result’s input list back as the next request.

TypeScript

Weave automatically instruments @openai/agents via module loader hooks when it is imported. The required setup differs slightly between module systems. For more background on CommonJS versus ESM and how Weave’s loader hooks work, see the TypeScript SDK third-party integration guide.

• CommonJS projects: No extra configuration needed. Require weave before @openai/agents so auto-instrumentation runs first.
• ESM projects: Start Node with the --import=weave/instrument flag so the instrumentation loads before any other modules.

The following code defines a wikipedia_search tool and a Research assistant agent, then runs three questions through the SDK’s Runner while Weave captures the trace. Each run() call is traced as a separate top-level trace, so the example creates a Runner with a shared groupId and reuses it across all three calls. Weave uses groupId to group traces from the same conversation, falling back to each trace’s own ID when no groupId is set.

main.mjs

import { randomUUID } from "node:crypto";
import * as weave from "weave";
import { Agent, Runner, tool, type AgentInputItem } from "@openai/agents";
import { z } from "zod";

const wikipediaSearch = tool({
    name: "wikipedia_search",
    description: "Search Wikipedia for a topic and return its title and intro paragraph.",
    parameters: z.object({
    query: z.string().describe("The topic to search for"),
    }),
    async execute({ query }) {
    const url = new URL("https://en.wikipedia.org/w/api.php");
    url.search = new URLSearchParams({
        action: "query",
        generator: "search",
        gsrsearch: query,
        gsrlimit: "1",
        prop: "extracts",
        exintro: "true",
        explaintext: "true",
        format: "json",
    }).toString();

    const response = await fetch(url, { headers: { "User-Agent": "weave-demo" } });
    const data = await response.json();
    const page = Object.values(data.query.pages)[0] as { title: string; extract: string };
    return `${page.title}: ${page.extract}`;
    },
});

async function main() {
    await weave.init("<your-team>/<your-project-name>");

    const agent = new Agent({
    name: "Research assistant",
    instructions:
        "You are a research assistant. Use the wikipedia_search tool to look up " +
        "topics when needed, and cite the article titles you used.",
    tools: [wikipediaSearch],
    });

    const runner = new Runner({ groupId: randomUUID() });

    const questions = [
    "Who founded Anthropic?",
    "What is Claude (the AI assistant)?",
    "Summarize what we discussed in one sentence.",
    ];

    let history: AgentInputItem[] = [];
    for (const question of questions) {
    history.push({ role: "user", content: question });
    console.log(`USER: ${question}`);
    const result = await runner.run(agent, history);
    console.log(`AGENT: ${result.finalOutput}\n`);
    history = result.history;
    }
}

main();

The example runs three turns in a single conversation. The first two turns trigger Wikipedia lookups, and the third uses the prior conversation context to produce a summary without a tool call. Each call to runner.run continues the conversation by passing the previous result’s history back as the next input, and shares the same groupId so Weave groups all three turns into one session in the Agents view.

Save the example as main.mjs and run it with the --import=weave/instrument flag so the loader hook runs before any other module:

node --import=weave/instrument main.mjs

Manual instrumentation

Manual instrumentation is only needed when module loader hooks cannot run, such as when using a bundler that combines dependencies into a single file, in environments that don’t support Node CLI flags, or with dynamic module loading patterns that bypass loader hooks.

Use instrumentOpenAIAgents() to register instrumentation explicitly:

import * as weave from "weave";

await weave.init("<your-team>/<your-project-name>");
await weave.instrumentOpenAIAgents();

If you need full control over the tracing processor — for example, to configure a custom processor or register it conditionally — create and register it directly:

import { addTraceProcessor } from "@openai/agents";
import { createOpenAIAgentsTracingProcessor } from "weave";

const processor = createOpenAIAgentsTracingProcessor();
addTraceProcessor(processor);

See your agent traces in the Agents view

After the script runs, weave.init() prints a link to your project. Open the Agents view to inspect:

• A session containing the conversation’s turns.
• Each turn rendered as an invoke_agent span with nested chat and execute_tool children.
• The full input, model, output, token usage, and tool results at each step.

For details about viewing Agents data in Weave, see View agent activity.