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Weave for Agents is in public preview. Features, APIs, and the Agents view UI may change before general availability.
Pi is a terminal-based coding agent. Weave traces Pi sessions, LLM calls, and tool executions automatically using the createOtelExtension integration, which conforms to the GenAI semantic conventions.
Pi is a TypeScript/Node.js framework with no Python equivalent. Pi requires the ESM module system — your project must use "type": "module" in package.json, or compile TypeScript to ESM output. CommonJS projects will error. For more information on setting up an ESM project, see Typescript SDK integration.

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Prerequisites

  • Node.js (v18 or later)
  • A W&B account and API key set as a WANDB_API_KEY environment variable

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Install packages

  • Install Weave, Pi, and Node type definitions as local project dependencies:
npm install weave @earendil-works/pi-coding-agent
npm install --save-dev @types/node tsx typescript

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Trace a Pi prompt and response

Call weave.init() before creating your agent session, then pass createOtelExtension() as an extension factory. Weave traces the full agent lifecycle: the session, each prompt/response cycle (invoke_agent), individual LLM calls (chat), and tool executions (execute_tool). The session ID is generated automatically by SessionManager.inMemory(). 
import {init, createOtelExtension} from 'weave';

import {
  createAgentSession,
  DefaultResourceLoader,
  SessionManager,
  getAgentDir,
} from '@earendil-works/pi-coding-agent';

async function main() {
  // 1. Initialize Weave — sets up the OTEL TracerProvider pointing at your
  //    Weave project. All spans created by createOtelExtension() are
  //    automatically exported here.
  await init('[YOUR-TEAM]/[YOUR-PROJECT]'); 

// 2. Create a resource loader and inject the Weave OTEL extension.
//    The resource loader provides the Pi runtime environment and
//    extension lifecycle used for tracing agent activity.
  const resourceLoader = new DefaultResourceLoader({
    cwd: process.cwd(),
    agentDir: getAgentDir(),
    extensionFactories: [createOtelExtension({})],
  });

  await resourceLoader.reload();

  // 3. Start the agent session
  const {session} = await createAgentSession({
    resourceLoader,
    sessionManager: SessionManager.inMemory(),
  });

  // 4. Bind extensions — triggers session_start event so the OTEL adapter
  //    creates the root session span and captures the conversation ID.
  await session.bindExtensions({});

  // 5. Stream assistant output to stdout
  session.subscribe(event => {
    if (
      event.type === 'message_update' &&
      event.assistantMessageEvent.type === 'text_delta'
    ) {
      process.stdout.write(event.assistantMessageEvent.delta);
    }
  });

  // 6. Send a prompt and wait for the full response
  await session.prompt('What files are in the current directory?');
  console.log();
}

main();
Build and run using: 
npx tsx [filename].ts
When you run your code, your traces appear in the Agents tab of your Weave project at https://wandb.ai/[YOUR-TEAM]/[YOUR-PROJECT]/weave/agents.

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Next steps

You can turn this example into a multi-turn session by adding additional prompts. Each call to session.prompt() is traced as a separate invoke_agent span, all nested under a single root span. The agent retains context across prompts automatically. After running the code, the Agents tab shows the full multi-turn timeline with nested LLM calls, tool executions, token usage, and cost.