Pi Durable observability

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Overview

• One trace per run, with a root span (pi.run) holding the user’s input and the final answer, or the error when the run ends unanswered.
• An LLM span (pi.chat) per model request: the messages as sent, including the system prompt, the tool definitions, the model, token counts, and cost.
• A TOOL span per tool call, named after the tool, with its arguments and result.
• The session ID (Pi’s per-conversation session ID by default), user ID, and metadata you set.

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Getting started

npm install @lmnr-ai/lmnr @earendil-works/pi-durable @earendil-works/pi-ai @earendil-works/chord

# .env
LMNR_PROJECT_API_KEY=your-laminar-project-api-key
OPENAI_API_KEY=your-openai-api-key

import 'dotenv/config';
import { BACKGROUND_CONTEXT } from '@earendil-works/chord/context';
import { createModels } from '@earendil-works/pi-ai/models';
import { openaiProvider } from '@earendil-works/pi-ai/providers/openai';
import * as piDurable from '@earendil-works/pi-durable';
import { AssistantEntry, createRegistry, Harness } from '@earendil-works/pi-durable';
import { openNodeSqliteStorage } from '@earendil-works/pi-durable/storage/sqlite/node';
import { Laminar } from '@lmnr-ai/lmnr';

Laminar.initialize({ instrumentModules: { piDurable } });

const context = BACKGROUND_CONTEXT;

const weather = {
  name: 'get_weather',
  description: 'Get the current weather in a city',
  parameters: {
    type: 'object',
    properties: { city: { type: 'string' } },
    required: ['city'],
  },
  execute: async ({ city }) => ({
    output: [{ type: 'text', text: `Sunny and 22°C in ${city}` }],
  }),
};

const app = {
  name: 'weather-app',
  tools: [weather],
  sections: [{ key: 'preamble', render: () => 'You are a concise weather assistant.', tag: false }],
};

const registry = createRegistry();
registry.install(app);

const models = createModels();
models.setProvider(openaiProvider());

const harness = await Harness.open(
  await openNodeSqliteStorage('./session.sqlite'),
  { models, registry },
  context,
);

const root = await harness.root(context, {
  agent: { model: { provider: 'openai', modelId: 'gpt-5.6-terra' } },
});

const submission = await root.submit(
  { type: 'input', content: "What's the weather in Paris?" },
  context,
);
const settled = await submission.wait(context);
if (settled.status === 'done' && settled.type === 'input') {
  const answer = await root.commit((tx) => tx.entry(AssistantEntry, settled.answer), context);
  console.log(answer?.model?.[0]);
}

await harness.close(context);
await Laminar.shutdown();

• If you open a Harness with your own telemetry, Laminar leaves that Harness alone.
• If the registry already has an extension named @lmnr-ai/lmnr that isn’t Laminar’s, Laminar logs a warning and leaves it in place.
• With Next.js, add @earendil-works/pi-durable to serverExternalPackages. When it’s bundled, the copy your app runs isn’t the one Laminar patched.

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Set a user ID, metadata, or span names with laminarPiDurable()

• lmnr.telemetry goes to Harness.open({ telemetry }). It gives every run its trace and carries that trace across restarts.
• lmnr.extension goes into the registry. Its hooks record the LLM and TOOL spans.

import { Laminar, laminarPiDurable } from '@lmnr-ai/lmnr';

const lmnr = laminarPiDurable({
  userId: 'user-123',
  metadata: { app: 'weather-bot' },
});

const registry = createRegistry();
registry.install(app);
registry.install(lmnr.extension); // install Laminar's extension last

const harness = await Harness.open(
  await openNodeSqliteStorage('./session.sqlite'),
  { models, registry, telemetry: lmnr.telemetry },
  context,
);

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Options

const lmnr = laminarPiDurable({
  sessionId: (conversation) => `chat-${conversation.conversationId}`, // default: Pi's session ID
  userId: 'user-123',
  metadata: { app: 'support-bot', env: 'prod' },
  spanNames: { run: 'support.run', llm: 'support.llm' },
  systemPrompt: 'You are a support assistant.',
});

• sessionId: a string, or a function of the conversation. Defaults to the session ID Pi keeps per conversation, so every run in a conversation lands in the same Laminar session.
• userId: a string, or a function of the conversation.
• metadata: key-value pairs added to every trace. See metadata.
• spanNames: { run, llm, tool }. Defaults are pi.run, pi.chat, and the tool’s name.
• systemPrompt: report this system prompt on LLM spans instead of the one Pi sent.
• projectApiKey or tracerProvider: send Pi Durable traces to a different project than the rest of your app. Laminar.initialize() isn’t needed in that case; call await lmnr.shutdown() before the process exits.

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Runs that resume after a restart

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See what happened in a trace

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Track outcomes with Signals

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Query across traces

• SQL editor for ad-hoc queries across traces, spans, signals, and evals.
• SQL API for programmatic access from scripts and pipelines.
• CLI (lmnr-cli sql query) for terminal-driven queries.
• MCP server to query Laminar from Claude Code, Cursor, or Codex.

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Troubleshooting

Laminar.initialize({
  baseUrl: 'http://localhost',
  httpPort: 8000,
  grpcPort: 8001,
});

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What’s next

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Related integrations