Drip Billing

Track AI agent usage and costs with Drip metered billing. Use when you need to record aggregate LLM usage, tool calls, agent runs, or other metered usage for billing.

Drip Billing Integration

Track usage and costs for AI agents, LLM calls, tool invocations, and any metered workload.

When to Use This Skill

• Recording LLM usage quantities (for example total tokens per call)
• Tracking tool/function call costs
• Logging agent execution traces
• Metering API requests for billing
• Attributing costs to customers or workflows

Security & Data Privacy

Key scoping (least privilege):

• Use pk_ (public) keys for usage tracking, customer management, and billing. This is sufficient for all skill operations.
• Only use sk_ (secret) keys if you need admin operations: webhook management, API key rotation, or feature flags.
• Public keys (pk_) cannot manage webhooks, rotate API keys, or toggle feature flags — this limits blast radius if the key is compromised.

Metadata safety:

• Include only minimal non-sensitive operational context in metadata.
• Never include PII, secrets, passwords, API keys, raw user prompts, model outputs, or full request/response bodies.
• Use a strict allowlist and redaction policy before telemetry writes.
• Prefer hashes/IDs (for example queryHash) instead of raw user text.

What data is transmitted:

• Usage quantities (meter name + numeric value)
• Customer identifiers
• Run lifecycle events (start/end, status, duration)
• Sanitized metadata you explicitly provide (model family, tool name, status code, latency, hashed IDs)

What is NOT transmitted:

• Raw prompts, completions, or model outputs
• Environment variables or secrets
• File contents or source code

Installation

npm install @drip-sdk/node

Environment Setup

# Recommended: public key — sufficient for all usage tracking and billing
export DRIP_API_KEY=pk_live_...

# Only if you need admin operations (webhooks, key management, feature flags):
# export DRIP_API_KEY=sk_live_...

Telemetry Safety Contract

• Send only metadata needed for billing and diagnostics.
• Do not send raw prompts, raw model outputs, raw query text, full request/response bodies, or credentials.
• Prefer stable identifiers and hashes (for example queryHash) over raw user content.
• Emit telemetry only to a trusted DRIP_BASE_URL.

Quick Start

1. Initialize the SDK

import { Drip } from '@drip-sdk/node';

// Reads DRIP_API_KEY from environment automatically (pk_live_... recommended)
const drip = new Drip({
  apiKey: process.env.DRIP_API_KEY
});

2. Track Usage (Simple)

await drip.trackUsage({
  customerId: 'customer_123',
  meter: 'llm_tokens',
  quantity: 1500,
  // metadata is optional — only include operational context, never PII or secrets
  metadata: { model: 'gpt-4' }
});

3. Record Agent Runs (Complete Execution)

await drip.recordRun({
  customerId: 'cus_123',
  workflow: 'research-agent',
  events: [
    { eventType: 'llm.call', model: 'gpt-4', quantity: 1700, units: 'tokens' },
    { eventType: 'tool.call', name: 'web-search', duration: 1500 },
    { eventType: 'llm.call', model: 'gpt-4', quantity: 1000, units: 'tokens' },
  ],
  status: 'COMPLETED',
});

4. Streaming Execution (Real-Time)

// Start the run
const run = await drip.startRun({
  customerId: 'cus_123',
  workflowSlug: 'document-processor',
});

// Log each step as it happens
await drip.emitEvent({
  runId: run.id,
  eventType: 'llm.call',
  model: 'gpt-4',
  quantity: 1700,
  units: 'tokens',
});

await drip.emitEvent({
  runId: run.id,
  eventType: 'tool.call',
  name: 'web-search',
  duration: 1500,
});

// Complete the run
await drip.endRun(run.id, { status: 'COMPLETED' });

Event Types

Event Type	Description	Key Fields
llm.call	LLM API call	model, quantity, units
tool.call	Tool invocation	name, duration, status
agent.plan	Planning step	description
agent.execute	Execution step	description, metadata
error	Error occurred	description, metadata

Common Patterns

Wrap Tool Calls

async function trackedToolCall<T>(runId: string, toolName: string, fn: () => Promise<T>): Promise<T> {
  const start = Date.now();
  try {
    const result = await fn();
    await drip.emitEvent({
      runId,
      eventType: 'tool.call',
      name: toolName,
      duration: Date.now() - start,
      status: 'success',
    });
    return result;
  } catch (error: unknown) {
    const message = error instanceof Error ? error.message : 'Unknown error';
    await drip.emitEvent({
      runId,
      eventType: 'tool.call',
      name: toolName,
      duration: Date.now() - start,
      status: 'error',
      // Only include the error message — never include stack traces, env vars, or user data
      metadata: { error: message },
    });
    throw error;
  }
}

LangChain Auto-Tracking

import { DripCallbackHandler } from '@drip-sdk/node/langchain';

const handler = new DripCallbackHandler({
  drip,
  customerId: 'cus_123',
  workflow: 'research-agent',
});

// All LLM calls and tool usage automatically tracked
const result = await agent.invoke(
  { input: 'Research the latest AI news' },
  { callbacks: [handler] }
);

API Reference

See references/API.md for complete SDK documentation.