Logging

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Stagehand provides comprehensive logging capabilities to help you debug automation workflows, track execution, and diagnose issues. Configure logging levels, structured output, and debugging tools for both development and production environments.

Quick Start

Choose your logging setup based on your environment:

import { Stagehand } from "@browserbasehq/stagehand";

const stagehand = new Stagehand({
  env: "LOCAL",
  verbose: 2,  // Full debug output
  // restOfYourConfiguration...
});

Operational Logging

Real-time event logging during automation execution.

Verbosity Level

Control how much detail you see in logs:

Level 2: Debug
Level 1: Info (Default)
Level 0: Errors Only

Use for: Development, debugging specific issues

const stagehand = new Stagehand({
  verbose: 2,  // Maximum detail
  // restOfYourConfiguration...
});

Example Output

[12:34:56] DEBUG: Capturing DOM snapshot
[12:34:57] DEBUG: DOM contains 847 elements
[12:34:58] DEBUG: LLM inference started
[12:34:59] DEBUG: LLM response: {"selector": "#btn-submit", "method": "click"}
[12:35:00] INFO: act completed successfully

Use for: Standard operations, staging, production

const stagehand = new Stagehand({
  verbose: 1,  // Default level
  // restOfYourConfiguration...
});

Example Output

[12:34:56] INFO: act started
[12:35:00] INFO: act completed successfully
[12:35:01] INFO: extract started
[12:35:03] INFO: extract completed

Use for: Production with external monitoring, minimal noise

const stagehand = new Stagehand({
  verbose: 0,  // Errors only
  // restOfYourConfiguration...
});

Example Output

[12:35:05] ERROR: act failed: element not found
[12:35:10] ERROR: navigation timeout exceeded

Log Destinations

Logs can be sent to different destinations, including your console and external observability platforms:

Pino (Default)
Console Fallback
Custom Logger
External Logger (Production)

Fast, structured, colorized JSON logger with console output.When to use: Development, staging, or production without external observability; can manage multiple Stagehand instances

// Enabled by default - Pino handles console output automatically
const stagehand = new Stagehand({
  verbose: 1,
  // restOfYourConfiguration...
});

Auto-disabled when

process.env.NODE_ENV === "test"
process.env.JEST_WORKER_ID !== undefined (Jest tests)
process.env.PLAYWRIGHT_TEST_BASE_DIR !== undefined (Playwright tests)
process.env.CI === "true" (CI/CD environments)

Why auto-disable? Pino uses worker threads for pretty-printing, which can cause issues in test runners.

Simple console.log/error output.When to use: Automatically activated in tests, or when disablePino: true without setting an external logger

const stagehand = new Stagehand({
  verbose: 1,
  disablePino: true, // Set to true automatically when a test is detected
  // restOfYourConfiguration...
});

Auto-disabled when

process.env.NODE_ENV === "test"
process.env.JEST_WORKER_ID !== undefined (Jest tests)
process.env.PLAYWRIGHT_TEST_BASE_DIR !== undefined (Playwright tests)
process.env.CI === "true" (CI/CD environments)

Why auto-disable? Pino uses worker threads for pretty-printing, which can cause issues in test runners.

Your custom logging function to receive all logs. Works independently of Pino - receives logs regardless of Pino setting.When to use: Development, debugging, or when you don’t need querying capabilities.

Create a simple logger

// Simple logger without parsing (for basic console output)
const simpleLogger = (logLine: LogLine) => {
  console.log(`[${logLine.level}] ${logLine.message}`);

  // Optional: log raw auxiliary data
  if (logLine.auxiliary) {
    console.log('  Context:', logLine.auxiliary);
  }
};

Pass the logger in your Stagehand instance

Then pass the logger in your Stagehand instance:

const stagehand = new Stagehand({
  env: "BROWSERBASE",
  verbose: 1,
  logger: simpleLogger,
  disablePino: true,  // Avoid duplicate processing
  // restOfYourConfiguration...
})

Your custom logging function to receive all logs. Works independently of Pino - receives logs regardless of Pino setting.When to use: Production with DataDog, Sentry, CloudWatch, or custom observability platforms for centralized monitoring and enable error alerting. Here’s examples using Sentry and DataDog:

Create a production logger

Sentry
DataDog

import * as Sentry from "@sentry/node";

const productionLogger = (logLine: LogLine) => {
  // Send errors to Sentry
  if (logLine.level === 0) {
    Sentry.captureMessage(logLine.message, {
      level: 'error',
      extra: aux,
    });
  }
}

// Helper to parse auxiliary data to be flat, numeric, and filterable
function parseAuxiliary(aux?: LogLine['auxiliary']): Record<string, any> {
  if (!aux) return {};
  const parsed: Record<string, any> = {};
  for (const [key, entry] of Object.entries(aux)) {
    parsed[key] = entry.type === 'object'
      ? JSON.parse(entry.value)
      : entry.value;
  }
  return parsed;
}

import { datadogLogs } from "@datadog/browser-logs";

const productionLogger = (logLine: LogLine) => {
  // Send all logs to DataDog
  datadogLogs.logger.log(logLine.message, {
    status: logLine.level === 0 ? 'error' : 'info',
    service: 'stagehand-automation',
    category: logLine.category,
    ...aux,
  });
}

// Helper to parse auxiliary data to be flat, numeric, and filterable
function parseAuxiliary(aux?: LogLine['auxiliary']): Record<string, any> {
  if (!aux) return {};
  const parsed: Record<string, any> = {};
  for (const [key, entry] of Object.entries(aux)) {
    parsed[key] = entry.type === 'object'
      ? JSON.parse(entry.value)
      : entry.value;
  }
  return parsed;
}

Pass the logger in your Stagehand instance

const stagehand = new Stagehand({
  env: "BROWSERBASE",
  verbose: 1,
  logger: productionLogger,
  disablePino: true,  // Avoid duplicate processing
  // restOfYourConfiguration...
})

LLM Inference Debugging

Development only - Creates large files and contains page content. Do not use in production.

Save complete LLM request/response dumps to disk for offline analysis. See exactly what DOM was sent to the LLM and why it chose the wrong element.

const stagehand = new Stagehand({
  env: "LOCAL",
  verbose: 2,
  logInferenceToFile: true,  // Writes files to ./inference_summary/
});

Creates timestamped files for each LLM call:

./inference_summary/
├── act_summary/
│   ├── act_summary.json                      # Aggregate metrics
│   ├── 20250127_123456_act_call.txt          # LLM request
│   ├── 20250127_123456_act_response.txt      # LLM response
│   ├── 20250127_123501_act_call.txt
│   └── 20250127_123501_act_response.txt
├── extract_summary/
│   ├── extract_summary.json
│   ├── 20250127_123510_extract_call.txt
│   ├── 20250127_123510_extract_response.txt
│   ├── 20250127_123511_metadata_call.txt
│   └── 20250127_123511_metadata_response.txt
└── observe_summary/
    ├── observe_summary.json
    └── ...

File Types:

Call File

Contains the complete LLM request:

{
  "modelCall": "act",
  "messages": [
    {
      "role": "system",
      "content": "You are a browser automation assistant. You have access to these actions:\n- click\n- type\n- scroll\n..."
    },
    {
      "role": "user",
      "content": "Click the sign in button\n\nDOM:\n<html>\n  <body>\n    <button id=\"btn-1\">Sign In</button>\n    <button id=\"btn-2\">Sign Up</button>\n  </body>\n</html>"
    }
  ]
}

Response File

Contains the LLM output:

{
  "modelResponse": "act",
  "rawResponse": {
    "selector": "#btn-1",
    "method": "click",
    "reasoning": "Found sign in button with ID btn-1"
  }
}

Summary File

Aggregates all calls with metrics:

{
  "act_summary": [
    {
      "act_inference_type": "act",
      "timestamp": "20250127_123456",
      "LLM_input_file": "20250127_123456_act_call.txt",
      "LLM_output_file": "20250127_123456_act_response.txt",
      "prompt_tokens": 3451,
      "completion_tokens": 45,
      "inference_time_ms": 951
    },
    {
      "act_inference_type": "act",
      "timestamp": "20250127_123501",
      "LLM_input_file": "20250127_123501_act_call.txt",
      "LLM_output_file": "20250127_123501_act_response.txt",
      "prompt_tokens": 2890,
      "completion_tokens": 38,
      "inference_time_ms": 823
    }
  ]
}

Reference

Logging Configuration

All logging options are passed to the Stagehand constructor:

const stagehand = new Stagehand({
  // ... your other configurations (env, model, etc.)

  // Logging options:
  verbose?: 0 | 1 | 2;                   // Log level (default: 1)
  logger?: (line: LogLine) => void;      // External logger function
  disablePino?: boolean;                 // Disable Pino backend (default: false)
  logInferenceToFile?: boolean;          // Save LLM requests to disk (default: false)
});

Option	Default	Description
`verbose`	`1`	Log level: `0` = errors only, `1` = info, `2` = debug
`logger`	`undefined`	Custom logger function for external platforms
`disablePino`	`false`	Disable Pino (auto `true` in tests)
`logInferenceToFile`	`false`	Save LLM requests to disk (default: false)

Log Structure

Each log entry follows a structured format:

interface LogLine {
  message: string;              // "act completed successfully"
  level?: 0 | 1 | 2;            // error | info | debug
  category?: string;            // "action", "llm", "browser", "cache"
  timestamp?: string;           // ISO 8601 timestamp
  auxiliary?: {                 // Additional structured metadata
    [key: string]: {
      value: string;             // Serialized value
      type: "object" | "string" | "integer" | "float" | "boolean";
    };
  };
}

Log Examples

Successful Action
LLM Inference
Error

{
  "category": "action",
  "message": "act completed successfully",
  "level": 1,
  "timestamp": "2025-01-27T12:35:00.123Z",
  "auxiliary": {
    "selector": {
      "value": "#btn-submit",
      "type": "string"
    },
    "executionTime": {
      "value": "1250",
      "type": "integer"
    }
  }
}

{
  "category": "llm",
  "message": "inference completed",
  "level": 1,
  "timestamp": "2025-01-27T12:34:58.456Z",
  "auxiliary": {
    "model": {
      "value": "gpt-4o",
      "type": "string"
    },
    "promptTokens": {
      "value": "3451",
      "type": "integer"
    },
    "completionTokens": {
      "value": "45",
      "type": "integer"
    }
  }
}

{
  "category": "action",
  "message": "action failed: element not found",
  "level": 0,
  "timestamp": "2025-01-27T12:35:05.789Z",
  "auxiliary": {
    "selector": {
      "value": "#missing-btn",
      "type": "string"
    },
    "url": {
      "value": "https://example.com/form",
      "type": "string"
    }
  }
}

Next Steps

Now that you have logging configured, explore additional debugging and monitoring tools in the Observability guide:

History API

Track all LLM operations (act, extract, observe, agent) with parameters, results, and timestamps. Perfect for debugging sequences and replaying workflows.

Metrics API

Monitor token usage and performance in real-time. Track costs per operation, identify expensive calls, and optimize resource usage.

LLM Inference Debugging

Save complete LLM request/response dumps to disk. See exactly what DOM was sent to the LLM and why it made specific decisions.

Browserbase Session Monitoring

Watch your automation visually with session recordings, network monitoring, and real-time browser inspection (Browserbase only).

First Steps

The Basics

Configuration

Best Practices

Integrations

Reference

Migration Guides

Quick Start

Operational Logging

Verbosity Level

Log Destinations

LLM Inference Debugging

Reference

Logging Configuration

Log Structure

Next Steps

History API

Metrics API

LLM Inference Debugging

Browserbase Session Monitoring

First Steps

The Basics

Configuration

Best Practices

Integrations

Reference

Migration Guides

​Quick Start

​Operational Logging

​Verbosity Level

​Log Destinations

​LLM Inference Debugging

​Reference

​Logging Configuration

​Log Structure

​Next Steps

History API

Metrics API

LLM Inference Debugging

Browserbase Session Monitoring

Quick Start

Operational Logging

Verbosity Level

Log Destinations

LLM Inference Debugging

Reference

Logging Configuration

Log Structure

Next Steps