Console Agent

Build AI agents with console.agent() - the jQuery of AI Agents. Drop console.agent(...) anywhere in your code for agentic workflows with the simplicity of console.log(). Use when adding AI agent capabilities, debugging with AI, security auditing, intelligent logging, or runtime analysis.

Audits

Pending

ClawScanReview

Agentic behavior and permission review.

Static analysisPending

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install skills-3

console.agent - The jQuery of AI Agents

Comprehensive guide for implementing @console-agent/agent — drop console.agent(...) anywhere in your code to execute agentic workflows with the simplicity of console.log().

Official Documentation: https://console-agent.github.io
Package: @console-agent/agent (npm) / console-agent (PyPI)
Version: v1.2.0
Provider: Google Gemini (gemini-2.5-flash-lite, gemini-3-flash-preview)

When to Apply

Reference this skill when:

User wants to add AI agent capabilities to their code
User asks about debugging with AI assistance
User mentions "console.agent" or agentic workflows
User wants runtime analysis, security audits, or code review
User needs intelligent logging, testing assistance, or data validation
User asks about tools like code execution, Google Search, or file analysis

Core Concepts

1. Fire-and-Forget by Default (Non-blocking)

// Returns immediately, agent runs async in background
console.agent("analyze this error", error);
// Code continues executing...

2. Blocking Mode (Await for Structured Results)

// Wait for complete AgentResult
const result = await console.agent("validate email", email);
if (!result.success) throw new Error(result.summary);

3. AI Personas (Auto-detected or Explicit)

Security 🛡️ - OWASP expert (detects: injection, xss, vulnerability, csrf)
Debugger 🐛 - Senior debugging expert (detects: slow, error, optimize, crash)
Architect 🏗️ - Principal engineer (detects: design, architecture, schema)
General 🔍 - Full-stack senior engineer (default fallback)

4. Output Modes

Default (verbose: false):

SQL injection detected in user input
fix: Use parameterized queries
severity: critical

Verbose (verbose: true):

[AGENT] ✓ 🛡️ Security audit Complete
[AGENT] ├─ ✓ SQL injection detected in user input
[AGENT] ├─ Tool: google_search
[AGENT] ├─ fix: Use parameterized queries
[AGENT] └─ confidence: 0.94 | 247ms | 156 tokens | model: gemini-2.5-flash-lite

Installation & Setup

JavaScript/TypeScript

npm install @console-agent/agent

Python

pip install console-agent

Get Free API Key

https://aistudio.google.com/apikey

Minimal Setup (Zero Config Works!)

// Just set environment variable
export GEMINI_API_KEY="your-key-here"

// Import and use
import '@console-agent/agent';
console.agent("analyze this", data);

Optional Configuration

import { init } from '@console-agent/agent';

init({
  apiKey: process.env.GEMINI_API_KEY,
  model: 'gemini-2.5-flash-lite',  // Default
  persona: 'general',
  mode: 'fire-and-forget',
  timeout: 10000,
  
  budget: {
    maxCallsPerDay: 100,
    maxTokensPerCall: 8000,
    costCapDaily: 1.00  // USD
  },
  
  anonymize: true,              // Auto-strip secrets/PII
  localOnly: false,             // Disable cloud tools
  includeCallerSource: true,    // Auto-read source file
  logLevel: 'info'
});

Python Configuration

from console_agent import init_agent, agent

init_agent(
    api_key=os.getenv('GEMINI_API_KEY'),
    model='gemini-2.5-flash-lite',
    persona='general',
    budget={'maxCallsPerDay': 100}
)

API Reference

`console.agent(prompt, context?, options?)`

Main API - call it like console.log().

// Simple fire-and-forget
console.agent("explain this error", error);

// Await structured results
const result = await console.agent("analyze", data, {
  persona: 'security',
  model: 'gemini-3-flash-preview',
  thinking: { level: 'high', includeThoughts: true },
  tools: ['google_search', 'code_execution']
});

Return Type: `AgentResult`

interface AgentResult {
  success: boolean;           // Overall task success
  summary: string;            // Human-readable conclusion
  reasoning?: string;         // Agent's thought process (if thinking enabled)
  data: Record<string, any>;  // Structured findings
  actions: string[];          // Tools used / steps taken
  confidence: number;         // 0-1 confidence score
  metadata: {
    model: string;
    tokensUsed: number;
    latencyMs: number;
    toolCalls: ToolCall[];
    cached: boolean;
  };
}

Persona Shortcuts

// Auto-selects security persona
console.agent.security("audit this query", sql);

// Auto-selects debugger persona
console.agent.debug("why is this slow?", metrics);

// Auto-selects architect persona
console.agent.architect("review API design", endpoint);

Built-in Tools

IMPORTANT: Tools are opt-in. Only activated when explicitly passed via tools: [...].

1. Google Search 🔍

Real-time web grounding - search for current info, CVEs, documentation.

const result = await console.agent(
  "What is the current population of Tokyo?",
  null,
  { tools: ['google_search'] }
);

2. Code Execution 💻

Python sandbox (Gemini-hosted) - calculations, data processing, algorithm verification.

const result = await console.agent(
  "Calculate the 20th Fibonacci number",
  null,
  { tools: ['code_execution'] }
);
// result.data.result → 6765

3. URL Context 🌐

Fetch and analyze web pages - read docs, analyze APIs, extract content.

const result = await console.agent(
  "Summarize this page",
  null,
  { tools: ['url_context'] }
);

Combining Multiple Tools

// Agent decides which tools to use based on prompt
const result = await console.agent(
  "Search for current world population, then calculate 1% of it",
  null,
  { tools: ['google_search', 'code_execution'] }
);
// 1. Uses google_search to find population
// 2. Uses code_execution to calculate 1%
// 3. Returns combined result

Common Use Cases

1. Security Auditing 🛡️

app.post('/api/search', async (req, res) => {
  const query = req.body.q;
  
  const audit = await console.agent.security(
    "check for SQL injection", 
    query
  );
  
  if (audit.data.severity === 'critical') {
    return res.status(400).json({ error: "Invalid input" });
  }
  
  const results = await db.search(query);
  res.json(results);
});

2. Debugging Failed Tests 🐛

import { agent } from '@console-agent/agent';
import { test, expect } from 'vitest';

test('payment processing', async () => {
  const result = await processPayment(order);
  
  if (!result.success) {
    await agent.debug("why did payment fail?", {
      order,
      result,
      testName: 'payment processing'
    });
  }
  
  expect(result.success).toBe(true);
});

Output:

Likely cause: Missing await on async fn
Suggested fix: Add 'await' on line 47
Confidence: 0.92 | 312ms | 189 tokens

3. Data Validation 📊

const records = await fetchBatch();

const validation = await console.agent(
  "validate batch meets schema", 
  records,
  { 
    schema: z.object({
      valid: z.boolean(),
      errors: z.array(z.string()),
      quality_score: z.number()
    })
  }
);

if (!validation.data.valid) {
  console.log("Issues:", validation.data.errors);
}

4. Architecture Review 🏗️

console.agent.architect("review API design", {
  endpoint: '/api/users',
  method: 'POST',
  handler: userController,
  middleware: [auth, rateLimit]
});

5. Performance Analysis

const startTime = Date.now();
const result = await slowOperation();
const duration = Date.now() - startTime;

if (duration > 1000) {
  agent.debug("why is this slow?", {
    operation: 'slowOperation',
    duration,
    input: operationInput
  });
}

6. Research with Tools 🔍

const research = await console.agent(
  "research known CVEs for lodash@4.17.20",
  null,
  { 
    tools: ['google_search'],
    persona: 'security'
  }
);
console.log(research.summary);

Advanced Features

Structured Output with Zod Schema

import { z } from 'zod';

const result = await console.agent(
  "analyze sentiment", 
  review,
  {
    schema: z.object({
      sentiment: z.enum(["positive", "negative", "neutral"]),
      score: z.number(),
      keywords: z.array(z.string())
    })
  }
);

result.data.sentiment;  // "positive" ✅ typed and validated

File Attachments

import { readFileSync } from 'fs';

const doc = await console.agent(
  "What does this document say?", 
  null,
  {
    files: [{
      data: readFileSync('./data/report.pdf'),
      mediaType: 'application/pdf',
      fileName: 'report.pdf'
    }]
  }
);

Thinking Mode (Extended Reasoning)

const result = await console.agent(
  "design optimal database schema for multi-tenant SaaS",
  requirements,
  {
    model: 'gemini-3-flash-preview',
    thinking: { 
      level: 'high',          // 'low' | 'medium' | 'high'
      includeThoughts: true   // Return reasoning summary
    }
  }
);

console.log(result.reasoning);  // Extended thought process

Best Practices

1. Provide Rich Context

// ❌ Too vague
agent("fix this");

// ✅ Specific with context
agent.debug("why does payment fail?", {
  error,
  order,
  user,
  timestamp,
  environment: process.env.NODE_ENV,
  recentLogs: logs.slice(-10)
});

2. Use Appropriate Personas

// Security tasks
agent.security("audit SQL query", query);

// Performance tasks
agent.debug("analyze slow response", { duration, query });

// Architecture tasks
agent.architect("review this pattern", codeStructure);

// General tasks (auto-detected)
agent("validate email format", email);

3. Handle Results Properly

// Fire-and-forget for logging/analysis
agent("log unusual event", eventData);

// Await for decisions that affect flow
const isValid = await agent("validate input", userInput);
if (!isValid.success) {
  throw new ValidationError(isValid.summary);
}

4. Enable Tools When Needed

// No tools - uses only LLM knowledge
agent("explain async/await");

// With search - gets current info
agent("latest React best practices", null, { 
  tools: ['google_search'] 
});

// With code execution - performs calculations
agent("optimize this algorithm", code, { 
  tools: ['code_execution'] 
});

5. Budget Monitoring

init({
  budget: {
    maxCallsPerDay: 50,
    costCapDaily: 0.50
  },
  onBudgetWarning: (usage) => {
    console.warn(`Budget: ${usage.calls}/50 calls used`);
  }
});

Available Models

Model	Best For	Speed	Cost	Default
`gemini-2.5-flash-lite`	General purpose, fast	~200ms	Very low	✅ Default
`gemini-3-flash-preview`	Complex reasoning, thinking	~400ms	Low

Configuration Options Reference

Full InitOptions

interface InitOptions {
  // Core
  apiKey?: string;                      // Default: GEMINI_API_KEY env
  model?: 'gemini-2.5-flash-lite' | 'gemini-3-flash-preview';
  persona?: 'debugger' | 'security' | 'architect' | 'general';
  
  // Execution
  mode?: 'fire-and-forget' | 'blocking';
  timeout?: number;                     // Default: 10000ms
  
  // Budget
  budget?: {
    maxCallsPerDay?: number;            // Default: 100
    maxTokensPerCall?: number;          // Default: 8000
    costCapDaily?: number;              // Default: 1.00 USD
  };
  
  // Privacy
  anonymize?: boolean;                  // Default: true
  localOnly?: boolean;                  // Default: false
  
  // Output
  logLevel?: 'silent' | 'errors' | 'info' | 'debug';
  includeCallerSource?: boolean;        // Default: true
  
  // Advanced
  dryRun?: boolean;                     // Default: false
}

Per-Call Options

agent(prompt, context, {
  persona?: string;
  model?: string;
  verbose?: boolean;
  timeout?: number;
  tools?: Array<'google_search' | 'code_execution' | 'url_context'>;
  thinking?: {
    level?: 'low' | 'medium' | 'high';
    includeThoughts?: boolean;
  };
  schema?: ZodSchema;                   // For structured output
  files?: Array<{
    data: Buffer;
    mediaType: string;
    fileName: string;
  }>;
});

Privacy & Security

Auto-Anonymization

When anonymize: true (default), automatically strips:

API keys and secrets
Email addresses
IP addresses
AWS keys
Private keys
Database connection strings

Budget Controls

Hard limits prevent cost explosion:

maxCallsPerDay: 100 calls/day default
maxTokensPerCall: 8K tokens/call default
costCapDaily: $1.00/day default

Rate Limiting

Token bucket algorithm spreads calls evenly across 24 hours with graceful degradation.

Troubleshooting

Issue: "No API key configured"

# Solution: Set environment variable
export GEMINI_API_KEY="your-key"

# Or configure explicitly
init({ apiKey: 'your-key' });

Issue: "Budget exceeded"

// Increase limits
init({
  budget: { 
    maxCallsPerDay: 200,
    costCapDaily: 2.00
  }
});

Issue: "Agent timeout"

// Increase timeout
agent("complex task", data, { timeout: 30000 });  // 30s

Issue: "Results not specific enough"

// ❌ Vague
agent("help");

// ✅ Specific with rich context
agent.debug("why does API return 500?", {
  endpoint: '/api/users',
  request: { method: 'POST', body },
  response: { status: 500, body: errorBody },
  logs: recentLogs,
  environment: process.env.NODE_ENV
});

Python-Specific Examples

Basic Usage

from console_agent import agent

# Fire-and-forget
agent("analyze this error", error)

# Blocking
result = agent("validate input", data)
if not result.valid:
    raise ValueError(result.reason)

With Pytest

import pytest
from console_agent import agent

def test_data_pipeline():
    result = process_batch(test_data)
    
    if result.errors:
        agent.debug("pipeline failure", {
            "input": test_data,
            "output": result,
            "errors": result.errors
        })
    
    assert len(result.errors) == 0

Persona Shortcuts

# Security persona
agent.security("audit SQL query", query)

# Debug persona
agent.debug("why is this slow?", metrics)

# Architect persona
agent.architect("review design", schema)

Implementation Tips for AI Assistants

Default to fire-and-forget unless user needs the result
Auto-suggest personas based on task context
Include rich context in examples - show what data to pass
Explain tools - they're opt-in, not automatic
Show budget controls for production scenarios
Emphasize zero-config - just set GEMINI_API_KEY
Use in tests - highlight debugging failed tests use case
Reference official docs at https://console-agent.github.io

Key Differences from Other Agents

Feature	console.agent	Langchain	Agno
Setup	0 lines (env var)	100+ lines	50+ lines
API	Like console.log()	Complex classes	Framework
Blocking	Optional (await)	Always	Configurable
Tools	Opt-in per call	Pre-configured	Pre-configured
Best for	Runtime utilities	Chat apps	Multi-agent systems

Official Links

Homepage: https://console-agent.github.io
API Reference: https://console-agent.github.io/reference
npm: https://www.npmjs.com/package/@console-agent/agent
PyPI: https://pypi.org/project/console-agent/
GitHub (JS): https://github.com/console-agent/console_agent
GitHub (Python): https://github.com/console-agent/console_agent_python
Get API Key: https://aistudio.google.com/apikey

Version

This SKILL.md is for @console-agent/agent v1.2.0

Console Agent

Audits

Install

console.agent - The jQuery of AI Agents

When to Apply

Core Concepts

1. Fire-and-Forget by Default (Non-blocking)

2. Blocking Mode (Await for Structured Results)

3. AI Personas (Auto-detected or Explicit)

4. Output Modes

Installation & Setup

JavaScript/TypeScript

Python

Get Free API Key

Minimal Setup (Zero Config Works!)

Optional Configuration

Python Configuration

API Reference

console.agent(prompt, context?, options?)

Return Type: AgentResult

Persona Shortcuts

Built-in Tools

1. Google Search 🔍

2. Code Execution 💻

3. URL Context 🌐

Combining Multiple Tools

Common Use Cases

1. Security Auditing 🛡️

2. Debugging Failed Tests 🐛

3. Data Validation 📊

4. Architecture Review 🏗️

5. Performance Analysis

6. Research with Tools 🔍

Advanced Features

Structured Output with Zod Schema

File Attachments

Thinking Mode (Extended Reasoning)

Best Practices

1. Provide Rich Context

2. Use Appropriate Personas

3. Handle Results Properly

4. Enable Tools When Needed

5. Budget Monitoring

Available Models

Configuration Options Reference

Full InitOptions

Per-Call Options

Privacy & Security

Auto-Anonymization

Budget Controls

Rate Limiting

Troubleshooting

Issue: "No API key configured"

Issue: "Budget exceeded"

Issue: "Agent timeout"

Issue: "Results not specific enough"

Python-Specific Examples

Basic Usage

With Pytest

Persona Shortcuts

Implementation Tips for AI Assistants

Key Differences from Other Agents

Official Links

Version

`console.agent(prompt, context?, options?)`

Return Type: `AgentResult`