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.
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.
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 backgroundconsole.agent("analyze this error", error);
// Code continues executing...
2. Blocking Mode (Await for Structured Results)
// Wait for complete AgentResultconst result = awaitconsole.agent("validate email", email);
if (!result.success) thrownewError(result.summary);
// Just set environment variableexportGEMINI_API_KEY="your-key-here"// Import and useimport'@console-agent/agent';
console.agent("analyze this", data);
// Auto-selects security personaconsole.agent.security("audit this query", sql);
// Auto-selects debugger personaconsole.agent.debug("why is this slow?", metrics);
// Auto-selects architect personaconsole.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 = awaitconsole.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 = awaitconsole.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 = awaitconsole.agent(
"Summarize this page",
null,
{ tools: ['url_context'] }
);
Combining Multiple Tools
// Agent decides which tools to use based on promptconst result = awaitconsole.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
// ❌ Vagueagent("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)
ifnot result.valid:
raise ValueError(result.reason)
With Pytest
import pytest
from console_agent import agent
deftest_data_pipeline():
result = process_batch(test_data)
if result.errors:
agent.debug("pipeline failure", {
"input": test_data,
"output": result,
"errors": result.errors
})
assertlen(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
