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Google Gemini Api

jackspace/claudeskillz

  • 41 installs
  • 16 repo stars
  • Updated November 20, 2025
  • jackspace/claudeskillz

Integrate the Google Gemini API using the current @google/genai SDK for text, chat, and multimodal generation.

About

A complete guide to the Google Gemini API using the correct current @google/genai SDK. A developer uses it when integrating Gemini models into JavaScript/TypeScript applications.

  • Uses the current @google/genai SDK (not the deprecated one)
  • Covers text, chat, and multimodal patterns

Google Gemini Api by the numbers

  • 41 all-time installs (skills.sh)
  • Ranked #8,067 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
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Installs41
repo stars★ 16
Last updatedNovember 20, 2025
Repositoryjackspace/claudeskillz ↗

What it does

Integrate the Google Gemini API using the current @google/genai SDK for text, chat, and multimodal generation.

Files

SKILL.mdMarkdownGitHub ↗

Google Gemini API - Complete Guide

Version: Phase 2 Complete ✅ Package: @google/genai@1.27.0 (⚠️ NOT @google/generative-ai) Last Updated: 2025-10-25

---

⚠️ CRITICAL SDK MIGRATION WARNING

DEPRECATED SDK: @google/generative-ai (sunset November 30, 2025) CURRENT SDK: @google/genai v1.27+

If you see code using `@google/generative-ai`, it's outdated!

This skill uses the correct current SDK and provides a complete migration guide.

---

Status

✅ Phase 1 Complete:

  • ✅ Text Generation (basic + streaming)
  • ✅ Multimodal Inputs (images, video, audio, PDFs)
  • ✅ Function Calling (basic + parallel execution)
  • ✅ System Instructions & Multi-turn Chat
  • ✅ Thinking Mode Configuration
  • ✅ Generation Parameters (temperature, top-p, top-k, stop sequences)
  • ✅ Both Node.js SDK (@google/genai) and fetch approaches

✅ Phase 2 Complete:

  • ✅ Context Caching (cost optimization with TTL-based caching)
  • ✅ Code Execution (built-in Python interpreter and sandbox)
  • ✅ Grounding with Google Search (real-time web information + citations)

📦 Separate Skills:

  • Embeddings: See google-gemini-embeddings skill for text-embedding-004

---

Table of Contents

Phase 1 - Core Features: 1. Quick Start 2. Current Models (2025) 3. SDK vs Fetch Approaches 4. Text Generation 5. Streaming 6. Multimodal Inputs 7. Function Calling 8. System Instructions 9. Multi-turn Chat 10. Thinking Mode 11. Generation Configuration

Phase 2 - Advanced Features: 12. Context Caching 13. Code Execution 14. Grounding with Google Search

Common Reference: 15. Error Handling 16. Rate Limits 17. SDK Migration Guide 18. Production Best Practices

---

Quick Start

Installation

CORRECT SDK:

npm install @google/genai@1.27.0

❌ WRONG (DEPRECATED):

npm install @google/generative-ai  # DO NOT USE!

Environment Setup

export GEMINI_API_KEY="..."

Or create .env file:

GEMINI_API_KEY=...

First Text Generation (Node.js SDK)

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Explain quantum computing in simple terms'
});

console.log(response.text);

First Text Generation (Fetch - Cloudflare Workers)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [{ parts: [{ text: 'Explain quantum computing in simple terms' }] }]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

---

Current Models (2025)

Gemini 2.5 Series (General Availability)

gemini-2.5-pro
  • Context: 1,048,576 input tokens / 65,536 output tokens
  • Description: State-of-the-art thinking model for complex reasoning
  • Best for: Code, math, STEM, complex problem-solving
  • Features: Thinking mode (default on), function calling, multimodal, streaming
  • Knowledge cutoff: January 2025
gemini-2.5-flash
  • Context: 1,048,576 input tokens / 65,536 output tokens
  • Description: Best price-performance workhorse model
  • Best for: Large-scale processing, low-latency, high-volume, agentic use cases
  • Features: Thinking mode (default on), function calling, multimodal, streaming
  • Knowledge cutoff: January 2025
gemini-2.5-flash-lite
  • Context: 1,048,576 input tokens / 65,536 output tokens
  • Description: Cost-optimized, fastest 2.5 model
  • Best for: High throughput, cost-sensitive applications
  • Features: Thinking mode (default on), function calling, multimodal, streaming
  • Knowledge cutoff: January 2025

Model Feature Matrix

FeatureProFlashFlash-Lite
Thinking Mode✅ Default ON✅ Default ON✅ Default ON
Function Calling✅✅✅
Multimodal✅✅✅
Streaming✅✅✅
System Instructions✅✅✅
Context Window1,048,576 in1,048,576 in1,048,576 in
Output Tokens65,536 max65,536 max65,536 max

⚠️ Context Window Correction

ACCURATE: Gemini 2.5 models support 1,048,576 input tokens (NOT 2M!) OUTDATED: Only Gemini 1.5 Pro (previous generation) had 2M token context window

Common mistake: Claiming Gemini 2.5 has 2M tokens. It doesn't. This skill prevents this error.

---

SDK vs Fetch Approaches

Node.js SDK (@google/genai)

Pros:

  • Type-safe with TypeScript
  • Easier API (simpler syntax)
  • Built-in chat helpers
  • Automatic SSE parsing for streaming
  • Better error handling

Cons:

  • Requires Node.js or compatible runtime
  • Larger bundle size
  • May not work in all edge runtimes

Use when: Building Node.js apps, Next.js Server Actions/Components, or any environment with Node.js compatibility

Fetch-based (Direct REST API)

Pros:

  • Works in any JavaScript environment (Cloudflare Workers, Deno, Bun, browsers)
  • Minimal dependencies
  • Smaller bundle size
  • Full control over requests

Cons:

  • More verbose syntax
  • Manual SSE parsing for streaming
  • No built-in chat helpers
  • Manual error handling

Use when: Deploying to Cloudflare Workers, browser clients, or lightweight edge runtimes

---

Text Generation

Basic Text Generation (SDK)

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Write a haiku about artificial intelligence'
});

console.log(response.text);

Basic Text Generation (Fetch)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        {
          parts: [
            { text: 'Write a haiku about artificial intelligence' }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

Response Structure

{
  text: string,                  // Convenience accessor for text content
  candidates: [
    {
      content: {
        parts: [
          { text: string }       // Generated text
        ],
        role: string             // "model"
      },
      finishReason: string,      // "STOP" | "MAX_TOKENS" | "SAFETY" | "OTHER"
      index: number
    }
  ],
  usageMetadata: {
    promptTokenCount: number,
    candidatesTokenCount: number,
    totalTokenCount: number
  }
}

---

Streaming

Streaming with SDK (Async Iteration)

const response = await ai.models.generateContentStream({
  model: 'gemini-2.5-flash',
  contents: 'Write a 200-word story about time travel'
});

for await (const chunk of response) {
  process.stdout.write(chunk.text);
}

Streaming with Fetch (SSE Parsing)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:streamGenerateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [{ parts: [{ text: 'Write a 200-word story about time travel' }] }]
    }),
  }
);

const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  buffer += decoder.decode(value, { stream: true });
  const lines = buffer.split('\n');
  buffer = lines.pop() || '';

  for (const line of lines) {
    if (line.trim() === '' || line.startsWith('data: [DONE]')) continue;
    if (!line.startsWith('data: ')) continue;

    try {
      const data = JSON.parse(line.slice(6));
      const text = data.candidates[0]?.content?.parts[0]?.text;
      if (text) {
        process.stdout.write(text);
      }
    } catch (e) {
      // Skip invalid JSON
    }
  }
}

Key Points:

  • Use streamGenerateContent endpoint (not generateContent)
  • Parse Server-Sent Events (SSE) format: data: {json}\n\n
  • Handle incomplete chunks in buffer
  • Skip empty lines and [DONE] markers

---

Multimodal Inputs

Gemini 2.5 models support text + images + video + audio + PDFs in the same request.

Images (Vision)

SDK Approach
import { GoogleGenAI } from '@google/genai';
import fs from 'fs';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

// From file
const imageData = fs.readFileSync('/path/to/image.jpg');
const base64Image = imageData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'What is in this image?' },
        {
          inlineData: {
            data: base64Image,
            mimeType: 'image/jpeg'
          }
        }
      ]
    }
  ]
});

console.log(response.text);
Fetch Approach
const imageData = fs.readFileSync('/path/to/image.jpg');
const base64Image = imageData.toString('base64');

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        {
          parts: [
            { text: 'What is in this image?' },
            {
              inlineData: {
                data: base64Image,
                mimeType: 'image/jpeg'
              }
            }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

Supported Image Formats:

  • JPEG (.jpg, .jpeg)
  • PNG (.png)
  • WebP (.webp)
  • HEIC (.heic)
  • HEIF (.heif)

Max Image Size: 20MB per image

Video

// Video must be < 2 minutes for inline data
const videoData = fs.readFileSync('/path/to/video.mp4');
const base64Video = videoData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Describe what happens in this video' },
        {
          inlineData: {
            data: base64Video,
            mimeType: 'video/mp4'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

Supported Video Formats:

  • MP4 (.mp4)
  • MPEG (.mpeg)
  • MOV (.mov)
  • AVI (.avi)
  • FLV (.flv)
  • MPG (.mpg)
  • WebM (.webm)
  • WMV (.wmv)

Max Video Length (inline): 2 minutes Max Video Size: 2GB (use File API for larger files - Phase 2)

Audio

const audioData = fs.readFileSync('/path/to/audio.mp3');
const base64Audio = audioData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Transcribe and summarize this audio' },
        {
          inlineData: {
            data: base64Audio,
            mimeType: 'audio/mp3'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

Supported Audio Formats:

  • MP3 (.mp3)
  • WAV (.wav)
  • FLAC (.flac)
  • AAC (.aac)
  • OGG (.ogg)
  • OPUS (.opus)

Max Audio Size: 20MB

PDFs

const pdfData = fs.readFileSync('/path/to/document.pdf');
const base64Pdf = pdfData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Summarize the key points in this PDF' },
        {
          inlineData: {
            data: base64Pdf,
            mimeType: 'application/pdf'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

Max PDF Size: 30MB PDF Limitations: Text-based PDFs work best; scanned images may have lower accuracy

Multiple Inputs

You can combine multiple modalities in one request:

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Compare these two images and describe the differences:' },
        { inlineData: { data: base64Image1, mimeType: 'image/jpeg' } },
        { inlineData: { data: base64Image2, mimeType: 'image/jpeg' } }
      ]
    }
  ]
});

---

Function Calling

Gemini supports function calling (tool use) to connect models with external APIs and systems.

Basic Function Calling (SDK)

import { GoogleGenAI, FunctionCallingConfigMode } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

// Define function declarations
const getCurrentWeather = {
  name: 'get_current_weather',
  description: 'Get the current weather for a location',
  parametersJsonSchema: {
    type: 'object',
    properties: {
      location: {
        type: 'string',
        description: 'City name, e.g. San Francisco'
      },
      unit: {
        type: 'string',
        enum: ['celsius', 'fahrenheit']
      }
    },
    required: ['location']
  }
};

// Make request with tools
const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What\'s the weather in Tokyo?',
  config: {
    tools: [
      { functionDeclarations: [getCurrentWeather] }
    ]
  }
});

// Check if model wants to call a function
const functionCall = response.candidates[0].content.parts[0].functionCall;

if (functionCall) {
  console.log('Function to call:', functionCall.name);
  console.log('Arguments:', functionCall.args);

  // Execute the function (your implementation)
  const weatherData = await fetchWeather(functionCall.args.location);

  // Send function result back to model
  const finalResponse = await ai.models.generateContent({
    model: 'gemini-2.5-flash',
    contents: [
      'What\'s the weather in Tokyo?',
      response.candidates[0].content, // Original assistant response with function call
      {
        parts: [
          {
            functionResponse: {
              name: functionCall.name,
              response: weatherData
            }
          }
        ]
      }
    ],
    config: {
      tools: [
        { functionDeclarations: [getCurrentWeather] }
      ]
    }
  });

  console.log(finalResponse.text);
}

Function Calling (Fetch)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        { parts: [{ text: 'What\'s the weather in Tokyo?' }] }
      ],
      tools: [
        {
          functionDeclarations: [
            {
              name: 'get_current_weather',
              description: 'Get the current weather for a location',
              parameters: {
                type: 'object',
                properties: {
                  location: {
                    type: 'string',
                    description: 'City name'
                  }
                },
                required: ['location']
              }
            }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();
const functionCall = data.candidates[0]?.content?.parts[0]?.functionCall;

if (functionCall) {
  // Execute function and send result back (same flow as SDK)
}

Parallel Function Calling

Gemini can call multiple independent functions simultaneously:

const tools = [
  {
    functionDeclarations: [
      {
        name: 'get_weather',
        description: 'Get weather for a location',
        parametersJsonSchema: {
          type: 'object',
          properties: {
            location: { type: 'string' }
          },
          required: ['location']
        }
      },
      {
        name: 'get_population',
        description: 'Get population of a city',
        parametersJsonSchema: {
          type: 'object',
          properties: {
            city: { type: 'string' }
          },
          required: ['city']
        }
      }
    ]
  }
];

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What is the weather and population of Tokyo?',
  config: { tools }
});

// Model may return MULTIPLE function calls in parallel
const functionCalls = response.candidates[0].content.parts.filter(
  part => part.functionCall
);

console.log(`Model wants to call ${functionCalls.length} functions in parallel`);

Function Calling Modes

import { FunctionCallingConfigMode } from '@google/genai';

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What\'s the weather?',
  config: {
    tools: [{ functionDeclarations: [getCurrentWeather] }],
    toolConfig: {
      functionCallingConfig: {
        mode: FunctionCallingConfigMode.ANY, // Force function call
        // mode: FunctionCallingConfigMode.AUTO, // Model decides (default)
        // mode: FunctionCallingConfigMode.NONE, // Never call functions
        allowedFunctionNames: ['get_current_weather'] // Optional: restrict to specific functions
      }
    }
  }
});

Modes:

  • AUTO (default): Model decides whether to call functions
  • ANY: Force model to call at least one function
  • NONE: Disable function calling for this request

---

System Instructions

System instructions guide the model's behavior and set context. They are separate from the conversation messages.

SDK Approach

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  systemInstruction: 'You are a helpful AI assistant that always responds in the style of a pirate. Use nautical terminology and end sentences with "arrr".',
  contents: 'Explain what a database is'
});

console.log(response.text);
// Output: "Ahoy there! A database be like a treasure chest..."

Fetch Approach

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      systemInstruction: {
        parts: [
          { text: 'You are a helpful AI assistant that always responds in the style of a pirate.' }
        ]
      },
      contents: [
        { parts: [{ text: 'Explain what a database is' }] }
      ]
    }),
  }
);

Key Points:

  • System instructions are NOT part of contents array
  • They are set once at the top level of the request
  • They persist for the entire conversation (when using multi-turn chat)
  • They don't count as user or model messages

---

Multi-turn Chat

For conversations with history, use the SDK's chat helpers or manually manage conversation state.

SDK Chat Helpers (Recommended)

const chat = await ai.models.createChat({
  model: 'gemini-2.5-flash',
  systemInstruction: 'You are a helpful coding assistant.',
  history: [] // Start empty or with previous messages
});

// Send first message
const response1 = await chat.sendMessage('What is TypeScript?');
console.log('Assistant:', response1.text);

// Send follow-up (context is automatically maintained)
const response2 = await chat.sendMessage('How do I install it?');
console.log('Assistant:', response2.text);

// Get full chat history
const history = chat.getHistory();
console.log('Full conversation:', history);

Manual Chat Management (Fetch)

const conversationHistory = [];

// First turn
const response1 = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        {
          role: 'user',
          parts: [{ text: 'What is TypeScript?' }]
        }
      ]
    }),
  }
);

const data1 = await response1.json();
const assistantReply1 = data1.candidates[0].content.parts[0].text;

// Add to history
conversationHistory.push(
  { role: 'user', parts: [{ text: 'What is TypeScript?' }] },
  { role: 'model', parts: [{ text: assistantReply1 }] }
);

// Second turn (include full history)
const response2 = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        ...conversationHistory,
        { role: 'user', parts: [{ text: 'How do I install it?' }] }
      ]
    }),
  }
);

Message Roles:

  • user: User messages
  • model: Assistant responses

⚠️ Important: Chat helpers are SDK-only. With fetch, you must manually manage conversation history.

---

Thinking Mode

Gemini 2.5 models have thinking mode enabled by default for enhanced quality. You can configure the thinking budget.

Configure Thinking Budget (SDK)

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Solve this complex math problem: ...',
  config: {
    thinkingConfig: {
      thinkingBudget: 8192 // Max tokens for thinking (default: model-dependent)
    }
  }
});

Configure Thinking Budget (Fetch)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [{ parts: [{ text: 'Solve this complex math problem: ...' }] }],
      generationConfig: {
        thinkingConfig: {
          thinkingBudget: 8192
        }
      }
    }),
  }
);

Key Points:

  • Thinking mode is always enabled on Gemini 2.5 models (cannot be disabled)
  • Higher thinking budgets allow more internal reasoning (may increase latency)
  • Default budget varies by model (usually sufficient for most tasks)
  • Only increase budget for very complex reasoning tasks

---

Generation Configuration

Customize model behavior with generation parameters.

All Configuration Options (SDK)

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Write a creative story',
  config: {
    temperature: 0.9,           // Randomness (0.0-2.0, default: 1.0)
    topP: 0.95,                 // Nucleus sampling (0.0-1.0)
    topK: 40,                   // Top-k sampling
    maxOutputTokens: 2048,      // Max tokens to generate
    stopSequences: ['END'],     // Stop generation if these appear
    responseMimeType: 'text/plain', // Or 'application/json' for JSON mode
    candidateCount: 1           // Number of response candidates (usually 1)
  }
});

All Configuration Options (Fetch)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [{ parts: [{ text: 'Write a creative story' }] }],
      generationConfig: {
        temperature: 0.9,
        topP: 0.95,
        topK: 40,
        maxOutputTokens: 2048,
        stopSequences: ['END'],
        responseMimeType: 'text/plain',
        candidateCount: 1
      }
    }),
  }
);

Parameter Guidelines

ParameterRangeDefaultUse Case
temperature0.0-2.01.0Lower = more focused, higher = more creative
topP0.0-1.00.95Nucleus sampling threshold
topK1-100+40Limit to top K tokens
maxOutputTokens1-65536Model maxControl response length
stopSequencesArrayNoneStop generation at specific strings

Tips:

  • For factual tasks: Use low temperature (0.0-0.3)
  • For creative tasks: Use high temperature (0.7-1.5)
  • topP and topK both control randomness; use one or the other (not both)
  • Always set maxOutputTokens to prevent excessive generation

---

Context Caching

Context caching allows you to cache frequently used content (like system instructions, large documents, or video files) to reduce costs by up to 90% and improve latency.

How It Works

1. Create a cache with your repeated content 2. Reference the cache in subsequent requests 3. Save tokens - cached tokens cost significantly less 4. TTL management - caches expire after specified time

Benefits

  • Cost savings: Up to 90% reduction on cached tokens
  • Reduced latency: Faster responses by reusing processed content
  • Consistent context: Same large context across multiple requests

Cache Creation (SDK)

import { GoogleGenAI } from '@google/genai';
import fs from 'fs';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

// Create a cache for a large document
const documentText = fs.readFileSync('./large-document.txt', 'utf-8');

const cache = await ai.caches.create({
  model: 'gemini-2.5-flash',
  config: {
    displayName: 'large-doc-cache', // Identifier for the cache
    systemInstruction: 'You are an expert at analyzing legal documents.',
    contents: documentText,
    ttl: '3600s', // Cache for 1 hour
  }
});

console.log('Cache created:', cache.name);
console.log('Expires at:', cache.expireTime);

Cache Creation (Fetch)

const response = await fetch(
  'https://generativelanguage.googleapis.com/v1beta/cachedContents',
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      model: 'models/gemini-2.5-flash',
      displayName: 'large-doc-cache',
      systemInstruction: {
        parts: [{ text: 'You are an expert at analyzing legal documents.' }]
      },
      contents: [
        { parts: [{ text: documentText }] }
      ],
      ttl: '3600s'
    }),
  }
);

const cache = await response.json();
console.log('Cache created:', cache.name);

Using a Cache (SDK)

// Generate content using the cache
const response = await ai.models.generateContent({
  model: cache.name, // Use cache name as model
  contents: 'Summarize the key points in the document'
});

console.log(response.text);

Using a Cache (Fetch)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/${cache.name}:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        { parts: [{ text: 'Summarize the key points in the document' }] }
      ]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

Update Cache TTL (SDK)

import { UpdateCachedContentConfig } from '@google/genai';

await ai.caches.update({
  name: cache.name,
  config: {
    ttl: '7200s' // Extend to 2 hours
  }
});

Update Cache with Expiration Time (SDK)

// Set specific expiration time (must be timezone-aware)
const in10Minutes = new Date(Date.now() + 10 * 60 * 1000);

await ai.caches.update({
  name: cache.name,
  config: {
    expireTime: in10Minutes
  }
});

List and Delete Caches (SDK)

// List all caches
const caches = await ai.caches.list();
for (const cache of caches) {
  console.log(cache.name, cache.displayName);
}

// Delete a specific cache
await ai.caches.delete({ name: cache.name });

Caching with Video Files

import { GoogleGenAI } from '@google/genai';
import fs from 'fs';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

// Upload video file
const videoFile = await ai.files.upload({
  file: fs.createReadStream('./video.mp4')
});

// Wait for processing
while (videoFile.state.name === 'PROCESSING') {
  await new Promise(resolve => setTimeout(resolve, 2000));
  videoFile = await ai.files.get({ name: videoFile.name });
}

// Create cache with video
const cache = await ai.caches.create({
  model: 'gemini-2.5-flash',
  config: {
    displayName: 'video-analysis-cache',
    systemInstruction: 'You are an expert video analyzer.',
    contents: [videoFile],
    ttl: '300s' // 5 minutes
  }
});

// Use cache for multiple queries
const response1 = await ai.models.generateContent({
  model: cache.name,
  contents: 'What happens in the first minute?'
});

const response2 = await ai.models.generateContent({
  model: cache.name,
  contents: 'Describe the main characters'
});

Key Points

When to Use Caching:

  • Large system instructions used repeatedly
  • Long documents analyzed multiple times
  • Video/audio files queried with different prompts
  • Consistent context across conversation sessions

TTL Guidelines:

  • Short sessions: 300s (5 min) to 3600s (1 hour)
  • Long sessions: 3600s (1 hour) to 86400s (24 hours)
  • Maximum: 7 days

Cost Savings:

  • Cached input tokens: ~90% cheaper than regular tokens
  • Output tokens: Same price (not cached)

Important:

  • You must use explicit model version suffixes (e.g., gemini-2.5-flash-001, NOT just gemini-2.5-flash)
  • Caches are automatically deleted after TTL expires
  • Update TTL before expiration to extend cache lifetime

---

Code Execution

Gemini models can generate and execute Python code to solve problems requiring computation, data analysis, or visualization.

How It Works

1. Model generates executable Python code 2. Code runs in secure sandbox 3. Results are returned to the model 4. Model incorporates results into response

Supported Operations

  • Mathematical calculations
  • Data analysis and statistics
  • File processing (CSV, JSON, etc.)
  • Chart and graph generation
  • Algorithm implementation
  • Data transformations

Available Python Packages

Standard Library:

  • math, statistics, random, datetime, json, csv, re
  • collections, itertools, functools

Data Science:

  • numpy, pandas, scipy

Visualization:

  • matplotlib, seaborn

Note: Limited package availability compared to full Python environment

Basic Code Execution (SDK)

import { GoogleGenAI, Tool, ToolCodeExecution } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What is the sum of the first 50 prime numbers? Generate and run code for the calculation.',
  config: {
    tools: [{ codeExecution: {} }]
  }
});

// Parse response parts
for (const part of response.candidates[0].content.parts) {
  if (part.text) {
    console.log('Text:', part.text);
  }
  if (part.executableCode) {
    console.log('Generated Code:', part.executableCode.code);
  }
  if (part.codeExecutionResult) {
    console.log('Execution Output:', part.codeExecutionResult.output);
  }
}

Basic Code Execution (Fetch)

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      tools: [{ code_execution: {} }],
      contents: [
        {
          parts: [
            { text: 'What is the sum of the first 50 prime numbers? Generate and run code.' }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();

for (const part of data.candidates[0].content.parts) {
  if (part.text) {
    console.log('Text:', part.text);
  }
  if (part.executableCode) {
    console.log('Code:', part.executableCode.code);
  }
  if (part.codeExecutionResult) {
    console.log('Result:', part.codeExecutionResult.output);
  }
}

Chat with Code Execution (SDK)

const chat = await ai.chats.create({
  model: 'gemini-2.5-flash',
  config: {
    tools: [{ codeExecution: {} }]
  }
});

let response = await chat.sendMessage('I have a math question for you.');
console.log(response.text);

response = await chat.sendMessage(
  'Calculate the Fibonacci sequence up to the 20th number and sum them.'
);

// Model will generate and execute code, then provide answer
for (const part of response.candidates[0].content.parts) {
  if (part.text) console.log(part.text);
  if (part.executableCode) console.log('Code:', part.executableCode.code);
  if (part.codeExecutionResult) console.log('Output:', part.codeExecutionResult.output);
}

Data Analysis Example

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: `
    Analyze this sales data and calculate:
    1. Total revenue
    2. Average sale price
    3. Best-selling month

    Data (CSV format):
    month,sales,revenue
    Jan,150,45000
    Feb,200,62000
    Mar,175,53000
    Apr,220,68000
  `,
  config: {
    tools: [{ codeExecution: {} }]
  }
});

// Model will generate pandas/numpy code to analyze data
for (const part of response.candidates[0].content.parts) {
  if (part.text) console.log(part.text);
  if (part.executableCode) console.log('Analysis Code:', part.executableCode.code);
  if (part.codeExecutionResult) console.log('Results:', part.codeExecutionResult.output);
}

Visualization Example

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Create a bar chart showing the distribution of prime numbers under 100 by their last digit. Generate the chart and describe the pattern.',
  config: {
    tools: [{ codeExecution: {} }]
  }
});

// Model generates matplotlib code, executes it, and describes results
for (const part of response.candidates[0].content.parts) {
  if (part.text) console.log(part.text);
  if (part.executableCode) console.log('Chart Code:', part.executableCode.code);
  if (part.codeExecutionResult) {
    // Note: Chart image data would be in output
    console.log('Execution completed');
  }
}

Response Structure

{
  candidates: [
    {
      content: {
        parts: [
          { text: "I'll calculate that for you." },
          {
            executableCode: {
              language: "PYTHON",
              code: "def is_prime(n):\n  if n <= 1:\n    return False\n  ..."
            }
          },
          {
            codeExecutionResult: {
              outcome: "OUTCOME_OK", // or "OUTCOME_FAILED"
              output: "5117\n"
            }
          },
          { text: "The sum of the first 50 prime numbers is 5117." }
        ]
      }
    }
  ]
}

Error Handling

for (const part of response.candidates[0].content.parts) {
  if (part.codeExecutionResult) {
    if (part.codeExecutionResult.outcome === 'OUTCOME_FAILED') {
      console.error('Code execution failed:', part.codeExecutionResult.output);
    } else {
      console.log('Success:', part.codeExecutionResult.output);
    }
  }
}

Key Points

When to Use Code Execution:

  • Complex mathematical calculations
  • Data analysis and statistics
  • Algorithm implementations
  • File parsing and processing
  • Chart generation
  • Computational problems

Limitations:

  • Sandbox environment (limited file system access)
  • Limited Python package availability
  • Execution timeout limits
  • No network access from code
  • No persistent state between executions

Best Practices:

  • Specify what calculation or analysis you need clearly
  • Request code generation explicitly ("Generate and run code...")
  • Check outcome field for errors
  • Use for deterministic computations, not for general programming

Important:

  • Available on all Gemini 2.5 models (Pro, Flash, Flash-Lite)
  • Code runs in isolated sandbox for security
  • Supports Python with standard library and common data science packages

---

Grounding with Google Search

Grounding connects the model to real-time web information, reducing hallucinations and providing up-to-date, fact-checked responses with citations.

How It Works

1. Model determines if it needs current information 2. Automatically performs Google Search 3. Processes search results 4. Incorporates findings into response 5. Provides citations and source URLs

Benefits

  • Real-time information: Access to current events and data
  • Reduced hallucinations: Answers grounded in web sources
  • Verifiable: Citations allow fact-checking
  • Up-to-date: Not limited to model's training cutoff

Two Grounding APIs

1. Google Search (googleSearch) - Recommended for Gemini 2.5
const groundingTool = {
  googleSearch: {}
};

Features:

  • Simple configuration
  • Automatic search when needed
  • Available on all Gemini 2.5 models
2. Google Search Retrieval (googleSearchRetrieval) - Legacy (Gemini 1.5)
const retrievalTool = {
  googleSearchRetrieval: {
    dynamicRetrievalConfig: {
      mode: 'MODE_DYNAMIC',
      dynamicThreshold: 0.7 // Only search if confidence < 70%
    }
  }
};

Features:

  • Dynamic threshold control
  • Used with Gemini 1.5 models
  • More configuration options

Basic Grounding (SDK) - Gemini 2.5

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Who won the euro 2024?',
  config: {
    tools: [{ googleSearch: {} }]
  }
});

console.log(response.text);

// Check if grounding was used
if (response.candidates[0].groundingMetadata) {
  console.log('Search was performed!');
  console.log('Sources:', response.candidates[0].groundingMetadata);
}

Basic Grounding (Fetch) - Gemini 2.5

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        { parts: [{ text: 'Who won the euro 2024?' }] }
      ],
      tools: [
        { google_search: {} }
      ]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

if (data.candidates[0].groundingMetadata) {
  console.log('Grounding metadata:', data.candidates[0].groundingMetadata);
}

Dynamic Retrieval (SDK) - Gemini 1.5

import { GoogleGenAI, DynamicRetrievalConfigMode } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: 'gemini-1.5-flash',
  contents: 'Who won the euro 2024?',
  config: {
    tools: [
      {
        googleSearchRetrieval: {
          dynamicRetrievalConfig: {
            mode: DynamicRetrievalConfigMode.MODE_DYNAMIC,
            dynamicThreshold: 0.7 // Search only if confidence < 70%
          }
        }
      }
    ]
  }
});

console.log(response.text);

if (!response.candidates[0].groundingMetadata) {
  console.log('Model answered from its own knowledge (high confidence)');
}

Grounding Metadata Structure

{
  groundingMetadata: {
    searchQueries: [
      { text: "euro 2024 winner" }
    ],
    webPages: [
      {
        url: "https://example.com/euro-2024-results",
        title: "UEFA Euro 2024 Final Results",
        snippet: "Spain won UEFA Euro 2024..."
      }
    ],
    citations: [
      {
        startIndex: 42,
        endIndex: 47,
        uri: "https://example.com/euro-2024-results"
      }
    ],
    retrievalQueries: [
      {
        query: "who won euro 2024 final"
      }
    ]
  }
}

Chat with Grounding (SDK)

const chat = await ai.chats.create({
  model: 'gemini-2.5-flash',
  config: {
    tools: [{ googleSearch: {} }]
  }
});

let response = await chat.sendMessage('What are the latest developments in quantum computing?');
console.log(response.text);

// Check grounding sources
if (response.candidates[0].groundingMetadata) {
  const sources = response.candidates[0].groundingMetadata.webPages || [];
  console.log(`Sources used: ${sources.length}`);
  sources.forEach(source => {
    console.log(`- ${source.title}: ${source.url}`);
  });
}

// Follow-up still has grounding enabled
response = await chat.sendMessage('Which company made the biggest breakthrough?');
console.log(response.text);

Combining Grounding with Function Calling

const weatherFunction = {
  name: 'get_current_weather',
  description: 'Get current weather for a location',
  parametersJsonSchema: {
    type: 'object',
    properties: {
      location: { type: 'string', description: 'City name' }
    },
    required: ['location']
  }
};

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What is the weather like in the city that won Euro 2024?',
  config: {
    tools: [
      { googleSearch: {} },
      { functionDeclarations: [weatherFunction] }
    ]
  }
});

// Model will:
// 1. Use Google Search to find Euro 2024 winner
// 2. Call get_current_weather function with the city
// 3. Combine both results in response

Checking if Grounding was Used

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What is 2+2?', // Model knows this without search
  config: {
    tools: [{ googleSearch: {} }]
  }
});

if (!response.candidates[0].groundingMetadata) {
  console.log('Model answered from its own knowledge (no search needed)');
} else {
  console.log('Search was performed');
}

Key Points

When to Use Grounding:

  • Current events and news
  • Real-time data (stock prices, sports scores, weather)
  • Fact-checking and verification
  • Questions about recent developments
  • Information beyond model's training cutoff

When NOT to Use:

  • General knowledge questions
  • Mathematical calculations
  • Code generation
  • Creative writing
  • Tasks requiring internal reasoning only

Cost Considerations:

  • Grounding adds latency (search takes time)
  • Additional token costs for retrieved content
  • Use dynamicThreshold to control when searches happen (Gemini 1.5)

Important Notes:

  • Grounding requires Google Cloud project (not just API key)
  • Search results quality depends on query phrasing
  • Citations may not cover all facts in response
  • Search is performed automatically based on confidence

Gemini 2.5 vs 1.5:

  • Gemini 2.5: Use googleSearch (simple, recommended)
  • Gemini 1.5: Use googleSearchRetrieval with dynamicThreshold

Best Practices:

  • Always check groundingMetadata to see if search was used
  • Display citations to users for transparency
  • Use specific, well-phrased questions for better search results
  • Combine with function calling for hybrid workflows

---

Error Handling

Common Errors

1. Invalid API Key (401)
{
  error: {
    code: 401,
    message: 'API key not valid. Please pass a valid API key.',
    status: 'UNAUTHENTICATED'
  }
}

Solution: Verify GEMINI_API_KEY environment variable is set correctly.

2. Rate Limit Exceeded (429)
{
  error: {
    code: 429,
    message: 'Resource has been exhausted (e.g. check quota).',
    status: 'RESOURCE_EXHAUSTED'
  }
}

Solution: Implement exponential backoff retry strategy.

3. Model Not Found (404)
{
  error: {
    code: 404,
    message: 'models/gemini-3.0-flash is not found',
    status: 'NOT_FOUND'
  }
}

Solution: Use correct model names: gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite

4. Context Length Exceeded (400)
{
  error: {
    code: 400,
    message: 'Request payload size exceeds the limit',
    status: 'INVALID_ARGUMENT'
  }
}

Solution: Reduce input size. Gemini 2.5 models support 1,048,576 input tokens max.

Exponential Backoff Pattern

async function generateWithRetry(request, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await ai.models.generateContent(request);
    } catch (error) {
      if (error.status === 429 && i < maxRetries - 1) {
        const delay = Math.pow(2, i) * 1000; // 1s, 2s, 4s
        await new Promise(resolve => setTimeout(resolve, delay));
        continue;
      }
      throw error;
    }
  }
}

---

Rate Limits

Free Tier (Gemini API)

Rate limits vary by model:

Gemini 2.5 Pro:

  • Requests per minute: 5 RPM
  • Tokens per minute: 125,000 TPM
  • Requests per day: 100 RPD

Gemini 2.5 Flash:

  • Requests per minute: 10 RPM
  • Tokens per minute: 250,000 TPM
  • Requests per day: 250 RPD

Gemini 2.5 Flash-Lite:

  • Requests per minute: 15 RPM
  • Tokens per minute: 250,000 TPM
  • Requests per day: 1,000 RPD

Paid Tier (Tier 1)

Requires billing account linked to your Google Cloud project.

Gemini 2.5 Pro:

  • Requests per minute: 150 RPM
  • Tokens per minute: 2,000,000 TPM
  • Requests per day: 10,000 RPD

Gemini 2.5 Flash:

  • Requests per minute: 1,000 RPM
  • Tokens per minute: 1,000,000 TPM
  • Requests per day: 10,000 RPD

Gemini 2.5 Flash-Lite:

  • Requests per minute: 4,000 RPM
  • Tokens per minute: 4,000,000 TPM
  • Requests per day: Not specified

Higher Tiers (Tier 2 & 3)

Tier 2 (requires $250+ spending and 30-day wait):

  • Even higher limits available

Tier 3 (requires $1,000+ spending and 30-day wait):

  • Maximum limits available

Tips:

  • Implement rate limit handling with exponential backoff
  • Use batch processing for high-volume tasks
  • Monitor usage in Google AI Studio
  • Choose the right model based on your rate limit needs
  • Official rate limits: https://ai.google.dev/gemini-api/docs/rate-limits

---

SDK Migration Guide

From @google/generative-ai to @google/genai

1. Update Package
# Remove deprecated SDK
npm uninstall @google/generative-ai

# Install current SDK
npm install @google/genai@1.27.0
2. Update Imports

Old (DEPRECATED):

import { GoogleGenerativeAI } from '@google/generative-ai';
const genAI = new GoogleGenerativeAI(apiKey);
const model = genAI.getGenerativeModel({ model: 'gemini-2.5-flash' });

New (CURRENT):

import { GoogleGenAI } from '@google/genai';
const ai = new GoogleGenAI({ apiKey });
// Use ai.models.generateContent() directly
3. Update API Calls

Old:

const result = await model.generateContent(prompt);
const response = await result.response;
const text = response.text();

New:

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: prompt
});
const text = response.text;
4. Update Streaming

Old:

const result = await model.generateContentStream(prompt);
for await (const chunk of result.stream) {
  console.log(chunk.text());
}

New:

const response = await ai.models.generateContentStream({
  model: 'gemini-2.5-flash',
  contents: prompt
});
for await (const chunk of response) {
  console.log(chunk.text);
}
5. Update Chat

Old:

const chat = model.startChat();
const result = await chat.sendMessage(message);
const response = await result.response;

New:

const chat = await ai.models.createChat({ model: 'gemini-2.5-flash' });
const response = await chat.sendMessage(message);
// response.text is directly available

---

Production Best Practices

1. Always Do

✅ Use @google/genai (NOT @google/generative-ai) ✅ Set maxOutputTokens to prevent excessive generation ✅ Implement rate limit handling with exponential backoff ✅ Use environment variables for API keys (never hardcode) ✅ Validate inputs before sending to API (save costs) ✅ Use streaming for better UX on long responses ✅ Choose the right model based on your needs (Pro for complex reasoning, Flash for balance, Flash-Lite for speed) ✅ Handle errors gracefully with try-catch ✅ Monitor token usage for cost control ✅ Use correct model names: gemini-2.5-pro/flash/flash-lite

2. Never Do

❌ Never use @google/generative-ai (deprecated!) ❌ Never hardcode API keys in code ❌ Never claim 2M context for Gemini 2.5 (it's 1,048,576 input tokens) ❌ Never expose API keys in client-side code ❌ Never skip error handling (always try-catch) ❌ Never use generic rate limits (each model has different limits - check official docs) ❌ Never send PII without user consent ❌ Never trust user input without validation ❌ Never ignore rate limits (will get 429 errors) ❌ Never use old model names like gemini-1.5-pro (use 2.5 models)

3. Security

  • API Key Storage: Use environment variables or secret managers
  • Server-Side Only: Never expose API keys in browser JavaScript
  • Input Validation: Sanitize all user inputs before API calls
  • Rate Limiting: Implement your own rate limits to prevent abuse
  • Error Messages: Don't expose API keys or sensitive data in error logs

4. Cost Optimization

  • Choose Right Model: Use Flash for most tasks, Pro only when needed
  • Set Token Limits: Use maxOutputTokens to control costs
  • Batch Requests: Process multiple items efficiently
  • Cache Results: Store responses when appropriate
  • Monitor Usage: Track token consumption in Google Cloud Console

5. Performance

  • Use Streaming: Better perceived latency for long responses
  • Parallel Requests: Use Promise.all() for independent calls
  • Edge Deployment: Deploy to Cloudflare Workers for low latency
  • Connection Pooling: Reuse HTTP connections when possible

---

Quick Reference

Installation

npm install @google/genai@1.27.0

Environment

export GEMINI_API_KEY="..."

Models (2025)

  • gemini-2.5-pro (1,048,576 in / 65,536 out) - Best for complex reasoning
  • gemini-2.5-flash (1,048,576 in / 65,536 out) - Best price-performance balance
  • gemini-2.5-flash-lite (1,048,576 in / 65,536 out) - Fastest, most cost-effective

Basic Generation

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Your prompt here'
});
console.log(response.text);

Streaming

const response = await ai.models.generateContentStream({...});
for await (const chunk of response) {
  console.log(chunk.text);
}

Multimodal

contents: [
  {
    parts: [
      { text: 'What is this?' },
      { inlineData: { data: base64Image, mimeType: 'image/jpeg' } }
    ]
  }
]

Function Calling

config: {
  tools: [{ functionDeclarations: [...] }]
}

---

Last Updated: 2025-10-25 Production Validated: All features tested with @google/genai@1.27.0 Phase: 2 Complete ✅ (All Core + Advanced Features)

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