Fixing 'Thoughts must be present in conversation history' in Gemini API: Thought Signatures in Multi-Turn Tool Calls
If you're hitting 'Thoughts must be present in conversation history when using thinking signature' in Gemini 2.5/3.x with multi-turn function calling, this guide walks through what's actually happening and how to fix it in five minutes — Python SDK, REST, and streaming all covered.
Custom Gemini API Agent Loop Without ADK — A Complete Production Guide to Tool Calling, Memory, and Parallel Execution
Build production-grade AI agents using Gemini API directly without Google ADK. This guide covers custom agent loops, tool calling patterns, sliding window memory, parallel execution, and battle-tested error recovery strategies.
Gemini Function Calling Isn't Firing: Five Symptoms and Their Causes
Fix Gemini API Function Calling issues fast. This guide covers the most common causes — bad schemas, wrong model, parse errors, and tool selection problems — with step-by-step solutions and working code examples.
Building Custom MCP Servers for Gemini API — Extending AI Agents with TypeScript
Learn how to build custom Model Context Protocol (MCP) servers in TypeScript and integrate them with Gemini API. Covers architecture, authentication, error handling, and production deployment patterns.
Building an Intelligent Email Classification System with Gemini API — Function Calling and Structured Output in Practice
Learn how to use Gemini API's Function Calling and structured output to build a system that automatically classifies, summarizes, and prioritizes incoming emails — with working TypeScript code.
Build an AI Data Analysis Agent with Gemini API — Combining Code Execution, Function Calling, and Structured Output
Learn how to build a production-ready AI data analysis agent in Python that combines Gemini API's Code Execution, Function Calling, and Structured Output to automatically analyze CSV/Excel data, generate visualizations, and produce structured reports.
Gemini 2.5 Pro × FastAPI: Building a Production-Ready AI Backend
Learn how to build a production-ready AI backend by combining Gemini 2.5 Pro with FastAPI, covering streaming, rate limiting, Function Calling, cost optimization, and Docker deployment.
Production-Grade Voice AI Agent with Gemini Live API & Google ADK [2026]
Build and deploy a production-grade voice AI agent by combining Gemini Live API with Google ADK, Function Calling, WebSocket management, and Cloud Run. Covers architecture design, connection stability, parallel tool execution, and cost optimization.