Create a human-like Evolution API WhatsApp agent with Redis, PostgreSQL and Gemini

🤖 Human-like Evolution API Agent with Redis & PostgreSQL This production-ready template builds a sophisticated AI Agent using Evolution API that mimics human interaction patterns. Unlike standard chatbots that reply instantly to every incoming message, this workflow uses a Smart Redis Buffering System. It waits for the user to finish typing their full thought (text, audio, or image albums) before processing, creating a natural, conversational flow. It features a Hybrid Memory Architecture: active conversations are cached in Redis for ultra-low latency, while the complete chat history is securely stored in PostgreSQL. To optimize token usage and maintain long-term coherence, a Context Refiner Agent summarizes the conversation history before the Main AI generates a response. ✨ Key Features - Human-like Buffering: The agent waits (configurable time) to group consecutive messages, voice notes, and media albums into a single context. This prevents fragmented replies and feels like talking to a real person. - Hybrid Memory: Combines Redis (Hot Cache) for speed and PostgreSQL (Cold Storage) for permanent history. - Context Refinement: A specialized AI step summarizes past interactions, allowing the Main Agent to understand long conversations without exceeding token limits or increasing costs. - Multi-Modal Support: Natively handles text, audio transcription, and image analysis via Evolution API. - Parallel Processing: Manages "typing..." status and session checks in parallel to reduce response latency. 📋 Requirements To use this workflow, you must configure the Evolution API correctly: 1. Evolution API Instance: You need a running instance of Evolution API. - Configuration Guide 2. N8n Community Node: Install the Evolution API node in your n8n instance. - n8n-nodes-evolution-api 3. Database: A PostgreSQL database for chat history and a Redis instance for the buffer/cache. 4. AI Models: API keys for your LLM (OpenAI, Anthropic, or Google Gemini). ⚙️ Setup Instructions 1. Install the Node: Go to Settings > Community Nodes in n8n and install n8n-nodes-evolution-api. 2. Credentials: Configure credentials for Redis, PostgreSQL, and your AI provider (e.g., OpenAI/Gemini). 3. Database Setup: Create a chathistory table in PostgreSQL (columns must match the Insert node). 4. Redis Connection: Configure your Redis credentials in the workflow nodes. 5. Global Variables: Set the following in the "Global Variables" node: - waitbuffer: Seconds to wait for the user to stop typing (e.g., 5s). - waitconversation: Seconds to keep the cache alive (e.g., 300s). - maxchathistory: Number of past messages to retrieve. 6. Webhook: Point your Evolution API instance to this workflow's Webhook URL. 🚀 How it Works 1. Ingestion: Receives data via Evolution API. Detects if it's text, audio, or an album. 2. Smart Buffering: Holds the execution to collect all parts of the user's message (simulating a human reading/listening). 3. Context Retrieval: Checks Redis for the active session. If empty, fetches from PostgreSQL. 4. Refinement: The Refiner Agent summarizes the history to extract key details. 5. Response: The Main Agent generates a reply based on the refined context and current buffer, then saves it to both Redis and Postgres. 💡 Need Assistance? If you’d like help customizing or extending this workflow, feel free to reach out: 📧 Email: johnsilva11031@gmail.com(mailto:johnsilva11031@gmail.com) 🔗 LinkedIn: John Alejandro Silva Rodríguez