Multi-Agent RAG (Firebase)

Sophisticated agent orchestration system built for real-time interaction.

Description

This blueprint focuses on a sophisticated multi-agent system where different specialized agents (e.g., researcher, summarizer, biller) collaborate in a serverless environment. It features real-time communication via WebRTC for low-latency video/audio interactions, providing a human-like interface for complex AI workflows.

Core Capabilities

• Agent Orchestration: Uses a central controller to delegate sub-tasks to specialized models based on semantic load.
• Low-Latency Interaction: WebRTC integration ensures sub-300ms latency for audio/video feedback loops.
• Infinite Scalability: Built on the Firebase serverless suite, handling traffic spikes without manual infrastructure management.
• RAG for Personalization: Long-term memory and context are managed via Firestore Vector Search, grounded in user-specific datasets.

Orchestration Strategy

The system employs a Router-Worker pattern. A primary "Supervisor" agent analyzes the user's intent and spawns ephemeral worker functions for tasks like data fetching, code execution, or financial calculation, aggregating the results into a unified response.

Expert Take

[!IMPORTANT] WebRTC Optimization For high-quality AI streams, prioritize VP9 or AV1 encoding if the client supports it. Implement a jitter buffer on the client side to handle network fluctuations, ensuring the AI's "voice" remains natural and uninterrupted.

Tech Stack