Agent

Build real-time voice and video AI agents with LLMs, STT/TTS, and MCP-style tools.

Updated Mar 1, 2026

Search Tags:#integration #agent #LLM #voice #real-time #vision-agents

Ready to run

npx skills add https://github.com/emSoumik/SuperPong --skill agent-emsoumik

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Agent
Source: https://github.com/emSoumik/SuperPong/tree/main
Command: npx skills add https://github.com/emSoumik/SuperPong --skill agent-emsoumik

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Builds and coordinates real-time voice and video AI agents that orchestrate multiple providers and run in production.

Core Features & Use Cases

Unified Agent class to manage LLMs, STT/TTS, video processing, and MCP-like tools.
Real-time voice and video streaming with WebRTC and edge deployments.
Use cases include building autonomous AI agents for live interactions, analytics, and automation.

Quick Start

Install Vision Agents, configure your environment, and run the agent in server mode to start an autonomous voice and vision agent.

Frequently Asked Questions about Agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build real-time voice and video AI agents for production?▼

To build real-time voice and video AI agents, you use a unified Agent class to orchestrate LLMs, STT/TTS, and computer vision pipelines. It targets production-grade deployments on HTTP servers with robust session management for live interactions.

Can I orchestrate multiple LLM providers and computer vision pipelines in a single agent?▼

Yes, you can orchestrate multiple LLM providers and computer vision pipelines using a unified Agent class. It manages function-call execution and MCP-style tooling to coordinate live voice, video, and AI streaming across different services.

What is needed to deploy autonomous voice and vision agents on an HTTP server?▼

Deploying autonomous voice and vision agents on an HTTP server requires the Vision Agents SDK, configured environment variables, and plugin integrations. You run the agent in server mode to start handling real-time WebRTC and edge deployments.

Does this approach support real-time WebRTC streaming for live AI interactions?▼

Yes, real-time WebRTC streaming is supported for live AI interactions. The framework handles voice and video streaming directly, enabling autonomous agents to process live inputs and manage function calls during active sessions.

How do I manage function calls and tool integrations for live voice agents?▼

You manage function calls and tool integrations for live voice agents using MCP-style tooling within the unified Agent class. This architecture handles robust session management and coordinates tool execution during real-time interactions.

When should I use a unified agent architecture for voice and video processing?▼

Use a unified agent architecture for voice and video processing when building production-grade systems that require coordinating LLMs, STT/TTS, and computer vision simultaneously. It is designed for autonomous live interactions, analytics, and automation across multiple providers.