> ## Documentation Index
> Fetch the complete documentation index at: https://visionagents.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> What Vision Agents is, what you can build, and where to start

Vision Agents is an open-source Python framework for real-time voice and video AI. You write an `Agent` that joins a session, connects to AI providers through swappable plugins, and responds in real time. The framework handles call lifecycle, audio/video routing, turn-taking, and deployment. You focus on instructions, provider choices and connecting to your existing knowledge bases.

## What can you build?

Teams ship **voice support bots**, **video coaches** that watch a camera, **phone agents** with knowledge bases, and **multimodal assistants** that see and hear. Common starting points:

* [Simple voice agent](/examples/simple-agent): STT, LLM, and TTS in a custom pipeline
* [Phone support with RAG](/examples/phone-and-rag): inbound calls backed by your docs
* [Golf coach](/examples/golf-coach): YOLO pose detection plus realtime voice feedback

More recipes on the [homepage](/) and in [Examples](/examples/simple-agent).

## How it fits together

A **client app** (web, iOS, Android, or local camera) connects through an **edge transport** to your **agent** process. The agent calls **AI plugins** (LLM, STT, TTS, vision), keeps **chat memory** for conversation context, and connects to **knowledge and tools** (RAG, function calling, MCP).

```mermaid theme={null}
flowchart LR
    Client[Client app]
    Edge[Edge transport]
    Agent[Your agent]
    AI[AI plugins]
    Chat[Chat memory]
    Data[Knowledge and tools]

    Client --> Edge --> Agent
    Agent --> AI
    Agent --> Chat
    Agent --> Data
```

**Edge transport** (pick one, swap in one line):

| Plugin                                                     | Best for                                                                                                                |
| ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| [Stream Video RTC](/integrations/edge-transport/getstream) | Production WebRTC, [Stream client SDKs](https://getstream.io/video/sdk/), [chat-backed memory](/guides/chat-and-memory) |
| [Local transport](/integrations/edge-transport/local)      | Dev on your machine with camera and mic, no Stream account                                                              |
| [Tencent RTC](/integrations/edge-transport/tencent)        | Low latency in Asia, [Tencent client SDKs](/integrations/edge-transport/tencent)                                        |

Your users join the same session as the agent via the transport's client SDK (or the Quickstart browser demo while prototyping). The agent runs server-side in Python.

**AI plugins**: realtime speech models, or separate STT + LLM + TTS, plus YOLO/VLM processors and avatars. See [Integrations](/integrations/introduction-to-integrations).

**Chat memory**: with Stream Video, transcripts and context persist to [Stream Chat](/guides/chat-and-memory) automatically. Use in-memory storage for local dev.

**Knowledge and tools**: TurboPuffer, Gemini FileSearch, `@llm.register_function`, and [MCP servers](/guides/mcp-tool-calling).

## Getting started

<Steps>
  <Step title="Run your first agent" icon="rocket">
    Follow the [Quickstart](/introduction/quickstart) to scaffold and talk to an agent in your browser (\~5 min).
  </Step>

  <Step title="Extend voice or video" icon="microphone">
    Read [Voice Agents](/introduction/voice-agents) for custom pipelines and function calling, or [Video Agents](/introduction/video-agents) for VLMs and YOLO processors.
  </Step>

  <Step title="Try an example" icon="code">
    Clone a recipe from [Examples](/examples/simple-agent): phone, RAG, golf coach, and more.
  </Step>

  <Step title="Deploy" icon="server">
    Ship with [Deploy to production](/guides/deploying-overview): Docker, Kubernetes, and metrics.
  </Step>
</Steps>