> ## Documentation Index
> Fetch the complete documentation index at: https://visionagents.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Build and run your first AI voice agent in under 5 minutes
You'll build a real-time voice agent you can talk to in your browser, using [Gemini Realtime](https://ai.google.dev/gemini-api/docs/live) on [Stream's](https://getstream.io/video/) edge network. About 18 lines of Python.
New to Vision Agents? Read the [Overview](/introduction/overview) first.
{`Create a Python project for a Vision Agents voice assistant using uv and Python 3.12.
Steps:
1. Scaffold: uvx vision-agents init my-agent && cd my-agent
2. Copy .env.example to .env and fill in: STREAM_API_KEY, STREAM_API_SECRET (from getstream.io), GOOGLE_API_KEY (from aistudio.google.com)
3. Review and customize agent.py (already created by init):
from dotenv import load_dotenv
from vision_agents.core import Agent, Runner, User
from vision_agents.core.agents import AgentLauncher
from vision_agents.plugins import getstream, gemini
load_dotenv()
INSTRUCTIONS = (
"You're a helpful voice AI assistant. "
"Keep responses short and conversational."
)
async def create_agent(\*\*kwargs) -> Agent:
return Agent(
edge=getstream.Edge(),
agent_user=User(name="My AI assistant", id="agent"),
instructions=INSTRUCTIONS,
llm=gemini.Realtime(),
)
async def join_call(agent: Agent, call_type: str, call_id: str, \*\*kwargs) -> None:
call = await agent.create_call(call_type, call_id)
async with agent.join(call):
await agent.simple_response(text="Say hi and introduce yourself.")
await agent.finish()
runner = Runner(AgentLauncher(create_agent=create_agent, join_call=join_call))
if **name** == "**main**":
runner.cli()
4. Run with: uv run agent.py run
Reference docs: https://visionagents.ai
MCP server: https://visionagents.ai/mcp
Skill.md: https://visionagents.ai/skill.md`}
## Build your agent
Install **[uv](https://docs.astral.sh/uv/getting-started/installation/)**
first; it includes `uvx`. The `init` command runs `uv sync` to provision your
virtual environment, so `uv` must be on your PATH. Python 3.10–3.13 is
supported.
One command creates a ready-to-run agent project with dependencies installed:
```bash theme={null}
uvx vision-agents init my-agent && cd my-agent
```
This generates `agent.py`, `pyproject.toml`, `.env.example`, `tests/`, and a `Dockerfile`, then runs `uv sync`.
Pass `--no-install` to skip `uv sync` if you only want the project files: `uvx vision-agents init my-agent --no-install`
Get the keys you'll need:
* Create a free **[Stream account](https://getstream.io/try-for-free/)** for `STREAM_API_KEY` and `STREAM_API_SECRET`. See [Why Stream Video RTC](/integrations/edge-transport/getstream) for what the edge transport provides.
* Get an API key from **[Google AI Studio](https://aistudio.google.com/)** for `GOOGLE_API_KEY`.
Copy the scaffolded template and fill in your keys. Vision Agents auto-loads these for each plugin.
```bash theme={null}
cp .env.example .env
```
```bash .env theme={null}
STREAM_API_KEY=your_stream_api_key
STREAM_API_SECRET=your_stream_api_secret
GOOGLE_API_KEY=your_google_api_key
```
Open `agent.py`. The `init` command already created this file. Walk through each section to see how the agent works, then customize as needed.
```python agent.py theme={null}
from dotenv import load_dotenv
from vision_agents.core import Agent, Runner, User
from vision_agents.core.agents import AgentLauncher
from vision_agents.plugins import getstream, gemini
load_dotenv()
INSTRUCTIONS = (
"You're a helpful voice AI assistant. "
"Keep responses short and conversational."
)
async def create_agent(**kwargs) -> Agent:
return Agent(
edge=getstream.Edge(),
agent_user=User(name="My AI assistant", id="agent"),
instructions=INSTRUCTIONS,
llm=gemini.Realtime(),
)
async def join_call(agent: Agent, call_type: str, call_id: str, **kwargs) -> None:
call = await agent.create_call(call_type, call_id)
async with agent.join(call):
await agent.simple_response(text="Say hi and introduce yourself.")
await agent.finish()
runner = Runner(AgentLauncher(create_agent=create_agent, join_call=join_call))
if __name__ == "__main__":
runner.cli()
```
* **`create_agent`**: builds the agent with Stream transport and Gemini Realtime.
* **`join_call`**: creates a call, joins it, and triggers the first response.
* **`runner`**: entry point for the CLI; `pyproject.toml` references it as `agent:runner`.
Start the agent. The CLI prints a join link. Open it to talk to your agent in the browser.
```bash theme={null}
uv run agent.py run
```
The agent greets you as soon as you join the call. The join link is a browser demo for testing. To embed the agent in your own app, use [Stream's Video SDKs](https://getstream.io/video/sdk/) on your target platform; your client and the agent join the same call.
**Core Reference** in the sidebar covers Agent, LLM, and processor APIs in
depth. Skip it until your first agent runs, then come back when you need API
details.
## Next steps
Custom STT/LLM/TTS pipelines, function calling, provider options
VLMs, YOLO processors, real-time video analysis
Docker, Kubernetes, and monitoring
35+ AI providers to mix and match