> ## Documentation Index
> Fetch the complete documentation index at: https://visionagents.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Gemini LLM
[Google's Gemini](https://ai.google.dev/gemini-api/docs/live) provides powerful language models with built-in tools for search, code execution, RAG, and URL context. The LLM mode requires separate STT/TTS.
Vision Agents requires a [Stream](https://getstream.io/try-for-free/) account
for real-time transport. Most providers offer free tiers to get started.
Gemini also provides [Realtime speech-to-speech](/integrations/realtime/gemini) with optional video over WebSocket.
## Installation
```sh theme={null}
uv add "vision-agents[gemini]"
```
## Quick Start
```python theme={null}
from vision_agents.core import Agent, User
from vision_agents.plugins import gemini, getstream, deepgram, elevenlabs
agent = Agent(
edge=getstream.Edge(),
agent_user=User(name="Assistant", id="agent"),
instructions="You are a helpful assistant.",
llm=gemini.LLM("gemini-3-flash-preview"),
stt=deepgram.STT(),
tts=elevenlabs.TTS(),
)
```
## Built-in Tools
Gemini provides built-in tools you can enable:
```python theme={null}
llm = gemini.LLM(
model="gemini-3-flash-preview",
tools=[
gemini.tools.GoogleSearch(),
gemini.tools.CodeExecution(),
gemini.tools.FileSearch(store), # RAG
gemini.tools.URLContext(),
]
)
```
| Tool | Description |
| --------------- | ------------------------------ |
| `GoogleSearch` | Ground responses with web data |
| `CodeExecution` | Run Python code |
| `FileSearch` | RAG over your documents |
| `URLContext` | Read specific web pages |
## File Search (RAG)
Managed RAG with automatic chunking and retrieval:
```python theme={null}
from vision_agents.plugins import gemini
store = gemini.GeminiFilesearchRAG(name="my-knowledge-base")
await store.create()
await store.add_directory("./knowledge")
llm = gemini.LLM(
model="gemini-3-flash-preview",
tools=[gemini.tools.FileSearch(store)]
)
```
See the [RAG guide](/guides/rag) for more details.
## Function Calling
```python theme={null}
@agent.llm.register_function(description="Get weather for a location")
async def get_weather(location: str) -> dict:
return {"temperature": "22°C", "condition": "Sunny"}
```
See the [Function Calling guide](/guides/mcp-tool-calling) for details.
## Events
The Gemini plugin emits events for connection state and responses. Most developers should use the core events ([LLMResponseCompletedEvent](/reference/events-reference#llmresponsecompletedevent), etc.) for provider-agnostic code.
```python theme={null}
from vision_agents.plugins.gemini.events import (
GeminiConnectedEvent,
GeminiErrorEvent,
)
@agent.events.subscribe
async def on_gemini_connected(event: GeminiConnectedEvent):
print(f"Connected to Gemini model: {event.model}")
@agent.events.subscribe
async def on_gemini_error(event: GeminiErrorEvent):
print(f"Gemini error: {event.error}")
```
| Event | Description |
| ---------------------- | ------------------------------- |
| `GeminiConnectedEvent` | Realtime connection established |
| `GeminiErrorEvent` | Error occurred |
| `GeminiAudioEvent` | Audio output received |
| `GeminiTextEvent` | Text output received |
| `GeminiResponseEvent` | Response chunk received |
## Next Steps
Speech-to-speech with optional video
Get started with voice