## Usage

### Python

```python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Used for embedding model
os.environ["GOOGLE_API_KEY"] = "your-gemini-api-key"

config = {
    "llm": {
        "provider": "gemini",
        "config": {
            "model": "gemini-2.0-flash-001",
            "api_key": "your-gemini-api-key",
            "temperature": 0.2,
            "max_tokens": 2000,
            "top_p": 1.0
        }
    }
}

m = Memory.from_config(config)

messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
]

m.add(messages, user_id="alice", metadata={"category": "movies"})
```

### TypeScript

```typescript
import { Memory } from "mem0ai/oss";

const config = {
    llm: {
        // You can also use "google" as provider ( for backward compatibility )
        provider: "gemini",
        config: {
            model: "gemini-2.0-flash-001",
            apiKey: process.env.GOOGLE_API_KEY || '',
            temperature: 0.1
        }
    }
}

const memory = new Memory(config);

const messages = [
    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
    { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
    { role: "user", content: "I’m not a big fan of thrillers, but I love sci-fi movies." },
    { role: "assistant", content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead." }
]

await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```

## Config

All available parameters for the `Gemini` config are present in [Master List of All Params in Config](https://docs.mem0.ai/components/llms/config).