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

# LLMs & Model Routing

> Configure inference engines for fact extraction, summarization, entity resolution, and context generation.

| Provider | Supported Models | Recommended For |
| :- | :- | :- |
| OpenAI | gpt-4o , gpt-4o-mini , o3-mini | Fast, accurate atomic fact extraction. |
| Anthropic | claude-3-7-sonnet , claude-3-5-haiku | High-fidelity reasoning and complex entity resolution. |
| Google Gemini | gemini-2.0-flash , gemini-1.5-pro | High-speed inference and multimodal context analysis. |
| DeepSeek | deepseek-v3 , deepseek-r1 | Cost-effective open-weights inference. |
| Ollama (Local) | llama3.3 , mistral-small , qwen2.5 | Air-gapped, zero-cost private deployments. |

## Supported Providers

## Local Ollama Configuration

Run 100% private, on-premise memory pipelines without sending tokens to third-party APIs:

```python python_ollama.py theme={null}
from negentro import Memory

config = {
    "llm": {
        "provider": "ollama",
        "config": {
            "model": "llama3.3:latest",
            "base_url": "http://localhost:11434"
        }
    }
}

m = Memory.from_config(config)
```


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