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

# Overview

> MemWire is model-agnostic. Use recalled memory context with any LLM provider.

MemWire never calls an LLM internally. Memory storage, recall, and search are pure vector operations. You choose the LLM and MemWire supplies the context to inject into it.

```python theme={null}
result = memory.recall("How should I format my answers?", user_id="alice")

# Inject into any LLM prompt
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
]
if result.formatted:
    messages.append({"role": "system", "content": f"Memory context:\n{result.formatted}"})
messages.append({"role": "user", "content": "How should I format my answers?"})
```

## Supported providers

<CardGroup cols={2}>
  <Card title="OpenAI" href="/llms/openai">
    GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo
  </Card>

  <Card title="Azure OpenAI" href="/llms/azure-openai">
    GPT-4o, GPT-4 Turbo deployed on Azure
  </Card>

  <Card title="Anthropic">
    Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku
  </Card>

  <Card title="Ollama">
    Llama 3, Mistral, Gemma and any locally hosted model
  </Card>

  <Card title="Google Gemini">
    Gemini 2.0 Flash, Gemini 1.5 Pro
  </Card>

  <Card title="Any OpenAI-compatible API">
    Together AI, Groq, Mistral, DeepSeek
  </Card>
</CardGroup>

***