## Documentation Index

Fetch the complete documentation index at: [/llms.txt](https://docs.mem0.ai/llms.txt)

Use this file to discover all available pages before exploring further.

Mem0 mirrors the OpenAI client interface so you can plug memories into existing chat-completion code with minimal changes. Point your OpenAI-compatible client at Mem0, keep the same request shape, and gain persistent memory between calls.

**You’ll use this when…**

- Your app already relies on OpenAI chat completions and you want Mem0 to feel familiar.
- You need to reuse existing middleware that expects OpenAI-compatible responses.
- You plan to switch between Mem0 Platform and the self-hosted client without rewriting code.

## Feature

- **Drop-in client:** `client.chat.completions.create(...)` works the same as OpenAI’s method signatures.
- **Shared parameters:** Mem0 accepts `messages`, `model`, and optional memory-scoping fields (`user_id`, `agent_id`, `run_id`).
- **Memory-aware responses:** Each call saves relevant facts so future prompts automatically reflect past conversations.
- **OSS parity:** Use the same API surface whether you call the hosted proxy or the OSS configuration.

Run one request with `user_id` set. If the next call references that ID and its reply uses the stored memory, compatibility is confirmed.

## Configure it

### Call the managed Mem0 proxy

```python
from mem0.proxy.main import Mem0

client = Mem0(api_key="m0-xxx")

messages = [\
    {"role": "user", "content": "I love Indian food but I cannot eat pizza since I'm allergic to cheese."}\
]

chat_completion = client.chat.completions.create(
    messages=messages,
    model="gpt-5-mini",
    user_id="alice"
)
```

Reuse the same identifiers your OpenAI client already sends so you can switch between providers without branching logic.

### Use the OpenAI-compatible OSS client

```python
from mem0.proxy.main import Mem0

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "host": "localhost",
            "port": 6333
        }
    }
}

client = Mem0(config=config)

chat_completion = client.chat.completions.create(
    messages=[{"role": "user", "content": "What's the capital of France?"}],
    model="gpt-5-mini"
)
```

## See it in action

### Memory-aware restaurant recommendation

```python
from mem0.proxy.main import Mem0

client = Mem0(api_key="m0-xxx")

# Store preferences
client.chat.completions.create(
    messages=[{"role": "user", "content": "I love Indian food but I'm allergic to cheese."}],
    model="gpt-5-mini",
    user_id="alice"
)

# Later conversation reuses the memory
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Suggest dinner options in San Francisco."}],
    model="gpt-5-mini",
    user_id="alice"
)

print(response.choices[0].message.content)
```

The second response should call out Indian restaurants and avoid cheese, proving Mem0 recalled the stored preference.

## Verify the feature is working

- Compare responses from Mem0 vs. OpenAI for identical prompts. Both should return the same structure (`choices`, `usage`, etc.).
- Inspect stored memories after each request to confirm the fact extraction captured the right details.
- Test switching between hosted (`Mem0(api_key=...)`) and OSS configurations to ensure both respect the same request body.

## Best practices

1. **Scope context intentionally:** Pass identifiers only when you want conversations to persist; skip them for one-off calls.
2. **Log memory usage:** Inspect `response.metadata.memories` (if enabled) to see which facts the model recalled.
3. **Reuse middleware:** Point your existing OpenAI client wrappers to the Mem0 proxy URL to avoid code drift.
4. **Handle fallbacks:** Keep a code path for plain OpenAI calls in case Mem0 is unavailable, then resync memory later.

## Parameter reference

| Parameter   | Type | Purpose                                                        |
| ----------- | ---- | -------------------------------------------------------------- |
| `user_id`   | `str`| Associates the conversation with a user so memories persist.   |
| `agent_id`  | `str`| Optional agent or bot identifier for multi-agent scenarios.    |
| `run_id`    | `str`| Optional session/run identifier for short-lived flows.         |
| `metadata`  | `dict`| Store extra fields alongside each memory entry.                |
| `filters`    | `dict`| Restrict retrieval to specific memories while responding.      |
| `top_k`     | `int`| Cap how many memories Mem0 pulls into the context (default 10).|

Other request fields mirror OpenAI’s chat completion API.