OpenAI Compatibility

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.

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.

Configure it

Call the managed Mem0 proxy

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"
)

Use the OpenAI-compatible OSS client

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

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)

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

Other request fields mirror OpenAI’s chat completion API.