## 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.

To use LM Studio with Mem0, you’ll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.

## Usage

Python

```
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model

config = {
    "llm": {
        "provider": "lmstudio",
        "config": {
            "model": "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf",
            "temperature": 0.2,
            "max_tokens": 2000,
            "lmstudio_base_url": "http://localhost:1234/v1", # default LM Studio API URL
            "lmstudio_response_format": {"type": "json_schema", "json_schema": {"type": "object", "schema": {}}},
        }
    }
}

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 thriller movies but I love sci-fi movies."},\
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}\
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```

### Running Completely Locally

You can also use LM Studio for both LLM and embedding to run Mem0 entirely locally:

```
from mem0 import Memory

# No external API keys needed!
config = {
    "llm": {
        "provider": "lmstudio"
    },
    "embedder": {
        "provider": "lmstudio"
    }
}

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 thriller movies but I love sci-fi movies."},\
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}\
]
m.add(messages, user_id="alice123", metadata={"category": "movies"})
```

When using LM Studio for both LLM and embedding, make sure you have:

1. An LLM model loaded for generating responses
2. An embedding model loaded for vector embeddings
3. The server enabled with the correct endpoints accessible

To use LM Studio, you need to:

1. Download and install [LM Studio](https://lmstudio.ai/)
2. Start a local server from the “Server” tab
3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually [http://localhost:1234/v1](http://localhost:1234/v1))

## Config

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