> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LM Studio

> Configure LM Studio as an LLM provider in Mem0 for running local language models via an OpenAI-compatible API.

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

<CodeGroup>
  ```python Python theme={null}
  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"})
  ```
</CodeGroup>

### Running Completely Locally

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

```python theme={null}
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"})
```

<Note>
  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
</Note>

<Note>
  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))
</Note>

## Config

All available parameters for the `lmstudio` config are present in [Master List of All Params in Config](../config).