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.

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Usage

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 a 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"})

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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 a 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
  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)

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Config

All available parameters for the lmstudio config are present in Master List of All Params in Config.