Configurations

Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules.

How to define configurations?

Python

The config is defined as a Python dictionary with two main keys:

• llm: Specifies the llm provider and its configuration
  • provider: The name of the llm (e.g., “openai”, “groq”)
  • config: A nested dictionary containing provider-specific settings

TypeScript

The config is defined as a TypeScript object with these keys:

• llm: Specifies the LLM provider and its configuration (required)
  • provider: The name of the LLM (e.g., “openai”, “groq”)
  • config: A nested object containing provider-specific settings
• embedder: Specifies the embedder provider and its configuration (optional)
• vectorStore: Specifies the vector store provider and its configuration (optional)
• historyDbPath: Path to the history database file (optional)

Config Values Precedence

Config values are applied in the following order of precedence (from highest to lowest):

1. Values explicitly set in the config object/dictionary
2. Environment variables (e.g., OPENAI_API_KEY, OPENAI_BASE_URL)
3. Default values defined in the LLM implementation

This means that values specified in the config will override corresponding environment variables, which in turn override default values.

How to Use Config

Here’s a general example of how to use the config with Mem0:

Python

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx" # for embedder

config = {
    "llm": {
        "provider": "your_chosen_provider",
        "config": {
            # Provider-specific settings go here
        }
    }
}

m = Memory.from_config(config)
m.add("Your text here", user_id="user", metadata={"category": "example"})

import { Memory } from 'mem0ai/oss';

// Minimal configuration with just the LLM settings
const config = {
  llm: {
    provider: 'your_chosen_provider',
    config: {
      // Provider-specific settings go here
    }
  }
};

const memory = new Memory(config);
await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });

Why is Config Needed?

Config is essential for:

1. Specifying which LLM to use.
2. Providing necessary connection details (e.g., model, api_key, temperature).
3. Ensuring proper initialization and connection to your chosen LLM.

Master List of All Params in Config

Here’s a comprehensive list of all parameters that can be used across different LLMs:

Python

Parameter	Description	Provider
model	Embedding model to use	All
temperature	Temperature of the model	All
api_key	API key to use	All
max_tokens	Tokens to generate	All
top_p	Probability threshold for nucleus sampling	All
top_k	Number of highest probability tokens to keep	All
http_client_proxies	Allow proxy server settings	All
models	List of models	Openrouter
route	Routing strategy	Openrouter
openrouter_base_url	Base URL for Openrouter API	Openrouter
site_url	Site URL	Openrouter
app_name	Application name	Openrouter
ollama_base_url	Base URL for Ollama API	Ollama
openai_base_url	Base URL for OpenAI API	OpenAI
azure_kwargs	Azure LLM args for initialization	AzureOpenAI
deepseek_base_url	Base URL for DeepSeek API	DeepSeek
xai_base_url	Base URL for XAI API	XAI
sarvam_base_url	Base URL for Sarvam API	Sarvam
reasoning_effort	Reasoning level (low, medium, high)	All
frequency_penalty	Penalize frequent tokens (-2.0 to 2.0)	Sarvam
presence_penalty	Penalize existing tokens (-2.0 to 2.0)	Sarvam
seed	Seed for deterministic sampling	Sarvam
stop	Stop sequences (max 4)	Sarvam
lmstudio_base_url	Base URL for LM Studio API	LM Studio
response_callback	LLM response callback function	OpenAI

TypeScript

Parameter	Description	Provider
model	Embedding model to use	All
temperature	Temperature of the model	All
apiKey	API key to use	All
maxTokens	Tokens to generate	All
topP	Probability threshold for nucleus sampling	All
topK	Number of highest probability tokens to keep	All
openaiBaseUrl	Base URL for OpenAI API	OpenAI

Supported LLMs

For detailed information on configuring specific LLMs, please visit the LLMs section. There you’ll find information for each supported LLM with provider-specific usage examples and configuration details.