Azure OpenAI

Configure Azure OpenAI as an LLM provider in Mem0 with Azure Identity authentication and deployment settings.

Mem0 Now Supports Azure OpenAI Models in TypeScript SDK

To use Azure OpenAI models, you have to set the LLM_AZURE_OPENAI_API_KEY, LLM_AZURE_ENDPOINT, LLM_AZURE_DEPLOYMENT and LLM_AZURE_API_VERSION environment variables. You can obtain the Azure API key from the Azure Portal.

Optionally, you can use Azure Identity to authenticate with Azure OpenAI, which allows you to use managed identities or service principals for production and Azure CLI login for development instead of an API key. If an Azure Identity is to be used, do not set the LLM_AZURE_OPENAI_API_KEY environment variable or the api_key in the config dictionary.

Note: The following are currently unsupported with reasoning models Parallel tool calling,temperature, top_p, presence_penalty, frequency_penalty, logprobs, top_logprobs, logit_bias, max_tokens

Usage

Python

import os
from mem0 import Memory

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

os.environ["LLM_AZURE_OPENAI_API_KEY"] = "your-api-key"
os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"

config = {
    "llm": {
        "provider": "azure_openai",
        "config": {
            "model": "your-deployment-name",
            "temperature": 0.1,
            "max_tokens": 2000,
            "azure_kwargs": {
                  "azure_deployment": "",
                  "api_version": "",
                  "azure_endpoint": "",
                  "api_key": "",
                  "default_headers": {
                    "CustomHeader": "your-custom-header",
                  }
              }
        }
    }
}

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"})

import { Memory } from 'mem0ai/oss';

const config = {
  llm: {
    provider: 'azure_openai',
    config: {
      apiKey: process.env.AZURE_OPENAI_API_KEY || '',
      modelProperties: {
        endpoint: 'https://your-api-base-url',
        deployment: 'your-deployment-name',
        modelName: 'your-model-name',
        apiVersion: 'version-to-use',
        // Any other parameters you want to pass to the Azure OpenAI API
      },
    },
  },
};

const memory = new Memory(config);
const 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."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

We also support the new OpenAI structured-outputs model. Typescript SDK does not support the azure_openai_structured model yet.

import os
from mem0 import Memory

os.environ["LLM_AZURE_OPENAI_API_KEY"] = "your-api-key"
os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"

config = {
    "llm": {
        "provider": "azure_openai_structured",
        "config": {
            "model": "your-deployment-name",
            "temperature": 0.1,
            "max_tokens": 2000,
            "azure_kwargs": {
                  "azure_deployment": "",
                  "api_version": "",
                  "azure_endpoint": "",
                  "api_key": "",
                  "default_headers": {
                    "CustomHeader": "your-custom-header",
                  }
              }
        }
    }
}

As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with Azure OpenAI role-based security.

If an API key is provided, it will be used for authentication over an Azure Identity

Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:

import os
from mem0 import Memory
# You can set the values directly in the config dictionary or use environment variables

os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"

config = {
    "llm": {
        "provider": "azure_openai_structured",
        "config": {
            "model": "your-deployment-name",
            "temperature": 0.1,
            "max_tokens": 2000,
            "azure_kwargs": {
                  "azure_deployment": "<your-deployment-name>",
                  "api_version": "<version-to-use>",
                  "azure_endpoint": "<your-api-base-url>",
                  "default_headers": {
                    "CustomHeader": "your-custom-header",
                  }
              }
        }
    }
}

Refer to Azure Identity troubleshooting tips for setting up an Azure Identity credential.

Config

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