chat_history

Azure AI Foundry Memory integration with LangChain.

Azure CosmosDB Memory History — DEPRECATED.

This module has moved to langchain_azure_cosmosdb. Install and import directly from there instead::

pip install langchain-azure-cosmosdb
from langchain_azure_cosmosdb import CosmosDBChatMessageHistory

History wrapper that updates Azure AI Foundry Memory per chat turn.

This class decorates a LangChain BaseChatMessageHistory, preserving short-term transcript storage while forwarding each turn to Foundry Memory for long-term extraction and consolidation.

Setup:

We will need to set the required environment variables:

export AZURE_AI_PROJECT_ENDPOINT="<YOUR_PROJECT_ENDPOINT>"
export MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME="<YOUR_CHAT_MODEL_DEPLOYMENT>"
export MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME="<YOUR_EMBEDDING_DEPLOYMENT>"

Before using this class, create the memory store explicitly in your Azure AI Foundry project. The store is not created automatically by this integration.

import os

from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    MemoryStoreDefaultDefinition,
    MemoryStoreDefaultOptions,
)
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential

client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

store_name = "my_store"
try:
    client.beta.memory_stores.get(store_name)
except ResourceNotFoundError:
    client.beta.memory_stores.create(
        name=store_name,
        description="Long-term memory store",
        definition=MemoryStoreDefaultDefinition(
            chat_model=os.environ[
                "MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME"
            ],
            embedding_model=os.environ[
                "MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME"
            ],
            options=MemoryStoreDefaultOptions(
                user_profile_enabled=True,
                chat_summary_enabled=True,
            ),
        ),
    )

Examples

With explicit endpoint and credential:

from azure.identity import DefaultAzureCredential
from langchain_core.chat_history import InMemoryChatMessageHistory

history = AzureAIMemoryChatMessageHistory(
    project_endpoint="https://myproject.api.azureml.ms",
    credential=DefaultAzureCredential(),
    store_name="my_store",
    scope="user:123",
    base_history=InMemoryChatMessageHistory(),
)

With endpoint from environment variable:

history = AzureAIMemoryChatMessageHistory(
    store_name="my_store",
    scope="user:123",
    base_history=InMemoryChatMessageHistory(),
)