langchain-memorae

Connect an existing LangChain, LangGraph, or local chatbot to MeMorae's project-key API. MeMorae handles ingestion, approval, and retrieval; this package only calls the existing endpoints. The MeMorae backend must already be running.

MCP exposes MeMorae tools to MCP clients. This package provides Python memory and retriever objects for application code using LangChain or LangGraph.

Install

From the repository root:

pip install -e integrations/langchain-memorae

When published, install with pip install langchain-memorae.

Basic usage

from langchain_memorae import MeMoraeMemory, MeMoraeRetriever

memory = MeMoraeMemory(api_key="MEMORAE_PROJECT_API_KEY",
                       base_url="http://localhost:8000",
                       subject_id="user_123", namespace="default")
context = memory.load_memory_variables({"input": "What food do I like?"})
memory.save_context({"input": "I love pizza"}, {"output": "Got it."})

retriever = MeMoraeRetriever(api_key="MEMORAE_PROJECT_API_KEY",
                             base_url="http://localhost:8000",
                             subject_id="user_123", namespace="default")
docs = retriever.invoke("What sports do I enjoy?")

context["history"] is a compact string of retrieved memories. docs contains LangChain Document objects with memory_id and source_ids metadata. Ingestion is asynchronous on the server; a new message may take time to appear in search. Assistant output is saved only with save_assistant=True.

subject_id identifies the end user whose memories are recalled. namespace separates memory spaces for that user; use the same values for saving and searching. A project API key determines the project scope. Keep it on your server, load it from an environment variable, and never put it in browser code or logs.

LangGraph

In a graph, call memory.load_memory_variables({"input": question}) in a recall node before the answer node. Call memory.save_context({"input": question}, {"output": answer}) in a save node afterward. See langgraph_chatbot.py for a complete small graph.

Local chatbot

Wrap your existing answer function: load history, pass it to the function as context, then save the question and answer. See local_chatbot_flask.py for a Flask route and basic_chatbot.py for ChatOpenAI. Examples read MEMORAE_API_KEY, MEMORAE_BASE_URL, and MEMORAE_SUBJECT_ID from the environment. The ChatOpenAI examples also need OPENAI_API_KEY and their optional dependencies.

clear() is a no-op because clearing remote memories is a separate MeMorae review action.