langchain-mengram

LangChain integration for Mengram — AI memory with semantic, episodic, and procedural memory types.

Installation

pip install langchain-mengram

Quick start

from langchain_mengram import MengramRetriever

retriever = MengramRetriever(
    api_key="om-...",
    user_id="user-123",
)

docs = retriever.invoke("deployment issues")
for doc in docs:
    print(doc.metadata["memory_type"], doc.page_content)

What it does

MengramRetriever searches across all three Mengram memory types and returns LangChain Document objects:

• Semantic — facts, entities, and knowledge graph relationships
• Episodic — events, experiences, and their outcomes
• Procedural — workflows, step-by-step procedures, and learned routines

Each document includes metadata["memory_type"] so you can filter or prioritize by type.

Use in a chain

from langchain_mengram import MengramRetriever
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

retriever = MengramRetriever(api_key="om-...", user_id="user-123")
llm = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_messages([
    ("system", "Use the following memory context to answer:\n\n{context}"),
    ("human", "{question}"),
])

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke("What deployment steps did we follow last time?")

Parameters

Links

• Mengram docs
• GitHub
• Get API key