LangChain

pip install langchain-mengram

from langchain_mengram import MengramRetriever

retriever = MengramRetriever(
    api_key="om-your-key",
    user_id="alice",
    top_k=5,
    memory_types=["semantic", "episodic", "procedural"],
)

# Use as any LangChain retriever
docs = retriever.invoke("deployment issues")
for doc in docs:
    print(doc.page_content)
    print(doc.metadata)  # {"source": "mengram", "memory_type": "semantic", ...}

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

retriever = MengramRetriever(api_key="om-...")

prompt = ChatPromptTemplate.from_template(
    "Context from memory:\n{context}\n\nQuestion: {question}"
)

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | ChatOpenAI(model="gpt-4o")
    | StrOutputParser()
)

answer = chain.invoke("What deployment stack am I using?")

from langchain_mengram import get_mengram_profile

# Get a system prompt string
prompt = get_mengram_profile(api_key="om-...", user_id="alice")