langchain-openmemory 1.0.0

One-line OpenMemory integration for LangChain with persistent, temporal, local-first memory.

langchain-openmemory

One-line, persistent, temporal memory for LangChain — powered by OpenMemory.

from langchain_openmemory import Memory

m = Memory()  # zero friction, no user id needed!

That’s it. Memory works as:

• a retriever
• a chat history backend
• a LCEL Runnable that injects rich context
• a persistent long-term memory across sessions

All backed by OpenMemory: local-first, temporal, explainable memory for AI agents.

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Features

• 🧠 One-line API — Memory() is all you need
• 🪢 LangChain-native — works as a Runnable, retriever, and chat history
• 🕒 Temporal memory — recall state across time, not just similar text
• 📚 Multi-chat context — memory persists over many conversations
• 💾 Local-first — backed by OpenMemory’s SQLite / engine, no vector DB required
• 🔍 Explainable (via OpenMemory metadata) — you can inspect what was recalled and why

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Installation

pip install openmemory-py langchain-core langchain-openmemory

Requires Python 3.9+.

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Quickstart

1. Create memory

from langchain_openmemory import Memory

memory = Memory()  # optional: Memory("user123")

2. Use with an LLM via LCEL

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

from langchain_openmemory import Memory

memory = Memory()

prompt = ChatPromptTemplate.from_template(
    "You are a helpful assistant."
    "Here is what you remember: {context}"
    "User: {question}"
)

llm = ChatOpenAI()

chain = (
    {"context": memory, "question": RunnablePassthrough()}
    | prompt
    | llm
)

print(chain.invoke("Remember that I like dark themes and short answers."))
print(chain.invoke("What did I say about themes?"))

3. Manual recall

print(memory("what does the user prefer?"))

4. Store extra facts

memory.store("user123 loves Minecraft and Pterodactyl panels.")

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How it works

Internally, Memory:

1. Uses the Python openmemory client in local mode by default.
2. Stores chat messages and facts into OpenMemory.
3. Retrieves relevant memories with temporal + sector-aware ranking.
4. Exposes a LangChain-compatible Runnable that returns a context block.
5. Provides an internal retriever and chat history implementation.

You get:

• real long-term memory
• across many sessions
• with minimal boilerplate

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Using as a retriever

from langchain_openai import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain_openmemory import Memory

memory = Memory()
retriever = memory.retriever

llm = ChatOpenAI()

qa = ConversationalRetrievalChain.from_llm(
    llm,
    retriever=retriever,
    return_source_documents=True,
)

res = qa.invoke({"question": "What does this user like?"})
print(res["answer"])

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Using as chat history

from langchain_core.runnables import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_openmemory import Memory

memory = Memory()

prompt = ChatPromptTemplate.from_messages(
    [("system", "You are a helpful assistant."),
     ("human", "{input}")]
)
llm = ChatOpenAI()
base_chain = prompt | llm

def get_history(session_id: str):
    return memory.history

chain = RunnableWithMessageHistory(
    base_chain,
    get_history,
    input_messages_key="input",
    history_messages_key="history",
)

print(chain.invoke({"input": "Remember that I live in Hyderabad."}, config={"configurable": {"session_id": "s1"}}))
print(chain.invoke({"input": "Where do I live?"}, config={"configurable": {"session_id": "s1"}}))

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Examples

See the examples/ folder for:

• chatbot.py — simple chatbot with persistent memory
• agent.py — agent-style usage
• retrieval.py — manual recall demo

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Roadmap

• Better temporal filters
• First-class LangChain docs integration
• Benchmarks vs vector DB + Redis memory

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License

MIT — see LICENSE.