LangChain Integration

LangChain Integration

Use SuperLocalMemory as a local-first chat message history backend for LangChain applications. All data stays on your machine — zero cloud calls, zero API keys for the memory layer.

Installation

Prerequisites: SuperLocalMemory installed (npm install -g superlocalmemory or ./install.sh)

pip install langchain-superlocalmemory

Quick Start

from langchain_superlocalmemory import SuperLocalMemoryChatMessageHistory
from langchain_core.messages import HumanMessage, AIMessage

# Create a session
history = SuperLocalMemoryChatMessageHistory(session_id="my-project")

# Add messages
history.add_messages([
    HumanMessage(content="I'm building a React app with TypeScript"),
    AIMessage(content="Great choice! Do you want to use Next.js or Vite?"),
])

# Messages persist across sessions
messages = history.messages  # Returns all messages for this session

Usage with RunnableWithMessageHistory

from langchain_core.chat_history import InMemoryChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_superlocalmemory import SuperLocalMemoryChatMessageHistory

# Store factory — returns persistent local memory per session
store = {}

def get_session_history(session_id: str):
    if session_id not in store:
        store[session_id] = SuperLocalMemoryChatMessageHistory(session_id=session_id)
    return store[session_id]

# Wrap your chain with persistent memory
conversation = RunnableWithMessageHistory(
    your_chain,
    get_session_history,
)

# Each session_id gets isolated, persistent memory
response = conversation.invoke(
    "What framework am I using?",
    config={"configurable": {"session_id": "my-project"}},
)

Multi-Session Support

# Work project
work = SuperLocalMemoryChatMessageHistory(session_id="work-api-redesign")

# Personal project
personal = SuperLocalMemoryChatMessageHistory(session_id="personal-blog")

# Sessions are fully isolated — no cross-contamination

API Reference

SuperLocalMemoryChatMessageHistory

Parameter	Type	Default	Description
session_id	str	Required	Unique identifier for this conversation session
db_path	Optional[str]	~/.claude-memory/memory.db	Path to SQLite database

Methods:

Method	Returns	Description
.messages	List[BaseMessage]	All messages for this session, ordered chronologically
.add_messages(messages)	None	Add one or more messages to the session
.clear()	None	Remove all messages for this session

How It Works

• Each message is stored as a memory entry in SLM's SQLite database
• Messages are tagged with langchain:session:{session_id} for isolation
• Content is serialized via LangChain's message_to_dict() for perfect round-trip fidelity
• All message types supported: HumanMessage, AIMessage, SystemMessage, FunctionMessage, ToolMessage
• Importance is set to 3 (lower than user memories) so chat history doesn't crowd search results

Links

• GitHub Repository
• Full Documentation
• LlamaIndex Integration
• Example Script