langchain-memorysync

MemorySync integration for LangChain: persistent chat message history, structured memory tools for agents, and prompt-ready recall context. Works with langchain-core 0.3.x and 1.x.

pip install langchain-memorysync

Set MEMORYSYNC_API_KEY (create a key at memorysync.io, or run npx memorysync-cli init).

Chat message history

MemorySyncChatMessageHistory implements BaseChatMessageHistory, so it plugs straight into RunnableWithMessageHistory:

from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_memorysync import MemorySyncChatMessageHistory

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: MemorySyncChatMessageHistory(session_id=session_id),
    input_messages_key="input",
    history_messages_key="history",
)

chain_with_history.invoke(
    {"input": "My name is Ada."},
    config={"configurable": {"session_id": "thread-42"}},
)

Turns are stored verbatim through MemorySync's episodic ingestion — no extraction gates, so short turns like "yes" survive — and the full LangChain message (tool calls, additional kwargs) is serialised into metadata for exact reconstruction. Writes are idempotent: a retried write is recognised, never duplicated.

history = MemorySyncChatMessageHistory(
    session_id="thread-42",
    user_id="customer-7",   # share memory across a user's sessions
    max_messages=30,        # cap the transcript tail handed to the LLM
)

Agent tools

from langchain.agents import create_agent
from langchain_memorysync import create_memorysync_tools

tools = create_memorysync_tools(end_user_id="customer-7")
agent = create_agent(model, tools=tools)

Returns add_memory, search_memory, list_memories, update_memory, and delete_memory. Tools return readable strings and never raise, so a memory failure cannot abort an agent run. Pass read_only=True to hand an agent only search_memory and list_memories.

add_memory derives an idempotency key from the content — an agent that repeats itself gets "already stored", not a duplicate.

Recall context

from langchain_memorysync import MemorySyncContextProvider

provider = MemorySyncContextProvider(user_id="customer-7")
context = provider.get_context("What should I cook tonight?")

Returns a grouped, type-labelled context block built by MemorySync's hierarchical retrieval, ready to inject into a system prompt. Empty string — not an exception — when the user has no relevant memories yet.

Documentation

Full guide: docs.memorysync.io/integrations/langchain