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LangGraph Checkpoint Aerospike

Store LangGraph checkpoints in Aerospike using the provided AerospikeSaver.

Installation

pip install -U langgraph-checkpoint-aerospike

Usage

  1. Bring up Aerospike locally using prebuilt Aerospike Docker Image:
docker run -d --name aerospike -p 3000-3002:3000-3002 container.aerospike.com/aerospike/aerospike-server
  1. Point the saver at your cluster (Default):

    • AEROSPIKE_HOST=127.0.0.1
    • AEROSPIKE_PORT=3000
    • AEROSPIKE_NAMESPACE=test
  2. Build a LangGraph graph and compile it with the saver as the checkpointer. Passing the same thread_id on a later run resumes from the persisted state:

    from typing import Annotated, TypedDict
    
    import aerospike
    from langgraph.graph import START, END, StateGraph
    from langgraph.graph.message import add_messages
    from langchain_core.messages import HumanMessage
    
    from langgraph.checkpoint.aerospike import AerospikeSaver
    
    # 1. Connect to Aerospike and build the checkpointer.
    client = aerospike.client({"hosts": [("127.0.0.1", 3000)]}).connect()
    checkpointer = AerospikeSaver(client=client, namespace="test")
    
    # 2. Define a minimal LangGraph graph.
    class State(TypedDict):
        messages: Annotated[list, add_messages]
    
    def chatbot(state: State) -> State:
        last = state["messages"][-1].content
        return {"messages": [("assistant", f"You said: {last}")]}
    
    builder = StateGraph(State)
    builder.add_node("chatbot", chatbot)
    builder.add_edge(START, "chatbot")
    builder.add_edge("chatbot", END)
    
    # 3. Compile with the Aerospike checkpointer.
    graph = builder.compile(checkpointer=checkpointer)
    
    # 4. Each thread_id is a separate, persisted conversation.
    config = {"configurable": {"thread_id": "demo"}}
    graph.invoke({"messages": [HumanMessage("hello")]}, config)
    
    # 5. A later call with the same thread_id resumes from the saved state,
    #    even in a brand-new process — the history lives in Aerospike.
    result = graph.invoke({"messages": [HumanMessage("are you still there?")]}, config)
    for message in result["messages"]:
        message.pretty_print()
    

You can also inspect persisted checkpoints directly through the saver, e.g. checkpointer.get_tuple(config) for the latest checkpoint or checkpointer.list(config) to walk a thread's history.

Metadata

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