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Aerospike is a high-performance, distributed NoSQL database designed for real-time applications at scale. It provides sub-millisecond latency, strong consistency, and automatic data distribution across clusters, making it well-suited for AI workloads that require fast, reliable state persistence.
Aerospike features a shared-nothing architecture with linear horizontal scaling, a Hybrid Memory Architecture (DRAM indexes with SSD/NVMe data storage), tunable strong or relaxed consistency per namespace, built-in TTL for automatic record expiration, active-active cross-datacenter replication, and multi-model support for key-value, document, graph, and vector data. The Aerospike LangGraph integrations provide a checkpointer for persisting graph execution state and a store for long-lived agent data such as user profiles, extracted entities, and cached tool outputs.

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Get started

The implementation is available in the aerospike-community/aerospike-langgraph repository. It includes a Docker-based Aerospike setup, reference implementations of AerospikeSaver and AerospikeStore, and basic tests that exercise checkpoint resume, TTL behavior, and namespace-scoped store access. To try it, start an Aerospike container, configure the saver and store in a LangGraph app, and run the included examples to observe execution recovery and agent state persistence.

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Installation

Install both packages: 
pip install -U langgraph-store-aerospike langgraph-checkpoint-aerospike

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Run Aerospike locally

Start Aerospike using the Aerospike Docker Image: 
docker run -d --name aerospike \
  -p 3000-3002:3000-3002 \
  container.aerospike.com/aerospike/aerospike-server

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Configuration

Both the store and checkpointer use the same Aerospike connection settings: 
export AEROSPIKE_HOST=127.0.0.1
export AEROSPIKE_PORT=3000

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LangGraph checkpointer

The Aerospike checkpointer persists LangGraph execution state and enables resume from any checkpoint. 
import aerospike
from langgraph.checkpoint.aerospike import AerospikeSaver

client = aerospike.client(
    {"hosts": [("127.0.0.1", 3000)]}
).connect()

saver = AerospikeSaver(
    client=client,
    namespace="langgraph",
)

compiled = graph.compile(checkpointer=saver)

compiled.invoke(
    {"input": "hello"},
    config={
        "configurable": {
            "thread_id": "demo-thread"
        }
    }
)

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LangGraph store

The Aerospike store is used for long-lived agent data such as user profiles, extracted entities, and cached tool outputs. 
import aerospike
from langgraph.store.aerospike import AerospikeStore
from langgraph.store.base import PutOp, GetOp, SearchOp

client = aerospike.client(
    {"hosts": [("127.0.0.1", 3000)]}
).connect()

store = AerospikeStore(
    client=client,
    namespace="langgraph",
    set="store",
)

# Write data
store.put(
    namespace=("users", "profiles"),
    key="user_123",
    value={"name": "Alice", "age": 30},
)

# Read data
item = store.get(
    namespace=("users", "profiles"),
    key="user_123",
)
print(item.value)

# Batch operations
results = store.batch([
    PutOp(namespace=("documents",), key="doc1", value={"status": "draft"}),
    GetOp(namespace=("documents",), key="doc1"),
])

# Search within a namespace
search_results = store.search(
    namespace_prefix=("documents",),
    filter={"status": {"$eq": "draft"}},
    limit=10,
)

# Delete data
store.delete(namespace=("users", "profiles"), key="user_123")

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Resources

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