langgraph-checkpoint-datastore

Implementation of a LangGraph CheckpointSaver that uses a GCP's Datastore

Inspiration

Based on: https://github.com/justinram11/langgraph-checkpoint-dynamodb

Required Datastore Tables

To be able to use this checkpointer, two Datastore kinds are needed, one to store checkpoints and the other to store writes.

Using the Checkpoint Saver

Default

To use the datastore checkpoint saver, you only need to specify the names of the checkpoints and writes kinds. In this scenario the datastore client will be instantiated with the default configuration, great for running on AWS Lambda.

from langgraph_checkpoint_datastore import DatastoreSaver
...
checkpoints_table_name = 'YourCheckpointsTableName'
writes_table_name = 'YourWritesTableName'

memory = DatastoreSaver(
    checkpoints_table_name=checkpoints_table_name,
    writes_table_name=writes_table_name,
)

graph = workflow.compile(checkpointer=memory)

Providing Client Configuration

If you need to provide custom configuration to the datastore client, you can pass in an object with the configuration options. Below is an example of how you can provide custom configuration.

memory = DatastoreSaver(
    checkpoints_table_name=checkpoints_table_name,
    writes_table_name=writes_table_name,
    client_config={
        'region': 'us-west-2',
    }
)

Important note, you will need to add an index to your kinds:

- kind: IntegrationCheckpoint
  properties:
  - name: thread_id
  - name: checkpoint_id
    direction: desc```