Which database should I use for production checkpointing?

PostgresSaver (PostgreSQL) is recommended for production environments requiring high availability, durability, and shared access across multiple application instances.

Is this skill compatible with asynchronous Python code?

Yes, it provides patterns for both synchronous and asynchronous operations, including support for AsyncSqliteSaver and async PostgreSQL drivers.

Can I resume a workflow after it crashes?

Yes, by using a persistent checkpointer and a consistent thread ID, you can use the invoke(None, config) pattern to resume the workflow from the last successful checkpoint.

What is the difference between a Checkpointer and a Store?

A Checkpointer handles short-term, thread-specific state like a single conversation history, while a Store manages long-term, cross-thread memory such as user preferences or learned facts.

How do I implement a manual review step in my workflow?

You can use the interrupt_before or interrupt_after parameters when compiling your LangGraph workflow to pause execution at specific nodes for human intervention.

LangGraph Checkpointing & Persistence

Name: LangGraph Checkpointing & Persistence
Author: yonatangross

byyonatangross

•

データサイエンスとML

Implements fault-tolerant AI workflows with robust state persistence and recovery for LangGraph applications.

概要

This skill provides production-ready patterns for managing state and persistence in LangGraph workflows, enabling fault tolerance and seamless execution recovery. It covers a wide range of backend options including in-memory storage for development and SQLite or PostgreSQL for production environments. By implementing deterministic thread management and distinguishing between short-term thread memory (Checkpointers) and long-term cross-thread memory (Stores), this skill helps developers build resilient AI agents that can resume after crashes, handle human-in-the-loop interruptions, and maintain user context across multiple sessions.

主な機能

Deterministic thread ID management for workflow resumption and recovery
29 GitHub stars
Production-grade configuration for human-in-the-loop interruption gates
Support for Memory, SQLite, and PostgreSQL persistence backends
Implementation of cross-thread long-term memory using the Store pattern
State history inspection for debugging and manual state rollbacks

ユースケース

Building production agents that must persist state across system restarts or crashes
Creating personalized AI assistants that remember user preferences across different conversation threads
Implementing human-in-the-loop approval gates where workflows pause for manual review

概要

主な機能

Deterministic thread ID management for workflow resumption and recovery
29 GitHub stars
Production-grade configuration for human-in-the-loop interruption gates
Support for Memory, SQLite, and PostgreSQL persistence backends
Implementation of cross-thread long-term memory using the Store pattern
State history inspection for debugging and manual state rollbacks

ユースケース

Building production agents that must persist state across system restarts or crashes
Creating personalized AI assistants that remember user preferences across different conversation threads
Implementing human-in-the-loop approval gates where workflows pause for manual review