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langgraph-patterns

Automate dynamic LangGraph workflows with routing, HITL, and checkpointing.

35|7|Updated Jun 12, 2025
Search Tags:#workflow#routing#checkpointing#langgraph#hitl#parallelism
One-click install
npx skills add https://github.com/Bbar0n234/learnflow-ai --skill langgraph-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langgraph-patterns
Source: https://github.com/Bbar0n234/learnflow-ai/tree/main/.claude/skills/langgraph-patterns
Command: npx skills add https://github.com/Bbar0n234/learnflow-ai --skill langgraph-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangGraph patterns solve the complexity of building dynamic, stateful workflows by providing structured patterns for command-based routing, HITL, persistence, and parallel execution.

Core Features & Use Cases

  • Command API vs pre-defined edges for flexible transitions between nodes
  • Checkpointing with a Postgres-backed saver for durable workflow state
  • HITL support to interrupt, collect user input, and resume workflows
  • State accumulation using operator.add for parallel worker results
  • Fan-out/fan-in parallelism with Send to process multiple data slices concurrently
  • Streaming and event-driven iteration with a runnable config that carries runtime context

Quick Start

Use this pattern library to implement dynamic LangGraph workflows and observe runtime routing, HITL prompts, and aggregated state across workers.

Frequently Asked Questions about langgraph-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement checkpointing for durable LangGraph workflows?▼

LangGraph checkpointing uses a Postgres-backed saver to persist durable workflow state, ensuring robust state management across distributed workers and enabling recovery after interruptions.

What is the difference between command-based routing and pre-defined edges in LangGraph?▼

Command-based routing in LangGraph provides flexible runtime transitions between nodes, allowing dynamic workflow decisions, whereas pre-defined edges establish static, fixed connections between nodes.

How does human-in-the-loop (HITL) work in dynamic graph-based workflows?▼

Human-in-the-loop (HITL) support in dynamic graph-based workflows allows you to interrupt execution, collect user input, and resume the workflow, facilitating runtime decision-making and human oversight.

Do I need a PostgreSQL database to use LangGraph patterns for state management?▼

Yes, a PostgreSQL database is required to configure the Postgres-backed checkpointer for durable workflow state management and checkpointing in LangGraph.

What is the best way to process multiple data slices concurrently in LangGraph?▼

LangGraph uses fan-out/fan-in parallelism with the Send command to process multiple data slices concurrently, aggregating the results through state accumulation using operator.add.

Can I stream events and carry runtime context through runnable config in LangGraph?▼

Yes, LangGraph supports streaming and event-driven iteration using a runnable config that carries runtime context, enabling responsive and dynamic workflow execution.

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