
Langgraph
- 10 repo stars
- Updated January 30, 2026
- vanman2024/ai-dev-marketplace
Build complex stateful LLM workflows with LangGraph — persistence, checkpointing, conditional routing, and human-in-the-loop.
About
Skill for LangGraph stateful graph orchestration of complex LLM workflows, with persistence, checkpointing, conditional routing, and human-in-the-loop. A developer uses it when constructing multi-step agent workflows that need durable state and controllable branching.
- Stateful graph orchestration
- Persistence & checkpointing
- Conditional routing
- Human-in-the-loop
Langgraph by the numbers
- Data as of Jul 7, 2026 (Skillselion catalog sync)
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| repo stars | ★ 10 |
|---|---|
| Last updated | January 30, 2026 |
| Repository | vanman2024/ai-dev-marketplace ↗ |
What it does
Build complex stateful LLM workflows with LangGraph — persistence, checkpointing, conditional routing, and human-in-the-loop.
README.md
LangGraph Plugin
LLM-native graph orchestration for complex stateful workflows.
Features
- State Machine Workflows - Define complex flows as graphs
- Durable Execution - Persist state across runs
- Human-in-the-Loop - Interrupt for approval/input
- Conditional Branching - Dynamic flow control
- Checkpointing - Save and restore state
- Streaming - Real-time node outputs
Quick Start
# Build complete LangGraph workflow
/langgraph:build my-workflow
# Add specific features
/langgraph:add node # Add a graph node
/langgraph:add conditional # Add conditional edge
/langgraph:add checkpoint # Add state persistence
/langgraph:add human-loop # Add human interruption
When to Use LangGraph
Use LangGraph when you need:
- Multi-step reasoning with state
- Human approval in AI workflows
- Retry/branching logic
- Persistent conversation state
- Complex agent orchestration
Agents
graph-architect- Workflow design and structurenode-specialist- Node implementationstate-specialist- State management and persistence