langgraph
sickn33/agentic-awesome-skills
Production-grade framework for building stateful, multi-actor AI applications with explicit graph structure.
What is langgraph?
LangGraph is a framework for constructing agents and workflows as directed graphs, enabling state management, persistence, and human-in-the-loop patterns. Use it when building production AI agents that need explicit control flow, debugging visibility, and reliability across multiple steps or actors.
- Design and construct agent graphs with explicit topology and state management
- Implement conditional branching and cycles while preventing infinite loops
- Persist agent state using checkpointers for recovery and debugging
- Integrate human-in-the-loop approval and intervention patterns
- Build ReAct agents and multi-step tool-using workflows
- Handle errors and implement recovery strategies in agent flows
How to install langgraph
npx skills add https://github.com/sickn33/agentic-awesome-skills --skill langgraph- Python 3.9 or higher
- langgraph package installed
- LLM API access (OpenAI, Anthropic, or compatible)
- Familiarity with async programming concepts
- Basic understanding of graph theory
How to use langgraph
- 1.Define your agent's state schema as a TypedDict or Pydantic model
- 2.Create graph nodes as functions that process state and return updates
- 3.Connect nodes with edges, using conditional_edge for branching logic
- 4.Add a checkpointer for persistence (MemorySaver for development, database-backed for production)
- 5.Compile the graph and invoke it with initial state
- 6.Monitor execution and adjust cycles, error handling, or state transitions based on behavior
Use cases
- Building a customer support agent that routes queries through classification, tool selection, and response generation steps
- Creating a research workflow where an agent iteratively searches, evaluates sources, and synthesizes findings with human review gates
- Implementing a multi-actor system where different agents handle different stages of a process with shared state
- Designing a tool-using agent that can call APIs, process results, and decide next actions based on outcomes
- Building agents that need to persist mid-execution state for audit trails or recovery from failures
- AI/ML engineers building production agents
- Backend engineers implementing agentic workflows
- Teams requiring debuggable, explicit agent control flow
- Organizations needing persistence and human oversight in agent systems
langgraph FAQ
Use LangGraph when you need explicit control flow, cycles (agent loops), state persistence, human approval steps, or debugging visibility. Simple sequential tasks may not need it.
Use max_iterations in invoke(), implement explicit stopping conditions in nodes, or use conditional edges that guarantee termination based on state changes.
Yes, LangGraph is provider-agnostic. You can use OpenAI, Anthropic, local models, or any LLM with a compatible API by configuring the appropriate client in your nodes.
Nodes are functions that process state; edges connect nodes and determine the flow. Conditional edges route based on state, while normal edges always execute next.
Use interrupt() in a node to pause execution, return control to the user, and resume with updated state. Checkpointers enable resuming from the exact pause point.
Full instructions (SKILL.md)
Source of truth, from sickn33/agentic-awesome-skills.
name: langgraph description: Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. risk: critical source: vibeship-spawner-skills (Apache 2.0) date_added: 2026-02-27
LangGraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents.
Role: LangGraph Agent Architect
You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production. You know when cycles are needed and how to prevent infinite loops.
Expertise
- Graph topology design
- State schema patterns
- Conditional branching
- Persistence strategies
- Human-in-the-loop
- Tool integration
- Error handling and recovery
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
Prerequisites
- 0: Python proficiency
- 1: LLM API basics
- 2: Async programming concepts
- 3: Graph theory fundamentals
- Required skills: Python 3.9+, langgraph package, LLM API access (OpenAI, Anthropic, etc.), Understanding of graph concepts
When to Use
- User mentions or implies: langgraph
- User mentions or implies: langchain agent
- User mentions or implies: stateful agent
- User mentions or implies: agent graph
- User mentions or implies: react agent
- User mentions or implies: agent workflow
- User mentions or implies: multi-step agent
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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