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langgraph skill

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This skill helps you design production-grade LangGraph agents with explicit graphs, state management, and persistence patterns for reliable, scalable workflows.

npx playbooks add skill omer-metin/skills-for-antigravity --skill langgraph

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
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---
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. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned. 
---

# Langgraph

## Identity


**Role**: LangGraph Agent Architect

**Personality**: 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

## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Overview

This skill is an expert guide for designing and running production-grade LangGraph agent graphs. It focuses on clear graph topology, explicit state schemas, reliable persistence, and safe control flows so agents behave predictably in multi-step, multi-actor scenarios. Use it to build, validate, and harden stateful agent workflows for real-world systems.

How this skill works

The skill inspects your agent graph topology, state definitions, reducers, checkpointing choices, and branching logic to identify weak spots. It reports risks from cyclic flows, missing persistence, unsafe side-effects, and unclear actor responsibilities. It also validates that transitions, guards, and recovery paths follow established LangGraph patterns and critical failure checks used in production.

When to use it

  • Designing a multi-step or multi-actor agent workflow
  • Converting ad-hoc prompts into a stateful agent graph
  • Adding persistence, checkpoints, or recovery to an existing agent
  • Reviewing conditional branching and cycle safety in an agent
  • Integrating human-in-the-loop approvals or tool orchestration

Best practices

  • Model state explicitly with a validated schema and minimal mutable fields
  • Use reducers to encapsulate state transitions and keep side-effects out of reducers
  • Add checkpoints and idempotent persistence for each critical stage
  • Limit cycle depth and add loop guards or counters to prevent infinite loops
  • Design clear actor boundaries and single-responsibility tools for each node
  • Include explicit failure and retry branches with human escalation paths

Example use cases

  • Build an email triage agent graph with tool calls, human approval, and durable checkpoints
  • Implement a ReAct-style agent where reasoning steps are nodes and tools are isolated actors
  • Migrate a LangChain agent to LangGraph to gain visibility, state management, and persistence
  • Create a multi-actor workflow combining model reasoning, DB writes, and human verification
  • Harden an agent used in production with loop guards, retries, and deterministic reducers

FAQ

How do I prevent my agent from looping forever?

Add loop guards such as max-iterations counters, state-based loop termination conditions, and explicit cycle exit transitions. Keep cycles small and validate them with the graph safety checks before deployment.

When is persistence required versus in-memory state?

Persist state when you need crash recovery, long-running workflows, or human-in-the-loop pauses. For short, ephemeral flows use in-memory state but still validate reducers and idempotency to avoid data corruption.