agentsop-langgraph

A decision guide for building and running LangGraph workflows. LangGraph is a tool for making agent systems that keep state, coordinate steps or agents, and pause for human input.

In plain words
What is it for?
Use it to design stateful agents, human approval steps, supervisor or swarm setups, checkpoint storage, and migrations to durable workflows. It also covers issues such as infinite loops and invalid updates.
Why use it?
It helps avoid fragile chains and recurring LangGraph errors by guiding design choices and troubleshooting. It also requires important claims to include source references.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agentsope/skillalchemy/agentsop-langgraph
Any agent
npx skills add agentsope/SkillAlchemy --skill agentsop-langgraph
Clone the repo
git clone --depth 1 https://github.com/agentsope/SkillAlchemy

Made for: Claude Code, Codex.

Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,819 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00124 $0.07819
Opus 5 $0.00062 $0.03909
Sonnet 5 $0.00025 $0.01564
Haiku 4.5 $0.00012 $0.00782

Measured 3d ago against content hash 9785e4a9d630, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentsop-langgraph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/agentsop-langgraph/SKILL.md · 566 lines

How it starts

The opening of the file, as written. The whole thing — 566 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LangGraph · SOP

Source posture: every non-trivial claim is cited inline. Citations use short tags like [lc-docs], [lc-blog/interrupt], [gh/6731], [zenml/uber] — resolve them against references/*.md for the full URL.


何时激活 (Activation Rules)

Activate this skill when any of the following triggers fire:

  • The task mentions LangGraph, StateGraph, MessageGraph, create_react_agent, interrupt(, Command(resume=, add_messages, checkpointer, PostgresSaver, Send(, or entrypoint / task decorators.
  • The user wants to build a stateful agent (memory across turns, long-running, must survive a process crash) — LangGraph's stated sweet spot [lc-docs/why-langgraph].
  • The user wants human-in-the-loop (approve a tool call, edit state, multi-turn validation) — LangGraph offers a first-class interrupt() primitive that competitors require "duct-taping" to achieve [bswen/hitl].
  • The user is hitting GRAPH_RECURSION_LIMIT errors, infinite loops, or InvalidUpdateError on parallel branches — these are LangGraph-specific failure modes with known fixes [lc-docs/errors] [cheatsheet/gotchas].
  • The user is choosing between LangGraph and CrewAI / AutoGen / OpenAI Swarm / raw LangChain — section 生态对照 gives the decision matrix.
  • The user is migrating an existing LangChain chain or a hand-rolled while-loop agent to something durable and observable.

Do not activate if the task is a single LLM call, a one-shot RAG query, or a stateless tool pipeline — Sec. 反模式 explains why graphs are overkill there.


核心心智模型 (Core Mental Model)

LangGraph is a state machine, not a chain. The cleanest one-liner from the 2026 docs: "If chains were about passing outputs between steps, graphs are about maintaining and evolving a shared state over time" [eastondev/2026]. Pre-LLM analog: think BPMN / finite state machine / Pregel-style "supersteps", not a Unix pipe. The official position is even more reductive: LangGraph is "a deterministic execution engine for AI reasoning workflows" [eastondev/2026].

Read the full file on GitHub · 566 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 566 lines · 124 tokens per session scan A 9785e4a9d630

Subscribe to this mod's changes

agentsop-langgraph is a skill published in the GitHub repository agentsope/SkillAlchemy (342 stars, last pushed 8d ago), licensed MIT. It adds 124 tokens to every session and 7,819 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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