What is langgraph-state-management?

A guide to managing the shared data used by LangGraph applications. LangGraph is a framework for building workflows and agents as connected steps.

Use it to define state schemas, write reducers that combine updates, configure persistence, and test or debug state handling.

It helps prevent unclear data definitions, incorrect merging of updates, lost state, and poorly configured saved checkpoints.

langgraph-state-management — Skill for Claude Code by soba-labs

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.

Any agent

npx skills add soba-labs/langchain-agent-skills --skill langgraph-state-management

Clone the repo

git clone --depth 1 https://github.com/soba-labs/langchain-agent-skills

Made for: Claude Code.

Or install langgraph-skills, the plugin that ships this one along with the rest of its 5 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-state-management/github.svg)](https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-state-management)

Your own site

<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-state-management"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-state-management/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/soba-labs/langchain-agent-skills/langgraph-state-management"><img src="https://agentmods.dev/badge/skills/soba-labs/langchain-agent-skills/langgraph-state-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 167 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,074 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00167	$0.03074
Opus 5.5	$0.00067	$0.01230
Sonnet 5.5	$0.00033	$0.00615
Haiku 4.5	$0.00017	$0.00307

Measured 2mo ago against content hash f1483a25d7b4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

langgraph-state-management 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 2mo ago.

The scan reads SKILL.md. This mod also ships 8 executable files (assets/chat_state.py, assets/research_state.py, assets/tool_calling_state.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/langgraph-state-management/SKILL.md · 389 lines

How it starts

The opening of the file, as written. The whole thing — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LangGraph State Management

State Design Workflow

Follow this workflow when designing or modifying state for a LangGraph application:

Identify data requirements — What data flows through the graph?
Choose a schema pattern — Match the use case to a template
Define reducers — Decide how concurrent updates merge
Configure persistence — Select and set up a checkpointer
Validate and test — Run schema validation and reducer tests

Quick Start

Python — Minimal Chat State

from langgraph.graph import StateGraph, START, END, MessagesState
from langchain_core.messages import AIMessage

class State(MessagesState):
    pass

def chat_node(state: State):
    return {"messages": [AIMessage(content="Hello!")]}

graph = StateGraph(State).add_node("chat", chat_node)
graph.add_edge(START, "chat").add_edge("chat", END)
app = graph.compile()

Python — Subclass MessagesState

For convenience, subclass the built-in MessagesState (includes messages with add_messages reducer):

from langgraph.graph import MessagesState

class State(MessagesState):
    documents: list[str]
    query: str

TypeScript — StateSchema with Zod

import { StateGraph, StateSchema, MessagesValue, ReducedValue, START, END } from "@langchain/langgraph";
import { AIMessage } from "@langchain/core/messages";
import { z } from "zod/v4";

const State = new StateSchema({
  messages: MessagesValue,
  documents: z.array(z.string()).default(() => []),
  count: new ReducedValue(
    z.number().default(0),
    { reducer: (current, update) => current + update }
  ),
});

const graph = new StateGraph(State)
  .addNode("chat", (state) => ({ messages: [new AIMessage("Hello!")] }))
  .addEdge(START, "chat")
  .addEdge("chat", END)
  .compile();

Schema Patterns

Choose the pattern matching the application type. See references/schema-patterns.md for complete examples with both Python and TypeScript.

Read the full file on GitHub · 389 lines

Files

What ships with it

13 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.

2mo ago First seen · 389 lines · 167 tokens per session scan A f1483a25d7b4

Subscribe to this mod's changes

langgraph-state-management is a skill published in the GitHub repository soba-labs/langchain-agent-skills (106 stars, last pushed 18d ago), licensed MIT. It adds 167 tokens to every session and 3,074 once invoked, about $0.0007 per session on Opus 5.5. 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.

Backend and APIs Building agents

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