langgraph-persistence

langgraph-persistence is a skill for Claude Code, Codex from joonlab/joonlab-claudecode-setting-for-share. It costs 55 tokens per session (4,334 once invoked), scanned A, a copy of langgraph-persistence, MIT.

A LangGraph skill for saving workflow state and remembering information. It distinguishes short-term memory for one conversation from long-term storage for facts and preferences shared across conversations.

In plain words
What is it for?
Use it to persist conversations, resume workflows, store user preferences, and travel through a graph's saved history.
Why use it?
It allows a workflow to continue with its previous state after another request and keeps separate conversations from being mixed together. It also covers revisiting earlier states and saving nested workflows.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to persist conversations, resume workflows, store user preferences, and travel through a graph's saved history.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/joonlab/joonlab-claudecode-setting-for-share/langgraph-persistence
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 joonlab/joonlab-claudecode-setting-for-share --skill langgraph-persistence
Clone the repo
git clone --depth 1 https://github.com/joonlab/joonlab-claudecode-setting-for-share

Made for: Claude Code, Codex.

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.

agentmods badge for langgraph-persistence

README.md
[![agentmods](https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/langgraph-persistence.svg)](https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/langgraph-persistence)
Your own site
<a href="https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/langgraph-persistence"><img src="https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/langgraph-persistence.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,334 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.
Origin 100% copy Near-identical to another mod 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 5.1 $0.00055 $0.04334
Opus 5 $0.00028 $0.02167
Sonnet 5 $0.00011 $0.00867
Haiku 4.5 $0.00006 $0.00433

Measured 8d ago against content hash a3c3fe4339f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

langgraph-persistence 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 8d 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.

Origin

This is a copy

100% identical to langgraph-persistence — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

claude/skills/langgraph-persistence/SKILL.md · 561 lines

How it starts

The opening of the file, as written. The whole thing — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.

  • Checkpointer: Saves/loads graph state at every super-step
  • Thread ID: Identifies separate checkpoint sequences (conversations)
  • Store: Cross-thread memory for user preferences, facts

Two memory types:

  • Short-term (checkpointer): Thread-scoped conversation history
  • Long-term (store): Cross-thread user preferences, facts
Checkpointer Use Case Production Ready
InMemorySaver Testing, development No
SqliteSaver Local development Partial
PostgresSaver Production Yes

Checkpointer Setup

class State(TypedDict): messages: Annotated[list, operator.add]

def add_message(state: State) -> dict: return {"messages": ["Bot response"]}

checkpointer = InMemorySaver()

graph = ( StateGraph(State) .add_node("respond", add_message) .add_edge(START, "respond") .add_edge("respond", END) .compile(checkpointer=checkpointer) # Pass at compile time )

ALWAYS provide thread_id

config = {"configurable": {"thread_id": "conversation-1"}}

result1 = graph.invoke({"messages": ["Hello"]}, config) print(len(result1["messages"])) # 2

result2 = graph.invoke({"messages": ["How are you?"]}, config) print(len(result2["messages"])) # 4 (previous + new)

</python>
<typescript>
Set up a basic graph with in-memory checkpointing and thread-based state persistence.
```typescript
import { MemorySaver, StateGraph, StateSchema, MessagesValue, START, END } from "@langchain/langgraph";
import { HumanMessage } from "@langchain/core/messages";

const State = new StateSchema({ messages: MessagesValue });

const addMessage = async (state: typeof State.State) => {
  return { messages: [{ role: "assistant", content: "Bot response" }] };
};

const checkpointer = new MemorySaver();

const graph = new StateGraph(State)
  .addNode("respond", addMessage)
  .addEdge(START, "respond")
  .addEdge("respond", END)
  .compile({ checkpointer });

// ALWAYS provide thread_id
const config = { configurable: { thread_id: "conversation-1" } };

const result1 = await graph.invoke({ messages: [new HumanMessage("Hello")] }, config);
console.log(result1.messages.length);  // 2

const result2 = await graph.invoke({ messages: [new HumanMessage("How are you?")] }, config);
console.log(result2.messages.length);  // 4 (previous + new)

with PostgresSaver.from_conn_string( "postgresql://user:pass@localhost/db" ) as checkpointer: checkpointer.setup() # only needed on first use to create tables graph = builder.compile(checkpointer=checkpointer)

</python>
<typescript>
Configure PostgreSQL-backed checkpointing for production deployments.
```typescript
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";

const checkpointer = PostgresSaver.fromConnString(
  "postgresql://user:pass@localhost/db"
);
await checkpointer.setup(); // only needed on first use to create tables

const graph = builder.compile({ checkpointer });

Read the full file on GitHub · 561 lines

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. 8d ago First seen · 561 lines · 55 tokens per session scan A a3c3fe4339f2

Subscribe to this mod's changes

langgraph-persistence is a skill published in the GitHub repository joonlab/joonlab-claudecode-setting-for-share (10 stars, last pushed 29d ago), licensed MIT. It adds 55 tokens to every session and 4,334 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-persistence, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

media-ingest

Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.

garrytan/gbrain · 52 tokens

mem0-oss-to-platform

Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…

mem0ai/mem0 · 273 tokens

Cortex

Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…

danielmiessler/LifeOS · 196 tokens

memory

Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.

opensquilla/opensquilla · 44 tokens

ha-data-stores

Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…

shiwenwen/hope-agent · 115 tokens

establishing-project-context

Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.

GanyuanRan/Aegis · 45 tokens