Official agent skill

Langgraph Persistence

by langchain-ai in langchain-ai/langchain-skills

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping.

OfficialMITAuto-check passedAI & LLM Engineering

Install Langgraph Persistence

skills CLI
$ npx skills add langchain-ai/langchain-skills --skill langgraph-persistence -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install langchain-ai/langchain-skills langgraph-persistence --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/skills/langgraph-persistence .claude/skills/langgraph-persistence && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
langgraph-persistence
GitHub stars
1.3k
Used in
1 other repo
Token cost
~4.6k tokens
SKILL.md length
635 words
Files
1
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping.

  • Tasks that involve Building AI agents
  • SKILL.md covers Checkpointer Setup, Thread Management, State History & Time Travel and Subgraph Checkpointer Scoping, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Langgraph Persistence is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, threadid, time travel, Store, and subgraph persistence modes.

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Building AI agents. It works with LangGraph, PostgreSQL, Python and TypeScript. The licence is MIT.

When your agent uses it

  • Tasks that involve Building AI agents

Example prompts

  • “/langgraph-persistence”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 16a992f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Langgraph Persistence loads about 4.6k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 635 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from langchain-ai/langchain-skills at commit 16a992f, republished under its MIT licence (© langchain-ai). 635 words, ~4,626 tokens.

Download SKILL.mdSave it as .claude/skills/langgraph-persistence/SKILL.md (or your agent's skills folder).
name
langgraph-persistence
description
INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.
<overview>
LangGraph's persistence layer enables durable execution by checkpointing graph state:
  • 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
    </overview>
<checkpointer-selection>
CheckpointerUse CaseProduction Ready
InMemorySaverTesting, developmentNo
SqliteSaverLocal developmentPartial
PostgresSaverProductionYes
</checkpointer-selection>

Checkpointer Setup

<ex-basic-persistence>
<python>
Set up a basic graph with in-memory checkpointing and thread-based state persistence.
python
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict, Annotated
import operator

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)
</typescript>
</ex-basic-persistence>
<ex-production-postgres>
<python>
Configure PostgreSQL-backed checkpointing for production deployments.
python
import os
from langgraph.checkpoint.postgres import PostgresSaver

# Run once during deployment (not at application startup):
#   PostgresSaver.from_conn_string(os.environ["DATABASE_URL"]).setup()

with PostgresSaver.from_conn_string(os.environ["DATABASE_URL"]) as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)
</python>
<typescript>
Configure PostgreSQL-backed checkpointing for production deployments.
typescript
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";

// Run once during deployment (not at application startup):
//   await PostgresSaver.fromConnString(process.env.DATABASE_URL!).setup();

const checkpointer = PostgresSaver.fromConnString(process.env.DATABASE_URL!);
const graph = builder.compile({ checkpointer });
</typescript>
</ex-production-postgres>

Thread Management

<ex-separate-threads>
<python>
Demonstrate isolated state between different thread IDs.
python
# Different threads maintain separate state
alice_config = {"configurable": {"thread_id": "user-alice"}}
bob_config = {"configurable": {"thread_id": "user-bob"}}

graph.invoke({"messages": ["Hi from Alice"]}, alice_config)
graph.invoke({"messages": ["Hi from Bob"]}, bob_config)

# Alice's state is isolated from Bob's
</python>
<typescript>
Demonstrate isolated state between different thread IDs.
typescript
// Different threads maintain separate state
const aliceConfig = { configurable: { thread_id: "user-alice" } };
const bobConfig = { configurable: { thread_id: "user-bob" } };

await graph.invoke({ messages: [new HumanMessage("Hi from Alice")] }, aliceConfig);
await graph.invoke({ messages: [new HumanMessage("Hi from Bob")] }, bobConfig);

// Alice's state is isolated from Bob's
</typescript>
</ex-separate-threads>

State History & Time Travel

<ex-resume-from-checkpoint>
<python>
Time travel: browse checkpoint history and replay or fork from a past state.
python
config = {"configurable": {"thread_id": "session-1"}}

result = graph.invoke({"messages": ["start"]}, config)

# Browse checkpoint history
states = list(graph.get_state_history(config))

# Replay from a past checkpoint
past = states[-2]
result = graph.invoke(None, past.config)  # None = resume from checkpoint

# Or fork: update state at a past checkpoint, then resume
fork_config = graph.update_state(past.config, {"messages": ["edited"]})
result = graph.invoke(None, fork_config)
</python>
<typescript>
Time travel: browse checkpoint history and replay or fork from a past state.
typescript
const config = { configurable: { thread_id: "session-1" } };

const result = await graph.invoke({ messages: ["start"] }, config);

// Browse checkpoint history (async iterable, collect to array)
const states: Awaited<ReturnType<typeof graph.getState>>[] = [];
for await (const state of graph.getStateHistory(config)) {
  states.push(state);
}

// Replay from a past checkpoint
const past = states[states.length - 2];
const replayed = await graph.invoke(null, past.config);  // null = resume from checkpoint

// Or fork: update state at a past checkpoint, then resume
const forkConfig = await graph.updateState(past.config, { messages: ["edited"] });
const forked = await graph.invoke(null, forkConfig);
</typescript>
</ex-resume-from-checkpoint>
<ex-update-state>
<python>
Manually update graph state before resuming execution.
python
config = {"configurable": {"thread_id": "session-1"}}

# Modify state before resuming
graph.update_state(config, {"data": "manually_updated"})

# Resume with updated state
result = graph.invoke(None, config)
</python>
<typescript>
Manually update graph state before resuming execution.
typescript
const config = { configurable: { thread_id: "session-1" } };

// Modify state before resuming
await graph.updateState(config, { data: "manually_updated" });

// Resume with updated state
const result = await graph.invoke(null, config);
</typescript>
</ex-update-state>

Subgraph Checkpointer Scoping

When compiling a subgraph, the checkpointer parameter controls persistence behavior. This is critical for subgraphs that use interrupts, need multi-turn memory, or run in parallel.

<subgraph-checkpointer-scoping-table>
Featurecheckpointer=FalseNone (default)True
Interrupts (HITL)NoYesYes
Multi-turn memoryNoNoYes
Multiple calls (different subgraphs)YesYesWarning (namespace conflicts possible)
Multiple calls (same subgraph)YesYesNo
State inspectionNoWarning (current invocation only)Yes
</subgraph-checkpointer-scoping-table>
<subgraph-checkpointer-when-to-use>
When to use each mode
  • checkpointer=False — Subgraph doesn't need interrupts or persistence. Simplest option, no checkpoint overhead.
  • None (default / omit checkpointer) — Subgraph needs interrupt() but not multi-turn memory. Each invocation starts fresh but can pause/resume. Parallel execution works because each invocation gets a unique namespace.
  • checkpointer=True — Subgraph needs to remember state across invocations (multi-turn conversations). Each call picks up where the last left off.
</subgraph-checkpointer-when-to-use>
<warning-stateful-subgraphs-parallel>

Warning: Stateful subgraphs (checkpointer=True) do NOT support calling the same subgraph instance multiple times within a single node — the calls write to the same checkpoint namespace and conflict.

</warning-stateful-subgraphs-parallel>
<ex-subgraph-checkpointer-modes>
<python>
Choose the right checkpointer mode for your subgraph.
python
# No interrupts needed — opt out of checkpointing
subgraph = subgraph_builder.compile(checkpointer=False)

# Need interrupts but not cross-invocation persistence (default)
subgraph = subgraph_builder.compile()

# Need cross-invocation persistence (stateful)
subgraph = subgraph_builder.compile(checkpointer=True)
</python>
<typescript>
Choose the right checkpointer mode for your subgraph.
typescript
// No interrupts needed — opt out of checkpointing
const subgraph = subgraphBuilder.compile({ checkpointer: false });

// Need interrupts but not cross-invocation persistence (default)
const subgraph = subgraphBuilder.compile();

// Need cross-invocation persistence (stateful)
const subgraph = subgraphBuilder.compile({ checkpointer: true });
</typescript>
</ex-subgraph-checkpointer-modes>
<parallel-subgraph-namespacing>
Show full SKILL.md (253 more words)Show less
Parallel subgraph namespacing

When multiple different stateful subgraphs run in parallel, wrap each in its own StateGraph with a unique node name for stable namespace isolation:

<python>
python
from langgraph.graph import MessagesState, StateGraph

def create_sub_agent(model, *, name, **kwargs):
    """Wrap an agent with a unique node name for namespace isolation."""
    agent = create_agent(model=model, name=name, **kwargs)
    return (
        StateGraph(MessagesState)
        .add_node(name, agent)  # unique name -> stable namespace
        .add_edge("__start__", name)
        .compile()
    )

fruit_agent = create_sub_agent(
    "gpt-4.1-mini", name="fruit_agent",
    tools=[fruit_info], prompt="...", checkpointer=True,
)
veggie_agent = create_sub_agent(
    "gpt-4.1-mini", name="veggie_agent",
    tools=[veggie_info], prompt="...", checkpointer=True,
)
</python>
<typescript>
typescript
import { StateGraph, StateSchema, MessagesValue, START } from "@langchain/langgraph";

function createSubAgent(model: string, { name, ...kwargs }: { name: string; [key: string]: any }) {
  const agent = createAgent({ model, name, ...kwargs });
  return new StateGraph(new StateSchema({ messages: MessagesValue }))
    .addNode(name, agent)  // unique name -> stable namespace
    .addEdge(START, name)
    .compile();
}

const fruitAgent = createSubAgent("gpt-4.1-mini", {
  name: "fruit_agent", tools: [fruitInfo], prompt: "...", checkpointer: true,
});
const veggieAgent = createSubAgent("gpt-4.1-mini", {
  name: "veggie_agent", tools: [veggieInfo], prompt: "...", checkpointer: true,
});
</typescript>

Note: Subgraphs added as nodes (via add_node) already get name-based namespaces automatically and don't need this wrapper.

</parallel-subgraph-namespacing>

Long-Term Memory (Store)

<ex-long-term-memory-store>
<python>
Use a Store for cross-thread memory to share user preferences across conversations.
python
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

# Save user preference (available across ALL threads)
store.put(("alice", "preferences"), "language", {"preference": "short responses"})

# Node with store — access via runtime
from langgraph.runtime import Runtime

def respond(state, runtime: Runtime):
    prefs = runtime.store.get((state["user_id"], "preferences"), "language")
    return {"response": f"Using preference: {prefs.value}"}

# Compile with BOTH checkpointer and store
graph = builder.compile(checkpointer=checkpointer, store=store)

# Both threads access same long-term memory
graph.invoke({"user_id": "alice"}, {"configurable": {"thread_id": "thread-1"}})
graph.invoke({"user_id": "alice"}, {"configurable": {"thread_id": "thread-2"}})  # Same preferences!
</python>
<typescript>
Use a Store for cross-thread memory to share user preferences across conversations.
typescript
import { MemoryStore } from "@langchain/langgraph";

const store = new MemoryStore();

// Save user preference (available across ALL threads)
await store.put(["alice", "preferences"], "language", { preference: "short responses" });

// Node with store — access via runtime
const respond = async (state: typeof State.State, runtime: any) => {
  const item = await runtime.store?.get(["alice", "preferences"], "language");
  return { response: `Using preference: ${item?.value?.preference}` };
};

// Compile with BOTH checkpointer and store
const graph = builder.compile({ checkpointer, store });

// Both threads access same long-term memory
await graph.invoke({ userId: "alice" }, { configurable: { thread_id: "thread-1" } });
await graph.invoke({ userId: "alice" }, { configurable: { thread_id: "thread-2" } });  // Same preferences!
</typescript>
</ex-long-term-memory-store>
<ex-store-operations>
<python>
Basic store operations: put, get, search, and delete.
python
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

store.put(("user-123", "facts"), "location", {"city": "San Francisco"})  # Put
item = store.get(("user-123", "facts"), "location")  # Get
results = store.search(("user-123", "facts"), filter={"city": "San Francisco"})  # Search
store.delete(("user-123", "facts"), "location")  # Delete
</python>
</ex-store-operations>

Fixes

<fix-thread-id-required>
<python>
Always provide thread_id in config to enable state persistence.
python
# WRONG: No thread_id - state NOT persisted!
graph.invoke({"messages": ["Hello"]})
graph.invoke({"messages": ["What did I say?"]})  # Doesn't remember!

# CORRECT: Always provide thread_id
config = {"configurable": {"thread_id": "session-1"}}
graph.invoke({"messages": ["Hello"]}, config)
graph.invoke({"messages": ["What did I say?"]}, config)  # Remembers!
</python>
<typescript>
Always provide thread_id in config to enable state persistence.
typescript
// WRONG: No thread_id - state NOT persisted!
await graph.invoke({ messages: [new HumanMessage("Hello")] });
await graph.invoke({ messages: [new HumanMessage("What did I say?")] });  // Doesn't remember!

// CORRECT: Always provide thread_id
const config = { configurable: { thread_id: "session-1" } };
await graph.invoke({ messages: [new HumanMessage("Hello")] }, config);
await graph.invoke({ messages: [new HumanMessage("What did I say?")] }, config);  // Remembers!
</typescript>
</fix-thread-id-required>
<fix-inmemory-not-for-production>
<python>
Use PostgresSaver instead of InMemorySaver for production persistence.
python
# WRONG: Data lost on process restart
checkpointer = InMemorySaver()  # In-memory only!

# CORRECT: Use persistent storage for production
from langgraph.checkpoint.postgres import PostgresSaver
with PostgresSaver.from_conn_string("postgresql://...") as checkpointer:
    checkpointer.setup()  # only needed on first use to create tables
    graph = builder.compile(checkpointer=checkpointer)
</python>
<typescript>
Use PostgresSaver instead of MemorySaver for production persistence.
typescript
// WRONG: Data lost on process restart
const checkpointer = new MemorySaver();  // In-memory only!

// CORRECT: Use persistent storage for production
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";
const checkpointer = PostgresSaver.fromConnString("postgresql://...");
await checkpointer.setup(); // only needed on first use to create tables
</typescript>
</fix-inmemory-not-for-production>
<fix-update-state-with-reducers>
<python>
Use Overwrite to replace state values instead of passing through reducers.
python
from langgraph.types import Overwrite

# State with reducer: items: Annotated[list, operator.add]
# Current state: {"items": ["A", "B"]}

# update_state PASSES THROUGH reducers
graph.update_state(config, {"items": ["C"]})  # Result: ["A", "B", "C"] - Appended!

# To REPLACE instead, use Overwrite
graph.update_state(config, {"items": Overwrite(["C"])})  # Result: ["C"] - Replaced
</python>
<typescript>
Use Overwrite to replace state values instead of passing through reducers.
typescript
import { Overwrite } from "@langchain/langgraph";

// State with reducer: items uses concat reducer
// Current state: { items: ["A", "B"] }

// updateState PASSES THROUGH reducers
await graph.updateState(config, { items: ["C"] });  // Result: ["A", "B", "C"] - Appended!

// To REPLACE instead, use Overwrite
await graph.updateState(config, { items: new Overwrite(["C"]) });  // Result: ["C"] - Replaced
</typescript>
</fix-update-state-with-reducers>
<fix-store-injection>
<python>
Access store via the Runtime object in graph nodes.
python
# WRONG: Store not available in node
def my_node(state):
    store.put(...)  # NameError! store not defined

# CORRECT: Access store via runtime
from langgraph.runtime import Runtime

def my_node(state, runtime: Runtime):
    runtime.store.put(...)  # Correct store instance
</python>
<typescript>
Access store via runtime parameter in graph nodes.
typescript
// WRONG: Store not available in node
const myNode = async (state) => {
  store.put(...);  // ReferenceError!
};

// CORRECT: Access store via runtime
const myNode = async (state, runtime) => {
  await runtime.store?.put(...);  // Correct store instance
};
</typescript>
</fix-store-injection>
<boundaries>
### What You Should NOT Do
  • Use InMemorySaver in production — data lost on restart; use PostgresSaver
  • Forget thread_id — state won't persist without it
  • Expect update_state to bypass reducers — it passes through them; use Overwrite to replace
  • Run the same stateful subgraph (checkpointer=True) in parallel within one node — namespace conflict
  • Access store directly in a node — use runtime.store via the Runtime param
    </boundaries>

© langchain-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in config/skills/langgraph-persistence of langchain-ai/langchain-skills.

Open the folder on GitHubat commit 16a992f

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in langchain-ai/langchain-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Tool Designagentailor/fullstack-langgraph-nextjs-agent132—~3.2kAutomated safety check: PassMIT
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    Official

    INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents.

    1.3k GitHub starsUsed in 1 repo~3.6k tokens
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Questions about Langgraph Persistence

What does Langgraph Persistence do?

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Langgraph Persistence is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping.

When should I use Langgraph Persistence?

Langgraph Persistence fits situations like: tasks that involve Building AI agents.

How do I install Langgraph Persistence in Claude Code?

Run `npx skills add langchain-ai/langchain-skills --skill langgraph-persistence -a claude-code`. Or copy the skill folder (config/skills/langgraph-persistence in langchain-ai/langchain-skills) into .claude/skills/langgraph-persistence in your project. Claude Code loads it when a task matches its description.

How do I install Langgraph Persistence in Codex?

Run `npx skills add langchain-ai/langchain-skills --skill langgraph-persistence -a codex`. Or copy the skill folder (config/skills/langgraph-persistence in langchain-ai/langchain-skills) into .agents/skills/langgraph-persistence in your project. Codex loads it when a task matches its description.

Can I use Langgraph Persistence in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add langchain-ai/langchain-skills --skill langgraph-persistence -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langgraph-persistence, .gemini/skills/langgraph-persistence, .github/skills/langgraph-persistence and .opencode/skills/langgraph-persistence in your project.

What does Langgraph Persistence need to run?

SKILL.md names no scripts, command-line tools or credentials: Langgraph Persistence is instructions for the agent only. Our summary lists: Python 3.

Does Langgraph Persistence access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Langgraph Persistence safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Langgraph Persistence use?

Langgraph Persistence is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langgraph Persistence use?

About 4.6k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Langgraph Persistence?

Skills that share tags, products or a category with Langgraph Persistence: Add Example Agent (GetBindu/Bindu, 10k stars), Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars), Tool Design (agentailor/fullstack-langgraph-nextjs-agent, 132 stars) and Uipath Functions (UiPath/skills, 167 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langgraph Persistence?

langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/langchain-skills, which has 1,270 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 5, 2026.

Source: langchain-ai/langchain-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.