Langgraph Persistence Agent Skill

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.

5k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

111

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langgraph-persistence

What it tells the agent to use

found in the instruction text

Task spawns other agents

The instruction itself

9 sections, as written by the author

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>

| Checkpointer | Use Case | Production Ready |

|--------------|----------|------------------|

| InMemorySaver | Testing, development | No |

| SqliteSaver | Local development | Partial |

| PostgresSaver | Production | Yes |

</checkpointer-selection>

Checkpointer Setup

<ex-basic-persistence>

Set up a basic graph with in-memory checkpointing and thread-based state persistence.

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>

Set up a basic graph with in-memory checkpointing and thread-based state persistence.

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>

Configure PostgreSQL-backed checkpointing for production deployments.

from langgraph.checkpoint.postgres import PostgresSaver

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>

Configure PostgreSQL-backed checkpointing for production deployments.

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 });

</typescript>

</ex-production-postgres>

Thread Management

<ex-separate-threads>

Demonstrate isolated state between different thread IDs.

# 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>

Demonstrate isolated state between different thread IDs.

// 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>

Time travel: browse checkpoint history and replay or fork from a past state.

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>

Time travel: browse checkpoint history and replay or fork from a past state.

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>

Manually update graph state before resuming execution.

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>

Manually update graph state before resuming execution.

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>

|---|---|---|---|

| Interrupts (HITL) | No | Yes | Yes |

| Multi-turn memory | No | No | Yes |

| Multiple calls (different subgraphs) | Yes | Yes | Warning (namespace conflicts possible) |

| Multiple calls (same subgraph) | Yes | Yes | No |

| State inspection | No | Warning (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>

Choose the right checkpointer mode for your subgraph.

# 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>

Choose the right checkpointer mode for your subgraph.

// 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>

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:

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>

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>

Use a Store for cross-thread memory to share user preferences across conversations.

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>

Use a Store for cross-thread memory to share user preferences across conversations.

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>

Basic store operations: put, get, search, and delete.

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>

Always provide thread_id in config to enable state persistence.

# 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>

Always provide thread_id in config to enable state persistence.

// 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>

Use PostgresSaver instead of InMemorySaver for production persistence.

# 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>

Use PostgresSaver instead of MemorySaver for production persistence.

// 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>

Use Overwrite to replace state values instead of passing through reducers.

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>

Use Overwrite to replace state values instead of passing through reducers.

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>

Access store via the Runtime object in graph nodes.

# 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>

Access store via runtime parameter in graph nodes.

// 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>

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>

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How to use it

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Take langchain-ai/skills-benchmarks-langgraph-persistence from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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