langchain-ai/langgraph-persistence

<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>

| Checkpointer | Use Case | Production Ready | |--------------|----------|------------------| | InMemorySaver | Testing, development | No | | SqliteSaver | Local development | Partial | | PostgresSaver | Production | Yes |

</checkpointer-selection>

---

Checkpointer Setup

<ex-basic-persistence> <python> 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> <typescript> 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> <python> Configure PostgreSQL-backed checkpointing for production deployments.

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.

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.

# 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.

// 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.

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.

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.

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.

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>

| Feature | checkpointer=False | None (default) | True | |---|---|---|---| | 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> <python> 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> <typescript> 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:

<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>

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.

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.

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.

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.

# 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.

// 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.

# 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.

// 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.

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.

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.

# 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.

// 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>