langgraph-persistence

SKILL.md

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

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)

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)

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)

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 });

Thread Management

# 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

// 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

State History & Time Travel

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)

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);

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)

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);

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.

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

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.

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.

# 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)

// 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 });

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,
)

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,
});

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

Long-Term Memory (Store)

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!

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!

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

Fixes

# 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!

// 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!

# 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)

// 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

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

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

# 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

// 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
};

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