• 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.

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 });

Thread Management

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

State History & Time Travel

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);

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);

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.

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 });

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:

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,
});

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

Long-Term Memory (Store)

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!

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!

// 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)

// 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

// 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

// 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>