langgraph-persistence-memory

name	LangGraph Persistence & Memory
description	INVOKE THIS SKILL when your LangGraph needs to remember state across calls, use memory, or persist conversations. Covers checkpointers (MemorySaver, Postgres), thread_id configuration, and Store for long-term memory.

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

Checkpointer	Use Case	Production Ready
MemorySaver	Testing, development	No
SqliteSaver	Local development	Partial
PostgresSaver	Production	Yes

---

Checkpointer Setup

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)

Configure PostgreSQL-backed checkpointing for production deployments. ```python from langgraph.checkpoint.postgres import PostgresSaver

from_conn_string returns a context manager in v3+

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

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

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);

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);

---

Long-Term Memory (Store)

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 injection

def respond(state, *, store): prefs = 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 { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

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

// Node with store - access via config
const respond = async (state: typeof State.State, config: any) => {
  const item = await config.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!

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>

<boundaries>
### What You CAN Configure

- Choose checkpointer implementation
- Specify thread IDs for conversation isolation
- Retrieve/update state at any checkpoint
- Use stores for cross-thread memory

### What You CANNOT Configure

- Checkpoint timing (happens every super-step)
- Share short-term memory across threads
- Skip checkpointer for persistence features
</boundaries>

<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!

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)

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-resume-with-none>
<python>
Pass None to resume from checkpoint instead of providing new input.
```python
# WRONG: Providing new input restarts from beginning
graph.invoke({"messages": ["New message"]}, config)  # Restarts!

# CORRECT: Use None to resume from checkpoint
graph.invoke(None, config)  # Continues from where it paused

Pass null to resume from checkpoint instead of providing new input. ```typescript // WRONG: Providing new input restarts from beginning await graph.invoke({ messages: ["New message"] }, config); // Restarts!

// CORRECT: Use null to resume from checkpoint await graph.invoke(null, config); // Continues from where it paused

</typescript>
</fix-resume-with-none>

<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

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>
Inject store via keyword parameter to access it in graph nodes.
```python
# WRONG: Store not available in node
def my_node(state):
    store.put(...)  # NameError! store not defined

# CORRECT: Inject store via keyword parameter
from langgraph.store.base import BaseStore

def my_node(state, *, store: BaseStore):
    store.put(...)  # Correct store instance injected

Access store via config parameter in graph nodes. ```typescript // WRONG: Store not available in node const myNode = async (state) => { store.put(...); // ReferenceError! };

// CORRECT: Access store via config parameter const myNode = async (state, config) => { await config.store.put(...); // Correct store instance };

</typescript>
</fix-store-injection>