LangGraph Persistence & Memory

What this skill does

<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 |
|--------------|----------|------------------|
| `MemorySaver` | 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.
```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)
```
</typescript>
</ex-basic-persistence>

<ex-production-postgres>
<python>
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 });
```
</typescript>
</ex-production-postgres>

---

## Thread Management

<ex-separate-threads>
<python>
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
```
</typescript>
</ex-separate-threads>

<ex-resume-from-checkpoint>
<python>
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);
```
</typescript>
</ex-resume-from-checkpoint>

<ex-update-state>
<python>
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);
```
</typescript>
</ex-update-state>

---

## Long-Term Memory (Store)

<ex-long-term-memory-store>
<python>
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 prefe