Oryntra: Skill for Claude Code

• 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.
```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)

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.
```typescript
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 });

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