memory

Memory

LangGraph supports two types of memory essential for building conversational agents:

• Short-term memory: Tracks the ongoing conversation by maintaining message history within a session.
• Long-term memory: Stores user-specific or application-level data across sessions.

This guide demonstrates how to use both memory types with agents in LangGraph. For a deeper understanding of memory concepts, refer to the LangGraph memory documentation.

!!! note "Terminology"

In LangGraph:

- *Short-term memory* is also referred to as **thread-level memory**.
- *Long-term memory* is also called **cross-thread memory**.

A [thread](../concepts/persistence.md#threads) represents a sequence of related runs
grouped by the same `thread_id`.

Short-term memory

Short-term memory enables agents to track multi-turn conversations. To use it, you must:

1. Provide a checkpointer when creating the agent. The checkpointer enables persistence of the agent's state.
2. Supply a thread_id in the config when running the agent. The thread_id is a unique identifier for the conversation session.

// highlight-next-line
import { MemorySaver } from "@langchain/langgraph-checkpoint";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

// highlight-next-line
const checkpointer = new MemorySaver();  // (1)!

const getWeather = tool(
  async (input: { city: string }) => {
    return `It's always sunny in ${input.city}!`;
  },
  {
    name: "getWeather",
    schema: z.object({
      city: z.string().describe("The city to get the weather for"),
    }),
    description: "Get weather for a given city.",
  }
);

const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
const agent = createReactAgent({
  llm,
  tools: [getWeather],
  // highlight-next-line
  checkpointer  // (2)!
});

// Run the agent
// highlight-next-line
const config = { configurable: { thread_id: "1" } };  // (3)!
const sfResponse = await agent.invoke(
  { messages: [ { role: "user", content: "what is the weather in sf" } ] },
  config  // (4)!
);
const nyResponse = await agent.invoke(
  { messages: [ { role: "user", content: "what about new york?" } ] },
  config
);