Memory

AI applications need memory to share context across multiple interactions. In LangGraph, you can add two types of memory:

• Add short-term memory as a part of your agent’s state to enable multi-turn conversations.
• Add long-term memory to store user-specific or application-level data across sessions.

Add short-term memory

Short-term memory (thread-level persistence) enables agents to track multi-turn conversations. To add short-term memory:

import { MemorySaver, StateGraph } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const builder = new StateGraph(...);
const graph = builder.compile({ checkpointer });

await graph.invoke(
  { messages: [{ role: "user", content: "hi! i am Bob" }] },
  { configurable: { thread_id: "1" } }
);

Use in production

In production, use a checkpointer backed by a database:

import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";

const DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable";
const checkpointer = PostgresSaver.fromConnString(DB_URI);

const builder = new StateGraph(...);
const graph = builder.compile({ checkpointer });

Example: using Postgres checkpointer

npm install @langchain/langgraph-checkpoint-postgres

You need to call checkpointer.setup() the first time you’re using Postgres checkpointer

import { ChatAnthropic } from "@langchain/anthropic";
import { StateGraph, MessagesZodMeta, START } from "@langchain/langgraph";
import { BaseMessage } from "@langchain/core/messages";
import { registry } from "@langchain/langgraph/zod";
import * as z from "zod";
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";

const MessagesZodState = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
});

const model = new ChatAnthropic({ model: "claude-haiku-4-5-20251001" });

const DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable";
const checkpointer = PostgresSaver.fromConnString(DB_URI);
// await checkpointer.setup();

const builder = new StateGraph(MessagesZodState)
  .addNode("call_model", async (state) => {
    const response = await model.invoke(state.messages);
    return { messages: [response] };
  })
  .addEdge(START, "call_model");

const graph = builder.compile({ checkpointer });

const config = {
  configurable: {
    thread_id: "1"
  }
};

for await (const chunk of await graph.stream(
  { messages: [{ role: "user", content: "hi! I'm bob" }] },
  { ...config, streamMode: "values" }
)) {
  console.log(chunk.messages.at(-1)?.content);
}

for await (const chunk of await graph.stream(
  { messages: [{ role: "user", content: "what's my name?" }] },
  { ...config, streamMode: "values" }
)) {
  console.log(chunk.messages.at(-1)?.content);
}

Use in subgraphs

If your graph contains subgraphs, you only need to provide the checkpointer when compiling the parent graph. LangGraph will automatically propagate the checkpointer to the child subgraphs.

import { StateGraph, START, MemorySaver } from "@langchain/langgraph";
import * as z from "zod";

const State = z.object({ foo: z.string() });

const subgraphBuilder = new StateGraph(State)
  .addNode("subgraph_node_1", (state) => {
    return { foo: state.foo + "bar" };
  })
  .addEdge(START, "subgraph_node_1");
const subgraph = subgraphBuilder.compile();

const builder = new StateGraph(State)
  .addNode("node_1", subgraph)
  .addEdge(START, "node_1");

const checkpointer = new MemorySaver();
const graph = builder.compile({ checkpointer });

If you want the subgraph to have its own memory, you can compile it with the appropriate checkpointer option. This is useful in multi-agent systems, if you want agents to keep track of their internal message histories.

const subgraphBuilder = new StateGraph(...);
const subgraph = subgraphBuilder.compile({ checkpointer: true });  

Add long-term memory

Use long-term memory to store user-specific or application-specific data across conversations.

import { InMemoryStore, StateGraph } from "@langchain/langgraph";

const store = new InMemoryStore();

const builder = new StateGraph(...);
const graph = builder.compile({ store });

Use in production

In production, use a store backed by a database:

import { PostgresStore } from "@langchain/langgraph-checkpoint-postgres/store";

const DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable";
const store = PostgresStore.fromConnString(DB_URI);

const builder = new StateGraph(...);
const graph = builder.compile({ store });

Example: using Postgres store

npm install @langchain/langgraph-checkpoint-postgres

You need to call store.setup() the first time you’re using Postgres store

import { ChatAnthropic } from "@langchain/anthropic";
import { StateGraph, MessagesZodMeta, START, LangGraphRunnableConfig } from "@langchain/langgraph";
import { PostgresSaver } from "@langchain/langgraph-checkpoint-postgres";
import { PostgresStore } from "@langchain/langgraph-checkpoint-postgres/store";
import { BaseMessage } from "@langchain/core/messages";
import { registry } from "@langchain/langgraph/zod";
import * as z from "zod";
import { v4 as uuidv4 } from "uuid";

const MessagesZodState = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
});

const model = new ChatAnthropic({ model: "claude-haiku-4-5-20251001" });

const DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable";

const store = PostgresStore.fromConnString(DB_URI);
const checkpointer = PostgresSaver.fromConnString(DB_URI);
// await store.setup();
// await checkpointer.setup();

const callModel = async (
  state: z.infer<typeof MessagesZodState>,
  config: LangGraphRunnableConfig,
) => {
  const userId = config.configurable?.userId;
  const namespace = ["memories", userId];
  const memories = await config.store?.search(namespace, { query: state.messages.at(-1)?.content });
  const info = memories?.map(d => d.value.data).join("\n") || "";
  const systemMsg = `You are a helpful assistant talking to the user. User info: ${info}`;

  // Store new memories if the user asks the model to remember
  const lastMessage = state.messages.at(-1);
  if (lastMessage?.content?.toLowerCase().includes("remember")) {
    const memory = "User name is Bob";
    await config.store?.put(namespace, uuidv4(), { data: memory });
  }

  const response = await model.invoke([
    { role: "system", content: systemMsg },
    ...state.messages
  ]);
  return { messages: [response] };
};

const builder = new StateGraph(MessagesZodState)
  .addNode("call_model", callModel)
  .addEdge(START, "call_model");

const graph = builder.compile({
  checkpointer,
  store,
});

const config = {
  configurable: {
    thread_id: "1",
    userId: "1",
  }
};

for await (const chunk of await graph.stream(
  { messages: [{ role: "user", content: "Hi! Remember: my name is Bob" }] },
  { ...config, streamMode: "values" }
)) {
  console.log(chunk.messages.at(-1)?.content);
}

const config2 = {
  configurable: {
    thread_id: "2",
    userId: "1",
  }
};

for await (const chunk of await graph.stream(
  { messages: [{ role: "user", content: "what is my name?" }] },
  { ...config2, streamMode: "values" }
)) {
  console.log(chunk.messages.at(-1)?.content);
}

Use semantic search

Enable semantic search in your graph’s memory store to let graph agents search for items in the store by semantic similarity.

import { OpenAIEmbeddings } from "@langchain/openai";
import { InMemoryStore } from "@langchain/langgraph";

// Create store with semantic search enabled
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const store = new InMemoryStore({
  index: {
    embeddings,
    dims: 1536,
  },
});

await store.put(["user_123", "memories"], "1", { text: "I love pizza" });
await store.put(["user_123", "memories"], "2", { text: "I am a plumber" });

const items = await store.search(["user_123", "memories"], {
  query: "I'm hungry",
  limit: 1,
});

Long-term memory with semantic search

import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { StateGraph, START, MessagesZodMeta, InMemoryStore } from "@langchain/langgraph";
import { BaseMessage } from "@langchain/core/messages";
import { registry } from "@langchain/langgraph/zod";
import * as z from "zod";

const MessagesZodState = z.object({
    messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
});

const model = new ChatOpenAI({ model: "gpt-4o-mini" });

// Create store with semantic search enabled
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const store = new InMemoryStore({
    index: {
    embeddings,
    dims: 1536,
    }
});

await store.put(["user_123", "memories"], "1", { text: "I love pizza" });
await store.put(["user_123", "memories"], "2", { text: "I am a plumber" });

const chat = async (state: z.infer<typeof MessagesZodState>, config) => {
    // Search based on user's last message
    const items = await config.store.search(
    ["user_123", "memories"],
    { query: state.messages.at(-1)?.content, limit: 2 }
    );
    const memories = items.map(item => item.value.text).join("\n");
    const memoriesText = memories ? `## Memories of user\n${memories}` : "";

    const response = await model.invoke([
    { role: "system", content: `You are a helpful assistant.\n${memoriesText}` },
    ...state.messages,
    ]);

    return { messages: [response] };
};

const builder = new StateGraph(MessagesZodState)
    .addNode("chat", chat)
    .addEdge(START, "chat");
const graph = builder.compile({ store });

for await (const [message, metadata] of await graph.stream(
    { messages: [{ role: "user", content: "I'm hungry" }] },
    { streamMode: "messages" }
)) {
    if (message.content) {
    console.log(message.content);
    }
}

Manage short-term memory

With short-term memory enabled, long conversations can exceed the LLM’s context window. Common solutions are:

• Trim messages: Remove first or last N messages (before calling LLM)
• Delete messages from LangGraph state permanently
• Summarize messages: Summarize earlier messages in the history and replace them with a summary
• Manage checkpoints to store and retrieve message history
• Custom strategies (e.g., message filtering, etc.)

This allows the agent to keep track of the conversation without exceeding the LLM’s context window.

Trim messages

Most LLMs have a maximum supported context window (denominated in tokens). One way to decide when to truncate messages is to count the tokens in the message history and truncate whenever it approaches that limit. If you’re using LangChain, you can use the trim messages utility and specify the number of tokens to keep from the list, as well as the strategy (e.g., keep the last maxTokens) to use for handling the boundary.

To trim message history, use the trimMessages function:

import { trimMessages } from "@langchain/core/messages";

const callModel = async (state: z.infer<typeof MessagesZodState>) => {
  const messages = trimMessages(state.messages, {
    strategy: "last",
    maxTokens: 128,
    startOn: "human",
    endOn: ["human", "tool"],
  });
  const response = await model.invoke(messages);
  return { messages: [response] };
};

const builder = new StateGraph(MessagesZodState)
  .addNode("call_model", callModel);
// ...

Full example: trim messages

import { trimMessages, BaseMessage } from "@langchain/core/messages";
import { ChatAnthropic } from "@langchain/anthropic";
import { StateGraph, START, MessagesZodMeta, MemorySaver } from "@langchain/langgraph";
import { registry } from "@langchain/langgraph/zod";
import * as z from "zod";

const MessagesZodState = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
});

const model = new ChatAnthropic({ model: "claude-3-5-sonnet-20241022" });

const callModel = async (state: z.infer<typeof MessagesZodState>) => {
  const messages = trimMessages(state.messages, {
    strategy: "last",
    maxTokens: 128,
    startOn: "human",
    endOn: ["human", "tool"],
    tokenCounter: model,
  });
  const response = await model.invoke(messages);
  return { messages: [response] };
};

const checkpointer = new MemorySaver();
const builder = new StateGraph(MessagesZodState)
  .addNode("call_model", callModel)
  .addEdge(START, "call_model");
const graph = builder.compile({ checkpointer });

const config = { configurable: { thread_id: "1" } };
await graph.invoke({ messages: [{ role: "user", content: "hi, my name is bob" }] }, config);
await graph.invoke({ messages: [{ role: "user", content: "write a short poem about cats" }] }, config);
await graph.invoke({ messages: [{ role: "user", content: "now do the same but for dogs" }] }, config);
const finalResponse = await graph.invoke({ messages: [{ role: "user", content: "what's my name?" }] }, config);

console.log(finalResponse.messages.at(-1)?.content);

Your name is Bob, as you mentioned when you first introduced yourself.

Delete messages

You can delete messages from the graph state to manage the message history. This is useful when you want to remove specific messages or clear the entire message history.

To delete messages from the graph state, you can use the RemoveMessage. For RemoveMessage to work, you need to use a state key with messagesStateReducer reducer, like MessagesZodState.

To remove specific messages:

import { RemoveMessage } from "@langchain/core/messages";

const deleteMessages = (state) => {
  const messages = state.messages;
  if (messages.length > 2) {
    // remove the earliest two messages
    return {
      messages: messages
        .slice(0, 2)
        .map((m) => new RemoveMessage({ id: m.id })),
    };
  }
};

When deleting messages, make sure that the resulting message history is valid. Check the limitations of the LLM provider you’re using. For example:

• Some providers expect message history to start with a user message
• Most providers require assistant messages with tool calls to be followed by corresponding tool result messages.

Full example: delete messages

import { RemoveMessage, BaseMessage } from "@langchain/core/messages";
import { ChatAnthropic } from "@langchain/anthropic";
import { StateGraph, START, MemorySaver, MessagesZodMeta } from "@langchain/langgraph";
import * as z from "zod";
import { registry } from "@langchain/langgraph/zod";

const MessagesZodState = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
});

const model = new ChatAnthropic({ model: "claude-3-5-sonnet-20241022" });

const deleteMessages = (state: z.infer<typeof MessagesZodState>) => {
  const messages = state.messages;
  if (messages.length > 2) {
    // remove the earliest two messages
    return { messages: messages.slice(0, 2).map(m => new RemoveMessage({ id: m.id })) };
  }
  return {};
};

const callModel = async (state: z.infer<typeof MessagesZodState>) => {
  const response = await model.invoke(state.messages);
  return { messages: [response] };
};

const builder = new StateGraph(MessagesZodState)
  .addNode("call_model", callModel)
  .addNode("delete_messages", deleteMessages)
  .addEdge(START, "call_model")
  .addEdge("call_model", "delete_messages");

const checkpointer = new MemorySaver();
const app = builder.compile({ checkpointer });

const config = { configurable: { thread_id: "1" } };

for await (const event of await app.stream(
  { messages: [{ role: "user", content: "hi! I'm bob" }] },
  { ...config, streamMode: "values" }
)) {
  console.log(event.messages.map(message => [message.getType(), message.content]));
}

for await (const event of await app.stream(
  { messages: [{ role: "user", content: "what's my name?" }] },
  { ...config, streamMode: "values" }
)) {
  console.log(event.messages.map(message => [message.getType(), message.content]));
}

[['human', "hi! I'm bob"]]
[['human', "hi! I'm bob"], ['ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?']]
[['human', "hi! I'm bob"], ['ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?'], ['human', "what's my name?"]]
[['human', "hi! I'm bob"], ['ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?'], ['human', "what's my name?"], ['ai', 'Your name is Bob.']]
[['human', "what's my name?"], ['ai', 'Your name is Bob.']]

Summarize messages

The problem with trimming or removing messages, as shown above, is that you may lose information from culling of the message queue. Because of this, some applications benefit from a more sophisticated approach of summarizing the message history using a chat model.

Prompting and orchestration logic can be used to summarize the message history. For example, in LangGraph you can include a summary key in the state alongside the messages key:

import { BaseMessage } from "@langchain/core/messages";
import { MessagesZodMeta } from "@langchain/langgraph";
import { registry } from "@langchain/langgraph/zod";
import * as z from "zod";

const State = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
  summary: z.string().optional(),
});

Then, you can generate a summary of the chat history, using any existing summary as context for the next summary. This summarizeConversation node can be called after some number of messages have accumulated in the messages state key.

import { RemoveMessage, HumanMessage } from "@langchain/core/messages";

const summarizeConversation = async (state: z.infer<typeof State>) => {
  // First, we get any existing summary
  const summary = state.summary || "";

  // Create our summarization prompt
  let summaryMessage: string;
  if (summary) {
    // A summary already exists
    summaryMessage =
      `This is a summary of the conversation to date: ${summary}\n\n` +
      "Extend the summary by taking into account the new messages above:";
  } else {
    summaryMessage = "Create a summary of the conversation above:";
  }

  // Add prompt to our history
  const messages = [
    ...state.messages,
    new HumanMessage({ content: summaryMessage })
  ];
  const response = await model.invoke(messages);

  // Delete all but the 2 most recent messages
  const deleteMessages = state.messages
    .slice(0, -2)
    .map(m => new RemoveMessage({ id: m.id }));

  return {
    summary: response.content,
    messages: deleteMessages
  };
};

Full example: summarize messages

import { ChatAnthropic } from "@langchain/anthropic";
import {
  SystemMessage,
  HumanMessage,
  RemoveMessage,
  type BaseMessage
} from "@langchain/core/messages";
import {
  MessagesZodMeta,
  StateGraph,
  START,
  END,
  MemorySaver,
} from "@langchain/langgraph";
import { BaseMessage } from "@langchain/core/messages";
import { registry } from "@langchain/langgraph/zod";
import * as z from "zod";
import { v4 as uuidv4 } from "uuid";

const memory = new MemorySaver();

// We will add a `summary` attribute (in addition to `messages` key,
// which MessagesZodState already has)
const GraphState = z.object({
  messages: z
    .array(z.custom<BaseMessage>())
    .register(registry, MessagesZodMeta),
  summary: z.string().default(""),
});

// We will use this model for both the conversation and the summarization
const model = new ChatAnthropic({ model: "claude-haiku-4-5-20251001" });

// Define the logic to call the model
const callModel = async (state: z.infer<typeof GraphState>) => {
  // If a summary exists, we add this in as a system message
  const { summary } = state;
  let { messages } = state;
  if (summary) {
    const systemMessage = new SystemMessage({
      id: uuidv4(),
      content: `Summary of conversation earlier: ${summary}`,
    });
    messages = [systemMessage, ...messages];
  }
  const response = await model.invoke(messages);
  // We return an object, because this will get added to the existing state
  return { messages: [response] };
};

// We now define the logic for determining whether to end or summarize the conversation
const shouldContinue = (state: z.infer<typeof GraphState>) => {
  const messages = state.messages;
  // If there are more than six messages, then we summarize the conversation
  if (messages.length > 6) {
    return "summarize_conversation";
  }
  // Otherwise we can just end
  return END;
};

const summarizeConversation = async (state: z.infer<typeof GraphState>) => {
  // First, we summarize the conversation
  const { summary, messages } = state;
  let summaryMessage: string;
  if (summary) {
    // If a summary already exists, we use a different system prompt
    // to summarize it than if one didn't
    summaryMessage =
      `This is summary of the conversation to date: ${summary}\n\n` +
      "Extend the summary by taking into account the new messages above:";
  } else {
    summaryMessage = "Create a summary of the conversation above:";
  }

  const allMessages = [
    ...messages,
    new HumanMessage({ id: uuidv4(), content: summaryMessage }),
  ];

  const response = await model.invoke(allMessages);

  // We now need to delete messages that we no longer want to show up
  // I will delete all but the last two messages, but you can change this
  const deleteMessages = messages
    .slice(0, -2)
    .map((m) => new RemoveMessage({ id: m.id! }));

  if (typeof response.content !== "string") {
    throw new Error("Expected a string response from the model");
  }

  return { summary: response.content, messages: deleteMessages };
};

// Define a new graph
const workflow = new StateGraph(GraphState)
  // Define the conversation node and the summarize node
  .addNode("conversation", callModel)
  .addNode("summarize_conversation", summarizeConversation)
  // Set the entrypoint as conversation
  .addEdge(START, "conversation")
  // We now add a conditional edge
  .addConditionalEdges(
    // First, we define the start node. We use `conversation`.
    // This means these are the edges taken after the `conversation` node is called.
    "conversation",
    // Next, we pass in the function that will determine which node is called next.
    shouldContinue,
  )
  // We now add a normal edge from `summarize_conversation` to END.
  // This means that after `summarize_conversation` is called, we end.
  .addEdge("summarize_conversation", END);

// Finally, we compile it!
const app = workflow.compile({ checkpointer: memory });

Manage checkpoints

You can view and delete the information stored by the checkpointer.

View thread state

const config = {
  configurable: {
    thread_id: "1",
    // optionally provide an ID for a specific checkpoint,
    // otherwise the latest checkpoint is shown
    // checkpoint_id: "1f029ca3-1f5b-6704-8004-820c16b69a5a"
  },
};
await graph.getState(config);

{
  values: { messages: [HumanMessage(...), AIMessage(...), HumanMessage(...), AIMessage(...)] },
  next: [],
  config: { configurable: { thread_id: '1', checkpoint_ns: '', checkpoint_id: '1f029ca3-1f5b-6704-8004-820c16b69a5a' } },
  metadata: {
    source: 'loop',
    writes: { call_model: { messages: AIMessage(...) } },
    step: 4,
    parents: {},
    thread_id: '1'
  },
  createdAt: '2025-05-05T16:01:24.680462+00:00',
  parentConfig: { configurable: { thread_id: '1', checkpoint_ns: '', checkpoint_id: '1f029ca3-1790-6b0a-8003-baf965b6a38f' } },
  tasks: [],
  interrupts: []
}

View the history of the thread

const config = {
  configurable: {
    thread_id: "1",
  },
};

const history = [];
for await (const state of graph.getStateHistory(config)) {
  history.push(state);
}

Delete all checkpoints for a thread

const threadId = "1";
await checkpointer.deleteThread(threadId);

Database management

If you are using any database-backed persistence implementation (such as Postgres or Redis) to store short and/or long-term memory, you will need to run migrations to set up the required schema before you can use it with your database.

By convention, most database-specific libraries define a setup() method on the checkpointer or store instance that runs the required migrations. However, you should check with your specific implementation of BaseCheckpointSaver or BaseStore to confirm the exact method name and usage.

We recommend running migrations as a dedicated deployment step, or you can ensure they’re run as part of server startup.

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