Streaming

Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:

1. Agent progress — get updates after each node in the agent graph is executed.
2. LLM tokens — stream tokens as they are generated by the language model.
3. Custom updates — emit custom data from tools during execution (e.g., "Fetched 10/100 records")

You can stream more than one type of data at a time.

Waiting is for pigeons.

Agent progress

To stream agent progress, use the stream() method with streamMode: "updates". This emits an event after every agent step.

For example, if you have an agent that calls a tool once, you should see the following updates:

• LLM node: AI message with tool call requests
• Tool node: Tool message with execution result
• LLM node: Final AI response

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";

const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
const agent = createReactAgent({
  llm,
  tools: [getWeather],
});
for await (const chunk of await agent.stream(
  { messages: "what is the weather in sf" },
  { streamMode: "updates" }
)) {
  console.log(chunk);
  console.log("\n");
}

LLM tokens

To stream tokens as they are produced by the LLM, use streamMode: "messages":

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";

const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
const agent = createReactAgent({
  llm,
  tools: [getWeather],
});
for await (const [token, metadata] of await agent.stream(
  { messages: "what is the weather in sf" },
  { streamMode: "messages" }
)) {
  console.log("Token", token);
  console.log("Metadata", metadata);
  console.log("\n");
}

Tool updates

To stream updates from tools as they are executed, you can use writer object available via config.writer:

import { LangGraphRunnableConfig } from "@langchain/langgraph";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";

const getWeather = tool(
  async (input: { city: string }, config: LangGraphRunnableConfig) => {
    // stream any arbitrary data
    config.writer?.(`Looking up data for city: ${input.city}`);
    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],
});

for await (const chunk of await agent.stream(
  { messages: "what is the weather in sf" },
  { streamMode: "custom" }
)) {
  console.log(chunk);
  console.log("\n");
}

Stream multiple modes

You can specify multiple streaming modes by passing stream mode as a list: streamMode: ["updates", "messages", "custom"]:

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { initChatModel } from "langchain/chat_models/universal";

const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
const agent = createReactAgent({
  llm,
  tools: [getWeather],
});

for await (const [streamMode, chunk] of await agent.stream(
  { messages: "what is the weather in sf" },
  { streamMode: ["updates", "messages", "custom"] }
)) {
  console.log(streamMode, chunk);
  console.log("\n");
}

Additional resources

• Streaming in LangGraph