How to stream agent data to the client

This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. The code in this doc is taken from the page.tsx and action.ts files in this directory. To view the full, uninterrupted code, click here for the actions file and here for the client file.

Prerequisites

This guide assumes familiarity with the following concepts:

• LangChain Expression Language
• Chat models
• Tool calling
• Agents

Setup

First, install the necessary LangChain & AI SDK packages:

npm

npm install langchain @langchain/core @langchain/community ai

Yarn

yarn add langchain @langchain/core @langchain/community ai

pnpm

pnpm add langchain @langchain/core @langchain/community ai

In this demo we'll be using the TavilySearchResults tool, which requires an API key. You can get one here, or you can swap it out for another tool of your choice, like WikipediaQueryRun which doesn't require an API key.

If you choose to use TavilySearchResults, set your API key like so:

export TAVILY_API_KEY=your_api_key

Get started

The first step is to create a new RSC file, and add the imports which we'll use for running our agent. In this demo, we'll name it action.ts:

"use server";

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
import { pull } from "langchain/hub";
import { createStreamableValue } from "ai/rsc";

Next, we'll define a runAgent function. This function takes in a single input of string, and contains all the logic for our agent and streaming data back to the client:

export async function runAgent(input: string) {
  "use server";
}

Next, inside our function we'll define our chat model of choice:

const llm = new ChatOpenAI({
  model: "gpt-4o-2024-05-13",
  temperature: 0,
});

Next, we'll use the createStreamableValue helper function provided by the ai package to create a streamable value:

const stream = createStreamableValue();

This will be very important later on when we start streaming data back to the client.

Next, lets define our async function inside which contains the agent logic:

  (async () => {
    const tools = [new TavilySearchResults({ maxResults: 1 })];

    const prompt = await pull<ChatPromptTemplate>(
      "hwchase17/openai-tools-agent",
    );

    const agent = createToolCallingAgent({
      llm,
      tools,
      prompt,
    });

    const agentExecutor = new AgentExecutor({
      agent,
      tools,
    });

tip

As of langchain version 0.2.8, the createToolCallingAgent function now supports OpenAI-formatted tools.

Here you can see we're doing a few things:

The first is we're defining our list of tools (in this case we're only using a single tool) and pulling in our prompt from the LangChain prompt hub.

After that, we're passing our LLM, tools and prompt to the createToolCallingAgent function, which will construct and return a runnable agent. This is then passed into the AgentExecutor class, which will handle the execution & streaming of our agent.

Finally, we'll call .streamEvents and pass our streamed data back to the stream variable we defined above,

    const streamingEvents = agentExecutor.streamEvents(
      { input },
      { version: "v1" },
    );

    for await (const item of streamingEvents) {
      stream.update(JSON.parse(JSON.stringify(item, null, 2)));
    }

    stream.done();
  })();

As you can see above, we're doing something a little wacky by stringifying and parsing our data. This is due to a bug in the RSC streaming code, however if you stringify and parse like we are above, you shouldn't experience this.

Finally, at the bottom of the function return the stream value:

return { streamData: stream.value };

Once we've implemented our server action, we can add a couple lines of code in our client function to request and stream this data:

First, add the necessary imports:

"use client";

import { useState } from "react";
import { readStreamableValue } from "ai/rsc";
import { runAgent } from "./action";

Then inside our Page function, calling the runAgent function is straightforward:

export default function Page() {
  const [input, setInput] = useState("");
  const [data, setData] = useState<StreamEvent[]>([]);

  async function handleSubmit(e: React.FormEvent) {
    e.preventDefault();

    const { streamData } = await runAgent(input);
    for await (const item of readStreamableValue(streamData)) {
      setData((prev) => [...prev, item]);
    }
  }
}

That's it! You've successfully built an agent that streams data back to the client. You can now run your application and see the data streaming in real-time.