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Introduction

A middleware implementation allows you to translate existing protocols and applications to AG-UI events. This approach creates a bridge between your existing system and AG-UI, making it perfect for adding agent capabilities to current applications.

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When to use a middleware implementation

Middleware is the flexible option. It allows you to translate existing protocols and applications to AG-UI events creating a bridge between your existing system and AG-UI.

Middleware is great for:

  • Taking your existing protocol or API and translating it universally
  • Working within the confines of an existing system or framework
  • When you don’t have direct control over the agent framework or system

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What you’ll build

In this guide, we’ll create a middleware agent that:

  1. Extends the AbstractAgent class
  2. Connects to OpenAI’s GPT-4o model
  3. Translates OpenAI responses to AG-UI events
  4. Runs in-process with your application

This approach gives you maximum flexibility to integrate with existing codebases while maintaining the full power of the AG-UI protocol.

Let’s get started!

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Prerequisites

Before we begin, make sure you have:

  • Node.js v16 or later
  • An OpenAI API key

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1. Provide your OpenAI API key

First, let’s set up your API key:

# Set your OpenAI API key
export OPENAI_API_KEY=your-api-key-here

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2. Install build utilities

Install the following tools:

brew install protobuf

npm i turbo

curl -fsSL https://get.pnpm.io/install.sh | sh -

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Step 1 – Scaffold your integration

Start by cloning the repo and navigating to the TypeScript SDK:

git clone git@github.com:ag-ui-protocol/ag-ui.git
cd ag-ui/typescript-sdk

Copy the middleware-starter template to create your OpenAI integration:

cp -r integrations/middleware-starter integrations/openai

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Update metadata

Open integrations/openai/package.json and update the fields to match your new folder:

{
  "name": "@ag-ui/openai",
  "author": "Your Name <your-email@example.com>",
  "version": "0.0.1",

  ... rest of package.json
}

Next, update the class name inside integrations/openai/src/index.ts:

// change the name to OpenAIAgent
export class OpenAIAgent extends AbstractAgent {}

Finally, introduce your integration to the dojo by adding it to apps/dojo/src/menu.ts:

// ...
export const menuIntegrations: MenuIntegrationConfig[] = [
  // ...

  {
    id: "openai",
    name: "OpenAI",
    features: ["agentic_chat"],
  },
]

And apps/dojo/src/agents.ts:

// ...
import { OpenAIAgent } from "@ag-ui/openai"

export const agentsIntegrations: AgentIntegrationConfig[] = [
  // ...

  {
    id: "openai",
    agents: async () => {
      return {
        agentic_chat: new OpenAIAgent(),
      }
    },
  },
]

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Step 2 – Add package to dojo dependencies

Open apps/dojo/package.json and add the package @ag-ui/openai:

{
  "name": "demo-viewer",
  "version": "0.1.0",
  "private": true,
  "scripts": {
    "dev": "next dev",
    "build": "next build",
    "start": "next start",
    "lint": "next lint"
  },
  "dependencies": {
    "@ag-ui/agno": "workspace:*",
    "@ag-ui/langgraph": "workspace:*",
    "@ag-ui/mastra": "workspace:*",
    "@ag-ui/middleware-starter": "workspace:*",
    "@ag-ui/server-starter": "workspace:*",
    "@ag-ui/server-starter-all-features": "workspace:*",
    "@ag-ui/vercel-ai-sdk": "workspace:*",
    "@ag-ui/openai": "workspace:*", <- Add this line

  ... rest of package.json
}

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Step 3 – Start the dojo

Now let’s see your work in action:

# Install dependencies
pnpm install

# Compile the project and run the dojo
turbo run dev

Head over to http://localhost:3000 and choose OpenAI from the drop-down. You’ll see the stub agent replies with Hello world! for now.

Here’s what’s happening with that stub agent:

// integrations/openai/src/index.ts
import {
  AbstractAgent,
  BaseEvent,
  EventType,
  RunAgentInput,
} from "@ag-ui/client"
import { Observable } from "rxjs"

export class OpenAIAgent extends AbstractAgent {
  protected run(input: RunAgentInput): Observable<BaseEvent> {
    const messageId = Date.now().toString()
    return new Observable<BaseEvent>((observer) => {
      observer.next({
        type: EventType.RUN_STARTED,
        threadId: input.threadId,
        runId: input.runId,
      } as any)

      observer.next({
        type: EventType.TEXT_MESSAGE_START,
        messageId,
      } as any)

      observer.next({
        type: EventType.TEXT_MESSAGE_CONTENT,
        messageId,
        delta: "Hello world!",
      } as any)

      observer.next({
        type: EventType.TEXT_MESSAGE_END,
        messageId,
      } as any)

      observer.next({
        type: EventType.RUN_FINISHED,
        threadId: input.threadId,
        runId: input.runId,
      } as any)

      observer.complete()
    })
  }
}

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Step 4 – Bridge OpenAI with AG-UI

Let’s transform our stub into a real agent that streams completions from OpenAI.

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Install the OpenAI SDK

First, we need the OpenAI SDK:

cd integrations/openai
pnpm install openai

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AG-UI recap

An AG-UI agent extends AbstractAgent and emits a sequence of events to signal:

  • lifecycle events (RUN_STARTED, RUN_FINISHED, RUN_ERROR)
  • content events (TEXT_MESSAGE_*, TOOL_CALL_*, and more)

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Implement the streaming agent

Now we’ll transform our stub agent into a real OpenAI integration. The key difference is that instead of sending a hardcoded “Hello world!” message, we’ll connect to OpenAI’s API and stream the response back through AG-UI events.

The implementation follows the same event flow as our stub, but we’ll add the OpenAI client initialization in the constructor and replace our mock response with actual API calls. We’ll also handle tool calls if they’re present in the response, making our agent fully capable of using functions when needed.

// integrations/openai/src/index.ts
import {
  AbstractAgent,
  RunAgentInput,
  EventType,
  BaseEvent,
} from "@ag-ui/client"
import { Observable } from "rxjs"

import { OpenAI } from "openai"

export class OpenAIAgent extends AbstractAgent {
  private openai: OpenAI

  constructor(openai?: OpenAI) {
    super()
    // Initialize OpenAI client - uses OPENAI_API_KEY from environment if not provided
    this.openai = openai ?? new OpenAI()
  }

  protected run(input: RunAgentInput): Observable<BaseEvent> {
    return new Observable<BaseEvent>((observer) => {
      // Same as before - emit RUN_STARTED to begin
      observer.next({
        type: EventType.RUN_STARTED,
        threadId: input.threadId,
        runId: input.runId,
      } as any)

      // NEW: Instead of hardcoded response, call OpenAI's API
      this.openai.chat.completions
        .create({
          model: "gpt-4o",
          stream: true, // Enable streaming for real-time responses
          // Convert AG-UI tools format to OpenAI's expected format
          tools: input.tools.map((tool) => ({
            type: "function",
            function: {
              name: tool.name,
              description: tool.description,
              parameters: tool.parameters,
            },
          })),
          // Transform AG-UI messages to OpenAI's message format
          messages: input.messages.map((message) => ({
            role: message.role as any,
            content: message.content ?? "",
            // Include tool calls if this is an assistant message with tools
            ...(message.role === "assistant" && message.toolCalls
              ? {
                  tool_calls: message.toolCalls,
                }
              : {}),
            // Include tool call ID if this is a tool result message
            ...(message.role === "tool"
              ? { tool_call_id: message.toolCallId }
              : {}),
          })),
        })
        .then(async (response) => {
          const messageId = Date.now().toString()

          // NEW: Stream each chunk from OpenAI's response
          for await (const chunk of response) {
            // Handle text content chunks
            if (chunk.choices[0].delta.content) {
              observer.next({
                type: EventType.TEXT_MESSAGE_CHUNK, // Chunk events open and close messages automatically
                messageId,
                delta: chunk.choices[0].delta.content,
              } as any)
            }
            // Handle tool call chunks (when the model wants to use a function)
            else if (chunk.choices[0].delta.tool_calls) {
              let toolCall = chunk.choices[0].delta.tool_calls[0]

              observer.next({
                type: EventType.TOOL_CALL_CHUNK,
                toolCallId: toolCall.id,
                toolCallName: toolCall.function?.name,
                parentMessageId: messageId,
                delta: toolCall.function?.arguments,
              } as any)
            }
          }

          // Same as before - emit RUN_FINISHED when complete
          observer.next({
            type: EventType.RUN_FINISHED,
            threadId: input.threadId,
            runId: input.runId,
          } as any)

          observer.complete()
        })
        // NEW: Handle errors from the API
        .catch((error) => {
          observer.next({
            type: EventType.RUN_ERROR,
            message: error.message,
          } as any)

          observer.error(error)
        })
    })
  }
}

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What happens under the hood?

Let’s break down what your agent is doing:

  1. Setup – We create an OpenAI client and emit RUN_STARTED
  2. Request – We send the user’s messages to chat.completions with stream: true
  3. Streaming – We forward each chunk as either TEXT_MESSAGE_CHUNK or TOOL_CALL_CHUNK
  4. Finish – We emit RUN_FINISHED (or RUN_ERROR if something goes wrong) and complete the observable

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Step 4 – Chat with your agent

Reload the dojo page and start typing. You’ll see GPT-4o streaming its answer in real-time, word by word.

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Bridging AG-UI to any protocol

The pattern you just implemented—translate inputs, forward streaming chunks, emit AG-UI events—works for virtually any backend:

  • REST or GraphQL APIs
  • WebSockets
  • IoT protocols such as MQTT

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Connect your agent to a frontend

Tools like CopilotKit already understand AG-UI and provide plug-and-play React components. Point them at your agent endpoint and you get a full-featured chat UI out of the box.

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Share your integration

Did you build a custom adapter that others could reuse? We welcome community contributions!

  1. Fork the AG-UI repository
  2. Add your package under typescript-sdk/integrations/. See Contributing for more details and naming conventions.
  3. Open a pull request describing your use-case and design decisions

If you have questions, need feedback, or want to validate an idea first, start a thread in the GitHub Discussions board: AG-UI GitHub Discussions board.

Your integration might ship in the next release and help the entire AG-UI ecosystem grow.

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Conclusion

You now have a fully-functional AG-UI adapter for OpenAI and a local playground to test it. From here you can:

  • Add tool calls to enhance your agent
  • Publish your integration to npm
  • Bridge AG-UI to any other model or service

Happy building!