Quickstart: Conversation

Get started with the Dapr conversation building block

Alpha

The conversation building block is currently in alpha.

Let’s take a look at how the Dapr conversation building block makes interacting with Large Language Models (LLMs) easier. In this quickstart, you use the echo component to communicate with the mock LLM and ask it to define Dapr.

You can try out this conversation quickstart by either:

Note

Currently, you can only use JavaScript for the quickstart sample using HTTP, not the JavaScript SDK.

Run the app with the template file

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/python/sdk/conversation

Install the dependencies:

pip3 install -r requirements.txt

Step 3: Launch the conversation service

Navigate back to the sdk directory and start the conversation service with the following command:

dapr run -f .

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

What happened?

When you ran dapr init during Dapr install, the dapr.yaml Multi-App Run template file was generated in the .dapr/components directory.

Running dapr run -f . in this Quickstart started conversation.go.

`dapr.yaml` Multi-App Run template file

Running the Multi-App Run template file with dapr run -f . starts all applications in your project. This Quickstart has only one application, so the dapr.yaml file contains the following:

version: 1
common:
  resourcesPath: ../../components/
apps:
  - appID: conversation
    appDirPath: ./conversation/
    command: ["python3", "app.py"]

Echo mock LLM component

In conversation/components directly of the quickstart, the conversation.yaml file configures the echo LLM component.

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: echo
spec:
  type: conversation.echo
  version: v1

To interface with a real LLM, swap out the mock component with one of the supported conversation components. For example, to use an OpenAI component, see the example in the conversation how-to guide

`app.py` conversation app

In the application code:

The app sends an input “What is dapr?” to the echo mock LLM component.
The mock LLM echoes “What is dapr?”.

from dapr.clients import DaprClient
from dapr.clients.grpc._request import ConversationInput

with DaprClient() as d:
    inputs = [
        ConversationInput(content="What is dapr?", role='user', scrub_pii=True),
    ]

    metadata = {
        'model': 'modelname',
        'key': 'authKey',
        'cacheTTL': '10m',
    }

    print('Input sent: What is dapr?')

    response = d.converse_alpha1(
        name='echo', inputs=inputs, temperature=0.7, context_id='chat-123', metadata=metadata
    )

    for output in response.outputs:
        print(f'Output response: {output.result}')

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/javascript/http/conversation

Install the dependencies:

npm install

Step 3: Launch the conversation service

Navigate back to the http directory and start the conversation service with the following command:

dapr run -f .

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

What happened?

When you ran dapr init during Dapr install, the dapr.yaml Multi-App Run template file was generated in the .dapr/components directory.

Running dapr run -f . in this Quickstart started conversation.go.

`dapr.yaml` Multi-App Run template file

Running the Multi-App Run template file with dapr run -f . starts all applications in your project. This Quickstart has only one application, so the dapr.yaml file contains the following:

version: 1
common:
  resourcesPath: ../../components/
apps:
  - appID: conversation
    appDirPath: ./conversation/
    daprHTTPPort: 3502
    command: ["npm", "run", "start"]

Echo mock LLM component

In conversation/components directly of the quickstart, the conversation.yaml file configures the echo LLM component.

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: echo
spec:
  type: conversation.echo
  version: v1

To interface with a real LLM, swap out the mock component with one of the supported conversation components. For example, to use an OpenAI component, see the example in the conversation how-to guide

`index.js` conversation app

In the application code:

The app sends an input “What is dapr?” to the echo mock LLM component.
The mock LLM echoes “What is dapr?”.

const conversationComponentName = "echo";

async function main() {
  const daprHost = process.env.DAPR_HOST || "http://localhost";
  const daprHttpPort = process.env.DAPR_HTTP_PORT || "3500";

  const inputBody = {
    name: "echo",
    inputs: [{ message: "What is dapr?" }],
    parameters: {},
    metadata: {},
  };

  const reqURL = `${daprHost}:${daprHttpPort}/v1.0-alpha1/conversation/${conversationComponentName}/converse`;

  try {
    const response = await fetch(reqURL, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify(inputBody),
    });

    console.log("Input sent: What is dapr?");

    const data = await response.json();
    const result = data.outputs[0].result;
    console.log("Output response:", result);
  } catch (error) {
    console.error("Error:", error.message);
    process.exit(1);
  }
}

main().catch((error) => {
  console.error("Unhandled error:", error);
  process.exit(1);
});

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/csharp/sdk

Step 3: Launch the conversation service

Start the conversation service with the following command:

dapr run -f .

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

What happened?

When you ran dapr init during Dapr install, the dapr.yaml Multi-App Run template file was generated in the .dapr/components directory.

Running dapr run -f . in this Quickstart started the conversation Program.cs.

`dapr.yaml` Multi-App Run template file

Running the Multi-App Run template file with dapr run -f . starts all applications in your project. This Quickstart has only one application, so the dapr.yaml file contains the following:

version: 1
common:
  resourcesPath: ../../components/
apps:
  - appDirPath: ./conversation/
    appID: conversation
    daprHTTPPort: 3500
    command: ["dotnet", "run"]

Echo mock LLM component

In conversation/components, the conversation.yaml file configures the echo mock LLM component.

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: echo
spec:
  type: conversation.echo
  version: v1

To interface with a real LLM, swap out the mock component with one of the supported conversation components. For example, to use an OpenAI component, see the example in the conversation how-to guide

`Program.cs` conversation app

In the application code:

The app sends an input “What is dapr?” to the echo mock LLM component.
The mock LLM echoes “What is dapr?”.

using Dapr.AI.Conversation;
using Dapr.AI.Conversation.Extensions;

class Program
{
  private const string ConversationComponentName = "echo";

  static async Task Main(string[] args)
  {
    const string prompt = "What is dapr?";

    var builder = WebApplication.CreateBuilder(args);
    builder.Services.AddDaprConversationClient();
    var app = builder.Build();

    //Instantiate Dapr Conversation Client
    var conversationClient = app.Services.GetRequiredService<DaprConversationClient>();

    try
    {
      // Send a request to the echo mock LLM component
      var response = await conversationClient.ConverseAsync(ConversationComponentName, [new(prompt, DaprConversationRole.Generic)]);
      Console.WriteLine("Input sent: " + prompt);

      if (response != null)
      {
        Console.Write("Output response:");
        foreach (var resp in response.Outputs)
        {
          Console.WriteLine($" {resp.Result}");
        }
      }
    }
    catch (Exception ex)
    {
      Console.WriteLine("Error: " + ex.Message);
    }
  }
}

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/go/sdk

Step 3: Launch the conversation service

Start the conversation service with the following command:

dapr run -f .

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

What happened?

When you ran dapr init during Dapr install, the dapr.yaml Multi-App Run template file was generated in the .dapr/components directory.

Running dapr run -f . in this Quickstart started conversation.go.

`dapr.yaml` Multi-App Run template file

Running the Multi-App Run template file with dapr run -f . starts all applications in your project. This Quickstart has only one application, so the dapr.yaml file contains the following:

version: 1
common:
  resourcesPath: ../../components/
apps:
  - appDirPath: ./conversation/
    appID: conversation
    daprHTTPPort: 3501
    command: ["go", "run", "."]

Echo mock LLM component

In conversation/components directly of the quickstart, the conversation.yaml file configures the echo LLM component.

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: echo
spec:
  type: conversation.echo
  version: v1

To interface with a real LLM, swap out the mock component with one of the supported conversation components. For example, to use an OpenAI component, see the example in the conversation how-to guide

`conversation.go` conversation app

In the application code:

The app sends an input “What is dapr?” to the echo mock LLM component.
The mock LLM echoes “What is dapr?”.

package main

import (
	"context"
	"fmt"
	"log"

	dapr "github.com/dapr/go-sdk/client"
)

func main() {
	client, err := dapr.NewClient()
	if err != nil {
		panic(err)
	}

	input := dapr.ConversationInput{
		Message: "What is dapr?",
		// Role:     nil, // Optional
		// ScrubPII: nil, // Optional
	}

	fmt.Println("Input sent:", input.Message)

	var conversationComponent = "echo"

	request := dapr.NewConversationRequest(conversationComponent, []dapr.ConversationInput{input})

	resp, err := client.ConverseAlpha1(context.Background(), request)
	if err != nil {
		log.Fatalf("err: %v", err)
	}

	fmt.Println("Output response:", resp.Outputs[0].Result)
}

Run the app without the template

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/python/sdk/conversation

Install the dependencies:

pip3 install -r requirements.txt

Step 3: Launch the conversation service

Navigate back to the sdk directory and start the conversation service with the following command:

dapr run --app-id conversation --resources-path ../../../components -- python3 app.py

Note: Since Python3.exe is not defined in Windows, you may need to use python app.py instead of python3 app.py.

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/javascript/http/conversation

Install the dependencies:

npm install

Step 3: Launch the conversation service

Navigate back to the http directory and start the conversation service with the following command:

dapr run --app-id conversation --resources-path ../../../components/ -- npm run start

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/csharp/sdk/conversation

Install the dependencies:

dotnet build

Step 3: Launch the conversation service

Start the conversation service with the following command:

dapr run --app-id conversation --resources-path ../../../components/ -- dotnet run

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

Step 1: Pre-requisites

For this example, you will need:

Docker Desktop

Step 2: Set up the environment

Clone the sample provided in the Quickstarts repo.

git clone https://github.com/dapr/quickstarts.git

From the root of the Quickstarts directory, navigate into the conversation directory:

cd conversation/go/sdk/conversation

Install the dependencies:

go build .

Step 3: Launch the conversation service

Start the conversation service with the following command:

dapr run --app-id conversation --resources-path ../../../components/ -- go run .

Expected output

== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?

Demo

Watch the demo presented during Diagrid’s Dapr v1.15 celebration to see how the conversation API works using the .NET SDK.

Tell us what you think!

We’re continuously working to improve our Quickstart examples and value your feedback. Did you find this Quickstart helpful? Do you have suggestions for improvement?

Join the discussion in our discord channel.

Next steps

HTTP samples of this quickstart:
Learn more about the conversation building block

Explore Dapr tutorials >>

Last modified July 18, 2025: Upgrade hugo docsy into 1.16 (#4733) (b2fdd345)

Quickstart: Conversation

Alpha

Note

Run the app with the template file

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

What happened?

dapr.yaml Multi-App Run template file

Echo mock LLM component

app.py conversation app

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

What happened?

dapr.yaml Multi-App Run template file

Echo mock LLM component

index.js conversation app

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

What happened?

dapr.yaml Multi-App Run template file

Echo mock LLM component

Program.cs conversation app

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

What happened?

dapr.yaml Multi-App Run template file

Echo mock LLM component

conversation.go conversation app

Run the app without the template

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

Step 1: Pre-requisites

Step 2: Set up the environment

Step 3: Launch the conversation service

Demo

Tell us what you think!

Next steps

`dapr.yaml` Multi-App Run template file

`app.py` conversation app

`dapr.yaml` Multi-App Run template file

`index.js` conversation app

`dapr.yaml` Multi-App Run template file

`Program.cs` conversation app

`dapr.yaml` Multi-App Run template file

`conversation.go` conversation app