More testing coverage (more quickstarts) (#25)

* Completes all OpenAI LLM calls quickstarts

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Completes all OpenAI LLM calls quickstarts

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* More tests for multi agent actor workflows (adds llm and random orchestrators)

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Adds nvidia tests

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Adds dapr llm tests

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Readds the diff workflows

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* fix orchestrator names

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Multi agent workflows - agents as dapr workflows

Signed-off-by: Elena Kolevska <elena@kolevska.com>

not needed

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Adds a github workflow to validate quickstarts

Signed-off-by: Elena Kolevska <elena@kolevska.com>

* Adds elevenlabs llm test

Signed-off-by: Elena Kolevska <elena@kolevska.com>

---------

Signed-off-by: Elena Kolevska <elena@kolevska.com>

.github/workflows/e2e-tests.yaml

@@ -0,0 +1,42 @@
+#
+# Copyright 2025 The Dapr Authors
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+name: E2E Tests
+
+on:
+  workflow_dispatch:
+
+jobs:
+  test:
+    name: Run tests
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.11
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+      - name: Validate quickstarts
+        env:
+          OPENAI_BASE_URL: "https://api.openai.com/v1"
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
+          ELEVENLABS_API_KEY: ${{ secrets.ELEVENLABS_API_KEY }}
+          HUGGINGFACE_API_KEY: ${{ secrets.HUGGINGFACE_API_KEY }}
+        run: |
+          make validate-quickstarts

.github/workflows/release.yml

@@ -6,7 +6,7 @@
 #     http://www.apache.org/licenses/LICENSE-2.0
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implieh.
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #

.gitignore

@@ -163,3 +163,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+.idea

README.md

@@ -116,6 +116,3 @@ pip install mkdocs-jupyter
 ```bash
 mkdocs serve
 ```
-
-## Acknowledgments
-Dapr Agents was born out of a desire to explore and learn more about [Dapr](https://dapr.io/) and its potential for building agentic systems. I wanted to understand how to deploy agents as services, manage message communication, and connect various components effectively. Along the way, I looked to several established frameworks for ideas and guidance, which helped shape my thinking and approach:

quickstarts/01-hello-world/README.md

@@ -255,4 +255,4 @@ In the later quickstarts, you'll see explicit Dapr integration through state sto
 
 ## Next Steps
 
-After completing these examples, move on to the [LLM Call quickstart](../02-llm-call) to learn more about structured outputs from LLMs.
+After completing these examples, move on to the [LLM Call quickstart](../02_llm_call_open_ai) to learn more about structured outputs from LLMs.

quickstarts/02-llm-call/text_completion.py

@@ -1,12 +0,0 @@
-from dapr_agents import OpenAIChatClient
-from dotenv import load_dotenv
-
-# Load environment variables from .env
-load_dotenv()
-
-# Initialize the chat client and call
-llm = OpenAIChatClient()
-response = llm.generate("Name a famous dog!")
-
-if len(response.get_content()) > 0:
-    print("Response: ", response.get_content())

quickstarts/02_llm_call_dapr/README.md

@@ -0,0 +1,77 @@
+# OpenAI LLM calls with Dapr Agents
+
+This quickstart demonstrates how to use Dapr Agents' LLM capabilities to interact with language models and generate both free-form text and structured data. You'll learn how to make basic calls to LLMs and how to extract structured information in a type-safe manner.
+
+## Prerequisites
+
+- Python 3.10 (recommended)
+- pip package manager
+
+## Environment Setup
+
+```bash
+# Create a virtual environment
+python3.10 -m venv .venv
+
+# Activate the virtual environment 
+# On Windows:
+.venv\Scripts\activate
+# On macOS/Linux:
+source .venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Examples
+
+### Text
+
+**1. Run the basic text completion example:**
+
+<!-- STEP
+name: Run text completion example
+expected_stdout_lines:
+  - "Response:"
+  - "Response with prompty:"
+  - "Response with user input:"
+timeout_seconds: 30
+output_match_mode: substring
+-->
+```bash
+dapr run --app-id daprllm --resources-path components/ -- python text_completion.py
+```
+<!-- END_STEP -->
+
+The script demonstrates basic usage of the DaprChatClient for text generation:
+
+```python
+import os
+
+from dapr_agents.llm import DaprChatClient
+from dapr_agents.types import UserMessage
+
+os.environ['DAPR_LLM_COMPONENT_DEFAULT'] = 'echo'
+
+# Basic chat completion
+llm = DaprChatClient()
+response = llm.generate("Name a famous dog!")
+
+if len(response.get_content()) > 0:
+    print("Response: ", response.get_content())
+
+# Chat completion using a prompty file for context
+llm = DaprChatClient.from_prompty('basic.prompty')
+response = llm.generate(input_data={"question":"What is your name?"})
+
+if len(response.get_content()) > 0:
+    print("Response with prompty: ", response.get_content())
+
+# Chat completion with user input
+llm = DaprChatClient()
+response = llm.generate(messages=[UserMessage("hello")])
+
+
+if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
+    print("Response with user input: ", response.get_content())
+```

quickstarts/02_llm_call_dapr/basic.prompty

@@ -0,0 +1,23 @@
+---
+name: Basic Prompt
+description: A basic prompt that uses the chat API to answer questions
+model:
+    api: chat
+    configuration:
+        type: nvidia
+        name: meta/llama3-8b-instruct
+    parameters:
+        max_tokens: 128
+        temperature: 0.2
+inputs:
+  question:
+    type: string
+sample:
+  "question": "Who is the most famous person in the world?"
+---
+system:
+You are an AI assistant who helps people find information.
+As the assistant, you answer questions briefly, succinctly.
+
+user:
+{{question}}

quickstarts/02_llm_call_dapr/components/echo.yaml

@@ -0,0 +1,7 @@
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: echo
+spec:
+  type: conversation.echo
+  version: v1

quickstarts/02_llm_call_dapr/requirements.txt

@@ -0,0 +1 @@
+dapr-agents==0.1.dev26

quickstarts/02_llm_call_dapr/text_completion.py

@@ -0,0 +1,28 @@
+import os
+
+from dapr_agents.llm import DaprChatClient
+from dapr_agents.types import UserMessage
+
+os.environ['DAPR_LLM_COMPONENT_DEFAULT'] = 'echo'
+
+# Basic chat completion
+llm = DaprChatClient()
+response = llm.generate("Name a famous dog!")
+
+if len(response.get_content()) > 0:
+    print("Response: ", response.get_content())
+
+# Chat completion using a prompty file for context
+llm = DaprChatClient.from_prompty('basic.prompty')
+response = llm.generate(input_data={"question":"What is your name?"})
+
+if len(response.get_content()) > 0:
+    print("Response with prompty: ", response.get_content())
+
+# Chat completion with user input
+llm = DaprChatClient()
+response = llm.generate(messages=[UserMessage("hello")])
+
+
+if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
+    print("Response with user input: ", response.get_content())

quickstarts/02_llm_call_elevenlabs/README.md

@@ -0,0 +1,85 @@
+# OpenAI LLM calls with Dapr Agents
+
+This quickstart demonstrates how to use Dapr Agents' LLM capabilities to interact with language models and generate both free-form text and structured data. You'll learn how to make basic calls to LLMs and how to extract structured information in a type-safe manner.
+
+## Prerequisites
+
+- Python 3.10 (recommended)
+- pip package manager
+- OpenAI API key
+
+## Environment Setup
+
+```bash
+# Create a virtual environment
+python3.10 -m venv .venv
+
+# Activate the virtual environment 
+# On Windows:
+.venv\Scripts\activate
+# On macOS/Linux:
+source .venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Configuration
+
+Create a `.env` file in the project root:
+
+```env
+ELEVENLABS_API_KEY=your_api_key_here
+```
+
+Replace `your_api_key_here` with your actual Elevenlabs API key.
+
+## Examples
+
+### Audio
+You can use the OpenAIAudioClient in `dapr-agents` for basic tasks with the OpenAI Audio API. We will explore:
+
+- Generating speech from text and saving it as an MP3 file.
+- Transcribing audio to text.
+- Translating audio content to English.
+
+**1. Run the text to speech example:**
+
+
+<!-- STEP
+name: Run audio generation example
+expected_stdout_lines:
+  - "Audio saved to output_speech.mp3"
+  - "File output_speech.mp3 has been deleted."
+-->
+```bash
+python text_to_speech.py
+```
+<!-- END_STEP -->
+
+## Key Concepts
+
+- **OpenAIChatClient**: The interface for interacting with OpenAI's language models
+- **generate()**: The primary method for getting responses from LLMs
+- **response_model**: Using Pydantic models to structure LLM outputs
+- **get_content()**: Extracting plain text from LLM responses
+
+## Dapr Integration
+
+While these examples don't explicitly use Dapr's distributed capabilities, Dapr Agents provides:
+
+- **Unified API**: Consistent interfaces for different LLM providers
+- **Type Safety**: Structured data extraction and validation
+- **Integration Path**: Foundation for building more complex, distributed LLM applications
+
+In later quickstarts, you'll see how these LLM interactions integrate with Dapr's building blocks.
+
+## Troubleshooting
+
+1. **Authentication Errors**: If you encounter authentication failures, check your OpenAI API key in the `.env` file
+2. **Structured Output Errors**: If the model fails to produce valid structured data, try refining your model or prompt
+3. **Module Not Found**: Ensure you've activated your virtual environment and installed the requirements
+
+## Next Steps
+
+After completing these examples, move on to the [Agent Tool Call quickstart](../03-agent-tool-call) to learn how to build agents that can use tools to interact with external systems.

quickstarts/02_llm_call_elevenlabs/requirements.txt

@@ -1,2 +1,3 @@
 dapr-agents==0.1.dev26
 python-dotenv
+tiktoken

quickstarts/02_llm_call_elevenlabs/text_to_speech.py

@@ -0,0 +1,41 @@
+import os
+
+from dapr_agents.types.llm import AudioSpeechRequest
+from dapr_agents import ElevenLabsSpeechClient
+from dotenv import load_dotenv
+
+load_dotenv()
+
+client = ElevenLabsSpeechClient(
+    model="eleven_multilingual_v2", # Default model
+    voice="JBFqnCBsd6RMkjVDRZzb" # 'name': 'George', 'language': 'en', 'labels': {'accent': 'British', 'description': 'warm', 'age': 'middle aged', 'gender': 'male', 'use_case': 'narration'}
+)
+
+
+# Define the text to convert to speech
+text = "Dapr Agents is an open-source framework for researchers and developers"
+
+# Create speech from text
+audio_bytes = client.create_speech(
+    text=text,
+    output_format="mp3_44100_128" # default output format, mp3 with 44.1kHz sample rate at 128kbps.
+)
+
+# You can also automatically create the audio file by passing the file name as an argument
+# client.create_speech(
+#     text=text,
+#     output_format="mp3_44100_128", # default output format, mp3 with 44.1kHz sample rate at 128kbps.,
+#     file_name='output_speech_auto.mp3'
+# )
+
+
+# Save the audio to an MP3 file
+output_path = "output_speech.mp3"
+with open(output_path, "wb") as audio_file:
+    audio_file.write(audio_bytes)
+
+print(f"Audio saved to {output_path}")
+
+os.remove(output_path)
+print(f"File {output_path} has been deleted.")
+

quickstarts/02_llm_call_nvidia/README.md

@@ -0,0 +1,158 @@
+# OpenAI LLM calls with Dapr Agents
+
+This quickstart demonstrates how to use Dapr Agents' LLM capabilities to interact with language models and generate both free-form text and structured data. You'll learn how to make basic calls to LLMs and how to extract structured information in a type-safe manner.
+
+## Prerequisites
+
+- Python 3.10 (recommended)
+- pip package manager
+- OpenAI API key
+
+## Environment Setup
+
+```bash
+# Create a virtual environment
+python3.10 -m venv .venv
+
+# Activate the virtual environment 
+# On Windows:
+.venv\Scripts\activate
+# On macOS/Linux:
+source .venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Configuration
+
+Create a `.env` file in the project root:
+
+```env
+NVIDIA_API_KEY=your_api_key_here
+```
+
+Replace `your_api_key_here` with your actual Nvidia key.
+
+## Examples
+
+### Text
+
+**1. Run the basic text completion example:**
+
+<!-- STEP
+name: Run text completion example
+expected_stdout_lines:
+  - "Response:"
+  - "Response with prompty:"
+  - "Response with user input:"
+timeout_seconds: 30
+output_match_mode: substring
+-->
+```bash
+python text_completion.py
+```
+<!-- END_STEP -->
+
+The script demonstrates basic usage of Dapr Agents' OpenAIChatClient for text generation:
+
+```python
+from dapr_agents import NVIDIAChatClient
+from dapr_agents.types import UserMessage
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+# Basic chat completion
+llm = NVIDIAChatClient()
+response = llm.generate("Name a famous dog!")
+
+if len(response.get_content()) > 0:
+    print("Response: ", response.get_content())
+
+# Chat completion using a prompty file for context
+llm = NVIDIAChatClient.from_prompty('basic.prompty')
+response = llm.generate(input_data={"question":"What is your name?"})
+
+if len(response.get_content()) > 0:
+    print("Response with prompty: ", response.get_content())
+
+# Chat completion with user input
+llm = NVIDIAChatClient()
+response = llm.generate(messages=[UserMessage("hello")])
+
+
+if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
+    print("Response with user input: ", response.get_content())
+```
+
+**2. Expected output:** The LLM will respond with the name of a famous dog (e.g., "Lassie", "Hachiko", etc.).
+
+**Run the structured text completion example:**
+
+<!-- STEP
+name: Run text completion example
+expected_stdout_lines:
+  - '"name":'
+  - '"breed":'
+  - '"reason":'
+timeout_seconds: 30
+output_match_mode: substring
+-->
+```bash
+python structured_completion.py
+```
+<!-- END_STEP -->
+
+This example shows how to use Pydantic models to get structured data from LLMs:
+
+```python
+import json
+
+from dapr_agents import NVIDIAChatClient
+from dapr_agents.types import UserMessage
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+# Define our data model
+class Dog(BaseModel):
+    name: str
+    breed: str
+    reason: str
+
+# Initialize the chat client
+llm = NVIDIAChatClient(
+    model="meta/llama-3.1-8b-instruct"
+)
+
+# Get structured response
+response = llm.generate(
+    messages=[UserMessage("One famous dog in history.")],
+    response_format=Dog
+)
+
+print(json.dumps(response.model_dump(), indent=2))
+```
+
+**Expected output:** A structured Dog object with name, breed, and reason fields (e.g., `Dog(name='Hachiko', breed='Akita', reason='Known for his remarkable loyalty...')`)
+
+### Embeddings
+You can use the `OpenAIEmbedder` in dapr-agents for generating text embeddings.
+
+**1. Embeddings a single text:**
+<!-- STEP
+name: Run audio transcription example
+expected_stdout_lines:
+  - "Embedding (first 5 values):"
+  - "Text 1 embedding (first 5 values):"
+  - "Text 2 embedding (first 5 values):"
+output_match_mode: substring
+-->
+```bash
+python embeddings.py
+```
+<!-- END_STEP -->

quickstarts/02_llm_call_nvidia/basic.prompty

@@ -0,0 +1,23 @@
+---
+name: Basic Prompt
+description: A basic prompt that uses the chat API to answer questions
+model:
+    api: chat
+    configuration:
+        type: nvidia
+        name: meta/llama3-8b-instruct
+    parameters:
+        max_tokens: 128
+        temperature: 0.2
+inputs:
+  question:
+    type: string
+sample:
+  "question": "Who is the most famous person in the world?"
+---
+system:
+You are an AI assistant who helps people find information.
+As the assistant, you answer questions briefly, succinctly.
+
+user:
+{{question}}

quickstarts/02_llm_call_nvidia/embeddings.py

@@ -0,0 +1,35 @@
+from dapr_agents.document.embedder import NVIDIAEmbedder
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Initialize the embedder
+embedder = NVIDIAEmbedder(
+    model="nvidia/nv-embedqa-e5-v5",  # Default embedding model
+)
+
+# Generate embedding with a single text
+text = "Dapr Agents is an open-source framework for researchers and developers"
+
+embedding = embedder.embed(text)
+
+# Display the embedding
+if len(embedding) > 0:
+    print(f"Embedding (first 5 values): {embedding[:5]}...")
+
+# Multiple input texts
+texts = [
+    "Dapr Agents is an open-source framework for researchers and developers",
+    "It provides tools to create, orchestrate, and manage agents"
+]
+
+# Generate embeddings
+embeddings = embedder.embed(texts)
+
+if len(embeddings) == 0:
+    print("No embeddings generated")
+    exit()
+
+# Display the embeddings
+for i, emb in enumerate(embeddings):
+    print(f"Text {i + 1} embedding (first 5 values): {emb[:5]}")

quickstarts/02_llm_call_nvidia/requirements.txt

@@ -0,0 +1,3 @@
+dapr-agents==0.1.dev26
+python-dotenv
+tiktoken

quickstarts/02_llm_call_nvidia/structured_completion.py

@@ -0,0 +1,28 @@
+import json
+
+from dapr_agents import NVIDIAChatClient
+from dapr_agents.types import UserMessage
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+# Define our data model
+class Dog(BaseModel):
+    name: str
+    breed: str
+    reason: str
+
+# Initialize the chat client
+llm = NVIDIAChatClient(
+    model="meta/llama-3.1-8b-instruct"
+)
+
+# Get structured response
+response = llm.generate(
+    messages=[UserMessage("One famous dog in history.")],
+    response_format=Dog
+)
+
+print(json.dumps(response.model_dump(), indent=2))

quickstarts/02_llm_call_nvidia/text_completion.py

@@ -0,0 +1,28 @@
+from dapr_agents import NVIDIAChatClient
+from dapr_agents.types import UserMessage
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+# Basic chat completion
+llm = NVIDIAChatClient()
+response = llm.generate("Name a famous dog!")
+
+if len(response.get_content()) > 0:
+    print("Response: ", response.get_content())
+
+# Chat completion using a prompty file for context
+llm = NVIDIAChatClient.from_prompty('basic.prompty')
+response = llm.generate(input_data={"question":"What is your name?"})
+
+if len(response.get_content()) > 0:
+    print("Response with prompty: ", response.get_content())
+
+# Chat completion with user input
+llm = NVIDIAChatClient()
+response = llm.generate(messages=[UserMessage("hello")])
+
+
+if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
+    print("Response with user input: ", response.get_content())

quickstarts/02_llm_call_open_ai/README.md

@@ -1,4 +1,4 @@
-# LLM Call with Dapr Agents
+# OpenAI LLM calls with Dapr Agents
 
 This quickstart demonstrates how to use Dapr Agents' LLM capabilities to interact with language models and generate both free-form text and structured data. You'll learn how to make basic calls to LLMs and how to extract structured information in a type-safe manner.
 
@@ -36,14 +36,16 @@ Replace `your_api_key_here` with your actual OpenAI API key.
 
 ## Examples
 
-### 1. Text Completion
+### Text
 
-Run the basic text completion example:
+**1. Run the basic text completion example:**
 
 <!-- STEP
 name: Run text completion example
 expected_stdout_lines:
   - "Response:"
+  - "Response with prompty:"
+  - "Response with user input:"
 timeout_seconds: 30
 output_match_mode: substring
 -->
@@ -56,24 +58,38 @@ The script demonstrates basic usage of Dapr Agents' OpenAIChatClient for text ge
 
 ```python
 from dapr_agents import OpenAIChatClient
+from dapr_agents.types import UserMessage
 from dotenv import load_dotenv
 
 # Load environment variables from .env
 load_dotenv()
 
-# Initialize the chat client and call
+# Basic chat completion
 llm = OpenAIChatClient()
 response = llm.generate("Name a famous dog!")
 
 if len(response.get_content()) > 0:
     print("Response: ", response.get_content())
+
+# Chat completion using a prompty file for context
+llm = OpenAIChatClient.from_prompty('basic.prompty')
+response = llm.generate(input_data={"question":"What is your name?"})
+
+if len(response.get_content()) > 0:
+    print("Response with prompty: ", response.get_content())
+
+# Chat completion with user input
+llm = OpenAIChatClient()
+response = llm.generate(messages=[UserMessage("hello")])
+
+
+if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
+    print("Response with user input: ", response.get_content())
 ```
 
-**Expected output:** The LLM will respond with the name of a famous dog (e.g., "Lassie", "Hachiko", etc.).
+**2. Expected output:** The LLM will respond with the name of a famous dog (e.g., "Lassie", "Hachiko", etc.).
 
-### 2. Structured Output
-
-Run the structured output example:
+**Run the structured text completion example:**
 
 <!-- STEP
 name: Run text completion example
@@ -122,6 +138,82 @@ print(json.dumps(response.model_dump(), indent=2))
 
 **Expected output:** A structured Dog object with name, breed, and reason fields (e.g., `Dog(name='Hachiko', breed='Akita', reason='Known for his remarkable loyalty...')`)
 
+### Audio
+You can use the OpenAIAudioClient in `dapr-agents` for basic tasks with the OpenAI Audio API. We will explore:
+
+- Generating speech from text and saving it as an MP3 file.
+- Transcribing audio to text.
+- Translating audio content to English.
+
+**1. Run the text to speech example:**
+
+
+<!-- STEP
+name: Run audio generation example
+expected_stdout_lines:
+  - "Audio saved to output_speech.mp3"
+  - "File output_speech.mp3 has been deleted."
+-->
+```bash
+python text_to_speech.py
+```
+<!-- END_STEP -->
+
+**2. Run the speech to text transcription example:**
+
+<!-- STEP
+name: Run audio transcription example
+expected_stdout_lines:
+  - "Transcription:"
+  - "Success! The transcription contains at least 5 out of 7 words."
+output_match_mode: substring
+-->
+```bash
+python audio_transcription.py
+```
+<!-- END_STEP -->
+
+
+**2. Run the speech to text translation example:**
+
+[//]: # (<!-- STEP)
+
+[//]: # (name: Run audio translation example)
+
+[//]: # (expected_stdout_lines:)
+
+[//]: # (  - "Translation:")
+
+[//]: # (  - "Success! The translation contains at least 5 out of 6 words.")
+
+[//]: # (-->)
+
+[//]: # (```bash)
+
+[//]: # (python audio_translation.py)
+
+[//]: # (```)
+
+[//]: # (<!-- END_STEP -->)
+
+### Embeddings
+You can use the `OpenAIEmbedder` in dapr-agents for generating text embeddings.
+
+**1. Embeddings a single text:**
+<!-- STEP
+name: Run audio transcription example
+expected_stdout_lines:
+  - "Embedding (first 5 values):"
+  - "Text 1 embedding (first 5 values):"
+  - "Text 2 embedding (first 5 values):"
+output_match_mode: substring
+-->
+```bash
+python embeddings.py
+```
+<!-- END_STEP -->
+
+
 ## Key Concepts
 
 - **OpenAIChatClient**: The interface for interacting with OpenAI's language models

quickstarts/02_llm_call_open_ai/audio_transcription.py

@@ -0,0 +1,49 @@
+from dapr_agents.types.llm import AudioTranscriptionRequest
+from dapr_agents import OpenAIAudioClient
+from dotenv import load_dotenv
+
+load_dotenv()
+client = OpenAIAudioClient()
+
+# Specify the audio file to transcribe
+audio_file_path = "speech.mp3"
+
+# Create a transcription request
+transcription_request = AudioTranscriptionRequest(
+    model="whisper-1",
+    file=audio_file_path
+)
+
+############
+# You can also use audio bytes:
+############
+#
+# with open(audio_file_path, "rb") as f:
+#     audio_bytes = f.read()
+#
+# transcription_request = AudioTranscriptionRequest(
+#     model="whisper-1",
+#     file=audio_bytes,  # File as bytes
+#     language="en"  # Optional: Specify the language of the audio
+# )
+
+
+# Generate transcription
+transcription_response = client.create_transcription(request=transcription_request)
+
+# Display the transcription result
+if not len(transcription_response.text) > 0:
+    exit(1)
+
+print("Transcription:", transcription_response.text)
+
+words = ["dapr", "agents", "open", "source", "framework", "researchers", "developers"]
+normalized_text = transcription_response.text.lower()
+
+count = 0
+for word in words:
+    if word in normalized_text:
+        count += 1
+
+if count >= 5:
+    print("Success! The transcription contains at least 5 out of 7 words.")

quickstarts/02_llm_call_open_ai/audio_translation.py

@@ -0,0 +1,36 @@
+from dapr_agents.types.llm import AudioTranslationRequest
+from dapr_agents import OpenAIAudioClient
+from dotenv import load_dotenv
+
+load_dotenv()
+client = OpenAIAudioClient()
+
+# Specify the audio file to translate
+audio_file_path = "speech_spanish.mp3"
+
+# Create a translation request
+translation_request = AudioTranslationRequest(
+    model="whisper-1",
+    file=audio_file_path,
+    prompt="The user will provide an audio file in Spanish. Translate the audio to English and transcribe the english text, word for word."
+)
+
+# Generate translation
+translation_response = client.create_translation(request=translation_request)
+
+# Display the transcription result
+if not len(translation_response.text) > 0:
+    exit(1)
+
+print("Translation:", translation_response)
+
+words = ["dapr", "agents", "open", "source", "framework", "researchers", "developers"]
+normalized_text = translation_response.text.lower()
+
+count = 0
+for word in words:
+    if word in normalized_text:
+        count += 1
+
+if count >= 5:
+    print("Success! The transcription contains at least 5 out of 7 words.")

quickstarts/02_llm_call_open_ai/basic.prompty

@@ -0,0 +1,23 @@
+---
+name: Basic Prompt
+description: A basic prompt that uses the chat API to answer questions
+model:
+    api: chat
+    configuration:
+        type: openai
+        name: gpt-4o
+    parameters:
+        max_tokens: 128
+        temperature: 0.2
+inputs:
+  question:
+    type: string
+sample:
+  "question": "Who is the most famous person in the world?"
+---
+system:
+You are an AI assistant who helps people find information.
+As the assistant, you answer questions briefly, succinctly.
+
+user:
+{{question}}

quickstarts/02_llm_call_open_ai/embeddings.py

@@ -0,0 +1,37 @@
+from dapr_agents.document.embedder import OpenAIEmbedder
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Initialize the embedder
+embedder = OpenAIEmbedder(
+    model="text-embedding-ada-002",  # Default embedding model
+    chunk_size=1000,  # Batch size for processing
+    max_tokens=8191   # Maximum tokens per input
+)
+
+# Generate embedding with a single text
+text = "Dapr Agents is an open-source framework for researchers and developers"
+
+embedding = embedder.embed(text)
+
+# Display the embedding
+if len(embedding) > 0:
+    print(f"Embedding (first 5 values): {embedding[:5]}...")
+
+# Multiple input texts
+texts = [
+    "Dapr Agents is an open-source framework for researchers and developers",
+    "It provides tools to create, orchestrate, and manage agents"
+]
+
+# Generate embeddings
+embeddings = embedder.embed(texts)
+
+if len(embeddings) == 0:
+    print("No embeddings generated")
+    exit()
+
+# Display the embeddings
+for i, emb in enumerate(embeddings):
+    print(f"Text {i + 1} embedding (first 5 values): {emb[:5]}")

quickstarts/02_llm_call_open_ai/requirements.txt

@@ -0,0 +1,3 @@
+dapr-agents==0.1.dev26
+python-dotenv
+tiktoken

quickstarts/02_llm_call_open_ai/text_completion.py

@@ -0,0 +1,28 @@
+from dapr_agents import OpenAIChatClient
+from dapr_agents.types import UserMessage
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
+
+# Basic chat completion
+llm = OpenAIChatClient()
+response = llm.generate("Name a famous dog!")
+
+if len(response.get_content()) > 0:
+    print("Response: ", response.get_content())
+
+# Chat completion using a prompty file for context
+llm = OpenAIChatClient.from_prompty('basic.prompty')
+response = llm.generate(input_data={"question":"What is your name?"})
+
+if len(response.get_content()) > 0:
+    print("Response with prompty: ", response.get_content())
+
+# Chat completion with user input
+llm = OpenAIChatClient()
+response = llm.generate(messages=[UserMessage("hello")])
+
+
+if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
+    print("Response with user input: ", response.get_content())

quickstarts/02_llm_call_open_ai/text_to_speech.py

@@ -0,0 +1,36 @@
+import os
+
+from dapr_agents.types.llm import AudioSpeechRequest
+from dapr_agents import OpenAIAudioClient
+from dotenv import load_dotenv
+
+load_dotenv()
+client = OpenAIAudioClient()
+
+# Define the text to convert to speech
+text_to_speech = "Dapr Agents is an open-source framework for researchers and developers"
+
+# Create a request for TTS
+tts_request = AudioSpeechRequest(
+    model="tts-1",
+    input=text_to_speech,
+    voice="fable",
+    response_format="mp3"
+)
+
+# Generate the audio - returns a byte string
+audio_bytes = client.create_speech(request=tts_request)
+
+# You can also automatically create the audio file by passing the file name as an argument
+# client.create_speech(request=tts_request, file_name=output_path)
+
+# Save the audio to an MP3 file
+output_path = "output_speech.mp3"
+with open(output_path, "wb") as audio_file:
+    audio_file.write(audio_bytes)
+
+print(f"Audio saved to {output_path}")
+
+os.remove(output_path)
+print(f"File {output_path} has been deleted.")
+

quickstarts/05-multi-agent-workflow-actors/README.md

@@ -61,7 +61,13 @@ services/                  # Directory for agent services
 │   └── app.py           # FastAPI app for elf
 └── workflow-random/      # Workflow orchestrator
     └── app.py           # Workflow service
-dapr.yaml                # Multi-App Run Template
+└── workflow-roundrobin/  # Roundrobin orchestrator
+    └── app.py           # Workflow service    
+└── workflow-llm/         # LLM orchestrator
+    └── app.py           # Workflow service        
+dapr-random.yaml         # Multi-App Run Template using the random orchestrator
+dapr-roundrobin.yaml     # Multi-App Run Template using the roundrobin orchestrator
+dapr-llm.yaml            # Multi-App Run Template using the LLM orchestrator
 ```
 
 ## Examples
@@ -108,9 +114,9 @@ if __name__ == "__main__":
 
 Similar implementations exist for the Wizard (Gandalf) and Elf (Legolas) agents.
 
-### Workflow Orchestrator Implementation
+### Workflow Orchestrator Implementations
 
-The workflow orchestrator manages the interaction between agents. Currently Dapr Agents support three workflow types: RoundRobin, Random, and LLM-based. Here's an example for the Random workflow orchestrator:
+The workflow orchestrators manage the interaction between agents. Currently, Dapr Agents support three workflow types: RoundRobin, Random, and LLM-based. Here's an example for the Random workflow orchestrator (you can find examples for RoundRobin and LLM-based orchestrators in the project):
 
 ```python
 # services/workflow-random/app.py
@@ -122,7 +128,7 @@ import logging
 async def main():
     try:
         random_workflow_service = RandomOrchestrator(
-            name="Orchestrator",
+            name="RandomOrchestrator",
             message_bus_name="messagepubsub",
             state_store_name="agenticworkflowstate",
             state_key="workflow_state",
@@ -146,8 +152,9 @@ if __name__ == "__main__":
 
 ### Running the Multi-Agent System
 
-The project includes a `dapr.yaml` configuration for running all services and an additional Client application for interacting with the agents:
+The project includes three dapr multi-app run configuration files (`dapr-random.yaml`, `dapr-roundrobin.yaml` and `dapr-llm.yaml` ) for running all services and an additional Client application for interacting with the agents:
 
+Example: `dapr-random.yaml`
 ```yaml
 version: 1
 common:
@@ -199,18 +206,62 @@ expected_stdout_lines:
   - "assistant:"
   - "user:"
   - "assistant:"
+  - "workflow completed with status 'ORCHESTRATION_STATUS_COMPLETED' workflowName 'RandomWorkflow'"
 timeout_seconds: 20
 output_match_mode: substring
 background: false
+sleep: 5
 -->
 ```bash
-dapr run -f . 
+dapr run -f dapr-random.yaml 
 ```
 <!-- END_STEP -->
 
 You will see the agents engaging in a conversation about getting to Mordor, with different agents contributing based on their character.
 
+You can also run the RoundRobin and LLM-based orchestrators using `dapr-roundrobin.yaml` and `dapr-llm.yaml` respectively:
 
+<!-- STEP
+name: Run text completion example
+match_order: none
+expected_stdout_lines:
+  - "Workflow started successfully!"
+  - "user:"
+  - "How to get to Mordor? We all need to help!"
+  - "assistant:"
+  - "user:"
+  - "assistant:"
+  - "workflow completed with status 'ORCHESTRATION_STATUS_COMPLETED' workflowName 'RoundRobinWorkflow'"
+timeout_seconds: 20
+output_match_mode: substring
+background: false
+sleep: 5
+-->
+```bash
+dapr run -f dapr-roundrobin.yaml 
+```
+<!-- END_STEP -->
+
+<!-- STEP
+name: Run text completion example
+match_order: none
+expected_stdout_lines:
+  - "Workflow started successfully!"
+  - "user:"
+  - "How to get to Mordor? We all need to help!"
+  - "assistant:"
+  - "user:"
+  - "assistant:"
+  - "workflow completed with status 'ORCHESTRATION_STATUS_COMPLETED' workflowName 'LLMWorkflow'"
+timeout_seconds: 20
+output_match_mode: substring
+background: false
+sleep: 5
+-->
+```bash
+dapr run -f dapr-llm.yaml 
+```
+<!-- END_STEP -->
 **Expected output:** The agents will engage in a conversation about getting to Mordor, with different agents contributing based on their character.
 
 ## Key Concepts

quickstarts/05-multi-agent-workflow-actors/dapr-llm.yaml

@@ -0,0 +1,37 @@
+# https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-template/#template-properties
+version: 1
+common:
+  resourcesPath: ./components
+  logLevel: info
+  appLogDestination: console
+  daprdLogDestination: console
+
+apps:
+- appId: HobbitApp
+  appDirPath: ./services/hobbit/
+  appPort: 8001
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50001
+
+- appId: WizardApp
+  appDirPath: ./services/wizard/
+  appPort: 8002
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50002
+
+- appId: ElfApp
+  appDirPath: ./services/elf/
+  appPort: 8003
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50003
+
+- appId: WorkflowApp
+  appDirPath: ./services/workflow-llm/
+  appPort: 8004
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50004
+
+- appId: ClientApp
+  appDirPath: ./services/client/
+  command: ["python3", "client.py"]
+  daprGRPCPort: 50011

quickstarts/05-multi-agent-workflow-actors/dapr-roundrobin.yaml

@@ -0,0 +1,37 @@
+# https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-template/#template-properties
+version: 1
+common:
+  resourcesPath: ./components
+  logLevel: info
+  appLogDestination: console
+  daprdLogDestination: console
+
+apps:
+- appId: HobbitApp
+  appDirPath: ./services/hobbit/
+  appPort: 8001
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50001
+
+- appId: WizardApp
+  appDirPath: ./services/wizard/
+  appPort: 8002
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50002
+
+- appId: ElfApp
+  appDirPath: ./services/elf/
+  appPort: 8003
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50003
+
+- appId: WorkflowApp
+  appDirPath: ./services/workflow-roundrobin/
+  appPort: 8004
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50004
+
+- appId: ClientApp
+  appDirPath: ./services/client/
+  command: ["python3", "client.py"]
+  daprGRPCPort: 50011

quickstarts/05-multi-agent-workflow-actors/services/workflow-llm/app.py

@@ -0,0 +1,31 @@
+from dapr_agents import LLMOrchestrator
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        workflow_service = LLMOrchestrator(
+            name="LLMOrchestrator",
+            message_bus_name="messagepubsub",
+            state_store_name="workflowstatestore",
+            state_key="workflow_state",
+            agents_registry_store_name="agentstatestore",
+            agents_registry_key="agents_registry",
+            service_port=8004,
+            daprGrpcPort=50004,
+            max_iterations=3
+        )
+
+        await workflow_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-actors/services/workflow-random/app.py

@@ -0,0 +1,31 @@
+from dapr_agents import RandomOrchestrator
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        workflow_service = RandomOrchestrator(
+            name="Random Orchestrator",
+            message_bus_name="messagepubsub",
+            state_store_name="workflowstatestore",
+            state_key="workflow_state",
+            agents_registry_store_name="agentstatestore",
+            agents_registry_key="agents_registry",
+            service_port=8004,
+            daprGrpcPort=50004,
+            max_iterations=3
+        )
+
+        await workflow_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-actors/services/workflow-roundrobin/app.py

@@ -0,0 +1,31 @@
+from dapr_agents import RoundRobinOrchestrator
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        workflow_service = RoundRobinOrchestrator(
+            name="RoundRobin Orchestrator",
+            message_bus_name="messagepubsub",
+            state_store_name="workflowstatestore",
+            state_key="workflow_state",
+            agents_registry_store_name="agentstatestore",
+            agents_registry_key="agents_registry",
+            service_port=8004,
+            daprGrpcPort=50004,
+            max_iterations=3
+        )
+
+        await workflow_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-dapr-workflows/README.md

@@ -0,0 +1,313 @@
+# Multi-Agent Event-Driven Workflows
+
+This quickstart demonstrates how to create and orchestrate event-driven workflows with multiple autonomous agents using Dapr Agents. You'll learn how to set up agents as services, implement workflow orchestration, and enable real-time agent collaboration through pub/sub messaging.
+
+## Prerequisites
+
+- Python 3.10 (recommended)
+- pip package manager
+- OpenAI API key
+- Dapr CLI and Docker installed
+
+## Environment Setup
+
+```bash
+# Create a virtual environment
+python3.10 -m venv .venv
+
+# Activate the virtual environment
+# On Windows:
+.venv\Scripts\activate
+# On macOS/Linux:
+source .venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Configuration
+
+1. Create a `.env` file for your API keys:
+
+```env
+OPENAI_API_KEY=your_api_key_here
+```
+
+2. Make sure Dapr is initialized on your system:
+
+```bash
+dapr init
+```
+
+3. The quickstart includes the necessary Dapr components in the `components` directory:
+
+- `statestore.yaml`: Agent state configuration
+- `pubsub.yaml`: Pub/Sub message bus configuration
+- `workflowstate.yaml`: Workflow state configuration
+
+## Project Structure
+
+```
+components/                # Dapr configuration files
+├── statestore.yaml       # State store configuration
+├── pubsub.yaml           # Pub/Sub configuration
+└── workflowstate.yaml    # Workflow state configuration
+services/                  # Directory for agent services
+├── hobbit/               # First agent's service
+│   └── app.py           # FastAPI app for hobbit
+├── wizard/              # Second agent's service
+│   └── app.py           # FastAPI app for wizard
+├── elf/                 # Third agent's service
+│   └── app.py           # FastAPI app for elf
+└── workflow-random/      # Workflow orchestrator
+    └── app.py           # Workflow service
+└── workflow-roundrobin/  # Roundrobin orchestrator
+    └── app.py           # Workflow service    
+└── workflow-llm/         # LLM orchestrator
+    └── app.py           # Workflow service        
+dapr-random.yaml         # Multi-App Run Template using the random orchestrator
+dapr-roundrobin.yaml     # Multi-App Run Template using the roundrobin orchestrator
+dapr-llm.yaml            # Multi-App Run Template using the LLM orchestrator
+```
+
+## Examples
+
+### Agent Service Implementation
+
+Each agent is implemented as a separate service. Here's an example for the Hobbit agent:
+
+```python
+# services/hobbit/app.py
+from dapr_agents import Agent, AgentActorService, AssistantAgent
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        hobbit_service = AssistantAgent(name="Frodo", role="Hobbit",
+                                      goal="Carry the One Ring to Mount Doom, resisting its corruptive power while navigating danger and uncertainty.",
+                                      instructions=[
+                                          "Speak like Frodo, with humility, determination, and a growing sense of resolve.",
+                                          "Endure hardships and temptations, staying true to the mission even when faced with doubt.",
+                                          "Seek guidance and trust allies, but bear the ultimate burden alone when necessary.",
+                                          "Move carefully through enemy-infested lands, avoiding unnecessary risks.",
+                                          "Respond concisely, accurately, and relevantly, ensuring clarity and strict alignment with the task."],
+                                      message_bus_name="messagepubsub",
+                                      state_store_name="workflowstatestore",
+                                      state_key="workflow_state",
+                                      agents_registry_store_name="agentstatestore",
+                                      agents_registry_key="agents_registry", service_port=8001,
+                                      daprGrpcPort=50001)
+
+        await hobbit_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())
+```
+
+Similar implementations exist for the Wizard (Gandalf) and Elf (Legolas) agents.
+
+### Workflow Orchestrator Implementations
+
+The workflow orchestrators manage the interaction between agents. Currently, Dapr Agents support three workflow types: RoundRobin, Random, and LLM-based. Here's an example for the Random workflow orchestrator (you can find examples for RoundRobin and LLM-based orchestrators in the project):
+
+```python
+# services/workflow-random/app.py
+from dapr_agents import RandomOrchestrator
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+async def main():
+    try:
+        random_workflow_service = RandomOrchestrator(
+            name="RandomOrchestrator",
+            message_bus_name="messagepubsub",
+            state_store_name="agenticworkflowstate",
+            state_key="workflow_state",
+            agents_registry_store_name="agentsregistrystore",
+            agents_registry_key="agents_registry",
+            service_port=8009,
+            daprGrpcPort=50009,
+            max_iterations=3
+        )
+        await random_workflow_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+    
+    asyncio.run(main())
+```
+
+### Running the Multi-Agent System
+
+The project includes three dapr multi-app run configuration files (`dapr-random.yaml`, `dapr-roundrobin.yaml` and `dapr-llm.yaml` ) for running all services and an additional Client application for interacting with the agents:
+
+Example: `dapr-random.yaml`
+```yaml
+version: 1
+common:
+  resourcesPath: ./components
+  logLevel: info
+  appLogDestination: console
+  daprdLogDestination: console
+
+apps:
+- appId: HobbitApp
+  appDirPath: ./services/hobbit/
+  appPort: 8001
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50001
+
+- appId: WizardApp
+  appDirPath: ./services/wizard/
+  appPort: 8002
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50002
+
+- appId: ElfApp
+  appDirPath: ./services/elf/
+  appPort: 8003
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50003
+
+- appId: WorkflowApp
+  appDirPath: ./services/workflow-random/
+  appPort: 8004
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50004
+
+- appId: ClientApp
+  appDirPath: ./services/client/
+  command: ["python3", "client.py"]
+  daprGRPCPort: 50011
+```
+
+Start all services using the Dapr CLI:
+
+<!-- STEP
+name: Run text completion example
+match_order: none
+expected_stdout_lines:
+  - "Workflow started successfully!"
+  - "user:"
+  - "How to get to Mordor? We all need to help!"
+  - "assistant:"
+  - "user:"
+  - "assistant:"
+  - "workflow completed with status 'ORCHESTRATION_STATUS_COMPLETED' workflowName 'RandomWorkflow'"
+timeout_seconds: 20
+output_match_mode: substring
+background: false
+sleep: 5
+-->
+```bash
+dapr run -f dapr-random.yaml 
+```
+<!-- END_STEP -->
+
+You will see the agents engaging in a conversation about getting to Mordor, with different agents contributing based on their character.
+
+You can also run the RoundRobin and LLM-based orchestrators using `dapr-roundrobin.yaml` and `dapr-llm.yaml` respectively:
+
+<!-- STEP
+name: Run text completion example
+match_order: none
+expected_stdout_lines:
+  - "Workflow started successfully!"
+  - "user:"
+  - "How to get to Mordor? We all need to help!"
+  - "assistant:"
+  - "user:"
+  - "assistant:"
+  - "workflow completed with status 'ORCHESTRATION_STATUS_COMPLETED' workflowName 'RoundRobinWorkflow'"
+timeout_seconds: 20
+output_match_mode: substring
+background: false
+sleep: 5
+-->
+```bash
+dapr run -f dapr-roundrobin.yaml 
+```
+<!-- END_STEP -->
+
+<!-- STEP
+name: Run text completion example
+match_order: none
+expected_stdout_lines:
+  - "Workflow started successfully!"
+  - "user:"
+  - "How to get to Mordor? We all need to help!"
+  - "assistant:"
+  - "user:"
+  - "assistant:"
+  - "workflow completed with status 'ORCHESTRATION_STATUS_COMPLETED' workflowName 'LLMWorkflow'"
+timeout_seconds: 20
+output_match_mode: substring
+background: false
+-->
+```bash
+dapr run -f dapr-llm.yaml 
+```
+<!-- END_STEP -->
+**Expected output:** The agents will engage in a conversation about getting to Mordor, with different agents contributing based on their character.
+
+## Key Concepts
+
+- **Agent Service**: Stateful service exposing an agent via API endpoints
+- **Pub/Sub Messaging**: Event-driven communication between agents
+- **Actor Model**: Stateful agent representation using Dapr Actors
+- **Workflow Orchestration**: Coordinating agent interactions
+- **Distributed System**: Multiple services working together
+
+## Workflow Types
+
+Dapr Agents supports multiple workflow orchestration patterns:
+
+1. **RoundRobin**: Cycles through agents sequentially
+2. **Random**: Selects agents randomly for tasks
+3. **LLM-based**: Uses GPT-4o to intelligently select agents based on context
+
+## Dapr Integration
+
+This quickstart showcases several Dapr building blocks:
+
+- **Pub/Sub**: Agent communication via Redis message bus
+- **State Management**: Persistence of agent and workflow states
+- **Service Invocation**: Direct HTTP communication between services
+- **Actors**: Stateful agent representation
+
+## Monitoring and Observability
+
+1. **Console Logs**: Monitor real-time workflow execution
+2. **Redis Insights**: View message bus and state data at http://localhost:5540/
+3. **Zipkin Tracing**: Access distributed tracing at http://localhost:9411/zipkin/
+
+## Troubleshooting
+
+1. **Service Startup**: If services fail to start, verify Dapr components configuration
+2. **Communication Issues**: Check Redis connection and pub/sub setup
+3. **Workflow Errors**: Check Zipkin traces for detailed request flows
+4. **System Reset**: Clear Redis data through Redis Insights if needed
+
+## Next Steps
+
+After completing this quickstart, you can:
+
+- Add more agents to the workflow
+- Switch to another workflow orchestration pattern (Random, LLM-based)
+- Extend agents with custom tools
+- Deploy to a Kubernetes cluster using Dapr

quickstarts/05-multi-agent-workflow-dapr-workflows/components/agentstate.yaml

@@ -0,0 +1,16 @@
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: agentstatestore
+spec:
+  type: state.redis
+  version: v1
+  metadata:
+  - name: redisHost
+    value: localhost:6379
+  - name: redisPassword
+    value: ""
+  - name: keyPrefix
+    value: none
+  - name: actorStateStore
+    value: "true"

quickstarts/05-multi-agent-workflow-dapr-workflows/components/pubsub.yaml

@@ -0,0 +1,12 @@
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: messagepubsub
+spec:
+  type: pubsub.redis
+  version: v1
+  metadata:
+  - name: redisHost
+    value: localhost:6379
+  - name: redisPassword
+    value: ""

quickstarts/05-multi-agent-workflow-dapr-workflows/components/workflowstate.yaml

@@ -0,0 +1,12 @@
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: workflowstatestore
+spec:
+  type: state.redis
+  version: v1
+  metadata:
+  - name: redisHost
+    value: localhost:6379
+  - name: redisPassword
+    value: ""

quickstarts/05-multi-agent-workflow-dapr-workflows/dapr-llm.yaml

@@ -0,0 +1,37 @@
+# https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-template/#template-properties
+version: 1
+common:
+  resourcesPath: ./components
+  logLevel: info
+  appLogDestination: console
+  daprdLogDestination: console
+
+apps:
+- appId: HobbitApp
+  appDirPath: ./services/hobbit/
+  appPort: 8001
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50001
+
+- appId: WizardApp
+  appDirPath: ./services/wizard/
+  appPort: 8002
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50002
+
+- appId: ElfApp
+  appDirPath: ./services/elf/
+  appPort: 8003
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50003
+
+- appId: WorkflowApp
+  appDirPath: ./services/workflow-llm/
+  appPort: 8004
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50004
+
+- appId: ClientApp
+  appDirPath: ./services/client/
+  command: ["python3", "client.py"]
+  daprGRPCPort: 50011

quickstarts/05-multi-agent-workflow-dapr-workflows/dapr-random.yaml

@@ -0,0 +1,37 @@
+# https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-template/#template-properties
+version: 1
+common:
+  resourcesPath: ./components
+  logLevel: info
+  appLogDestination: console
+  daprdLogDestination: console
+
+apps:
+- appId: HobbitApp
+  appDirPath: ./services/hobbit/
+  appPort: 8001
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50001
+
+- appId: WizardApp
+  appDirPath: ./services/wizard/
+  appPort: 8002
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50002
+
+- appId: ElfApp
+  appDirPath: ./services/elf/
+  appPort: 8003
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50003
+
+- appId: WorkflowApp
+  appDirPath: ./services/workflow-random/
+  appPort: 8004
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50004
+
+- appId: ClientApp
+  appDirPath: ./services/client/
+  command: ["python3", "client.py"]
+  daprGRPCPort: 50011

quickstarts/05-multi-agent-workflow-dapr-workflows/dapr-roundrobin.yaml

@@ -0,0 +1,37 @@
+# https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-template/#template-properties
+version: 1
+common:
+  resourcesPath: ./components
+  logLevel: info
+  appLogDestination: console
+  daprdLogDestination: console
+
+apps:
+- appId: HobbitApp
+  appDirPath: ./services/hobbit/
+  appPort: 8001
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50001
+
+- appId: WizardApp
+  appDirPath: ./services/wizard/
+  appPort: 8002
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50002
+
+- appId: ElfApp
+  appDirPath: ./services/elf/
+  appPort: 8003
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50003
+
+- appId: WorkflowApp
+  appDirPath: ./services/workflow-roundrobin/
+  appPort: 8004
+  command: ["python3", "app.py"]
+  daprGRPCPort: 50004
+
+- appId: ClientApp
+  appDirPath: ./services/client/
+  command: ["python3", "client.py"]
+  daprGRPCPort: 50011

quickstarts/05-multi-agent-workflow-dapr-workflows/requirements.txt

@@ -0,0 +1,3 @@
+dapr-agents==0.1.dev26
+python-dotenv
+requests

quickstarts/05-multi-agent-workflow-dapr-workflows/services/client/client.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+import requests
+import time
+import sys
+
+
+if __name__ == "__main__":
+    workflow_url = "http://localhost:8004/RunWorkflow"
+    task_payload = {"task": "How to get to Mordor? We all need to help!"}
+
+    attempt = 1
+
+    while attempt <= 10:
+        try:
+            print(f"Attempt {attempt}...")
+            response = requests.post(workflow_url, json=task_payload, timeout=5)
+
+            if response.status_code == 202:
+                print("Workflow started successfully!")
+                sys.exit(0)
+            else:
+                print(f"Received status code {response.status_code}: {response.text}")
+
+        except requests.exceptions.RequestException as e:
+            print(f"Request failed: {e}")
+
+        attempt += 1
+        print(f"Waiting 1s seconds before next attempt...")
+        time.sleep(1)
+
+    print(f"Maximum attempts (10) reached without success.")
+
+    print("Failed to get successful response")
+    sys.exit(1)

quickstarts/05-multi-agent-workflow-dapr-workflows/services/elf/app.py

@@ -0,0 +1,32 @@
+from dapr_agents import Agent, AgentActorService, AssistantAgent
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        elf_service = AssistantAgent(name="Legolas", role="Elf",
+                                     goal="Act as a scout, marksman, and protector, using keen senses and deadly accuracy to ensure the success of the journey.",
+                                     instructions=[
+                                         "Speak like Legolas, with grace, wisdom, and keen observation.",
+                                         "Be swift, silent, and precise, moving effortlessly across any terrain.",
+                                         "Use superior vision and heightened senses to scout ahead and detect threats.",
+                                         "Excel in ranged combat, delivering pinpoint arrow strikes from great distances.",
+                                         "Respond concisely, accurately, and relevantly, ensuring clarity and strict alignment with the task."],
+                                     message_bus_name="messagepubsub",
+                                     state_store_name="workflowstatestore",
+                                     state_key="workflow_state",
+                                     agents_registry_store_name="agentstatestore",
+                                     agents_registry_key="agents_registry", service_port=8003,
+                                     daprGrpcPort=50003)
+        await elf_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-dapr-workflows/services/hobbit/app.py

@@ -0,0 +1,34 @@
+from dapr_agents import Agent, AgentActorService, AssistantAgent
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        hobbit_service = AssistantAgent(name="Frodo", role="Hobbit",
+                                      goal="Carry the One Ring to Mount Doom, resisting its corruptive power while navigating danger and uncertainty.",
+                                      instructions=[
+                                          "Speak like Frodo, with humility, determination, and a growing sense of resolve.",
+                                          "Endure hardships and temptations, staying true to the mission even when faced with doubt.",
+                                          "Seek guidance and trust allies, but bear the ultimate burden alone when necessary.",
+                                          "Move carefully through enemy-infested lands, avoiding unnecessary risks.",
+                                          "Respond concisely, accurately, and relevantly, ensuring clarity and strict alignment with the task."],
+                                      message_bus_name="messagepubsub",
+                                      state_store_name="workflowstatestore",
+                                      state_key="workflow_state",
+                                      agents_registry_store_name="agentstatestore",
+                                      agents_registry_key="agents_registry", service_port=8001,
+                                      daprGrpcPort=50001)
+
+        await hobbit_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-dapr-workflows/services/wizard/app.py

@@ -0,0 +1,34 @@
+from dapr_agents import Agent, AgentActorService, AssistantAgent
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        wizard_service = AssistantAgent(name="Gandalf", role="Wizard",
+                                        goal="Guide the Fellowship with wisdom and strategy, using magic and insight to ensure the downfall of Sauron.",
+                                        instructions=[
+                                            "Speak like Gandalf, with wisdom, patience, and a touch of mystery.",
+                                            "Provide strategic counsel, always considering the long-term consequences of actions.",
+                                            "Use magic sparingly, applying it when necessary to guide or protect.",
+                                            "Encourage allies to find strength within themselves rather than relying solely on your power.",
+                                            "Respond concisely, accurately, and relevantly, ensuring clarity and strict alignment with the task."],
+                                        message_bus_name="messagepubsub",
+                                        state_store_name="workflowstatestore",
+                                        state_key="workflow_state",
+                                        agents_registry_store_name="agentstatestore",
+                                        agents_registry_key="agents_registry", service_port=8002,
+                                        daprGrpcPort=50002)
+
+        await wizard_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-dapr-workflows/services/workflow-llm/app.py

@@ -0,0 +1,31 @@
+from dapr_agents import LLMOrchestrator
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        workflow_service = LLMOrchestrator(
+            name="LLMOrchestrator",
+            message_bus_name="messagepubsub",
+            state_store_name="workflowstatestore",
+            state_key="workflow_state",
+            agents_registry_store_name="agentstatestore",
+            agents_registry_key="agents_registry",
+            service_port=8004,
+            daprGrpcPort=50004,
+            max_iterations=3
+        )
+
+        await workflow_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/05-multi-agent-workflow-dapr-workflows/services/workflow-random/app.py

@@ -7,7 +7,7 @@ import logging
 async def main():
     try:
         workflow_service = RandomOrchestrator(
-            name="Orchestrator",
+            name="RandomOrchestrator",
             message_bus_name="messagepubsub",
             state_store_name="workflowstatestore",
             state_key="workflow_state",

quickstarts/05-multi-agent-workflow-dapr-workflows/services/workflow-roundrobin/app.py

@@ -0,0 +1,31 @@
+from dapr_agents import RoundRobinOrchestrator
+from dotenv import load_dotenv
+import asyncio
+import logging
+
+
+async def main():
+    try:
+        workflow_service = RoundRobinOrchestrator(
+            name="RoundRobinOrchestrator",
+            message_bus_name="messagepubsub",
+            state_store_name="workflowstatestore",
+            state_key="workflow_state",
+            agents_registry_store_name="agentstatestore",
+            agents_registry_key="agents_registry",
+            service_port=8004,
+            daprGrpcPort=50004,
+            max_iterations=3
+        )
+
+        await workflow_service.start()
+    except Exception as e:
+        print(f"Error starting service: {e}")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+
+    asyncio.run(main())

quickstarts/README.md

@@ -31,7 +31,7 @@ Learn how to interact with Language Models using Dapr Agents:
 
 This quickstart shows both basic text generation and structured data extraction from LLMs.
 
-[Go to LLM Call](./02-llm-call)
+[Go to LLM Call](./02_llm_call_open_ai)
 
 ### 03 - Agent Tool Call