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Add Microsoft Agent Framework Python integration support (#645)
* Add Microsoft Agent Framework Python integration support * Cleanup * Update to use latest MAF package * Update to README * Cleanup * Bump agent-framework-ag-ui pkg to 1.0.0b251112.post1 * feat: update prep/run scripts to include Microsoft Agent Framework * Use token auth by default --------- Co-authored-by: Tyler Slaton <[email protected]> Co-authored-by: Brandon McConnell <[email protected]>
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‎apps/dojo/scripts/prep-dojo-everything.js‎

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@@ -112,6 +112,16 @@ const ALL_TARGETS = {
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name: "Dojo",
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cwd: gitRoot,
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},
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"microsoft-agent-framework-python": {
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command: "uv sync",
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name: "Microsoft Agent Framework (Python)",
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cwd: path.join(integrationsRoot, "microsoft-agent-framework/python/examples"),
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},
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"microsoft-agent-framework-dotnet": {
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command: "dotnet restore AGUIDojoServer/AGUIDojoServer.csproj",
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name: "Microsoft Agent Framework (.NET)",
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cwd: path.join(integrationsRoot, "microsoft-agent-framework/dotnet/examples"),
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},
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};
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function printDryRunServices(procs) {

‎apps/dojo/scripts/run-dojo-everything.js‎

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cwd: path.join(middlewaresRoot, "a2a-middleware/examples"),
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env: { PORT: 8014 },
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}],
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'microsoft-agent-framework-python': [{
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command: 'uv run dev',
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name: 'Microsoft Agent Framework (Python)',
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cwd: path.join(integrationsRoot, 'microsoft-agent-framework/python/examples'),
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env: { PORT: 8015 },
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}],
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'microsoft-agent-framework-dotnet': [{
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command: 'dotnet run --project AGUIDojoServer/AGUIDojoServer.csproj --urls "http://localhost:8889" --no-build',
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name: 'Microsoft Agent Framework (.NET)',
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cwd: path.join(integrationsRoot, 'microsoft-agent-framework/dotnet/examples'),
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env: { PORT: 8016 },
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}],
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'dojo': [{
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command: 'pnpm run start',
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name: 'Dojo',

‎apps/dojo/src/agents.ts‎

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};
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},
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},
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{
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id: "microsoft-agent-framework-python",
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agents: async () => {
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return {
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agentic_chat: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/agentic_chat`,
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}),
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backend_tool_rendering: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/backend_tool_rendering`,
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}),
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human_in_the_loop: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/human_in_the_loop`,
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}),
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agentic_generative_ui: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/agentic_generative_ui`,
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}),
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shared_state: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/shared_state`,
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}),
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tool_based_generative_ui: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/tool_based_generative_ui`,
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}),
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predictive_state_updates: new HttpAgent({
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url: `${envVars.agentFrameworkPythonUrl}/predictive_state_updates`,
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}),
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};
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},
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},
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{
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id: "a2a-basic",
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agents: async () => {

‎apps/dojo/src/env.ts‎

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crewAiUrl: string;
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pydanticAIUrl: string;
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adkMiddlewareUrl: string;
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agentFrameworkPythonUrl: string;
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a2aUrl: string;
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agentFrameworkDotnetUrl: string;
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a2aMiddlewareBuildingsManagementUrl: string;
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crewAiUrl: process.env.CREW_AI_URL || 'http://localhost:9002',
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pydanticAIUrl: process.env.PYDANTIC_AI_URL || 'http://localhost:9000',
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adkMiddlewareUrl: process.env.ADK_MIDDLEWARE_URL || 'http://localhost:8000',
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agentFrameworkPythonUrl: process.env.AGENT_FRAMEWORK_PYTHON_URL || 'http://localhost:8888',
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agentFrameworkDotnetUrl: process.env.AGENT_FRAMEWORK_DOTNET_URL || 'http://localhost:5018',
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springAiUrl: process.env.SPRING_AI_URL || 'http://localhost:8080',
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a2aUrl: process.env.A2A_URL || 'http://localhost:10002',

‎apps/dojo/src/menu.ts‎

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"tool_based_generative_ui",
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],
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},
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{
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id: "microsoft-agent-framework-python",
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name: "Microsoft Agent Framework (Python)",
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features: [
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"agentic_chat",
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"backend_tool_rendering",
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"human_in_the_loop",
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"agentic_generative_ui",
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"predictive_state_updates",
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"shared_state",
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"tool_based_generative_ui",
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],
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},
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{
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id: "a2a",
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name: "A2A",

‎integrations/microsoft-agent-framework/python/examples/.env.example‎

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# Environment variables for Microsoft Agent Framework examples
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# Azure OpenAI Configuration
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AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
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AZURE_OPENAI_API_KEY=your_azure_openai_api_key_here
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AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your_deployment_name
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# OpenAI Configuration (alternative to Azure OpenAI)
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# OPENAI_API_KEY=your_openai_api_key_here
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# OPENAI_CHAT_MODEL_ID=your_chat_model_id_here
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# Server Configuration
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PORT=8888

‎integrations/microsoft-agent-framework/python/examples/.gitignore‎

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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual environments
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venv/
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ENV/
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env/
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.venv
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# Poetry
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poetry.lock
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# Environment variables
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.env
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.env.local
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# IDEs
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# OS
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.DS_Store
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Thumbs.db
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# Logs
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*.log

‎integrations/microsoft-agent-framework/python/examples/README.md‎

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# Microsoft Agent Framework AG-UI Integration
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This directory contains examples for using the Microsoft Agent Framework with the AG-UI protocol in the Dojo application.
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## Prerequisites
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- Python 3.10 or higher
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- [uv](https://docs.astral.sh/uv/) for dependency management
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- An OpenAI API key or Azure OpenAI endpoint
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## Installation
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1. Install dependencies:
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```bash
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cd integrations/microsoft-agent-framework/python/examples
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uv sync
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```
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2. Create a `.env` file based on `.env.example`:
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```bash
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cp .env.example .env
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```
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3. Add your API credentials to `.env`:
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```bash
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# For OpenAI
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OPENAI_API_KEY=your_api_key_here
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OPENAI_CHAT_MODEL_ID=your_model_here
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# Or for Azure OpenAI
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AZURE_OPENAI_ENDPOINT=your_endpoint_here
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# If using token auth, this env var is not necessary
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# AZURE_OPENAI_API_KEY=your_api_key_here
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AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your_deployment_here
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```
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## Authentication
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The sample uses `AzureCliCredential` for authentication. Run `az login` in your terminal before running the examples, or replace `AzureCliCredential` with your preferred authentication method.
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## Required role-based access control (RBAC) roles
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To access the Azure OpenAI API, your Azure account or service principal needs one of the following RBAC roles assigned to the Azure OpenAI resource:
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- **Cognitive Services OpenAI User**: Provides read access to Azure OpenAI resources and the ability to call the inference APIs. This is the minimum role required for running these examples.
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- **Cognitive Services OpenAI Contributor**: Provides full access to Azure OpenAI resources, including the ability to create, update, and delete deployments and models.
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For most scenarios, the **Cognitive Services OpenAI User** role is sufficient. You can assign this role through the Azure portal under the Azure OpenAI resource's "Access control (IAM)" section.
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For more detailed information about Azure OpenAI RBAC roles, see: [Role-based access control for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control)
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## Running the Examples
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### 1. Start the Backend Server
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In the examples directory, start the Dojo backend server:
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```bash
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cd integrations/microsoft-agent-framework/python/examples
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uv run dev
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```
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The server will start on `http://localhost:8888` by default.
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### 2. Start the Dojo Frontend
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In a separate terminal, start the Dojo web application:
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```bash
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cd apps/dojo
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pnpm dev
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```
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The Dojo frontend will be available at `http://localhost:3000`.
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### 3. Connect to Your Agent
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1. Open `http://localhost:3000` in your browser
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2. Configure the server URL to `http://localhost:8888`
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3. Select one "Microsoft Agent Framework (Python)" from the dropdown
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4. Start exploring the samples
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## Available Endpoints
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The server exposes the following example agents demonstrating all 7 AG-UI features:
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- `/agentic_chat` - Basic conversational agent with tool calling (Feature 1: Agentic Chat)
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- `/backend_tool_rendering` - Agent demonstrating backend tool rendering (Feature 2: Backend Tool Rendering)
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- `/human_in_the_loop` - Agent with human-in-the-loop workflows (Feature 3: Human in the Loop)
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- `/agentic_generative_ui` - Agent that breaks down tasks into steps with streaming updates (Feature 4: Agentic Generative UI)
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- `/tool_based_generative_ui` - Agent that generates custom UI components (Feature 5: Tool-based Generative UI)
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- `/shared_state` - Agent with bidirectional state synchronization (Feature 6: Shared State)
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- `/predictive_state_updates` - Agent with predictive state updates during tool execution (Feature 7: Predictive State Updates)
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## Project Structure
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```
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examples/
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├── agents/
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│ ├── agentic_chat/ # Feature 1: Basic chat agent
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│ ├── backend_tool_rendering/ # Feature 2: Backend tool rendering
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│ ├── human_in_the_loop/ # Feature 3: Human-in-the-loop
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│ ├── agentic_generative_ui/ # Feature 4: Streaming state updates
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│ ├── tool_based_generative_ui/ # Feature 5: Custom UI components
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│ ├── shared_state/ # Feature 6: Bidirectional state sync
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│ ├── predictive_state_updates/ # Feature 7: Predictive state updates
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│ └── dojo.py # FastAPI application setup
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├── pyproject.toml # Dependencies and scripts
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├── .env.example # Environment variable template
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└── README.md # This file
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```
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## Using Different Chat Clients
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The Microsoft Agent Framework supports multiple chat clients. You can mix and match different clients for different agents:
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### Azure OpenAI (Default)
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```python
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from agent_framework.azure import AzureOpenAIChatClient
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azure_client = AzureOpenAIChatClient()
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agent = simple_agent(azure_client)
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```
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### OpenAI
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```python
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from agent_framework.openai import OpenAIChatClient
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openai_client = OpenAIChatClient(model_id="gpt-4o")
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agent = weather_agent(openai_client)
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```
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### Mixing Clients
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You can use different chat clients for different agents in the same application:
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```python
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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# Create clients
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azure_client = AzureOpenAIChatClient()
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openai_client = OpenAIChatClient(model_id="gpt-4o")
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# Use different clients for different agents
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agent1 = simple_agent(azure_client)
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agent2 = weather_agent(openai_client)
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agent3 = recipe_agent(azure_client)
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```
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See `agents/dojo.py` for a complete example.
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## Development
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To add a new example agent:
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1. Create a new directory under `agents/`
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2. Add an `agent.py` file with your agent implementation
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3. Import and register it in `agents/dojo.py`
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## Dependencies
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This integration uses:
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- `agent-framework-ag-ui` - Microsoft Agent Framework AG-UI adapter
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- `fastapi` - Web framework for the server
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- `uvicorn` - ASGI server
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- `python-dotenv` - Environment variable management
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## License
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MIT

‎integrations/microsoft-agent-framework/python/examples/agents/__init__.py‎

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"""Agent package."""

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