├── examples ├── mcp │ ├── streamable-http │ │ ├── mcp-client │ │ │ ├── .python-version │ │ │ ├── main.py │ │ │ ├── .env_sample │ │ │ ├── pyproject.toml │ │ │ ├── README.md │ │ │ └── mcp_as_streamable_plugin.py │ │ ├── mcp-server │ │ │ ├── .python-version │ │ │ ├── main.py │ │ │ ├── pyproject.toml │ │ │ ├── Dockerfile │ │ │ ├── README.md │ │ │ ├── weather.py │ │ │ └── uv.lock │ │ ├── ai-foundry-agent │ │ │ ├── .python-version │ │ │ ├── main.py │ │ │ ├── .env_sample │ │ │ ├── pyproject.toml │ │ │ ├── README.md │ │ │ ├── sample_agents_mcp.py │ │ │ └── uv.lock │ │ ├── apim-petstore-mcp │ │ │ ├── pyproject.toml │ │ │ ├── .env_sample │ │ │ ├── policy.xml │ │ │ ├── README.md │ │ │ └── pet_store_mcp.py │ │ └── README.md │ └── filesystem_example │ │ ├── requirements.txt │ │ ├── sample_files │ │ ├── favorite_cities.txt │ │ ├── favorite_songs.txt │ │ └── favorite_books.txt │ │ ├── .env_sample │ │ ├── README.md │ │ └── main_azure_ai_foundry.py └── mem0 │ ├── pyproject.toml │ ├── .env_sample │ ├── README.md │ ├── mem0_config.py │ ├── azure_ai_foundry_mem0_demo.py │ └── uv.lock ├── CHANGELOG.md ├── .github ├── CODE_OF_CONDUCT.md ├── ISSUE_TEMPLATE.md └── PULL_REQUEST_TEMPLATE.md ├── LICENSE.md ├── README.md ├── CONTRIBUTING.md └── .gitignore /examples/mcp/streamable-http/mcp-client/.python-version: -------------------------------------------------------------------------------- 1 | 3.13 2 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-server/.python-version: -------------------------------------------------------------------------------- 1 | 3.13 2 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/ai-foundry-agent/.python-version: -------------------------------------------------------------------------------- 1 | 3.13 2 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/requirements.txt: -------------------------------------------------------------------------------- 1 | dotenv-python 2 | openai-agents==0.0.11 3 | openai==1.75.0 -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-client/main.py: -------------------------------------------------------------------------------- 1 | def main(): 2 | print("Hello from mcp-client!") 3 | 4 | 5 | if __name__ == "__main__": 6 | main() 7 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-server/main.py: -------------------------------------------------------------------------------- 1 | def main(): 2 | print("Hello from mcp-server!") 3 | 4 | 5 | if __name__ == "__main__": 6 | main() 7 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/ai-foundry-agent/main.py: -------------------------------------------------------------------------------- 1 | def main(): 2 | print("Hello from ai-foundry-agent!") 3 | 4 | 5 | if __name__ == "__main__": 6 | main() 7 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | ## [project-title] Changelog 2 | 3 | 4 | # x.y.z (yyyy-mm-dd) 5 | 6 | *Features* 7 | * ... 8 | 9 | *Bug Fixes* 10 | * ... 11 | 12 | *Breaking Changes* 13 | * ... 14 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/sample_files/favorite_cities.txt: -------------------------------------------------------------------------------- 1 | - In the summer, I love visiting London. 2 | - In the winter, Tokyo is great. 3 | - In the spring, San Francisco. 4 | - In the fall, New York is the best. -------------------------------------------------------------------------------- /examples/mcp/streamable-http/ai-foundry-agent/.env_sample: -------------------------------------------------------------------------------- 1 | MODEL_DEPLOYMENT_NAME= 2 | PROJECT_ENDPOINT= 3 | WEATHER_MCP_SERVER_URL= -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-server/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "mcp-server" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "mcp>=1.9.4", 9 | ] 10 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/.env_sample: -------------------------------------------------------------------------------- 1 | # rename this to .env and fill in your own keys 2 | AZURE_OPENAI_ENDPOINT= 3 | AZURE_OPENAI_API_KEY= 4 | AZURE_OPENAI_CHAT_DEPLOYMENT_NAME= 5 | AZURE_OPENAI_API_VERSION= -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-client/.env_sample: -------------------------------------------------------------------------------- 1 | AZURE_OPENAI_CHAT_DEPLOYMENT_NAME= 2 | AZURE_OPENAI_ENDPOINT= 3 | AZURE_OPENAI_API_KEY= 4 | AZURE_OPENAI_API_VERSION= 5 | WEATHER_MCP_SERVER_URL=http://localhost:8001/mcp/ -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-client/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "mcp-client" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "mcp>=1.9.4", 9 | "python-dotenv>=1.1.0", 10 | "semantic-kernel>=1.32.2", 11 | ] 12 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/apim-petstore-mcp/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "mcp-client" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "mcp>=1.9.4", 9 | "python-dotenv>=1.1.0", 10 | "semantic-kernel>=1.32.2", 11 | ] 12 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/apim-petstore-mcp/.env_sample: -------------------------------------------------------------------------------- 1 | AZURE_OPENAI_CHAT_DEPLOYMENT_NAME= 2 | AZURE_OPENAI_ENDPOINT= 3 | AZURE_OPENAI_API_KEY= 4 | AZURE_OPENAI_API_VERSION= 5 | PETSTORE_MCP_SERVER_URL= -------------------------------------------------------------------------------- /examples/mem0/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "mem0" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "azure-identity>=1.25.0", 9 | "azure-search-documents>=11.5.3", 10 | "mem0ai>=0.1.117", 11 | "openai>=1.99.9", 12 | "python-dotenv>=1.1.1", 13 | ] 14 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/ai-foundry-agent/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "ai-foundry-agent" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "azure-ai-agents>=1.2.0b1", 9 | "azure-ai-projects>=1.0.0", 10 | "azure-identity>=1.24.0", 11 | "python-dotenv>=1.1.1", 12 | ] 13 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-client/README.md: -------------------------------------------------------------------------------- 1 | # Streamable HTTP MCP Client using Semantic Kernel 2 | 3 | ## Running the MCP Client 4 | 5 | Save the file `.env_sample` as `.env` and update the environment variables. 6 | 7 | Run the following commands 8 | ``` 9 | cd examples/mcp/streamable-http/mcp-client 10 | uv venv 11 | uv sync 12 | uv run mcp_as_streamable_plugin.py 13 | ``` 14 | 15 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/sample_files/favorite_songs.txt: -------------------------------------------------------------------------------- 1 | 1. "Here Comes the Sun" – The Beatles 2 | 2. "Imagine" – John Lennon 3 | 3. "Bohemian Rhapsody" – Queen 4 | 4. "Shake It Off" – Taylor Swift 5 | 5. "Billie Jean" – Michael Jackson 6 | 6. "Uptown Funk" – Mark Ronson ft. Bruno Mars 7 | 7. "Don’t Stop Believin’" – Journey 8 | 8. "Dancing Queen" – ABBA 9 | 9. "Happy" – Pharrell Williams 10 | 10. "Wonderwall" – Oasis 11 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-server/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.13-slim 2 | 3 | # Install uv. 4 | COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ 5 | 6 | # Copy the application into the container. 7 | COPY . /app 8 | 9 | # Install the application dependencies. 10 | WORKDIR /app 11 | RUN uv sync --frozen --no-cache 12 | 13 | # Run the application. 14 | CMD ["uv", "run", "/app/weather.py"] 15 | 16 | EXPOSE 8001 17 | -------------------------------------------------------------------------------- /examples/mem0/.env_sample: -------------------------------------------------------------------------------- 1 | LLM_AZURE_OPENAI_ENDPOINT= 2 | LLM_AZURE_OPENAI_API_KEY= 3 | LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT=gpt-4.1 4 | LLM_AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-small 5 | LLM_AZURE_CHAT_COMPLETION_API_VERSION=2024-12-01-preview 6 | LLM_AZURE_EMBEDDING_API_VERSION=2024-12-01-preview 7 | SEARCH_SERVICE_NAME= 8 | SEARCH_SERVICE_API_KEY= -------------------------------------------------------------------------------- /.github/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Microsoft Open Source Code of Conduct 2 | 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 4 | 5 | Resources: 6 | 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns 10 | -------------------------------------------------------------------------------- /examples/mem0/README.md: -------------------------------------------------------------------------------- 1 | # Long Term Memory Agent using mem0, Azure AI Foundry and Azure AI Search 2 | 3 | **Read the Blog Post**: https://medium.com/@eitansela/implement-long-term-memory-in-your-ai-agents-with-mem0-azure-ai-foundry-and-ai-search-56efd8683c03 4 | 5 | ## Running the sample code 6 | 7 | Save the file `.env_sample` as `.env` and update the environment variables. 8 | 9 | Run the following commands 10 | ``` 11 | cd examples/mem0 12 | uv venv 13 | uv sync 14 | uv run azure_ai_foundry_mem0_demo.py 15 | ``` 16 | 17 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/ai-foundry-agent/README.md: -------------------------------------------------------------------------------- 1 | # Streamable HTTP MCP Client using Azure AI Foundry Agent Service 2 | 3 | **Read the Blog Post**: https://medium.com/@eitansela/build-an-mcp-client-with-azure-ai-foundry-agent-service-3c99c805f4d9 4 | 5 | ## Running the Azure AI Foundry Agent Service MCP Client 6 | 7 | Save the file `.env_sample` as `.env` and update the environment variables. 8 | 9 | Run the following commands 10 | ``` 11 | cd examples/mcp/streamable-http/ai-foundry-agent 12 | uv venv 13 | uv sync 14 | uv run sample_agents_mcp.py 15 | ``` 16 | 17 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/README.md: -------------------------------------------------------------------------------- 1 | # Remote Streamable HTTP MCP Server deployed in Azure Container Apps 2 | 3 | This folder contains MCP server using Streamable HTTP transport Python sample code, and MCP Client implemented with Semantic Kernel Python. 4 | 5 | **Read the Blog Post**: https://medium.com/@eitansela/azure-mcp-serverless-guide-deploy-with-container-apps-debug-with-inspector-connect-with-semantic-612d8abb5c75 6 | 7 | ## Project folders 8 | - `mcp-server` - MCP server using Streamable HTTP transport Python sample code. 9 | - `mcp-client` - MCP Client implemented with Semantic Kernel Python SDK. 10 | - `ai-foundry-agent` - Azure AI Foundry Agent Service MCP Client. -------------------------------------------------------------------------------- /examples/mcp/streamable-http/apim-petstore-mcp/policy.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | https://azure-api.net/authorization-manager 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-server/README.md: -------------------------------------------------------------------------------- 1 | # Streamable HTTP MCP Server 2 | 3 | ## Running the MCP Server locally 4 | Run the following commands 5 | ``` 6 | cd examples/mcp/streamable-http/mcp-server 7 | uv venv 8 | uv sync 9 | uv run weather.py 10 | ``` 11 | 12 | ## Run MCP Inspector to test and debug MCP Server 13 | Open a new terminal window and run the following commands 14 | ``` 15 | npx @modelcontextprotocol/inspector 16 | ``` 17 | 18 | Open a browser and navigate to http://127.0.0.1:6274 19 | 20 | In the URL enter: http://localhost:8001/mcp and click Connect. 21 | 22 | 23 | ## Deploy to Azure Container Apps 24 | Run the following commands 25 | ``` 26 | az containerapp up \ 27 | --resource-group \ 28 | --name streamable-weather-mcp \ 29 | --environment mcp \ 30 | --location \ 31 | --source . 32 | ``` 33 | 34 | 35 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/sample_files/favorite_books.txt: -------------------------------------------------------------------------------- 1 | 1. To Kill a Mockingbird – Harper Lee 2 | 2. Pride and Prejudice – Jane Austen 3 | 3. 1984 – George Orwell 4 | 4. The Hobbit – J.R.R. Tolkien 5 | 5. Harry Potter and the Sorcerer’s Stone – J.K. Rowling 6 | 6. The Great Gatsby – F. Scott Fitzgerald 7 | 7. Charlotte’s Web – E.B. White 8 | 8. Anne of Green Gables – Lucy Maud Montgomery 9 | 9. The Alchemist – Paulo Coelho 10 | 10. Little Women – Louisa May Alcott 11 | 11. The Catcher in the Rye – J.D. Salinger 12 | 12. Animal Farm – George Orwell 13 | 13. The Chronicles of Narnia: The Lion, the Witch, and the Wardrobe – C.S. Lewis 14 | 14. The Book Thief – Markus Zusak 15 | 15. A Wrinkle in Time – Madeleine L’Engle 16 | 16. The Secret Garden – Frances Hodgson Burnett 17 | 17. Moby-Dick – Herman Melville 18 | 18. Fahrenheit 451 – Ray Bradbury 19 | 19. Jane Eyre – Charlotte Brontë 20 | 20. The Little Prince – Antoine de Saint-Exupéry -------------------------------------------------------------------------------- /examples/mcp/streamable-http/apim-petstore-mcp/README.md: -------------------------------------------------------------------------------- 1 | # MCP Client for Pet Store MCP Server on Azure API Management, using Semantic Kernel 2 | 3 | **Read the Blog Post**: https://medium.com/@eitansela/expose-your-rest-api-as-an-mcp-server-in-minutes-with-azure-api-management-1deb51142a04 4 | 5 | ## Running the MCP Client 6 | 7 | Save the file `.env_sample` as `.env` and update the environment variables. 8 | 9 | Run the following commands 10 | ``` 11 | cd examples/mcp/streamable-http/apim-petstore-mcp 12 | uv venv 13 | uv sync 14 | uv run pet_store_mcp.py 15 | ``` 16 | 17 | **NOTE**: when running this sample code, you'll get `HTTP/1.1 401 Unauthorized` error, when connecting to the Pet Store MCP Server on APIM. 18 | This demonstrates that the MCP Server is secure and can be reachable only by providing the Bearer token. 19 | To fix this, uncomment the line setting the headers in the `MCPStreamableHttpPlugin` definition (around line 115). -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | 4 | > Please provide us with the following information: 5 | > --------------------------------------------------------------- 6 | 7 | ### This issue is for a: (mark with an `x`) 8 | ``` 9 | - [ ] bug report -> please search issues before submitting 10 | - [ ] feature request 11 | - [ ] documentation issue or request 12 | - [ ] regression (a behavior that used to work and stopped in a new release) 13 | ``` 14 | 15 | ### Minimal steps to reproduce 16 | > 17 | 18 | ### Any log messages given by the failure 19 | > 20 | 21 | ### Expected/desired behavior 22 | > 23 | 24 | ### OS and Version? 25 | > Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?) 26 | 27 | ### Versions 28 | > 29 | 30 | ### Mention any other details that might be useful 31 | 32 | > --------------------------------------------------------------- 33 | > Thanks! We'll be in touch soon. 34 | -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | ## Purpose 2 | 3 | * ... 4 | 5 | ## Does this introduce a breaking change? 6 | 7 | ``` 8 | [ ] Yes 9 | [ ] No 10 | ``` 11 | 12 | ## Pull Request Type 13 | What kind of change does this Pull Request introduce? 14 | 15 | 16 | ``` 17 | [ ] Bugfix 18 | [ ] Feature 19 | [ ] Code style update (formatting, local variables) 20 | [ ] Refactoring (no functional changes, no api changes) 21 | [ ] Documentation content changes 22 | [ ] Other... Please describe: 23 | ``` 24 | 25 | ## How to Test 26 | * Get the code 27 | 28 | ``` 29 | git clone [repo-address] 30 | cd [repo-name] 31 | git checkout [branch-name] 32 | npm install 33 | ``` 34 | 35 | * Test the code 36 | 37 | ``` 38 | ``` 39 | 40 | ## What to Check 41 | Verify that the following are valid 42 | * ... 43 | 44 | ## Other Information 45 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Azure AI Foundry Agents Samples 2 | 3 | Welcome to the **Azure AI Foundry Agents Samples** repository! This collection showcases sample implementations, templates, and demos for building intelligent agents using **Azure AI Foundry**. Whether you're just getting started or looking to deepen your skills, these examples will help you harness the power of AI in your applications. 4 | 5 | ## 🔍 What is Azure AI Foundry? 6 | 7 | Azure AI Foundry is a trusted, integrated platform for Developers and IT Administrators to design, customize, and manage AI applications and agents. 8 | 9 | For more details, please refer to [Azure AI Foundry documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry). 10 | 11 | ## 🛠️ Requirements 12 | 13 | - An active Azure subscription 14 | - Access to Azure AI Foundry 15 | - Python 3.10+ 16 | - NPM and Node.js to run the MCP Server 17 | - VS Code with Azure extensions recommended 18 | 19 | ## 🚀 Getting Started 20 | 21 | 1. Clone the repo: 22 | ``` 23 | git clone https://github.com/Azure-Samples/ai-foundry-agents-samples.git 24 | ``` 25 | 26 | 2. Navigate to the folder of interest: 27 | ``` 28 | cd examples/ 29 | ``` 30 | 31 | 3. Follow the instructions in the `README.md` file within each sample directory to run the example. 32 | 33 | ## 📄 License 34 | 35 | This project is licensed under the MIT License. See `LICENSE.txt` for more information. 36 | 37 | ## 🙌 Contributing 38 | 39 | Have a cool agent or MCP integration to share? 40 | Pull requests are welcome! Please open an issue first to discuss what you would like to add. 41 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/README.md: -------------------------------------------------------------------------------- 1 | # MCP Filesystem Example 2 | 3 | This example uses the [filesystem MCP server](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem), running locally via `npx`. 4 | 5 | ## Details 6 | 7 | The example uses the `MCPServerStdio` class from `agents.mcp`, with the command: 8 | 9 | ```bash 10 | npx -y "@modelcontextprotocol/server-filesystem" 11 | ``` 12 | 13 | It's only given access to the `sample_files` directory adjacent to the example, which contains some sample data. 14 | 15 | Under the hood: 16 | 17 | 1. The server is spun up in a subprocess, and exposes a bunch of tools like `list_directory()`, `read_file()`, etc. 18 | 2. We add the server instance to the Agent via `mcp_agents`. 19 | 3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`. 20 | 4. If the LLM chooses to use an MCP tool, we call the MCP server to run the tool via `server.run_tool()`. 21 | 22 | ## Runing the sample code 23 | 24 | ### Create and activate Python virtual environment 25 | 26 | ``` 27 | python3 -m venv .venv 28 | ``` 29 | 30 | #### MacOS/Linux 31 | ``` 32 | source .venv/bin/activate 33 | ``` 34 | 35 | #### Windows 36 | ``` 37 | venv\Scripts\activate 38 | ``` 39 | 40 | ### Install dependencies 41 | 42 | ``` 43 | pip install -r requirements.txt 44 | ``` 45 | 46 | ### Install npx 47 | ``` 48 | npm install -g npx 49 | ``` 50 | 51 | ### Rename `.env_sample` to `.env` 52 | 53 | Update the following ENV variables: 54 | 55 | - `AZURE_OPENAI_ENDPOINT` 56 | - `AZURE_OPENAI_API_KEY` 57 | - `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME` 58 | - `AZURE_OPENAI_API_VERSION` 59 | 60 | ### Run the Python file 61 | 62 | ``` 63 | python main_azure_ai_foundry.py 64 | ``` -------------------------------------------------------------------------------- /examples/mem0/mem0_config.py: -------------------------------------------------------------------------------- 1 | """Shared configuration for connecting Mem0 to Azure resources.""" 2 | 3 | from dotenv import load_dotenv 4 | import os 5 | 6 | # Pull secrets from the local .env so running the demo remains simple. 7 | load_dotenv() 8 | 9 | LLM_AZURE_OPENAI_API_KEY = os.environ["LLM_AZURE_OPENAI_API_KEY"] 10 | LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT = os.environ["LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT"] 11 | LLM_AZURE_OPENAI_ENDPOINT = os.environ["LLM_AZURE_OPENAI_ENDPOINT"] 12 | LLM_AZURE_CHAT_COMPLETION_API_VERSION = os.environ["LLM_AZURE_CHAT_COMPLETION_API_VERSION"] 13 | LLM_AZURE_EMBEDDING_DEPLOYMENT = os.environ["LLM_AZURE_EMBEDDING_DEPLOYMENT"] 14 | LLM_AZURE_EMBEDDING_API_VERSION = os.environ["LLM_AZURE_EMBEDDING_API_VERSION"] 15 | SEARCH_SERVICE_NAME = os.environ["SEARCH_SERVICE_NAME"] 16 | SEARCH_SERVICE_API_KEY = os.environ["SEARCH_SERVICE_API_KEY"] 17 | 18 | # Central config object consumed by Mem0 when building the memory backend. 19 | CONFIG = { 20 | "llm": { 21 | "provider": "azure_openai", 22 | "config": { 23 | "model": LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT, 24 | "temperature": 0.1, 25 | "max_tokens": 2000, 26 | "azure_kwargs": { 27 | "azure_deployment": LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT, 28 | "api_version": LLM_AZURE_CHAT_COMPLETION_API_VERSION, 29 | "azure_endpoint": LLM_AZURE_OPENAI_ENDPOINT, 30 | "api_key": LLM_AZURE_OPENAI_API_KEY, 31 | }, 32 | }, 33 | }, 34 | "embedder": { 35 | "provider": "azure_openai", 36 | "config": { 37 | "model": LLM_AZURE_EMBEDDING_DEPLOYMENT, 38 | "embedding_dims": 1536, 39 | "azure_kwargs": { 40 | "api_version": LLM_AZURE_EMBEDDING_API_VERSION, 41 | "azure_deployment": LLM_AZURE_EMBEDDING_DEPLOYMENT, 42 | "azure_endpoint": LLM_AZURE_OPENAI_ENDPOINT, 43 | "api_key": LLM_AZURE_OPENAI_API_KEY, 44 | }, 45 | }, 46 | }, 47 | "vector_store": { 48 | "provider": "azure_ai_search", 49 | "config": { 50 | "service_name": SEARCH_SERVICE_NAME, 51 | "api_key": SEARCH_SERVICE_API_KEY, 52 | "collection_name": "my-demo-mem0-agent-memories", 53 | "embedding_model_dims": 1536, 54 | "compression_type": "binary", 55 | }, 56 | }, 57 | } 58 | -------------------------------------------------------------------------------- /examples/mem0/azure_ai_foundry_mem0_demo.py: -------------------------------------------------------------------------------- 1 | from mem0 import Memory 2 | from openai import AzureOpenAI 3 | 4 | # Load reusable Azure configuration and Mem0 wiring. 5 | from mem0_config import ( 6 | CONFIG, 7 | LLM_AZURE_CHAT_COMPLETION_API_VERSION, 8 | LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT, 9 | LLM_AZURE_OPENAI_ENDPOINT, 10 | LLM_AZURE_OPENAI_API_KEY, 11 | SEARCH_SERVICE_NAME, 12 | ) 13 | 14 | print("Using Azure OpenAI deployment:", LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT) 15 | print("Using Azure AI Search service:", SEARCH_SERVICE_NAME) 16 | 17 | 18 | # Create Azure OpenAI client that powers chat completions. 19 | azure_openai_client = AzureOpenAI( 20 | azure_endpoint=LLM_AZURE_OPENAI_ENDPOINT, 21 | api_key=LLM_AZURE_OPENAI_API_KEY, 22 | api_version=LLM_AZURE_CHAT_COMPLETION_API_VERSION, 23 | ) 24 | 25 | # Build the Mem0 memory interface using the shared config block. 26 | memory = Memory.from_config(CONFIG) 27 | 28 | 29 | def chat_with_memories(message: str, user_id: str = "default_user") -> str: 30 | # Retrieve relevant memories 31 | relevant_memories = memory.search(query=message, user_id=user_id, limit=3) 32 | memories_string = "\n".join( 33 | f"- {entry['memory']}" for entry in relevant_memories["results"] 34 | ) 35 | 36 | # Generate Assistant response 37 | system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_string}" 38 | messages = [ 39 | {"role": "system", "content": system_prompt}, 40 | {"role": "user", "content": message}, 41 | ] 42 | response = azure_openai_client.chat.completions.create( 43 | model=LLM_AZURE_CHAT_COMPLETION_DEPLOYMENT, messages=messages 44 | ) 45 | assistant_response = response.choices[0].message.content 46 | 47 | # Create new memories from the conversation 48 | messages.append({"role": "assistant", "content": assistant_response}) 49 | # This is where the magic happens 50 | memory.add(messages, user_id=user_id, metadata={"source": "my-demo-mem0-agent"}) 51 | 52 | return assistant_response 53 | 54 | 55 | def main(): 56 | 57 | # This is a demo user ID. In a real application, use actual user IDs 58 | demo_user_id = "user_123" 59 | 60 | print("Chat with AI (type 'exit' to quit)") 61 | while True: 62 | user_input = input("You: ").strip() 63 | if user_input.lower() == "exit": 64 | print("Goodbye!") 65 | break 66 | print(f"AI: {chat_with_memories(user_input, demo_user_id)}") 67 | 68 | 69 | if __name__ == "__main__": 70 | main() 71 | -------------------------------------------------------------------------------- /examples/mcp/filesystem_example/main_azure_ai_foundry.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import os 3 | import shutil 4 | 5 | from agents import Agent, OpenAIChatCompletionsModel, Runner, set_tracing_disabled 6 | from agents.mcp import MCPServer, MCPServerStdio 7 | from dotenv import load_dotenv 8 | from openai import AsyncAzureOpenAI 9 | 10 | 11 | def get_azure_open_ai_client(): 12 | """ 13 | Creates and returns Azure OpenAI client instance. 14 | 15 | Returns: 16 | AsyncAzureOpenAI: Configured Azure OpenAI client 17 | """ 18 | load_dotenv() 19 | 20 | return AsyncAzureOpenAI( 21 | api_key=os.getenv("AZURE_OPENAI_API_KEY"), 22 | api_version=os.getenv("AZURE_OPENAI_API_VERSION"), 23 | azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), 24 | ) 25 | 26 | 27 | async def run(mcp_server: MCPServer): 28 | 29 | azure_open_ai_client = get_azure_open_ai_client() 30 | set_tracing_disabled(disabled=True) 31 | 32 | agent = Agent( 33 | name="Assistant", 34 | instructions="Use the tools to read the filesystem and answer questions based on those files.", 35 | model=OpenAIChatCompletionsModel(model=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"), 36 | openai_client=azure_open_ai_client), 37 | mcp_servers=[mcp_server], 38 | ) 39 | 40 | # List the files it can read 41 | message = "Read the files in `sample_files` folder, and list them." 42 | print(f"Running: {message}") 43 | result = await Runner.run(starting_agent=agent, input=message) 44 | print(result.final_output) 45 | 46 | # Ask about books 47 | message = "What is my #1 favorite book?" 48 | print(f"\n\nRunning: {message}") 49 | result = await Runner.run(starting_agent=agent, input=message) 50 | print(result.final_output) 51 | 52 | # Ask a question that reads then reasons. 53 | message = "Look at my favorite songs. Suggest one new song that I might like." 54 | print(f"\n\nRunning: {message}") 55 | result = await Runner.run(starting_agent=agent, input=message) 56 | print(result.final_output) 57 | 58 | 59 | async def main(): 60 | current_dir = os.path.dirname(os.path.abspath(__file__)) 61 | samples_dir = os.path.join(current_dir, "sample_files") 62 | 63 | async with MCPServerStdio( 64 | name="Filesystem Server, via npx", 65 | params={ 66 | "command": "npx", 67 | "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], 68 | }, 69 | ) as server: 70 | await run(server) 71 | 72 | 73 | if __name__ == "__main__": 74 | # Let's make sure the user has npx installed 75 | if not shutil.which("npx"): 76 | raise RuntimeError("npx is not installed. Please install it with `npm install -g npx`.") 77 | 78 | asyncio.run(main()) 79 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-server/weather.py: -------------------------------------------------------------------------------- 1 | from typing import Any 2 | import httpx 3 | from mcp.server.fastmcp import FastMCP 4 | 5 | # Initialize FastMCP server 6 | mcp = FastMCP("weather", port=8001, host="0.0.0.0", stateless_http=True) 7 | 8 | # Constants 9 | NWS_API_BASE = "https://api.weather.gov" 10 | USER_AGENT = "weather-app/1.0" 11 | 12 | async def make_nws_request(url: str) -> dict[str, Any] | None: 13 | """Make a request to the NWS API with proper error handling.""" 14 | headers = { 15 | "User-Agent": USER_AGENT, 16 | "Accept": "application/geo+json" 17 | } 18 | async with httpx.AsyncClient() as client: 19 | try: 20 | response = await client.get(url, headers=headers, timeout=30.0) 21 | response.raise_for_status() 22 | return response.json() 23 | except Exception: 24 | return None 25 | 26 | def format_alert(feature: dict) -> str: 27 | """Format an alert feature into a readable string.""" 28 | props = feature["properties"] 29 | return f""" 30 | Event: {props.get('event', 'Unknown')} 31 | Area: {props.get('areaDesc', 'Unknown')} 32 | Severity: {props.get('severity', 'Unknown')} 33 | Description: {props.get('description', 'No description available')} 34 | Instructions: {props.get('instruction', 'No specific instructions provided')} 35 | """ 36 | 37 | @mcp.tool() 38 | async def get_alerts(state: str) -> str: 39 | """Get weather alerts for a US state. 40 | 41 | Args: 42 | state: Two-letter US state code (e.g. CA, NY) 43 | """ 44 | url = f"{NWS_API_BASE}/alerts/active/area/{state}" 45 | data = await make_nws_request(url) 46 | 47 | if not data or "features" not in data: 48 | return "Unable to fetch alerts or no alerts found." 49 | 50 | if not data["features"]: 51 | return "No active alerts for this state." 52 | 53 | alerts = [format_alert(feature) for feature in data["features"]] 54 | return "\n---\n".join(alerts) 55 | 56 | @mcp.tool() 57 | async def get_forecast(latitude: float, longitude: float) -> str: 58 | """Get weather forecast for a location. 59 | 60 | Args: 61 | latitude: Latitude of the location 62 | longitude: Longitude of the location 63 | """ 64 | # First get the forecast grid endpoint 65 | points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}" 66 | points_data = await make_nws_request(points_url) 67 | 68 | if not points_data: 69 | return "Unable to fetch forecast data for this location." 70 | 71 | # Get the forecast URL from the points response 72 | forecast_url = points_data["properties"]["forecast"] 73 | forecast_data = await make_nws_request(forecast_url) 74 | 75 | if not forecast_data: 76 | return "Unable to fetch detailed forecast." 77 | 78 | # Format the periods into a readable forecast 79 | periods = forecast_data["properties"]["periods"] 80 | forecasts = [] 81 | for period in periods[:5]: # Only show next 5 periods 82 | forecast = f""" 83 | {period['name']}: 84 | Temperature: {period['temperature']}°{period['temperatureUnit']} 85 | Wind: {period['windSpeed']} {period['windDirection']} 86 | Forecast: {period['detailedForecast']} 87 | """ 88 | forecasts.append(forecast) 89 | 90 | return "\n---\n".join(forecasts) 91 | 92 | 93 | if __name__ == "__main__": 94 | # Initialize and run the server 95 | mcp.run(transport="streamable-http") -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to [project-title] 2 | 3 | This project welcomes contributions and suggestions. Most contributions require you to agree to a 4 | Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us 5 | the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. 6 | 7 | When you submit a pull request, a CLA bot will automatically determine whether you need to provide 8 | a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions 9 | provided by the bot. You will only need to do this once across all repos using our CLA. 10 | 11 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 12 | For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or 13 | contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. 14 | 15 | - [Code of Conduct](#coc) 16 | - [Issues and Bugs](#issue) 17 | - [Feature Requests](#feature) 18 | - [Submission Guidelines](#submit) 19 | 20 | ## Code of Conduct 21 | Help us keep this project open and inclusive. Please read and follow our [Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 22 | 23 | ## Found an Issue? 24 | If you find a bug in the source code or a mistake in the documentation, you can help us by 25 | [submitting an issue](#submit-issue) to the GitHub Repository. Even better, you can 26 | [submit a Pull Request](#submit-pr) with a fix. 27 | 28 | ## Want a Feature? 29 | You can *request* a new feature by [submitting an issue](#submit-issue) to the GitHub 30 | Repository. If you would like to *implement* a new feature, please submit an issue with 31 | a proposal for your work first, to be sure that we can use it. 32 | 33 | * **Small Features** can be crafted and directly [submitted as a Pull Request](#submit-pr). 34 | 35 | ## Submission Guidelines 36 | 37 | ### Submitting an Issue 38 | Before you submit an issue, search the archive, maybe your question was already answered. 39 | 40 | If your issue appears to be a bug, and hasn't been reported, open a new issue. 41 | Help us to maximize the effort we can spend fixing issues and adding new 42 | features, by not reporting duplicate issues. Providing the following information will increase the 43 | chances of your issue being dealt with quickly: 44 | 45 | * **Overview of the Issue** - if an error is being thrown a non-minified stack trace helps 46 | * **Version** - what version is affected (e.g. 0.1.2) 47 | * **Motivation for or Use Case** - explain what are you trying to do and why the current behavior is a bug for you 48 | * **Browsers and Operating System** - is this a problem with all browsers? 49 | * **Reproduce the Error** - provide a live example or a unambiguous set of steps 50 | * **Related Issues** - has a similar issue been reported before? 51 | * **Suggest a Fix** - if you can't fix the bug yourself, perhaps you can point to what might be 52 | causing the problem (line of code or commit) 53 | 54 | You can file new issues by providing the above information at the corresponding repository's issues link: https://github.com/[organization-name]/[repository-name]/issues/new]. 55 | 56 | ### Submitting a Pull Request (PR) 57 | Before you submit your Pull Request (PR) consider the following guidelines: 58 | 59 | * Search the repository (https://github.com/[organization-name]/[repository-name]/pulls) for an open or closed PR 60 | that relates to your submission. You don't want to duplicate effort. 61 | 62 | * Make your changes in a new git fork: 63 | 64 | * Commit your changes using a descriptive commit message 65 | * Push your fork to GitHub: 66 | * In GitHub, create a pull request 67 | * If we suggest changes then: 68 | * Make the required updates. 69 | * Rebase your fork and force push to your GitHub repository (this will update your Pull Request): 70 | 71 | ```shell 72 | git rebase master -i 73 | git push -f 74 | ``` 75 | 76 | That's it! Thank you for your contribution! 77 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/mcp-client/mcp_as_streamable_plugin.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Microsoft. All rights reserved. 2 | 3 | import asyncio 4 | import logging 5 | import os 6 | from dotenv import load_dotenv 7 | from semantic_kernel import Kernel 8 | from semantic_kernel.connectors.ai import FunctionChoiceBehavior 9 | from semantic_kernel.connectors.mcp import MCPStreamableHttpPlugin 10 | from semantic_kernel.contents import ChatHistory 11 | from semantic_kernel.utils.logging import setup_logging 12 | from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureChatPromptExecutionSettings 13 | 14 | 15 | """ 16 | This sample demonstrates how to build a conversational chatbot 17 | using Semantic Kernel, 18 | it creates a Plugin from a MCP server config and adds it to the kernel. 19 | The chatbot is designed to interact with the user, call MCP tools 20 | as needed, and return responses. 21 | """ 22 | 23 | # Step 1: Configure the logging module 24 | logging.basicConfig( 25 | level=logging.INFO, # Set the desired logging level (DEBUG, INFO, WARNING, etc.) 26 | format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" 27 | ) 28 | 29 | # Step 2: Create a logger 30 | logger = logging.getLogger("mcp.streamable-http.mcp-client") 31 | 32 | load_dotenv() 33 | weather_mcp_server_url = os.getenv("WEATHER_MCP_SERVER_URL") 34 | api_key=os.getenv("AZURE_OPENAI_API_KEY") 35 | endpoint=os.getenv("AZURE_OPENAI_ENDPOINT") 36 | deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME") 37 | api_version=os.getenv("AZURE_OPENAI_API_VERSION") 38 | logger.info(f"Using MCP server URL: {weather_mcp_server_url}") 39 | logger.info(f"Using Azure OpenAI endpoint: {endpoint}") 40 | logger.info(f"Using Azure OpenAI deployment name: {deployment_name}") 41 | logger.info(f"Using Azure OpenAI API version: {api_version}") 42 | 43 | # System message defining the behavior and persona of the chat bot. 44 | system_message = """ 45 | You are a chat bot. And you help users interact with weather services. 46 | You can call functions to get the information you need. 47 | """ 48 | 49 | # Create and configure the kernel. 50 | kernel = Kernel() 51 | 52 | chat_service = AzureChatCompletion( 53 | api_key=api_key, 54 | endpoint=endpoint, 55 | deployment_name=deployment_name, 56 | api_version=api_version 57 | ) 58 | settings = AzureChatPromptExecutionSettings() 59 | 60 | 61 | # Configure the function choice behavior. Here, we set it to Auto, where auto_invoke=True by default. 62 | # With `auto_invoke=True`, the model will automatically choose and call functions as needed. 63 | settings.function_choice_behavior = FunctionChoiceBehavior.Auto() 64 | 65 | kernel.add_service(chat_service) 66 | 67 | # Create a chat history to store the system message, initial messages, and the conversation. 68 | history = ChatHistory() 69 | history.add_system_message(system_message) 70 | 71 | 72 | async def chat() -> bool: 73 | """ 74 | Continuously prompt the user for input and show the assistant's response. 75 | Type 'exit' to exit. 76 | """ 77 | try: 78 | user_input = input("User:> ") 79 | except (KeyboardInterrupt, EOFError): 80 | print("\n\nExiting chat...") 81 | return False 82 | if user_input.lower().strip() == "exit": 83 | print("\n\nExiting chat...") 84 | return False 85 | 86 | history.add_user_message(user_input) 87 | result = await chat_service.get_chat_message_content(history, settings, kernel=kernel) 88 | if result: 89 | print(f"Bot:> {result}") 90 | history.add_message(result) 91 | 92 | return True 93 | 94 | 95 | async def main() -> None: 96 | # Create a plugin from the MCP server config and add it to the kernel. 97 | # The MCP server plugin is defined using the MCPStreamableHttpPlugin, which take a URL. 98 | async with MCPStreamableHttpPlugin( 99 | name="Weather", 100 | description="Weather Plugin", 101 | url=weather_mcp_server_url, 102 | load_prompts=False, 103 | ) as weather_plugin: 104 | 105 | # Add the plugin to the kernel. 106 | kernel.add_plugin(weather_plugin) 107 | 108 | # Start the chat loop. 109 | print("Welcome to the chat bot!\n Type 'exit' to exit.\n") 110 | chatting = True 111 | while chatting: 112 | chatting = await chat() 113 | 114 | 115 | if __name__ == "__main__": 116 | asyncio.run(main()) 117 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/apim-petstore-mcp/pet_store_mcp.py: -------------------------------------------------------------------------------- 1 | # This sample demonstrates how to use the Azure API Management (APIM) service 2 | # to expose Pet Store APIs as a MCP server, to create a chatbot using the Semantic Kernel. 3 | # 4 | # NOTE: when running this sample code, you'll get `HTTP/1.1 401 Unauthorized` error, when connecting to the Pet Store MCP Server on APIM. 5 | # This demonstrates that the MCP Server is secure and can be reachable only by providing the Bearer token. 6 | # To fix this, uncomment the line setting the headers in the `MCPStreamableHttpPlugin` definition (around line 115). 7 | 8 | 9 | import asyncio 10 | import logging 11 | import os 12 | from dotenv import load_dotenv 13 | from semantic_kernel import Kernel 14 | from semantic_kernel.connectors.ai import FunctionChoiceBehavior 15 | from semantic_kernel.connectors.mcp import MCPStreamableHttpPlugin 16 | from semantic_kernel.contents import ChatHistory 17 | from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureChatPromptExecutionSettings 18 | from azure.identity import DefaultAzureCredential 19 | 20 | 21 | """ 22 | This sample demonstrates how to build a conversational chatbot 23 | using Semantic Kernel, 24 | it creates a Plugin from a MCP server config and adds it to the kernel. 25 | The chatbot is designed to interact with the user, call MCP tools 26 | as needed, and return responses. 27 | """ 28 | 29 | # Configure the logging module 30 | logging.basicConfig( 31 | level=logging.INFO, # Set the desired logging level (DEBUG, INFO, WARNING, etc.) 32 | format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" 33 | ) 34 | 35 | # Step 2: Create a logger 36 | logger = logging.getLogger("mcp.streamable-http.mcp-client") 37 | 38 | load_dotenv() 39 | petstore_mcp_server_url = os.getenv("PETSTORE_MCP_SERVER_URL") 40 | api_key=os.getenv("AZURE_OPENAI_API_KEY") 41 | endpoint=os.getenv("AZURE_OPENAI_ENDPOINT") 42 | deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME") 43 | api_version=os.getenv("AZURE_OPENAI_API_VERSION") 44 | logger.info(f"Using MCP server URL: {petstore_mcp_server_url}") 45 | logger.info(f"Using Azure OpenAI endpoint: {endpoint}") 46 | logger.info(f"Using Azure OpenAI deployment name: {deployment_name}") 47 | logger.info(f"Using Azure OpenAI API version: {api_version}") 48 | 49 | # System message defining the behavior and persona of the chat bot. 50 | system_message = """ 51 | You are a chat bot. And you help users interact with pet store services. 52 | You can call functions to get the information you need. 53 | """ 54 | 55 | # Create and configure the kernel. 56 | kernel = Kernel() 57 | 58 | chat_service = AzureChatCompletion( 59 | api_key=api_key, 60 | endpoint=endpoint, 61 | deployment_name=deployment_name, 62 | api_version=api_version 63 | ) 64 | settings = AzureChatPromptExecutionSettings() 65 | 66 | 67 | # Configure the function choice behavior. Here, we set it to Auto, where auto_invoke=True by default. 68 | # With `auto_invoke=True`, the model will automatically choose and call functions as needed. 69 | settings.function_choice_behavior = FunctionChoiceBehavior.Auto() 70 | 71 | kernel.add_service(chat_service) 72 | 73 | # Create a chat history to store the system message, initial messages, and the conversation. 74 | history = ChatHistory() 75 | history.add_system_message(system_message) 76 | 77 | 78 | async def chat() -> bool: 79 | """ 80 | Continuously prompt the user for input and show the assistant's response. 81 | Type 'exit' to exit. 82 | """ 83 | try: 84 | user_input = input("User:> ") 85 | except (KeyboardInterrupt, EOFError): 86 | print("\n\nExiting chat...") 87 | return False 88 | if user_input.lower().strip() == "exit": 89 | print("\n\nExiting chat...") 90 | return False 91 | 92 | history.add_user_message(user_input) 93 | result = await chat_service.get_chat_message_content(history, settings, kernel=kernel) 94 | if result: 95 | print(f"Bot:> {result}") 96 | history.add_message(result) 97 | 98 | return True 99 | 100 | 101 | async def main() -> None: 102 | 103 | # Get an access token for the Azure API 104 | credential = DefaultAzureCredential() 105 | access_token = credential.get_token("https://azure-api.net/authorization-manager") 106 | 107 | # Create a plugin from the MCP server config and add it to the kernel. 108 | # The MCP server plugin is defined using the MCPStreamableHttpPlugin, which take a URL. 109 | async with MCPStreamableHttpPlugin( 110 | name="Petstore", 111 | description="Petstore Plugin", 112 | url=petstore_mcp_server_url, 113 | load_prompts=False, 114 | # UNCOMMENT THE BELOW LINE TO MAKE THIS SAMPLE CODE WORK 115 | # headers={"Authorization": f"Bearer {access_token.token}"} 116 | ) as petstore_plugin: 117 | 118 | # Add the plugin to the kernel. 119 | kernel.add_plugin(petstore_plugin) 120 | 121 | # Start the chat loop. 122 | print("Welcome to the chat bot!\n Type 'exit' to exit.\n") 123 | chatting = True 124 | while chatting: 125 | chatting = await chat() 126 | 127 | 128 | if __name__ == "__main__": 129 | asyncio.run(main()) 130 | -------------------------------------------------------------------------------- /examples/mcp/streamable-http/ai-foundry-agent/sample_agents_mcp.py: -------------------------------------------------------------------------------- 1 | # pylint: disable=line-too-long,useless-suppression 2 | # ------------------------------------ 3 | # Copyright (c) Microsoft Corporation. 4 | # Licensed under the MIT License. 5 | # ------------------------------------ 6 | 7 | """ 8 | DESCRIPTION: 9 | This sample demonstrates how to use agent operations with code interpreter from 10 | the Azure Agents service using a synchronous client. 11 | 12 | USAGE: 13 | Before running the sample: 14 | 15 | Set these environment variables with your own values: 16 | 1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview 17 | page of your Azure AI Foundry portal. 18 | 2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in 19 | the "Models + endpoints" tab in your Azure AI Foundry project. 20 | """ 21 | 22 | import os 23 | import time 24 | import json 25 | 26 | from azure.ai.agents.models import MessageTextContent, ListSortOrder 27 | from azure.ai.projects import AIProjectClient 28 | from azure.identity import DefaultAzureCredential 29 | from azure.ai.agents.models import McpTool, RequiredMcpToolCall, SubmitToolApprovalAction, ToolApproval 30 | from dotenv import load_dotenv 31 | 32 | load_dotenv() 33 | 34 | project_client = AIProjectClient( 35 | endpoint=os.environ["PROJECT_ENDPOINT"], 36 | credential=DefaultAzureCredential() 37 | ) 38 | 39 | # [START create_agent_with_mcp_tool] 40 | # Initialize agent MCP tool 41 | mcp_tool = McpTool( 42 | server_label="weather", 43 | server_url=os.environ["WEATHER_MCP_SERVER_URL"] 44 | ) 45 | 46 | # Create agent with MCP tool and process agent run 47 | with project_client: 48 | agents_client = project_client.agents 49 | 50 | # Create a new agent. 51 | # NOTE: To reuse existing agent, fetch it with get_agent(agent_id) 52 | agent = agents_client.create_agent( 53 | model=os.environ["MODEL_DEPLOYMENT_NAME"], 54 | name="my-weather-mcp-agent", 55 | instructions="You are a helpful agent that can use MCP tools to assist users. Use the available MCP tools to answer questions and perform tasks.", 56 | tools=mcp_tool.definitions, 57 | ) 58 | # [END create_agent_with_mcp_tool] 59 | 60 | print(f"Created agent, ID: {agent.id}") 61 | print(f"MCP Server: {mcp_tool.server_label} at {mcp_tool.server_url}") 62 | 63 | # Create thread for communication 64 | thread = agents_client.threads.create() 65 | print(f"Created thread, ID: {thread.id}") 66 | 67 | # Create message to thread 68 | message = agents_client.messages.create( 69 | thread_id=thread.id, 70 | role="user", 71 | content="please list the weather alerts in NY", 72 | ) 73 | print(f"Created message, ID: {message.id}") 74 | 75 | # [START handle_tool_approvals] 76 | mcp_tool.set_approval_mode("never") 77 | run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources) 78 | print(f"Created run, ID: {run.id}") 79 | 80 | while run.status in ["queued", "in_progress", "requires_action"]: 81 | time.sleep(1) 82 | run = agents_client.runs.get(thread_id=thread.id, run_id=run.id) 83 | 84 | if run.status == "requires_action" and isinstance(run.required_action, SubmitToolApprovalAction): 85 | tool_calls = run.required_action.submit_tool_approval.tool_calls 86 | if not tool_calls: 87 | print("No tool calls provided - cancelling run") 88 | agents_client.runs.cancel(thread_id=thread.id, run_id=run.id) 89 | break 90 | 91 | print(f"Current run status: {run.status}") 92 | # [END handle_tool_approvals] 93 | 94 | print(f"Run completed with status: {run.status}") 95 | if run.status == "failed": 96 | print(f"Run failed: {run.last_error}") 97 | 98 | # Display run steps and tool calls 99 | run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id) 100 | 101 | # Loop through each step 102 | for step in run_steps: 103 | print(f"Step {step['id']} status: {step['status']}") 104 | 105 | # Check if there are tool calls in the step details 106 | step_details = step.get("step_details", {}) 107 | tool_calls = step_details.get("tool_calls", []) 108 | 109 | if tool_calls: 110 | print(" MCP Tool calls:") 111 | for call in tool_calls: 112 | print(f" Tool Call ID: {call.get('id')}") 113 | print(f" Type: {call.get('type')}") 114 | 115 | print() # add an extra newline between steps 116 | 117 | # Fetch and log all messages 118 | messages = agents_client.messages.list(thread_id=thread.id) 119 | print("\nConversation:") 120 | print("-" * 50) 121 | for msg in messages: 122 | if msg.text_messages: 123 | last_text = msg.text_messages[-1] 124 | print(f"{msg.role.upper()}: {last_text.text.value}") 125 | print("-" * 50) 126 | 127 | # Example of dynamic tool management 128 | print(f"\nDemonstrating dynamic tool management:") 129 | print(f"Current allowed tools: {mcp_tool.allowed_tools}") 130 | 131 | agents_client.delete_agent(agent.id) 132 | print("Deleted agent") -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ## Ignore Visual Studio temporary files, build results, and 2 | ## files generated by popular Visual Studio add-ons. 3 | ## 4 | ## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore 5 | 6 | # User-specific files 7 | *.rsuser 8 | *.suo 9 | *.user 10 | *.userosscache 11 | *.sln.docstates 12 | 13 | # User-specific files (MonoDevelop/Xamarin Studio) 14 | *.userprefs 15 | 16 | # Mono auto generated files 17 | mono_crash.* 18 | 19 | # Build results 20 | [Dd]ebug/ 21 | [Dd]ebugPublic/ 22 | [Rr]elease/ 23 | [Rr]eleases/ 24 | x64/ 25 | x86/ 26 | [Ww][Ii][Nn]32/ 27 | [Aa][Rr][Mm]/ 28 | [Aa][Rr][Mm]64/ 29 | bld/ 30 | [Bb]in/ 31 | [Oo]bj/ 32 | [Ll]og/ 33 | [Ll]ogs/ 34 | 35 | # Visual Studio 2015/2017 cache/options directory 36 | .vs/ 37 | # Uncomment if you have tasks that create the project's static files in wwwroot 38 | #wwwroot/ 39 | 40 | # Visual Studio 2017 auto generated files 41 | Generated\ Files/ 42 | 43 | # MSTest test Results 44 | [Tt]est[Rr]esult*/ 45 | [Bb]uild[Ll]og.* 46 | 47 | # NUnit 48 | *.VisualState.xml 49 | TestResult.xml 50 | nunit-*.xml 51 | 52 | # Build Results of an ATL Project 53 | [Dd]ebugPS/ 54 | [Rr]eleasePS/ 55 | dlldata.c 56 | 57 | # Benchmark Results 58 | BenchmarkDotNet.Artifacts/ 59 | 60 | # .NET Core 61 | project.lock.json 62 | project.fragment.lock.json 63 | artifacts/ 64 | 65 | # ASP.NET Scaffolding 66 | ScaffoldingReadMe.txt 67 | 68 | # StyleCop 69 | StyleCopReport.xml 70 | 71 | # Files built by Visual Studio 72 | *_i.c 73 | *_p.c 74 | *_h.h 75 | *.ilk 76 | *.meta 77 | *.obj 78 | *.iobj 79 | *.pch 80 | *.pdb 81 | *.ipdb 82 | *.pgc 83 | *.pgd 84 | *.rsp 85 | # but not Directory.Build.rsp, as it configures directory-level build defaults 86 | !Directory.Build.rsp 87 | *.sbr 88 | *.tlb 89 | *.tli 90 | *.tlh 91 | *.tmp 92 | *.tmp_proj 93 | *_wpftmp.csproj 94 | *.log 95 | *.tlog 96 | *.vspscc 97 | *.vssscc 98 | .builds 99 | *.pidb 100 | *.svclog 101 | *.scc 102 | 103 | # Chutzpah Test files 104 | _Chutzpah* 105 | 106 | # Visual C++ cache files 107 | ipch/ 108 | *.aps 109 | *.ncb 110 | *.opendb 111 | *.opensdf 112 | *.sdf 113 | *.cachefile 114 | *.VC.db 115 | *.VC.VC.opendb 116 | 117 | # Visual Studio profiler 118 | *.psess 119 | *.vsp 120 | *.vspx 121 | *.sap 122 | 123 | # Visual Studio Trace Files 124 | *.e2e 125 | 126 | # TFS 2012 Local Workspace 127 | $tf/ 128 | 129 | # Guidance Automation Toolkit 130 | *.gpState 131 | 132 | # ReSharper is a .NET coding add-in 133 | _ReSharper*/ 134 | *.[Rr]e[Ss]harper 135 | *.DotSettings.user 136 | 137 | # TeamCity is a build add-in 138 | _TeamCity* 139 | 140 | # DotCover is a Code Coverage Tool 141 | *.dotCover 142 | 143 | # AxoCover is a Code Coverage Tool 144 | .axoCover/* 145 | !.axoCover/settings.json 146 | 147 | # Coverlet is a free, cross platform Code Coverage Tool 148 | coverage*.json 149 | coverage*.xml 150 | coverage*.info 151 | 152 | # Visual Studio code coverage results 153 | *.coverage 154 | *.coveragexml 155 | 156 | # NCrunch 157 | _NCrunch_* 158 | .*crunch*.local.xml 159 | nCrunchTemp_* 160 | 161 | # MightyMoose 162 | *.mm.* 163 | AutoTest.Net/ 164 | 165 | # Web workbench (sass) 166 | .sass-cache/ 167 | 168 | # Installshield output folder 169 | [Ee]xpress/ 170 | 171 | # DocProject is a documentation generator add-in 172 | DocProject/buildhelp/ 173 | DocProject/Help/*.HxT 174 | DocProject/Help/*.HxC 175 | DocProject/Help/*.hhc 176 | DocProject/Help/*.hhk 177 | DocProject/Help/*.hhp 178 | DocProject/Help/Html2 179 | DocProject/Help/html 180 | 181 | # Click-Once directory 182 | publish/ 183 | 184 | # Publish Web Output 185 | *.[Pp]ublish.xml 186 | *.azurePubxml 187 | # Note: Comment the next line if you want to checkin your web deploy settings, 188 | # but database connection strings (with potential passwords) will be unencrypted 189 | *.pubxml 190 | *.publishproj 191 | 192 | # Microsoft Azure Web App publish settings. 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Backup files are not needed, 258 | # because we have git ;-) 259 | _UpgradeReport_Files/ 260 | Backup*/ 261 | UpgradeLog*.XML 262 | UpgradeLog*.htm 263 | ServiceFabricBackup/ 264 | *.rptproj.bak 265 | 266 | # SQL Server files 267 | *.mdf 268 | *.ldf 269 | *.ndf 270 | 271 | # Business Intelligence projects 272 | *.rdl.data 273 | *.bim.layout 274 | *.bim_*.settings 275 | *.rptproj.rsuser 276 | *- [Bb]ackup.rdl 277 | *- [Bb]ackup ([0-9]).rdl 278 | *- [Bb]ackup ([0-9][0-9]).rdl 279 | 280 | # Microsoft Fakes 281 | FakesAssemblies/ 282 | 283 | # GhostDoc plugin setting file 284 | *.GhostDoc.xml 285 | 286 | # Node.js Tools for Visual Studio 287 | .ntvs_analysis.dat 288 | node_modules/ 289 | 290 | # Visual Studio 6 build log 291 | *.plg 292 | 293 | # Visual Studio 6 workspace options file 294 | *.opt 295 | 296 | # Visual Studio 6 auto-generated workspace file (contains which files were open etc.) 297 | *.vbw 298 | 299 | # Visual Studio 6 auto-generated project file (contains which files were 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BizTalk build output 343 | *.btp.cs 344 | *.btm.cs 345 | *.odx.cs 346 | *.xsd.cs 347 | 348 | # OpenCover UI analysis results 349 | OpenCover/ 350 | 351 | # Azure Stream Analytics local run output 352 | ASALocalRun/ 353 | 354 | # MSBuild Binary and Structured Log 355 | *.binlog 356 | 357 | # NVidia Nsight GPU debugger configuration file 358 | *.nvuser 359 | 360 | # MFractors (Xamarin productivity tool) working folder 361 | .mfractor/ 362 | 363 | # Local History for Visual Studio 364 | .localhistory/ 365 | 366 | # Visual Studio History (VSHistory) files 367 | .vshistory/ 368 | 369 | # BeatPulse healthcheck temp database 370 | healthchecksdb 371 | 372 | # Backup folder for Package Reference Convert tool in Visual Studio 2017 373 | MigrationBackup/ 374 | 375 | # Ionide (cross platform F# VS Code tools) working folder 376 | .ionide/ 377 | 378 | # Fody - auto-generated XML schema 379 | FodyWeavers.xsd 380 | 381 | # VS Code files for those working on multiple tools 382 | .vscode/* 383 | !.vscode/settings.json 384 | !.vscode/tasks.json 385 | !.vscode/launch.json 386 | !.vscode/extensions.json 387 | *.code-workspace 388 | 389 | # Local History for Visual Studio Code 390 | .history/ 391 | 392 | # Windows Installer files from build outputs 393 | *.cab 394 | *.msi 395 | *.msix 396 | *.msm 397 | *.msp 398 | 399 | # JetBrains Rider 400 | *.sln.iml 401 | .DS_Store 402 | examples/.DS_Store 403 | examples/mcp/.DS_Store 404 | examples/mcp/filesystem_example/.env 405 | examples/mcp/streamable-http/mcp-client/.env 406 | examples/mcp/streamable-http/ai-foundry-agent/.env 407 | main.py 408 | .python-version 409 | examples/mcp/streamable-http/apim-petstore-mcp/.env 410 | examples/mcp/streamable-http/ai-foundry-agent/browser_automation_tool.py 411 | examples/mcp/streamable-http/ai-foundry-agent/list_agents.py 412 | examples/mcp/streamable-http/mcp-client/coralogix_mcp.py 413 | examples/mcp/streamable-http/mcp-client/playwright_mcp.py 414 | examples/mem0/.env 415 | 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