How to use any Python AI agent framework with free GitHub Models
I ❤️ when companies offer free tiers for developer services, since it gives everyone a way to learn new technologies without breaking the bank. Free tiers are especially important for students and people between jobs, when the desire to learn is high but the available cash is low. That's why I'm such a fan of GitHub Models: free, high-quality generative AI models available to anyone with a GitHub account. The available models include the latest OpenAI LLMs (like o3-mini), LLMs from the research community (like Phi and Llama), LLMs from other popular providers (like Mistral and Jamba), multimodal models (like gpt-4o and llama-vision-instruct) and even a few embedding models (from OpenAI and Cohere). With access to such a range of models, you can prototype complex multi-model workflows to improve your productivity or heck, just make something fun for yourself. 🤗 To use GitHub Models, you can start off in no-code mode: open the playground for a model, send a few requests, tweak the parameters, and check out the answers. When you're ready to write code, select "Use this model". A screen will pop up where you can select a programming language (Python/JavaScript/C#/Java/REST) and select an SDK (which varies depending on model). Then you'll get instructions and code for that model, language, and SDK. But here's what's really cool about GitHub Models: you can use them with all the popular Python AI frameworks, even if the framework has no specific integration with GitHub Models. How is that possible? The vast majority of Python AI frameworks support the OpenAI Chat Completions API, since that API became a defacto standard supported by many LLM API providers besides OpenAI itself. GitHub Models also provide OpenAI-compatible endpoints for chat completion models. Therefore, any Python AI framework that supports OpenAI-like models can be used with GitHub Models as well. 🎉 To prove it, I've made a new repository with examples from eight different Python AI agent packages, all working with GitHub Models: python-ai-agent-frameworks-demos. There are examples for AutoGen, LangGraph, Llamaindex, OpenAI Agents SDK, OpenAI standard SDK, PydanticAI, Semantic Kernel, and SmolAgents. You can open that repository in GitHub Codespaces, install the packages, and get the examples running immediately. Now let's walk through the API connection code for GitHub Models for each framework. Even if I missed your favorite framework, I hope my tips here will help you connect any framework to GitHub Models. OpenAI I'll start with openai , the package that started it all! import openai client = openai.OpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") The code above demonstrates the two key parameters we'll need to configure for all frameworks: api_key : When using OpenAI.com, you pass your OpenAI API key here. When using GitHub Models, you pass in a Personal Access Token (PAT). If you open the repository (or any repository) in GitHub Codespaces, a PAT is already stored in the GITHUB_TOKEN environment variable. However, if you're working locally with GitHub Models, you'll need to generate a PAT yourself and store it. PATs expire after a while, so you need to generate new PATs every so often. base_url : This parameter tells the OpenAI client to send all requests to "https://models.inference.ai.azure.com" instead of the OpenAI.com API servers. That's the domain that hosts the OpenAI-compatible endpoint for GitHub Models, so you'll always pass that domain as the base URL. If we're working with the new openai-agents SDK, we use very similar code, but we must use the AsyncOpenAI client from openai instead. Lately, Python AI packages are defaulting to async, because it's so much better for performance. import agents import openai client = openai.AsyncOpenAI( base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"]) model = agents.OpenAIChatCompletionsModel( model="gpt-4o", openai_client=client) spanish_agent = agents.Agent( name="Spanish agent", instructions="You only speak Spanish.", model=model) PydanticAI Now let's look at all of the packages that make it really easy for us, by allowing us to directly bring in an instance of either OpenAI or AsyncOpenAI . For PydanticAI, we configure an AsyncOpenAI client, then construct an OpenAIModel object from PydanticAI, and pass that model to the agent: import openai import pydantic_ai import pydantic_ai.models.openai client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") model = pydantic_ai.models.openai.OpenAIModel( "gpt-4o", provider=OpenAIProvider(openai_client=client)) spanish_agent = pydantic_ai.Agent( model, system_prompt="You only speak Spanish.") Semantic Kernel For Semantic Kernel, the code is very similar. We configure an AsyncOpenAI client, then construct an OpenAIChatCompletion object from Semantic Kernel, and add that object to the kernel. import openai import semantic_kernel.connectors.ai.open_ai import semantic_kernel.agents chat_client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") chat = semantic_kernel.connectors.ai.open_ai.OpenAIChatCompletion( ai_model_id="gpt-4o", async_client=chat_client) kernel.add_service(chat) spanish_agent = semantic_kernel.agents.ChatCompletionAgent( kernel=kernel, name="Spanish agent" instructions="You only speak Spanish") AutoGen Next, we'll check out a few frameworks that have their own wrapper of the OpenAI clients, so we won't be using any classes from openai directly. For AutoGen, we configure both the OpenAI parameters and the model name in the same object, then pass that to each agent: import autogen_ext.models.openai import autogen_agentchat.agents client = autogen_ext.models.openai.OpenAIChatCompletionClient( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") spanish_agent = autogen_agentchat.agents.AssistantAgent( "spanish_agent", model_client=client, system_message="You only speak Spanish") LangGraph For LangGraph, we configure a very similar object, which even has the same parameter names: import langchain_openai import langgraph.graph model = langchain_openai.ChatOpenAI( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com", ) def call_model(state): messages = state["messages"] response = model.invoke(messages) return {"messages": [response]} workflow = langgraph.graph.StateGraph(MessagesState) workflow.add_node("agent", call_model) SmolAgents Once again, for SmolAgents, we configure a similar object, though with slightly different parameter names: import smolagents model = smolagents.OpenAIServerModel( model_id="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = smolagents.CodeAgent(model=model) Llamaindex I saved Llamaindex for last, as it is the most different. The llama-index package has a different constructor for OpenAI.com versus OpenAI-like servers, so I opted to use that OpenAILike constructor instead. However, I also needed an embeddings model for my example, and the package doesn't have an OpenAIEmbeddingsLike constructor, so I used the standard OpenAIEmbedding constructor. import llama_index.embeddings.openai import llama_index.llms.openai_like import llama_index.core.agent.workflow Settings.llm = llama_index.llms.openai_like.OpenAILike( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com", is_chat_model=True) Settings.embed_model = llama_index.embeddings.openai.OpenAIEmbedding( model="text-embedding-3-small", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = llama_index.core.agent.workflow.ReActAgent( tools=query_engine_tools, llm=Settings.llm) Choose your models wisely! In all of the examples above, I specified the gpt-4o model. The gpt-4o model is a great choice for agents because it supports function calling, and many agent frameworks only work (or work best) with models that natively support function calling. Fortunately, GitHub Models includes multiple models that support function calling, at least in my basic experiments: gpt-4o gpt-4o-mini o3-mini AI21-Jamba-1.5-Large AI21-Jamba-1.5-Mini Codestral-2501 Cohere-command-r Ministral-3B Mistral-Large-2411 Mistral-Nemo Mistral-small You might find that some models work better than others, especially if you're using agents with multiple tools. With GitHub Models, it's very easy to experiment and see for yourself, by simply changing the model name and re-running the code. Join the AI Agents Hackathon We are currently running a free virtual hackathon from April 8th - 30th, to challenge developers to create agentic applications using Microsoft technologies. You could build an agent entirely using GitHub Models and submit it to the hackathon for a chance to win amazing prizes! You can also join our 30+ streams about building AI agents, including a stream all about prototyping with GitHub Models. Learn more and register at https://aka.ms/agentshackModel Context Protocol (MCP) Server for Azure Database for MySQL
We are excited to introduce a new MCP Server for integrating your AI models with data hosted in Azure Database for MySQL. By utilizing this server, you can effortlessly connect any AI application that supports MCP to your MySQL flexible server (using either MySQL password-based authentication or Microsoft Entra authentication methods), enabling you to provide your business data as meaningful context in a standardized and secure manner.Is it a bug or a feature? Using Prompty to automatically track and tag issues.
Introduction You’ve probably noticed a theme in my recent posts: tackling challenges with AI-powered solutions. In my latest project, I needed a fast way to classify and categorize GitHub issues using a predefined set of tags. The tag data was there, but the connections between issues and tags weren’t. To bridge that gap, I combined Azure OpenAI Service, Prompty, and a GitHub to automatically extract and assign the right labels. By automating issue tagging, I was able to: Streamline contributor workflows with consistent, on-time labels that simplify triage Improve repository hygiene by keeping issues well-organized, searchable, and easy to navigate Eliminate repetitive maintenance so the team can focus on community growth and developer empowerment Scale effortlessly as the project expands, turning manual chores into intelligent automation Challenge: 46 issues, no tags The Prompty repository currently hosts 46 relevant, but untagged, issues. To automate labeling, I first defined a complete tag taxonomy. Then I built a solution using: Prompty for prompt templating and function calling Azure OpenAI (gpt-4o-mini) to classify each issue Azure AI Search for retrieval-augmented context (RAG) Python to orchestrate the workflow and integrate with GitHub By the end, you’ll have an autonomous agent that fetches open issues, matches them against your custom taxonomy, and applies labels back on GitHub. Prerequisites: An Azure account with Azure AI Search and Azure OpenAI enabled Python and Prompty installed Clone the repo and install dependencies: pip install -r requirements.txt Step 1: Define the prompt template We’ll use Prompty to structure our LLM instructions. If you haven’t yet, install the Prompty VS Code extension and refer to the Prompty docs to get started. Prompty combines: Tooling to configure and deploy models Runtime for executing prompts and function calls Specification (YAML) for defining prompts, inputs, and outputs Our Prompty is set to use gpt-4o-mini and below is our sample input: sample: title: Including Image in System Message tags: ${file:tags.json} description: An error arises in the flow, coming up starting from the "complete" block. It seems like it is caused by placing a static image in the system prompt, since removing it causes the issue to go away. Please let me know if I can provide additional context. The inputs will be the tags file implemented using RAG, then we will fetch the issue title and description from GitHub once a new issue is posted. Next, in our Prompty file, we gave instructions of how the LLLM should work as follows: system: You are an intelligent GitHub issue tagging assistant. Available tags: ${inputs} {% if tags.tags %} ## Available Tags {% for tag in tags.tags %} name: {{tag.name}} description: {{tag.description}} {% endfor %} {% endif %} Guidelines: 1. Only select tags that exactly match the provided list above 2. If no tags apply, return an empty array [] 3. Return ONLY a valid JSON array of strings, nothing else 4. Do not explain your choices or add any other text Use your understanding of the issue and refer to documentation at https://prompty.ai to match appropriate tags. Tags may refer to: - Issue type (e.g., bug, enhancement, documentation) - Tool or component (e.g., tool:cli, tracer:json-tracer) - Technology or integration (e.g., integration:azure, runtime:python) - Conceptual elements (e.g., asset:template-loading) Return only a valid JSON array of the issue title, description and tags. If the issue does not fit in any of the categories, return an empty array with: ["No tags apply to this issue. Please review the issue and try again."] Example: Issue Title: "App crashes when running in Azure CLI" Issue Body: "Running the generated code in Azure CLI throws a Python runtime error." Tag List: ["bug", "tool:cli", "runtime:python", "integration:azure"] Output: ["bug", "tool:cli", "runtime:python", "integration:azure"] user: Issue Title: {{title}} Issue Description: {{description}} Once the Prompty file was ready, I right clicked on the file and converted it to Prompty code, which provided a Python base code to get started from, instead of building from scratch. Step 2: enrich with context using Azure AI Search To be able to generate labels for our issues, I created a sample of tags, around 20, each with a title and a description of what it does. As a starting point, I started with Azure AI Foundry, where I uploaded the data and created an index. This typically takes about 1hr to successfully complete. Next, I implemented a retrieval function: def query_azure_search(query_text): """Query Azure AI Search for relevant documents and tags.""" search_client = SearchClient( endpoint=SEARCH_SERVICE_ENDPOINT, index_name=SEARCH_INDEX_NAME, credential=AzureKeyCredential(SEARCH_API_KEY) ) # Perform the search results = search_client.search( search_text=query_text, query_type=QueryType.SIMPLE, top=5 # Retrieve top 5 results ) # Extract content and tags from results documents = [doc["content"] for doc in results] tags = [doc.get("tags", []) for doc in results] # Assuming "tags" is a field in the index # Flatten and deduplicate tags unique_tags = list(set(tag for tag_list in tags for tag in tag_list)) return documents, unique_tags Step 3: Orchestrate the Workflow In addition, to adding RAG, I added functions in the basic.py file to: fetch_github_issues: calls the GitHub REST API to list open issues and filters out any that already have labels. run_with_rag: on the issues selected, calls the query_azure_search to append any retrieved docs, tags the issues and parses the JSON output from the prompt to a list for the labels label_issue: patches the issue to apply a list of labels. process_issues: this fetches all unlabelled issues, extracts the rag pipeline to generate the tags, and calls the labels_issue tag to apply the tags scheduler loop: this runs every so often to check if there's a new issue and apply a label Step 4: Validate and Run Ensure all .env variables are set (API keys, endpoints, token). Install dependencies and execute using: python basic.py Create a new GitHub issue and watch as your agent assigns tags in real time. Below is a short demo video here to illustrate the workflow. Next Steps Migrate from PATs to a GitHub App for tighter security Create multi-agent application and add an evaluator agent to review tags before publishing Integrate with GitHub Actions or Azure Pipelines for CI/CD Conclusion and Resources By combining Prompty, Azure AI Search, and Azure OpenAI, you can fully automate GitHub issue triage—improving consistency, saving time, and scaling effortlessly. Adapt this pattern to any classification task in your own workflows! You can learn more using the following resources: Prompty documentation to learn more on Prompty Agents for Beginners course to learn how you can build your own agentAI Sparks: Unleashing Agents with the AI Toolkit
The final episode of our "AI Sparks" series delved deep into the exciting world of AI Agents and their practical implementation. We also covered a fair part of MCP with Microsoft AI Toolkit extension for VS Code. We kicked off by charting the evolutionary path of intelligent conversational systems. Starting with the rudimentary rule-based Basic Chatbots, we then explored the advancements brought by Basic Generative AI Chatbots, which offered contextually aware interactions. Then we explored the Retrieval-Augmented Generation (RAG), highlighting its ability to ground generative models in specific knowledge bases, significantly enhancing accuracy and relevance. The limitations were also discussed for the above mentioned techniques. The session was then centralized to the theme – Agents and Agentic Frameworks. We uncovered the fundamental shift from basic chatbots to autonomous agents capable of planning, decision-making, and executing tasks. We moved forward with detailed discussion on the distinction between Single Agentic systems, where one core agent orchestrates the process, and Multi-Agent Architectures, where multiple specialized agents collaborate to achieve complex goals. A key part of building robust and reliable AI Agents, as we discussed, revolves around carefully considering four critical factors. Firstly, Knowledge-Providing agents with the right context is paramount for them to operate effectively and make informed decisions. Secondly, equipping agents with the necessary Actions by granting them access to the appropriate tools allows them to execute tasks and achieve desired outcomes. Thirdly, Security is non-negotiable; ensuring agents have access only to the data and services they genuinely need is crucial for maintaining privacy and preventing unintended actions. Finally, establishing robust Evaluations mechanisms is essential to verify that agents are completing tasks correctly and meeting the required standards. These four pillars – Knowledge, Actions, Security, and Evaluation – form the bedrock of any successful agentic implementation. To illustrate the transformative power of AI Agents, we explored several interesting use cases and applications. These ranged from intelligent personal assistants capable of managing schedules and automating workflows to sophisticated problem-solving systems in domains like customer service. A significant portion of the session was dedicated to practical implementation through demonstrations. We highlighted key frameworks that are empowering developers to build agentic systems.: Semantic Kernel: We highlighted its modularity and rich set of features for integrating various AI services and tools. Autogen Studio: The focus here was on its capabilities for facilitating the creation and management of multi-agent conversations and workflows. Agent Service: We discussed its role in providing a more streamlined and managed environment for deploying and scaling AI agents. The major point of attraction was that these were demonstrated using the local LLMs which were hosted using AI Toolkit. This showcased the ease with which developers can utilize VS Code AI toolkit to build and experiment with agentic workflows directly within their familiar development environment. Finally, we demystified the concept of Model Context Protocol (MCP) and demonstrated how seamlessly it can be implemented using the Agent Builder within the VS Code AI Toolkit. We demonstrated this with a basic Website development using MCP. This practical demonstration underscored the toolkit's power in simplifying the development of complex solutions that can maintain context and engage in more natural, multi-step interactions. The "AI Sparks" series concluded with a discussion, where attendees had a clearer understanding of the evolution, potential and practicalities of AI Agents. The session underscored that we are on the cusp of a new era of intelligent systems that are not just reactive but actively work alongside us to achieve goals. The tools and frameworks are maturing, and the possibilities for agentic applications are sparking innovation across various industries. It was an exciting journey, and engagement during the final session on AI Sparks around Agents truly highlighted the transformative potential of this field. "AI Sparks" Series Roadmap: The "AI Sparks" series delved deeper into specific topics using AI Toolkit for Visual Studio Code, including: Introduction to AI toolkit and feature walkthrough: Introduction to the AI Toolkit extension for VS Code a powerful way to explore and integrate the latest AI models from OpenAI, Meta, Deepseek, Mistral, and more. Introduction to SLMs and local model with use cases: Explore Small Language Models (SLMs) and how they compare to larger models. Building RAG Applications: Create powerful applications that combine the strengths of LLMs with external knowledge sources. Multimodal Support and Image Analysis: Working with vision models and building multimodal applications. Evaluation and Model Selection: Evaluate model performance and choose the best model for your needs. Agents and Agentic Frameworks: Exploring the cutting edge of AI agents and how they can be used to build more complex and autonomous systems. The full playlist of the series with all the episodes of "AI Sparks" is available at AI Sparks Playlist. Continue the discussion and questions in Microsoft AI Discord Community where we have a dedicated AI-sparks channel. All the code samples can be found on AI_Toolkit_Samples .We look forward to continuing these insightful discussions in future series!Swagger Auto-Generation on MCP Server
Would you like to generate a swagger.json directly on an MCP server on-the-fly? In many use cases, using remote MCP servers is not uncommon. In particular, if you're using Azure API Management (APIM), Azure API Center (APIC) or Copilot Studio in Power Platform, integrating with remote MCP servers is inevitable.Use Prompty with Foundry Local
Prompty is a powerful tool for managing prompts in AI applications. Not only does it allow you to easily test your prompts during development, but it also provides observability, understandability and portability. Here's how to use Prompty with Foundry Local to support your AI applications with on-device inference. Foundry Local At the Build '25 conference, Microsoft announced Foundry Local, a new tool that allows developers to run AI models locally on their devices. Foundry Local offers developers several benefits, including performance, privacy, and cost savings. Why Prompty? When you build AI applications with Foundry Local, but also other language model hosts, consider using Prompty to manage your prompts. With Prompty, you store your prompts in separate files, making it easy to test and adjust them without changing your code. Prompty also supports templating, allowing you to create dynamic prompts that adapt to different contexts or user inputs. Using Prompty with Foundry Local The most convenient way to use Prompty with Foundry Local is to create a new configuration for Foundry Local. Using a separate configuration allows you to seamlessly test your prompts without having to repeat the configuration for every prompt. It also allows you to easily switch between different configurations, such as Foundry Local and other language model hosts. Install Prompty and Foundry Local To get started, install the Prompty Visual Studio Code extension and Foundry Local. Start Foundry Local from the command line by running foundry service start and note the URL on which it listens for requests, such as http://localhost:5272 or http://localhost:5273. Create a new Prompty configuration for Foundry Local If you don't have a Prompty file yet, create one to easily access Prompty settings. In Visual Studio Code, open Explorer, click right to open the context menu, and select New Prompty. This creates a basic.prompty file in your workspace. Create the Foundry Local configuration From the status bar, select default to open the Prompty configuration picker. When prompted to select the configuration, choose Add or Edit.... In the settings pane, choose Edit in settings.json. In the settings.json file, to the prompty.modelConfigurations collection, add a new configuration for Foundry Local, for example (ignore comments): { // Foundry Local model ID that you want to use "name": "Phi-4-mini-instruct-generic-gpu", // API type; Foundry Local exposes OpenAI-compatible APIs "type": "openai", // API key required for the OpenAI SDK, but not used by Foundry Local "api_key": "local", // The URL where Foundry Local exposes its API "base_url": "http://localhost:5272/v1" } Important: Be sure to check that you use the correct URL for Foundry Local. If you started Foundry Local with a different port, adjust the URL accordingly. Save your changes, and go back to the .prompty file. Once again, select the default configuration from the status bar, and choose Phi-4-mini-instruct-generic-gpu from the list. Since the model and API are configured, you can remove them from the .prompty file. Test your prompts With the newly created Foundry Local configuration selected, in the .prompty file, press F5 to test the prompt. The first time you run the prompt, it may take a few seconds because Foundry Local needs to load the model. Eventually, you should see the response from Foundry Local in the output pane. Summary Using Prompty with Foundry Local allows you to easily manage and test your prompts while running AI models locally. By creating a dedicated Prompty configuration for Foundry Local, you can conveniently test your prompts with Foundry Local models and switch between different model hosts and models if needed.European AI and Cloud Summit 2025
Dusseldorf , Germany added 3,000+ tech enthusiast and hosted the Microsoft for Startups Cloud AI Pitch Competition between May 26-28, 2025. We were pleased to attend the European AI Cloud and Collaboration Summits and Biz Apps Summit– to participate and to gain so much back from everyone. It was a packed week filled with insights, feedback, and fun. Below is a recap of various aspects of the event - across keynotes, general sessions, breakout sessions, and the Expo Hall. The event in a nutshell: 3,000+ attendees 237 speakers overall – 98 Microsoft Valued Professionals, 16 Microsoft Valued Professional Regional Directorss, 51 from Microsoft Product Groups and Engineering 306 sessions | 13 tutorials (workshops) 70 sponsors | One giant Expo Hall PreDay Workshop AI Beginner Development Powerclass This workshop is designed to give you a hands-on introduction to the core concepts and best practices for interacting with OpenAI models in Azure AI Foundry portal. Innovate with Azure OpenAI's GPT-4o multimodal model in this hands-on experience in Azure AI Foundry. Learn the core concepts and best practices to effectively generate with text, sound, and images using GPT-4o-mini, DALL-E and GPT-4o-realtime. Create AI assistants that enhance user experiences and drive innovation. Workshop for you azure-ai-foundry/ai-tutorials: This repo includes a collection of tutorials to help you get started with building Generative AI applications using Azure AI Foundry. Each tutorial is designed to be self-contained and provides step-by-step instructions to guide you in the development process. Keynotes & general sessions Day 1 Keynote The Future of AI Is Already Here, Marco Casalaina, VP Products of Azure AI and AI Futurist at Microsoft This session discussed the new and revolutionary changes that you're about to see in AI - and how many of them are available for you to try now. Marco shared how AI is becoming ubiquitous, multimodal, multilingual, and autonomous, and how it will change our lives and our businesses. This session covered: • Incredible advances in multilingual AI • How Copilot (and every AI) are grounded to data, and how we do it in Azure OpenAI • Responsible AI, including evaluation for correctness, and real time content safety • The rise of AI Agents • And how AI is going to move from question-answering to taking action Day 2 Keynote Leveraging Microsoft AI: Navigating the EU AI Act and Unlocking Future Opportunities, Azar Koulibaly, General Manager and Associate General Counsel This session was for developers and business decision makers, Azar set the stage for Microsoft’s advancements in AI and how they align with the latest regulatory framework. Exploring the EU AI Act, its key components, and its implications for AI development and deployment within the European Union. The audience gained a comprehensive understanding of the EU AI Act's objectives, including the promotion of trustworthy AI, the mitigation of risks, and the enhancement of transparency and accountability. Learning aboutMicrosoft's Cloud and AI Services provide robust support for compliance with these new regulations, ensuring that your AI projects are both innovative and legally sound and Microsoft trust center resources. He delved into the opportunities that come with using Microsoft’s state-of-the-art tools, services, and technologies. Discover how partnering with Microsoft can accelerate your AI initiatives, drive business growth, and create competitive advantages in an evolving regulatory landscape. Join us to unlock the full potential of AI while navigating the complexities of the EU AI Act with confidence. General Sessions It is crucial to ensure your organization is technically ready for the full potential of AI. The sessions focused on technical readiness and ensuring you have the latest guidance. Our experts will shared the best practices and provide guidance on how to leverage AI and Azure AI Foundry to maximize the benefits of Agents, LLM and Generative within your organization. Expo Hall + Cloud AI Statup Stage + tutorials The Expo Hall was Buzzing with demos, discussions, interviews, podcasts, lightning talks, popcorn, catering trucks, cotton candy, SWAG, prizes, and community. There was a busy Cloud AI StartupStage, a Business Stage, and shorter talks delivered in front of a shiny airstream trailer. Cloud AI Startup Stage This was a highly informative and engaging event focused on Artificial Intelligence (AI) and its potential for startups. The Microsoft for Startups is a platform to provide startups with the resources, tools, and support they need to succeed. This portion of the event offered value to budding entrepreneurs and established startups looking to scale. For example, on day 2, we focused on accelerating innovation with Microsoft Founders Hub and Azure AI. Startups could kickstart their journey with Azure credits and gain access to 30+ tools and services, benefiting from additional credits and offerings as they evolve. It’s a great way for Startups to navigate technical and business challenges. Cloud AI Startup Pitch The Microsoft for Startups AI Pitch Competition and Startup Stage at European Cloud Summit 2025 This was a highly informative and engaging event focused on Artificial Intelligence (AI) and its potential for startups. The Microsoft Startup Programme was introduced as a platform that provides startups with the resources, tools, and support they need to succeed. The AI Empowerment session provided an in-depth overview of the various AI services available through Microsoft Azure AI Foundry and how these cutting-edge technologies can be integrated into business operations. This was perfect for startups looking to get started with AI or those interested in joining the Microsoft Startup Programme. The Spotlight on Innovation session showcased innovative startups from the European Cloud Summit, giving attendees a unique insight into the cutting-edge ideas that are shaping our future. The Empowering Innovation session featured a panel of experts and successful startup founders sharing insights on leveraging Microsoft technologies, navigating the startup ecosystem, and securing funding. This was valuable for budding entrepreneurs or established startups looking to scale. Startup Showcases Holistic AI - End to End AI Governance Platform, Raj Bharat Patel, Securing Advantage in the Era of Agentic AI With this autonomy comes high variability: the difference between a minor efficiency and a major one could mean millions in savings. Conversely, a seemingly small misstep could cascade into catastrophic reputational or compliance risk. The stakes are high—but so is the potential. The next frontier introduces a new paradigm: AI managing AI. As organizations deploy swarms of autonomous agents across business functions, the challenge expands beyond governing human-AI interactions. Now, it's about ensuring that AI agents can monitor, evaluate, and optimize each other—in real time, at scale. This demands a shift in both architecture and mindset: toward native-AI platforms and a new human role, moving from human-in-the-loop to human-on-the-loop—strategically overseeing autonomous systems, not micromanaging them. In this session, Raj Bharat Patel will explore how forward-thinking enterprises can prepare for this emerging reality—building governance and orchestration infrastructures that enable scale, speed, and safety in the age of agentic AI. D-ID | The #1 Choice for AI Generated Video Creation Platform, Yaniv Levi In a world increasingly powered by AI, how do we make digital experiences feel more human? At D-ID we enable businesses to create lifelike, interactive avatars that are transforming the way users communicate with AI, making it more intuitive, personal, and memorable. In this session, we’ll share how our collaboration with Microsoft for Startups helped us scale and innovate, enabling seamless integration with Microsoft’s tools to enhance customer-facing AI agents, and LLM-powered solutions while adding a powerful and personalized new layer of humanlike expression. Startup Pitch Competition Day 2 of the event focused on accelerating innovation with Microsoft Starups and Azure AI Foundry. Startups can kickstart their journey with Azure credits and gain access to 30+ tools and services, benefiting from additional credits and offerings as they evolve. The Azure AI Foundry provides access to an extensive range of AI models, including OpenAI, Meta, Nvidia, Hugging Face. Moreover, startups can navigate through technical and business challenges with the help of free 1:1 sessions with Microsoft experts. The Cloud Startup Stage Competition showcased the most innovative startups in the Microsoft Azure and Azure OpenAI ecosystem, highlighting their groundbreaking solutions and business models. This was a celebration of innovation and success and provided insights into the experiences, challenges, and future plans of these startups The Judges The Pitches . The Winners 1 st Place Graia 2 nd Place iThink365 3 rd Place ShArc Overall, this event was highly informative, engaging, and valuable for anyone interested in AI and its potential for startups. The Microsoft Startup Programme and Azure AI Foundry are powerful tools that can help startups achieve success and transform their ideas into successful businesses. In the end... We are grateful for this year's active and engaging #CollabSummit & #CloudSummit #BizAppSummit— so much goodness, caring, learning, and fun! Great questions, stories, understanding of your concerns, and the sharing in fun. Thank you and see you next year! We look forward to seeing in Cololgne - May 5-7, 2025 – Collaboration Summit (@CollabSummit), Cloud Summit (@EUCloudSummit), and BizApps Summit (@BizAppsSummit) and continue the discussion around AI in our Azure AI Discord CommunityFeedback Loops in GenAI with Azure Functions, Azure OpenAI and Neon serverless Postgres
Generative Feedback Loops (GFL) are focused on optimizing and improving the AI’s outputs over time through a cycle of feedback and learning based on the production data. Learn how to build GenAI solution with feedback loops using Azure OpenAI, Azure Functions and Neon Serverless PostgresExploring Azure AI Model Inference: A Comprehensive Guide
Azure AI model inference provides access to a wide range of flagship models from leading providers such as AI21 Labs, Azure OpenAI, Cohere, Core42, DeepSeek, Meta, Microsoft, Mistral AI, and NTT Data https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models . These models can be consumed as APIs, allowing you to integrate advanced AI capabilities into your applications seamlessly. Model Families and Their Capabilities Azure AI Foundry categorises its models into several families, each offering unique capabilities: AI21 Labs: Known for the Jamba family models, which are production-grade large language models (LLMs) using AI21's hybrid Mamba-Transformer architecture. These models support chat completions, tool calling, and multiple languages including English, French, Spanish, Portuguese, German, Arabic, and Hebrew. https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Azure OpenAI: Offers diverse models designed for tasks such as reasoning, problem-solving, natural language understanding, and code generation. These models support text and image inputs, multiple languages, and tool calling https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Cohere: Provides models for embedding and command tasks, supporting multilingual capabilities and various response formats https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Core42: Features the Jais-30B-chat model, designed for chat completions https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models DeepSeek: Includes models like DeepSeek-V3 and DeepSeek-R1, focusing on advanced AI tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Meta: Offers the Llama series models, which are instruction-tuned for various AI tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Microsoft: Provides the Phi series models, supporting multimodal instructions and vision tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Mistral AI: Features models like Ministral-3B and Mistral-large, designed for high-performance AI tasks https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models NTT Data: Offers the Tsuzumi-7b model, focusing on specific AI capabilities https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models Deployment and Integration Azure AI model inference supports global standard deployment, ensuring consistent throughput and performance. Models can be deployed in various configurations, including regional deployments and sovereign clouds such as Azure Government, Azure Germany, and Azure China https://learn.microsoft.com/azure/ai-foundry/model-inference/concepts/models To integrate these models into your applications, you can use the Azure AI model inference API, which supports multiple programming languages including Python, C#, JavaScript, and Java. This flexibility allows you to deploy models multiple times under different configurations, providing a robust and scalable solution for your AI needs https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/overview Conclusion Azure AI model inference in Azure AI Foundry offers a comprehensive solution for integrating advanced AI models into your applications. With a wide range of models from leading providers, flexible deployment options, and robust API support, Azure AI Foundry empowers you to leverage cutting-edge AI capabilities without the complexity of hosting and managing the infrastructure. Explore the Azure AI model catalog today and unlock the potential of AI for your business. Join the Conversation on Azure AI Foundry Discussions! Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 👉 Click here to join the discussion!
