AutoGen

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AI Agents: Planning and Orchestration with the Planning Design Pattern - Part 7
This blog post, Part 7 in a series on AI agents, focuses on the Planning Design Pattern for effective task orchestration. It explains how to define clear goals, decompose complex tasks into manageable subtasks, and leverage structured output (e.g., JSON) for seamless communication between agents. The post includes code snippets demonstrating how to create a planning agent, orchestrate multi-agent workflows, and implement iterative planning for dynamic adaptation. It also links to a practical example notebook (07-autogen.ipynb) and further resources like AutoGen Magnetic One, encouraging readers to explore advanced planning concepts. Links to the previous posts in the series are provided for easy access to foundational AI agent concepts.
ShivamGoyal03
Apr 14, 2025 Place Educator Developer Blog
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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/agentshack
Pamela_Fox
Apr 11, 2025 Place Educator Developer Blog
1.1KViews
3likes
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AI Agents: Building Trustworthy Agents- Part 6
This blog post, Part 6 in a series on AI agents, focuses on building trustworthy AI agents. It emphasizes the importance of safety and security in agent design and deployment. The post details a system message framework for creating robust and scalable prompts, outlining a four-step process from meta prompt to iterative refinement. It then explores various threats to AI agents, including task manipulation, unauthorized access, resource overloading, knowledge base poisoning, and cascading errors, providing mitigation strategies for each. The post also highlights the human-in-the-loop approach for enhanced trust and control, providing a code example using AutoGen. Finally, it links to further resources on responsible AI, model evaluation, and risk assessment, along with the previous posts in the series.
ShivamGoyal03
Apr 07, 2025 Place Educator Developer Blog
394Views
3likes
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The Launch of "AI Agents for Beginners": Your Gateway to Building Intelligent Systems
🌱 Getting Started Each lesson covers fundamental aspects of building AI Agents. Whether you're a novice or have some experience, you'll find valuable insights and practical knowledge. We also support multiple languages, so you can learn in your preferred language. To see the available languages, click here. If this is your first time working with Generative AI models, we highly recommend our "Generative AI For Beginners" course, which includes 21 lessons on building with GenAI. Remember to star (🌟) this repository and fork it to run the code! 📋 What You Need The course includes code examples that you can find in the code_samples folder. Feel free to fork this repository to create your own copy. The exercises utilize Azure AI Foundry and GitHub Model Catalogs for interacting with Language Models: Github Models - Free / Limited Azure AI Foundry - Azure Account Required We also leverage the following AI Agent frameworks and services from Microsoft: Azure AI Agent Service Semantic Kernel AutoGen For more information on running the code for this course, visit the Course Setup. 🙏 Want to Help? We welcome contributions from the community! If you have suggestions or spot any errors, please raise an issue or create a pull request. If you encounter any difficulties or have questions about building AI Agents, join our Azure AI Community on Discord. 📂 Each Lesson Includes A written lesson located in the README (Videos Coming March 2025) Python code samples supporting Azure AI Foundry and Github Models (Free) Links to extra resources to continue your learning 🗃️ Lessons Overview Intro to AI Agents and Use Cases Exploring Agentic Frameworks Understanding Agentic Design Patterns Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Pattern Multi-Agent Design Pattern Metacognition Design Pattern AI Agents in Production 🌐 Multi-Language Support We offer translations in several languages and will updating these on a regular basis. 🚀 Go Fork or Clone this repo and get started on your AI Agents journey 🤖 at https://aka.ms/ai-agents-beginners
Lee_Stott
Mar 18, 2025 Place Educator Developer Blog
12KViews
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4Comments
Building AI Agent Applications Series - Using AutoGen to build your AI Agents
In the previous content, we learned about AI Agent. If you didn't read it, please read my previous content - Understanding AI Agents. We have many different frameworks to implement AI Agents. AutoGen from Microsoft is a relatively mature AI Agents framework. Now AutoGen is mainly based on two programming languages .NET and Python. The more mature version is the Python version. The content in this article is mainly based on the Python version https://microsoft.github.io/autogen. If you want to learn the .NET version, you can visit here https://microsoft.github.io/autogen-for-net
kinfey
Feb 26, 2025 Place Educator Developer Blog
38KViews
1like
3Comments
Microsoft Semantic Kernel and AutoGen: Open Source Frameworks for AI Solutions
Explore Microsoft’s open-source frameworks, Semantic Kernel and AutoGen. Semantic Kernel enables developers to create AI solutions across various domains using a single Large Language Model (LLM). AutoGen, on the other hand, uses AI Agents to perform smart tasks through agent dialogues. Discover how these technologies serve different scenarios and can be used to build powerful AI applications.
Lee_Stott
Jan 07, 2025 Place Educator Developer Blog
46KViews
6likes
1Comment
DocAider: Automated Documentation Maintenance for Open-source GitHub Repositories
Code–level documentation of a software system provides explanations of the code functionality and usages. Documentation is crucial for giving clear insights into the code for end–users and future developers. However, creating and updating documentation manually is a demanding task, requiring significant resources and labour. With the advancement of generative AI, there is a potential to reduce human labour in documentation tasks significantly. We propose DocAider, an automation tool powered by GPT–4 that integrates the processes of documentation generation and update. DocAider can generate comprehensive and structured documentation in markdown format and update it in response to any changes made in pull requests. The mission of DocAider is to reduce developers’ burden on maintaining documentation for GitHub repositories.
Dias_Jakupov
Sep 17, 2024 Place Educator Developer Blog
3.5KViews
2likes
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Building AI Agent Applications Series - Understanding AI Agents
Do you know about AI Agents? How to apply AI Agents in different scenarios? For AI Agents, Microsoft released the open source framework Autogen. But what is its relationship with Semantic Kernel and Prompt flow?
kinfey
May 13, 2024 Place Educator Developer Blog
20KViews
8likes
3Comments
Building AI Agent Applications Series - Assembling your AI agent with the Semantic Kernel
In the previous series of articles, we learned about the basic concepts of AI agents and how to use AutoGen or Semantic Kernel combined with the Azure OpenAI Service Assistant API to build AI agent applications. For different scenarios and workflows, powerful tools need to be assembled to support the operation of the AI agent. If you only use your own tool chain in the AI agent framework to solve enterprise workflow, it will be very limited. AutoGen supports defining tool chains through Function Calling, and developers can define different methods to assemble extended business work chains. As mentioned before, Semantic Kernel has good business-based plug-in creation, management and engineering capabilities. Through AutoGen + Semantic Kernel, powerful AI agent solutions can be built.
kinfey
Feb 26, 2024 Place Educator Developer Blog
5.2KViews
1like
1Comment
Autogen: Microsoft’s Open-Source Tool for Streamlining Development
Are you a technical student looking for a tool that can help you generate high-quality code, documentation, and tests for your projects? If so, you might want to check out AutoGen a framework that enables development of large language model (LLM) applications using multiple agents that can converse with each other to solve tasks.
Lee_Stott
Jan 30, 2024 Place Educator Developer Blog
11KViews
1like
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7","conversation":{"__ref":"Conversation:conversation:4399204"},"id":"message:4399204","revisionNum":3,"uid":4399204,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:2181140"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This blog post, Part 7 in a series on AI agents, focuses on the Planning Design Pattern for effective task orchestration. It explains how to define clear goals, decompose complex tasks into manageable subtasks, and leverage structured output (e.g., JSON) for seamless communication between agents. The post includes code snippets demonstrating how to create a planning agent, orchestrate multi-agent workflows, and implement iterative planning for dynamic adaptation. It also links to a practical example notebook (07-autogen.ipynb) and further resources like AutoGen Magnetic One, encouraging readers to explore advanced planning concepts. Links to the previous posts in the series are provided for easy access to foundational AI agent concepts. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":512},"postTime":"2025-04-14T04:04:52.172-07:00","lastPublishTime":"2025-04-14T04:04:52.172-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Hi everyone, Shivam Goyal here! This blog series, based on Microsoft's AI Agents for Beginners repository, continues with a focus on the Planning Design Pattern. In previous posts (links at the end!), we've built a strong foundation in AI agent concepts. Now, we'll explore how to design agents that can effectively plan and orchestrate complex tasks, breaking them down into manageable subtasks and coordinating their execution. \n Introduction to Planning Design \n The Planning Design Pattern helps AI agents tackle complex goals by providing a structured approach to task decomposition and execution. This involves: \n \n Defining a clear overall goal. \n Breaking down the task into smaller, manageable subtasks. \n Leveraging structured output for easier processing. \n Using an event-driven approach for dynamic adaptation. \n \n Defining Goals and Decomposing Tasks \n A well-defined goal is crucial for effective agent planning. Consider the goal \"Generate a 3-day travel itinerary.\" While seemingly simple, it requires refinement to ensure the agent understands the desired outcome (flights, hotels, activities, etc.). \n Task Decomposition: We break down complex tasks into smaller, more manageable subtasks: \n \n Flight Booking \n Hotel Booking \n Car Rental \n Personalization \n \n This allows specialized agents or processes to handle each subtask, improving modularity and enabling incremental enhancements (e.g., adding food recommendations later). \n Structured Output for Seamless Communication \n Structured output (like JSON) simplifies processing for other agents and services, especially in multi-agent systems. Here's how to define the structure for a travel plan: \n class TravelSubTask(BaseModel):\n task_details: str\n assigned_agent: AgentEnum\n\nclass TravelPlan(BaseModel):\n main_task: str\n subtasks: List[TravelSubTask]\n is_greeting: bool \n Planning Agent with Multi-Agent Orchestration \n A Semantic Router Agent can coordinate multiple specialized agents. It receives a user request, generates a structured plan, routes subtasks, and summarizes the outcome. Here's the core logic for creating the plan: \n messages = [\n SystemMessage(content=\"\"\"You are a planner agent...\"\"\", source=\"system\"),\n UserMessage(content=\"Create a travel plan...\", source=\"user\"),\n]\nresponse = await client.create(messages=messages, extra_create_args={\"response_format\": TravelPlan}) \n This generates a structured plan like this: \n {\n \"main_task\": \"Plan a family trip...\",\n \"subtasks\": [\n {\"assigned_agent\": \"flight_booking\", \"task_details\": \"Book flights...\"},\n // ... other subtasks\n ]\n} \n Iterative Planning and Adaptation \n Iterative planning allows agents to adapt to changing requirements or unexpected data. Here's how to incorporate a previous plan for re-planning: \n messages = [\n // ... previous messages\n AssistantMessage(content=f\"Previous travel plan - {TravelPlan}\", source=\"assistant\")\n]\n# ... re-plan based on the previous plan \n Summary \n This post demonstrated how a planner agent can dynamically select and assign subtasks to specialized agents, generating structured plans for execution. We also touched upon iterative planning and adaptation. Explore the provided code examples and resources to deepen your understanding. \n Further Resources and Learning \n \n AutoGen Magnetic One \n AI Agents for Beginners Repository \n \n Catch up on the series: \n \n Part 1: Introduction to AI Agents \n Part 2: Exploring Agentic Frameworks \n Part 3: Agentic Design Principles \n Part 4: Tool Use Design Pattern \n Part 5: AI Agents: Mastering Agentic RAG \n Part 6: Building Trustworthy Agents \n \n If you have any further questions or would like to connect for more discussion, feel free to reach out to me on the Microsoft AI Community Discord ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3814","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5MjA0LUI3Uk5GZA?revision=3\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5MjA0LUI3Uk5GZA?revision=3"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4403526":{"__typename":"Conversation","id":"conversation:4403526","topic":{"__typename":"BlogTopicMessage","uid":4403526},"lastPostingActivityTime":"2025-04-11T09:54:46.142-07:00","solved":false},"User:user:1604078":{"__typename":"User","uid":1604078,"login":"Pamela_Fox","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0xNjA0MDc4LTQxODI4MWk5MjkyQjFBMEVGOUE5NkM5"},"id":"user:1604078"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAzNTI2LWMzYXNRTA?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAzNTI2LWMzYXNRTA?revision=3","title":"githubmodels_agents_square.png","associationType":"COVER","width":783,"height":490,"altText":""},"BlogTopicMessage:message:4403526":{"__typename":"BlogTopicMessage","subject":"How to use any Python AI agent framework with free GitHub Models","conversation":{"__ref":"Conversation:conversation:4403526"},"id":"message:4403526","revisionNum":3,"uid":4403526,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1604078"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":1128},"postTime":"2025-04-11T09:43:55.656-07:00","lastPublishTime":"2025-04-11T09:54:46.142-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" 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. \n 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). \n 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. 🤗 \n 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. \n 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? \n \n 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. \n GitHub Models also provide OpenAI-compatible endpoints for chat completion models. \n Therefore, any Python AI framework that supports OpenAI-like models can be used with GitHub Models as well. 🎉 \n \n 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. \n 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. \n OpenAI \n I'll start with openai , the package that started it all! \n import openai\n\nclient = openai.OpenAI(\n api_key=os.environ[\"GITHUB_TOKEN\"],\n base_url=\"https://models.inference.ai.azure.com\") \n The code above demonstrates the two key parameters we'll need to configure for all frameworks: \n \n 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. \n 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. \n \n 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. \n import agents\nimport openai\n\nclient = openai.AsyncOpenAI(\n base_url=\"https://models.inference.ai.azure.com\",\n api_key=os.environ[\"GITHUB_TOKEN\"])\n\nmodel = agents.OpenAIChatCompletionsModel(\n model=\"gpt-4o\",\n openai_client=client)\n\nspanish_agent = agents.Agent(\n name=\"Spanish agent\",\n instructions=\"You only speak Spanish.\",\n model=model) \n PydanticAI \n 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 . \n For PydanticAI, we configure an AsyncOpenAI client, then construct an OpenAIModel object from PydanticAI, and pass that model to the agent: \n import openai\nimport pydantic_ai\nimport pydantic_ai.models.openai\n\n\nclient = openai.AsyncOpenAI(\n api_key=os.environ[\"GITHUB_TOKEN\"],\n base_url=\"https://models.inference.ai.azure.com\")\n\nmodel = pydantic_ai.models.openai.OpenAIModel(\n \"gpt-4o\", provider=OpenAIProvider(openai_client=client))\n\nspanish_agent = pydantic_ai.Agent(\n model,\n system_prompt=\"You only speak Spanish.\") \n Semantic Kernel \n 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. \n import openai\nimport semantic_kernel.connectors.ai.open_ai\nimport semantic_kernel.agents\n\nchat_client = openai.AsyncOpenAI(\n api_key=os.environ[\"GITHUB_TOKEN\"],\n base_url=\"https://models.inference.ai.azure.com\")\n\nchat = semantic_kernel.connectors.ai.open_ai.OpenAIChatCompletion(\n ai_model_id=\"gpt-4o\",\n async_client=chat_client)\n\nkernel.add_service(chat)\n \nspanish_agent = semantic_kernel.agents.ChatCompletionAgent(\n kernel=kernel,\n name=\"Spanish agent\"\n instructions=\"You only speak Spanish\") \n AutoGen \n 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. \n For AutoGen, we configure both the OpenAI parameters and the model name in the same object, then pass that to each agent: \n import autogen_ext.models.openai\nimport autogen_agentchat.agents\n\nclient = autogen_ext.models.openai.OpenAIChatCompletionClient(\n model=\"gpt-4o\",\n api_key=os.environ[\"GITHUB_TOKEN\"],\n base_url=\"https://models.inference.ai.azure.com\")\n\nspanish_agent = autogen_agentchat.agents.AssistantAgent(\n \"spanish_agent\",\n model_client=client,\n system_message=\"You only speak Spanish\") \n LangGraph \n For LangGraph, we configure a very similar object, which even has the same parameter names: \n import langchain_openai\nimport langgraph.graph\n\nmodel = langchain_openai.ChatOpenAI(\n model=\"gpt-4o\",\n api_key=os.environ[\"GITHUB_TOKEN\"],\n base_url=\"https://models.inference.ai.azure.com\", \n)\n\ndef call_model(state):\n messages = state[\"messages\"]\n response = model.invoke(messages)\n return {\"messages\": [response]}\n\nworkflow = langgraph.graph.StateGraph(MessagesState)\nworkflow.add_node(\"agent\", call_model) \n SmolAgents \n Once again, for SmolAgents, we configure a similar object, though with slightly different parameter names: \n import smolagents\n\nmodel = smolagents.OpenAIServerModel(\n model_id=\"gpt-4o\",\n api_key=os.environ[\"GITHUB_TOKEN\"],\n api_base=\"https://models.inference.ai.azure.com\")\n \nagent = smolagents.CodeAgent(model=model) \n Llamaindex \n 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. \n import llama_index.embeddings.openai\nimport llama_index.llms.openai_like\nimport llama_index.core.agent.workflow\n\nSettings.llm = llama_index.llms.openai_like.OpenAILike(\n model=\"gpt-4o\",\n api_key=os.environ[\"GITHUB_TOKEN\"],\n api_base=\"https://models.inference.ai.azure.com\",\n is_chat_model=True)\n\nSettings.embed_model = llama_index.embeddings.openai.OpenAIEmbedding(\n model=\"text-embedding-3-small\",\n api_key=os.environ[\"GITHUB_TOKEN\"],\n api_base=\"https://models.inference.ai.azure.com\")\n\nagent = llama_index.core.agent.workflow.ReActAgent(\n tools=query_engine_tools,\n llm=Settings.llm) \n Choose your models wisely! \n 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. \n Fortunately, GitHub Models includes multiple models that support function calling, at least in my basic experiments: \n \n gpt-4o \n gpt-4o-mini \n o3-mini \n AI21-Jamba-1.5-Large \n AI21-Jamba-1.5-Mini \n Codestral-2501 \n Cohere-command-r \n Ministral-3B \n Mistral-Large-2411 \n Mistral-Nemo \n Mistral-small \n \n 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. \n Join the AI Agents Hackathon \n 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. \n Learn more and register at https://aka.ms/agentshack ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"9491","kudosSumWeight":3,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAzNTI2LWMzYXNRTA?revision=3\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAzNTI2LWMzYXNRTA?revision=3"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4399202":{"__typename":"Conversation","id":"conversation:4399202","topic":{"__typename":"BlogTopicMessage","uid":4399202},"lastPostingActivityTime":"2025-04-07T00:00:00.030-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5MjAyLVhqV0pLcg?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5MjAyLVhqV0pLcg?revision=2","title":"system-message-framework.png","associationType":"COVER","width":1920,"height":1080,"altText":""},"BlogTopicMessage:message:4399202":{"__typename":"BlogTopicMessage","subject":"AI Agents: Building Trustworthy Agents- Part 6","conversation":{"__ref":"Conversation:conversation:4399202"},"id":"message:4399202","revisionNum":2,"uid":4399202,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:2181140"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This blog post, Part 6 in a series on AI agents, focuses on building trustworthy AI agents. It emphasizes the importance of safety and security in agent design and deployment. The post details a system message framework for creating robust and scalable prompts, outlining a four-step process from meta prompt to iterative refinement. It then explores various threats to AI agents, including task manipulation, unauthorized access, resource overloading, knowledge base poisoning, and cascading errors, providing mitigation strategies for each. The post also highlights the human-in-the-loop approach for enhanced trust and control, providing a code example using AutoGen. Finally, it links to further resources on responsible AI, model evaluation, and risk assessment, along with the previous posts in the series. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":394},"postTime":"2025-04-07T00:00:00.030-07:00","lastPublishTime":"2025-04-07T00:00:00.030-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Hi everyone, Shivam Goyal here! This blog series, based on Microsoft's AI Agents for Beginners repository, continues with a critical topic: building trustworthy AI agents. In previous posts (links at the end!), we explored agent fundamentals, frameworks, design principles, tool usage, and Agentic RAG. Now, we'll focus on ensuring safety, security, and user privacy in your AI agent applications. \n Building Safe and Effective AI Agents \n Safety in AI agents means ensuring they behave as intended. A core component of this is a robust system message (or prompt) framework. \n Building a System Message Framework \n System messages define the rules, instructions, and guidelines for LLMs within agents. A scalable framework for crafting these messages is crucial: \n \n Meta System Message: A template prompt used by the LLM to generate agent-specific system prompts. This meta prompt sets the overall tone and expectations for agent behavior. You are an expert at creating AI agent assistants. You will be provided with company information, roles, responsibilities, and other details to craft a system prompt. Be as descriptive as possible, providing structure for an LLM-based system to understand the AI assistant's role \n Basic Prompt: A concise description of the agent's role, tasks, and responsibilities. You are a travel agent for Contoso Travel, specializing in booking flights. You can lookup flights, book them, ask for seating/time preferences, cancel bookings, and alert customers about delays/cancellations. \n LLM-Generated System Message: Combine the meta system message and the basic prompt to generate a more refined and structured system message for the agent. The example in the full blog post demonstrates the output of this process. \n Iterate and Improve: Refine the basic prompt and regenerate the system message until it effectively guides the agent's behavior. \n \n Understanding and Mitigating Threats \n Building trustworthy agents requires understanding potential threats: \n \n Task and Instruction Manipulation: Attackers might try to alter the agent's instructions. Mitigate this with input validation, filters, and limits on conversation turns. \n Access to Critical Systems: Restrict agent access to sensitive systems to a need-only basis. Secure communication channels and implement authentication/access control. \n Resource and Service Overloading: Prevent denial-of-service attacks by limiting the agent's requests to external services. \n Knowledge Base Poisoning: Regularly verify and secure the agent's knowledge base to prevent data corruption and biased responses. \n Cascading Errors: Limit the agent's operational environment (e.g., Docker containers) and implement fallback mechanisms to prevent errors from spreading. \n \n Human-in-the-Loop for Enhanced Trust \n Incorporating a human-in-the-loop allows users to provide feedback and act as agents within the system, enhancing trust and control. The AutoGen code example demonstrates this: \n # Create the agents.\nmodel_client = OpenAIChatCompletionClient(model=\"gpt-4o-mini\")\nassistant = AssistantAgent(\"assistant\", model_client=model_client)\nuser_proxy = UserProxyAgent(\"user_proxy\", input_func=input) # Use input() to get user input from console.\n\n# Create the termination condition which will end the conversation when the user says \"APPROVE\".\ntermination = TextMentionTermination(\"APPROVE\")\n\n# Create the team.\nteam = RoundRobinGroupChat([assistant, user_proxy], termination_condition=termination)\n\n# Run the conversation and stream to the console.\nstream = team.run_stream(task=\"Write a 4-line poem about the ocean.\")\n# Use asyncio.run(...) when running in a script.\nawait Console(stream) \n Conclusion \n Building trustworthy AI agents involves a multifaceted approach. By implementing robust system message frameworks, understanding potential threats, and incorporating mitigation strategies like human-in-the-loop, developers can create AI agents that are both secure and effective. As AI evolves, prioritizing security, privacy, and ethical considerations will be essential for building truly trustworthy AI systems. \n Further Resources \n \n Responsible AI overview \n Evaluation of generative AI models and AI applications \n Safety system messages \n Risk Assessment Template \n AI Agents for Beginners Repository \n \n Catch up on the series: \n \n Part 1: Introduction to AI Agents \n Part 2: Exploring Agentic Frameworks \n Part 3: Agentic Design Principles \n Part 4: Tool Use Design Pattern \n Part 5: AI Agents: Mastering Agentic RAG \n \n If you have any further questions or would like to connect for more discussion, feel free to reach out to me on LinkedIn | GitHub ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4736","kudosSumWeight":3,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5MjAyLVhqV0pLcg?revision=2\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5MjAyLVhqV0pLcg?revision=2"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4382281":{"__typename":"Conversation","id":"conversation:4382281","topic":{"__typename":"BlogTopicMessage","uid":4382281},"lastPostingActivityTime":"2025-03-18T09:07:47.486-07:00","solved":false},"User:user:210546":{"__typename":"User","uid":210546,"login":"Lee_Stott","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yMTA1NDYtODM5MjVpMDI2ODNGQTMwMzAwNDFGQQ"},"id":"user:210546"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyMjgxLUhBTnhQRg?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyMjgxLUhBTnhQRg?revision=1","title":"Agents-thumbnail.png","associationType":"COVER","width":1920,"height":1080,"altText":""},"BlogTopicMessage:message:4382281":{"__typename":"BlogTopicMessage","subject":"The Launch of \"AI Agents for Beginners\": Your Gateway to Building Intelligent Systems","conversation":{"__ref":"Conversation:conversation:4382281"},"id":"message:4382281","revisionNum":1,"uid":4382281,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:210546"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"We are excited to announce the launch of our new course, \"AI Agents for Beginners,\" designed to teach you everything you need to know to start building AI Agents. This comprehensive course consists of 10 lessons, each focusing on a specific topic, enabling you to jump in wherever you feel most comfortable.","metrics":{"__typename":"MessageMetrics","views":12333},"postTime":"2025-02-18T00:27:28.893-08:00","lastPublishTime":"2025-02-18T00:27:28.893-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" 🌱 Getting Started \n Each lesson covers fundamental aspects of building AI Agents. Whether you're a novice or have some experience, you'll find valuable insights and practical knowledge. We also support multiple languages, so you can learn in your preferred language. To see the available languages, click here. \n If this is your first time working with Generative AI models, we highly recommend our \"Generative AI For Beginners\" course, which includes 21 lessons on building with GenAI. \n Remember to star (🌟) this repository and fork it to run the code! \n 📋 What You Need \n The course includes code examples that you can find in the code_samples folder. Feel free to fork this repository to create your own copy. The exercises utilize Azure AI Foundry and GitHub Model Catalogs for interacting with Language Models: \n \n Github Models - Free / Limited \n Azure AI Foundry - Azure Account Required \n \n We also leverage the following AI Agent frameworks and services from Microsoft: \n \n Azure AI Agent Service \n Semantic Kernel \n AutoGen \n \n For more information on running the code for this course, visit the Course Setup. \n 🙏 Want to Help? \n We welcome contributions from the community! If you have suggestions or spot any errors, please raise an issue or create a pull request. If you encounter any difficulties or have questions about building AI Agents, join our Azure AI Community on Discord. \n 📂 Each Lesson Includes \n \n A written lesson located in the README (Videos Coming March 2025) \n Python code samples supporting Azure AI Foundry and Github Models (Free) \n Links to extra resources to continue your learning \n \n 🗃️ Lessons Overview \n \n Intro to AI Agents and Use Cases \n Exploring Agentic Frameworks \n Understanding Agentic Design Patterns \n Tool Use Design Pattern \n Agentic RAG \n Building Trustworthy AI Agents \n Planning Design Pattern \n Multi-Agent Design Pattern \n Metacognition Design Pattern \n AI Agents in Production \n \n 🌐 Multi-Language Support \n We offer translations in several languages and will updating these on a regular basis. 🚀 Go Fork or Clone this repo and get started on your AI Agents journey 🤖 at https://aka.ms/ai-agents-beginners ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2238","kudosSumWeight":3,"repliesCount":4,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyMjgxLUhBTnhQRg?revision=1\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyMjgxLUhBTnhQRg?revision=1"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4052280":{"__typename":"Conversation","id":"conversation:4052280","topic":{"__typename":"BlogTopicMessage","uid":4052280},"lastPostingActivityTime":"2025-02-26T07:43:57.019-08:00","solved":false},"User:user:1158870":{"__typename":"User","uid":1158870,"login":"kinfey","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0xMTU4ODcwLTU0ODQxMWlERTQ5OEYxMkNFQTBBQzcw"},"id":"user:1158870"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA2OWk3QjQ5N0VFRkJENUYwQ0RG?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA2OWk3QjQ5N0VFRkJENUYwQ0RG?revision=6","title":"autogen_s1.png","associationType":"BODY","width":2551,"height":1407,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3MGlGQjlDNjcwNjEzRDhBOUYz?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3MGlGQjlDNjcwNjEzRDhBOUYz?revision=6","title":"autogen_studio.png","associationType":"BODY","width":1448,"height":941,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3MmlDNzFDNkJCMDQyOTBCNEJC?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3MmlDNzFDNkJCMDQyOTBCNEJC?revision=6","title":"autogen_skills.png","associationType":"BODY","width":1426,"height":336,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3M2k4OTUxMzE1NzgwODNBMUYz?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3M2k4OTUxMzE1NzgwODNBMUYz?revision=6","title":"autogen_models.png","associationType":"BODY","width":789,"height":420,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3NGk2QjJEREM4MzhBNTlGOUUx?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3NGk2QjJEREM4MzhBNTlGOUUx?revision=6","title":"autogen_agents.png","associationType":"BODY","width":789,"height":927,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3OGlDQjZFMTk3MDg5MjcxMTk1?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3OGlDQjZFMTk3MDg5MjcxMTk1?revision=6","title":"autogen_wf1.png","associationType":"BODY","width":782,"height":479,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3OWkzNTZBQTkzRjE1QTFFQTc1?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA3OWkzNTZBQTkzRjE1QTFFQTc1?revision=6","title":"autogen_wf2.png","associationType":"BODY","width":700,"height":922,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA4MGlEMzRBQTNEMzY4MzIyQzdG?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA4MGlEMzRBQTNEMzY4MzIyQzdG?revision=6","title":"autogen_sessions.png","associationType":"BODY","width":463,"height":188,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA4MWlENDIwMzU2NDVFQUIzMTNF?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUyMjgwLTU1MDA4MWlENDIwMzU2NDVFQUIzMTNF?revision=6","title":"autogen_pg.png","associationType":"BODY","width":1452,"height":933,"altText":null},"BlogTopicMessage:message:4052280":{"__typename":"BlogTopicMessage","subject":"Building AI Agent Applications Series - Using AutoGen to build your AI Agents","conversation":{"__ref":"Conversation:conversation:4052280"},"id":"message:4052280","revisionNum":6,"uid":4052280,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1158870"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In the previous content, we learned about AI Agent. If you didn't read it, please read my previous content - Understanding AI Agents. We have many different frameworks to implement AI Agents. AutoGen from Microsoft is a relatively mature AI Agents framework. Now AutoGen is mainly based on two programming languages .NET and Python. The more mature version is the Python version. The content in this article is mainly based on the Python version https://microsoft.github.io/autogen. If you want to learn the .NET version, you can visit here https://microsoft.github.io/autogen-for-net ","introduction":"","metrics":{"__typename":"MessageMetrics","views":37571},"postTime":"2024-02-09T00:00:00.033-08:00","lastPublishTime":"2024-02-09T00:00:00.033-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In the previous content, we learned about AI Agent. If you didn't read it, please read my previous content - Understanding AI Agents. We have many different frameworks to implement AI Agents. AutoGen from Microsoft is a relatively mature AI Agents framework. Now AutoGen is mainly based on two programming languages .NET and Python. The more mature version is the Python version. The content in this article is mainly based on the Python version https://microsoft.github.io/autogen. If you want to learn the .NET version, you can visit here https://microsoft.github.io/autogen-for-net \n AutoGen Features \n From the perspective of AI agents, AutoGen has the ability to be compatible with different LLMs, tool chains for different tasks, and human-computer interaction capabilities. It is an open source framework used to solve the interaction between agents. Its biggest feature is its ability to automate task orchestration, optimize workflow, and have powerful multi-agent conversation capabilities to adjust to workflow or goals. Combined with the different APIs provided in the framework, the cache construction, error handling, different LLMs configurations, context association, dialogue process settings required by the AI agent . Compared with Semantic Kernel and LangChain, the frameworks based on Copilot applications, AutoGen has more advantages in automated task orchestration scenarios and is more front-end-oriented. After receiving the target task, AutoGen can arrange the task, and Semantic Kernel or LangChain are more like providing an ammunition library to solve the task arrangement process, providing various tools and methods that can support the completion of the task. \n The construction of AutoGen is very simple. You only need simple code to quickly configure the agent. By building simple anthropomorphic user agents and assistants, you can complete the construction of a simple agent. Here's how to quickly build a single agent \n \n 1. Configuration file, AutoGen. For configuration files, Azure OpenAI Service is generally placed in the AOAI_CONFIG_LIST in the root directory, such as \n \n \n[\n {\n \"model\": \"Your Azure OpenAI Service Deployment Model Name\",\n \"api_key\": \"Your Azure OpenAI Service API Key\",\n \"base_url\": \"Your Azure OpenAI Service Endpoin\",\n \"api_type\": \"azure\",\n \"api_version\": \"Your Azure OpenAI Service version, eg 2023-12-01-preview\"\n },\n {\n \"model\": \"Your Azure OpenAI Service Deployment Model Name\",\n \"api_key\": \"Your Azure OpenAI Service API Key\",\n \"base_url\": \"Your Azure OpenAI Service Endpoin\",\n \"api_type\": \"azure\",\n \"api_version\": \"Your Azure OpenAI Service version, eg 2023-12-01-preview\"\n },\n {\n \"model\": \"Your Azure OpenAI Service Deployment Model Name\",\n \"api_key\": \"Your Azure OpenAI Service API Key\",\n \"base_url\": \"Your Azure OpenAI Service Endpoin\",\n \"api_type\": \"azure\",\n \"api_version\": \"Your Azure OpenAI Service version, eg 2023-12-01-preview\"\n }\n]\n\n\n \n If it is OpenAI Service, OAI_CONFIG_LIST placed in the root directory, the content includes \n \n[\n {\n \"model\": \"Your OpenAI Model Name\",\n \"api_key\": \"Your OpenAI API Key\"\n },\n {\n \"model\": \"Your OpenAI Model Name\",\n \"api_key\": \"Your OpenAI API Key\"\n },\n {\n \"model\": \"Your OpenAI Model Name\",\n \"api_key\": \"Your OpenAI API Key\"\n },\n]\n\n\n \n After completing the configuration, you can use Python to initial \n \nconfig_list = autogen.config_list_from_json(\n env_or_file=\"AOAI_CONFIG_LIST\",\n file_location=\".\",\n filter_dict={\n \"model\": {\n \"Your Model list\"\n\n }\n },\n)\n\n\n \n \n 2. Create user proxy agent and assistant agent \n \n \n# Create an AssistantAgent instance named \"assistant\"\nassistant = autogen.AssistantAgent(\n name=\"assistant\",\n llm_config={\n \"cache_seed\": 42,\n \"config_list\": config_list,\n }\n)\n# create a UserProxyAgent instance named \"user_proxy\"\nuser_proxy = autogen.UserProxyAgent(\n name=\"user_proxy\",\n human_input_mode=\"ALWAYS\"\n)\n\n \n Notice \n A. The AI agent assistant corresponds to the configuration file and adds a cache. The role of the configuration is to give the agent a powerful \"brain\" and cache memory. \n B. User proxy agent can simulate human behavior, and you can set whether human intervention occurs. We know that the characteristics of AI agents are not only human thinking, but also human interactive behaviors. When we solve problems through AI agents, we need to consider whether human intervention is needed. You can choose Never. But sometimes you must choose ALWAYS. Because when we need to obtain some APIs, we need some Keys or the cooperation of some network address files. You can set it according to your own scene. \n \n 3. The last step is to associate the user proxy agent and the assistant agent together and give them a task \n \n \nmessages = \"tell me today's top 10 news in the world \"\n\nuser_proxy.initiate_chat(assistant, message=messages)\n\n \n We can clearly see how the agent completes the complete interaction and generates code to obtain today's latest news. If you want to learn this example, please visit \n \n https://github.com/kinfey/AutoGenDemo/blob/main/agent_demo_step01.ipynb \n \n AutoGen scenarios \n There are many implementation scenarios based on AutoGen. You can learn from the cases in https://microsoft.github.io/autogen/docs/Examples. I would like to tell you how to use AutoGen from two scenarios and how Autogen works through a detailed application scenario. \n Scenarios \n Case 1: Combining multi-modal capabilities to complete object detection \n Requirement: During our production process, we need to conduct safety helmet detection. If you find that employees are not wearing safety helmets, please mark it. \n From traditional AI applications, what we need is to collect data of people wearing helmets, label them, complete the model through deep learning training, and then inference and label it. Now that we have multimodal models, we can simplify a lot of our work. In this scenario, we can combine multi-modal agents, code agents, and running-code agents to complete related work. \n \n \n \n AutoGen supports group chat, and multiple agents can be combined to complete tasks in a session. The code is as follows: \n \ngroupchat = autogen.GroupChat(agents=[user_proxy, checker,coder, commander], messages=[], max_round=10)\n\nmanager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n\n \n Group chat can combine different agents to complete tasks, which is very interesting work. If you want to know more, please visit \n \n https://github.com/kinfey/AutoGenDemo/blob/main/agent_demo_step02.ipynb \n \n \n Case 2: AutoGen powered by Assistant API \n The Assistant API is designed for AI agents. You can build AI agent applications with less code through the Assistant API. It integrates capabilities such as state management, context association, chat threads, and code execution, making it easier to access third-party extensions ( Code interpreter, knowledge retrieval and Function Calling, etc.). Although AutoGen already had similar functions before the Assistant API, with the support of the Assistant API, AutoGen can be more flexibly defined in multi-agent scenarios, can set more interactive scenarios, more flexible task execution, and more Good end-to-end process management. \n Note: Before the article was written, AutoGen did not support the Assistant API provided on Azure OpenAI Service, so this article will be completed based on the OpenAI Assistant API. \n Before using the Assistant API, the relevant Assistants must be created on the OpenAI or Azure OpenAI Service portal. For details, please refer to https://learn.microsoft.com/azure/ai-services/openai/assistants-quickstart \n Using the Assistant API in AutoGen requires adjusting the configuration. The tools type can be set to code_interpreter, retrival, function calling. \n \nllm_config = {\n \"config_list\": config_list,\n \"assistant_id\": \"Your OpenAI Assistant ID \", \n \"tools\": [{\"type\": \"code_interpreter\"}],\n \"file_ids\": [\n \"Your OpenAI Assistant File 1\",\n \"Your OpenAI Assistant File 2\"\n ],\n}\n\n \n AI Agent-based settings \n \ngpt_assistant = GPTAssistantAgent(\n name=\"Your Assistant Agent Name\", instructions=\"Your Assistant Agent Instructions \", llm_config=llm_config\n)\n\n \n If you want to try running the contents of this repo, please visit \n \n https://github.com/kinfey/AutoGenDemo/blob/main/agent_demo_step03.ipynb \n \n \n Build a visualization solution for AutoGen - AutoGen Studio \n For enterprise solutions, more people like to use a combination of visualization and low-code methods to complete relevant workflow settings. Use AutoGen Studio to bring better workflow-based agent-customized visualization solutions to enterprises. \n \n \n \n Installation \n AutoGen Studio is recommended to be started in the Python 3.11 environment. You can use conda to install the Python environment and install the AutoGen Studio package. \n \nconda create -n agstudioenv python=3.11.7\n\nconda activate agstudioenv\n\npip install autogenstudio \n\n \n Remember to configure OPENAI_API_KEY or your AZURE_OPENAI_API_KEY before starting \n \nexport OPENAI_API_KEY='Your OpenAI Key'\n\nexport AZURE_OPENAI_API_KEY='Your Azure OpenAI Service Key'\n\n\n \n Start your AutoGen Studio, where port is the network port and can be set as needed \n \nautogenstudio ui --port 8088\n\n\n \n Use Case/Scenario \n Everyone knows that I am a Premier League fan. I hope to build an AI agent to help me analyze the situation of each Premier League team in the new season based on the standings. \n Assemble ammunition for your AI agent \n AutoGen Studio now supports configuring skills, models, agents, and workflows. These four functions can be seen by selecting the Build menu. \n \n 1. Skills Different functions can be added to the agent through Python. Here I add a get_league_standing Skills. \n \n Note: You need to register https://www.football-data.org/ to get API Key \n \n \nimport requests\nimport json\n\ndef get_league_standings(api_key='Your football-data API Key'):\n url = \"http://api.football-data.org/v4/competitions/PL/standings\"\n headers = {\"X-Auth-Token\": api_key}\n response = requests.get(url, headers=headers)\n data = response.json()\n\n standings = [] \n\n if 'standings' in data:\n for standing in data['standings']:\n if standing['type'] == 'TOTAL': \n for team in standing['table']:\n team_data = {\n \"position\": team['position'],\n \"teamName\": team['team']['name'],\n \"playedGames\": team['playedGames'],\n \"won\": team['won'],\n \"draw\": team['draw'],\n \"lost\": team['lost'],\n \"points\": team['points'],\n \"goalsFor\": team['goalsFor'],\n \"goalsAgainst\": team['goalsAgainst'],\n \"goalDifference\": team['goalDifference']\n }\n standings.append(team_data)\n break \n\n standings_json = json.dumps(standings, ensure_ascii=False, indent=4)\n return standings_json\n else:\n return \"Error\"\n\n\n \n After saving, as shown in the figure \n \n \n \n 2. Models corresponds to the binding of the LLMs. The design needs to set the Key of OpenAI or Azure OpenAI Service before starting. We add a binding of the gpt-4-turbo model. Here we use Azure OpenAI Services service, so the name and EndPoint of the deployment need to correspond one-to-one with your Azure OpenAI Service \n \n \n \n 3. Agents Add your AI agent. You can set different agents here. In our case, we only need to set up a single agent. Add a football_expert_assistant agent here and set a role for the system and bind the Skill - get_league_standing and Models just added, as shown in the figure. \n \n \n \n 4. Workflows We can set the workflow of the agent and the interactive dialogue workflow of the agent. We set the simplest two agent interaction mode - \"Two Agents.\" \n \n \n \n We need to set up the receiver - Receiver and bind the set football_expert_assistant's agent and LLMs. \n \n \n \n Running Your Agents \n You can run your application through the Playground in the AutoGen Studio UI. You only need to create a Session to correspond to the set Workflows. \n \n The result: \n \n Of course, you can also publish the agent by selecting Session, which can be viewed through the Gallery menu. \n Summary \n AutoGen is a relatively comprehensive AI agent framework. For enterprises that want to build AI agents, it not only provides an application framework, but also provides a visual and interactive visual UI of AutoGen, which lowers the entry barrier for intelligent agents and allows more people to take advantage of intelligent agents. We have taken the first step to build an AI agent using AutoGen, and will incorporate more advanced content in the following series. \n Resources \n \n \n Microsoft AutoGen https://microsoft.github.io/autogen/ \n \n \n Microsoft AutoGen Studio UI 2.0 https://microsoft.github.io/autogen/blog/2023/12/01/AutoGenStudio/ \n \n \n AutoGen Studio: Interactively Explore Multi-Agent Workflows https://microsoft.github.io/autogen/blog/2023/12/01/AutoGenStudio/ \n \n \n Azure OpenAI Service Assistant API Docs https://learn.microsoft.com/azure/ai-services/openai/assistants-quickstart \n \n 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Semantic Kernel and AutoGen: Open Source Frameworks for AI Solutions","conversation":{"__ref":"Conversation:conversation:4051305"},"id":"message:4051305","revisionNum":4,"uid":4051305,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:210546"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Explore Microsoft’s open-source frameworks, Semantic Kernel and AutoGen. Semantic Kernel enables developers to create AI solutions across various domains using a single Large Language Model (LLM). AutoGen, on the other hand, uses AI Agents to perform smart tasks through agent dialogues. Discover how these technologies serve different scenarios and can be used to build powerful AI applications. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":45580},"postTime":"2024-02-08T00:00:00.049-08:00","lastPublishTime":"2024-02-12T09:23:06.440-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Microsoft Semantic Kernel (SK) and Microsoft AutoGen are both open sources framework developed by Microsoft, but they serve different scenarios \n \n Microsoft Semantic Kernel (SK) is a framework for using and managing a single Large Language Model (LLM). It enables developers to create powerful AI solutions for various domains such as copilot, RAG. vision, speech, language, decision, knowledge and search. Semantic Kernel is built on the Copilot application. It benefits from rich Connectors, which can work with different enterprise application scenarios, and has strong capabilities of orchestrating tasks based on individuals. It can also customize different plugins (embedded components/Prompt/custom extension methods) to fulfill the relevant application scenarios of the enterprise. Semantic Kernel has cross-platform capabilities and supports multiple programming languages, such as C #, Java, and Python. It is more suitable for traditional engineering projects to access LLMs and build Copilot applications. \n \n \n What is Microsoft AutoGen \n Microsoft AutoGen uses AI Agents that can work together with other AI Agents based on tasks to do smart tasks through dialogue between agents. Unlike the usual Copilot application, more AI Agents are involved, and people only need simple intervention to finish the related work. AutoGen has the features of AI Agents, such as powerful memory abilities, task coordination abilities, and rich tool chains. It now supports Python and .NET, and has AutogenStudio to do the work of low-code configuration of AI Agents within a UI experience. \n \n Semantic Kernel vs AutoGen \n \n \n \n AutoGen can implement different forms of AI Agents, including single AI agent, multi-AI agents, and hybrid AI agent \n \n \n \n Single AI Agent \n Work completed in specific task scenarios, such as the agent workspace under GitHub Copilot Chat, is an example of completing specific programming tasks based on user needs. Based on the capabilities of LLMs, a single agent can perform different actions based on tasks, such as requirements analysis, project reading, code generation, etc. It can also be used in smart homes and autonomous driving. \n \n Multi-AI agents \n This is the work of mutual interaction between AI agents. For example, the above-mentioned Semantic Kernel agent implementation is an example. The AI agent generated by the script interacts with your AI agent that executes the script. Multi-agent application scenarios are very helpful in highly collaborative work, such as software industry development, intelligent production, enterprise management, etc. \n \n Hybrid AI Agent \n This is human-computer interaction, making decisions in the same environment. For example, smart medical care, smart cities and other professional fields can use hybrid intelligence to complete complex professional work. \n At present, the application of intelligent agents is still very preliminary. Many enterprises and individual developers are in the exploratory stage. Taking the first step is very critical. I hope you can try it more. I also hope that everyone can use Azure OpenAI Service to build more agent applications. \n \n Semantic Kernel and AutoGen are both Microsoft technologies, but they serve different purposes and are used in different ways. Semantic Kernel is an open-source Software Development Kit (SDK) that allows developers to build AI agents that can call existing code. It's designed to work with models from various AI providers like OpenAI, Azure OpenAI, and Hugging Face. By integrating your existing C#, Python, and Java code with these models, you can create agents that answer questions and automate processes. \n \n Semantic Kernel is at the heart of the agent stack, enabling AI orchestration by combining AI models and plugins to create new experiences for users. It's particularly useful for automating business processes and making AI agents more productive by calling existing code. \n \n Semantic Kernel, which is more focused on Copilot applications. It is characterized by task orchestration and division of steps for a single individual. \n \n Autogen was born to serve AI Agents. In addition to arranging tasks for a single individual, it can also complete task division for multiple agents. Autogen is a Multi Agent conversation framework. It simplifies the creation, orchestration and automation of conversational applications where LLMs, tools and humans collaborate through diverse communication patterns to perform complex tasks. Agents can be structured statically or dynamically to support applications where the topology of agents adapts to the conversation. Microsoft AutoGen is designed for integrating and controlling multiple LLMs. It’s a research project that shows the potential of using multiple agents together. AutoGen allows for the creation of diverse teams of agents, each with their own specialized skills or goals. These agents can chat with each other, facilitating greater diversity in opinion and outcomes. \n \n \n \n While AutoGen and Semantic Kernel have some overlap in features, they are not exactly the same. AutoGen is not a superset of Semantic Kernel , and it does not delegate to Semantic Kernel for using individual LLMs. However, they can be used together in certain scenarios. For example, agents within AutoGen can retrieve information, create content, and complete tasks using plugins provided by Semantic Kernel. \n The two are compatible with each other because AI agents have three characteristics: task, memory, and tools. These can be provided by Semantic Kernel. \n \n In summary \n Both Semantic Kernel and AutoGen offer unique capabilities for working with LLMs, and the choice between them depends on the specific requirements of your project. Semantic Kernel is about creating single AI agents and equipping them with the tools to do tasks, while AutoGen is about managing complicated workflows that involve multiple agents, each with different skills and functions. Both technologies can work together; for example, you can use Semantic Kernel to give tools (via plugins) to agents made in AutoGen. This lets AutoGen agents access real-time information and communicate more efficiently. \n Resources Microsoft AutoGen Semantic Kernel: Integrate cutting-edge LLM technology quickly and easily into your apps (github.com) This is a Semantic Kernel's book for beginners (github.com) \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"6567","kudosSumWeight":6,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUxMzA1LTU0OTgwM2k0MUU5OEM4NjJCMkE5NUE5?revision=4\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUxMzA1LTU0OTgwNGlEMjY1N0IzOEY5NUE1ODQy?revision=4\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUxMzA1LTU0OTgwNWkwNDU3MzQ5MDdCOTBGREQ4?revision=4\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDUxMzA1LTU0OTgwNmk2NjQwMEQ5ODVDN0M0OURE?revision=4\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4245588":{"__typename":"Conversation","id":"conversation:4245588","topic":{"__typename":"BlogTopicMessage","uid":4245588},"lastPostingActivityTime":"2024-09-17T00:00:00.026-07:00","solved":false},"User:user:2588119":{"__typename":"User","uid":2588119,"login":"Dias_Jakupov","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-1.svg?time=0"},"id":"user:2588119"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDExMGlDOTE4OEY1MDU5OTBFOUY3?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDExMGlDOTE4OEY1MDU5OTBFOUY3?revision=11","title":"DocAider.png","associationType":"TEASER","width":1258,"height":483,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDExMWkyMTdBNDE0NEU2QzI4MzBC?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDExMWkyMTdBNDE0NEU2QzI4MzBC?revision=11","title":"Multi_agent_blog_post.png","associationType":"BODY","width":4189,"height":1097,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDE1OGlCN0EzNEQyQjY5OTEwMDc4?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDE1OGlCN0EzNEQyQjY5OTEwMDc4?revision=11","title":"update.png","associationType":"BODY","width":2325,"height":1067,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDE1NWlCM0NGMUI4N0ExOEU5MkIy?revision=11\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDE1NWlCM0NGMUI4N0ExOEU5MkIy?revision=11","title":"QA_Table.png","associationType":"BODY","width":934,"height":215,"altText":null},"BlogTopicMessage:message:4245588":{"__typename":"BlogTopicMessage","subject":"DocAider: Automated Documentation Maintenance for Open-source GitHub Repositories","conversation":{"__ref":"Conversation:conversation:4245588"},"id":"message:4245588","revisionNum":11,"uid":4245588,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:2588119"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n Code–level documentation of a software system provides explanations of the code functionality and usages. Documentation is crucial for giving clear insights into the code for end–users and future developers. However, creating and updating documentation manually is a demanding task, requiring significant resources and labour. With the advancement of generative AI, there is a potential to reduce human labour in documentation tasks significantly. We propose DocAider, an automation tool powered by GPT–4 that integrates the processes of documentation generation and update. DocAider can generate comprehensive and structured documentation in markdown format and update it in response to any changes made in pull requests. The mission of DocAider is to reduce developers’ burden on maintaining documentation for GitHub repositories. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3546},"postTime":"2024-09-17T00:00:00.026-07:00","lastPublishTime":"2024-09-17T00:00:00.026-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Project Overview \n Comprehensive documentation is crucial for end users in open-source software projects, but manual creation and maintenance are time-consuming and costly. Traditional documentation generators like Pydoc rely on predefined rules and in-line code information. However, emerging generative AI techniques, particularly Large Language Models (LLMs), offer new possibilities for enhanced documentation generation. Developed in partnership with Microsoft and UCL, DocAider aims to create an AI-powered documentation tool that automatically generates and updates code documentation. The tool leverages Github Actions workflows to trigger documentation tasks upon pull requests (PRs) opening, providing valuable insights into continuous documentation maintenance. This approach addresses the challenges of automating documentation and ensures that project documentation remains current with minimal human intervention. This project aims to leverage LLM technologies, combined with Microsoft Semantic Kernel, Microsoft Autogen, and Azure AI Studio, to mitigate the burden of maintaining up-to-date documentation. This system uses a multi-agent architecture where multiple agents work together to complete the task. It offers two innovative features: a recursive update mechanism, which ensures that changes ripple throughout all related documentation, and continuous monitoring and updating code via pull requests. DocAider offers a promising solution for software engineers, with the potential to automatically maintain clean and up-to-date documentation. It allows developers to concentrate more on coding while simplifying the onboarding process for new team members. Additionally, it helps reduce costs and boosts overall efficiency. \n \n Project Journey \n This project was completed over 3 months. The few weeks, the team focused on the requirements engineering portion of the project, where we set functional and non-functional requirements, created context and architecture diagrams and broke the project down with the stakeholders, so that it was easy for us to implement in the following months, making sure we included the most important features and requirements. This process also allowed the team to see how much is realistically achievable, and what should be kept as optional if time allowed us to complete. During implementation, our team employed agile methodologies, Git practices, and continuous integration and testing. We chose agile as our development approach because it facilitated constant communication between the team and stakeholders. This strategy proved crucial to our product's success. Bi-weekly meetings with stakeholders allowed us to report progress, plan upcoming tasks and clarify the requirements. Additionally, we held weekly internal meetings for team members to showcase their work and assess overall progress. \n \n Technical Details \n DocAider is an LLM-powered tool that generates and updates documentation automatically. It performs the documentation tasks using a customised GitHub Actions workflow and runs in the background. We developed DocAider by integrating Semantic Kernel and AutoGen. The tools facilitate the development of AI-based software. Furthermore, we deployed and managed Azure OpenAI LLMs on Azure AI Studio. To obtain good results, we used the GPT-4-0125-preview model to create documentation for the source code. The temperature parameter was set between 0 and 0.2 for more deterministic and factual LLM responses. \n \n Documentation Generation \n \n \n AutoGen provides multiple conversation patterns to orchestrate AI agents, such as sequential chats, group chats, nested chats, etc. We used the sequential chats to create documentation. The figure shows our multi-agent architecture, which reduces LLM hallucinations in two ways: appropriate code context information and self-improvement. Four agents perform different tasks in sequence and an agent manager controls the multi-agent conversation. \n \n Code Context Agent creates a graph representation of the entire repository, mapping the relationships between function calls. It then generates comprehensive information about the codebase using the actual source code and the relationship graph. \n Documentation Generation Agent produces baseline documentation taking into account the contextual information passed from the previous agent. The documentation contains three basic sections: overview, class/function/method descriptions and input/output examples. \n Review Agent assesses the baseline documentation, and suggests the improvements. \n Revise Agent modifies the baseline documentation according to the suggestions and returns the improved documentation to Agent Manager. \n Agent Manager controls the conversation process and responds to function calling requests from LLM-configured agents. \n \n By using Semantic Kernel, we built skilled agents for performing specific tasks. AutoGen facilitates agent interactions to complete complex workflows. The LLM function calling capability helps to reduce programming efforts and makes agents flexible. An agent can autonomously execute external functions defined in the associated plugins to complete a variety of tasks. \n \n Documentation Update \n To maintain consistency and accuracy across all related documentation when a class/function in a file is changed, the Documentation Update feature performs the update recursively. If a class/function is modified, the system will automatically update the documentation for all dependent files. This includes documentation of the source file, as well as documentation of files that use functions dependent on the changed class/function. This recursive update feature ensures that all related documentation remains up–to–date with the latest changes in the code. \n Additionally, Documentation update on PR Comment allows reviewers to trigger the documentation updates on specified files by commenting in a specific format. The reviewer can specify which file needs an update and provide instructions on what changes should be made. The system will then process this comment and update the documentation as instructed. This feature ensures that precise and targeted documentation updates can be made based on reviewer feedback, improving the overall quality and relevance of the documentation. Furthermore, it removes the need for developers to manually change documentation according to reviewers’ comments. The comment triggering this process needs to be in this format: \n \n “Documentation {file_path}: {comment}”. For example, “Documentation main.py: Add more I/O examples”. \n \n Results and Outcome \n Our evaluation process involved three stages: a case study executing our system on a well–known repository to showcase our system, a comparison of our system against RepoAgent, and a quantitative analysis. Through this process, we could determine our system’s performance. \n \n Case Study: \n The section presents results from applying our tool to generate and update documentation for the Graphviz repository's Python files. The system produced well-structured documentation, including overviews, global variables, function/class descriptions, and I/O examples, providing clear explanations of file purposes and usage guidelines. When updates were made to the base.py file, adding logging functionality and a new method, the system successfully incorporated these changes while preserving existing content. The system also demonstrated its ability to handle recursive updates, propagating changes from the ParameterBase class in base.py to dependent files like engine.py. Additionally, it responded effectively to a PR comment, requesting more input/output examples, showcasing its capacity to incorporate reviewer feedback. Overall, the multi-agent system proved capable of generating, updating, and maintaining comprehensive documentation across all files in a software repository. \n \n \n Comparison with RepoAgent \n We compared DocAider, a multi-agent documentation system, with RepoAgent, another LLM-based documentation generation tool. While RepoAgent produces lengthy paragraphs, DocAider generates concise, bullet-pointed documentation, aligning with developers' preferences for brevity. DocAider's multi-agent approach potentially enhances accuracy and reduces hallucinations compared to RepoAgent's single-agent system. DocAider also implements a Reviewer and Revisor agent to suggest and apply improvements. A notable feature of DocAider is its HTML-based front-end interface, which improves documentation accessibility and organization - factors highly valued by developers. While our system is well-designed and offers unique features like recursive updates, RepoAgent stands out by providing thorough I/O examples for every function. However, LLMs can make incorrect assumptions as function complexity increases, leading to factual inaccuracies or nonsensical outputs. To mitigate this, we restrict the LLM from making such assumptions, resulting in some functions/classes lacking I/O examples. \n Quantitive Analysis \n \n \n We conducted a quantitative analysis of DocAider's performance across six popular GitHub repositories: collarama, fake-useragent, graphiz, photon, progress, and pywhat. These repositories were selected based on their popularity (over 1000 stars each) and size (small to medium, limited to 20 files per repository). All of them varied in the number of functions and classes. Scores are normalised between 0 and 1, reflecting the presence of these attributes in the documentation. For instance, a score of 1 for Function/Class Description indicates that every class and function in the repository is described in the documentation, while a score of 0.5 for I/O examples means that only half of the functions have I/O examples provided in the documentation. \n DocAider achieved perfect scores (1.0) for function/class descriptions across all repositories, demonstrating consistent performance. For parameters/attributes, most repositories received perfect scores, with only collamara scoring slightly lower at 0.94 due to two functions lacking parameter documentation. I/O examples showed the most variation, with scores ranging from 0.54 (photon and progress) to 0.88 (collamara). Lower scores were often due to specific function types without return values (e.g., class init methods) or complex logic that made example generation challenging. Return value documentation was consistently strong, with all repositories scoring 1.0. \n Overall, DocAider is proficient in many areas, such as generating function/class descriptions and handling most documentation aspects. However, there is room for improvement in consistently documenting I/O examples, particularly for functions with more complex logic. \n \n Lessons Learned \n The development of DocAider provided valuable insights across several key areas. Firstly, the adoption of a multi-agent approach proved crucial in managing system's complexity. Initially, a single-agent design led to issues such as hallucinations and incomplete documentation. By transitioning to a multi-agent architecture, the team was able to distribute tasks across specialized agents, each handling specific aspects of the documentation process. This approach significantly improved the accuracy and reliability of the documentation while also enhancing system scalability. The success of this strategy highlighted the importance of modular design and task specialization in complex AI-driven systems. Secondly, prompt engineering emerged as a critical and unexpectedly challenging aspect of the project. The quality of generated documentation was heavily dependent on the prompts given to the Large Language Models (LLMs). Initial struggles with overly broad or contextually lacking prompts led to irrelevant or inaccurate outputs. Through iterative testing and refinement, the team developed more precise and context-aware prompts, significantly improving documentation quality. This experience underscored the complexity and importance of effective prompt engineering in applications requiring high accuracy and relevance. Lastly, the team learned the critical importance of managing dependency versions. An incident where a new version of Semantic Kernel (1.3.0) caused the software to crash in Docker due to API changes highlighted the need for version consistency across development and deployment environments. This experience emphasized the importance of carefully managing and aligning dependency versions to ensure system stability and functionality. \n \n Team Contributions \n Jakupov Dias (Team Leader): Team management, Stakeholder communication, development of Documentation Update, Recursive Update, Update on PR comment, prompt engineering. \n Chengqi Ke: development of Retrieval Augmented Generation and multi-agent communication using Semantic Kernel and AutoGen. \n Fatima Hussain: development of GitHub workflows, evaluation of DocAider's performance and effectiveness. \n Tanmay Thaware: development of Retrieval Augmented Generation, evaluation of DocAider's performance. \n Tomas Kopunec: development of Abstract Syntax Tree, Recursive Update and HTML front-end . \n Zena Wang: development of GitHub workflows and handled deployment, packaging the tool into a Docker image. \n \n Future Work \n DocAider's current implementation successfully automates code documentation, but there are several areas for future improvement. First, enhancing the tool's ability to provide comprehensive and accurate I/O examples is a priority. This can be achieved by refining agent prompts and potentially integrating context-specific agents to better interpret complex functions. Second, future evaluations should extend to larger, more complex repositories to assess DocAider's scalability and performance beyond small and medium sized projects. This expansion was previously limited by budget constraints. Third, while initial attempts to use Retrieval-Augmented Generation (RAG) didn't significantly improve documentation quality due to limited contextual knowledge in the repository, future iterations could explore more effective RAG implementations. For instance, retrieving test cases from repositories could enhance the accuracy of I/O examples, and RAG could support the development of repository-specific chatbots to assist developers. Lastly, DocAider's modular and loosely coupled multi-agent design allows for significant scalability potential. The system can easily integrate new features such as code validation, static analysis, or security evaluation without major architectural changes. This flexibility extends to adding or replacing agents, supporting various LLM models, and expanding language support beyond Python, all while maintaining core functionality. \n \n Conclusion \n DocAider successfully automated the creation and upkeep of accurate, up-to-date documentation, significantly reducing the manual workload for developers. By leveraging AI tools like Microsoft AutoGen, Semantic Kernel, and Azure AI Studio, the project addressed key challenges in maintaining consistent, real-time documentation. \n While budget constraints, missing I/O examples, and the limitations of LLMs posed challenges, the project established a solid foundation for future improvements. Beyond solving the immediate need for documentation management, DocAider raised the bar for efficiency and accuracy in software development, showcasing the potential of AI-driven solutions for more advanced applications. \n \n \n Call To Action \n We invite you to explore DocAider further and consider how its innovative approach to documentation maintenance can be applied in your projects. Here are some steps you can take and explore the tools we used: \n \n Connect with Us: Feel free to reach out to our team for more information or collaboration opportunities. \n AutoGen: https://www.microsoft.com/en-us/research/project/autogen/ \n Semantic Kernel: https://learn.microsoft.com/en-us/semantic-kernel/overview/?tabs=Csharp \n \n \n \n Special Thanks to Contributors \n Each contributor’s continuous support and involvement all plays a crucial role in the success of the project, here, we present a special thanks to all following contributors. \n \n Lee Stott, Principal Cloud Advocate Manager at Microsoft \n Diego Colombo, Principal Software Engineer at Microsoft \n Jens Krinke, Senior Lecturer and Academic Supervisor \n \n \n \n Team \n The team involved in developing this project included 6 members. All of us are Masters students at UCL studying Software Systems Engineering \n Dias Jakupov - Team Leader - Full Stack Developer \n GitHub URL: https://github.com/Dias2406/ \n LinkedIn URL: https://www.linkedin.com/in/dias-jakupov-a05258221/ \n Chengqi Ke - Full Stack Developer \n GitHub URL: https://github.com/CQ-Ke/ \n LinkedIn URL: http://linkedin.com/in/chengqi-ke-9b91a8313/ \n Tomas Kopunec - Full Stack Developer \n GitHub URL: https://github.com/TomasKopunec/ \n LinkedIn URL: https://www.linkedin.com/in/tomas-kopunec-425b0199/ \n Fatima Hussain - Full Stack Developer \n GitHub URL: https://github.com/fatimahuss/ \n LinkedIn URL: http://linkedin.com/in/fatima-noor-hussain/ \n Tanmay Thaware - Full Stack Developer \n GitHub URL: https://github.com/tanmaythaware/ \n LinkedIn URL: http://linkedin.com/in/tanmaythaware/ \n Zena Wang - Full Stack Developer \n GitHub URL: https://github.com/ZenaWangqwq/ \n LinkedIn URL: https://www.linkedin.com/in/zena-wang-b63a8822b/ \n \n \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"17675","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDExMGlDOTE4OEY1MDU5OTBFOUY3?revision=11\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDExMWkyMTdBNDE0NEU2QzI4MzBC?revision=11\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDE1OGlCN0EzNEQyQjY5OTEwMDc4?revision=11\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MjQ1NTg4LTYyMDE1NWlCM0NGMUI4N0ExOEU5MkIy?revision=11\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4046944":{"__typename":"Conversation","id":"conversation:4046944","topic":{"__typename":"BlogTopicMessage","uid":4046944},"lastPostingActivityTime":"2024-05-13T09:01:02.927-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5NGk0MDE0NjgxNERCRjA5NjM1?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5NGk0MDE0NjgxNERCRjA5NjM1?revision=4","title":"aiagent.png","associationType":"BODY","width":1363,"height":681,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5M2kzNjE2MkRFMjM5QjgyODYz?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5M2kzNjE2MkRFMjM5QjgyODYz?revision=4","title":"dotNETAgent.png","associationType":"BODY","width":1669,"height":1071,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5MmkyNUFBQjk2RjJGNjI3RjMw?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5MmkyNUFBQjk2RjJGNjI3RjMw?revision=4","title":"hybridAgent.png","associationType":"BODY","width":1902,"height":658,"altText":null},"BlogTopicMessage:message:4046944":{"__typename":"BlogTopicMessage","subject":"Building AI Agent Applications Series - Understanding AI Agents","conversation":{"__ref":"Conversation:conversation:4046944"},"id":"message:4046944","revisionNum":4,"uid":4046944,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1158870"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Do you know about AI Agents? How to apply AI Agents in different scenarios? For AI Agents, Microsoft released the open source framework Autogen. But what is its relationship with Semantic Kernel and Prompt flow? ","introduction":"","metrics":{"__typename":"MessageMetrics","views":19854},"postTime":"2024-02-03T00:52:39.757-08:00","lastPublishTime":"2024-02-03T00:52:39.757-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Do you know about AI Agents? How to apply AI Agents in different scenarios? For AI Agents, Microsoft released the open source framework Autogen. But what is its relationship with Semantic Kernel and Prompt flow? I hope this series can answer your questions. Let everyone have a clear understanding of AI Agents, how to combine Autogen, Semantic Kernel, and Prompt flow to build intelligent applications Around artificial intelligence, humans have made many attempts in different industries and different application scenarios. With the emergence of LLMs, we have transitioned from traditional chatbots with process predefinition plus semantic matching to Copilot applications that interact with LLMs through natural language. In the past year or so, everyone has mainly focused on basic theories based on LLMs. In 2024 we should enter the application scenario of LLMs. We have a lot of papers, application frameworks, and practices from large companies to support the implementation of LLMs applications. So what is the final form of our so-called artificial intelligence applications? What you can think of is GitHub Copilot for programming assistance, Microsoft 365 Copilot for office scenarios, and Microsoft Copilot on Windows or Bing, etc. But think about the application of Copilot, which relies more on individuals to guide or correct through prompt words, and does not achieve fully intelligent applications. In the 1980s, we began to try to do fully intelligent work, and AI Agent is a fully intelligent best practice. \n \n The agent interacts with the scene where it is located, receives instructions or data in the application scene, and decides different responses based on the instructions or data to achieve the final goal. Intelligent agents not only have human thinking capabilities, but can also simulate human behavior. They can be simple systems based on business processes, or they can be as complex as machine learning models. Agents use pre-established rules or models trained through machine learning/deep learning to make decisions, and sometimes require external control or supervision. \n \n Characteristics of the AI agent: \n \n \n Planning, divide steps based on tasks, and have a chain of though. With LLMs, it can be said that the planning ability of the agent is greatly enhanced, and the understanding of the task can be more accurate. \n \n \n Memory the ability to remember behavior and part of logic, the ability to store experiences, and the ability to self-reflect. \n \n \n Tool Chain, such as code execution capabilities, search capabilities, and computing capabilities. It can be said that he has strong mobility \n \n \n perceive and obtain information such as pictures, sounds, temperatures, etc. based on the scene, thus providing better conditions for execution. \n \n \n Technical support for realizing intelligent agents \n There is considerable application practice in the application of LLMs. \n There are many frameworks for implementing intelligent agents. The previously mentioned Semantic Kernel or Autogen can implement intelligent agents. The Assitants API has also been added under OpenAI to enhance the model's capabilities in agents. Now OpenAI’s Assitants API opens up the capabilities of code interpretation, retrieval, and function calling. Assitants API of Azure OpenAI Service is also coming soon, which can be said to provide enough wisdom for the application capabilities of agents. \n Many people pay more attention to the application layer framework. People often compare Semantic Kernel and Autogen. After all, both are from Microsoft and have good task or plan orchestration capabilities. However, some people always feel that the two have many similarities. \n Semantic Kernel vs Autogen \n Semantic Kernel focuses on effectively dividing individual tasks into steps in Copilot applications. This is also the charm of the Semantic Kernel Planner API. Autogen, on the other hand, focuses more on the construction of agents, dividing tasks to complete goals and assigning tasks to different agents. Each agent executes individually or interactively according to the assigned tasks. Behind each agent's task can be a streaming task arrangement, or an extended method for solving problems, or skills triggered by corresponding prompts, which can be organized in conjunction with Semantic Kernel plugins. When we want to have a stable task output, we can also add prompt flow to evaluate the output. \n \n \n Use Semantic Kernel to implement AI agents. \n Semantic Kernel has added support for agents in the Experimental library, introduced AgentBuilder, and combined with the Assistant API to complete the brain configuration of the agent. The corresponding planning, memory and tools are defined using different plugins. \n \nvar yourAgent = await new AgentBuilder()\n .WithOpenAIChatCompletion(\"OpenAI Assitants API\", \"OpenAI Key\")\n .WithInstructions(\"Your agent instruction\")\n //.FromTemplate(EmbeddedResource.Read(\"Your agent YAML\"))\n .WithName(\"Your Agent Name\")\n .WithDescription(\"Your Agent Desctiption\")\n .WithPlugin(\"Your Agent Plugins\")\n .BuildAsync();\n \n Notice \n \n WithOpenAIChatCompletion requires OpenAI/Azure OpenAI Service models that support Assistants API (soon to be released). Currently supported OpenAI models are GPT-3.5 or GPT-4 models. \n WithInstructions We need to give clear task instructions and inform the agent how to execute it. This is equivalent to a process. You need to describe it clearly, otherwise the accuracy will be reduced. \n .FromTemplate can also use Template to describe task instructions \n .WithName The name is required to make the call more clear. \n .WithPlugin is based on different skills and tool chains for the agent to complete tasks. This corresponds to the content of Semantic Kernel. \n \n Let's take a simple scenario and hope to build a .NET console application through an agent, compile and run it, and require it to be completed through an agent. From this scenario, we need two agents - the agent that generates the .NET CLI script and the agent that runs the .NET CLI script. In Semantic Kernel, we use different plugins to define the required planning, memory and tools. The following is the relevant structure diagram. \n \n \n \n You can get sample code from Semantic Kernel CookBook https://github.com/microsoft/SemanticKernelCookBook/tree/main/workshop/dotNET/workshop3/dotNETAgent \n Application scenarios of AI agents \n AI Agents are an important scenario for LLMs applications, and building agent applications will be an important technical field in 2024. We currently have three main forms of intelligence, such as single AI agent, multi- AI agents, and hybrid AI agent. \n \n \n \n Single AI Agent \n Work completed in specific task scenarios, such as the agent workspace under GitHub Copilot Chat, is an example of completing specific programming tasks based on user needs. Based on the capabilities of LLMs, a single agent can perform different actions based on tasks, such as requirements analysis, project reading, code generation, etc. It can also be used in smart homes and autonomous driving. \n \n Multi-AI agents \n This is the work of mutual interaction between AI agents. For example, the above-mentioned Semantic Kernel agent implementation is an example. The AI agent generated by the script interacts with your AI agent that executes the script. Multi-agent application scenarios are very helpful in highly collaborative work, such as software industry development, intelligent production, enterprise management, etc. \n \n Hybrid AI Agent \n This is human-computer interaction, making decisions in the same environment. For example, smart medical care, smart cities and other professional fields can use hybrid intelligence to complete complex professional work. \n At present, the application of intelligent agents is still very preliminary. Many enterprises and individual developers are in the exploratory stage. Taking the first step is very critical. I hope you can try it more. I also hope that everyone can use Azure OpenAI Service to build more agent applications. \n \n Resources \n \n Microsoft Semantic Kernel https://github.com/microsoft/semantic-kernel \n Microsoft Autogen https://github.com/microsoft/autogen \n Microsoft Semantic Kernel CookBook https://github.com/microsoft/SemanticKernelCookBook \n Pursuit of \"wicked smartness\" in VS Code https://code.visualstudio.com/blogs/2023/11/13/vscode-copilot-smarter \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"8763","kudosSumWeight":8,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5NGk0MDE0NjgxNERCRjA5NjM1?revision=4\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5M2kzNjE2MkRFMjM5QjgyODYz?revision=4\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQ2OTQ0LTU0ODM5MmkyNUFBQjk2RjJGNjI3RjMw?revision=4\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4059215":{"__typename":"Conversation","id":"conversation:4059215","topic":{"__typename":"BlogTopicMessage","uid":4059215},"lastPostingActivityTime":"2024-02-25T23:17:36.480-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2M2lCOUM4NjkwMDlBOENDNzlG?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2M2lCOUM4NjkwMDlBOENDNzlG?revision=6","title":"agsk001.png","associationType":"BODY","width":1454,"height":1248,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2NWlFMjUxNDY0OEZEODA4MkJF?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2NWlFMjUxNDY0OEZEODA4MkJF?revision=6","title":"agsk002.png","associationType":"BODY","width":2844,"height":1554,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2Nmk1ODMzNEVGM0Q4OTc3QjJE?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2Nmk1ODMzNEVGM0Q4OTc3QjJE?revision=6","title":"agsk003.png","associationType":"BODY","width":2644,"height":1098,"altText":null},"BlogTopicMessage:message:4059215":{"__typename":"BlogTopicMessage","subject":"Building AI Agent Applications Series - Assembling your AI agent with the Semantic Kernel","conversation":{"__ref":"Conversation:conversation:4059215"},"id":"message:4059215","revisionNum":6,"uid":4059215,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1158870"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In the previous series of articles, we learned about the basic concepts of AI agents and how to use AutoGen or Semantic Kernel combined with the Azure OpenAI Service Assistant API to build AI agent applications. For different scenarios and workflows, powerful tools need to be assembled to support the operation of the AI agent. If you only use your own tool chain in the AI agent framework to solve enterprise workflow, it will be very limited. AutoGen supports defining tool chains through Function Calling, and developers can define different methods to assemble extended business work chains. As mentioned before, Semantic Kernel has good business-based plug-in creation, management and engineering capabilities. Through AutoGen + Semantic Kernel, powerful AI agent solutions can be built. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":5172},"postTime":"2024-02-17T08:53:41.324-08:00","lastPublishTime":"2024-02-25T23:17:36.480-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In the previous series of articles, we learned about the basic concepts of AI agents and how to use AutoGen or Semantic Kernel combined with the Azure OpenAI Service Assistant API to build AI agent applications. For different scenarios and workflows, powerful tools need to be assembled to support the operation of the AI agent. If you only use your own tool chain in the AI agent framework to solve enterprise workflow, it will be very limited. AutoGen supports defining tool chains through Function Calling, and developers can define different methods to assemble extended business work chains. As mentioned before, Semantic Kernel has good business-based plug-in creation, management and engineering capabilities. Through AutoGen + Semantic Kernel, powerful AI agent solutions can be built. \n Scenario 1 - Constructing a single AI agent for writing technical blogs \n \n \n \n As a cloud advocate, I often need to write some technical blogs. In the past, I needed a lot of supporting materials. Although I could write some of the materials through Prompt + LLMs, some professional content might not be enough to meet the requirements. For example, I want to write based on the recorded YouTube video and the syllabus. As shown in the picture above, combine the video script and outline around the three questions as basic materials, and then start writing the blog. \n \n \n \n Note: We need to save the data as vector first. There are many methods. You can choose to use different frameworks for embedded vector processing. Here we use Semantic Kernel combined with Qdrant. Of course, the more ideal step is to add this part to the entire technical blog writing agent, which we will introduce in the next scenario. \n Because the AI agent simulates human behavior, when designing the AI agent, the steps that need to be set are the same as in my daily work. \n \n Find relevant content based on the question \n Set a blog title, extended content and related guidance, and write it in markdown \n Save \n \n We can complete steps 1 and 2 through Semantic Kernel. As for step 3, we can directly use the traditional method of reading and writing files. We need to define these three functions ask, writeblog, and saveblog here. After completion, we need to configure Function Calling and set the parameters and function names corresponding to these three functions. \n \nllm_config={\n \"config_list\": config_list,\n \"functions\": [\n {\n \"name\": \"ask\",\n \"description\": \"ask question about Machine Learning, get basic knowledge\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"question\": {\n \"type\": \"string\",\n \"description\": \"About Machine Learning\",\n }\n },\n \"required\": [\"question\"],\n },\n },\n {\n \"name\": \"writeblog\",\n \"description\": \"write blogs in markdown format\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"content\": {\n \"type\": \"string\",\n \"description\": \"basic content\",\n }\n },\n \"required\": [\"content\"],\n },\n },\n {\n \"name\": \"saveblog\",\n \"description\": \"save blogs\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"blog\": {\n \"type\": \"string\",\n \"description\": \"basic content\",\n }\n },\n \"required\": [\"blog\"],\n },\n }\n ],\n}\n\n \n Because this is a single AI agent application, we only need to define an Assistant and a UserProxy. We only need to define our goals and inform the relevant steps to run. \n \nassistant = autogen.AssistantAgent(\n name=\"assistant\",\n llm_config=llm_config,\n)\n\nuser_proxy = autogen.UserProxyAgent(\n name=\"user_proxy\",\n is_termination_msg=lambda x: x.get(\"content\", \"\") and x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n human_input_mode=\"NEVER\",\n max_consecutive_auto_reply=10,\n code_execution_config=False\n)\n\nuser_proxy.register_function(\n function_map={\n \"ask\": ask,\n \"writeblog\": writeblog,\n \"saveblog\": saveblog\n }\n)\n\n\nwith Cache.disk():\n await user_proxy.a_initiate_chat(\n assistant,\n message=\"\"\"\n I'm writing a blog about Machine Learning. Find the answers to the 3 questions below and write an introduction based on them. After preparing these basic materials, write a blog and save it.\n\n 1. What is Machine Learning?\n 2. The difference between AI and ML\n 3. The history of Machine Learning\n\n Let's go\n \"\"\"\n )\n\n\n \n We tried running it and it worked fine. For specific effects, please refer to： \n \n https://github.com/kinfey/AutoGenDemo/blob/main/autogenwithSK/blogs/blog-12af6af4-ba79-48a4-b1e5-b6094675c22e.md \n \n Sample code: \n \n https://github.com/kinfey/AutoGenDemo/blob/main/autogenwithSK/02.autogenwithsk.ipynb \n \n \n \n Scenario 2 - Building a multi-agent interactive technical blog editor solution. \n In the above scenario, we successfully built a single AI agent for technical blog writing. We hope that our technology will be more intelligent. From content search to writing and saving to translation, it is all completed through AI agent interaction. We can use different job roles to achieve this goal. This can be done by generating code from LLMs in AutoGen, but the uncertainty of this is a bit high. Therefore, it is more reliable to define additional methods through Function Calling to ensure the accuracy of the call. The following is a structural diagram of the division of labor roles: \n \n \n \n Notice \n \n \n Admin - Define various operations through UserProxy, including the most important methods. \n \n \n Collector KB Assistant - Responsible for downloading relevant subtitle scripts of technical videos from YouTube, saving them locally, and vectorizing them by extracting different knowledge points and saving them to the vector database. Here I only made a video subtitle script. You can also add local documents and support for different types of audio files. \n \n \n Blog Editor Assistant - When the data collection assistant completes its work, it can hand over the work to the blog writing assistant, who will write the blog as required based on a simple question outline (title setting, content expansion, and usage markdown format, etc.), and automatically save the blog to the local after writing. \n \n \n Translation Assistant - Responsible for the translation of blogs in different languages. What I am talking about here is translating Chinese (can be expanded to support more languages) \n \n \n Based on the above division of labor, we need to define different methods to support it. At this time, we can use SK to complete related operations. \n Here we use AutoGen's group chat mode to complete related blog work. You can clearly see that you have a team working, which is also the charm of the agent. Set it up with the following code. \n \ngroupchat = autogen.GroupChat(\n agents=[user_proxy, collect_kb_assistant, blog_editor_assistant,translate_assistant], messages=[],max_round=30)\n\nmanager = autogen.GroupChatManager(groupchat=groupchat, llm_config={'config_list': config_list})\n\"\"\"\n )\n\n\n \n The code for group chat dispatch is as follows: \n \nawait user_proxy.a_initiate_chat(\n manager,\n message=\"\"\"\n Use this link https://www.youtube.com/watch?v=1qs6QKk0DVc as knowledge with collect knowledge assistant. Find the answers to the 3 questions below to write blog and save and save this blog to local file with blog editor assistant. And translate this blog to Chinese with translate assistant.\n\n 1. What is GitHub Copilot ?\n 2. How to Install GitHub Copilot ?\n 3. Limitations of GitHub Copilot\n\n Let's go\n\"\"\"\n )\n\n\n \n Different from a single AI agent, a manager is configured to coordinate the communication work of multiple AI agents. Of course, you also need to have clear instructions to assign work. \n You can view the complete code on this Repo. \n \n https://github.com/kinfey/AutoGenDemo/blob/main/autogenwithSK/03.autogenwithsk_groupchat.ipynb \n \n \n If you want to see the result about English blog, you can also click this link. \n \n https://github.com/kinfey/AutoGenDemo/blob/main/autogenwithSK/blogs/blog-28bdf106-b234-4559-b8e8-4f7dbd0fcc36.md \n \n If you want to see the result about Chinese blog, you can also click this link. \n \n https://github.com/kinfey/AutoGenDemo/blob/main/autogenwithSK/blogs/zh-blogs-f55e2e3e-292b-4028-8b7b-1b4dde9fc819.md \n \n \n More \n AutoGen helps us easily define different AI agents and plan how different AI agents interact and operate. The Semantic Kernel is more like a middle layer to help support different ways for agents to solve tasks, which will be of great help to enterprise scenarios. When AutoGen appears, some people may think that it overlaps with Semantic Kernel in many places. In fact, it complements and does not replace it. With the arrival of the Azure OpenAI Service Assistant API, you can believe that the agent will have stronger capabilities as the technical framework and API are improved. \n Resources \n \n Microsoft Semantic Kernel https://github.com/microsoft/semantic-kernel \n Microsoft Autogen https://github.com/microsoft/autogen \n Microsoft Semantic Kernel CookBook https://aka.ms/SemanticKernelCookBook \n Get started using Azure OpenAI Assistants. https://learn.microsoft.com/en-us/azure/ai-services/openai/assistants-quickstart \n What is an agent? https://learn.microsoft.com/en-us/semantic-kernel/agents \n What are Memories? https://learn.microsoft.com/en-us/semantic-kernel/memories/ \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"10137","kudosSumWeight":1,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2M2lCOUM4NjkwMDlBOENDNzlG?revision=6\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2NWlFMjUxNDY0OEZEODA4MkJF?revision=6\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDU5MjE1LTU1MjI2Nmk1ODMzNEVGM0Q4OTc3QjJE?revision=6\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4040417":{"__typename":"Conversation","id":"conversation:4040417","topic":{"__typename":"BlogTopicMessage","uid":4040417},"lastPostingActivityTime":"2024-01-30T07:45:34.377-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQwNDE3LTU0NjE2Nmk0OTIzNUY2RDlFMDc5NjAz?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQwNDE3LTU0NjE2Nmk0OTIzNUY2RDlFMDc5NjAz?revision=6","title":"autogen_agentchat.png","associationType":"BODY","width":1576,"height":756,"altText":null},"BlogTopicMessage:message:4040417":{"__typename":"BlogTopicMessage","subject":"Autogen: Microsoft’s Open-Source Tool for Streamlining Development","conversation":{"__ref":"Conversation:conversation:4040417"},"id":"message:4040417","revisionNum":6,"uid":4040417,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:210546"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Are you a technical student looking for a tool that can help you generate high-quality code, documentation, and tests for your projects? If so, you might want to check out \n AutoGen a framework that enables development of large language model (LLM) applications using multiple agents that can converse with each other to solve tasks. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":10949},"postTime":"2024-01-26T01:10:32.986-08:00","lastPublishTime":"2024-01-30T07:45:34.377-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Today, we're going to dive into an exciting tool from Microsoft Research called Autogen. \n What is Autogen? \n Autogen is an open-source project by Microsoft, designed to simplify the process of creating and maintaining libraries of data structures and routines. It's a powerful tool that can significantly speed up your development process. \n Why Autogen? \n Autogen is incredibly versatile and can be used in a variety of programming projects. It's especially useful when you're dealing with large codebases or complex data structures. With Autogen, you can automate repetitive tasks, reduce errors, and ensure consistency across your project. \n Getting Started with Autogen \n To get started with Autogen, visit the official Autogen GitHub page microsoft/autogen: Enable Next-Gen Large Language Model Applications. \n Join the Autogen Discord: https://discord.gg/pAbnFJrkgZ Here, you'll find comprehensive documentation, installation instructions, and a wealth of examples to help you understand how to use Autogen effectively. \n AutoGen is a framework that enables development of large language model (LLM) applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. \n \n \n \n AutoGen is useful for technical students because: \n \n \n It simplifies the orchestration, automation, and optimization of complex LLM workflows, such as code-based question answering, retrieval-augmented generation, and conversational AI. \n It offers a collection of examples spanning a wide range of applications from various domains and complexities. \n It supports enhanced LLM inference APIs, which can be used to improve inference performance and reduce cost. \n It leverages the strongest capabilities of the most advanced LLMs, like GPT-4, while addressing their limitations by integrating with humans and tools and having conversations between multiple agents via automated chat. \n \n AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. \n \n To get started with AutoGen, you need to: \n \n Install AutoGen: You can use AutoGen from Codespaces or Docker or locally with pip by running pip install pyautogen . You also need to install Docker and the python docker package for code execution. See Installation for more details. \n Create Agents: AutoGen provides customizable and conversable agents that can integrate LLMs, tools, and humans. You can create different types of agents, such as AssistantAgent , UserProxyAgent , HumanAgent , etc. See Agents for more details. \n Initiate Chat: You can initiate a chat between two or more agents by calling the initiate_chat method on one of the agents. You can also specify the message, the task, and the conversation mode. See Chat for more details. \n Build Applications: You can use AutoGen to build a wide range of applications from various domains and complexities, such as code generation, data analysis, summarization, etc. See Applications for some examples. \n \n \n Conclusion \n Autogen is a powerful tool that can greatly enhance your coding experience. Whether you're a beginner just starting out or an experienced developer looking for ways to improve your workflow, Autogen has something to offer. So why wait? Dive into Autogen today and discover a new way of coding! You can also check out the Quickstart guide and the Documentation for more information. \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3762","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDQwNDE3LTU0NjE2Nmk0OTIzNUY2RDlFMDc5NjAz?revision=6\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"CachedAsset:text:en_US-components/community/Navbar-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/community/Navbar-1745505307000","value":{"community":"Community Home","inbox":"Inbox","manageContent":"Manage Content","tos":"Terms of Service","forgotPassword":"Forgot Password","themeEditor":"Theme Editor","edit":"Edit Navigation Bar","skipContent":"Skip to content","gxcuf89792":"Tech Community","external-1":"Events","s-m-b":"Nonprofit Community","windows-server":"Windows Server","education-sector":"Education Sector","driving-adoption":"Driving Adoption","Common-content_management-link":"Content Management","microsoft-learn":"Microsoft Learn","s-q-l-server":"Content Management","partner-community":"Microsoft Partner Community","microsoft365":"Microsoft 365","external-9":".NET","external-8":"Teams","external-7":"Github","products-services":"Products","external-6":"Power Platform","communities-1":"Topics","external-5":"Microsoft Security","planner":"Outlook","external-4":"Microsoft 365","external-3":"Dynamics 365","azure":"Azure","healthcare-and-life-sciences":"Healthcare and Life Sciences","external-2":"Azure","microsoft-mechanics":"Microsoft Mechanics","microsoft-learn-1":"Community","external-10":"Learning Room Directory","microsoft-learn-blog":"Blog","windows":"Windows","i-t-ops-talk":"ITOps Talk","external-link-1":"View All","microsoft-securityand-compliance":"Microsoft Security","public-sector":"Public Sector","community-info-center":"Lounge","external-link-2":"View All","microsoft-teams":"Microsoft Teams","external":"Blogs","microsoft-endpoint-manager":"Microsoft Intune","startupsat-microsoft":"Startups at Microsoft","exchange":"Exchange","a-i":"AI and Machine Learning","io-t":"Internet of Things (IoT)","Common-microsoft365-copilot-link":"Microsoft 365 Copilot","outlook":"Microsoft 365 Copilot","external-link":"Community Hubs","communities":"Products"},"localOverride":false},"CachedAsset:text:en_US-components/community/NavbarHamburgerDropdown-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/community/NavbarHamburgerDropdown-1745505307000","value":{"hamburgerLabel":"Side Menu"},"localOverride":false},"CachedAsset:text:en_US-components/community/BrandLogo-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/community/BrandLogo-1745505307000","value":{"logoAlt":"Khoros","themeLogoAlt":"Brand Logo"},"localOverride":false},"CachedAsset:text:en_US-components/community/NavbarTextLinks-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/community/NavbarTextLinks-1745505307000","value":{"more":"More"},"localOverride":false},"CachedAsset:text:en_US-components/authentication/AuthenticationLink-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/authentication/AuthenticationLink-1745505307000","value":{"title.login":"Sign In","title.registration":"Register","title.forgotPassword":"Forgot Password","title.multiAuthLogin":"Sign In"},"localOverride":false},"CachedAsset:text:en_US-components/nodes/NodeLink-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/nodes/NodeLink-1745505307000","value":{"place":"Place {name}"},"localOverride":false},"CachedAsset:text:en_US-components/tags/TagSubscriptionAction-1745505307000":{"__typename":"CachedAsset","id":"text:en_US-components/tags/TagSubscriptionAction-1745505307000","value":{"success.follow.title":"Following Tag","success.unfollow.title":"Unfollowed Tag","success.follow.message.followAcrossCommunity":"You will be notified when this tag is used anywhere across the community","success.unfollowtag.message":"You will no longer be notified when this tag is used anywhere in this place","success.unfollowtagAcrossCommunity.message":"You will no longer be notified when this tag is used anywhere across the community","unexpected.error.title":"Error - 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