{"id":198057,"date":"2026-01-28T17:07:05","date_gmt":"2026-01-28T16:07:05","guid":{"rendered":"https:\/\/liora.io\/en\/?p=198057"},"modified":"2026-02-06T07:18:14","modified_gmt":"2026-02-06T06:18:14","slug":"all-about-autogen","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/all-about-autogen","title":{"rendered":"Autogen: AI Agent Collaboration by Microsoft"},"content":{"rendered":"<p><b>Autogen is the open-source framework that Microsoft has developed to orchestrate multiple AI agents, enabling them to collaborate as a real team. Discover how it is transforming the use of generative AIs by encouraging communication between agents, integrating human involvement, and collectively tackling complex tasks!<\/b><\/p>\n<p><a href=\"https:\/\/liora.io\/en\/top-10-ai-image-generators\">Current artificial intelligences<\/a> have a significant limitation: they often operate in isolation. Chatbots, assistants, copilots\u2014 <b>each resides in its own silo, with its own constraints<\/b>. This solitary algorithmic existence becomes a real impediment when tasks grow complex, such as <b>coding<\/b>, data analysis, or <b>decision-making<\/b>.<\/p>\n<p>To address this issue, <b>Microsoft<\/b> launched an open-source framework that enables <b>multiple AIs to collaborate<\/b> like a cohesive team. Even better, these agents can interact with humans, organize themselves, assign roles, and work together to solve problems that were previously insurmountable. This framework is called <b>Autogen<\/b>.<\/p>\n<p><a href=\"https:\/\/liora.io\/en\/artificial-intelligence-definition\"><br \/>\nExploring artificial intelligence<br \/>\n<\/a><\/p>\n<style>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style>\n<h2>A Platform for AI Collective Intelligence<\/h2>\n<p>Developed by <b>Microsoft Research<\/b>, this open-source framework orchestrates <b>collaborative AI agents<\/b>. The concept? Design systems where multiple models can engage in continuous interaction, hand-off tasks, ask questions, and self-correct\u2014 akin to a <b>project team<\/b>. It can include <a href=\"https:\/\/liora.io\/en\/all-about-generated-pre-trained-transformers\">GPT<\/a>, specialized tools, scripts, and even humans.<\/p>\n<p>It&#8217;s important to clarify that Autogen does not offer another <a href=\"https:\/\/liora.io\/en\/large-language-models-llm-everything-you-need-to-know\">LLM<\/a>, but rather an <b>interaction infrastructure<\/b>. It facilitates conversations among multiple intelligent entities, enabling them to <b>coordinate their actions<\/b> and tackle more ambitious tasks collectively than a single agent could manage alone.<\/p>\n<p>Whereas single-agent assistants were traditionally built, Autogen promotes a <b>multi-agent dialogue approach<\/b>. Each agent assumes a role: a coder, a reviewer, a coordinator, a decision-maker&#8230; and everything operates seamlessly, guided by an <b>orchestration engine<\/b> that adheres to dialogic logic.<\/p>\n<p>Technically, it&#8217;s open-source, <a href=\"https:\/\/liora.io\/en\/all-about-courses-on-python\">written in Python<\/a>, and based on LLM models accessible through APIs (<a href=\"https:\/\/liora.io\/en\/meta-goes-head-to-head-with-openai-and-gpt-4\">OpenAI<\/a>, <a href=\"https:\/\/liora.io\/en\/microsoft-azure-empower-yourself-with-knowledge\">Azure<\/a>, etc.). The aim is to make the system <b>modular<\/b>, <b>testable<\/b>, and <b>reusable<\/b> in any project.<\/p>\n<style>\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\n<p>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-1.webp\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-1.webp 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-1-300x200.webp 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-1-1024x683.webp 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-1-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\"><\/p>\n<h2>Collaboration Among AI&#8230; and With Humans<\/h2>\n<p>What sets Autogen apart is not merely its ability for multiple AI agents to communicate. Similar frameworks are indeed in existence. Its uniqueness lies in its <b>structured dialogue logic<\/b>, which is designed to incorporate humans into the process.<\/p>\n<p>Central to the system are two types of entities: <b><i>agents<\/i><\/b> (autonomous, such as a specialized GPT assistant) and <b><i>user proxies<\/i><\/b>, which act as interfaces for human users. A <b>user proxy<\/b> acts as a developer who can jump into a conversation to <b>validate a decision, fix a bug, or pose a new question<\/b>. They can communicate directly within the interaction thread among agents.<\/p>\n<p>Each agent, on its end, can be assigned a specific role: &#8220;coding a function,&#8221; &#8220;testing a module,&#8221; &#8220;reformulating an instruction,&#8221; &#8220;posing questions to the client,&#8221; and each <b>interaction<\/b> unfolds in a controlled cycle. An agent speaks, another responds, and the system evaluates whether to persist, adjust, or terminate the dialogue.<\/p>\n<p>This format enables the creation of a <b>realistic and efficient dynamic among AIs<\/b>. As a result, Autogen becomes a true platform for <b>cognitive orchestration<\/b>, capable of structuring a <b>collective reasoning<\/b> that extends beyond a mere prompt.<\/p>\n<p><a href=\"\/en\/courses\/data-ai\/\"><br \/>\nLearn to orchestrate AI agents with Autogen<br \/>\n<\/a><\/p>\n<h2>The Architecture of Autogen Explained<\/h2>\n<p>The framework relies on a <b>modular architecture<\/b>, where each agent is a Python object capable of engaging in dialogue according to predefined rules. These rules encompass a <b>personality<\/b> (system prompt, style, role), a <b>response strategy<\/b> (based on an LLM or a custom function), and <b>criteria for when to speak or not<\/b> given the conversational context.<\/p>\n<p>The core of the process is the <b>cyclical conversation<\/b>. An <i>orchestrator<\/i> (potentially an agent itself) manages speaking turns, oversees outcomes, and decides whether to continue or halt dialogue. This setup allows for the simulation of genuine <b>AI work sessions<\/b>, complete with iterative cycles, restarts, and arbitrations.<\/p>\n<p>For instance, you might construct a loop where one agent proposes code, another agent reviews it, a third agent conducts tests, and a fourth agent determines if the code is satisfactory or requires another attempt&#8230; It\u2019s fluid, logical, reproducible, and significantly <b>more reliable than a single-shot prompt<\/b>.<\/p>\n<p>What enhances this even more is Autogen&#8217;s capability to include <b>external tools<\/b>. Agents can execute <a href=\"https:\/\/liora.io\/en\/top-10-native-python-functions\">Python functions<\/a>, engage with APIs, read files, and operate in a real-world environment, not solely within simulated dialogues!<\/p>\n<p><img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-4.webp\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-4.webp 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-4-300x200.webp 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-4-1024x683.webp 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-4-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\"><\/p>\n<h2>Some Notable Use Cases<\/h2>\n<p>Autogen excels in complex scenarios where traditional AIs reach their limitations. Microsoft has notably tested it in <b>software development<\/b> contexts with striking outcomes. Imagine being able to task it with an instruction like &#8220;<i>create a Python function to clean a dataset, then generate a correlation graph<\/i>&#8220;.<\/p>\n<p>Rather than attempting everything at once, and often doing so with difficulty, Autogen distributes the workload. A <b>Data Cleaner agent writes the preprocessing code<\/b>, a <b>Debugger agent checks it line by line<\/b>. Meanwhile, a <b>Visualizer agent suggests relevant graphical outputs<\/b>, and you, as a User Proxy, can pause the cycle, adjust the command, or initiate a new cycle.<\/p>\n<p>This methodology enables a <b>modular approach<\/b>, fully documented, tested, and devoid of major hallucinations. It overcomes many of the significant shortcomings of traditional AIs. Another notable application is the <b>creation of specialized agents<\/b> to automate <b>business processes<\/b>. For instance, one agent collects the data, while another aggregates it.<\/p>\n<p><b>A third compiles a daily report<\/b>, and a fourth communicates with users to send everything via Slack or email. This is <strong>RPA (process automation)<\/strong> enhanced with generative intelligence, and it&#8217;s thoroughly customizable.<\/p>\n<p><a href=\"\/en\/courses\/data-ai\/\"><br \/>\nUsing Autogen for your AI projects<br \/>\n<\/a><\/p>\n<h2>Why Does This Mark a Turning Point for Generative AI?<\/h2>\n<p>With Autogen, we transition from the &#8220;AI assistant&#8221; paradigm to the <b>&#8220;AI team&#8221;<\/b> model. It&#8217;s not just a change in tools, but a philosophical shift. The aim is no longer to <b>turn an AI into a universal genius<\/b>, but rather to coordinate specialized agents, each with its own <b>expertise<\/b>.<\/p>\n<p>We no longer delegate a task to a solitary AI, but to an <b>intelligent collective<\/b> capable of debate, iteration, and decision-making. The focus shifts from crafting massive prompts to creating <b>collaborative architectures<\/b> that can evolve with our needs.<\/p>\n<p>Moreover, Autogen introduces a degree of <b>resilience in AI endeavors<\/b>. If one agent delivers an unsatisfactory result, another can <b>challenge it<\/b>, <b>offer an alternative<\/b>, or <b>seek clarification<\/b>. This mirrors the <b>cognitive mechanisms<\/b> of human deliberation, now applied to the realm of AIs&#8230;<\/p>\n<p>For companies, this offers a significant advantage. Complex tasks, like <b>project management<\/b>, <b>automating analyses<\/b>, or <b>drafting technical documents<\/b>, can be assigned to autonomous AI teams.<\/p>\n<p><img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-3.webp\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-3.webp 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-3-300x200.webp 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-3-1024x683.webp 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-3-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\"><\/p>\n<h2>Autogen vs. Crew AI vs. LangChain Agents: Who Does What?<\/h2>\n<p>The landscape of <b>collaborative AI agents<\/b> is growing denser. Alongside Autogen, two other frameworks are emerging prominently: <b>Crew AI and LangChain<\/b> Agents. So how do you choose? And more importantly, what truly distinguishes Autogen?<\/p>\n<p>Focused on productivity, <b>Crew AI<\/b> offers an <b>highly structured approach<\/b>: roles are defined (developer, reviewer, planner, etc.), <b>&#8220;tasks&#8221;<\/b> are configured, and the AI team organizes itself to reach its objective. It&#8217;s effective, but also more rigid. Every agent follows a specific plan, and the system operates entirely on a <b>sequential workflow<\/b>.<\/p>\n<p>On the <b>LangChain<\/b> side, <i>agents<\/i> are designed to <b>make real-time decisions<\/b>. They&#8217;re provided with a goal, tools, and a thought process (through the <b>ReAct<\/b> framework, for example). The <b>system is flexible<\/b> and powerful, but it emphasizes &#8220;dialogue between agents&#8221; less. That\u2019s where Autogen&#8217;s uniqueness lies: <b>dialogue is central to its concept<\/b>.<\/p>\n<p>No forced sequences, no fixed logic. You configure a <b>team of agents<\/b> that can discuss, correct, contradict, and discover solutions autonomously. Essentially, Autogen doesn&#8217;t build processing chains, but <b>authentic collective brains<\/b>.<\/p>\n<p><a href=\"\/en\/courses\/data-ai\/\"><br \/>\nMaster AI agent orchestration<br \/>\n<\/a><\/p>\n<h2>How to Test Autogen Today?<\/h2>\n<p>Good news: Autogen is <b>freely available as<\/b> open source, and it&#8217;s fairly straightforward to get started if you&#8217;re comfortable with Python and AI APIs. The initial step is to <b>clone the repository<\/b>. Visit the official <a href=\"https:\/\/liora.io\/en\/github-course-mastering-the-platform-made-easy\">GitHub<\/a> page. Everything you need is there: documentation, example scripts, and ready-to-use code.<\/p>\n<p>Next, you&#8217;ll need to set up your environment. This requires an <b>OpenAI key<\/b> (or another compatible provider), a <b>Python environment<\/b> (like <a href=\"https:\/\/liora.io\/en\/python-virtualenv-your-essential-guide-to-virtual-environments\">virtualenv<\/a> or <a href=\"https:\/\/liora.io\/en\/anaconda-prompt-all-you-need-to-know\">conda<\/a>), and a bit of patience to explore the examples.<\/p>\n<p>It\u2019s time to run an example. Among the demos is a <b>collaborative coding session<\/b> involving two agents, a <b>multi-agent chatbot<\/b> simulation, or even a full circle of data analysis plus visualization.<\/p>\n<p>It\u2019s concrete, instructive, and clear enough for you to quickly adapt it for your own projects. Autogen isn\u2019t a <b>&#8220;plug-and-play&#8221; tool<\/b> akin to ChatGPT, but a framework for creators. It&#8217;s meant for those looking beyond simple prompts, for those aiming to design intelligence that thinks collaboratively.<\/p>\n<p><img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-5.webp\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-5.webp 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-5-300x200.webp 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-5-1024x683.webp 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2025\/07\/illustration-autogen-Liora-5-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\"><\/p>\n<h2>Autogen &#8211; When Artificial Intelligence Collaborative Play<\/h2>\n<p>Autogen signifies an exciting new direction in AI: the world of <b>collaborative AI agents<\/b> that can dialogue, iterate, and coordinate like a real team. This transition from a single-agent model to <b>collective intelligence<\/b> changes the way we imagine, design, and utilize generative AIs.<\/p>\n<p>For <b>developers<\/b>, <b>businesses<\/b>, or <b>any professional eager to harness this revolution<\/b>, understanding and mastering such frameworks provides a competitive edge. Being able to <b>orchestrate multiple agents<\/b> and manage intricate dialogues ensures that tasks previously undelegatable to a single AI can now be automated.<\/p>\n<p>So, to dive deeper into Autogen and other <b>advanced artificial intelligence technologies<\/b>, consider exploring Liora. The Artificial Intelligence Engineer program offered by Liora immerses you in the concepts, tools, and modern methods: <a href=\"https:\/\/liora.io\/en\/all-about-machine-learning-metrics\">machine learning<\/a>, deep learning, <a href=\"https:\/\/liora.io\/en\/natural-language-processing-definition-and-principles\">NLP<\/a>, <b>multi-agent orchestration<\/b>, and more.<\/p>\n<p>With a practice-focused educational approach, you will learn to <b>design, deploy, and manage complex AI projects<\/b>, from data to industrialization. Through this course, you will gain all the necessary skills to <b>master frameworks like Autogen<\/b>, and earn a <b>recognized professional certification<\/b> that boosts your appeal to employers.<\/p>\n<p><a href=\"\/en\/courses\/data-ai\/\">Our courses are tailored to your needs<\/a>, whether you prefer intensive BootCamp, apprenticeship, or ongoing training. Furthermore, Liora is eligible for funding via CPF or France Travail. <b>Discover Liora<\/b>, and accelerate your journey in AI!<\/p>\n<p><a href=\"\/en\/courses\/data-ai\/\"><br \/>\nOur AI training courses<br \/>\n<\/a><\/p>\n<p>Now you know all about Autogen. For further insights on related subjects, explore <a href=\"https:\/\/liora.io\/en\/all-about-ai-agents\">our comprehensive article on AI agents<\/a>, our piece on LangChain, and our exploration of Crew AI!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Autogen is the open-source framework that Microsoft has developed to orchestrate multiple AI agents, enabling them to collaborate as a real team. Discover how it is transforming the use of generative AIs by encouraging communication between agents, integrating human involvement, and collectively tackling complex tasks!<\/p>\n","protected":false},"author":85,"featured_media":198059,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-198057","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/198057","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/85"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=198057"}],"version-history":[{"count":5,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/198057\/revisions"}],"predecessor-version":[{"id":205269,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/198057\/revisions\/205269"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/198059"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=198057"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=198057"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}