diff --git a/website/blog/2024-10-02-new-autogen-architecture-preview/index.mdx b/website/blog/2024-10-02-new-autogen-architecture-preview/index.mdx deleted file mode 100644 index 42a873d38..000000000 --- a/website/blog/2024-10-02-new-autogen-architecture-preview/index.mdx +++ /dev/null @@ -1,103 +0,0 @@ ---- -title: New AutoGen Architecture Preview -authors: - - autogen-team -tags: [AutoGen] ---- - -# New AutoGen Architecture Preview - -
- -![What are they doing?](img/robots.jpeg) - -
- -One year ago, we launched AutoGen, a programming framework designed to build -agentic AI systems. The release of AutoGen sparked massive interest within the -developer community. As an early release, it provided us with a unique -opportunity to engage deeply with users, gather invaluable feedback, and learn -from a diverse range of use cases and contributions. By listening and engaging -with the community, we gained insights into what people were building or -attempting to build, how they were approaching the creation of agentic systems, -and where they were struggling. This experience was both humbling and -enlightening, revealing significant opportunities for improvement in our initial -design, especially for power users developing production-level applications with -AutoGen. - -Through engagements with the community, we learned many lessons: - -- Developers value modular and reusable agents. For example, our built-in agents - that could be directly plugged in or easily customized for specific use cases - were particularly popular. At the same time, there was a desire for more - customizability, such as integrating custom agents built using other - programming languages or frameworks. -- Chat-based agent-to-agent communication was an intuitive collaboration - pattern, making it easy for developers to get started and involve humans in - the loop. As developers began to employ agents in a wider range of scenarios, - they sought more flexibility in collaboration patterns. For instance, - developers wanted to build predictable, ordered workflows with agents, and to - integrate them with new user interfaces that are not chat-based. -- Although it was easy for developers to get started with AutoGen, debugging and - scaling agent teams applications proved more challenging. -- There were many opportunities for improving code quality. - -These learnings, along with many others from other agentic efforts across -Microsoft, prompted us to take a step back and lay the groundwork for a new -direction. A few months ago, we started dedicating time to distilling these -learnings into a roadmap for the future of AutoGen. This led to the development -of AutoGen 0.4, a complete redesign of the framework from the foundation up. -AutoGen 0.4 embraces the actor model of computing to support distributed, highly -scalable, event-driven agentic systems. This approach offers many advantages, -such as: - -- **Composability**. Systems designed in this way are more composable, allowing - developers to bring their own agents implemented in different frameworks or - programming languages and to build more powerful systems using complex agentic - patterns. -- **Flexibility**. It allows for the creation of both deterministic, ordered - workflows and event-driven or decentralized workflows, enabling customers to - bring their own orchestration or integrate with other systems more easily. It - also opens more opportunities for human-in-the-loop scenarios, both active and - reactive. -- **Debugging and Observability**. Event-driven communication moves message delivery - away from agents to a centralized component, making it easier to observe and - debug their activities regardless of agent implementation. -- **Scalability**. An event-based architecture enables distributed and - cloud-deployed agents, which is essential for building scalable AI services - and applications. - -Today, we are delighted to share our progress and invite everyone to collaborate -with us and provide feedback to evolve AutoGen and help shape the future of -multi-agent systems. - -As the first step, we are opening a [pull request](https://github.com/microsoft/autogen/pull/3600) into the main branch with the -current state of development of 0.4. After approximately a week, we plan to -merge this into main and continue development. There's still a lot left to do -before 0.4 is ready for release though, so keep in mind this is a work in -progress. - -Starting in AutoGen 0.4, the project will have three main libraries: - -- **Core** - the building blocks for an event-driven agentic system. -- **AgentChat** - a task-driven, high-level API built with core, including group - chat, code execution, pre-built agents, and more. This is the most similar API - to AutoGen [0.2](https://github.com/microsoft/autogen/tree/0.2) and will be the easiest API to migrate to. -- **Extensions** - implementations of core interfaces and third-party integrations - (e.g., Azure code executor and OpenAI model client). - -AutoGen [0.2](https://github.com/microsoft/autogen/tree/0.2) is still available, developed and maintained out of the [0.2 branch](https://github.com/microsoft/autogen/tree/0.2). -For everyone looking for a stable version, we recommend continuing to use [0.2](https://github.com/microsoft/autogen/tree/0.2) -for the time being. It can be installed using: - -```sh -pip install autogen-agentchat~=0.2 -``` - -This new package name was used to align with the new packages that will come with 0.4: -`autogen-core`, `autogen-agentchat`, and `autogen-ext`. - -Lastly, we will be using [GitHub -Discussion](https://github.com/microsoft/autogen/discussions) as the official -community forum for the new version and, going forward, all discussions related -to the AutoGen project. We look forward to meeting you there.