AutoGen
AutoGen is an open-source framework from Microsoft for building applications that coordinate multiple agents powered by large language models (LLMs) and tools (or functions) to automate complex tasks (machine learning frameworks / agent orchestration).
- Multi-agent orchestration framework for LLM-based and tool-augmented agents (machine learning frameworks).
- Support for Human-in-the-Loop (HITL) interaction and feedback within agent workflows (human-computer interaction).
- Configurable agent communication patterns, including cooperative and conversational task solving (agent orchestration).
- Extensibility for integrating external tools, APIs, and custom logic into agent behaviors (application integration).
- Support for various Large Language Model (LLM) backends via configurable model clients and connectors (AI infrastructure).
More About AutoGen
AutoGen is an open-source framework from Microsoft designed to simplify the development of applications that rely on multiple coordinated agents built on large language models (LLMs) and external tools (machine learning frameworks / agent orchestration). The project focuses on enabling developers to define agents with specific roles and capabilities, and to configure how these agents communicate with each other and with human users to complete tasks that may require reasoning, tool use, and iterative refinement.
The framework provides abstractions for agents that can use LLMs, call tools or functions, and exchange messages in structured dialogues (AI application frameworks). Developers can define agents such as task planners, code executors, data retrievers, or domain-specific assistants, and connect them through predefined or custom interaction patterns. AutoGen supports HITL workflows, where a human user can review, approve, or modify agent outputs, join conversations, or steer the progress of a task, which is relevant for scenarios that require oversight, compliance checks, or domain review (human-computer interaction).
AutoGen includes components for configuring model backends, such as connectors to cloud-based LLM services or other model providers, through a uniform interface (AI infrastructure). This allows an application to switch or combine models without changing the overall agent logic. The framework also offers mechanisms to integrate external tools and APIs as callable functions that agents can invoke when they need capabilities such as web access, code execution, or retrieval from enterprise data sources (application integration).
In enterprise and institutional environments, AutoGen is positioned as a framework for building task-oriented multi-agent systems, such as code assistance workflows, data analysis pipelines, customer support assistants, or knowledge-centric copilots (enterprise Artificial Intelligence (AI) applications). Its abstractions help teams structure complex workflows into interacting agents that can be tested, logged, and monitored, which is relevant for observability, governance, and iterative improvement. The human participation features support review loops and operational controls that many enterprises require.
From an architectural perspective, AutoGen belongs to the category of LLM application and agent orchestration frameworks. It interoperates with LLM providers through client interfaces, and it exposes extensibility points for tool plugins and custom agents. For technical stakeholders, it can serve as a layer between raw model APIs and business applications, encapsulating prompt design, interaction logic, and tool-calling flows. In a technical directory or catalog, AutoGen fits under AI/ML frameworks, multi-agent orchestration, and LLM-powered application tooling, supporting use cases that combine automated reasoning, tool use, and human oversight.