Author: Manus AI
Version: 1.0
Last Updated: July 2025

Table of Contents

1. Introduction and Foundations
2. Core Agent Concepts
3. Human-in-the-Loop Workflows
4. Multi-Agent Orchestration

Module 1: Introduction and Foundations

1.1 What is AG2?

AG2, formerly known as AutoGen, represents a paradigm shift in how we approach artificial intelligence automation and multi-agent systems [1]. At its core, AG2 is an open-source programming framework designed specifically for building AI agents and facilitating sophisticated cooperation among multiple agents to solve complex tasks [2]. This framework has emerged as a leading solution in the rapidly evolving landscape of agentic AI, offering developers and organizations a robust platform for creating intelligent, collaborative systems that can operate autonomously while maintaining human oversight capabilities.

The fundamental philosophy behind AG2 centers on the concept of "AgentOS" - an operating system for agentic AI that provides the infrastructure necessary for building production-ready multi-agent systems [3]. Unlike traditional single-agent approaches that rely on monolithic AI systems, AG2 embraces a distributed architecture where specialized agents work together, each contributing their unique capabilities to achieve common objectives. This approach mirrors how human teams operate, with different individuals bringing distinct skills and perspectives to collaborative problem-solving efforts.

The framework's architecture is built around several key principles that distinguish it from other AI automation tools. First, AG2 emphasizes conversational intelligence, enabling agents to communicate naturally through structured message exchanges that can include text, data, and even code execution results [4]. Second, it provides flexible orchestration patterns that allow developers to define how agents interact, whether through simple two-agent conversations, complex group discussions, or sophisticated swarm behaviors. Third, AG2 integrates seamlessly with various large language models (LLMs) from different providers, ensuring that developers are not locked into any single AI platform or service.

One of the most compelling aspects of AG2 is its ability to handle both autonomous operations and human-in-the-loop workflows with equal sophistication. The framework provides granular control over when and how human input is solicited, allowing for systems that can operate independently when appropriate while escalating to human oversight for critical decisions or complex scenarios that require domain expertise [5]. This flexibility makes AG2 particularly valuable for enterprise applications where automation must be balanced with accountability and human judgment.

The technical foundation of AG2 rests on a modular design that separates concerns between agent behavior, communication protocols, and execution environments. Agents in AG2 are built around the ConversableAgent class, which serves as the fundamental building block for all agent types [6]. This base class handles message routing, response generation, and state management, while specialized agent types like AssistantAgent and UserProxyAgent extend this functionality for specific use cases. The framework also provides sophisticated tools for managing conversation flow, including automatic speaker selection in group settings, context carryover between conversation segments, and termination conditions that ensure conversations reach meaningful conclusions.

1.2 Evolution from Microsoft AutoGen to AG2

The story of AG2's evolution from Microsoft's AutoGen project represents one of the most significant developments in the open-source AI community in recent years [7]. Originally developed as part of Microsoft Research's exploration into multi-agent AI systems, AutoGen gained considerable traction among developers and researchers for its innovative approach to agent collaboration and its practical applications in various domains. However, the transition from AutoGen to AG2 reflects broader trends in the AI industry regarding open-source development, community governance, and the need for truly independent platforms that can evolve rapidly without corporate constraints.

The split between Microsoft's AutoGen and the community-driven AG2 occurred in November 2024, when the original contributors and creators of AutoGen made the decision to establish an independent project [8]. This transition was not merely a rebranding exercise but represented a fundamental shift in how the project would be developed, maintained, and governed. The AG2 team, led by original AutoGen architects Chi Wang and Qingyun Wu, established a new organizational structure that prioritizes community input, rapid iteration, and broad accessibility over corporate strategic alignment.

Several factors contributed to this evolution, with the most significant being the desire for greater development velocity and community responsiveness. Under Microsoft's stewardship, AutoGen development was necessarily aligned with broader corporate priorities and release cycles, which sometimes created friction with the fast-moving needs of the developer community [9]. The AG2 project was established to address these concerns by creating a governance model that allows for more agile development, faster bug fixes, and more responsive feature development based on community feedback and real-world usage patterns.

The technical implications of this transition have been substantial and largely positive for users and developers. AG2 has maintained full backward compatibility with AutoGen 0.2.x while introducing significant improvements in performance, stability, and feature completeness [10]. The independent development model has allowed the AG2 team to make architectural decisions based purely on technical merit and user needs, rather than having to consider broader corporate ecosystem compatibility. This has resulted in cleaner APIs, better documentation, and more intuitive development patterns that reduce the learning curve for new users.

From a licensing and legal perspective, the transition to AG2 has provided important benefits for enterprise users and commercial applications. While AutoGen was already open-source under the Apache 2.0 license, the independent AG2 project offers additional assurances regarding long-term availability and freedom from potential corporate policy changes [11]. This independence has been particularly important for organizations that require stable, long-term platforms for critical business applications and cannot afford to be subject to changing corporate priorities or strategic shifts.

The community response to the AG2 transition has been overwhelmingly positive, with the project quickly gaining momentum and attracting contributors from diverse backgrounds and organizations [12]. The AG2 Discord community has grown to over 20,000 active members, with daily technical discussions, weekly community calls, and an open RFC process that allows community members to propose and discuss new features and architectural changes. This level of community engagement has accelerated development and resulted in a more robust, well-tested platform that reflects the real-world needs of its users.

1.3 Key Features and Capabilities

AG2's feature set represents a comprehensive approach to multi-agent AI development, encompassing everything from basic agent creation to sophisticated orchestration patterns and production deployment capabilities [13]. The framework's design philosophy emphasizes both ease of use for beginners and powerful extensibility for advanced users, resulting in a platform that can support everything from simple automation scripts to complex enterprise applications with hundreds of interacting agents.

The conversational intelligence capabilities of AG2 form the foundation of its multi-agent approach. Unlike traditional AI systems that operate in isolation, AG2 agents are designed from the ground up to communicate effectively with each other through structured message exchanges [14]. These conversations can include not only natural language text but also structured data, code snippets, execution results, and even multimedia content. The framework provides sophisticated message routing and filtering capabilities that ensure agents receive only relevant information while maintaining context across extended conversations.

One of AG2's most powerful features is its flexible orchestration system, which supports multiple patterns for agent collaboration. The framework includes built-in support for two-agent conversations, group chats with dynamic speaker selection, sequential chats with context carryover, and nested conversations that allow for modular problem-solving approaches [15]. Each of these patterns can be customized and extended to meet specific application requirements, and developers can create entirely custom orchestration patterns by registering specialized reply methods and conversation handlers.

The human-in-the-loop capabilities of AG2 represent a significant advancement in making AI systems more trustworthy and controllable. The framework provides three distinct modes for human interaction: ALWAYS mode, which requires human input for every agent response; NEVER mode, which operates fully autonomously; and TERMINATE mode, which only requests human input when ending conversations [16]. This granular control allows developers to create systems that balance automation efficiency with human oversight, ensuring that critical decisions receive appropriate review while routine tasks proceed automatically.

Tool integration represents another cornerstone of AG2's capabilities, addressing one of the fundamental limitations of large language models by providing seamless access to external systems and data sources [17]. The framework's tool system allows agents to invoke Python functions, call web APIs, interact with databases, execute code in sandboxed environments, and integrate with virtually any external service or system. Tools can be registered with specific agents or shared across multiple agents, and the framework provides sophisticated error handling and security features to ensure safe execution of external operations.

AG2's support for multiple LLM providers ensures that developers are not locked into any single AI platform or service. The framework includes native support for OpenAI's GPT models, Anthropic's Claude, Google's Gemini, and various open-source models through providers like Ollama and Hugging Face [18]. This flexibility allows developers to choose the most appropriate model for each agent based on factors like cost, performance, specialized capabilities, and deployment requirements. The LLMConfig system provides a unified interface for managing these different providers while maintaining the ability to optimize configurations for specific use cases.

The framework's code execution capabilities enable agents to write, execute, and iterate on code in real-time, making AG2 particularly powerful for applications involving data analysis, automation scripting, and dynamic problem-solving [19]. Agents can execute Python code in isolated environments, install packages as needed, and share execution results with other agents or human users. This capability transforms AG2 from a simple conversation framework into a powerful platform for building intelligent systems that can adapt and evolve their behavior based on changing requirements and feedback.

1.4 Current Ecosystem and Community

The AG2 ecosystem has experienced remarkable growth since its establishment as an independent project, evolving into a vibrant community of developers, researchers, and organizations building innovative applications with multi-agent AI [20]. This ecosystem encompasses not only the core AG2 framework but also a rich collection of extensions, tools, integrations, and real-world applications that demonstrate the platform's versatility and practical value across diverse domains.

The community structure around AG2 reflects a modern, inclusive approach to open-source development that prioritizes accessibility, collaboration, and knowledge sharing. The project maintains active presence across multiple platforms, with the primary hub being the AG2 Discord server, which hosts over 20,000 members engaged in daily technical discussions, troubleshooting sessions, and collaborative development efforts [21]. This community includes everyone from individual developers exploring multi-agent concepts to enterprise teams deploying production systems, creating a rich environment for knowledge exchange and mutual support.

The technical governance of AG2 follows an open, community-driven model that encourages participation from contributors across different organizations and backgrounds. The project maintains a transparent RFC (Request for Comments) process that allows community members to propose new features, architectural changes, and improvements to the framework [22]. This process has resulted in numerous community-driven enhancements, including improved tool integration patterns, new orchestration capabilities, and enhanced debugging and monitoring features that reflect real-world usage patterns and requirements.

The ecosystem of applications built with AG2 demonstrates the framework's versatility and practical value across numerous domains. The official "Build with AG2" repository showcases a curated collection of production-ready applications, including deep research agents that can synthesize information from multiple sources, travel planning systems that coordinate multiple specialized agents, e-commerce customer service platforms that handle complex order management workflows, and financial analysis tools that generate comprehensive market insights [23]. These applications serve not only as practical tools but also as learning resources and templates for developers building their own agent-based systems.

Educational resources within the AG2 ecosystem have grown substantially, reflecting the community's commitment to making multi-agent AI accessible to developers with varying levels of experience. The official documentation includes comprehensive tutorials, API references, and best practices guides, while community members have contributed additional resources including video tutorials, blog posts, and interactive Jupyter notebooks [24]. The framework's integration with educational platforms like DeepLearning.AI has further expanded access to structured learning opportunities for developers interested in mastering multi-agent development techniques.

The commercial ecosystem around AG2 has also begun to mature, with numerous organizations building products and services based on the framework. These range from specialized consulting services that help enterprises implement agent-based automation to SaaS platforms that provide hosted AG2 environments for teams that prefer managed solutions [25]. The framework's Apache 2.0 license and independent governance structure have made it particularly attractive for commercial applications, as organizations can build proprietary solutions without concerns about licensing restrictions or corporate policy changes.

Integration partnerships have become an important aspect of the AG2 ecosystem, with the framework now supporting seamless integration with popular development tools, cloud platforms, and AI services. Notable integrations include CopilotKit for building AI-powered user interfaces, various cloud deployment platforms for production hosting, and specialized tools for monitoring and debugging multi-agent systems [26]. These integrations reduce the complexity of building and deploying AG2-based applications while providing developers with familiar tools and workflows.

The research community has also embraced AG2 as a platform for exploring advanced concepts in multi-agent AI, distributed problem-solving, and human-AI collaboration. Academic institutions and research organizations have used the framework to investigate topics ranging from automated scientific discovery to collaborative creative processes, contributing both to the advancement of multi-agent AI theory and to the practical improvement of the AG2 platform itself [27]. This research activity has resulted in numerous publications, conference presentations, and open-source contributions that benefit the entire community.

Looking toward the future, the AG2 ecosystem continues to evolve rapidly, with active development in areas such as improved scalability for large agent networks, enhanced security and privacy features for enterprise deployments, and more sophisticated orchestration patterns for complex multi-agent workflows. The community's commitment to open development and collaborative innovation suggests that AG2 will continue to serve as a leading platform for multi-agent AI development, adapting to emerging needs and technologies while maintaining its core principles of accessibility, flexibility, and practical utility.

Module 2: Core Agent Concepts

2.1 Understanding Conversable Agents

The ConversableAgent class serves as the fundamental building block of the entire AG2 framework, embodying the core philosophy that effective AI systems should be built around communication and collaboration rather than isolated processing [28]. This foundational class represents a significant departure from traditional AI architectures by treating conversation as a first-class citizen in the system design, enabling agents to engage in sophisticated dialogues that can span multiple turns, incorporate complex reasoning, and maintain context across extended interactions.

At its most basic level, a ConversableAgent is designed to handle three primary functions: sending messages to other agents or humans, receiving and processing incoming messages, and generating appropriate responses based on the conversation context and the agent's configured behavior [29]. However, this simple description belies the sophisticated machinery that operates beneath the surface, including message routing systems, context management, response generation pipelines, and integration points for external tools and services.

The architecture of ConversableAgent reflects careful consideration of the challenges inherent in multi-agent communication. Unlike human conversation, where participants can rely on shared context, cultural understanding, and non-verbal cues, agent-to-agent communication must be explicitly structured to ensure clarity and prevent misunderstandings [30]. The framework addresses this challenge through a combination of structured message formats, explicit role definitions, and sophisticated context management that ensures agents maintain awareness of conversation history, participant roles, and ongoing objectives.

One of the most powerful aspects of the ConversableAgent design is its flexibility in response generation. Agents can generate responses using large language models, execute programmatic logic, invoke external tools, or request human input, depending on their configuration and the nature of the incoming message [31]. This flexibility allows developers to create agents that range from simple rule-based responders to sophisticated AI-powered entities that can engage in complex reasoning and problem-solving activities.

The message handling capabilities of ConversableAgent include sophisticated filtering and routing mechanisms that ensure agents receive only relevant communications while maintaining awareness of the broader conversation context. The framework supports both direct agent-to-agent communication and broadcast messaging patterns, allo