Microsoft AutoGen

The v0.4 redesign replaces the previous synchronous model with a fully asynchronous, event-driven runtime that supports distributed agent networks across multiple processes and machines. This architecture enables non-blocking agent communication through typed messages, improving throughput and scalability for production workloads. It also supports both reactive agents that respond to events and proactive agents that initiate actions autonomously.

AutoGen natively integrates OpenTelemetry for comprehensive monitoring of multi-agent systems, providing distributed tracing, metrics collection, and structured logging out of the box. Teams can visualize agent conversations, measure latency across agent interactions, and debug complex workflows using standard observability tools like Jaeger or Grafana. This eliminates the need to build custom monitoring infrastructure for understanding agent behavior in production.

Agents built in Python and .NET can participate in the same multi-agent system seamlessly, with additional language support under development. This enables organizations to leverage existing codebases and team expertise without being locked into a single language ecosystem. The interoperability layer handles serialization, message routing, and type mapping between language runtimes transparently.

The Extensions API allows developers to plug in custom agents, tools, memory backends, and LLM clients without modifying the core framework. First- and third-party extensions can be composed freely, enabling teams to tailor the framework to their specific domain requirements. This modularity also facilitates testing by allowing easy substitution of components with mocks or alternative implementations.

AutoGen's GroupChat manager coordinates multi-agent conversations with dynamic speaker selection, context windowing, and configurable flow control policies. It supports round-robin, automatic LLM-based selection, and custom selection functions to determine which agent speaks next based on conversation context. Nested conversation structures allow sub-groups of agents to collaborate on subtasks before reporting results back to the parent conversation.