Official Resources

Microsoft Research AG2 Fork Studio Paper Research Paper Docs v0.2 Semantic Kernel Blog v0.4 Release Blog Roadmap

Key Features

  • Core - Event-Driven Actor Model: Async message passing with distributed runtime for scalable multi-agent systems.
  • AgentChat - High-Level APIs: AssistantAgent, UserProxyAgent, RoundRobinGroupChat, and other pre-built agent types.
  • Extensions - Plug-in Ecosystem: LLM clients, code executors, web-surfers, MCP servers, and custom extensions.
  • Studio - Drag-and-Drop UI: No-code interface for prototyping, profiling, and exporting workflows.
  • Asynchronous Messaging: Event-driven messaging system for responsive multi-agent coordination.
  • Cross-Language Support: Python ≥ 3.10, .NET, with future Java/Go support for distributed agent teams.
  • Enterprise Observability: OpenTelemetry tracing, metrics, cost tracking, and stateful workflows with save/load.

Code Examples

Installation

bash 
pip install -U "autogen-agentchat" "autogen-ext[openai]"

Basic Agent Setup (v0.4)

python 
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    model = OpenAIChatCompletionClient(model="gpt-4o")
    agent = AssistantAgent("coder", model_client=model)
    result = await agent.run(task="Write a Python one-liner to list even numbers 0–10")
    print(result.messages[-1].content)
    await model.close()

asyncio.run(main())

AutoGen Studio Setup

bash 
# Run AutoGen Studio (no-code GUI)
pip install -U autogenstudio
autogenstudio ui --port 8080
# Visit http://localhost:8080 to drag-and-drop agents, trace runs, and export workflows as JSON

Use Cases

  • Rich modular design - Swap LLMs, tools, memory without touching orchestration
  • Enterprise-grade observability - Built-in metrics, OpenTelemetry, cost tracking
  • Distributed & multi-language - Agents can live in separate processes, languages
  • Studio accelerates prototyping - From drag-and-drop to containerized deploy

Pros & Cons

Advantages

  • Rich modular design - Swap LLMs, tools, memory without touching orchestration
  • Enterprise-grade observability - Built-in metrics, OpenTelemetry, cost tracking
  • Distributed & multi-language - Agents in separate processes, languages
  • Backed by Microsoft Research - Active roadmap, Semantic Kernel convergence
  • Studio accelerates prototyping - Drag-and-drop to containerized deploy

Disadvantages

  • API surface still stabilizing - Breaking changes between 0.2 → 0.4
  • Documentation gaps - Advanced patterns need more guides
  • Studio UX rough edges - Export/import semantics still beta
  • Streaming & structured output - Community reports intermittent issues

Future Outlook & Integrations

  • Semantic Kernel Unification [Early 2025]: AutoGen's multi-agent runtime (autogen-core) will merge into SK's Process Framework early 2025
  • Copilot Studio Integration [In Development]: AutoGen workflows will be importable as custom agents inside Copilot Studio
  • Language Expansion [Ongoing]: .NET GA, Java SDK preview, Go SDK on roadmap
  • Community Extensions [Active]: MCP servers, vector-store memories, Azure Container Apps executors shipping monthly