AutoGen's conversational multi-agent framework from Microsoft Research delivers the most sophisticated agent-to-agent collaboration patterns available today.
Open-source multi-agent framework from Microsoft Research with asynchronous architecture, AutoGen Studio GUI, and OpenTelemetry observability. Now part of the unified Microsoft Agent Framework alongside Semantic Kernel.
Open-source framework for building multi-agent AI systems where different AI agents collaborate to solve complex problems.
AutoGen is the multi-agent framework with Microsoft Research behind it and a merger with Semantic Kernel ahead of it.
If you need multiple AI agents talking to each other, executing code, and coordinating on complex tasks, AutoGen was among the first frameworks to make that practical. The v0.4 release (January 2025) rebuilt the architecture from scratch: asynchronous, event-driven, with OpenTelemetry observability baked in. For teams already in the Microsoft ecosystem (Azure OpenAI, .NET, Visual Studio), AutoGen fits naturally. For everyone else, the question is whether to invest in a framework that is merging into something bigger.
AutoGen's layered API design gives you three entry points depending on your needs. The Core API provides low-level agent building for teams that want full control. The AgentChat API offers familiar conversation patterns (two-agent chat, group chat) for faster prototyping. The Extensions API handles LLM client integrations and capability plugins.
AutoGen Studio is the no-code GUI that lets non-developers build multi-agent applications by dragging agents, defining their roles, and connecting them visually. No other major agent framework ships a comparable visual builder. CrewAI has a cloud platform but charges for it; AutoGen Studio is free and runs locally.
The cross-language support (Python and .NET) matters for enterprise teams with mixed codebases. LangChain and CrewAI are Python-only. If your backend runs on C# and you want to add AI agents, AutoGen is one of the few options that supports .NET natively.
Source: github.com/microsoft/autogen
Your only cost is the LLM API usage your agents generate. AutoGen supports OpenAI, Azure OpenAI, and other providers. A multi-agent workflow running GPT-4o with 3 agents discussing a problem might consume 10,000-50,000 tokens per run ($0.05-0.25 per run on GPT-4o). Budget controls are your responsibility; AutoGen does not enforce spending limits by default.
On Reddit's r/AutoGenAI, developers describe AutoGen as "the most forward-looking agent framework architecture" and praise the v0.4 design decisions. Users note that Microsoft backing means no revenue pressure driving feature bloat or premature monetization, unlike venture-backed competitors.
The criticism centers on documentation quality. Multiple Reddit threads flag documentation as hard to read, with insufficient examples and inconsistencies between v0.2 and v0.4 content. Some developers report that features like structured outputs do not work as documented. The AG2 fork (a community split from earlier versions) also creates confusion about which project to use.
Sources: arepeopleusingmicrosoftautogenvsother/" class="text-blue-700 dark:text-blue-300 underline decoration-current underline-offset-2 hover:no-underline" target="_blank" rel="noopener noreferrer">Reddit r/AutoGenAI, autogenmicrosoftcansomeonesharea/" class="text-blue-700 dark:text-blue-300 underline decoration-current underline-offset-2 hover:no-underline" target="_blank" rel="noopener noreferrer">Reddit r/AutoGenAI
The Microsoft Agent Framework launched in October 2025, unifying AutoGen and Semantic Kernel. Microsoft Foundry now offers hosted agent deployments with enterprise-grade identity, observability, governance, and autoscaling. AutoGen v0.4 continues receiving updates with Core, AgentChat, and Extensions APIs. AutoGen Studio provides a no-code GUI for multi-agent application building.
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AutoGen is the right choice for teams in the Microsoft ecosystem who need flexible multi-agent orchestration with .NET support and OpenTelemetry observability. The v0.4 rewrite is architecturally strong but documentation lags behind. The unification with Semantic Kernel into Microsoft Agent Framework makes it a bet on Microsoft's long-term AI agent vision — now with hosted deployment options through Microsoft Foundry.
Three-tier API design with Core (low-level agent primitives), AgentChat (conversation patterns like group chat), and Extensions (LLM integrations and plugins) so teams choose the right abstraction level.
Use Case:
A team building a code review system uses AgentChat for quick prototyping with two-agent conversations, then drops to Core API for custom routing logic when the prototype outgrows simple patterns.
Visual builder for multi-agent applications where non-developers can drag agents, define roles, connect workflows, and test agent interactions — all running locally without cloud dependencies.
Use Case:
A product manager designs a customer support workflow with three specialized agents (triage, technical, escalation) in the visual builder, then hands the configuration to engineering for production deployment.
Native support for both Python and .NET runtimes, allowing enterprise teams with C# backends to build AI agents without switching languages or maintaining separate toolchains.
Use Case:
An enterprise with a C# microservices architecture adds AI agents that coordinate between services using the .NET SDK, avoiding the need to introduce Python into their deployment pipeline.
Built-in distributed tracing and metrics via OpenTelemetry, providing visibility into agent interactions, token usage, latency, and decision chains across multi-agent workflows.
Use Case:
A DevOps team monitors a 5-agent research pipeline in Grafana, tracking which agents consume the most tokens and identifying bottleneck conversations that slow down task completion.
v0.4's async-first design enables agents to operate concurrently, handle events without blocking, and scale across distributed systems with message-based communication.
Use Case:
A data processing pipeline runs 10 analysis agents in parallel, each handling different data streams, with results aggregating asynchronously into a synthesis agent.
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View Pricing Options →Build workflows where multiple specialized agents research, analyze, and synthesize information by coordinating through structured conversations and code execution
Add AI agent capabilities to existing C# and .NET microservices architectures without introducing Python dependencies or maintaining separate toolchains
Use AutoGen Studio's visual builder to quickly design and test multi-agent workflows before committing to production code
Create agent teams that write, review, execute, and iterate on code — leveraging AutoGen's built-in code execution sandboxing
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Microsoft launched the open-source Microsoft Agent Framework in October 2025, unifying AutoGen and Semantic Kernel. AutoGen provides simple abstractions for multi-agent patterns, while Semantic Kernel adds enterprise features like session management, type safety, and telemetry. For new projects, this means you can start with AutoGen's agent patterns and scale to Semantic Kernel's enterprise capabilities within the same framework. Microsoft Foundry enables hosted deployments with built-in identity, governance, and autoscaling.
CrewAI gives you role-based agents with built-in orchestration and a commercial cloud platform — easier to start, more opinionated. LangGraph provides graph-based state machines for precise control flow. AutoGen sits between them: more flexible than CrewAI with lower-level building blocks, but with a steeper learning curve. AutoGen's unique advantages are .NET support, the free AutoGen Studio visual builder, and OpenTelemetry observability. Choose CrewAI for fastest time-to-working-prototype, LangGraph for precise workflow control, and AutoGen for Microsoft ecosystem integration.
Yes, AutoGen is MIT-licensed with no commercial restrictions. Your only costs are the LLM API fees from your chosen provider (OpenAI, Azure OpenAI, etc.). A typical multi-agent workflow with 3 agents running GPT-4o might consume 10,000-50,000 tokens per run ($0.05-0.25). There are no AutoGen-specific fees, usage limits, or premium tiers.
Use v0.4. It is a complete architectural rewrite with async support, better observability, and the layered API design. However, be aware that most tutorials and Stack Overflow answers reference v0.2 — the APIs are incompatible. Start with the official v0.4 documentation and examples on GitHub rather than blog posts that may reference the old API.
Complete UI overhaul with drag-and-drop agent builder and workflow templates.
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Microsoft Agent Framework launched October 2025, unifying AutoGen and Semantic Kernel with hosted deployment via Microsoft Foundry. v0.4 continues as the async, event-driven multi-agent runtime with OpenTelemetry observability and AutoGen Studio for visual agent building.
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Designing Agent Conversations That Work
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