AutoGen entered maintenance mode in October 2025. Microsoft's announcement was explicit: AutoGen would receive bug fixes and security patches, but "will not receive new feature investments." The company's new supported path is Microsoft Agent Framework 1.0, which reached general availability in April 2026. At the same time, Mastra — the TypeScript-native agent framework built by the Gatsby.js team — shipped its 1.0 release in January 2026 and crossed 22,000 GitHub stars and 300,000 weekly npm downloads in the same window.

If you are currently on AutoGen, or evaluating agent frameworks for a new project in 2026, you are staring at three options: migrate to MAF (the Microsoft-supported path), switch to Mastra (the TypeScript-first alternative), or stay on the community-managed AG2 fork. This article is the head-to-head comparison to make that decision.

TL;DR: TypeScript team? Mastra, no contest. Python team building new agentic workflows? MAF if you are in the Microsoft ecosystem; LangGraph or the OpenAI Agents SDK otherwise. Staying on AutoGen with no migration plan is using a framework with a ceiling, not a dead framework, but one without a future.

Key takeaways:

  • AutoGen is in maintenance mode since October 2025: bug fixes continue, features do not.
  • Mastra 1.0 (January 2026) is TypeScript-native; AutoGen is Python-only.
  • The community-maintained AG2 fork (v0.11.2, February 2026) keeps AutoGen's Python API alive with A2A support.
  • MAF 1.0 (April 3, 2026) is Microsoft's official successor for both AutoGen and Semantic Kernel users.
  • Mastra's architecture is workflow-first; AutoGen's is conversation-first. The choice maps to your use case.

At a glance

DimensionMastra 1.0AutoGen (maintenance mode)AG2 (community fork)MAF 1.0
StatusActively maintainedMaintenance modeCommunity managedActively maintained
Latest stable1.0 (Jan 2026)0.4.x0.11.2 (Feb 2026)1.0 (Apr 2026)
LanguageTypeScriptPythonPythonPython + .NET
ArchitectureWorkflow-first, deterministicConversation-first, GroupChatConversation-firstMulti-pattern orchestrator
MCP supportNativeNo (maintenance)Via A2A bridgeNative
Memory4-tier, built-in, 94.87% LongMemEvalBasicBasicPluggable
LicenseApache 2.0 (core)MITMITMIT
MaintainerMastra team (Gatsby.js)Microsoft (bugs/security only)CommunityMicrosoft

(Source: Mastra documentation, Microsoft Agent Framework blog, VentureBeat AutoGen)

AutoGen in 2026: what maintenance mode means

When Microsoft launched its Agent Framework public preview on October 1, 2025, AutoGen and Semantic Kernel moved to maintenance mode on the same day. The announcement said both would "remain in maintenance mode, which means they will not receive new feature investments but will continue to receive bug fixes, security patches and stability updates." (Source: VentureBeat AutoGen)

That status has concrete implications for projects: (Source: Microsoft Agent Framework blog)

Any integration with newer protocol standards (MCP, A2A) will not be added to AutoGen. Microsoft Agent Framework ships both; AutoGen does not. If your agent stack needs to talk to MCP servers or participate in multi-agent A2A workflows, you cannot add that on top of AutoGen without forking the framework yourself.

AutoGen's GroupChat and ConversableAgent primitives still work. Existing projects run. The maintenance commitment means those primitives will remain stable, not that they will improve or gain new capabilities.

The community fork AG2 changes the calculus somewhat. AG2 (v0.11.2, February 2026) is a Python-only community-managed fork that added A2A protocol support and continues active development. (Source: Microsoft Agent Framework blog) If your team is committed to AutoGen's conversation-first mental model and you do not want to migrate to MAF's architecture, AG2 is the viable path for staying in that ecosystem with continued protocol support. The tradeoff: AG2 is community-maintained without Microsoft's long-term support guarantee.

Mastra 1.0: what it actually ships

Mastra's pitch is bundled completeness. The 1.0 release ships agents, workflows, RAG pipelines, memory, evaluation frameworks, and MCP tool connections as a single integrated TypeScript package. The team described the gap they were filling: "the TypeScript options have historically been either ports of Python libraries or thin wrappers that leave you wiring together a dozen packages by hand." (Source: Mastra documentation)

Four capabilities stand out as differentiators relative to AutoGen:

Workflow primitives. Mastra Workflow is a deterministic, step-based execution graph with .suspend() and .resume() for human-in-the-loop checkpoints. Steps are explicit TypeScript functions. You know exactly what will run in what order.

Memory. Mastra ships a four-tier memory system (working memory, episodic memory, semantic memory, procedural memory) that scored 94.87% on the LongMemEval benchmark — a test that measures how well agents recall and apply information across very long sessions. AutoGen has no comparable built-in memory system.

MCP native. Mastra connects to MCP servers without an adapter layer. You configure an MCP server once and Mastra exposes its tools directly to any agent in your stack.

Observability. Native OpenTelemetry integration means traces ship to your existing observability stack without a separate SDK. (Source: Mastra documentation)

Architecture and workflow primitives: the real difference

The architectural difference between Mastra and AutoGen is the difference between a workflow engine and a conversation engine.

AutoGen's primary abstraction is ConversableAgent: agents that pass messages in a GroupChat session. The model drives what happens next. This is powerful for open-ended tasks where you want the LLM to plan dynamically, but it creates variability in production — the same input does not always produce the same execution path.

Mastra's primary abstraction is the Workflow: a directed graph of TypeScript steps with explicit control flow. The developer controls what happens next; the LLM is a component within steps, not the orchestrator of the overall flow.

A sequential two-agent handoff in each system makes the difference concrete.

AutoGen (GroupChat pattern):

from autogen import ConversableAgent, GroupChat, GroupChatManager

agent_a = ConversableAgent("researcher", llm_config={"config_list": config})
agent_b = ConversableAgent("writer", llm_config={"config_list": config})

groupchat = GroupChat(agents=[agent_a, agent_b], messages=[], max_round=4)
manager = GroupChatManager(groupchat=groupchat, llm_config={"config_list": config})
agent_a.initiate_chat(manager, message="Research and then write a report on topic X")

The GroupChat manager (itself an LLM call) decides when to hand off from researcher to writer. This is non-deterministic.

Mastra (Workflow pattern):

import { Workflow, Step } from "@mastra/core";

const researchStep = new Step({
id: "research",
execute: async ({ context }) => {
const result = await researchAgent.generate(context.topic);
return { research: result.text };
},
});

const writeStep = new Step({
id: "write",
execute: async ({ context }) => {
const result = await writerAgent.generate(context.research);
return { report: result.text };
},
});

const reportWorkflow = new Workflow({ name: "report" })
.step(researchStep)
.then(writeStep);

The handoff is explicit. Researcher runs, then writer runs. No LLM decides the sequence.

Operator note (first-hand): this distinction shows up most sharply in testing. The AutoGen GroupChat version requires mocking the GroupChatManager's routing decisions to write deterministic unit tests. The Mastra Workflow version tests each step independently as a TypeScript function. On a production pipeline with five agents, that difference in testability compounds significantly.

When to pick Mastra

Choose Mastra when:

  • Your team writes TypeScript and does not want to maintain a Python/TypeScript polyglot stack.
  • You need deterministic, auditable agent execution where the same input reliably produces the same execution path.
  • You are building agent-native features in a Next.js or Node.js application and want memory and MCP integration without a separate service.
  • Observability is a requirement from day one.
  • You need human-in-the-loop checkpoints at known points in the workflow.

When to stay on AutoGen (or move to AG2 or MAF)

AutoGen's GroupChat pattern remains the right mental model when you need genuinely dynamic multi-agent routing where the task structure is not known in advance. Research workflows where an orchestrating agent assigns sub-tasks to specialists based on the problem content, not a fixed schema, suit this model.

Move to AG2 if you want to stay in that Python conversation-first ecosystem with continued protocol support. Move to MAF 1.0 if you are in an enterprise Python or .NET environment and want Microsoft's long-term support commitment. Do not start a new project on AutoGen if you need MCP or A2A integration — neither will arrive there.

FAQ

Is AutoGen still worth using in 2026?

AutoGen still works and receives security patches, but it is in maintenance mode with no new features planned. It no longer receives MCP or A2A support. For new projects, the choices are MAF 1.0 (Microsoft's supported successor), AG2 (the community Python fork with continued development), or Mastra if you work in TypeScript. For existing projects, plan a migration timeline; the framework is not going away soon, but the window for protocol adoption is closing.

What is the difference between AutoGen and AG2?

AG2 is a community-maintained Python fork of AutoGen that started when AutoGen entered maintenance mode in October 2025. It maintains the same ConversableAgent and GroupChat API while adding A2A protocol support and continuing active development. Microsoft is not involved; the AG2 community manages the repository independently. AG2 v0.11.2 shipped in February 2026.

Should I migrate from AutoGen to Mastra or to MAF?

Migrate to Mastra if your team works primarily in TypeScript and wants a workflow-first architecture. Migrate to MAF if your team works in Python or .NET and you are already in the Microsoft/Azure ecosystem; MAF's migration assistant automates much of the transition from AutoGen's API. If neither fits, LangGraph and the OpenAI Agents SDK are strong Python-native alternatives without the AutoGen baggage.

Is Mastra production-ready in 2026?

Yes. Mastra 1.0 launched in January 2026 with a stable API and Apache 2.0 licensing for the core framework. The framework reached 22,000+ GitHub stars and 300,000 weekly npm downloads at launch, with 1.8 million monthly downloads by February 2026. The team is the same one that built and maintained Gatsby.js, a framework with a multi-year production track record.

What is Microsoft Agent Framework (MAF)?

Microsoft Agent Framework 1.0 is the unified Python and .NET SDK that replaced AutoGen and Semantic Kernel as Microsoft's supported agent development platform. It reached GA on April 3, 2026, with a commitment to "stable APIs and long-term support." It supports MCP and A2A protocols, multi-agent orchestration patterns, and Azure AI Foundry integration. Install via pip install agent-framework or dotnet add package Microsoft.Agents.AI.

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References