AutoGen to Microsoft Agent Framework: The 2026 Migration Guide

AutoGen is going into maintenance. Here is the migration

Microsoft spent 2025-2026 consolidating its agent story. AutoGen — the pioneering multi-agent conversation framework — is officially in maintenance mode, and the consolidated successor to AutoGen and Semantic Kernel is the Microsoft Agent Framework (MAF). For teams that built agents on AutoGen, the news is both unsettling and clarifying. This guide is the practical migration play for production AutoGen codebases, complete with the MAF-vs-LangGraph comparison, a step-by-step migration path, and the technical choices that decide the success of your move.

By Deepak Bagada, CEO at SaaSNext & AI Principal Architect.

What made AutoGen obsolete

AutoGen's core idea — agents as conversations that call each other and tools — was brilliant in 2023. Two years of production experience exposed the problems:

• Conversation-first orchestration is hard to test. AutoGen "conversations" are implicit control flow; deterministic evaluation, checkpoints, and retries become painful.
• Tool-calling grew alongside. AutoGen's tool execution was bolted on, not designed in, and it lagged the ecosystem behind MCP-centric stacks.
• Ecosystem split. The team's output fragmented; the community's models moved to graph-based orchestration (LangGraph, Crew), and MAF became the consolidate-and-replace.

The outcome: Microsoft paused feature work on AutoGen, redirecting developers to MAF. Code still runs, but new ecosystem momentum and security updates will slow, which — for production — is effectively a deprecation signal.

MAF vs LangGraph: the two real options

Both are graph-based frameworks. The differentiators are deployment, memory, and tooling type.

Read the pattern: MAF moves you into the Microsoft stack (Azure, connectors, Copilot Status), while LangGraph is the framework-agnostic, OSS-led choice. Neither is "better"; the decision is citadel vs portability.

Migration path from AutoGen, step by step

1. Inventory agents and conversations -> express as a graph of states
2. Replace GroupChat with explicit graph edges
3. Move tools -> MCP connectors (either framework supports MCP)
4. Add memory explicitly (MAF state or LangGraph checkpointers)
5. Port tests to graph-level assertions
6. Wrap LLM calls in OTel spans for parity visibility

The migration pattern number 2 hardest: your old AutoGen group chat encoded a workflow implicitly. In MAF or LangGraph the same workflow becomes a set of discrete nodes and edges. The work is translating implicit conversation order into explicit graph order, which is precisely the refactoring that usually takes the largest effort.

A minimal example in each framework

LangGraph — a two-stage agent:

from langgraph.graph import StateGraph

def node_classify(state):
    return {"category": "support" if "refund" in state["query"] else "other"}

def node_action(state):
    return {"answer": f"Handling {state['category']}"}

g = StateGraph(dict)
g.add_node("classify", node_classify)
g.add_node("action", node_action)
g.add_edge("classify", "action")
g.set_entry_point("classify")
app = g.compile()
print(app.invoke({"query": "Refund my order"}))

MAF — the same with Microsoft abstractions:

from microsoft_agent_framework import Agent, Message

async def handle_flow(agent, context):
    if "refund" in context.messages[-1].content:
        await agent.execute("refund-flow", context=context)

agent = Agent("order-support")
agent.add_handler("on_user_message", handle_flow)

Both are literal graphs; you pick the one whose graph primitives match your deployment target.

Financial ROI of migrating

Migration cost is one-time, benefit is recurring (maintenance + feature velocity + observability).

A typical 2026 math: 25 production agents on AutoGen, migration of 6 weeks, labor cost ~$45K, versus the cost of staying: ~8 CVEs never patched, a frozen skill set, and every new request re-forking a dead framework. The value balance comes out decisively in favor of moving, and most teams target the first 10% in month one.

To lighten the tunnel, treat Level-1s as proof: migrate the lowest-risk agent first and re-measure latency and cost before commiting the fleet.

Where the two go next

MAF's roadmap concentrates: Copilot 2 policies, deep MCP settings, memory standard, and enterprise identity. LangGraph's: more agent products, more OTel, an MCP ecosystem worth porting to. The believe the long-term game favors whoever makes the graph primitives predictable: entry cost low, checkpoints standard, evaluation built-in. Either framework gives you that; staying deppend in AutoGen does not.

See agentic recipes in AI Workflows and keep tracking framework anniversaries on Latest AI News.

Common migration mistakes (and how to avoid them)

Most AutoGen migrations fail for predictable reasons, and they are operational rather than technical:

• Porting GroupChat transcripts instead of behavior. Your GroupChat exchanged dozens of conversational turns; the migration should capture the decision points, not the dialogue. Encode the routing decisions as explicit conditional edges and discard the chatter. Teams that try to replay conversations as agent memory end up with bloated checkpoints and worse latency.
• Skipping checkpoint parity. AutoGen conversations could be resumed manually; MAF and LangGraph resume automatically from a checkpointer. If you do not verify that a mid-stream crash resumes at the exact tool call, you lose the deterministic guarantee you migrated for. Test this on the very first agent before touching the fleet.
• Keeping AutoGen-era prompts verbatim. Prompts written for a conversational runtime assume the model sees the whole transcript. Graph runtimes feed the model only the relevant node state, so prompts must be rewritten to be self-contained per node. This is the single largest quality regression source in real migrations.
• Ignoring the tooling layer. AutoGen's bolted-on tool execution was MCP-agnostic. Both successor frameworks are MCP-native, so this is the moment to standardize every external capability behind an MCP server rather than re-implementing vendor SDK calls inside each agent.

Build a runbook and a kill-switch

A production migration deserves the same rigor as a cloud migration. Keep a runbook that records, per agent: the old control flow, the new graph, the checkpoint recovery test, and the latency/cost regression gate. Pair it with a per-agent kill-switch so that if the new graph misbehaves in week one, you can route that agent back to the maintenance build without a release cycle. A safety valve is what lets you migrate in production instead of betting the fleet on a single cutover.

Migration checklist you can paste into your tracker

[ ] Inventory: list every AutoGen agent, its triggers, and its tools
[ ] Translate each GroupChat into an explicit state graph
[ ] Port all tools onto MCP connectors
[ ] Add explicit memory (MAF state or LangGraph checkpointer)
[ ] Rewrite node prompts to be self-contained
[ ] Write graph-level tests (assert node order + checkpoint resume)
[ ] Wrap LLM calls in OTel spans for parity visibility
[ ] Migrate one Level-1 agent as proof, measure latency + cost
[ ] Roll out in waves with per-agent kill-switch
[ ] Remove AutoGen dependency from build manifest

Bottom line

AutoGen is retirement, not pasteurization. The right response is a deliberate migration: inventory, translate the implicit control flow into an explicit graph, port tools onto MCP, and keep observability in OTel. If you are Microsoft-locked, MAF is the natural upgrade path; if you want portability and OTel-native control, LangGraph is the mature default. Either way, move in units of the first agent, measure the performance, and roll the fleet. The best time to migrate was before the maintenance announcement; the second best time is now.