LangGraph vs AutoGen 2026: Comparison + AG2 Migration Notes

LangGraph vs AutoGen 2026: which framework should you actually use?

In short

In 2026, use LangGraph 1.0 when durability, checkpointer-backed persistence, and human-in-the-loop dominate. Use Microsoft Agent Framework 1.0 when Azure and Foundry are the runtime. Use AG2 1.0 when you want to preserve AutoGen's conversational philosophy under community governance. Legacy AutoGen (v0.2 or v0.4) is no longer a valid new-project target — the microsoft/autogen repo has entered maintenance mode.

The LangGraph vs AutoGen question got messier in 2026 because AutoGen fractured. In October 2025 Microsoft moved microsoft/autogen into maintenance mode. The original creators forked the project into AG2 (community, Apache 2.0) and continued their conversational philosophy under an independent governance protocol. Microsoft then merged its AutoGen and Semantic Kernel teams and shipped Microsoft Agent Framework 1.0 on April 3, 2026 as the direct successor. On the LangChain side, LangGraph 1.0 reached GA on October 22, 2025 with zero breaking changes and durable execution as a first-class primitive.

Alice Labs' one-line rule of thumb across our 100+ production agent implementations: LangGraph 1.0 for durability, MAF 1.0 for Azure/Foundry, AG2 1.0 for conversational continuity. Nothing in this piece is theoretical — every claim is grounded in the changelogs, migration guides, and repository state as of August 3, 2026.

This article is the head-to-head that most enterprise architects arrive at after reading our broader best AI agent frameworks 2026 guide. If you specifically care about the AutoGen escape hatch — which is what most 2026 readers do — read every section below. If you already know you're on Claude, our LangGraph vs Claude Agent SDK comparison is the more precise fork.

Is AutoGen deprecated in 2026?

In short

AutoGen is not fully deprecated but has been in maintenance mode since October 2025. The microsoft/autogen GitHub banner explicitly states it will not receive new features or enhancements and is community managed going forward. Bug fixes and security patches continue. Microsoft directs new users to Microsoft Agent Framework 1.0, which reached GA on April 3, 2026.

Maintenance mode is not deprecation, but it is the strongest possible signal short of it. The banner at the top of microsoft/autogen reads verbatim: "AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward." At the point the mode change took effect, the repository had 60.2k stars, 9.1k forks, and 558 open issues.

Operationally, three things are true in 2026:

• Bug fixes and security patches continue. Existing production deployments will not silently break. But no new features, no async improvements, no new backends.
• Microsoft directs new users to Microsoft Agent Framework. The official learn.microsoft.com documentation frames MAF as "the direct successor" to both AutoGen and Semantic Kernel.
• The community fork is AG2. Governed by AutoGen's original creators and maintainers under an open contribution-based governance protocol.

The practical implication for a senior engineer inheriting an AutoGen v0.2 system: treat it like a legacy Django 1.x codebase. Working, patched, but on borrowed time. Alice Labs plans a migration on every engagement where AutoGen v0.2 is present, usually targeting LangGraph 1.0 or Microsoft Agent Framework 1.0 based on cloud posture and durability requirements.

LangGraph 1.0 architecture: durable execution, checkpointers, and human-in-the-loop

In short

LangGraph 1.0 centres on a StateGraph with a typed state schema and reducer functions. Durable state persists across server restarts via a checkpointer (in-memory, SQLite, or Postgres). Human-in-the-loop is first-class through the interrupt() primitive and Command return values. LangGraph 1.0 reached GA on October 22, 2025 with zero breaking changes from 0.x; the only meaningful deprecation moved langgraph.prebuilt into langchain.agents.

LangGraph 1.0's primitives read as if they were designed by a group of engineers who had already lived through the observability nightmares of every prior agent framework — because they were. The core concepts:

• StateGraph — a typed state object flows through nodes; each node returns a partial update; reducer functions merge updates deterministically.
• Checkpointer — every super-step is persisted (in-memory, SQLite, or Postgres). A crashed process resumes from the last checkpoint with full context.
• interrupt() + Command — pause the graph, hand control to a human, receive a decision back, resume without losing state. First-class, not a hack.
• Streaming — tokens, values, and updates all stream with typed boundaries; no more "is this a message or an intermediate step" guessing.

LangGraph 1.0 shipped on October 22, 2025 with zero breaking changes from 0.x. The single meaningful deprecation: langgraph.prebuilt moved into langchain.agents. Any 0.x code that avoids prebuilt upgrades without a diff.

Production references matter here more than benchmarks. LangChain's public case-study library lists Uber, LinkedIn, Klarna, JP Morgan, BlackRock, and Cisco as LangGraph production users. The pattern is consistent: durability-heavy workflows where a multi-hour agent run must survive infrastructure hiccups.

A minimal LangGraph 1.0 example that illustrates checkpointing:

# Python 3.10+
from typing import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.sqlite import SqliteSaver

class State(TypedDict):
    ticket_id: str
    diagnosis: str
    resolution: str

def diagnose(state: State) -> State:
    return {"diagnosis": f"Root cause for {state['ticket_id']}"}

def resolve(state: State) -> State:
    return {"resolution": f"Applied fix based on {state['diagnosis']}"}

graph = StateGraph(State)
graph.add_node("diagnose", diagnose)
graph.add_node("resolve", resolve)
graph.add_edge(START, "diagnose")
graph.add_edge("diagnose", "resolve")
graph.add_edge("resolve", END)

# Checkpointer = durable resume across process restarts
checkpointer = SqliteSaver.from_conn_string("agent_state.db")
app = graph.compile(checkpointer=checkpointer)

config = {"configurable": {"thread_id": "ticket-42"}}
app.invoke({"ticket_id": "ticket-42"}, config=config)

The thread_id is the equivalent of a session ID in the Claude Agent SDK. Store it alongside your business ID (ticket, user, conversation) and every subsequent call resumes with full state. For a deeper look at the graph pattern, our LangGraph implementation guide covers supervisor, swarm, and multi-agent topologies end-to-end.

AutoGen v0.2 vs AutoGen v0.4: the async rewrite that split the community

In short

AutoGen v0.4 was a ground-up rewrite adopting an async event-driven architecture, replacing the synchronous v0.2 conversational model. ConversableAgent became AssistantAgent with on_messages()/on_reset(); GroupChat/GroupChatManager became RoundRobinGroupChat teams; model config shifted from OpenAIWrapper + config_list to direct OpenAIChatCompletionClient. Several v0.2 features — Teachable Agents, Model Client Cost tracking — did not carry forward. The rewrite is what preceded the AG2 fork and the eventual maintenance-mode decision.

To understand why AG2 exists and why teams get burned trying to skip straight from v0.2 to MAF, you have to look at v0.4. It was not a point release. It was a rewrite. Async event-driven core, new agent primitives, new team primitives, new model client surface. The Microsoft migration guide is the primary source and worth reading end-to-end before any migration.

The high-level v0.2 to v0.4 API shifts:

• ConversableAgent → AssistantAgent — agents now define on_messages() and on_reset() handlers; message dispatch is async by default.
• GroupChat / GroupChatManager → RoundRobinGroupChat teams — team primitives replace the old orchestrator-with-manager pattern.
• OpenAIWrapper + config_list → OpenAIChatCompletionClient — model clients are now first-class typed objects, no more config-list gymnastics.
• Deprecated: Teachable Agents, Model Client Cost tracking, and several v0.2 conveniences did not carry into v0.4.

The community split happened because v0.4 broke the mental model that made AutoGen popular in the first place. AG2's positioning is essentially: "we preserve the v0.2 conversational philosophy, on modern infrastructure, under community governance." Microsoft's positioning with MAF is essentially: "the v0.4 async rewrite was the right idea, but the delivery surface should be productised — here it is, with checkpointing and OTel baked in."

The practical implication: a v0.2 to v0.4 migration is nearly as much work as a v0.2 to a different framework. Alice Labs has stopped recommending v0.4 as a migration target and now routes clients directly to LangGraph 1.0, MAF 1.0, or AG2 1.0 depending on their runtime and philosophy.

AG2 1.0: the community AutoGen fork's AgentOS vision

In short

AG2 1.0.1 shipped on July 29, 2026 on PyPI under Apache 2.0, hosted at the ag2ai GitHub organization. It positions an 'AgentOS' vision — interoperability across AG2, Google ADK, OpenAI, and LangChain agents via A2A and MCP. AG2 1.0 is NOT drop-in with pre-1.0 autogen; the classic namespace with ConversableAgent and GroupChat has moved to a separate ag2-classic package. Governance is community-based under AutoGen's original creators and maintainers.

AG2 is not a rebrand — it is a governance decision. The AG2 team frames itself as: "created and maintained by AutoGen's original creators and maintainers following a contribution-based open governance protocol." The project lives at the ag2ai GitHub organization and had 4,828 stars at the 1.0 release.

The version 1.0 story matters. AG2 1.0.1 landed on PyPI on July 29, 2026, and it is deliberately not drop-in with what was on PyPI before. The pre-1.0 conversational API (ConversableAgent, GroupChat, and friends) has moved to a separate ag2-classic package. If you pip install ag2 today, you get the new v1.0 API — not the classic AutoGen API you may remember.

The version-pinning story is worse than that. During the fork transition, the PyPI name autogen aliased to AG2 in some configurations, silently giving developers AG2's API when they thought they were installing Microsoft AutoGen. Any production system with a loose autogen pin should tighten it explicitly today.

The technical bet AG2 is making is called AgentOS: interop across AG2, Google ADK, OpenAI, and LangChain agents through A2A (Agent-to-Agent) and MCP (Model Context Protocol). This is the strongest positive differentiator AG2 has versus MAF, which is Microsoft-first, and LangGraph, which is LangChain-first.

Microsoft Agent Framework 1.0: the official AutoGen and Semantic Kernel successor

In short

Microsoft Agent Framework 1.0 reached GA on April 3, 2026 for Python and .NET, with Go in public preview. It was built by the merged AutoGen and Semantic Kernel teams and is documented as the direct successor to both. MAF ships three primitives: Agents, Harness (batteries-included runtime), and Workflows (graph-based orchestration). Providers include Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and AWS Bedrock. It adds checkpointing, session state, middleware, telemetry, and type-safe routing.

Microsoft Agent Framework is the productised path Microsoft chose after AutoGen and Semantic Kernel had duplicated too much surface. The two teams merged; the result reached 1.0 GA on April 3, 2026 for both Python and .NET, with a Go SDK in public preview.

MAF's three primitives are worth naming precisely because they do not map cleanly onto AutoGen or LangGraph terminology:

• Agents — the base unit; multi-turn by default; tool-augmented via the @tool decorator with automatic schema inference.
• Harness — the batteries-included runtime; sessions, middleware, telemetry, and provider config all live here.
• Workflows — the graph-based orchestration primitive; how you express multi-step, multi-agent flows with type-safe routing.

Provider coverage is broader than most teams expect. MAF speaks to Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and AWS Bedrock out of the box. This is the piece that surprises Azure-native teams who assumed vendor lock-in.

The features that carried over cleanly from Semantic Kernel: middleware, telemetry via native OpenTelemetry, and type-safe routing between agents. The features that carried over from AutoGen: async event-driven core, multi-agent teams. The features Microsoft added: checkpointing GA (resume from a specific checkpoint), session state with optional external stores like Redis, and distributed execution on the roadmap.

Feature-by-feature: LangGraph 1.0 vs AG2 1.0 vs Microsoft Agent Framework 1.0

In short

LangGraph is explicit-graph; AG2 is conversational + workflow; MAF combines graph-based Workflows with first-class Agents. Persistence: LangGraph ships a first-class checkpointer with SQLite/Postgres/Redis backends; MAF ships checkpointing GA; AG2 relies on session/history compaction with no first-class durable executor. All three are Apache 2.0 or MIT-compatible open source. LangGraph is the ecosystem gravity leader; AG2 targets cross-framework interop; MAF is Azure-adjacent.

Below is the head-to-head we use as the reference sheet in Alice Labs client engagements. Every row reflects the state of the frameworks as of August 3, 2026 — not marketing promises.

LangGraph 1.0 vs AG2 1.0 vs Microsoft Agent Framework 1.0

The rows that decide most enterprise selections are durable persistence and observability. LangGraph wins on persistence for the same reason it won the pre-1.0 mindshare: the checkpointer was a design primitive, not a bolt-on. MAF closes the gap for Azure-native shops; AG2 has intentionally not built one because its philosophy centres on conversational patterns, not durable state.

Migrating from AutoGen v0.2 to LangGraph 1.0

In short

The Alice Labs migration path: map ConversableAgent to a LangGraph node with a tools-bound LLM; map GroupChat to a Supervisor or Swarm topology with conditional edges for handoff; replace UserProxyAgent with interrupt() for human-in-the-loop; replace ad-hoc summarization with the LangGraph checkpointer plus thread_id resume. Typical Alice Labs client migration runs 2-4 weeks per production agent system, plus 2 weeks of canary and replay.

Alice Labs has run this migration across enough production systems to know exactly where the trapdoors are. The high-level mapping:

• ConversableAgent → a LangGraph node with a tools-bound LLM. The LangChain bind_tools() API is the direct equivalent of AutoGen's function-calling registration.
• GroupChat / GroupChatManager → a Supervisor pattern or Swarm pattern. Conditional edges from the supervisor node express the handoff logic.
• UserProxyAgent → the interrupt() primitive plus Command return values. This is what makes the migration a net upgrade, not a lateral move — UserProxyAgent was always the weakest AutoGen v0.2 primitive.
• Ad-hoc summarisation / history compression → LangGraph checkpointer plus thread_id resume. The graph state is your durable memory; you write summarisation only when a summary is genuinely useful, not to fit within a context window.

The concrete before/after in a small conversational-triage agent:

# AutoGen v0.2 (legacy)
from autogen import ConversableAgent, UserProxyAgent, GroupChat, GroupChatManager

triage = ConversableAgent(
    "triage", llm_config={"model": "gpt-4"},
    system_message="Route the ticket.",
)
solver = ConversableAgent(
    "solver", llm_config={"model": "gpt-4"},
    system_message="Draft a resolution.",
)
user = UserProxyAgent("user", human_input_mode="ALWAYS")
chat = GroupChat(agents=[triage, solver, user], messages=[])
manager = GroupChatManager(groupchat=chat)
user.initiate_chat(manager, message="Ticket 42: shipping delay")

# LangGraph 1.0 target
from typing import TypedDict, Literal
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.types import interrupt

class State(TypedDict):
    ticket: str
    route: str
    draft: str

def triage(state: State) -> State:
    return {"route": "solver"}  # LLM call in real code

def solver(state: State) -> State:
    approved = interrupt({"draft": "proposed resolution"})
    return {"draft": approved["text"]}

def route(state: State) -> Literal["solver", END]:
    return state["route"] or END

graph = StateGraph(State)
graph.add_node("triage", triage)
graph.add_node("solver", solver)
graph.add_edge(START, "triage")
graph.add_conditional_edges("triage", route, ["solver", END])
graph.add_edge("solver", END)

app = graph.compile(checkpointer=SqliteSaver.from_conn_string("tickets.db"))

Alice Labs' typical migration budget: 2-4 weeks per production agent system, plus a further 2 weeks of golden-trace replay and canary. For the full playbook including replay methodology and canary thresholds, see the dedicated section further down and our LangGraph implementation guide.

Migrating from AutoGen v0.4 to Microsoft Agent Framework 1.0

In short

Microsoft's official migration guide provides direct mappings: autogen_agentchat.AssistantAgent maps to agent_framework.Agent (multi-turn by default); GroupChat teams map to a Workflow with graph-based routing while preserving the RoundRobinGroupChat pattern; OpenAIChatCompletionClient signatures are largely preserved; FunctionTool maps to the @tool decorator with automatic schema inference; ChatCompletionContext maps to AgentSession with optional external stores like Redis.

The v0.4 to MAF migration is the smoothest of the three because Microsoft owns both ends. The official migration guide at learn.microsoft.com is the primary source; the mappings below are drawn directly from it and validated on Alice Labs client engagements.

• autogen_agentchat.AssistantAgent → agent_framework.Agent. Multi-turn by default; tools attached via the @tool decorator with automatic schema inference.
• GroupChat teams → Workflow with graph-based routing. The RoundRobinGroupChat pattern is preserved as a Workflow template, so team semantics carry over cleanly.
• OpenAIChatCompletionClient → largely preserved; provider config lives in the Harness instead of on the client object.
• FunctionTool → @tool decorator. Type hints drive schema inference; you rarely write a JSON schema by hand.
• ChatCompletionContext → AgentSession, with optional external stores (Redis is the documented example).

The one place the migration bites: middleware. MAF's middleware model is closer to Semantic Kernel's than AutoGen v0.4's, and any custom v0.4 hook that hand-rolled interception logic needs to be re-expressed as MAF middleware. Alice Labs' rule is to spend the first day of any v0.4-to-MAF engagement enumerating custom interception points before touching any agent code.

When to migrate to AG2 1.0 instead of LangGraph or MAF

In short

AG2 1.0 is the right target in a narrow but valid set of cases: the team has deep AutoGen conversational-pattern muscle memory; multi-vendor agent interop (Google ADK plus OpenAI plus LangChain) is the primary requirement; the team prefers community governance over Microsoft or LangChain corporate stewardship. Downsides: AG2 1.0 is NOT drop-in with pre-1.0 autogen (ag2-classic maintains the pre-1.0 API), and AG2 has a smaller production-user reference base than LangGraph.

AG2 is not the mass-market answer, and Alice Labs will say so on any engagement where durability or Azure alignment dominates. But there are three cases where AG2 wins on merits:

• Deep AutoGen conversational muscle memory. If a team has spent a year internalising ConversableAgent, GroupChat, and multi-agent debate patterns, AG2 is the shortest cognitive-distance migration target.
• Multi-vendor agent interop as a first requirement. AgentOS is AG2's headline bet: A2A and MCP to talk to Google ADK, OpenAI, and LangChain agents in the same runtime. MAF is Microsoft-first; LangGraph is LangChain-first.
• Governance preference for community over corporate. A real engineering-culture question. AG2's contribution-based governance protocol is the option for organisations that will not build critical infrastructure on a single vendor's stewardship.

The trade-offs to name explicitly on any AG2 migration engagement:

• AG2 1.0 is not drop-in with the pre-1.0 autogen namespace. Legacy code that used ConversableAgent and GroupChat needs an explicit switch to ag2-classic, or a refactor onto the new 1.0 API.
• The production-user reference base is smaller than LangGraph's. If your board wants a case-study story, this matters.
• No first-class durable executor. If durability is a hard requirement, AG2 is not the right target — LangGraph or MAF are.

Durability and checkpointing: the real production differentiator in 2026

In short

LangGraph auto-persists durable state; interrupted workflows resume without context loss. MAF ships checkpointing GA with resume-from-checkpoint semantics and distributed execution planned. AG2 relies on history compaction and structured outputs with no first-class durable executor equivalent. AutoGen v0.4 has no durable execution primitive; state lives in-process. Alice Labs' rule: durability requirement → LangGraph or MAF, not AG2 or legacy AutoGen.

Durability is now the primary axis of framework choice in 2026, and it deserves its own section because the marketing muddies it. What "durable" actually means in production:

• Process crashes — the container OOMs. Does the agent pick up from the last successful super-step, or restart from turn zero?
• Redeploys — you ship a bugfix. Do in-flight agent runs survive, or do you lose whatever was mid-conversation?
• Long-running HITL — a human takes 4 hours to approve a step. Does the process stay resident (expensive), or does the state persist and the process free itself (cheap)?
• Fan-out — parallel subtasks. Can each survive independently, or does a single subtask crash torch the whole run?

LangGraph 1.0: durable state auto-persists at every super-step. All four scenarios above resolve correctly out of the box. The checkpointer is a first-class primitive, not a bolt-on.

Microsoft Agent Framework 1.0: checkpointing GA with resume-from-checkpoint semantics. Distributed execution is on the roadmap. Not yet at LangGraph's maturity, but closing quickly.

AG2 1.0: history compaction and structured outputs, but no first-class durable executor. Long-pause HITL and redeploy survival require additional engineering.

AutoGen v0.4: no durable execution primitive. State lives in-process. Any production system needed to hand-roll checkpointing on top.

Alice Labs' one-line recommendation across our 100+ implementations: durability requirement → LangGraph or MAF, not AG2 or legacy AutoGen. The engineering cost of retrofitting durability onto a framework that did not design for it is always larger than the cost of picking the right framework at the start.

Observability and evals: LangSmith vs Microsoft OpenTelemetry

In short

LangGraph ships with LangSmith tracing, evals, and prompt hub integration. MAF emits OpenTelemetry traces natively and integrates with Azure Monitor and any OTel backend. AG2 supports OpenTelemetry via extensions but has no first-party trace UI. AutoGen Studio (v0.4) provides a GUI for prototyping but not production observability. Alice Labs' default: LangSmith for LangGraph clients, Azure Monitor for MAF clients, and Grafana + Tempo for AG2 clients.

Second-order tooling is where two frameworks with similar core primitives diverge in practice. In 2026, observability is not optional — every production agent needs traced runs, per-step latency, tool-call errors surfaced, and a way to replay a failed production trace in staging.

LangGraph + LangSmith. Deeply integrated. Traces flow automatically; evals sit alongside; the prompt hub is a versioned registry. This is the fastest zero-to-production observability story in the market.

MAF + OpenTelemetry. Native OTel emission is the default. Azure Monitor gets first-party integration; any OTel backend (Grafana Tempo, Honeycomb, Datadog, New Relic) works out of the box. No lock-in to a specific trace UI.

AG2 + OpenTelemetry extensions. Supported via extensions; no first-party trace UI. You bring your own backend, you bring your own dashboard. Fine for teams with an established observability stack; friction for teams without one.

AutoGen Studio. A GUI for prototyping v0.4 agents. Explicitly not production observability — it is a design surface, not a monitoring surface. Do not plan production incident response around it.

Alice Labs' default recommendations by client stack: LangSmith for LangGraph, Azure Monitor for MAF, Grafana with Tempo for AG2. All three route to the same underlying OTel semantic conventions, so cross-framework migration does not lose historical trace data.

Cost and enterprise considerations: SOC 2, HIPAA, EU AI Act

In short

MAF 1.0 ships with SOC 2 and HIPAA compliance certifications baked in via Azure. LangGraph 1.0 self-hosted is compliance-agnostic; LangGraph Platform offers SOC 2. AG2 is fully self-hosted and compliance is the operator's responsibility. EU AI Act obligations attach to the operator regardless of framework choice. Alice Labs' preferred stack for EU AI Act high-risk systems: LangGraph 1.0 self-hosted plus LangSmith EU region.

Compliance is where senior engineers earn their keep in 2026. The framework choice does not satisfy compliance — the operator satisfies compliance — but the framework can either help or hinder. What actually holds up in an audit:

• Microsoft Agent Framework 1.0. SOC 2 and HIPAA are inherited from the Azure platform. If you are already running Azure at compliance scale, MAF drops cleanly into that envelope. This is the fastest path to a compliance sign-off in a Microsoft-native shop.
• LangGraph 1.0 self-hosted. Compliance-agnostic — you run it on your own infrastructure, you inherit whatever certifications that infrastructure carries. LangGraph Platform (managed) offers SOC 2. For EU AI Act high-risk deployments, LangSmith has an EU region.
• AG2 1.0. Fully self-hosted; compliance is entirely the operator's responsibility. Fine for teams with existing compliance infrastructure; heavier lift for teams starting from scratch.

EU AI Act obligations — logging, human oversight, transparency for high-risk systems — attach to the operator regardless of framework. What matters is whether the framework supports the primitives you need. LangGraph's checkpointer plus interrupt() makes Article 14 human-oversight logging a five-line addition. MAF's middleware layer plus OTel makes the same. AG2 works but requires more custom middleware.

Alice Labs' preferred stack for EU AI Act high-risk deployments across our Nordic enterprise clients: LangGraph 1.0 self-hosted plus LangSmith EU region. For deeper coverage see our EU AI Act compliance checklist for 2026.

Decision matrix: which framework for which use case in 2026

In short

Long-running, durable, HITL-heavy workflows → LangGraph 1.0. Azure/Foundry-native enterprise (.NET or Python) → Microsoft Agent Framework 1.0. Multi-vendor agent interop and community governance → AG2 1.0. Existing AutoGen v0.2 production with low migration budget → AG2 1.0 with ag2-classic bridge. Greenfield with no vendor lock-in preference → LangGraph 1.0.

The table below is the sheet Alice Labs walks buyers through in the first hour of an agent-framework engagement. It compresses the rest of this article into an actionable grid.

2026 framework decision matrix by use case

If your use case is not on the grid, it usually decomposes into one of these rows plus a specific constraint. Bring the constraint to Alice Labs' scoping conversation and we will map it explicitly.

Alice Labs migration playbook: from AutoGen v0.2 to LangGraph 1.0 or MAF 1.0

In short

Five phases: (1) pin autogen==0.2.x and snapshot conversation traces as a golden set; (2) rewrite agents as LangGraph nodes or MAF Agents while keeping tool signatures stable; (3) replay golden traces and require semantic parity before cutover; (4) add durability (LangGraph checkpointer or MAF checkpointing) and OpenTelemetry emission; (5) run a 2-week canary at 5% traffic before full cutover. Alice Labs delivers this end-to-end in 4-8 weeks per production agent system.

The Alice Labs migration playbook is the same across LangGraph and MAF targets — only the framework primitives differ. Five phases, in this order, no shortcuts:

1. Pin and snapshot. Freeze the existing AutoGen v0.2 codebase at autogen==0.2.x. Do not let a stray upgrade change behaviour mid-migration. Collect a representative set of production conversation traces — 50 to 200 is usually enough — and save them as a golden set. These are your regression suite.
2. Rewrite agents; keep tool signatures stable. Map ConversableAgent to LangGraph node or MAF Agent; map GroupChat to Supervisor/Swarm (LangGraph) or Workflow (MAF). Keep the tool interfaces identical so tool implementations can be reused verbatim. Any tool-signature change is a separate PR after the migration.
3. Replay and require semantic parity. Run the golden traces through the new implementation. Compare outputs — not string-equal, but semantically equivalent (LLM-judge or embedding similarity). Any regression is a blocker. Alice Labs' rule: 90%+ semantic parity before cutover, 100% parity on the top-decile-value traces.
4. Add durability and OTel. LangGraph checkpointer (SQLite for single-container, Postgres for fleet) or MAF checkpointing. OpenTelemetry emission from day one. This is the migration's payoff — the new system is measurably more observable and more resilient than the old one.
5. Canary at 5% traffic for 2 weeks. Route 5% of production traffic to the new implementation. Compare error rates, latency, and cost against the legacy system. If anything regresses, fix it before scaling. Full cutover is at the end of week 2 assuming no regressions.

End-to-end Alice Labs delivery for a single production agent system: 4-8 weeks. Two engineers full-time. A senior reviewer at 20%. This is the shape we have delivered across 100+ enterprise implementations across the Nordics, Europe, and global clients.

For the broader multi-agent architecture context underneath the playbook, see our multi-agent systems explained guide and the surrounding best AI agent frameworks 2026 guide. For build-vs-buy economics on the migration itself, our build vs buy AI guide documents the same decision framework we run with buyers.