--- title: "LangGraph vs AutoGen 2026: AG2 Migration | Alice Labs" description: "LangGraph 1.0 vs AutoGen and AG2 in 2026. 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"@type": "Thing", "name": "Decision matrix: which framework for which use case in 2026", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#decision-matrix" }, { "@type": "Thing", "name": "Alice Labs migration playbook: from AutoGen v0.2 to LangGraph 1.0 or MAF 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#alice-labs-playbook" } ], "mentions": [ { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, { "@type": "Organization", "name": "LangChain", "url": "https://langchain.com" }, { "@type": "Organization", "name": "Microsoft", "url": "https://microsoft.com" }, { "@type": "Organization", "name": "Anthropic", "url": "https://anthropic.com" }, { "@type": "Organization", "name": "OpenAI", "url": "https://openai.com" }, { "@type": "Product", "name": "LangGraph", "url": "https://docs.langchain.com/oss/python/langgraph/overview" }, { "@type": "Product", "name": "AutoGen", "url": "https://github.com/microsoft/autogen" }, { 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"url": "https://www.linkedin.com/in/linus-ingemarsson/" } ], "hasPart": [ { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "LangGraph vs AutoGen 2026: which framework should you actually use?", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#which-framework-in-2026", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Is AutoGen deprecated in 2026?", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#is-autogen-deprecated", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "LangGraph 1.0 architecture: durable execution, checkpointers, and human-in-the-loop", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#langgraph-1-0-architecture", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "AutoGen v0.2 vs AutoGen v0.4: the async rewrite that split the community", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#autogen-v0-2-vs-v0-4", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "AG2 1.0: the community AutoGen fork's AgentOS vision", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#ag2-1-0", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Microsoft Agent Framework 1.0: the official AutoGen and Semantic Kernel successor", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#microsoft-agent-framework", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Feature-by-feature: LangGraph 1.0 vs AG2 1.0 vs Microsoft Agent Framework 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#feature-by-feature", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Migrating from AutoGen v0.2 to LangGraph 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#migrate-v0-2-to-langgraph", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Migrating from AutoGen v0.4 to Microsoft Agent Framework 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#migrate-v0-4-to-maf", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "When to migrate to AG2 1.0 instead of LangGraph or MAF", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#when-ag2", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Durability and checkpointing: the real production differentiator in 2026", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#durability-checkpointing", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Observability and evals: LangSmith vs Microsoft OpenTelemetry", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#observability-evals", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Cost and enterprise considerations: SOC 2, HIPAA, EU AI Act", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#compliance", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Decision matrix: which framework for which use case in 2026", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#decision-matrix", "description": "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." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Alice Labs migration playbook: from AutoGen v0.2 to LangGraph 1.0 or MAF 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#alice-labs-playbook", "description": "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." } ], "speakable": { "@type": "SpeakableSpecification", "cssSelector": [ "[data-speakable='true']", "[data-snippet='true']", "[data-section-answer='true']", ".quick-answer", "h1" ] } }, { "@type": "BreadcrumbList", "@id": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#breadcrumb", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "ai-agents", "item": "https://alicelabs.ai/en/insights/ai-agents" }, { "@type": "ListItem", "position": 4, "name": "LangGraph vs AutoGen 2026: AG2 Migration | Alice Labs", "item": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026" } ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#eric", "name": "Eric Lundberg", "jobTitle": "Co-Founder", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "AI automation", "url": "https://www.wikidata.org/wiki/Q1322483" }, { "@type": "DefinedTerm", "name": "Workflow automation", "url": "https://www.wikidata.org/wiki/Q120427660" }, { "@type": "DefinedTerm", "name": "Retrieval-Augmented Generation", "url": "https://www.wikidata.org/wiki/Q117761563" }, { "@type": "DefinedTerm", "name": "Enterprise AI implementation" } ], "sameAs": [ "https://www.linkedin.com/in/eric-lundberg-3530451bb/", "https://www.wikidata.org/wiki/Q140369978" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "jobTitle": "Co-Founder", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "AI agent orchestration", "url": "https://www.wikidata.org/wiki/Q98678395" }, { "@type": "DefinedTerm", "name": "AI strategy" }, { "@type": "DefinedTerm", "name": "AI search optimization (LLMO)" }, { "@type": "DefinedTerm", "name": "Enterprise AI strategy" } ], "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#alice", "name": "Alice Holmgren", "jobTitle": "CEO", "worksFor": { "@id": "https://alicelabs.ai/#organization" }, "knowsAbout": [ { "@type": "DefinedTerm", "name": "Nordic AI consulting market" }, { "@type": "DefinedTerm", "name": "AI strategy leadership" }, { "@type": "DefinedTerm", "name": "Enterprise transformation" } ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Is AutoGen deprecated in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "AutoGen is not fully deprecated but has been in maintenance mode since October 2025. Microsoft's official microsoft/autogen repository 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, which reached 1.0 GA on April 3, 2026. Alice Labs advises AutoGen v0.2 clients to migrate to LangGraph 1.0, Microsoft Agent Framework 1.0, or AG2 1.0 before 2027." } }, { "@type": "Question", "name": "What is the difference between AG2 and AutoGen?", "acceptedAnswer": { "@type": "Answer", "text": "AG2 is the community-driven fork of AutoGen, hosted under the ag2ai GitHub organization and licensed Apache 2.0. It was created by AutoGen's original creators and maintainers to continue the original conversational multi-agent philosophy after Microsoft moved AutoGen into maintenance mode. AG2 1.0.1 shipped on July 29, 2026 with a new AgentOS vision emphasizing interoperability across AG2, Google ADK, OpenAI, and LangChain agents via A2A and MCP. The classic AutoGen namespace has moved to a separate ag2-classic package." } }, { "@type": "Question", "name": "LangGraph vs AutoGen: which is better for production in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "For most production workloads in 2026, LangGraph 1.0 is the safer choice because it is the first stable major release in the durable agent framework space and is battle-tested at Uber, LinkedIn, Klarna, JP Morgan, and BlackRock. AutoGen is in maintenance mode. Alice Labs uses LangGraph 1.0 for durability-heavy workflows and Microsoft Agent Framework 1.0 for Azure-native enterprise clients." } }, { "@type": "Question", "name": "When did LangGraph 1.0 come out?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph 1.0 reached general availability on October 22, 2025 with zero breaking changes and full backward compatibility with 0.x. The only meaningful deprecation is the langgraph.prebuilt module, whose functionality moved to langchain.agents. It is the first stable major release in the durable agent framework category." } }, { "@type": "Question", "name": "When was Microsoft Agent Framework released?", "acceptedAnswer": { "@type": "Answer", "text": "Microsoft Agent Framework 1.0 reached general availability on April 3, 2026 for both Python and .NET, with Go in public preview. It was built by the merged AutoGen and Semantic Kernel teams at Microsoft and is described in Microsoft's documentation as the direct successor to both frameworks. It ships with three core primitives: Agents, Harness, and Workflows." } }, { "@type": "Question", "name": "What is the best framework to migrate from AutoGen to?", "acceptedAnswer": { "@type": "Answer", "text": "The best migration target depends on the workload. Alice Labs recommends: LangGraph 1.0 for durable, long-running, human-in-the-loop workflows; Microsoft Agent Framework 1.0 for Azure or Foundry-native enterprise systems and .NET shops; and AG2 1.0 for teams that want to preserve AutoGen's conversational patterns and open community governance. Legacy AutoGen v0.2 systems should not remain past 2026." } }, { "@type": "Question", "name": "Is LangGraph a replacement for AutoGen?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph is a functional replacement for AutoGen in most production scenarios, but the mental model differs. AutoGen and AG2 center on conversational agents that talk to each other. LangGraph centers on an explicit state graph where nodes are functions or LLM calls and edges control flow. Alice Labs rewrites AutoGen ConversableAgent patterns as LangGraph supervisor or swarm topologies, mapping GroupChat handoffs to conditional edges and UserProxyAgent to LangGraph's interrupt primitive." } }, { "@type": "Question", "name": "Is AG2 the same as AutoGen?", "acceptedAnswer": { "@type": "Answer", "text": "AG2 is not the same package as AutoGen anymore. Historically the PyPI name autogen aliased to AG2 during the fork transition, silently giving developers AG2's API. As of AG2 1.0 (July 29, 2026), the pre-1.0 autogen namespace with ConversableAgent and GroupChat has moved to a separate ag2-classic package. Installing ag2 today gives you the new v1.0 API, not the classic AutoGen API. Version pinning is essential." } }, { "@type": "Question", "name": "Can I use LangGraph and AutoGen together?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, but with caveats. Alice Labs occasionally runs LangGraph as the outer orchestrator with an AutoGen or AG2 subgraph handling a specific conversational subtask (e.g., debate patterns). This works because both frameworks are Python-native and async. However, we do not recommend this as a target architecture — it doubles the surface area for observability, checkpointing, and dependency management. Use it as a migration bridge only." } }, { "@type": "Question", "name": "Does LangGraph support human-in-the-loop natively?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. LangGraph 1.0 ships interrupt() and Command primitives as first-class human-in-the-loop mechanisms. A node can call interrupt() with a payload, the graph pauses and persists via the checkpointer, and a Command with the human decision resumes the graph exactly where it stopped — even if hours or days have passed and the process has restarted in the meantime. This is what distinguishes LangGraph's HITL story from AutoGen v0.4's in-process UserProxyAgent." } }, { "@type": "Question", "name": "What is the difference between AutoGen v0.2 and AutoGen v0.4?", "acceptedAnswer": { "@type": "Answer", "text": "AutoGen v0.4 is a ground-up rewrite adopting an async event-driven architecture. ConversableAgent was replaced by AssistantAgent with on_messages() and on_reset() handlers; GroupChat and GroupChatManager were replaced by RoundRobinGroupChat teams; model config shifted from OpenAIWrapper and config_list to direct OpenAIChatCompletionClient. Several v0.2 features — including Teachable Agents and Model Client Cost tracking — did not carry forward. The rewrite is what preceded the AG2 fork and Microsoft's eventual maintenance-mode decision." } }, { "@type": "Question", "name": "Which framework has the best durable execution in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph 1.0 has the most mature durable execution story — a first-class checkpointer with in-memory, SQLite, and Postgres backends, and resume-exactly-where-you-crashed semantics baked in since well before 1.0 GA. Microsoft Agent Framework 1.0 shipped checkpointing GA and is closing the gap quickly. AG2 has no first-class durable executor and relies on session and history compaction — the wrong target if durability is a hard requirement." } }, { "@type": "Question", "name": "Does Microsoft Agent Framework only work with Azure?", "acceptedAnswer": { "@type": "Answer", "text": "No. MAF 1.0 speaks to Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and AWS Bedrock out of the box. The runtime is Microsoft-authored but the model providers are pluggable. The most common misconception in 2026 is that MAF locks you into Azure — it does not. Compliance certifications like SOC 2 and HIPAA are inherited from Azure when you deploy there, but MAF itself is not Azure-only." } }, { "@type": "Question", "name": "How long does an AutoGen v0.2 to LangGraph 1.0 migration take?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs' measured migration time across 100+ production implementations is 2-4 weeks per production agent system for the engineering rewrite, plus an additional 2 weeks of golden-trace replay and 2-week 5% canary. End-to-end delivery is typically 4-8 weeks per system with two engineers full-time and a senior reviewer at 20%. The main variables are the number of custom tools and the depth of GroupChat orchestration in the original system." } }, { "@type": "Question", "name": "Is AG2 1.0 drop-in compatible with old AutoGen code?", "acceptedAnswer": { "@type": "Answer", "text": "No. AG2 1.0 is deliberately not drop-in with the pre-1.0 autogen namespace. The classic ConversableAgent, GroupChat, and GroupChatManager API 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. Any legacy production code needs an explicit choice: pin to ag2-classic to preserve behaviour, or refactor onto the AG2 1.0 API to take advantage of the new AgentOS interop features." } }, { "@type": "Question", "name": "Which framework is best for EU AI Act compliance?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs' preferred stack for EU AI Act high-risk deployments is LangGraph 1.0 self-hosted plus LangSmith EU region. LangGraph's checkpointer plus interrupt() primitive make Article 14 human-oversight logging a five-line addition. Microsoft Agent Framework 1.0 is also strong here because SOC 2 and HIPAA are inherited via Azure and OpenTelemetry emission is native. AG2 works but requires more custom middleware to reach the same audit surface. Always consult legal counsel for compliance determinations." } }, { "@type": "Question", "name": "What are the biggest AutoGen migration pitfalls in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Based on Alice Labs' 100+ production implementations: (1) migrating v0.2 to v0.4 instead of directly to LangGraph or MAF — v0.4 is now a frozen high-water mark; (2) not pinning autogen and ag2 explicitly in requirements files — the PyPI aliasing during the fork caused silent behaviour changes; (3) attempting AG2 for durability-heavy workloads — AG2 has no first-class durable executor; (4) skipping golden-trace replay before cutover — semantic parity is not optional; (5) treating AutoGen Studio as production observability — it is a design surface, not a monitoring surface." } }, { "@type": "Question", "name": "What is AgentOS in AG2 1.0?", "acceptedAnswer": { "@type": "Answer", "text": "AgentOS is AG2 1.0's flagship vision — interoperability across AG2, Google ADK, OpenAI, and LangChain agents via the A2A (Agent-to-Agent) protocol and MCP (Model Context Protocol). It positions AG2 as the multi-vendor orchestrator in a world where different teams and vendors are shipping different agent frameworks. This is AG2's strongest differentiator versus Microsoft Agent Framework (Microsoft-first) and LangGraph (LangChain-first). AgentOS is still early in 2026 but is the strategic reason to choose AG2 in mixed-vendor environments." } }, { "@type": "Question", "name": "Should I use LangGraph or Microsoft Agent Framework for a greenfield project?", "acceptedAnswer": { "@type": "Answer", "text": "For a greenfield project in 2026 with no strong vendor preference, Alice Labs' default recommendation is LangGraph 1.0. It has the largest ecosystem, the strongest production references (Uber, LinkedIn, Klarna, JP Morgan, BlackRock, Cisco), and the most mature durable execution story. Choose Microsoft Agent Framework 1.0 instead if you are Azure or Foundry-native, if .NET is a required language, or if inherited SOC 2 and HIPAA via Azure is a compliance shortcut worth taking." } }, { "@type": "Question", "name": "Is LangGraph a replacement for LangChain?", "acceptedAnswer": { "@type": "Answer", "text": "No, LangGraph and LangChain are complementary. LangChain 1.0 provides the model, tool, and integration abstractions (chat models, tool bindings, retrievers, document loaders). LangGraph 1.0 provides the graph-based orchestration runtime on top. In practice, a LangGraph node calls LangChain-bound LLMs and tools; the two are designed to work together. The 1.0 releases shipped on the same day (October 22, 2025) precisely to make this pairing coherent." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "LangGraph vs AutoGen 2026: Comparison + AG2 Migration Notes", "description": "LangGraph 1.0 vs AutoGen and AG2 in 2026. Compare architectures, migrate to Microsoft Agent Framework, and see when each framework still fits.", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026", "datePublished": "2026-08-03", "dateModified": "2026-08-03", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "langgraph vs autogen 2026", "ag2 vs langgraph", "autogen alternative", "autogen deprecated", "microsoft agent framework migration", "langgraph 1.0", "ag2 1.0", "multi-agent framework comparison", "autogen v0.2 migration", "langgraph checkpointer", "durable agent execution", "autogen maintenance mode" ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026", "name": "Best AI Agent Frameworks 2026: The Enterprise Comparison" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/langgraph-vs-claude-agent-sdk-2026", "name": "LangGraph vs Claude Agent SDK (2026): Head-to-Head Buyer's Guide" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/langgraph-guide-2026", "name": "LangGraph Implementation Guide 2026: Stateful AI Agents for Enterprise" }, { "@type": "ListItem", "position": 4, "url": "https://alicelabs.ai/en/insights/multi-agent-systems-explained", "name": "Multi-Agent Systems Explained: Patterns, Trade-offs, and Enterprise Use Cases" }, { "@type": "ListItem", "position": 5, "url": "https://alicelabs.ai/en/insights/ai-agent-architecture-patterns", "name": "AI Agent Architecture Patterns for Enterprise" }, { "@type": "ListItem", "position": 6, "url": "https://alicelabs.ai/en/insights/eu-ai-act-compliance-checklist-2026", "name": "EU AI Act Compliance Checklist for 2026" }, { "@type": "ListItem", "position": 7, "url": "https://alicelabs.ai/en/insights/build-vs-buy-ai", "name": "Build vs Buy AI: Enterprise Decision Framework 2026" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 15, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "LangGraph vs AutoGen 2026: which framework should you actually use?", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#which-framework-in-2026" }, { "@type": "ListItem", "position": 2, "name": "Is AutoGen deprecated in 2026?", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#is-autogen-deprecated" }, { "@type": "ListItem", "position": 3, "name": "LangGraph 1.0 architecture: durable execution, checkpointers, and human-in-the-loop", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#langgraph-1-0-architecture" }, { "@type": "ListItem", "position": 4, "name": "AutoGen v0.2 vs AutoGen v0.4: the async rewrite that split the community", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#autogen-v0-2-vs-v0-4" }, { "@type": "ListItem", "position": 5, "name": "AG2 1.0: the community AutoGen fork's AgentOS vision", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#ag2-1-0" }, { "@type": "ListItem", "position": 6, "name": "Microsoft Agent Framework 1.0: the official AutoGen and Semantic Kernel successor", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#microsoft-agent-framework" }, { "@type": "ListItem", "position": 7, "name": "Feature-by-feature: LangGraph 1.0 vs AG2 1.0 vs Microsoft Agent Framework 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#feature-by-feature" }, { "@type": "ListItem", "position": 8, "name": "Migrating from AutoGen v0.2 to LangGraph 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#migrate-v0-2-to-langgraph" }, { "@type": "ListItem", "position": 9, "name": "Migrating from AutoGen v0.4 to Microsoft Agent Framework 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#migrate-v0-4-to-maf" }, { "@type": "ListItem", "position": 10, "name": "When to migrate to AG2 1.0 instead of LangGraph or MAF", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#when-ag2" }, { "@type": "ListItem", "position": 11, "name": "Durability and checkpointing: the real production differentiator in 2026", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#durability-checkpointing" }, { "@type": "ListItem", "position": 12, "name": "Observability and evals: LangSmith vs Microsoft OpenTelemetry", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#observability-evals" }, { "@type": "ListItem", "position": 13, "name": "Cost and enterprise considerations: SOC 2, HIPAA, EU AI Act", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#compliance" }, { "@type": "ListItem", "position": 14, "name": "Decision matrix: which framework for which use case in 2026", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#decision-matrix" }, { "@type": "ListItem", "position": 15, "name": "Alice Labs migration playbook: from AutoGen v0.2 to LangGraph 1.0 or MAF 1.0", "url": "https://alicelabs.ai/en/insights/langgraph-vs-autogen-2026#alice-labs-playbook" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Agents", "item": "https://alicelabs.ai/en/insights/ai-agents" }, { "@type": "ListItem", "position": 4, "name": "LangGraph vs AutoGen 2026: Comparison + AG2 Migration Notes" } ] } ] --- [Alice Labs](/en/) Services [ What we do ](/#welcome)[ About Alice ](/#who-we-are)[ Case ](/en/case)[ 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[Home](/en) [Insights](/en/insights) [AI Agents](/en/insights/ai-agents) LangGraph vs AutoGen 2026: Comparison + AG2 Migration Notes AI Agents Comparison Fresh · Last reviewed: 3 August 2026 · 43d ago # LangGraph vs AutoGen 2026: Comparison + AG2 Migration Notes ## TL;DR Quick Answer Cited by AI > In 2026 LangGraph 1.0 (Oct 2025 GA) is the durable-execution choice; Microsoft moved AutoGen into maintenance mode and shipped Agent Framework 1.0 on April 3, 2026 as the successor; the community-driven AG2 1.0 (July 29, 2026) continues the original AutoGen philosophy under Apache 2.0. Alice Labs migrates AutoGen v0.2 systems to LangGraph 1.0 for durability, MAF for Azure, and AG2 for conversational continuity. A senior engineer's 2026 comparison of LangGraph 1.0, AG2 1.0, and Microsoft Agent Framework 1.0 with concrete migration paths from AutoGen v0.2 and v0.4, based on Alice Labs' 100+ production agent implementations across the Nordics and Europe. LangGraph vs AutoGen is the 2026 comparison between two dominant Python multi-agent frameworks: LangChain's LangGraph 1.0 (graph-based, durable execution, checkpointer-backed persistence) and Microsoft's AutoGen (now in maintenance mode, split into AG2 1.0 and Microsoft Agent Framework 1.0). The comparison matters because AutoGen users must choose a forward path in 2026 without inheriting a frozen async stack. ![Eric Lundberg - Author at Alice Labs](/images/eric-lundberg.png) Written by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) ![Linus Ingemarsson - Reviewer at Alice Labs](/images/linus-ingemarsson.png) Reviewed by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) Published August 3, 2026 14-18 min ## Key Takeaways - 01 LangGraph 1.0 reached GA on October 22, 2025 with zero breaking changes; the only meaningful deprecation is langgraph.prebuilt, whose functionality moved to langchain.agents. - 02 Microsoft's microsoft/autogen repository banner explicitly states AutoGen is in maintenance mode — no new features, community managed going forward; the repo entered maintenance mode with 60.2k stars and 9.1k forks. - 03 Microsoft Agent Framework 1.0 reached GA on April 3, 2026 for Python and .NET, built by the merged AutoGen and Semantic Kernel teams as the direct successor to both. - 04 AG2 1.0.1 shipped on July 29, 2026 under Apache 2.0 with an AgentOS vision targeting cross-framework interop (AG2, Google ADK, OpenAI, LangChain) via A2A and MCP. - 05 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. - 06 For durability, LangGraph 1.0 remains the strongest choice — LangSmith case studies include Uber, LinkedIn, Klarna, JP Morgan, BlackRock, and Cisco. - 07 MAF 1.0 ships SOC 2 and HIPAA compliance via Azure and native OpenTelemetry — the right target for Foundry-native and Azure OpenAI enterprises. - 08 Alice Labs' 100+ production migrations run 2-4 weeks per system for AutoGen v0.2 to LangGraph, and 4-8 weeks end-to-end including canary and replay. ### Contents 14 min left - [01 LangGraph vs AutoGen 2026: which framework should you actually use? ](#which-framework-in-2026) - [02 Is AutoGen deprecated in 2026? ](#is-autogen-deprecated) - [03 LangGraph 1.0 architecture: durable execution, checkpointers, and human-in-the-loop ](#langgraph-1-0-architecture) - [04 AutoGen v0.2 vs AutoGen v0.4: the async rewrite that split the community ](#autogen-v0-2-vs-v0-4) - [05 AG2 1.0: the community AutoGen fork's AgentOS vision ](#ag2-1-0) - [06 Microsoft Agent Framework 1.0: the official AutoGen and Semantic Kernel successor ](#microsoft-agent-framework) - [07 Feature-by-feature: LangGraph 1.0 vs AG2 1.0 vs Microsoft Agent Framework 1.0 ](#feature-by-feature) - [08 Migrating from AutoGen v0.2 to LangGraph 1.0 ](#migrate-v0-2-to-langgraph) - [09 Migrating from AutoGen v0.4 to Microsoft Agent Framework 1.0 ](#migrate-v0-4-to-maf) - [10 When to migrate to AG2 1.0 instead of LangGraph or MAF ](#when-ag2) - [11 Durability and checkpointing: the real production differentiator in 2026 ](#durability-checkpointing) - [12 Observability and evals: LangSmith vs Microsoft OpenTelemetry ](#observability-evals) - [13 Cost and enterprise considerations: SOC 2, HIPAA, EU AI Act ](#compliance) - [14 Decision matrix: which framework for which use case in 2026 ](#decision-matrix) - [15 Alice Labs migration playbook: from AutoGen v0.2 to LangGraph 1.0 or MAF 1.0 ](#alice-labs-playbook) Part of [Best AI Agent Frameworks 2026](/en/insights/best-ai-agent-frameworks-2026) 01 / 15 Dimension ## 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](/en/insights/best-ai-agent-frameworks-2026). 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](/en/insights/langgraph-vs-claude-agent-sdk-2026) is the more precise fork. The 2026 default: pick one of three, not four AutoGen v0.2 and v0.4 are no longer valid new-project targets. If you are starting greenfield in 2026, choose between LangGraph 1.0, Microsoft Agent Framework 1.0, and AG2 1.0. Every legacy AutoGen deployment should have a migration plan on the roadmap for 2026-2027. 02 / 15 Dimension ## 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. Maintenance mode ≠ safe indefinitely Community-managed maintenance mode covers security patches, but the async event-driven rewrite that became AutoGen v0.4 is now the frozen high-water mark. Any new async or observability improvements will land in AG2 or MAF, not upstream AutoGen. 03 / 15 Dimension ## 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](/en/insights/langgraph-guide-2026) covers supervisor, swarm, and multi-agent topologies end-to-end. Upgrade path from 0.x is trivial in most codebases LangGraph 1.0 shipped with zero breaking changes for code that avoided langgraph.prebuilt. If you did use prebuilt, the enhanced replacements live in langchain.agents and take a small targeted refactor. Do it on a branch, rerun evals, ship. 04 / 15 Dimension ## 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. Do not migrate v0.2 → v0.4 in 2026 AutoGen v0.4 is now the frozen high-water mark of a maintenance-mode project. Any v0.2 to v0.4 migration effort is better spent going directly to LangGraph 1.0, MAF 1.0, or AG2 1.0 — the API delta is comparable and the target project is under active development. 05 / 15 Dimension ## 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. Version-pin autogen and ag2 explicitly in production During the fork the PyPI 'autogen' name aliased to AG2 in some configurations. Any production requirements.txt or pyproject.toml using a loose autogen pin should be tightened. Prefer 'pyautogen==0.2.x' for legacy Microsoft AutoGen, 'ag2==1.0.x' for new AG2, and 'ag2-classic==0.x' for the classic API. 06 / 15 Dimension ## 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. MAF is not Azure-only The most common misconception in 2026 is that Microsoft Agent Framework locks you into Azure. It does not — MAF speaks to Anthropic, OpenAI, Ollama, and AWS Bedrock out of the box. The runtime is Microsoft-authored; the model providers are pluggable. 07 / 15 Dimension ## 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 Dimension LangGraph 1.0 AG2 1.0 Microsoft Agent Framework 1.0 Orchestration model Explicit StateGraph Conversational + workflow Agents + graph-based Workflows Durable persistence First-class checkpointer (SQLite / Postgres / Redis) Session + history compaction; no first-class durable executor Checkpointing GA; resume semantics Human-in-the-loop interrupt() + Command primitives UserProxy-style patterns Middleware + checkpoint-driven pause Streaming Tokens, values, updates typed Async message streams Typed event streams Multi-vendor interop LangChain agents; any LLM via LangChain integrations AgentOS via A2A + MCP (ADK, OpenAI, LangChain) Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, Bedrock Observability LangSmith tracing, evals, prompt hub OpenTelemetry via extensions Native OpenTelemetry; Azure Monitor integration License MIT Apache 2.0 MIT Ecosystem gravity 5M+ monthly downloads (LangChain-wide) Community-driven; ~4.8k stars Microsoft-backed; Azure-adjacent 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. 08 / 15 Dimension ## 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](/en/insights/langgraph-guide-2026). 2-4 weeks per system Alice Labs' measured migration time for a production AutoGen v0.2 system to LangGraph 1.0 with tool parity is 2-4 weeks of engineering effort, plus 2 weeks of golden-trace replay and canary. Full end-to-end delivery is typically 4-8 weeks. 09 / 15 Dimension ## 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. MAF middleware is a re-architecture, not a rename Custom v0.4 message-interception hooks do not translate 1:1 into MAF middleware. Enumerate every interception point on day one of the migration and design the middleware chain deliberately — retrofitting later is more expensive than it looks. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Migrating off AutoGen? We've done it 100+ times. Alice Labs is a Stockholm-headquartered enterprise AI consultancy with 100+ production agent implementations across the Nordics, Europe, and global clients — including AutoGen v0.2 to LangGraph 1.0 and Microsoft Agent Framework migrations with 2-week replay and 5% canary already solved. [Talk to an Agent Migration Expert](#contact) 10 / 15 Dimension ## 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. AG2 has no first-class durable executor If durability across process restarts is a hard requirement — long-running agents, HITL that can pause overnight, workflows that survive redeploys — AG2 is the wrong target. Choose LangGraph 1.0 or MAF 1.0 instead. 11 / 15 Dimension ## 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. Durability is the 2026 framework-choice pivot Across Alice Labs' 100+ enterprise agent implementations, the single most predictive question for framework fit in 2026 is: 'must in-flight runs survive process restarts and redeploys?' If yes, LangGraph or MAF. If no, any framework works — but the answer is almost always yes for production. 12 / 15 Dimension ## 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. Wire OTel from day one Whichever framework you pick, emit OpenTelemetry traces from day one — even in development. Trace data is the difference between debugging a production incident in an hour versus a week, and retrofitting tracing onto a live system is always more painful than starting with it. 13 / 15 Dimension ## 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](/en/insights/eu-ai-act-compliance-checklist-2026). Framework choice does not create compliance SOC 2, HIPAA, and EU AI Act obligations attach to the operator, not the framework. MAF's Azure certifications shorten the path if you are already Azure-native; LangGraph's design supports the primitives; AG2 works with more custom work. Always consult legal counsel for compliance determinations. 14 / 15 Dimension ## 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 Use case Recommended framework Why Long-running, durable, HITL-heavy workflows LangGraph 1.0 First-class checkpointer + interrupt() + battle-tested at Uber, LinkedIn, Klarna Azure or Foundry-native enterprise (.NET or Python) Microsoft Agent Framework 1.0 Inherited SOC 2/HIPAA via Azure, native OTel, Foundry integration Multi-vendor agent interop, community governance AG2 1.0 AgentOS via A2A + MCP; Apache 2.0; community-governed Existing AutoGen v0.2 production, low migration budget AG2 1.0 with ag2-classic bridge Preserves ConversableAgent/GroupChat semantics; smallest cognitive distance Greenfield with no vendor lock-in preference LangGraph 1.0 Largest ecosystem, strongest production references, best default choice Prototype / one-off / non-production Any of the three Choose based on team familiarity; migration cost is low at prototype scale 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. 15 / 15 Dimension ## 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](/en/insights/multi-agent-systems-explained) and the surrounding [best AI agent frameworks 2026 guide](/en/insights/best-ai-agent-frameworks-2026). For build-vs-buy economics on the migration itself, our [build vs buy AI guide](/en/insights/build-vs-buy-ai) documents the same decision framework we run with buyers. 4-8 weeks end-to-end per production system Alice Labs' measured delivery time for AutoGen v0.2 to LangGraph 1.0 or MAF 1.0, including golden-trace replay, durability, OpenTelemetry, and a 2-week 5% canary: 4-8 weeks per production agent system, with two engineers full-time and a senior reviewer at 20%. ## About the Authors & Reviewers Published August 3, 2026 Written by ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Reviewed by August 3, 2026 ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Published August 3, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### Is AutoGen deprecated in 2026? ▾ AutoGen is not fully deprecated but has been in maintenance mode since October 2025. Microsoft's official microsoft/autogen repository 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, which reached 1.0 GA on April 3, 2026. Alice Labs advises AutoGen v0.2 clients to migrate to LangGraph 1.0, Microsoft Agent Framework 1.0, or AG2 1.0 before 2027. ### What is the difference between AG2 and AutoGen? ▾ AG2 is the community-driven fork of AutoGen, hosted under the ag2ai GitHub organization and licensed Apache 2.0. It was created by AutoGen's original creators and maintainers to continue the original conversational multi-agent philosophy after Microsoft moved AutoGen into maintenance mode. AG2 1.0.1 shipped on July 29, 2026 with a new AgentOS vision emphasizing interoperability across AG2, Google ADK, OpenAI, and LangChain agents via A2A and MCP. The classic AutoGen namespace has moved to a separate ag2-classic package. ### LangGraph vs AutoGen: which is better for production in 2026? ▾ For most production workloads in 2026, LangGraph 1.0 is the safer choice because it is the first stable major release in the durable agent framework space and is battle-tested at Uber, LinkedIn, Klarna, JP Morgan, and BlackRock. AutoGen is in maintenance mode. Alice Labs uses LangGraph 1.0 for durability-heavy workflows and Microsoft Agent Framework 1.0 for Azure-native enterprise clients. ### When did LangGraph 1.0 come out? ▾ LangGraph 1.0 reached general availability on October 22, 2025 with zero breaking changes and full backward compatibility with 0.x. The only meaningful deprecation is the langgraph.prebuilt module, whose functionality moved to langchain.agents. It is the first stable major release in the durable agent framework category. ### When was Microsoft Agent Framework released? ▾ Microsoft Agent Framework 1.0 reached general availability on April 3, 2026 for both Python and .NET, with Go in public preview. It was built by the merged AutoGen and Semantic Kernel teams at Microsoft and is described in Microsoft's documentation as the direct successor to both frameworks. It ships with three core primitives: Agents, Harness, and Workflows. ### What is the best framework to migrate from AutoGen to? ▾ The best migration target depends on the workload. Alice Labs recommends: LangGraph 1.0 for durable, long-running, human-in-the-loop workflows; Microsoft Agent Framework 1.0 for Azure or Foundry-native enterprise systems and .NET shops; and AG2 1.0 for teams that want to preserve AutoGen's conversational patterns and open community governance. Legacy AutoGen v0.2 systems should not remain past 2026. ### Is LangGraph a replacement for AutoGen? ▾ LangGraph is a functional replacement for AutoGen in most production scenarios, but the mental model differs. AutoGen and AG2 center on conversational agents that talk to each other. LangGraph centers on an explicit state graph where nodes are functions or LLM calls and edges control flow. Alice Labs rewrites AutoGen ConversableAgent patterns as LangGraph supervisor or swarm topologies, mapping GroupChat handoffs to conditional edges and UserProxyAgent to LangGraph's interrupt primitive. ### Is AG2 the same as AutoGen? ▾ AG2 is not the same package as AutoGen anymore. Historically the PyPI name autogen aliased to AG2 during the fork transition, silently giving developers AG2's API. As of AG2 1.0 (July 29, 2026), the pre-1.0 autogen namespace with ConversableAgent and GroupChat has moved to a separate ag2-classic package. Installing ag2 today gives you the new v1.0 API, not the classic AutoGen API. Version pinning is essential. ### Can I use LangGraph and AutoGen together? ▾ Yes, but with caveats. Alice Labs occasionally runs LangGraph as the outer orchestrator with an AutoGen or AG2 subgraph handling a specific conversational subtask (e.g., debate patterns). This works because both frameworks are Python-native and async. However, we do not recommend this as a target architecture — it doubles the surface area for observability, checkpointing, and dependency management. Use it as a migration bridge only. ### Does LangGraph support human-in-the-loop natively? ▾ Yes. LangGraph 1.0 ships interrupt() and Command primitives as first-class human-in-the-loop mechanisms. A node can call interrupt() with a payload, the graph pauses and persists via the checkpointer, and a Command with the human decision resumes the graph exactly where it stopped — even if hours or days have passed and the process has restarted in the meantime. This is what distinguishes LangGraph's HITL story from AutoGen v0.4's in-process UserProxyAgent. ### What is the difference between AutoGen v0.2 and AutoGen v0.4? ▾ AutoGen v0.4 is a ground-up rewrite adopting an async event-driven architecture. ConversableAgent was replaced by AssistantAgent with on\_messages() and on\_reset() handlers; GroupChat and GroupChatManager were replaced by RoundRobinGroupChat teams; model config shifted from OpenAIWrapper and config\_list to direct OpenAIChatCompletionClient. Several v0.2 features — including Teachable Agents and Model Client Cost tracking — did not carry forward. The rewrite is what preceded the AG2 fork and Microsoft's eventual maintenance-mode decision. ### Which framework has the best durable execution in 2026? ▾ LangGraph 1.0 has the most mature durable execution story — a first-class checkpointer with in-memory, SQLite, and Postgres backends, and resume-exactly-where-you-crashed semantics baked in since well before 1.0 GA. Microsoft Agent Framework 1.0 shipped checkpointing GA and is closing the gap quickly. AG2 has no first-class durable executor and relies on session and history compaction — the wrong target if durability is a hard requirement. ### Does Microsoft Agent Framework only work with Azure? ▾ No. MAF 1.0 speaks to Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and AWS Bedrock out of the box. The runtime is Microsoft-authored but the model providers are pluggable. The most common misconception in 2026 is that MAF locks you into Azure — it does not. Compliance certifications like SOC 2 and HIPAA are inherited from Azure when you deploy there, but MAF itself is not Azure-only. ### How long does an AutoGen v0.2 to LangGraph 1.0 migration take? ▾ Alice Labs' measured migration time across 100+ production implementations is 2-4 weeks per production agent system for the engineering rewrite, plus an additional 2 weeks of golden-trace replay and 2-week 5% canary. End-to-end delivery is typically 4-8 weeks per system with two engineers full-time and a senior reviewer at 20%. The main variables are the number of custom tools and the depth of GroupChat orchestration in the original system. ### Is AG2 1.0 drop-in compatible with old AutoGen code? ▾ No. AG2 1.0 is deliberately not drop-in with the pre-1.0 autogen namespace. The classic ConversableAgent, GroupChat, and GroupChatManager API 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. Any legacy production code needs an explicit choice: pin to ag2-classic to preserve behaviour, or refactor onto the AG2 1.0 API to take advantage of the new AgentOS interop features. ### Which framework is best for EU AI Act compliance? ▾ Alice Labs' preferred stack for EU AI Act high-risk deployments is LangGraph 1.0 self-hosted plus LangSmith EU region. LangGraph's checkpointer plus interrupt() primitive make Article 14 human-oversight logging a five-line addition. Microsoft Agent Framework 1.0 is also strong here because SOC 2 and HIPAA are inherited via Azure and OpenTelemetry emission is native. AG2 works but requires more custom middleware to reach the same audit surface. Always consult legal counsel for compliance determinations. ### What are the biggest AutoGen migration pitfalls in 2026? ▾ Based on Alice Labs' 100+ production implementations: (1) migrating v0.2 to v0.4 instead of directly to LangGraph or MAF — v0.4 is now a frozen high-water mark; (2) not pinning autogen and ag2 explicitly in requirements files — the PyPI aliasing during the fork caused silent behaviour changes; (3) attempting AG2 for durability-heavy workloads — AG2 has no first-class durable executor; (4) skipping golden-trace replay before cutover — semantic parity is not optional; (5) treating AutoGen Studio as production observability — it is a design surface, not a monitoring surface. ### What is AgentOS in AG2 1.0? ▾ AgentOS is AG2 1.0's flagship vision — interoperability across AG2, Google ADK, OpenAI, and LangChain agents via the A2A (Agent-to-Agent) protocol and MCP (Model Context Protocol). It positions AG2 as the multi-vendor orchestrator in a world where different teams and vendors are shipping different agent frameworks. This is AG2's strongest differentiator versus Microsoft Agent Framework (Microsoft-first) and LangGraph (LangChain-first). AgentOS is still early in 2026 but is the strategic reason to choose AG2 in mixed-vendor environments. ### Should I use LangGraph or Microsoft Agent Framework for a greenfield project? ▾ For a greenfield project in 2026 with no strong vendor preference, Alice Labs' default recommendation is LangGraph 1.0. It has the largest ecosystem, the strongest production references (Uber, LinkedIn, Klarna, JP Morgan, BlackRock, Cisco), and the most mature durable execution story. Choose Microsoft Agent Framework 1.0 instead if you are Azure or Foundry-native, if .NET is a required language, or if inherited SOC 2 and HIPAA via Azure is a compliance shortcut worth taking. ### Is LangGraph a replacement for LangChain? ▾ No, LangGraph and LangChain are complementary. LangChain 1.0 provides the model, tool, and integration abstractions (chat models, tool bindings, retrievers, document loaders). LangGraph 1.0 provides the graph-based orchestration runtime on top. In practice, a LangGraph node calls LangChain-bound LLMs and tools; the two are designed to work together. The 1.0 releases shipped on the same day (October 22, 2025) precisely to make this pairing coherent. ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) [Previous in AI Agents ### CrewAI vs AutoGen 2026: Which Multi-Agent Framework Wins? ](/en/insights/crewai-vs-autogen-2026)[Next in AI Agents ### LangGraph vs CrewAI 2026: Head-to-Head Comparison ](/en/insights/langgraph-vs-crewai-2026) ## Further reading - [LangGraph 1.0 is Now Generally Available — LangChain Changelog](https://changelog.langchain.com/announcements/langgraph-1-0-is-now-generally-available)· changelog.langchain.com - [LangChain & LangGraph 1.0 — LangChain Blog](https://www.langchain.com/blog/langchain-langgraph-1dot0)· langchain.com - [microsoft/autogen GitHub repository (maintenance mode)](https://github.com/microsoft/autogen)· github.com - [AutoGen v0.2 to v0.4 Migration Guide](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/migration-guide.html)· microsoft.github.io - [Microsoft Agent Framework Overview](https://learn.microsoft.com/en-us/agent-framework/overview/)· learn.microsoft.com - [Microsoft Agent Framework — From AutoGen Migration Guide](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-autogen/)· learn.microsoft.com - [AG2 on PyPI](https://pypi.org/project/ag2/)· pypi.org - [AG2 Official Site](https://www.ag2.ai/)· ag2.ai - [LangGraph Documentation — Overview](https://docs.langchain.com/oss/python/langgraph/overview)· docs.langchain.com ## Related services [AI agents development and deployment ](/en/ai-agents) ## Related reading [pillar ### Best AI Agent Frameworks 2026: The Enterprise Comparison The pillar comparison across LangGraph, Claude Agent SDK, AG2, Microsoft Agent Framework, CrewAI, and others — state, multi-agent support, and production fit. ](/en/insights/best-ai-agent-frameworks-2026)[comparison ### LangGraph vs Claude Agent SDK (2026): Head-to-Head Buyer's Guide The other 2026 shortlist most enterprise architects arrive at — architecture, MCP, subagents, cost, and when each one wins. ](/en/insights/langgraph-vs-claude-agent-sdk-2026)[deepdive ### LangGraph Implementation Guide 2026: Stateful AI Agents for Enterprise The Python-first implementation guide — checkpointers, supervisor and swarm patterns, and the langgraph.prebuilt migration path. ](/en/insights/langgraph-guide-2026)[deepdive ### Multi-Agent Systems Explained: Patterns, Trade-offs, and Enterprise Use Cases Architectural context for supervisor, hierarchical, and peer-to-peer patterns underneath every framework covered in this comparison. ](/en/insights/multi-agent-systems-explained)[deepdive ### AI Agent Architecture Patterns for Enterprise The five core agent architecture patterns — ReAct, Plan-and-Execute, Supervisor, Reflection, and Tool-Use — with implementation guidance. ](/en/insights/ai-agent-architecture-patterns)[deepdive ### EU AI Act Compliance Checklist for 2026 The compliance frame behind Alice Labs' preferred stack for high-risk deployments — logging, oversight, transparency, and framework choice. ](/en/insights/eu-ai-act-compliance-checklist-2026)[deepdive ### Build vs Buy AI: Enterprise Decision Framework 2026 The economic framework behind the migration playbook — when a rewrite pays back and when it does not, based on Alice Labs' 100+ implementations. ](/en/insights/build-vs-buy-ai) ## Sources 1. [LangGraph 1.0 is Now Generally Available](https://changelog.langchain.com/announcements/langgraph-1-0-is-now-generally-available)LangChain · LangChain “LangGraph 1.0 reached general availability on October 22, 2025 with zero breaking changes. The only meaningful deprecation is the langgraph.prebuilt module, whose functionality moved to langchain.agents.” (accessed 2026-08-03) 2. [LangChain & LangGraph 1.0](https://www.langchain.com/blog/langchain-langgraph-1dot0)LangChain · LangChain “LangGraph 1.0 shipped with StateGraph, durable execution via checkpointer, and first-class interrupt()/Command primitives for human-in-the-loop. Production users include Uber, LinkedIn, Klarna, JP Morgan, BlackRock, and Cisco.” (accessed 2026-08-03) 3. [microsoft/autogen GitHub Repository (Maintenance Mode)](https://github.com/microsoft/autogen)Microsoft · Microsoft “The microsoft/autogen banner states: 'AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward.' Repository stats at maintenance-mode entry: 60.2k stars, 9.1k forks, 558 open issues.” (accessed 2026-08-03) 4. [AutoGen v0.2 to v0.4 Migration Guide](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/migration-guide.html)Microsoft · Microsoft “AutoGen v0.4 is a ground-up rewrite adopting async event-driven architecture. ConversableAgent replaced by AssistantAgent with on\_messages()/on\_reset(); GroupChat/GroupChatManager replaced by RoundRobinGroupChat teams; model config shifted from OpenAIWrapper+config\_list to direct OpenAIChatCompletionClient.” (accessed 2026-08-03) 5. [Microsoft Agent Framework Overview](https://learn.microsoft.com/en-us/agent-framework/overview/)Microsoft · Microsoft “Microsoft Agent Framework 1.0 reached general availability on April 3, 2026 for Python and .NET, with Go in public preview. Built by the merged AutoGen and Semantic Kernel teams. Three primitives: Agents, Harness, Workflows. Providers include Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and AWS Bedrock.” (accessed 2026-08-03) 6. [Microsoft Agent Framework — From AutoGen Migration Guide](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-autogen/)Microsoft · Microsoft “Official mappings from AutoGen v0.4 to MAF 1.0: autogen\_agentchat.AssistantAgent → agent\_framework.Agent; GroupChat teams → Workflow with graph-based routing; FunctionTool → @tool decorator with automatic schema inference; ChatCompletionContext → AgentSession with optional external stores (Redis).” (accessed 2026-08-03) 7. [AG2 on PyPI](https://pypi.org/project/ag2/)AG2 Team · ag2ai “AG2 1.0.1 released on PyPI on July 29, 2026 under Apache 2.0. Hosted at the ag2ai GitHub organization (4,828 stars at release). AG2 1.0 is not drop-in with pre-1.0 autogen; classic namespace with ConversableAgent and GroupChat moved to a separate ag2-classic package. AgentOS vision targets interop with Google ADK, OpenAI, and LangChain via A2A and MCP.” (accessed 2026-08-03) 8. [AG2 Official Site](https://www.ag2.ai/)AG2 Team · ag2ai “AG2 governance statement: 'created and maintained by AutoGen's original creators and maintainers following a contribution-based open governance protocol.' Community-driven Apache 2.0 project.” (accessed 2026-08-03) 9. [LangGraph Documentation — Overview](https://docs.langchain.com/oss/python/langgraph/overview)LangChain · LangChain “LangGraph 1.0 durable state auto-persists via checkpointer; interrupted workflows resume without context loss. Checkpointer backends: in-memory, SQLite, Postgres. Streaming primitives cover tokens, values, and updates with typed boundaries.” (accessed 2026-08-03) 10. [Enterprise Multi-Agent Framework Migration Data](https://alicelabs.ai/en/ai-agents)Alice Labs · Alice Labs “Alice Labs has delivered 100+ production AI agent implementations since 2023 across the Nordics, Europe, and global enterprises — including AutoGen v0.2 to LangGraph 1.0 and Microsoft Agent Framework migrations. Measured migration time: 2-4 weeks engineering rewrite plus 2 weeks replay/canary; end-to-end delivery 4-8 weeks per production agent system.” (accessed 2026-08-03) Next scheduled review: 2026-11-03 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Ready to Migrate Off AutoGen? Alice Labs has delivered 100+ production AI agent implementations across the Nordics and Europe — from AutoGen v0.2 to LangGraph 1.0 and Microsoft Agent Framework, with golden-trace replay, canary rollout, and EU AI Act audit hooks built in. 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