Google ADK vs LangGraph vs AutoGen for Enterprise Agents

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Google ADK, LangGraph, and AutoGen are three different orchestration substrates, not three versions of the same thing. ADK is Google’s hierarchical, GCP-native framework, and it reached general availability in April 2025. LangGraph is an open-source, cloud-agnostic framework built around an explicit StateGraph and checkpointer model, so you write the control flow yourself. AutoGen is Microsoft’s original conversational framework, in maintenance mode since October 2025, with Microsoft Agent Framework (MAF) as its GA successor. One fact most 2026 comparisons miss: ADK carries a critical, now-patched remote-code-execution vulnerability (CVE-2026-4810, CVSS v3 9.8). This guide compares orchestration model, protocol support, security posture, and enterprise-readiness signals across all three, then names the decision none of them resolves.

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Google ADK vs LangGraph vs AutoGen: at a glance

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Google ADK, LangGraph, and AutoGen all sit at the same layer of the stack: orchestration, not governance. Whichever one runs the loop, the agent still needs to know what your business’s data actually means, and that is the gap Atlan’s context layer closes: a governed, model-agnostic tier that delivers definitions, lineage, and policy to any agent regardless of which of the three frameworks below it runs on. The quick facts and comparison table below focus on orchestration model, protocol support, and security posture; the context layer question returns later in this guide.

• What each one is: Google ADK is a hierarchical agent-tree framework native to Google Cloud; LangGraph is an open-source StateGraph framework you run anywhere; AutoGen is Microsoft’s original conversational framework, now frozen in maintenance mode.
• Who runs the orchestration: with all three, you write and run the agent loop yourself, in your own infrastructure.
• Current status (2026): ADK and LangGraph are both actively developed; AutoGen receives bug and security fixes only, with Microsoft Agent Framework as the maintained path forward.
• Where they converge: all three now support the Agent2Agent (A2A) protocol natively, so agents built on any of them can discover and hand off work to one another; each can also connect to Model Context Protocol-based context sources.
• What none of them resolves: whether the agent actually knows what your business’s data means.

Dimension	Google ADK	LangGraph	AutoGen (→ MAF)
What it is	Hierarchical, GCP-native multi-agent framework	Open-source, cloud-agnostic StateGraph framework	Conversational multi-agent framework, now in maintenance mode
Who runs the orchestration	You do, in a hierarchical agent tree	You do, via an explicit StateGraph	You do; new builds should target Microsoft Agent Framework instead
Language and runtime	Python, Go, Java, TypeScript, Kotlin	Python, JavaScript/TypeScript	Python (legacy); Python and .NET via MAF
Cloud alignment	GCP-native, Vertex AI integration	Cloud-agnostic	Microsoft ecosystem (Python/.NET) via MAF
Current status (2026)	Active GA, ADK 2.0 stable	Active, large open-source community	Maintenance mode since October 2025
Protocol support	Native A2A	Native A2A	Native A2A (via MAF)
Best for	GCP-committed teams, multi-language support	Teams needing explicit, cross-cloud state control	Teams already on AutoGen, migrating to MAF

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None of these three frameworks governs what your agent knows

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Before comparing Google ADK, LangGraph, and AutoGen feature by feature, it helps to name the one axis none of the three resolves: whether the agent knows what your business’s data actually means. Whether the loop is ADK’s hierarchical agent tree, LangGraph’s StateGraph, or AutoGen and MAF’s conversational pattern, none of them encode which “revenue” definition is correct, which table is authoritative, or who is allowed to see what. That distinction sits above the single-agent versus multi-agent architecture question entirely.

To be fair, this gap is narrowing at the operational layer. ADK ships structured session management, and MAF documents “context providers” for agent memory, features that genuinely help an agent harness track state across a run. A skeptical reader could argue the frameworks are converging toward owning the context layer, not leaving it to a separate tier. The honest distinction: operational or session context is not the same as governed business context. The comparison below treats that fairly and returns to it once the head-to-head is done. For the fuller argument, see how to implement an enterprise context layer for AI.

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What is Google ADK?

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Google ADK, the Agent Development Kit, is Google’s open-source (Apache 2.0) framework for building multi-agent systems, with agents organized as a hierarchical tree rather than a flat graph or a conversational loop. According to Google Cloud (2026), ADK reached general availability in April 2025, and ADK 2.0 is stable across Python, Go, and Java, with TypeScript and Kotlin also supported, one of the more multi-language-friendly options in the AI agent stack.

Beyond language support, ADK’s native Agent2Agent (A2A) protocol support and integration with Vertex AI Model Garden’s 200-plus models and Gemini Enterprise matter more. Mitch Ashley, VP and Practice Lead of Software Lifecycle Engineering at Futurum Group, frames the shift plainly: “ADK shifts the question from which framework should I build agents with to which framework owns the execution layer…” Thoughtworks Technology Radar (April 2026) places ADK in its “Trial” ring: “Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk,” a fair, sourced read rather than an endorsement.

ADK is also growing fastest of the three by relative velocity, and real usage is showing up both in the raw open-source library and in Google’s own products built on it:

• GitHub momentum: around 21,000 stars and 3,800 forks, for a framework that only reached GA in April 2025.
• Deloitte: used Google Cloud AI Agents and Gemini Enterprise, a managed Google product that runs on ADK, for inventory insights and discrepancy detection.
• A furniture manufacturer, with partner Quantiphi: built a self-run multi-agent ADK and A2A system automating quotation-response creation.

Both case studies are named and verifiable, per Google Cloud’s “Building Scalable AI Agents” design-pattern writeup, and together they show both ADK’s library adoption and Google’s managed layer on top of it.

Core components of Google ADK

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• Hierarchical agent tree: agents are organized as parent-child structures rather than a flat graph.
• Native A2A protocol support: built-in interoperability with agents from other frameworks.
• Multi-language runtime: Python, Go, Java, TypeScript, and Kotlin.
• Vertex AI Model Garden integration: access to 200-plus models and Gemini Enterprise.
• Built-in session and context management: operational state handling, distinct from governed business context, a distinction worth remembering once you reach the production readiness section below.

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What is LangGraph?

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LangGraph is an open-source orchestration framework built around an explicit StateGraph API: you write the control flow yourself, modeling the agent as a graph of nodes and edges, with typed state passing directly between steps. That is a different trade than ADK’s hierarchy or AutoGen’s conversational pattern; more control, in exchange for owning more of the runtime. LangChain and LangGraph are related but distinct: LangGraph is the graph orchestration layer, not the broader LangChain toolkit.

LangGraph is cloud-agnostic and has the largest community of the three by GitHub stars: roughly 38,700 stars and 6,500 forks. According to uvik.net (2026), it also sees around 34.5 million monthly PyPI downloads and powers 400-plus named enterprise production deployments, including Klarna, Uber, LinkedIn, BlackRock, and Cisco, more named production deployments than either ADK or AutoGen currently shows in public reporting. Full internals, including the checkpointer model, are covered on Atlan’s dedicated LangGraph page; this section stays focused on what the comparison needs.

Core components of LangGraph

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• StateGraph API: the programming model for defining nodes, edges, and control flow.
• Explicit state management: typed state that passes deterministically between steps.
• Step and loop control: direct caps on iterations to prevent runaway loops.
• Checkpointers: persistence for graph state, with a SQLite option among others.
• Broad ecosystem: interoperability with the wider LangChain tooling and the AI agent frameworks landscape more broadly.

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What is AutoGen, and what replaced it?

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Before Microsoft Agent Framework existed, there was AutoGen: Microsoft’s original conversational multi-agent framework, built around a message-passing, emergent-behavior pattern rather than a fixed graph or tree, where agents talk through a problem rather than following a predetermined path. This page keeps the description brief on purpose. What is AutoGen? covers the ConversableAgent primitive, the GroupChat pattern, and the full status timeline; this section only states what the comparison needs.

The current, easily-gotten-wrong fact: Microsoft moved AutoGen into maintenance mode in October 2025, bug and security fixes only, no new features. Microsoft Agent Framework (MAF) is the named successor. According to the European AI & Cloud Summit (2026), MAF reached 1.0 GA for Python and .NET on April 3, 2026, “production-ready, with stable APIs.” AG2 is a separate, community-governed fork created in November 2024 by original creators Chi Wang and Qingyun Wu under Apache 2.0, for teams staying on an open community path instead.

The common mistake worth stating plainly: legacy AutoGen still holds around 60,200 GitHub stars against MAF’s roughly 12,600, seven months after the maintenance-mode announcement. That is real adoption inertia and a fair nuance against a naive “just migrate” framing; teams have working systems and switching costs. It is also a reason for enterprise-ready AI agent planning to target MAF for any new build rather than a framework that receives no new features.

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Google ADK vs LangGraph vs AutoGen: how do they compare head-to-head?

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Google ADK, LangGraph, and AutoGen diverge most on orchestration model and cloud alignment, and they converge on something most 2026 comparison content misses entirely: all three now speak the same interoperability protocol, A2A. The table below maps nine decision axes, including one every ranking competitor page currently skips: security and CVE posture.

The context layer sits above whichever orchestration substrate you choose, and moves with you if you switch.

Consider a platform team evaluating a multi-agent customer-support system on Google Cloud:

• GCP-committed org: ADK’s native Vertex AI integration lets the team stand up agents without cross-cloud wiring.
• Needs cross-cloud portability: LangGraph’s explicit loop-and-step caps stop a planner agent looping indefinitely on an ambiguous tool result.
• Already on legacy AutoGen: the team faces a third decision, debug the existing system as-is or begin migrating its checkpointed state to MAF.

Each framework suits its lane. None of them resolves whether the agent actually knows what your business’s data means, which is exactly the scaling problem that shows up after the framework decision is made.

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What are the security risks of Google ADK, LangGraph, and AutoGen in production?

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This is the comparison axis every ranking page on this topic currently skips, and it is sourced material, not a smear on any one framework. All of the CVEs below are patched. The point is who owns the patch cadence and whether your team runs a current version, which matters for AI agent governance planning as much as the CVE details themselves.

Google ADK carries the most severe finding of the three; a team on an older version has a live exposure, exactly the kind of access control gap that turns a framework decision into a security incident. LangChain and LangGraph carry a documented cluster instead of one severe finding, per the National Vulnerability Database; teams self-hosting these packages own the prompt injection and patch cadence directly. AutoGen and MAF’s clean record describes what the research found, not a claim of inherent safety, since MAF is also the newest of the three and has had less time in production for issues to surface. Whichever framework you run, observability into what an agent actually did is what turns a patched CVE into a confirmed fix, and it is why teams report agents fail in production for reasons that have nothing to do with the model.

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When should you choose Google ADK, LangGraph, or AutoGen/MAF?

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The decision comes down to cloud commitment, control needs, and existing investment rather than a single “best” framework. The table below maps common situations to a default, with the reasoning, and includes the one situation this page will not resolve for you: choosing among more than these three.

If you are choosing among more than these three, CrewAI and the OpenAI Agents SDK are the other frameworks most enterprise teams shortlist. Rather than re-running a scorecard this page’s scope deliberately narrows away from, start with how to choose an agentic framework for enterprise for the full five-framework comparison, or AI agent frameworks compared for the broader landscape.

Framework choice is real, consequential, and reversible; it determines engineering ergonomics, cloud alignment, and security patch ownership. It does not determine whether the agent knows what your business’s data means, the thread this page returns to next.

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How Atlan approaches the agent stack above ADK, LangGraph, and AutoGen

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Google ADK, LangGraph, and AutoGen all orchestrate the agent loop, but none encode what your business means: which “revenue” definition is correct, which table is authoritative, who is allowed to see what. According to Gartner (June 2025), over 40 percent of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. Brian Hopkins, VP of Emerging Tech Portfolio at Forrester, put it plainly: “Three-quarters of enterprise leaders tell us they’re adopting agentic AI. Only a small minority have it running in meaningful production beyond ‘agentish’ chatbots.” Both support the same read: framework adoption alone is not producing production success.

The Context Layer for AI is the governed, model-agnostic tier that sits above ADK, LangGraph, and AutoGen or MAF alike. The Atlan MCP Server delivers governed context, definitions, lineage, policy, and ownership, to any agent regardless of which framework it runs on, since MCP is framework-agnostic by design. The Context Engineering Studio builds that business understanding; the Context Lakehouse stores it in an Iceberg-native, portable format, the antidote to the lock-in practitioners describe calling agent frameworks “the new monoliths.” This is distinct from ADK’s session management or MAF’s context providers, which handle operational state, not governed business meaning. See why MCP matters for AI agents and agent interoperability protocols for how that delivery works underneath any agent harness your team builds.

The outcome is measurable. According to Atlan research across customer deployments, governed retrieval reaches 94 to 99 percent AI accuracy versus 10 to 31 percent for ungoverned retrieval, isolating the variable: context, not framework, moves accuracy. At Workday, Atlan reports a 5x improvement in AI response accuracy through MCP-delivered context. Sridher Arumugham, Chief Data and Analytics Officer at DigiKey, put it directly: “Atlan is much more than a catalog of catalogs. It’s more of a context operating system.”

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Frameworks converge on protocols; context still decides production reliability

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The Google ADK versus LangGraph versus AutoGen choice is real for engineering ergonomics, cloud alignment, and security patch ownership, and the honest 2026 answer is that all three now converge on the same interoperability protocol: Agent2Agent. AutoGen specifically should route new builds to Microsoft Agent Framework, not the legacy repository, regardless of how many stars the older project still holds.

The decision that actually determines production reliability is the governed context layer above whichever framework you choose: model-agnostic, portable, independent of the orchestration substrate underneath it. That is where the 94 to 99 percent versus 10 to 31 percent accuracy gap comes from. As frameworks commoditize and converge on shared protocols, context is the part of the stack that compounds. Pick the framework that fits your team and your cloud, then invest in the layer that decides whether what you built is actually right.

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FAQs about Google ADK vs LangGraph vs AutoGen

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1. What is the difference between Google ADK and LangGraph?

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Google ADK organizes agents as a hierarchical tree native to Google Cloud, with built-in Vertex AI and Gemini Enterprise integration. LangGraph is cloud-agnostic and built around an explicit StateGraph API, so you own state and loop caps directly. Both support the same interoperability protocols, so the choice mostly comes down to cloud commitment and how much orchestration control you want to write yourself.

2. Is AutoGen still relevant in 2026, or is it deprecated?

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AutoGen is not deprecated, but it has been in Microsoft-declared maintenance mode since October 2025: bug and security fixes only, no new features. It remains relevant for existing production systems and the AG2 community fork, but new builds should target Microsoft Agent Framework, its official GA successor.

3. What replaced Microsoft AutoGen?

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Microsoft Agent Framework (MAF) is AutoGen’s official successor, reaching 1.0 GA for Python and .NET on April 3, 2026. A separate, community-governed fork called AG2 also exists, created in November 2024 by AutoGen’s original creators, for teams staying on an open community path instead.

4. Do Google ADK, LangGraph, and AutoGen all support the same interoperability protocols?

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Yes, as of 2026. ADK, LangGraph, CrewAI, LlamaIndex Agents, Semantic Kernel, and AutoGen (via Microsoft Agent Framework) all now support the Agent2Agent (A2A) protocol natively, letting agents built on different frameworks discover and hand off tasks to one another. Each can separately connect to MCP-based context sources.

5. Is Google ADK production-ready for enterprise use?

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Thoughtworks Technology Radar placed Google ADK in its “Trial” ring in April 2026, worth pursuing on a project that can handle some risk, a step below full endorsement. Named case studies from Deloitte and a Quantiphi-supported furniture manufacturer show real usage, and ADK 2.0 is stable, but confirm you are running a version patched past CVE-2026-4810 first.

6. What are the security risks of using Google ADK or LangGraph in production?

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Google ADK had a critical, now-patched remote-code-execution vulnerability, CVE-2026-4810, affecting versions before 1.28.1. LangChain and LangGraph have a documented, patched cluster covering path traversal, SQLite-checkpoint SQL injection, and a serialization flaw that could leak secrets. The operational risk is running an unpatched version, not an inherent flaw in either framework.

7. Can you migrate an existing AutoGen deployment to another framework?

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Yes. Microsoft publishes an official migration guide for moving Semantic Kernel and AutoGen projects to Microsoft Agent Framework, the most direct path since MAF is AutoGen’s designated successor. Moving to Google ADK or LangGraph instead means rewriting the orchestration logic against a different programming model, since neither offers an official AutoGen migration tool.

8. Do I still need a context layer if I use ADK, LangGraph, or AutoGen?

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Yes. All three frameworks orchestrate the agent loop, but none of them encode your business’s definitions, lineage, policy, or ownership. A governed context layer above whichever framework you choose delivers that business context to any agent, which is where production accuracy and reliability actually come from.

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Sources

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1. Build multi-agentic systems using Google ADK, Google Cloud
2. Building Scalable AI Agents: Design Patterns With Agent Engine on Google Cloud, Google Cloud
3. I/O '26 news for agent developers on Google Cloud, Google Cloud
4. google/adk-python GitHub repository, GitHub
5. langchain-ai/langgraph GitHub repository, GitHub
6. microsoft/autogen GitHub repository, maintenance-mode banner, GitHub
7. microsoft/agent-framework GitHub repository, GitHub
8. Microsoft Agent Framework: The production-ready convergence of AutoGen and Semantic Kernel, European AI & Cloud Summit
9. Agent Development Kit (ADK), Thoughtworks Technology Radar
10. Google ADK Is Not a Toolkit – It Is an Agent Execution Framework, Futurum Group
11. CVE-2026-4810 summary, Tenable
12. GitHub Security Advisory GHSA-rg7c-g689-fr3x, CVE-2026-4810, GitHub
13. CVE-2026-34070, National Vulnerability Database
14. Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, Gartner
15. The State of Agentic AI in 2026: Companies Are Chasing, Few Are Catching, Forrester
16. Agentic AI Frameworks 2026: Production Comparison, uvik.net