Agentic AI Frameworks in 2026: LangGraph vs CrewAI vs Microsoft Agent Framework vs OpenAI Responses API vs Google ADK
Five agentic AI stacks compete for the enterprise in 2026. This is the engineering comparison — programming model, state handling, tool ergonomics, observability, and production readiness — updated for Microsoft consolidating AutoGen and Semantic Kernel into Microsoft Agent Framework and OpenAI retiring the Assistants API.
No agentic AI framework is universally correct. LangGraph wins on stateful graph orchestration and evaluation tooling. CrewAI wins on role-based clarity and a quick first agent. Microsoft Agent Framework is the path for Microsoft shops, and it is the successor to both AutoGen and Semantic Kernel. The OpenAI Responses API and Agents SDK win on managed simplicity when you are committed to OpenAI. Google ADK wins when Google Cloud is already the spine of your data stack. The engineering decision is not which framework is best but which framework's abstractions match the shape of your workflow and the constraints of your organisation.
What changed since this comparison was first published (checked 1 November 2026): Microsoft's AutoGen README now carries a banner saying the project is archived and no longer actively maintained by Microsoft, that Microsoft is not providing bug fixes, security fixes, new features or support, and that new users should start with Microsoft Agent Framework. The Semantic Kernel README states that Semantic Kernel is now Microsoft Agent Framework, the enterprise-ready successor to Semantic Kernel. And OpenAI's documentation states that the Assistants API was officially sunset on 26 August 2026 and is no longer available, with the Responses API as the replacement. Earlier versions of this post compared those three products head to head; this version compares what you can actually adopt today.
Why the Framework Choice Matters More Than It Used To
In 2023 the agentic framework landscape was dominated by LangChain, and the question most teams asked was "should we use LangChain or build from scratch?" By 2026 the landscape has matured into a genuine ecosystem. LangChain's own README now points teams that need agent orchestration to LangGraph, its low-level framework for long-running, stateful agents. For how the two relate since v1.0, see LangChain vs LangGraph in LangChain 1.0. CrewAI is a popular choice for role-based multi-agent systems. Microsoft has consolidated its two earlier entries — Semantic Kernel and AutoGen — into Microsoft Agent Framework. OpenAI has moved from the Assistants API to the Responses API and the open-source Agents SDK. Google's Agent Development Kit (ADK) is the code-first option for teams on Google Cloud.
The stakes of the framework decision are also higher. An agentic framework shapes how state is represented, how tools are discovered, how errors propagate, how traces are emitted, and how the system can be tested. Switching frameworks mid-project is expensive — often closer to a rewrite than a refactor, as every team that built on the Assistants API has now discovered. The goal of this comparison is to make the production limits visible up front.
Our multi-agent orchestration enterprise guide covers multi-agent patterns in depth; this post compares the five stacks an enterprise architect is likely to evaluate in 2026, with the dimensions that matter for production choice.
LangGraph: The Stateful Graph Standard
Programming Model
LangGraph models an agent as a directed graph of nodes and edges. Nodes are Python functions (or LLM calls); edges define which node runs next based on state. Conditional edges route dynamically based on the agent's output, enabling branching, loops, and parallel execution. State is a typed object that persists across nodes and can be checkpointed.
Strengths
First-class durable execution and human-in-the-loop: the LangGraph README lists agents that persist through failures and resume where they left off, inspect-and-modify interrupts, short- and long-term memory, and tracing through LangSmith. Checkpointing every state transition lets you resume and replay for debugging. Deployment is available through LangSmith deployments. Strong for complex stateful workflows that need to survive restarts and support interrupts.
Weaknesses
Steeper learning curve than role-based frameworks. The graph abstraction is powerful but forces you to think in state machines, which is unfamiliar to teams accustomed to sequential code. For simple sequential agents, the graph overhead adds complexity without obvious benefit. Python-first, with a JavaScript/TypeScript port (LangGraph.js); there is no first-party .NET or Java version, so those teams are second-class citizens. Our LangGraph multi-agent workflow deep-dive covers the production patterns.
CrewAI: Role-Based Multi-Agent, Quick to a First Agent
Programming Model
CrewAI models multi-agent systems as a crew of agents, each with a defined role, goal, and set of tools. A process (sequential or hierarchical) defines how tasks flow between agents. CrewAI also offers Flows for event-driven automations that combine precise workflow control with Crews. The core abstraction is close to how non-engineers describe a team: 'the researcher collects information, the writer drafts an article, the editor reviews it.'
Strengths
Designed for a quick path to a first working agent. The mental model matches how product teams describe the work, which accelerates cross-functional collaboration. Strong default patterns for hierarchical supervision and task delegation. The commercial CrewAI AMP Suite adds managed deployment, observability, governance and enterprise support around the open-source framework. Well suited to PoCs that need to demonstrate agentic value in days, not weeks.
Weaknesses
In our experience it is less expressive than LangGraph for stateful workflows that need explicit branching, loops, or checkpointing, and teams that need production-grade tool orchestration often move the orchestration layer to LangGraph. Tool-calling reliability depends heavily on the underlying model. Our CrewAI enterprise deployment guide covers the patterns that make it work beyond PoC.
Microsoft Agent Framework: The Successor to AutoGen and Semantic Kernel
Where AutoGen and Semantic Kernel Stand
Earlier editions of this comparison profiled AutoGen (conversational multi-agent research heritage) and Semantic Kernel (.NET-native enterprise integration) separately. Microsoft has since consolidated both. The AutoGen README calls Microsoft Agent Framework (MAF) the enterprise-ready successor to AutoGen, says AutoGen is archived and Microsoft is not providing bug fixes, security fixes, new features or support, advises against production use, and tells new users to start with MAF. The Semantic Kernel README says Semantic Kernel is now Microsoft Agent Framework. Microsoft publishes migration guides from both. If you have AutoGen or Semantic Kernel code in production, treat migration as a planned project, not an emergency; if you are starting fresh, there is no reason to choose either predecessor.
Programming Model
MAF is an open, multi-language framework for production-grade agents and multi-agent workflows, with full Python and .NET implementations and a Go SDK in a separate repository. Workflows are graph-based and support sequential, concurrent, handoff and group-collaboration patterns, with checkpointing, streaming, human-in-the-loop and time-travel. It supports multiple model providers and interoperates through A2A and MCP. Agents can also be defined declaratively in YAML.
Strengths
A single Microsoft-backed framework with stable 1.0 APIs and a stated commitment to long-term support, replacing two overlapping ones. Group collaboration covers the debate, review and consensus patterns that AutoGen was known for, while the graph-based workflow engine brings checkpointing and human-in-the-loop control. Python and .NET parity matters for enterprises with mixed estates. The repository ships OpenTelemetry-based telemetry samples, middleware and a developer UI, and agents can be hosted on Foundry if you are on Azure.
Weaknesses
It is the youngest stack in this comparison under its current name, so expect fewer third-party tutorials and recipes than LangGraph or CrewAI, and verify its behaviour on your own workloads. Teams migrating from AutoGen or Semantic Kernel face real porting work, and older blog posts and examples refer to APIs that no longer apply. Hosted deployment is shaped around Azure and Foundry, though the framework itself is open and multi-provider.
OpenAI Responses API and Agents SDK: Managed Simplicity, Vendor Coupling
Where the Assistants API Went
OpenAI's migration documentation states that the Assistants API was officially sunset on 26 August 2026 and is no longer available. It maps Assistants to Prompts, Threads to Conversations, Runs to Responses and Run steps to Items. OpenAI recommends the Responses API for all new projects (Chat Completions remains supported). Anything built on Assistants needs to have been migrated already.
Programming Model
The Responses API is OpenAI's unified interface for agent-like applications: built-in tools (web search, file search, computer use, code interpreter and remote MCP servers), custom function calling, and state that you can chain with previous_response_id, persist with store, or hold in a Conversations object. On top of it, the open-source OpenAI Agents SDK is a lightweight, Python-first framework with agents, handoffs (and agents as tools), guardrails, sessions and built-in tracing; it uses the Responses API by default for OpenAI models and documents adapters for non-OpenAI providers. Our OpenAI Agents SDK enterprise architecture guide covers it in depth.
Strengths
Low operational burden when you are already an OpenAI customer: hosted tools and stored conversation state mean less infrastructure to run. The Agents SDK keeps the programming model small, with handoffs, guardrails and tracing built in, and includes mechanisms for involving humans during runs.
Weaknesses
Managed state and hosted tools couple you to OpenAI; the Agents SDK's provider adapters reduce that coupling for the agent loop, but the managed pieces do not travel with you. Hosted tools are billed separately, so check current pricing. The Assistants API retirement is also a practical reminder that managed APIs can be deprecated on a timetable you do not control, so keep the agent logic behind your own interface.
Google Agent Development Kit (ADK): Google Cloud's Code-First Path
Programming Model
Google's ADK is an open-source, code-first framework for building, evaluating and deploying agents, available in Python with Java and TypeScript versions. It composes agents, tools and sub-agents, includes a graph-based workflow runtime with human-in-the-loop support, and can use OpenAPI specs and MCP tools. It is designed to be model-agnostic and deployment-agnostic, with the strongest fit for Gemini and the Google Cloud data stack.
Strengths
If your data stack is Google — BigQuery as the warehouse, Vertex AI Search as the retrieval layer, Gemini as the primary model — ADK removes integration work. Agent evaluation is part of the framework's stated scope, and agents can be containerised and deployed to Cloud Run.
Weaknesses
Newer than the older frameworks, so a smaller community and fewer patterns. Strongly associated with Google Cloud; enterprises on AWS or Azure will find the integration benefits much smaller. Choice is typically downstream of a decision to standardise on Google Cloud for the broader AI platform.
Framework Comparison Matrix
| Dimension | LangGraph | CrewAI | Microsoft Agent Framework | OpenAI Responses API + Agents SDK | Google ADK |
|---|---|---|---|---|---|
| Primary language | Python (JS/TS port available) | Python | Python / .NET (Go SDK separate) | Python (Agents SDK) / REST (Responses API) | Python (Java, TypeScript versions) |
| Programming model | Stateful graph | Role-based crews + event-driven Flows | Agents + graph-based workflows | Agents, handoffs, guardrails over Responses API | Code-first agents + graph workflow runtime |
| State persistence | Durable execution, checkpointing | Verify in current docs | Workflow checkpointing | Stored responses, Conversations API, SDK sessions | Sessions and state services |
| Multi-agent pattern | Explicit graph | Hierarchical / sequential crews | Sequential, concurrent, handoff, group collaboration | Handoffs / agents as tools | Sub-agents and workflows |
| Tool ergonomics | LangChain ecosystem | Decorator-based tools | Function tools, MCP, A2A | Built-in tools (web, file search, code interpreter, computer use, remote MCP) + functions | OpenAPI specs, MCP tools |
| Observability | LangSmith | CrewAI AMP tracing | OpenTelemetry-based telemetry | Built-in tracing | Built-in evaluation |
| Human-in-the-loop | Native interrupts | Task-level human input | Workflow human-in-the-loop | Built-in mechanisms in the SDK | Workflow human-in-the-loop |
| Commercial deployment option | LangSmith deployments | CrewAI AMP Suite | Foundry Hosted Agents | OpenAI platform (managed API) | Cloud Run / Google Cloud |
| Best fit | Complex stateful workflows | Multi-agent PoCs, role clarity | Microsoft / .NET shops; migrating AutoGen or Semantic Kernel code | OpenAI-committed teams | Google Cloud / BigQuery shops |
How to Choose — A Decision Rubric
Start with the stack, not the framework
If your organisation runs on Microsoft 365 and Azure, Microsoft Agent Framework reduces integration work regardless of its other features. If your data stack is BigQuery plus Vertex, Google ADK is similarly advantaged. If you have no strong cloud commitment, the choice is between LangGraph and CrewAI on technical merits. Treat the surrounding stack as a hard constraint before considering framework ergonomics.
Match the framework's abstraction to your workflow shape
If your workflow is a stateful process with branching, retries, and checkpointing — procurement orchestration, multi-day investigations — LangGraph's graph model maps cleanly. If it is a team of specialists with distinct roles — researcher, writer, reviewer — CrewAI maps more cleanly. If it is a multi-agent conversation with dynamic participation — debate, consensus, peer review — Microsoft Agent Framework's group-collaboration pattern (the successor to AutoGen's group chat) is the best fit. Forcing a workflow into the wrong abstraction is the single biggest source of framework-level pain in production.
Weight observability early
The framework that makes it hardest to debug agent behaviour is the framework that will hurt you in month six, not month one. LangGraph pairs with LangSmith; Microsoft Agent Framework ships OpenTelemetry-based telemetry; the OpenAI Agents SDK has built-in tracing; CrewAI AMP includes tracing and observability. Check which of these your platform team already operates. If your team does not have strong telemetry discipline independently, bias toward frameworks where observability is built in.
Plan for the framework evolution
Every framework in this list is under rapid development, and the last year proved it: AutoGen and Semantic Kernel were folded into Microsoft Agent Framework, and OpenAI's Assistants API was sunset on a published date. Budget for framework upgrades in the same way you budget for library upgrades — quarterly at minimum — and keep your agent logic behind interfaces you own so that a vendor's retirement notice becomes a migration ticket, not a rewrite.
The most common framework mistake is starting with LangChain for everything. LangChain is still excellent as a library of integrations — model wrappers, retrievers, parsers — but LangChain's own README points to LangGraph for more advanced agent orchestration. Pick the orchestration framework explicitly rather than inheriting one by default.
The Decision Most Enterprises Actually Face
For most enterprises the real choice in 2026 is between LangGraph, CrewAI, and the platform option of their cloud provider (the OpenAI Responses API and Agents SDK for OpenAI-committed shops, Microsoft Agent Framework for Microsoft shops, Google ADK for Google Cloud shops). Our LangGraph vs CrewAI comparison covers the first two in depth. AutoGen and Semantic Kernel should not be chosen for new projects: Microsoft's own repositories direct new users to Microsoft Agent Framework.
Across Inductivee Intelligence's agentic deployments, the pattern we see most is a hybrid: LangGraph for the primary orchestration layer because it provides strong observability and state-persistence primitives, with tools or sub-agents implemented in whatever is most convenient — a Microsoft Agent Framework agent to access Microsoft 365, a Google ADK agent to query BigQuery, an OpenAI Responses API call for a specific summarisation task. Frameworks are not religions; pick the best tool for each layer and wire them together deliberately. Our enterprise AI consulting work is built around that pragmatic view.
Sources & Further Reading
Framework status and capabilities were checked against the primary sources below on 1 November 2026. Framework status changes quickly; confirm before you commit to a stack.
- Microsoft AutoGen — README (archived, no longer maintained by Microsoft, Microsoft Agent Framework successor)
- Semantic Kernel — README (Semantic Kernel is now Microsoft Agent Framework)
- Microsoft Agent Framework — GitHub repository
- OpenAI — Assistants API migration guide (sunset 26 August 2026)
- OpenAI — Migrate to the Responses API
- OpenAI Agents SDK (Python) — documentation
- LangGraph — GitHub repository (langchain-ai)
- LangChain — GitHub repository (langchain-ai)
- CrewAI — GitHub repository (crewAIInc)
- Google Agent Development Kit — adk-python repository
Frequently Asked Questions
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Written By
Inductivee Intelligence Team
AuthorAgentic AI Engineering Team
The Inductivee Intelligence engineering team — multi-agent orchestration, RAG pipeline architecture, and data liquidity engineering. Our writing is grounded in what we actually build, break, and operate in production.
Inductivee Intelligence is a remote-first agentic AI engineering studio. Our engineering content is written by active practitioners and technically reviewed before publication.
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