{"id":140,"date":"2026-08-30T14:00:12","date_gmt":"2026-08-30T14:00:12","guid":{"rendered":"https:\/\/www.interviewbit.com\/varsity\/blog\/?p=140"},"modified":"2026-09-23T06:29:56","modified_gmt":"2026-09-23T06:29:56","slug":"top-agentic-ai-frameworks-compared","status":"publish","type":"post","link":"https:\/\/www.interviewbit.com\/varsity\/blog\/top-agentic-ai-frameworks-compared\/","title":{"rendered":"Top Agentic AI Frameworks Compared: LangGraph, CrewAI, AutoGen and Beyond"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Among agentic AI frameworks in 2026, LangGraph is the default for stateful production workflows that need checkpointing, retries and human approval. CrewAI is the fastest route to a working multi-agent prototype. AutoGen has been superseded by the Microsoft Agent Framework, which Microsoft describes as the successor to both AutoGen and Semantic Kernel, so new Azure and .NET builds should start there rather than with AutoGen. Vendor SDKs from OpenAI, Anthropic and Google are the right choice when you have already committed to one model provider.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most framework comparisons you will read are feature checklists, and feature checklists are close to useless here, because every one of these frameworks now does tool calling, memory, multi-agent handoff and MCP. The differences that actually decide your project are elsewhere: how much state the framework will manage for you, how many tokens it burns to coordinate, how debuggable it is at 3am, and whether the vendor is still going to be maintaining it in eighteen months.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That last question matters more than usual right now, because one of the three frameworks in this article&#8217;s title has already been replaced by its own maintainer.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Takeaways<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Feature parity has arrived. Tool calling, memory, multi-agent handoff and MCP are table stakes, so choose on state model, token cost, debuggability and maintenance risk instead.<\/li>\n\n\n\n<li>AutoGen was superseded by the Microsoft Agent Framework in late 2025. Any tutorial recommending AutoGen for new enterprise builds predates that.<\/li>\n\n\n\n<li>LangGraph is the safest production default when workflows need durable state, replay and approval gates.<\/li>\n\n\n\n<li>CrewAI is the fastest to a demo, and its role metaphor is also its constraint when workflows stop matching job titles.<\/li>\n\n\n\n<li>Multi-agent is not automatically better. Coordination costs tokens and debuggability, and a single well-scoped agent frequently wins.<\/li>\n\n\n\n<li>The best migration path for most teams is prototype in CrewAI, productionise the critical path in LangGraph.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The 2026 Landscape: What Actually Changed<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Three shifts define this year, and they matter more than any individual feature.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Consolidation around a few winners.<\/strong> The crowded middle of the framework market has thinned. LangGraph and the vendor SDKs became the defaults, and the rest now differentiate on ergonomics or a specific niche rather than on core capability.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Microsoft merged its two frameworks.<\/strong> In October 2025 Microsoft introduced the <a href=\"https:\/\/learn.microsoft.com\/en-us\/agent-framework\/overview\/agent-framework-overview\">Microsoft Agent Framework<\/a>, and its documentation is unambiguous about what that means: the framework &#8220;combines AutoGen&#8217;s simple agent abstractions with Semantic Kernel&#8217;s enterprise features&#8221; and is &#8220;the next generation of both Semantic Kernel and AutoGen.&#8221; This is the single most important fact in this comparison, and most articles still ranking for this keyword do not mention it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Protocols beat frameworks.<\/strong> MCP has been adopted across effectively all major frameworks. Tools you write against it are portable, which lowers the cost of choosing wrong.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you want the conceptual grounding first meaning what an agent framework actually is and what its components do you can explore the specialized curriculum tracks across <a href=\"https:\/\/www.interviewbit.com\/varsity\">Varsity&#8217;s AI Programs<\/a>, which break down the foundational architecture of autonomous workflows. This page assumes you already know it and need to pick one.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Agentic AI Frameworks Compared<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Framework<\/strong><\/td><td><strong>Orchestration model<\/strong><\/td><td><strong>Languages<\/strong><\/td><td><strong>Model lock-in<\/strong><\/td><td><strong>Production maturity<\/strong><\/td><td><strong>Best for<\/strong><\/td><\/tr><tr><td>LangGraph<\/td><td>Explicit state graph<\/td><td>Python, TypeScript<\/td><td>Low<\/td><td>High<\/td><td>Stateful, auditable, regulated workflows<\/td><\/tr><tr><td>CrewAI<\/td><td>Role-based crews and flows<\/td><td>Python<\/td><td>Low<\/td><td>Medium to high<\/td><td>Fast multi-agent prototypes<\/td><\/tr><tr><td>Microsoft Agent Framework<\/td><td>Graph workflows plus agents<\/td><td>Python, .NET, Go preview<\/td><td>Medium, Azure-leaning<\/td><td>High for Azure<\/td><td>Enterprise .NET and Azure builds<\/td><\/tr><tr><td>AutoGen<\/td><td>Conversational multi-agent<\/td><td>Python, .NET<\/td><td>Low<\/td><td>Maintained, superseded<\/td><td>Research, conversational experiments<\/td><\/tr><tr><td>AG2<\/td><td>Conversational, community fork<\/td><td>Python<\/td><td>Low<\/td><td>Medium<\/td><td>Teams committed to the AutoGen model<\/td><\/tr><tr><td>OpenAI Agents SDK<\/td><td>Handoff chains<\/td><td>Python, TypeScript<\/td><td>Low to medium<\/td><td>High<\/td><td>OpenAI-native builds, voice, tracing<\/td><\/tr><tr><td>Claude Agent SDK<\/td><td>Tool use plus sandbox<\/td><td>Python, TypeScript<\/td><td>High, Claude only<\/td><td>High<\/td><td>Autonomous coding and research agents<\/td><\/tr><tr><td>Google ADK<\/td><td>Hierarchical agents<\/td><td>Python<\/td><td>Medium, Gemini-leaning<\/td><td>High<\/td><td>Multimodal, GCP-native stacks<\/td><\/tr><tr><td>Pydantic AI<\/td><td>Type-safe composable<\/td><td>Python<\/td><td>Low<\/td><td>Medium to high<\/td><td>Type-safe production Python<\/td><\/tr><tr><td>LlamaIndex<\/td><td>Retrieval-centric<\/td><td>Python, TypeScript<\/td><td>Low<\/td><td>Medium to high<\/td><td>RAG-heavy knowledge agents<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Maturity ratings reflect ecosystem depth and documented production use, not code quality.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>LangGraph: The Production Default<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/docs.langchain.com\/oss\/python\/langgraph\/overview\">LangGraph<\/a> models your agent as an explicit state graph. You define nodes, edges, and the conditions for moving between them, and the framework persists state at each step.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Why it wins in production.<\/strong> Durable execution means a crashed run resumes from its last checkpoint instead of restarting. Time-travel debugging lets you rewind to any prior state and replay from there, which is the difference between diagnosing a failure in ten minutes and never reproducing it. Human-in-the-loop is a first-class primitive rather than something you bolt on. State persists to Postgres or SQLite that you control, which is usually what regulated environments require.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The real cost.<\/strong> You do the architecture work up front. Teams that treat LangGraph as a drop-in chain replacement write bad graphs and conclude the framework is complicated. You have to think in states and transitions before writing code, which is unfamiliar to engineers used to linear chains and is exactly why the learning curve is real.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Choose It When:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Workflows must survive sudden infrastructure restarts.<\/li>\n\n\n\n<li>Operations need explicit approval checkpoints or human oversight.<\/li>\n\n\n\n<li>The business requires an audit trail of every state mutation.<\/li>\n\n\n\n<li>Pipelines must be debugged by someone other than the original author.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For a deeper dive into how LangChain and LangGraph work together including structural updates from the 1.0 consolidation see our comprehensive architectural breakdown on <a href=\"https:\/\/www.interviewbit.com\/varsity\">Varsity<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>CrewAI: Fastest to a Working Prototype<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/docs.crewai.com\/en\/introduction\">CrewAI<\/a> organises agents as a crew: each has a role, a goal, and a backstory, and tasks are assigned and delegated between them. The metaphor maps onto how people already think about dividing work, which is why teams get something running in hours rather than days.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Why it wins.<\/strong> Speed to demo is genuinely unmatched for multi-agent work. YAML configuration keeps boilerplate low. For content pipelines, research workflows and operations automation, where the work really does decompose into researcher, analyst and writer, the abstraction fits the problem.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The real cost.<\/strong> Two things. First, the role metaphor becomes a constraint the moment your workflow stops resembling a team of humans; you end up inventing fake job titles to satisfy the abstraction. Second, agent-to-agent conversation costs tokens, and coordination overhead on simple tasks can run substantially higher than an equivalent explicit graph. Debugging is also harder, because behaviour emerges from delegation rather than from a control flow you wrote.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Choose it when:<\/strong> you need a stakeholder demo this week, or the workflow genuinely decomposes into specialist roles.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>AutoGen, AG2 and the Microsoft Agent Framework<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This is where most comparison articles are now out of date, so it deserves care.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What AutoGen was.<\/strong> Microsoft Research&#8217;s conversational multi-agent framework, built on the idea that agents solve problems by talking to each other: proposing, critiquing, and iterating. It pioneered patterns that everything else borrowed, and it remains genuinely good at debate-style workflows and code execution loops. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What happened.<\/strong> Microsoft built the Agent Framework as the direct successor to both AutoGen and Semantic Kernel, by the same teams. It keeps AutoGen&#8217;s agent abstractions, adds Semantic Kernel&#8217;s enterprise features including session state, type safety, middleware and telemetry, and introduces graph-based workflows for explicit orchestration. It supports Python, .NET and Go in preview, and is MCP-native.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What AG2 is.<\/strong> When Microsoft&#8217;s direction shifted, part of the community continued the original line as <a href=\"https:\/\/docs.ag2.ai\/latest\/\">AG2<\/a>, an independent fork with event-driven architecture and async messaging. It is a legitimate option if your team is invested in the conversational model and wants a community-governed project.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What to actually do.<\/strong> For a new enterprise build on Azure or .NET, start with the Microsoft Agent Framework. For research or conversational multi-agent experiments, AutoGen or AG2 both remain reasonable. If you have an existing AutoGen system in production, there is no emergency, but plan the migration path deliberately.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>CrewAI vs AutoGen: The Direct Comparison<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Dimension<\/strong><\/td><td><strong>CrewAI<\/strong><\/td><td><strong>AutoGen \/ AG2<\/strong><\/td><\/tr><tr><td>Core metaphor<\/td><td>Roles and tasks, like a team<\/td><td>Conversation, like a discussion<\/td><\/tr><tr><td>Time to first agent<\/td><td>Under an hour<\/td><td>A few hours<\/td><\/tr><tr><td>Best-fit workflow<\/td><td>Sequential specialist handoff<\/td><td>Debate, critique, iterative refinement<\/td><\/tr><tr><td>Code execution<\/td><td>Supported<\/td><td>A traditional strength<\/td><\/tr><tr><td>Determinism<\/td><td>Moderate, delegation-driven<\/td><td>Lower, conversation-driven<\/td><\/tr><tr><td>Token efficiency<\/td><td>Moderate<\/td><td>Lower on chatty patterns<\/td><\/tr><tr><td>Enterprise path<\/td><td>CrewAI Enterprise<\/td><td>Microsoft Agent Framework<\/td><\/tr><tr><td>Maintenance risk<\/td><td>Active, independent<\/td><td>AutoGen superseded, AG2 community-run<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Short version:<\/strong> CrewAI when the work divides into roles, AutoGen or AG2 when the value comes from agents disagreeing with each other, and the Microsoft Agent Framework when this is going into an enterprise Azure estate.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The Vendor SDKs<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Three of these matter, and they follow the same logic: give up portability, get tighter integration and less scaffolding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/openai.github.io\/openai-agents-python\/\"><strong>OpenAI Agents SDK<\/strong><\/a><strong>.<\/strong> Four primitives, agents, handoffs, guardrails and sessions, with tracing built into the OpenAI platform. Fastest path to a working agent if you are already on GPT models, and it supports other providers through Chat Completions compatible APIs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Claude Agent SDK.<\/strong> The infrastructure behind Claude Code, with sandboxed shell execution, file editing and native MCP support. Strong for autonomous coding and long-running research agents. Claude models only.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/google.github.io\/adk-docs\/\"><strong>Google ADK<\/strong><\/a><strong>.<\/strong> Hierarchical agent composition, strong multimodal support, natural fit for GCP and Gemini.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Choose a vendor SDK when:<\/strong> model choice is already settled, you value less scaffolding over portability, and you can accept that switching providers later means a rewrite.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Two Worth Knowing Beyond the Big Names<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/ai.pydantic.dev\/\"><strong>Pydantic AI<\/strong><\/a> brings static typing and structured output validation to agents, from the team behind Pydantic. If your organisation already enforces type safety in Python, this will feel like the natural choice, and it pairs well with the structured-output discipline that separates production code from prototypes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/docs.llamaindex.ai\/en\/stable\/\"><strong>LlamaIndex<\/strong><\/a> is retrieval-first. LlamaIndex is retrieval-first. When your agent&#8217;s actual job is reasoning over a large internal corpus rather than orchestrating tools, its indexing and connector ecosystem outperforms general orchestration frameworks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You can deep-dive into how these data ingestion models operate in production through the advanced engineering modules in <a href=\"https:\/\/www.interviewbit.com\/varsity\">Varsity&#8217;s AI Programs<\/a>, which break down exactly where complex architectural retrieval mechanisms fit within broader agentic software patterns.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>How to Choose: A Decision Path<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Work down this list and stop at the first match.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Is this a single agent with fewer than about four tools?<\/strong> Use raw API calls. No framework. You will debug faster.<\/li>\n\n\n\n<li><strong>Are you locked to one model provider already?<\/strong> Use that vendor&#8217;s SDK.<\/li>\n\n\n\n<li><strong>Is the stack .NET or Azure?<\/strong> Microsoft Agent Framework.<\/li>\n\n\n\n<li><strong>Does the workflow need durable state, replay, audit trails or approval gates?<\/strong> LangGraph.<\/li>\n\n\n\n<li><strong>Is the agent mostly reasoning over documents?<\/strong> LlamaIndex.<\/li>\n\n\n\n<li><strong>Do you need a multi-agent demo within a week?<\/strong> CrewAI.<\/li>\n\n\n\n<li><strong>Does your team enforce strict typing?<\/strong> Pydantic AI.<\/li>\n\n\n\n<li><strong>Is the value in agents critiquing each other?<\/strong> AG2 or AutoGen.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The most common successful pattern in Indian product teams right now is number 6 followed by number 4: prototype in CrewAI to prove the concept to stakeholders, then rebuild the critical path in LangGraph when it has to survive real users.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What This Costs, and the Mistake That Causes Bill Shock<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The frameworks are free. Tokens are not, and orchestration style is a cost decision disguised as an architecture decision.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Pattern<\/strong><\/td><td><strong>Relative token cost<\/strong><\/td><td><strong>Why<\/strong><\/td><\/tr><tr><td>Single agent, explicit tools<\/td><td>Baseline<\/td><td>One reasoning loop<\/td><\/tr><tr><td>Explicit state graph<\/td><td>Low to moderate<\/td><td>Only invoked nodes consume tokens<\/td><\/tr><tr><td>Role-based delegation<\/td><td>Moderate to high<\/td><td>Task handoffs carry context<\/td><\/tr><tr><td>Free-form agent conversation<\/td><td>Highest<\/td><td>Every exchange is billed both ways<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Three controls matter more than framework choice: cache stable context aggressively, cap iterations so no loop can run unbounded, and route simple sub-tasks to smaller models. The classic failure is an agent that retries a failing tool indefinitely overnight. Every framework here lets you cap it, and forgetting to is a self-inflicted wound rather than a framework flaw.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Mistakes When Choosing Agentic AI Frameworks<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Reaching for multi-agent by default.<\/strong> Multi-agent is a coordination strategy, not a quality upgrade. It adds token cost, latency and non-determinism. Start with one agent and split only when a single one demonstrably cannot hold the task.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Choosing on GitHub stars.<\/strong> Stars measure attention, not production suitability. Ask instead who runs it at scale, how you debug a failed run, and whether state survives a restart.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Ignoring the exit cost.<\/strong> Ask how much code changes if you switch frameworks in a year. Business logic in plain Python functions, with the framework only orchestrating, keeps that number small. MCP tools travel between frameworks; deeply framework-coupled logic does not.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Skipping evaluation.<\/strong> Framework choice can move agent task performance materially on identical models, but you cannot detect that without an evaluation harness. Build the golden dataset before you commit, then compare frameworks on your workload rather than on someone else&#8217;s benchmark.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Learning from stale tutorials.<\/strong> Given what happened to AutoGen and the LangChain v1.0 reset, any material older than about a year needs verification against current documentation before you trust it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Building Agentic AI Skills That Outlast the Frameworks<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The frameworks in this article will keep churning. What transfers is the underlying model: state management, tool contracts, failure recovery, human oversight design, evaluation, and cost control. An engineer who understands those can pick up any of these in a week; an engineer who only knows a single framework&#8217;s decorators cannot. [<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For structured depth, the advanced engineering modules in <a href=\"https:\/\/www.interviewbit.com\/varsity\">Varsity&#8217;s AI Programs<\/a> cover production-grade orchestration, multi-agent evaluation, and live infrastructure deployment. These live online programs are co-created with India\u2019s premier institutes like <strong>IIT Delhi<\/strong>, allowing you to pair practical systems mastery with an elite executive credential.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To practice this rather than read about it, look for gen AI projects that include explicit agent builds with spend caps, approval gates, and failure injection. This is the exact boundary where framework differences disappear and true systems engineering begins<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQ<\/strong><\/h2>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1787641246491\"><strong class=\"schema-faq-question\"><strong>Which agentic AI framework should I learn first in 2026?<\/strong><\/strong> <p class=\"schema-faq-answer\">LangGraph, because its explicit state model teaches concepts that transfer everywhere: persistence, checkpointing, conditional routing and human-in-the-loop. Add CrewAI afterwards, since it takes about a day once you understand agents properly.<br><\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1787641306469\"><strong class=\"schema-faq-question\"><br><strong>Is CrewAI or LangGraph better for production?<\/strong><\/strong> <p class=\"schema-faq-answer\">LangGraph for most production systems, because of durable execution, replay and state you control. CrewAI is production-capable, and it is the better answer when the workflow maps cleanly to roles and speed of delivery outweighs fine-grained control.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1787641327437\"><strong class=\"schema-faq-question\"><strong>Has AutoGen been discontinued?<\/strong><\/strong> <p class=\"schema-faq-answer\">No. It is still maintained and documented, but it has been superseded. Microsoft describes the Agent Framework as the next generation of both AutoGen and Semantic Kernel, and the community fork AG2 continues the original line independently.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1787641347042\"><strong class=\"schema-faq-question\"><strong>Do all agentic AI frameworks support MCP?<\/strong><\/strong> <p class=\"schema-faq-answer\">Effectively all major ones do, natively or through an adapter. That is why MCP support is no longer a useful selection criterion, though it does usefully reduce lock-in for the tools you write.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1787641360849\"><strong class=\"schema-faq-question\"><strong>Can I mix frameworks in one system?<\/strong><\/strong> <p class=\"schema-faq-answer\">\u00a0Yes, and it is common. Teams often use LlamaIndex for retrieval inside a LangGraph-orchestrated workflow, or expose CrewAI crews as MCP tools. Keep business logic framework-agnostic and this stays manageable.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1787641372999\"><strong class=\"schema-faq-question\"><strong>How much does it cost to run an agentic system in production?<\/strong><\/strong> <p class=\"schema-faq-answer\">The frameworks are free; tokens dominate, typically the largest share of operating cost. Cost varies enormously between designs for similar accuracy, so measure your own workload rather than trusting published figures.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1787641390544\"><strong class=\"schema-faq-question\"><strong>Are multi-agent systems better than single agents?<\/strong><\/strong> <p class=\"schema-faq-answer\">Usually not by default. They add token cost, latency and debugging difficulty. Use multiple agents when tasks genuinely require different tools, permissions or context windows, not because the architecture diagram looks more impressive.<\/p> <\/div> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>Among agentic AI frameworks in 2026, LangGraph is the default for stateful production workflows that need checkpointing, retries and human approval. CrewAI is the fastest route to a working multi-agent prototype. AutoGen has been superseded by the Microsoft Agent Framework, which Microsoft describes as the successor to both AutoGen and Semantic Kernel, so new Azure [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":206,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[52],"class_list":["post-140","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai","tag-agentic-ai-frameworks"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"acf":{"reviewed_by":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Top Agentic AI Frameworks Compared: LangGraph, CrewAI, AutoGen and Beyond - Varsity Blog<\/title>\n<meta name=\"description\" content=\"Compare the top Agentic 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