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CrewAI 1.14.7 introduced pluggable memory/knowledge/RAG backends, Snowflake Cortex, and Chat API. Pydantic AI V2 and LlamaIndex Workflows 1.0 both went stable in the same 48-hour window (June 22–23). Google ADK 2.0 shipped Go 2.0 and expanded the Java 1.0 line. MCP 2026-07-28 spec is in release candidate. A2A crossed 150 adopting organisations." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Enterprise Platforms (Managed): 8 Options for 2026", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#enterprise-platforms-managed", "description": "If you need governance, identity, audit trails, SLAs, and procurement-friendly licensing — not raw code — the question is which managed agent platform fits your existing stack. The eight that matter in 2026: Microsoft Copilot Studio + Microsoft 365 Agents (was 'Agent 365'), AWS Bedrock AgentCore, Google Vertex AI Agent Builder, OpenAI Agent Platform, Salesforce Agentforce 360, ServiceNow AI Agents, IBM watsonx Orchestrate, and UiPath Agentic Automation." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "How to Choose Between Them", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#how-to-choose", "description": "Start from your dominant constraint: control (LangGraph), Anthropic-native production (Claude Agent SDK), team velocity (CrewAI), conversational research (AutoGen/AG2), enterprise stack (Semantic Kernel), data layer (LlamaIndex), or type safety (Pydantic AI). 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None replaces the top-10 for enterprise production yet, but all three are on the Alice Labs 'evaluate every quarter' watchlist." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Most Popular AI Agent Frameworks 2026: Ranked by GitHub Stars, PyPI Downloads, and Ecosystem Signals", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#most-popular-ai-agent-frameworks-2026", "description": "Popularity and production-readiness are not the same axis. As of August 2026 the popularity leaders (by GitHub stars) are Hermes Agent (~220K), LangGraph, CrewAI, and Mastra (~22-24K, dominant on the TypeScript side). But Alice Labs' ranking above is by production-readiness across our 100+ implementations — a different question that only partially overlaps." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "What Changed in 2025 (historical context)", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#what-changed-2025", "description": "2025 set up 2026's consolidation. The AutoGen / AG2 split, LangGraph 1.0 GA (October 22), Claude Code SDK launch (later renamed to Claude Agent SDK), CrewAI commercialization, and Pydantic AI V1 (September) were the year's five biggest movers. Q2 2026 releases are covered in the dedicated section above." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Production Considerations Beyond the Framework", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#production-considerations", "description": "Framework choice is necessary but insufficient. Production agents also need observability (LangSmith / Langfuse / Arize), guardrails, evaluation harnesses, and a deployment story. Underestimating these is the most common reason agent projects stall after a successful demo." } ], "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/best-ai-agent-frameworks-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": "Best AI Agent Frameworks 2026: Alice Labs Top 10 Ranked", "item": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-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": "What is the official full name of AutoGen?", "acceptedAnswer": { "@type": "Answer", "text": "AutoGen is Microsoft Research's Automated Generation of Multi-Agent Conversation framework (github.com/microsoft/autogen), first released September 2023. The v0.2 lineage lives on as the community fork AG2 (ag2.ai), while Microsoft's v0.4+ AutoGen is now succeeded by Microsoft Agent Framework 1.0, which shipped on 3 April 2026 and merges AutoGen with Semantic Kernel into a single production SDK. In Alice Labs' agent engagements we default new Microsoft-stack projects to Microsoft Agent Framework 1.0 and only touch AutoGen / AG2 for existing v0.2 codebases." } }, { "@type": "Question", "name": "What are the best open source AI agent frameworks in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs' ranking of open-source-only AI agent frameworks for 2026, drawn from our 100+ production AI implementations: (1) LangGraph 1.0 (MIT) — best overall for stateful production workflows; (2) Claude Agent SDK (MIT) — best Anthropic-native primitives; (3) CrewAI 1.14 (MIT) — fastest path to a role-based multi-agent prototype; (4) Microsoft Agent Framework 1.0 (MIT) — best for .NET / Python enterprise stacks; (5) LlamaIndex Workflows 1.0 (MIT) — best RAG-grounded agents; (6) Pydantic AI V2 (MIT) — best type-safe Python DX; (7) AutoGen / AG2 (Apache 2.0) — legacy option for existing v0.2 code. All seven now ship native or first-class MCP support." } }, { "@type": "Question", "name": "What are the best frameworks for building AI agents in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "If you are building AI agents from scratch in 2026, Alice Labs recommends starting with LangGraph 1.0 for stateful production workflows that need explicit branching and human-in-the-loop, or Claude Agent SDK if you are Anthropic-native and want the same architecture that powers Claude Code. Choose CrewAI 1.14 when the work decomposes into role-based tasks (researcher, writer, reviewer) and you need a working prototype in days, not weeks. Choose Microsoft Agent Framework 1.0 if your dominant stack is .NET / Azure / M365. All four ship native MCP, durable state, and production observability hooks." } }, { "@type": "Question", "name": "Which enterprise AI agent platform is best for 2026?", "acceptedAnswer": { "@type": "Answer", "text": "There is no single best — pick by your dominant stack. Microsoft 365 / Azure → Microsoft Copilot Studio + Microsoft 365 Agents. AWS / Bedrock → AWS Bedrock AgentCore. Google Cloud → Vertex AI Agent Builder. OpenAI-first → OpenAI Agent Platform (AgentKit). Salesforce CRM → Agentforce 360. ServiceNow → ServiceNow AI Agents (Now Assist). Regulated / hybrid → IBM watsonx Orchestrate. Large UiPath estate → UiPath Agentic Automation. All eight align to NIST AI RMF, ISO/IEC 42001 and the EU AI Act." } }, { "@type": "Question", "name": "What's the difference between an enterprise agent platform and an open-source agent framework?", "acceptedAnswer": { "@type": "Answer", "text": "Enterprise platforms (Microsoft Copilot Studio, AWS Bedrock AgentCore, Vertex AI Agent Builder, OpenAI Agent Platform, Agentforce 360, ServiceNow AI Agents, watsonx Orchestrate, UiPath) are managed, governed, and procurement-friendly — agents inherit identity, audit, data residency, and SLAs from a vendor tenant. Open-source frameworks (LangGraph, Claude Agent SDK, CrewAI, AutoGen/AG2, Semantic Kernel, LlamaIndex, Pydantic AI) are libraries — your engineering team owns deployment, observability, and governance. Most large programmes run both: platform for breadth, framework for depth." } }, { "@type": "Question", "name": "Which AI agent framework is best in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "There is no single best framework — they target different problems. For most new production projects with explicit control needs, LangGraph is the safest pick. For fast multi-agent prototypes, CrewAI is the fastest. Match framework to your dominant constraint." } }, { "@type": "Question", "name": "Is LangGraph the same as LangChain?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph is built by the LangChain team but is a separate library. LangChain provides the underlying primitives (LLM wrappers, tools, prompts); LangGraph is the orchestration layer that models agents as state graphs. You can use LangGraph without using LangChain agents directly." } }, { "@type": "Question", "name": "What's the difference between AutoGen and AG2?", "acceptedAnswer": { "@type": "Answer", "text": "AG2 is the community continuation of the original Microsoft AutoGen v0.2 lineage, hosted at ag2.ai. Microsoft has continued the AutoGen name with a v0.4+ rewrite that uses a different API. Both are open-source. Choose based on which API you started with and where the community you depend on lives." } }, { "@type": "Question", "name": "Should I use Semantic Kernel or LangChain?", "acceptedAnswer": { "@type": "Answer", "text": "If your stack is .NET / C# or Microsoft / Azure-centric, Semantic Kernel is the right pick — it has first-class C# support and tight Azure integration. For Python-first stacks not tied to Microsoft, LangChain (with LangGraph) has a larger ecosystem and more community resources." } }, { "@type": "Question", "name": "Is Pydantic AI production-ready?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, but with the caveat that it's newer than LangGraph or LangChain. Teams that prioritize type safety, structured responses, and FastAPI-style ergonomics report excellent developer experience. Production references are growing but the ecosystem is smaller than LangChain's." } }, { "@type": "Question", "name": "Can I switch frameworks later?", "acceptedAnswer": { "@type": "Answer", "text": "Partially. The LLM-facing prompts and tool definitions tend to be portable. The orchestration layer (state, control flow, multi-agent patterns) is framework-specific and requires rewrite. Plan to commit to one framework for at least the first year of a production system." } }, { "@type": "Question", "name": "Do I need a framework at all, or can I use the OpenAI Assistants API directly?", "acceptedAnswer": { "@type": "Answer", "text": "For very simple single-agent tools you can use the OpenAI Assistants API or Anthropic's tool use directly. As soon as you need multi-step control flow, multi-agent patterns, model-agnostic deployment, or production-grade observability, a framework saves significant engineering time." } }, { "@type": "Question", "name": "What changed in AI agent frameworks in Q2 2026 (April-July 2026)?", "acceptedAnswer": { "@type": "Answer", "text": "The biggest release was Microsoft Agent Framework 1.0 on April 3, 2026 — the unified successor to Semantic Kernel and AutoGen, shipping with native MCP and A2A protocol support for both .NET and Python. LangGraph added per-node timeouts, DeltaChannel, and a v2 typed streaming API. Anthropic's Claude Agent SDK shipped hierarchical subagent spawning (up to 3 levels deep), fallback model chains, and a community MCP tool marketplace. CrewAI 1.14.6 (May 28) plus a June 11 release introduced pluggable memory/knowledge/RAG/flow backends, a Chat API, and native Snowflake Cortex. Pydantic AI V2 (June 23) shipped a harness-first redesign with capabilities as a core primitive. LlamaIndex Workflows 1.0 landed on June 22. The MCP 2026-07-28 spec release candidate reworks the protocol to be stateless at the layer." } }, { "@type": "Question", "name": "Which frameworks support the Model Context Protocol (MCP) natively?", "acceptedAnswer": { "@type": "Answer", "text": "As of July 2026 the deepest native MCP integration is in the Claude Agent SDK (MCP is the primary tool contract, with a community tool marketplace) and Microsoft Agent Framework 1.0 (native, not bolt-on). LangGraph 1.0 supports MCP tools as first-class graph nodes with full streaming. CrewAI 1.14, LlamaIndex Workflows 1.0, and Pydantic AI V2 all ship native MCP support. AutoGen v0.4 and AG2 use community adapters — new Microsoft-stack builds should use Microsoft Agent Framework 1.0 for native MCP + A2A." } }, { "@type": "Question", "name": "What is the Agent-to-Agent (A2A) protocol and which frameworks support it?", "acceptedAnswer": { "@type": "Answer", "text": "A2A is Google's open protocol for direct communication between autonomous agents across organisations, frameworks, and vendors. As of April 2026, 150+ organisations support the standard (Google, Microsoft, AWS, Salesforce, SAP, ServiceNow, Workday, IBM among them), and A2A ships production SDKs in Python, JavaScript, Java, Go, and .NET. Native A2A integration is live in Azure AI Foundry, AWS Bedrock AgentCore, and Google Cloud. Microsoft Agent Framework 1.0 is the first open-source SDK with native A2A. Other frameworks use community adapters; if agent-to-agent interoperability is a hard requirement, prefer Microsoft Agent Framework 1.0 or pair another framework with a small A2A shim service." } }, { "@type": "Question", "name": "Should I migrate from Semantic Kernel or AutoGen to Microsoft Agent Framework 1.0?", "acceptedAnswer": { "@type": "Answer", "text": "For new Microsoft-stack projects — yes, start on Microsoft Agent Framework 1.0. It merges the enterprise features of Semantic Kernel (session state, type safety, middleware, telemetry) with the multi-agent orchestration of AutoGen into one SDK, and ships native MCP + A2A. For existing production systems on Semantic Kernel, Microsoft has committed to critical bug fixes for at least one year after MAF GA (April 2026) — plan a migration but don't rush it. Microsoft has published a formal Semantic Kernel → Microsoft Agent Framework migration guide on Microsoft Learn." } }, { "@type": "Question", "name": "What is Semantic Kernel? (Microsoft's official definition)", "acceptedAnswer": { "@type": "Answer", "text": "Semantic Kernel is Microsoft's open-source SDK for integrating LLMs with conventional programming languages (C#, Python, Java). It was Microsoft's primary agent SDK from 2023 through early 2026. In April 2026 Microsoft folded Semantic Kernel and AutoGen into a single successor product: Microsoft Agent Framework 1.0. Semantic Kernel remains supported with critical bug fixes for at least one year post-MAF-GA, but new Microsoft-stack projects should start on Microsoft Agent Framework 1.0. Official docs: learn.microsoft.com/semantic-kernel." } }, { "@type": "Question", "name": "Which AI agent frameworks shipped an update in August 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Two major August 2026 updates: (1) LangGraph shipped workflow updates for Python and JS — node caching (skip redundant computation on re-runs), deferred nodes (fan-in barriers that wait for all upstream branches), pre/post model hooks for context trimming / guardrails / PII redaction, and a content-block-centric streaming API replacing the legacy token stream (see changelog.langchain.com). (2) Microsoft previewed Agent Framework Agent Harness, Hosted Agents (managed runtime + sandbox execution), and CodeAct-style tool synthesis at BUILD 2026 (devblogs.microsoft.com/agent-framework). Claude Agent SDK continued rolling updates via Claude Code Week 24+ releases (5-level subagent hierarchy, 200-spawn per-session ceiling, @tool decorator with typing.Annotated)." } }, { "@type": "Question", "name": "Which AI agent framework has the best TypeScript support in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Mastra (@mastra/core 1.35) is the de-facto TypeScript-first default with ~300K weekly npm downloads and ~22-24K GitHub stars — it was designed from the ground up for TS teams shipping web-integrated agents (Next.js / Node). For frameworks with true Python and TypeScript parity, LlamaIndex Workflows 1.0 (June 22, 2026) and Google ADK 2.0 (TS + Python at parity) are the strongest picks. OpenAI Agents SDK ships TypeScript but the Python SDK still leads by 1-2 quarters on major features. LangGraph has a JS/TS SDK but Python-first ergonomics leak in." } }, { "@type": "Question", "name": "Which agent frameworks support Java or Go?", "acceptedAnswer": { "@type": "Answer", "text": "As of August 2026, Google ADK is the only major agent framework with production-grade Java (ADK Java 1.0, March 2026) and Go (ADK Go 2.0, June 2026) SDKs at parity with its Python and TypeScript SDKs. Microsoft Agent Framework 1.0 supports Python and .NET (C#) — not Java or Go. LangGraph, CrewAI, LlamaIndex, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, and Mastra are Python and/or TypeScript only. If Java or Go is a hard requirement, Google ADK is the answer." } }, { "@type": "Question", "name": "Are there any new AI agent frameworks worth watching in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Three new-in-2026 entrants worth an evaluation cycle: (1) Hermes Agent (Nous Research, launched Feb 25, 2026, MIT) — persistent memory + auto-generated skills; crossed ~220K GitHub stars by late July 2026 per startupfortune.com, though enterprise governance is still forming. (2) Strands Agents (AWS open-source) — framework-agnostic agent SDK designed to feed into Bedrock AgentCore. (3) BeeAI (IBM Research) — open-source agent framework built around interoperable protocols; complements watsonx Orchestrate. None replaces the top-10 for enterprise production yet, but all three are on Alice Labs' 'evaluate every quarter' watchlist." } }, { "@type": "Question", "name": "What is 'openclaw' AI agent framework? Is it real?", "acceptedAnswer": { "@type": "Answer", "text": "'Openclaw' does not correspond to a real AI agent framework as of August 2026. The query most likely stems from a typo or confusion with 'OpenClaude' / 'OpenAI Claude' / the Claude Agent SDK. If you were searching for an Anthropic-native agent framework, the correct answer is the Claude Agent SDK (github.com/anthropics/claude-agent-sdk, ranked #3 in Alice Labs' August 2026 ranking). If you were searching for an open-source AutoGen alternative, see AG2 (community fork) or Microsoft Agent Framework 1.0 (the official successor)." } }, { "@type": "Question", "name": "What are the best AI agent orchestration frameworks in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "For multi-agent orchestration specifically (as opposed to single-agent tool use) the strongest 2026 picks are: (1) Microsoft Agent Framework 1.0 — supports sequential, concurrent, handoff, group chat, and Magentic-One patterns natively with C# + Python parity and native A2A cards; (2) LangGraph 1.x — supervisor and hierarchical patterns via subgraphs, plus deferred nodes (Aug 2026) for native fan-in barriers; (3) CrewAI 1.14.7 — hierarchical process + Flow DSL for role-based crews; (4) Claude Agent SDK — hierarchical subagents up to 5 levels deep with a 200-spawn ceiling; (5) Google ADK 2.0 — graph-based Workflow Runtime with a slider from dynamic to fully deterministic flows." } }, { "@type": "Question", "name": "Which frameworks support the Anthropic Claude models natively?", "acceptedAnswer": { "@type": "Answer", "text": "The Claude Agent SDK is Anthropic's official first-party SDK and runs against Claude 5, Opus 4.8, and Haiku 4.5. Every other major framework in the top-10 (LangGraph, Microsoft Agent Framework 1.0, OpenAI Agents SDK, Google ADK 2.0, CrewAI 1.14.7, LlamaIndex Workflows 1.0, Pydantic AI 2.0, Mastra, AG2) supports Claude models via provider abstractions — Anthropic API keys work everywhere, but only the Claude Agent SDK is optimised specifically for Claude's tool-use and subagent behaviour." } }, { "@type": "Question", "name": "What's the difference between the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol?", "acceptedAnswer": { "@type": "Answer", "text": "MCP (Model Context Protocol, Anthropic origin, 2026-07-28 spec release candidate) connects agents to tools — an MCP server exposes tools that any MCP-capable agent can call. A2A (Agent-to-Agent, Google origin, 150+ adopting organisations as of April 2026) connects agents to other agents across frameworks and vendors. They are complementary, not competing. In Alice Labs' current architecture calls: an agent uses MCP to call any tool it needs and A2A to delegate to any other agent it doesn't want to reimplement itself. Both are open standards, both ship in Microsoft Agent Framework 1.0 natively, and both are the interoperability layer that matters more than framework choice in 2026." } }, { "@type": "Question", "name": "Which AI agent framework did Alice Labs pick for its own production stack?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs runs a hybrid across our 100+ production AI implementations. For Nordic customer-service deployments we default to a supervisor + swarm hybrid on LangGraph 1.x (with LangSmith for observability). For Anthropic-native coding, research, and back-office agents we use the Claude Agent SDK with hierarchical subagents. For Microsoft-stack clients we use Microsoft Agent Framework 1.0. For our own internal Python type-safety-critical extraction agents we use Pydantic AI 2.0. For fast role-based prototypes handed to junior engineers we use CrewAI 1.14.7. Framework selection is per project — no single framework wins every axis." } }, { "@type": "Question", "name": "How does the Alice Labs Production Score work?", "acceptedAnswer": { "@type": "Answer", "text": "The Alice Labs Production Score is a 1-10 rating we assign each framework based on four axes we've measured across our 100+ production AI implementations: (1) durable state and human-in-the-loop maturity, (2) native MCP + A2A protocol coverage, (3) observability and evaluation-harness integration, and (4) time-to-first-production-deploy for a new team. As of August 2026: LangGraph 1.x and Claude Agent SDK both score 9/10; Microsoft Agent Framework 1.0, OpenAI Agents SDK, Google ADK 2.0, and Pydantic AI 2.0 all score 8/10; CrewAI 1.14.7, LlamaIndex Workflows 1.0, and Mastra score 7/10; AG2 0.12.2 scores 6/10. Scores update at every quarterly review." } }, { "@type": "Question", "name": "What is Pydantic AI 2.0's 'capability' primitive?", "acceptedAnswer": { "@type": "Answer", "text": "Pydantic AI 2.0 (shipped 23 June 2026) introduced a single 'capability' primitive that unifies instructions, tools, hooks, and settings into reusable composable units — one abstraction replaces four. Everything above the primitive lives in the separately versioned Pydantic AI Harness (which first shipped April 2026, ~2 months before v2 core) and includes 40+ features across 9 categories: memory, guardrails, sandbox, evals, and more. The clean separation means the stable core moves slowly while moving-target features (Harness) can iterate independently. Version policy tightened: no-breaking-changes window shrunk from 6 months to 3." } }, { "@type": "Question", "name": "Is CrewAI still relevant now that Microsoft Agent Framework 1.0 has shipped?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. CrewAI 1.14.7 (11 June 2026) sits in a different position than Microsoft Agent Framework 1.0: it is the fastest path from idea to a working role-based multi-agent prototype (researcher → writer → reviewer), independent of Microsoft's cloud gravity, with ergonomic Python, pluggable memory/knowledge/RAG/flow backends, native Snowflake Cortex + Bedrock + Databricks + OpenAI + Anthropic + Gemini providers, and a Chat API. Alice Labs still recommends CrewAI for early-stage prototypes and role-based multi-agent workloads that don't need Azure-specific integration." } } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/what-is-an-ai-agent", "name": "What Is an AI Agent?" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/enterprise-ai-strategy-framework", "name": "Enterprise AI Strategy: 6-Step Framework" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/why-ai-projects-fail", "name": "Why AI Projects Fail: 7 Root Causes" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "@id": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#listicle", "name": "Best AI Agent Frameworks 2026: The Complete Comparison (Last verified: August 2026)", "description": "Alice Labs ranks the 10 best AI agent frameworks for August 2026 from 100+ production deployments: LangGraph, MAF 1.0, Claude SDK, OpenAI Agents, Google ADK, CrewAI, LlamaIndex, Pydantic AI 2.0, Mastra, AG2. MCP + A2A tracker included.", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026", "numberOfItems": 10, "itemListOrder": "https://schema.org/ItemListOrderDescending", "datePublished": "2026-04-15", "dateModified": "2026-08-02", "author": { "@id": "https://alicelabs.ai/#linus" }, "reviewedBy": { "@id": "https://alicelabs.ai/#eric" }, "itemListElement": [ { "@type": "ListItem", "position": 1, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Explicit graph model — easy to reason about, debug, and version-control" }, { "@type": "ListItem", "position": 2, "name": "First-class HITL, durable state, checkpoints, and time-travel debugging (GA 22 Oct 2025)" }, { "@type": "ListItem", "position": 3, "name": "Aug 2026: node caching, deferred nodes (fan-in barriers), pre/post model hooks, content-block streaming API, per-node timeouts + graceful shutdown" }, { "@type": "ListItem", "position": 4, "name": "Deepest MCP tool support of any framework — MCP servers become first-class graph nodes with streaming" }, { "@type": "ListItem", "position": 5, "name": "Python and JavaScript/TypeScript SDKs; LangSmith observability out of the box" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Runtime is opinionated and heavy for simple linear tool-use loops" }, { "@type": "ListItem", "position": 2, "name": "Python-first ergonomics leak into the JS SDK" }, { "@type": "ListItem", "position": 3, "name": "The LangChain-classic split still confuses new teams" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/langgraph-1-x", "name": "LangGraph 1.x", "description": "Graph-based agent orchestration from the LangChain team. Models agents as explicit state machines — the default choice for LangChain-shop production systems that need explicit control over routing, checkpoints, and human-in-the-loop. LangGraph 1.0 went GA on October 22, 2025 alongside LangChain 1.0. August 2026 workflow updates add node caching (skip redundant computation on re-runs), deferred nodes (native fan-in barriers that hold execution until all upstream branches complete), pre/post model hooks (inject context trimming, guardrails, or PII redaction around every model call), a new content-block-centric streaming API, and finer-grained per-node timeouts and error recovery. Alice Labs Production Score: 9/10.", "url": "https://github.com/langchain-ai/langgraph" } }, { "@type": "ListItem", "position": 2, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Semantic Kernel enterprise features + AutoGen orchestration in one SDK, C# + Python parity from day one" }, { "@type": "ListItem", "position": 2, "name": "Native MCP client and A2A cards (not adapters) baked into the agent primitive" }, { "@type": "ListItem", "position": 3, "name": "Multi-agent orchestration patterns: sequential, concurrent, handoff, group chat, Magentic-One" }, { "@type": "ListItem", "position": 4, "name": "Declarative YAML agent config for version-controlled deployments" }, { "@type": "ListItem", "position": 5, "name": "Tight Azure AI Foundry, Azure OpenAI, and Entra ID integration; long-term support commitment for the 1.x line" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Azure-gravity: Foundry integration is where the polish lives — running MSAF outside Azure works but you leave observability, hosting, and safety infra on the table" }, { "@type": "ListItem", "position": 2, "name": "Migration from AutoGen or Semantic Kernel is guided but non-trivial (SK gets critical bug fixes for ≥1 year post-GA)" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/microsoft-agent-framework-1-0-successor-to-semantic-kernel-autogen", "name": "Microsoft Agent Framework 1.0 (successor to Semantic Kernel + AutoGen)", "description": "The unified Microsoft agent SDK — shipped as Agent Framework 1.0 on 3 April 2026 for Python and .NET simultaneously, merging Semantic Kernel's enterprise features (session-based state, type safety, middleware, telemetry, connectors) with AutoGen's multi-agent orchestration (sequential, concurrent, handoff, group chat, Magentic-One) into a single production SDK under Microsoft.Agents.AI. First-class MCP client + tool support and native A2A cards baked into the agent primitive. Declarative YAML agent configuration for version-controlled deployments. BUILD 2026 previewed Agent Harness, Hosted Agents (managed runtime, sandbox execution), and CodeAct-style tool synthesis. Alice Labs Production Score: 8/10.", "url": "https://learn.microsoft.com/en-us/agent-framework/overview/" } }, { "@type": "ListItem", "position": 3, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Same agent architecture that powers Claude Code in production" }, { "@type": "ListItem", "position": 2, "name": "Hierarchical subagents up to 5 levels deep with 200-spawn per-session ceiling" }, { "@type": "ListItem", "position": 3, "name": "First-class MCP client baked in; every user-connected MCP server available inside subagents" }, { "@type": "ListItem", "position": 4, "name": "Hooks are the primary customization surface: pre-tool, post-response, on-error" }, { "@type": "ListItem", "position": 5, "name": "@tool decorator supports typing.Annotated for per-parameter descriptions" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Single-vendor by design: the primitives assume Claude models; multi-provider fallback happens outside the SDK" }, { "@type": "ListItem", "position": 2, "name": "Fewer multi-agent choreography patterns than Microsoft Agent Framework or LangGraph" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/claude-agent-sdk-2026-line", "name": "Claude Agent SDK (2026 line)", "description": "Anthropic's official agent SDK (renamed from the Claude Code SDK in early 2026 to reflect broader agent scope). The production baseline for Anthropic Managed Agents and for the 'file + bash + web + MCP' loop that Claude Code itself runs on. 2026 updates ship hierarchical subagents — subagents can spawn subagents, capped at 5 levels deep with a default 200-spawn per-session ceiling — first-class MCP client integration (every MCP server the user has connected is available inside subagents), hooks at every lifecycle point (pre-tool, post-response, on-error) as the primary customization surface, and @tool decorator support for typing.Annotated so per-parameter descriptions live next to the function signature. Runs against Claude 5, Opus 4.8, Haiku 4.5. Alice Labs Production Score: 9/10.", "url": "https://code.claude.com/docs/en/agent-sdk/overview" } }, { "@type": "ListItem", "position": 4, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Native sandbox execution across 7 providers (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel)" }, { "@type": "ListItem", "position": 2, "name": "Model-native harness: files + tools + computer use exposed as a standard runtime" }, { "@type": "ListItem", "position": 3, "name": "Zod-based tool() function in TypeScript SDK; typed schemas across both languages" }, { "@type": "ListItem", "position": 4, "name": "Clean integration with Responses API and OpenAI hosted tools (web search, code interpreter, file search)" }, { "@type": "ListItem", "position": 5, "name": "Works with 100+ non-OpenAI LLMs via Chat Completions when required" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Python leads TypeScript by 1-2 quarters on every major feature (subagents, code mode)" }, { "@type": "ListItem", "position": 2, "name": "Single-vendor optimization means less clean support for multi-provider fallback chains" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/openai-agents-sdk", "name": "OpenAI Agents SDK", "description": "OpenAI's production successor to Swarm — shipped GA in March 2026 with typed tools, handoffs, guardrails, and tracing in Python and TypeScript. April 2026 added native sandbox execution for file, bash, and code operations across 7 providers out of the box (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel) plus a model-native harness that exposes files, tools, and computer use as a standard runtime. Subagents and code mode launched Python-first with TypeScript parity in progress. Integrates cleanly with OpenAI's Responses API and hosted tools (web search, code interpreter, file search). Alice Labs Production Score: 8/10.", "url": "https://openai.github.io/openai-agents-python/" } }, { "@type": "ListItem", "position": 5, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Only major framework with Python, TypeScript, Java 1.0, and Go 2.0 SDKs at parity" }, { "@type": "ListItem", "position": 2, "name": "Unified graph-based Workflow Runtime: slide from model-led to fully deterministic" }, { "@type": "ListItem", "position": 3, "name": "Native A2A protocol and MCP client support" }, { "@type": "ListItem", "position": 4, "name": "Visual Agent Designer in Google Cloud console; Agentspace deployment surface" }, { "@type": "ListItem", "position": 5, "name": "Feb 2026 Tools & Integrations Ecosystem: GitHub, Jira, MongoDB + 5 observability platforms as first-class connectors" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Best experience is inside Gemini Enterprise Agent Platform; running fully outside GCP loses Agent Designer + deploy pipeline + grounding integrations" }, { "@type": "ListItem", "position": 2, "name": "Newer than LangGraph / MSAF outside Google-native shops" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/google-adk-2-0-agent-development-kit", "name": "Google ADK 2.0 (Agent Development Kit)", "description": "Google's open-source agent framework — the only major framework with four language SDKs at parity: Python and TypeScript (both 2.0), Java 1.0 (March 2026), and Go 2.0 (June 2026). ADK 2.0's unified graph-based Workflow Runtime provides a slider from dynamic model-led reasoning to strict deterministic flows. Native A2A protocol support baked in; visual Agent Designer in the Google Cloud console; February 2026 Tools & Integrations Ecosystem added GitHub, Jira, MongoDB, and 5 observability platforms as first-class connectors. Best experience is inside Gemini Enterprise Agent Platform. Alice Labs Production Score: 8/10.", "url": "https://google.github.io/adk-docs/" } }, { "@type": "ListItem", "position": 6, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Very low barrier to entry — readable, declarative agent definitions" }, { "@type": "ListItem", "position": 2, "name": "Pluggable backends for memory, knowledge, RAG, and flow (Jun 2026): swap persistence without forking" }, { "@type": "ListItem", "position": 3, "name": "Native Snowflake Cortex + Bedrock + Databricks + OpenAI + Anthropic + Gemini as first-class LLM providers" }, { "@type": "ListItem", "position": 4, "name": "Chat API + Flow DSL + per-execution runtime state isolation" }, { "@type": "ListItem", "position": 5, "name": "Real finish_reason and response.id on LLM events — observability parity with LangGraph / LangSmith" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Role-play abstraction leaks: complex handoff logic still requires dropping into Flows" }, { "@type": "ListItem", "position": 2, "name": "Observability thinner than LangGraph/LangSmith or MSAF/Foundry" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/crewai-1-14-7", "name": "CrewAI 1.14.7", "description": "Role-based multi-agent framework. You define a 'crew' of agents (researcher, writer, reviewer), assign tasks, and CrewAI orchestrates collaboration. Still the easiest framework to hand to a non-framework specialist. Version 1.14.7 (11 June 2026) added pluggable default backends for memory, knowledge, RAG, and flow (swap the persistence layer without forking); a native Snowflake Cortex LLM provider (joining Bedrock, Databricks, OpenAI, Anthropic, Gemini as first-class); a Chat API for conversational flows alongside the classic crew/task/agent primitives; real finish_reason, sampling params, and response.id surfaced on all LLM events (observability parity with LangGraph); and a Flow DSL with type decorators and per-execution runtime state isolation. Alice Labs Production Score: 7/10.", "url": "https://github.com/crewAIInc/crewAI" } }, { "@type": "ListItem", "position": 7, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Event-driven step composition — cleanest abstraction if you dislike graph-shaped state" }, { "@type": "ListItem", "position": 2, "name": "Async-first runtime with typed event handlers" }, { "@type": "ListItem", "position": 3, "name": "Python and TypeScript parity from day one (rare)" }, { "@type": "ListItem", "position": 4, "name": "Native LlamaCloud integration (parsers + indexes) for RAG-heavy agents" }, { "@type": "ListItem", "position": 5, "name": "Best-in-class indexing, retrievers, and query engines from the core LlamaIndex library" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Multi-agent orchestration patterns are thinner than MSAF or CrewAI" }, { "@type": "ListItem", "position": 2, "name": "You're renting Workflows on top of a framework that's still primarily a data framework" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/llamaindex-workflows-1-0", "name": "LlamaIndex Workflows 1.0", "description": "Event-driven step composition as the recommended pattern for any non-trivial agent — Workflows 1.0 shipped 22 June 2026 as the first stable release. Async-first runtime with typed event handlers for every agent step, Python and TypeScript parity from day one, and native LlamaCloud integration (parsers + indexes) so RAG-heavy agents don't have to leave the ecosystem. Positioned as a lightweight LangGraph alternative for teams already on LlamaIndex's data plane. Alice Labs Production Score: 7/10.", "url": "https://github.com/run-llama/llama_index" } }, { "@type": "ListItem", "position": 8, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Single 'capability' primitive unifies instructions, tools, hooks, and settings into composable units" }, { "@type": "ListItem", "position": 2, "name": "Slim core + separately versioned Harness (40+ features across 9 categories)" }, { "@type": "ListItem", "position": 3, "name": "Best-in-class Python type safety and IDE support" }, { "@type": "ListItem", "position": 4, "name": "Model-agnostic: OpenAI, Anthropic, Gemini, Groq, local models" }, { "@type": "ListItem", "position": 5, "name": "Ergonomically familiar to FastAPI users" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "3-month major-version window is aggressive — if you pin to v2.0, expect v3 breakage sooner than the ecosystem average" }, { "@type": "ListItem", "position": 2, "name": "Ecosystem still smaller than LangGraph or CrewAI" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/pydantic-ai-2-0", "name": "Pydantic AI 2.0", "description": "Type-safe agent framework from the Pydantic team. Reached V1 in September 2025; V2 shipped stable on 23 June 2026 with a harness-first redesign. One 'capability' primitive unifies instructions, tools, hooks, and settings into reusable composable units, and the slimmer core has everything above the primitive living in the separately versioned Pydantic AI Harness (40+ features across 9 categories: memory, guardrails, sandbox, evals, etc.). The Harness first shipped April 2026, ~2 months before v2 core — clean separation of stable core vs moving-target features. Version policy tightened: no-breaking-changes window shrunk from 6 months to 3. Alice Labs Production Score: 8/10.", "url": "https://github.com/pydantic/pydantic-ai" } }, { "@type": "ListItem", "position": 9, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "TypeScript-first: agents, workflows, memory, workspaces, and observability in one SDK" }, { "@type": "ListItem", "position": 2, "name": "Model router across 40+ LLM providers behind one API" }, { "@type": "ListItem", "position": 3, "name": "Multi-environment deploys (prod / staging / preview) with per-env DB + env vars" }, { "@type": "ListItem", "position": 4, "name": "Native MCP client integration" }, { "@type": "ListItem", "position": 5, "name": "~300K weekly npm downloads and ~22-24K GitHub stars — de facto TS default" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Younger project — fewer enterprise reference customers than LangGraph or Microsoft Agent Framework" }, { "@type": "ListItem", "position": 2, "name": "Multi-agent orchestration semantics lighter than the Python heavyweights" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/mastra-mastra-core-1-35", "name": "Mastra (@mastra/core 1.35)", "description": "TypeScript-first framework covering agents, workflows, memory, workspaces, and observability in one SDK. Graduated from YC W25 with $13M raised; ~22-24K GitHub stars and 300K+ weekly npm downloads make it the de facto TypeScript-first default. Ships a model router across 40+ LLM providers behind one API, multi-environment deploys (prod / staging / preview, each with its own URL, env vars, database), and native MCP client integration for external tools and services. 88+ releases through May 2026 (active release cadence). Alice Labs Production Score: 7/10.", "url": "https://github.com/mastra-ai/mastra" } }, { "@type": "ListItem", "position": 10, "positiveNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Preserves AutoGen 0.2 group-chat / nested-chat mental model" }, { "@type": "ListItem", "position": 2, "name": "Open governance — core maintainers are the original AutoGen creators" }, { "@type": "ListItem", "position": 3, "name": "Beta API + legacy API run side-by-side without breakage" }, { "@type": "ListItem", "position": 4, "name": "Continued MCP convergence; A2A support via community adapters" } ] }, "negativeNotes": { "@type": "ItemList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Still pre-1.0" }, { "@type": "ListItem", "position": 2, "name": "Ecosystem gravity is now with Microsoft Agent Framework — hiring, docs, and vendor integrations increasingly assume MSAF as 'the AutoGen answer'" } ] }, "item": { "@type": "Organization", "@id": "https://alicelabs.ai/entity/ag2-0-12-2-community-autogen-fork", "name": "AG2 0.12.2 (community AutoGen fork)", "description": "The community continuation of the original AutoGen 0.2 lineage, forked in November 2024 by the same core maintainers who created AutoGen at Microsoft Research. Currently 0.12.2 with a v1.0 roadmap announced (legacy API deprecated at v0.14). Beta API launched March 2026 and expanded in 0.12.x — the foundation for v1.0 — runs alongside legacy without breakage. Continued convergence with MCP; A2A support via community adapters. Positioned explicitly as the non-Microsoft option for teams that want AutoGen's group-chat / nested-chat semantics without Azure gravity. Original Microsoft AutoGen has been in maintenance mode since October 2025. Alice Labs Production Score: 6/10.", "url": "https://github.com/ag2ai/ag2" } } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 11, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "AI Agent Framework Release Timeline (Feb – Aug 2026)", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#ai-agent-framework-release-timeline-2026" }, { "@type": "ListItem", "position": 2, "name": "What Changed in Q2 – Q3 2026 (April – August) — AI Agent Framework Releases", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#q2-2026-framework-releases" }, { "@type": "ListItem", "position": 3, "name": "Enterprise Platforms (Managed): 8 Options for 2026", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#enterprise-platforms-managed" }, { "@type": "ListItem", "position": 4, "name": "How to Choose Between Them", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#how-to-choose" }, { "@type": "ListItem", "position": 5, "name": "LangGraph vs CrewAI vs AutoGen 2026: Which One Should You Ship?", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#langgraph-vs-crewai-vs-autogen-head-to-head-2026" }, { "@type": "ListItem", "position": 6, "name": "MCP and A2A: The Two Protocols Every 2026 Agent Framework Must Support", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#mcp-a2a-protocols-2026" }, { "@type": "ListItem", "position": 7, "name": "AI Agent Architecture 2026: Design Patterns from LangGraph, CrewAI, and Multi-Agent Systems", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#ai-agent-architecture-patterns-2026" }, { "@type": "ListItem", "position": 8, "name": "Emerging AI Agent Frameworks 2026: New Entrants and Ones to Watch", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#emerging-frameworks-2026" }, { "@type": "ListItem", "position": 9, "name": "Most Popular AI Agent Frameworks 2026: Ranked by GitHub Stars, PyPI Downloads, and Ecosystem Signals", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#most-popular-ai-agent-frameworks-2026" }, { "@type": "ListItem", "position": 10, "name": "What Changed in 2025 (historical context)", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#what-changed-2025" }, { "@type": "ListItem", "position": 11, "name": "Production Considerations Beyond the Framework", "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026#production-considerations" } ] } ] }, { "@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": 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[Home](/en) [Insights](/en/insights) [AI Agents](/en/insights/ai-agents) Best AI Agent Frameworks 2026: The Complete Comparison (Last verified: August 2026) AI Agents Top 10 Fresh Last reviewed: 2 August 2026 · 23d ago # Best AI Agent Frameworks 2026: The Complete Comparison (Last verified: August 2026) ## TL;DR Quick Answer Cited by AI > Alice Labs — a Stockholm-headquartered enterprise AI consultancy with 100+ production AI implementations since 2023 — ranks LangGraph 1.x as the #1 AI agent framework for August 2026 for stateful production workloads, and Microsoft Agent Framework 1.0 as the #1 for enterprises on Microsoft/Azure. TL;DR (last verified: August 2026) — The 10 best AI agent frameworks in 2026, ranked by production-readiness across our deployments: (1) LangGraph 1.x (MIT; Aug 2026 added node caching, deferred nodes, pre/post model hooks, content-block streaming; native MCP) — best overall for durable stateful graphs; (2) Microsoft Agent Framework 1.0 (MIT; GA Apr 3 2026 — Semantic Kernel + AutoGen merged; native MCP + A2A; Python + .NET parity) — best for enterprise Microsoft/.NET stacks; (3) Claude Agent SDK (MIT SDK; hierarchical subagents 5 levels deep, deepest MCP integration, hooks + @tool decorator) — best for Anthropic-native coding, research, and back-office agents; (4) OpenAI Agents SDK (MIT; GA Mar 2026, sandbox harness across 7 providers, TypeScript now shipping) — best for OpenAI-first shops with computer-use workloads; (5) Google ADK 2.0 (Apache 2.0; Python + TypeScript + Java 1.0 + Go 2.0; native A2A) — best for Google Cloud, Java, or Go teams; (6) CrewAI 1.14.7 (MIT; Jun 11 2026 pluggable memory/knowledge/RAG/flow backends, Snowflake Cortex, Chat API) — fastest path to role-based multi-agent prototypes; (7) LlamaIndex Workflows 1.0 (MIT; Jun 22 2026 event-driven, Python + TypeScript parity) — best for RAG-grounded agents; (8) Pydantic AI 2.0 (MIT; Jun 23 2026 single 'capability' primitive, separately versioned Harness) — best for type-safe Python; (9) Mastra @mastra/core 1.35 (MIT; ~300K weekly npm downloads) — de-facto TypeScript-first default; (10) AG2 0.12.2 (Apache 2.0; community AutoGen fork, pre-1.0) — legacy option for teams that started on AutoGen 0.4/0.5. All 10 now support MCP (7 natively); A2A is native in Microsoft Agent Framework 1.0 and Google ADK, community adapters elsewhere. An engineer's ranking of the 10 leading AI agent frameworks as of August 2026, benchmarked across 100+ Alice Labs production AI implementations. Covers LangGraph 1.x, Microsoft Agent Framework 1.0, Claude Agent SDK, OpenAI Agents SDK, Google ADK 2.0, CrewAI 1.14.7, LlamaIndex Workflows 1.0, Pydantic AI 2.0, Mastra, and AG2 — with license, MCP + A2A protocol support, latest release date, and the Alice Labs Production Score for each. An AI agent framework is a software library that provides primitives for building LLM-powered agents: tool calling, multi-step reasoning, memory, multi-agent orchestration, subagents, hooks, streaming, and human-in-the-loop control. As of August 2026, the ten frameworks that matter (ranked by Alice Labs — a Stockholm-headquartered enterprise AI consultancy with 100+ production AI implementations since 2023) are: LangGraph 1.x (GA Oct 22 2025; Aug 2026 added node caching, deferred nodes, pre/post model hooks, content-block streaming); Microsoft Agent Framework 1.0 (GA Apr 3 2026 — the unified successor merging Semantic Kernel and AutoGen with native MCP + A2A); Claude Agent SDK (2026 line — hierarchical subagents up to 5 levels, native MCP client, @tool decorator with typing.Annotated); OpenAI Agents SDK (GA Mar 2026 — sandbox harness across 7 providers, model-native file/tool runtime); Google ADK 2.0 (Python + TypeScript + Java 1.0 + Go 2.0 — the only framework with native Java and Go at parity); CrewAI 1.14.7 (Jun 11 2026 — pluggable memory/knowledge/RAG/flow backends, Snowflake Cortex, Chat API); LlamaIndex Workflows 1.0 (Jun 22 2026 event-driven step composition); Pydantic AI 2.0 (Jun 23 2026 — harness-first with a single 'capability' primitive); Mastra @mastra/core 1.35 (May 2026 — TypeScript-first, ~300K weekly downloads); and AG2 0.12.2 (community AutoGen fork, pre-1.0). All ten now support Model Context Protocol (MCP); Agent-to-Agent (A2A) is native in Microsoft Agent Framework 1.0 and Google ADK and available via adapters elsewhere. ## How we picked these - Active maintenance in 2026 (weekly-to-monthly release cadence; primary-source changelog verified in August 2026) - Production-capable: durable state, observability hooks, error recovery, deterministic control - First-class support for tool use, memory, multi-agent patterns, and MCP - Language SDK parity or a defensible language-first position (e.g. Mastra = TypeScript-first, Google ADK = Java/Go) - Deployed in Alice Labs' 100+ production AI implementations, or a credible near-term evaluation candidate ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by Eric Lundberg Published April 15, 2026 · Updated August 2, 2026 18 min read ## The list at a glance 1. [01 LangGraph 1.x Best overall for stateful production graphs ](#rank-1) 2. [02 Microsoft Agent Framework 1.0 (successor to Semantic Kernel + AutoGen) Best for enterprise Microsoft / Azure / .NET stacks ](#rank-2) 3. [03 Claude Agent SDK (2026 line) Best for Anthropic-native coding, research, and back-office agents ](#rank-3) 4. [04 OpenAI Agents SDK Best for OpenAI-first shops with computer-use / sandbox workloads ](#rank-4) 5. [05 Google ADK 2.0 (Agent Development Kit) Best for Google Cloud, Java, or Go teams ](#rank-5) 6. [06 CrewAI 1.14.7 Best for fast role-based multi-agent prototypes ](#rank-6) 7. [07 LlamaIndex Workflows 1.0 Best for RAG-grounded agents ](#rank-7) 8. [08 Pydantic AI 2.0 Best DX for type-safe Python ](#rank-8) 9. [09 Mastra (@mastra/core 1.35) Best TypeScript-first framework for web-integrated agents ](#rank-9) 10. [10 AG2 0.12.2 (community AutoGen fork) Legacy — for teams on AutoGen 0.4/0.5 who don't want to migrate to MSAF ](#rank-10) ## Key Takeaways - 10 frameworks, not 7: the August 2026 landscape spans LangGraph 1.x, Microsoft Agent Framework 1.0, Claude Agent SDK, OpenAI Agents SDK, Google ADK 2.0, CrewAI 1.14.7, LlamaIndex Workflows 1.0, Pydantic AI 2.0, Mastra @mastra/core 1.35, and AG2 0.12.2 — plus eight managed enterprise platforms (Microsoft Copilot Studio, AWS Bedrock AgentCore, Vertex AI Agent Builder, OpenAI Agent Platform, Salesforce Agentforce 360, ServiceNow AI Agents, IBM watsonx Orchestrate, UiPath Agentic Automation). - Pick by your stack first: M365/Azure → Copilot Studio + Microsoft Agent Framework 1.0; AWS → Bedrock AgentCore + LangGraph or Claude Agent SDK; Google Cloud → Vertex AI Agent Builder + Google ADK 2.0; Salesforce CRM → Agentforce 360; ServiceNow → AI Agents on Now Platform; IBM/regulated → watsonx Orchestrate; TypeScript-native web → Mastra; custom Python code → LangGraph or Claude Agent SDK. - LangGraph 1.x (LangChain) is the default choice for complex stateful graphs that need explicit control. Aug 2026 added node caching, deferred nodes (native fan-in barriers), pre/post model hooks (context trimming, guardrails, PII redaction), and a content-block-centric streaming API. - Microsoft Agent Framework 1.0 (GA Apr 3 2026) is the direct successor to both Semantic Kernel and AutoGen — one SDK with C# + Python parity, native MCP + A2A cards, and orchestration patterns (sequential, concurrent, handoff, group chat, Magentic-One). BUILD 2026 previewed Agent Harness, Hosted Agents, and CodeAct as follow-on. - Claude Agent SDK is Anthropic's official agent framework — hierarchical subagents capped at 5 levels deep (200-spawn per-session default), first-class MCP client, hooks at every lifecycle point, and @tool decorator supporting typing.Annotated. Runs against Claude 5, Opus 4.8, Haiku 4.5. - OpenAI Agents SDK (GA Mar 2026) shipped native sandbox execution across 7 providers (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel). Python leads TypeScript by roughly 1-2 quarters on every major feature. - Google ADK 2.0 is the only major framework with Python, TypeScript, Java 1.0, and Go 2.0 SDKs at parity — plus native A2A and a visual Agent Designer in Google Cloud console. - CrewAI 1.14.7 (Jun 11 2026) added pluggable memory/knowledge/RAG/flow backends, native Snowflake Cortex LLM provider, Chat API, and real finish\_reason + response.id on LLM events (observability parity with LangGraph). - AG2 is the community continuation of AutoGen 0.2, pre-1.0 at 0.12.2. Microsoft's own AutoGen lineage is in maintenance since Oct 2025; new Microsoft-stack builds should start on Microsoft Agent Framework 1.0. - Pydantic AI 2.0 (Jun 23 2026) shipped a single 'capability' primitive that unifies instructions, tools, hooks, and settings — with a separately versioned Pydantic AI Harness (40+ features across 9 categories: memory, guardrails, sandbox, evals, etc.). Major-version window tightened from 6 months to 3. - MCP + A2A are table stakes: all 10 frameworks support MCP (7 natively), and A2A has crossed 150+ adopting organisations with native integration in Azure AI Foundry, AWS Bedrock AgentCore, and Google Cloud. - All enterprise platforms align to NIST AI RMF, ISO/IEC 42001, and the EU AI Act — verify governance feature parity before committing. 1. ## LangGraph 1.x Best overall for stateful production graphs Graph-based agent orchestration from the LangChain team. Models agents as explicit state machines — the default choice for LangChain-shop production systems that need explicit control over routing, checkpoints, and human-in-the-loop. LangGraph 1.0 went GA on October 22, 2025 alongside LangChain 1.0. August 2026 workflow updates add node caching (skip redundant computation on re-runs), deferred nodes (native fan-in barriers that hold execution until all upstream branches complete), pre/post model hooks (inject context trimming, guardrails, or PII redaction around every model call), a new content-block-centric streaming API, and finer-grained per-node timeouts and error recovery. Alice Labs Production Score: 9/10. Best for: Enterprise-grade, durable, stateful agent graphs with explicit routing, checkpoints, and HITL · Price: Open source (MIT). LangGraph Platform (managed) is paid. Pros - Explicit graph model — easy to reason about, debug, and version-control - First-class HITL, durable state, checkpoints, and time-travel debugging (GA 22 Oct 2025) - Aug 2026: node caching, deferred nodes (fan-in barriers), pre/post model hooks, content-block streaming API, per-node timeouts + graceful shutdown - Deepest MCP tool support of any framework — MCP servers become first-class graph nodes with streaming - Python and JavaScript/TypeScript SDKs; LangSmith observability out of the box Cons - Runtime is opinionated and heavy for simple linear tool-use loops - Python-first ergonomics leak into the JS SDK - The LangChain-classic split still confuses new teams [github.com/langchain-ai/langgraph](https://github.com/langchain-ai/langgraph) 2. #2 ## Microsoft Agent Framework 1.0 (successor to Semantic Kernel + AutoGen) Best for enterprise Microsoft / Azure / .NET stacks The unified Microsoft agent SDK — shipped as Agent Framework 1.0 on 3 April 2026 for Python and .NET simultaneously, merging Semantic Kernel's enterprise features (session-based state, type safety, middleware, telemetry, connectors) with AutoGen's multi-agent orchestration (sequential, concurrent, handoff, group chat, Magentic-One) into a single production SDK under Microsoft.Agents.AI. First-class MCP client + tool support and native A2A cards baked into the agent primitive. Declarative YAML agent configuration for version-controlled deployments. BUILD 2026 previewed Agent Harness, Hosted Agents (managed runtime, sandbox execution), and CodeAct-style tool synthesis. Alice Labs Production Score: 8/10. Best for: Enterprises already on Azure AI Foundry, .NET shops, and teams that need one SDK covering both single-agent and Magentic-style multi-agent orchestration · Price: Open source (MIT). Azure AI Foundry runtime is paid. Pros - Semantic Kernel enterprise features + AutoGen orchestration in one SDK, C# + Python parity from day one - Native MCP client and A2A cards (not adapters) baked into the agent primitive - Multi-agent orchestration patterns: sequential, concurrent, handoff, group chat, Magentic-One - Declarative YAML agent config for version-controlled deployments - Tight Azure AI Foundry, Azure OpenAI, and Entra ID integration; long-term support commitment for the 1.x line Cons - Azure-gravity: Foundry integration is where the polish lives — running MSAF outside Azure works but you leave observability, hosting, and safety infra on the table - Migration from AutoGen or Semantic Kernel is guided but non-trivial (SK gets critical bug fixes for ≥1 year post-GA) [learn.microsoft.com/agent-framework](https://learn.microsoft.com/en-us/agent-framework/overview/) 3. #3 ## Claude Agent SDK (2026 line) Best for Anthropic-native coding, research, and back-office agents Anthropic's official agent SDK (renamed from the Claude Code SDK in early 2026 to reflect broader agent scope). The production baseline for Anthropic Managed Agents and for the 'file + bash + web + MCP' loop that Claude Code itself runs on. 2026 updates ship hierarchical subagents — subagents can spawn subagents, capped at 5 levels deep with a default 200-spawn per-session ceiling — first-class MCP client integration (every MCP server the user has connected is available inside subagents), hooks at every lifecycle point (pre-tool, post-response, on-error) as the primary customization surface, and @tool decorator support for typing.Annotated so per-parameter descriptions live next to the function signature. Runs against Claude 5, Opus 4.8, Haiku 4.5. Alice Labs Production Score: 9/10. Best for: Anthropic-first teams building coding, research, and back-office agents where the primary loop is 'file + bash + web + MCP' and you want subagent delegation without hand-rolling orchestration · Price: Open source SDK (MIT). Anthropic API tokens billed at Claude pricing. Anthropic Managed Agents adds scheduling + rubric grading on top. Pros - Same agent architecture that powers Claude Code in production - Hierarchical subagents up to 5 levels deep with 200-spawn per-session ceiling - First-class MCP client baked in; every user-connected MCP server available inside subagents - Hooks are the primary customization surface: pre-tool, post-response, on-error - @tool decorator supports typing.Annotated for per-parameter descriptions Cons - Single-vendor by design: the primitives assume Claude models; multi-provider fallback happens outside the SDK - Fewer multi-agent choreography patterns than Microsoft Agent Framework or LangGraph [code.claude.com/docs/en/agent-sdk/overview](https://code.claude.com/docs/en/agent-sdk/overview) 4. #4 ## OpenAI Agents SDK Best for OpenAI-first shops with computer-use / sandbox workloads OpenAI's production successor to Swarm — shipped GA in March 2026 with typed tools, handoffs, guardrails, and tracing in Python and TypeScript. April 2026 added native sandbox execution for file, bash, and code operations across 7 providers out of the box (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel) plus a model-native harness that exposes files, tools, and computer use as a standard runtime. Subagents and code mode launched Python-first with TypeScript parity in progress. Integrates cleanly with OpenAI's Responses API and hosted tools (web search, code interpreter, file search). Alice Labs Production Score: 8/10. Best for: OpenAI-first shops that want the vendor-native answer, especially for computer-use / sandbox-heavy agents where OpenAI's hosted tools do half the work · Price: Open source SDK (MIT). OpenAI API tokens billed at model pricing. Sandbox providers billed separately. Pros - Native sandbox execution across 7 providers (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel) - Model-native harness: files + tools + computer use exposed as a standard runtime - Zod-based tool() function in TypeScript SDK; typed schemas across both languages - Clean integration with Responses API and OpenAI hosted tools (web search, code interpreter, file search) - Works with 100+ non-OpenAI LLMs via Chat Completions when required Cons - Python leads TypeScript by 1-2 quarters on every major feature (subagents, code mode) - Single-vendor optimization means less clean support for multi-provider fallback chains [openai.github.io/openai-agents-python](https://openai.github.io/openai-agents-python/) 5. #5 ## Google ADK 2.0 (Agent Development Kit) Best for Google Cloud, Java, or Go teams Google's open-source agent framework — the only major framework with four language SDKs at parity: Python and TypeScript (both 2.0), Java 1.0 (March 2026), and Go 2.0 (June 2026). ADK 2.0's unified graph-based Workflow Runtime provides a slider from dynamic model-led reasoning to strict deterministic flows. Native A2A protocol support baked in; visual Agent Designer in the Google Cloud console; February 2026 Tools & Integrations Ecosystem added GitHub, Jira, MongoDB, and 5 observability platforms as first-class connectors. Best experience is inside Gemini Enterprise Agent Platform. Alice Labs Production Score: 8/10. Best for: Google Cloud / Gemini Enterprise shops, and any team that needs Java or Go as a first-class agent language (still the only major framework with both) · Price: Open source (Apache 2.0). Gemini API + Vertex AI Agent Engine billed separately. Pros - Only major framework with Python, TypeScript, Java 1.0, and Go 2.0 SDKs at parity - Unified graph-based Workflow Runtime: slide from model-led to fully deterministic - Native A2A protocol and MCP client support - Visual Agent Designer in Google Cloud console; Agentspace deployment surface - Feb 2026 Tools & Integrations Ecosystem: GitHub, Jira, MongoDB + 5 observability platforms as first-class connectors Cons - Best experience is inside Gemini Enterprise Agent Platform; running fully outside GCP loses Agent Designer + deploy pipeline + grounding integrations - Newer than LangGraph / MSAF outside Google-native shops [google.github.io/adk-docs](https://google.github.io/adk-docs/) 6. #6 ## CrewAI 1.14.7 Best for fast role-based multi-agent prototypes Role-based multi-agent framework. You define a 'crew' of agents (researcher, writer, reviewer), assign tasks, and CrewAI orchestrates collaboration. Still the easiest framework to hand to a non-framework specialist. Version 1.14.7 (11 June 2026) added pluggable default backends for memory, knowledge, RAG, and flow (swap the persistence layer without forking); a native Snowflake Cortex LLM provider (joining Bedrock, Databricks, OpenAI, Anthropic, Gemini as first-class); a Chat API for conversational flows alongside the classic crew/task/agent primitives; real finish\_reason, sampling params, and response.id surfaced on all LLM events (observability parity with LangGraph); and a Flow DSL with type decorators and per-execution runtime state isolation. Alice Labs Production Score: 7/10. Best for: Role-based collaboration crews (research → write → review) where ergonomic Python + fast time-to-first-agent matter more than deep control · Price: Open source (MIT). CrewAI+ Enterprise is paid (optional). Pros - Very low barrier to entry — readable, declarative agent definitions - Pluggable backends for memory, knowledge, RAG, and flow (Jun 2026): swap persistence without forking - Native Snowflake Cortex + Bedrock + Databricks + OpenAI + Anthropic + Gemini as first-class LLM providers - Chat API + Flow DSL + per-execution runtime state isolation - Real finish\_reason and response.id on LLM events — observability parity with LangGraph / LangSmith Cons - Role-play abstraction leaks: complex handoff logic still requires dropping into Flows - Observability thinner than LangGraph/LangSmith or MSAF/Foundry [github.com/crewAIInc/crewAI](https://github.com/crewAIInc/crewAI) ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Need help picking the right agent stack? We've shipped production agents on LangGraph, CrewAI, AutoGen, and Semantic Kernel for clients in financial services, media, and the public sector. Book a 30-minute architecture call. [Book an architecture call](#contact) 7. #7 ## LlamaIndex Workflows 1.0 Best for RAG-grounded agents Event-driven step composition as the recommended pattern for any non-trivial agent — Workflows 1.0 shipped 22 June 2026 as the first stable release. Async-first runtime with typed event handlers for every agent step, Python and TypeScript parity from day one, and native LlamaCloud integration (parsers + indexes) so RAG-heavy agents don't have to leave the ecosystem. Positioned as a lightweight LangGraph alternative for teams already on LlamaIndex's data plane. Alice Labs Production Score: 7/10. Best for: RAG-heavy agents where the retrieval quality dominates and orchestration is a supporting cast — or teams that dislike graph-shaped state · Price: Open source (MIT). LlamaCloud is paid (optional). Pros - Event-driven step composition — cleanest abstraction if you dislike graph-shaped state - Async-first runtime with typed event handlers - Python and TypeScript parity from day one (rare) - Native LlamaCloud integration (parsers + indexes) for RAG-heavy agents - Best-in-class indexing, retrievers, and query engines from the core LlamaIndex library Cons - Multi-agent orchestration patterns are thinner than MSAF or CrewAI - You're renting Workflows on top of a framework that's still primarily a data framework [github.com/run-llama/llama\_index](https://github.com/run-llama/llama_index) 8. #8 ## Pydantic AI 2.0 Best DX for type-safe Python Type-safe agent framework from the Pydantic team. Reached V1 in September 2025; V2 shipped stable on 23 June 2026 with a harness-first redesign. One 'capability' primitive unifies instructions, tools, hooks, and settings into reusable composable units, and the slimmer core has everything above the primitive living in the separately versioned Pydantic AI Harness (40+ features across 9 categories: memory, guardrails, sandbox, evals, etc.). The Harness first shipped April 2026, ~2 months before v2 core — clean separation of stable core vs moving-target features. Version policy tightened: no-breaking-changes window shrunk from 6 months to 3. Alice Labs Production Score: 8/10. Best for: Type-safe Python teams who value 'FastAPI for agents' ergonomics and want tools + hooks + settings to compose the same way pydantic models do · Price: Open source (MIT) Pros - Single 'capability' primitive unifies instructions, tools, hooks, and settings into composable units - Slim core + separately versioned Harness (40+ features across 9 categories) - Best-in-class Python type safety and IDE support - Model-agnostic: OpenAI, Anthropic, Gemini, Groq, local models - Ergonomically familiar to FastAPI users Cons - 3-month major-version window is aggressive — if you pin to v2.0, expect v3 breakage sooner than the ecosystem average - Ecosystem still smaller than LangGraph or CrewAI [github.com/pydantic/pydantic-ai](https://github.com/pydantic/pydantic-ai) 9. #9 ## Mastra (@mastra/core 1.35) Best TypeScript-first framework for web-integrated agents TypeScript-first framework covering agents, workflows, memory, workspaces, and observability in one SDK. Graduated from YC W25 with $13M raised; ~22-24K GitHub stars and 300K+ weekly npm downloads make it the de facto TypeScript-first default. Ships a model router across 40+ LLM providers behind one API, multi-environment deploys (prod / staging / preview, each with its own URL, env vars, database), and native MCP client integration for external tools and services. 88+ releases through May 2026 (active release cadence). Alice Labs Production Score: 7/10. Best for: TypeScript teams shipping web-integrated agents (Next.js / Node) that want a framework designed for their runtime instead of a Python port · Price: Open source (MIT). Mastra Cloud (managed) is paid. Pros - TypeScript-first: agents, workflows, memory, workspaces, and observability in one SDK - Model router across 40+ LLM providers behind one API - Multi-environment deploys (prod / staging / preview) with per-env DB + env vars - Native MCP client integration - ~300K weekly npm downloads and ~22-24K GitHub stars — de facto TS default Cons - Younger project — fewer enterprise reference customers than LangGraph or Microsoft Agent Framework - Multi-agent orchestration semantics lighter than the Python heavyweights [github.com/mastra-ai/mastra](https://github.com/mastra-ai/mastra) 10. #10 ## AG2 0.12.2 (community AutoGen fork) Legacy — for teams on AutoGen 0.4/0.5 who don't want to migrate to MSAF The community continuation of the original AutoGen 0.2 lineage, forked in November 2024 by the same core maintainers who created AutoGen at Microsoft Research. Currently 0.12.2 with a v1.0 roadmap announced (legacy API deprecated at v0.14). Beta API launched March 2026 and expanded in 0.12.x — the foundation for v1.0 — runs alongside legacy without breakage. Continued convergence with MCP; A2A support via community adapters. Positioned explicitly as the non-Microsoft option for teams that want AutoGen's group-chat / nested-chat semantics without Azure gravity. Original Microsoft AutoGen has been in maintenance mode since October 2025. Alice Labs Production Score: 6/10. Best for: Teams that started on AutoGen 0.4/0.5 and want to keep the group-chat / nested-chat mental model without migrating to Microsoft Agent Framework · Price: Open source (Apache 2.0) Pros - Preserves AutoGen 0.2 group-chat / nested-chat mental model - Open governance — core maintainers are the original AutoGen creators - Beta API + legacy API run side-by-side without breakage - Continued MCP convergence; A2A support via community adapters Cons - Still pre-1.0 - Ecosystem gravity is now with Microsoft Agent Framework — hiring, docs, and vendor integrations increasingly assume MSAF as 'the AutoGen answer' [github.com/ag2ai/ag2](https://github.com/ag2ai/ag2) 01 / 11 Context ## AI Agent Framework Release Timeline (Feb – Aug 2026) In short Dated changelog of every major AI agent framework release from February through August 2026 — the seven months that reshaped the landscape. Alice Labs tracks these releases across our 100+ production AI implementations because framework version choice locks a project's durability, protocol interoperability, and observability story for the year that follows. The table below is the compressed release timeline Alice Labs uses internally when advising clients on which framework version to pin for a Q3/Q4 2026 production build. Every row links to a primary source (vendor blog, GitHub release, or official spec page). If you are searching for "ai agent framework release February 2026" or "ai agent frameworks updates 2026", this is the section to bookmark — it is refreshed after every material release across the seven frameworks we cover. Month Framework Version / Change Source Feb 2026 Claude Agent SDK Renamed from Claude Code SDK; broader agent scope, unified Anthropic tool-use surface [docs.claude.com/agent-sdk](https://docs.claude.com/en/api/agent-sdk) Feb 2026 Google ADK Tools & Integrations Ecosystem: GitHub, Jira, MongoDB + 5 observability platforms as first-class connectors [cloud.google.com/blog/io26](https://cloud.google.com/blog/topics/developers-practitioners/io26-news-for-agent-developers-on-google-cloud) Feb 25 2026 Hermes Agent (Nous Research) 1.0 launched (MIT) — persistent memory + auto-generated skills; ~140K GitHub stars in <3 months [startupfortune.com/hermes-agent](https://startupfortune.com/hermes-agent-crosses-214000-github-stars-as-developers-abandon-commercial-ai-agent-frameworks/) Feb 2026 Microsoft Agent Framework Release Candidate; migration guides from Semantic Kernel and AutoGen published [devblogs.microsoft.com/migration](https://devblogs.microsoft.com/agent-framework/migrate-your-semantic-kernel-and-autogen-projects-to-microsoft-agent-framework-release-candidate/) Mar 2026 OpenAI Agents SDK GA — production successor to Swarm; typed tools, handoffs, guardrails, tracing (Python + TypeScript) [helpnetsecurity.com/agents-sdk](https://www.helpnetsecurity.com/2026/04/16/openai-agents-sdk-harness-and-sandbox-update/) Mar 2026 Google ADK Java 1.0.0 — Maps grounding, human-in-the-loop confirmation, automated event compaction [developers.googleblog.com/adk-java](https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/) Mar 2026 AG2 Beta API launch — foundation for v1.0 without breaking existing users [chatforest.com/ag2-review](https://chatforest.com/reviews/ag2-autogen-multi-agent-framework/) Feb 2026 LangGraph 1.0.x patch series; DeltaChannel prototype; per-node timeout RFC [github.com/langchain-ai/langgraph/releases](https://github.com/langchain-ai/langgraph/releases) Mar 2026 Pydantic AI V2 beta 1–3; capabilities primitive introduced [ai.pydantic.dev](https://ai.pydantic.dev) Apr 3 2026 Microsoft Agent Framework 1.0 GA — Semantic Kernel + AutoGen merger, native MCP + A2A [devblogs.microsoft.com](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/) Apr 15 2026 OpenAI Agents SDK Sandbox execution (UnixLocal + Docker + hosted backends), TypeScript parity [openai.com/index/agents-sdk](https://openai.com/index/the-next-evolution-of-the-agents-sdk/) May 28 2026 CrewAI 1.14.6 stable [github.com/crewAIInc/crewAI/releases](https://github.com/crewAIInc/crewAI/releases) Jun 11 2026 CrewAI Pluggable memory/knowledge/RAG/flow backends, Chat API, Snowflake Cortex [github.com/crewAIInc/crewAI/releases](https://github.com/crewAIInc/crewAI/releases) Jun 2026 Claude Agent SDK Hierarchical subagents (3 levels), fallback model chains, MCP marketplace [code.claude.com/agent-sdk](https://code.claude.com/docs/en/agent-sdk/overview) Jun 22 2026 LlamaIndex Workflows 1.0 — event-driven, async-first, Python + TypeScript [llamaindex.ai/blog/workflows-1-0](https://www.llamaindex.ai/blog/announcing-workflows-1-0-a-lightweight-framework-for-agentic-systems) Jun 23 2026 Pydantic AI V2 stable — harness-first redesign, capabilities primitive [pydantic.dev/articles/pydantic-ai-v1](https://pydantic.dev/articles/pydantic-ai-v1) Jul 2026 Model Context Protocol 2026-07-28 spec release candidate — stateless protocol layer, cache scoping [blog.modelcontextprotocol.io](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/) Jul 2026 Pydantic AI V2.5.0 patch — additional model provider adapters and harness fixes [ai.pydantic.dev](https://ai.pydantic.dev) Jul 2026 Claude Agent SDK Week 24+ rolling updates: subagent hierarchy expanded to 5 levels deep, 200-spawn per-session ceiling, hooks refined, @tool decorator gains typing.Annotated support [code.claude.com/whats-new/2026-w24](https://code.claude.com/docs/en/whats-new/2026-w24) Aug 2026 LangGraph Workflow updates — node caching (skip redundant computation on re-runs), deferred nodes (fan-in barriers), pre/post model hooks (context trimming, guardrails, PII redaction), content-block streaming API [changelog.langchain.com/langgraph-workflow-updates](https://changelog.langchain.com/announcements/langgraph-workflow-updates-python-js) Aug 2026 Microsoft Agent Framework BUILD 2026 preview: Agent Harness, Hosted Agents (managed runtime + sandbox execution), CodeAct-style tool synthesis [devblogs.microsoft.com/build-2026](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-at-build-2026-announce/) The single most consequential row is Microsoft Agent Framework 1.0 on 3 April 2026 — because it collapsed the two most-adopted Microsoft agent SDKs into one and shipped with both interoperability protocols native. Every other row is incremental. In Alice Labs' agent engagements this quarter, "pin to the release below" is the most common architectural decision the team asks us to underwrite: if you are building for open-source only, pin LangGraph 1.0 with the Q2 additions; if you are building on Microsoft, pin Microsoft Agent Framework 1.0; if you are Anthropic-native, pin the Claude Agent SDK June 2026 subagent release. 02 / 11 Context ## What Changed in Q2 – Q3 2026 (April – August) — AI Agent Framework Releases In short Q2–Q3 2026 (April–August) delivered more shipped features across the agent-framework ecosystem than any prior period. Microsoft merged Semantic Kernel and AutoGen into Microsoft Agent Framework 1.0 (April 3) and previewed Agent Harness + Hosted Agents + CodeAct at BUILD 2026. LangGraph shipped node caching, deferred nodes, pre/post model hooks, and content-block streaming (August). Anthropic expanded Claude Agent SDK subagent hierarchy to 5 levels with a 200-spawn ceiling. CrewAI 1.14.7 introduced pluggable memory/knowledge/RAG backends, Snowflake Cortex, and Chat API. Pydantic AI V2 and LlamaIndex Workflows 1.0 both went stable in the same 48-hour window (June 22–23). Google ADK 2.0 shipped Go 2.0 and expanded the Java 1.0 line. MCP 2026-07-28 spec is in release candidate. A2A crossed 150 adopting organisations. The window from 1 April to 5 July 2026 delivered more shipped features across the agent-framework ecosystem than any quarter since agent frameworks began shipping. The headline is Microsoft's consolidation — but the underlying story is that every major framework now ships production primitives (durable state, subagents, pluggable backends, harness-first design) that were community recipes twelve months ago. For a deeper look at the runtime patterns behind these primitives, see our reference on [best practices for autonomous agent orchestration](/en/insights/ai-agent-orchestration). 1. **Microsoft Agent Framework 1.0 GA — 3 April 2026.** [devblogs.microsoft.com/agent-framework](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/). Microsoft shipped Agent Framework 1.0 for .NET and Python, merging Semantic Kernel's enterprise primitives (session state, type safety, middleware, telemetry) with AutoGen's multi-agent orchestration into a single SDK under Microsoft.Agents.AI. MCP and A2A are native — not adapters. Existing Semantic Kernel projects will receive critical bug fixes for ≥1 year post-GA; new Microsoft-stack builds should start on MAF. This is the biggest consolidation of the year. 2. **OpenAI Agents SDK next evolution — 15 April 2026.** [openai.com/index/the-next-evolution-of-the-agents-sdk](https://openai.com/index/the-next-evolution-of-the-agents-sdk/). OpenAI shipped sandbox execution (UnixLocalSandboxClient, DockerSandboxClient + hosted backends: Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel), a model-native harness for long-horizon file/tool work, and TypeScript SDK parity. Subagents and code mode are staged. The SDK now works with 100+ non-OpenAI LLMs via Chat Completions. 3. **CrewAI 1.14.6 stable + pluggable backends — 28 May & 11 June 2026.** [github.com/crewAIInc/crewAI/releases](https://github.com/crewAIInc/crewAI/releases). Version 1.14.6 became the current stable on 28 May 2026; the 11 June release added pluggable default backends for memory, knowledge, RAG, and flow, plus a Chat API for conversational flows, a native Snowflake Cortex LLM provider, and scoped runtime state per run to isolate concurrent executions. 4. **Claude Agent SDK — hierarchical subagents & fallback chains, June 2026.** [code.claude.com/docs/en/agent-sdk/overview](https://code.claude.com/docs/en/agent-sdk/overview). Anthropic shipped hierarchical agent spawning — parent agents can create child agents which can each spawn their own children, up to three levels deep — enabling layered task decomposition. Also added: fallback model chains, per-agent cost attribution, scoped permissions, and a community MCP tool marketplace. Separate Agent SDK credit pool went live 15 June 2026 for subscription plans. 5. **LangGraph — per-node timeouts, DeltaChannel & v2 streaming, Q2 2026.** [github.com/langchain-ai/langgraph/releases](https://github.com/langchain-ai/langgraph/releases). Building on the LangGraph 1.0 GA (22 October 2025 — durable state, built-in persistence, first-class HITL), Q2 2026 releases added per-node timeouts via TimeoutPolicy (run\_timeout / idle\_timeout), node-level error handlers that receive a typed NodeError and can route to a recovery node for Saga/compensation patterns, cooperative graceful shutdown, a DeltaChannel type that stores only incremental deltas per step (cutting checkpoint overhead for long-running threads), and a v2 typed streaming API with unified StreamPart output. 6. **LlamaIndex Workflows 1.0 — 22 June 2026.** [llamaindex.ai/blog/announcing-workflows-1-0](https://www.llamaindex.ai/blog/announcing-workflows-1-0-a-lightweight-framework-for-agentic-systems). A lightweight event-driven, async-first, step-based framework for orchestrating agentic systems in Python and TypeScript. Announced alongside LlamaCloud's rebrand to LlamaParse (reflecting the platform's evolution into agentic document processing) and new pre-built document-agent templates. 7. **Pydantic AI V2 stable — 23 June 2026.** [pydantic.dev/articles/pydantic-ai-v1](https://pydantic.dev/articles/pydantic-ai-v1). After seven betas, V2 shipped stable with a harness-first redesign — capabilities as a core primitive bundling tools, hooks, instructions, and model settings into a single composable unit that reaches every layer of the agent. V1 (September 2025) remains supported; V2 is where new development should start. Patch releases through 4 July 2026 (v2.5.0). 8. **MCP 2026-07-28 spec — release candidate.** [blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/). The largest MCP revision to date. The protocol layer is now stateless — a remote MCP server can run behind a plain round-robin load balancer, route traffic on an Mcp-Method header, and let clients cache tools/list responses (with ttlMs and cacheScope on list and resource-read results). Ships with the Extensions framework, refined Tasks primitive, MCP Apps, authorization hardening, and a formal deprecation policy. Final spec locks 28 July 2026. 9. **Agent-to-Agent (A2A) protocol — 150+ organisations, native in three major clouds.** [prnewswire.com/a2a-protocol-150-organizations](https://www.prnewswire.com/news-releases/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year-302737641.html). As of April 2026, A2A has crossed 150 adopting organisations, is natively integrated in Azure AI Foundry, AWS Bedrock AgentCore, and Google Cloud, and ships production SDKs in Python, JavaScript, Java, Go, and .NET. Adopters include Google, Microsoft, AWS, Salesforce, SAP, ServiceNow, Workday, and IBM. Complements MCP: MCP connects agents to tools; A2A connects agents to other agents across frameworks and vendors. ### MCP + A2A protocol adoption tracker (Q2 2026) Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol adoption is now the single biggest interoperability signal in the agent-framework market. Native support means the protocol ships in the SDK; adapter support means a community or first-party bridge exists but the framework doesn't ship it as a primitive. Framework MCP (tools) A2A (agent-to-agent) Notes Claude Agent SDK Native — deepest integration Adapter MCP is the primary tool contract; community A2A bridges shipped Q2 2026. Microsoft Agent Framework 1.0 Native Native Both protocols ship in 1.0 — not bolt-on. Azure AI Foundry runtime aware. LangGraph 1.0 Native — MCP tools as first-class graph nodes with streaming Adapter (community) Deepest streaming MCP support of any framework. CrewAI 1.14 Native — MCP tool servers as agent tools Adapter MCP tools plug into the pluggable-backend model. AutoGen / AG2 Adapter (community) — native in MAF 1.0 successor Adapter For new Microsoft-stack builds, use MAF 1.0 for native protocol support. LlamaIndex Workflows 1.0 Native Adapter Agent Client Protocol integrations shipped early 2026 for cross-framework flows. Pydantic AI V2 Native Adapter Harness-first V2 exposes MCP tools through the capabilities primitive. If you're building today and interoperability is a hard requirement — for example an agent that must call tools exposed by another team's MCP server, or must delegate to a partner agent behind an A2A endpoint — the safest picks in Q2 2026 are Microsoft Agent Framework 1.0 (both protocols native) or a pairing of the Claude Agent SDK (native MCP) with a small A2A shim service. For teams already delivering on LangGraph, the community A2A bridges are production-ready but you should read them before adopting. How to read Q2 2026 releases as a buyer, not a builder If you're evaluating frameworks in July 2026, ignore the version numbers — every framework in this list ships weekly. What matters is whether the framework has crossed three thresholds: (1) durable state and HITL as primitives (LangGraph 1.0, MAF 1.0, Claude Agent SDK — yes; others emerging); (2) native MCP for tool interoperability (all seven now); (3) native A2A for cross-agent interoperability (MAF 1.0 today; others via adapters). Pick a framework that already sits above all three, or pin your architecture around a framework that will get there before your production date. 03 / 11 Context ## Enterprise Platforms (Managed): 8 Options for 2026 In short If you need governance, identity, audit trails, SLAs, and procurement-friendly licensing — not raw code — the question is which managed agent platform fits your existing stack. The eight that matter in 2026: Microsoft Copilot Studio + Microsoft 365 Agents (was 'Agent 365'), AWS Bedrock AgentCore, Google Vertex AI Agent Builder, OpenAI Agent Platform, Salesforce Agentforce 360, ServiceNow AI Agents, IBM watsonx Orchestrate, and UiPath Agentic Automation. Open-source frameworks below are the right answer for engineering teams building custom agents — and if you need a delivery partner rather than a library, our [AI agent development](/en/ai-agents) practice ships production systems on the frameworks below. But many enterprises ask a different question first: _which managed agent platform does our existing vendor offer, and is it production-ready?_ The eight enterprise-grade platforms below cover >90% of Fortune 500 stack decisions for 2026. All eight ship with role-based access control, audit logging, regional data residency options, and alignment to [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework), [ISO/IEC 42001](https://www.iso.org/standard/81230.html), and the [EU AI Act](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai). 1. **Microsoft Copilot Studio + Microsoft 365 Agents** — [microsoft.com/microsoft-copilot/microsoft-copilot-studio](https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio). Low-code agent builder bound to Entra ID identity, Microsoft Graph data, Power Platform connectors, and Azure AI Foundry models. The default pick for M365 / Azure tenants. Licensing: Copilot Studio messages metered + Microsoft 365 Copilot seat ($30/user/month list). 2. **AWS Bedrock AgentCore** — [aws.amazon.com/bedrock/agentcore](https://aws.amazon.com/bedrock/agentcore/). Managed runtime, memory, identity, gateway, and browser/code-interpreter tools for agents on Amazon Bedrock. Framework-agnostic (works with LangGraph, CrewAI, Strands, Claude Agent SDK). Pricing: per-invocation + model tokens; no platform seat. 3. **Google Vertex AI Agent Builder** — [cloud.google.com/products/agent-builder](https://cloud.google.com/products/agent-builder). Vertex-native agent SDK (Agent Development Kit / ADK), Agent Engine runtime, and Agentspace deployment surface. Gemini models, BigQuery and Cloud Storage data, and Google Workspace context. Default pick for GCP-centric stacks. 4. **OpenAI Agent Platform (AgentKit + Responses API)** — [platform.openai.com/docs/guides/agents](https://platform.openai.com/docs/guides/agents). OpenAI's first-party agent stack: Agents SDK + AgentKit + Responses API with built-in tools (web search, file search, code interpreter, computer use). Best when you're already standardised on the OpenAI API and want a vendor-managed control plane. 5. **Salesforce Agentforce 360** — [salesforce.com/agentforce](https://www.salesforce.com/agentforce/). Agents grounded in Data Cloud and the Salesforce metadata layer (CRM, Service Cloud, Sales Cloud, Slack). Atlas reasoning engine, deterministic guardrails, and consumption-based pricing per conversation. The default if Salesforce is your system of record. 6. **ServiceNow AI Agents (Now Assist)** — [servicenow.com/products/ai-agents](https://www.servicenow.com/products/ai-agents.html). Pre-built and custom agents on the Now Platform with native access to ITSM, HR Service Delivery, Customer Service Management workflows. Strongest for ITSM-led enterprises with ServiceNow as the workflow backbone. 7. **IBM watsonx Orchestrate** — [ibm.com/products/watsonx-orchestrate](https://www.ibm.com/products/watsonx-orchestrate). Pre-built domain agents (HR, sales, procurement) and an Agent Builder with governance hooks via watsonx.governance. Strong story for regulated industries (financial services, healthcare, public sector) that need hybrid-cloud / on-prem. 8. **UiPath Agentic Automation** — [uipath.com/product/agentic-automation](https://www.uipath.com/product/agentic-automation). Combines RPA with LLM agents (Agent Builder, Maestro orchestration, Autopilot). Best when you already operate a large UiPath RPA estate and want to upgrade deterministic bots with agentic reasoning. **Honourable mentions** for narrower or adjacent use cases: Cognigy, Cohere North, Glean Agents, Sierra, Sana AI, Decagon, Adept, Anthropic Claude for Enterprise (the platform, distinct from the Claude Agent SDK). These remain important but are either domain-specific (customer support, internal search) or earlier in enterprise maturity. If your evaluation is stalling because you also need delivery capacity, our [AI agent implementation services](/en/ai-agents) page covers the full lifecycle — and our directory of [AI agent consultants](/en/insights/ai-agent-development-companies-2026) lists the firms we benchmark against. ### Pick by your stack — decision table Your dominant stack Default platform Pair with (open source) Why Microsoft 365 / Azure / Entra ID Copilot Studio + M365 Agents Semantic Kernel Identity, Graph data, Power Platform connectors out of the box. AWS / Bedrock / IAM Bedrock AgentCore LangGraph or Claude Agent SDK Framework-agnostic runtime, AWS IAM-native, VPC isolation. Google Cloud / Workspace / BigQuery Vertex AI Agent Builder ADK (open-source) + LangGraph Gemini-native, BigQuery grounding, Agentspace deployment. OpenAI API-first OpenAI Agent Platform (AgentKit) Pydantic AI or LangGraph First-party tools, Responses API, no vendor abstraction tax. Salesforce CRM as system of record Agentforce 360 LangGraph (via MuleSoft) Data Cloud grounding, deterministic guardrails, Slack-native. ServiceNow workflow backbone ServiceNow AI Agents (Now Assist) LangGraph (via Integration Hub) ITSM/HRSD/CSM workflows native; minimal integration work. Regulated / hybrid / IBM-heavy watsonx Orchestrate LangGraph + watsonx.governance On-prem option, governance suite, regulated-industry references. Large UiPath RPA estate UiPath Agentic Automation LangGraph or CrewAI Upgrades deterministic RPA bots with LLM reasoning in one estate. No dominant stack / build custom — (no managed platform) LangGraph or Claude Agent SDK Maximum control; pay observability + deployment cost yourself. **Sources for governance alignment:** [NIST AI RMF 1.0](https://www.nist.gov/itl/ai-risk-management-framework), [ISO/IEC 42001:2023](https://www.iso.org/standard/81230.html), [EU AI Act (digital-strategy.ec.europa.eu)](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai), [Gartner research](https://www.gartner.com/en/newsroom). Enterprise platform vs. open-source framework — the simple rule Use a managed enterprise platform when your buying centre is procurement, legal, or IT and the agent must inherit identity, data residency, and audit from an existing tenant. Use an open-source framework when your buying centre is engineering and the agent must do something the platform cannot express. Most large programmes end up running both — platform for breadth, framework for depth. 04 / 11 Context ## How to Choose Between Them In short Start from your dominant constraint: control (LangGraph), Anthropic-native production (Claude Agent SDK), team velocity (CrewAI), conversational research (AutoGen/AG2), enterprise stack (Semantic Kernel), data layer (LlamaIndex), or type safety (Pydantic AI). Frameworks are not interchangeable — picking the right one saves weeks. In client engagements we use a single decision rule: identify the dominant constraint for the project, and pick the framework whose core abstraction matches it. If you'd rather compare vendors than libraries, see our shortlist of [AI agent development companies](/en/insights/ai-agent-development-companies-2026) for 2026, and the deeper technical head-to-head in [LangGraph vs CrewAI vs AutoGen](/en/insights/langgraph-vs-crewai-vs-autogen). - **Need explicit control?** LangGraph 1.0. Graph state, retries, HITL, per-node timeouts (Q2 2026), time-travel debugging. - **Building Anthropic-native production agents?** Claude Agent SDK. Same architecture as Claude Code — hooks, deepest MCP integration, skills, subagents (now hierarchical up to 3 levels, June 2026). - **Need fast multi-agent prototype?** CrewAI 1.14. Define roles, assign tasks, ship. Pluggable memory/knowledge/RAG backends since Jun 2026. - **Building research-style assistants?** AG2 (community) — for new Microsoft-stack builds, prefer Microsoft Agent Framework 1.0 for native protocol support. - **On Microsoft / .NET?** Microsoft Agent Framework 1.0 (GA April 3, 2026). Unified successor to Semantic Kernel and AutoGen with C# + Python parity, Azure AI Foundry integration, and native MCP + A2A. - **RAG-first agent?** LlamaIndex Workflows 1.0. Retrieval and indexes are first-class; Workflows 1.0 (Jun 2026) is the current orchestration API. - **Python team that values types?** Pydantic AI V2 (Jun 2026). Harness-first redesign, capabilities primitive, model-agnostic. - **Need agent-to-agent interoperability?** Microsoft Agent Framework 1.0 for native A2A, or pair any framework with a small A2A shim. See the MCP + A2A adoption tracker above. **Building or evaluating a coding agent (Claude Code, Cursor, Aider) rather than a custom agent?** See our companion article: [Best AI Coding Agents 2026](/en/insights/best-ai-coding-agents-2026). For teams sketching the architecture behind a single agent, start with [how to build an AI agent](/en/insights/how-to-build-ai-agent) and, for coordination across multiple agents, our [AI agent orchestration](/en/insights/ai-agent-orchestration) playbook and primer on [multi-agent systems](/en/insights/multi-agent-systems-explained). 05 / 11 Context ## LangGraph vs CrewAI vs AutoGen 2026: Which One Should You Ship? In short For net-new production projects in Q3/Q4 2026, Alice Labs' default answer across our 100+ production AI implementations is LangGraph 1.x for stateful graphs, Microsoft Agent Framework 1.0 for enterprise Microsoft/.NET stacks (this is where AutoGen's lineage now lives), and CrewAI 1.14.7 for fast role-based prototypes. AutoGen itself is in maintenance since October 2025; the community fork AG2 is pre-1.0 and mainly relevant to teams already on AutoGen 0.4/0.5. This is the single most-searched head-to-head in the agent-framework market — and the most-often-confused. LangChain and LangGraph are related but distinct (LangChain is the underlying primitives library; LangGraph is the graph-based orchestration layer built by the same team). AutoGen is a lineage, not a single product: the original Microsoft AutoGen (0.2 → 0.4 → 0.5) is in maintenance since October 2025 and its features have been folded into Microsoft Agent Framework 1.0; the community fork AG2 continues under open governance at 0.12.2 with a v1.0 roadmap. Below is the decision matrix we use internally when a client asks us to underwrite the pick. Axis LangGraph 1.x CrewAI 1.14.7 Microsoft Agent Framework 1.0 (AutoGen successor) License MIT MIT MIT Core abstraction State graph with explicit nodes + edges Role-based crew (agent → task → crew) Agent primitive + orchestration patterns (sequential, concurrent, handoff, group chat, Magentic-One) Languages Python, TypeScript Python Python, .NET (C# at parity) Durable state / checkpoints First-class (checkpointer + time-travel) Pluggable backends (Jun 2026) Session-based state, telemetry, middleware Human-in-the-loop Native (interrupt / resume) Via Flow DSL Native MCP support Native — first-class graph nodes with streaming First-class (via pluggable-backend model) Native — MCP client + tool support baked into agent primitive A2A support Community adapter Community adapter Native — A2A cards baked into agent primitive Observability LangSmith (deep) finish\_reason + response.id on all events (Jun 2026) Azure AI Foundry telemetry + middleware Aug 2026 headline feature Node caching, deferred nodes, pre/post model hooks, content-block streaming Pluggable memory/knowledge/RAG/flow backends + Snowflake Cortex + Chat API BUILD 2026: Agent Harness + Hosted Agents + CodeAct preview Winner: customer service agents Yes (with LangSmith) Fast prototype Yes (Copilot Studio + Agentforce integration) Winner: RAG-heavy Runner-up (pair with LlamaIndex retrievers) Runner-up Runner-up (pair with Azure AI Search) Winner: research assistants Runner-up Yes (role-based decomposition) Yes (Magentic-One) Winner: coding agents Runner-up No Yes (with CodeAct + Hosted Agents preview) — but Claude Agent SDK is the category leader **Migration cost between them, from Alice Labs deployments:** LangGraph ↔ CrewAI is roughly 3-6 engineering weeks per medium-complexity agent (the tool and prompt layer ports cleanly; the orchestration layer needs full rewrite). Either → Microsoft Agent Framework 1.0 is 4-8 weeks and mostly worth doing only if you are consolidating onto Azure. AutoGen 0.5 → Microsoft Agent Framework 1.0 is Microsoft's guided path and 2-4 weeks with the official migration doc. AG2 stays where it is because its users explicitly do not want to be on the Microsoft path. LangChain is not LangGraph About one in three inbound Alice Labs calls that mention 'LangChain agents' actually needs LangGraph. LangChain provides the underlying primitives (LLM wrappers, tools, prompts, retrievers); LangGraph is the graph-based orchestration layer built by the same team. Both are MIT. You can use LangGraph without using LangChain's agents directly, and most 2026 production teams do. 06 / 11 Context ## MCP and A2A: The Two Protocols Every 2026 Agent Framework Must Support In short Model Context Protocol (MCP, Anthropic origin, 2026-07-28 spec release candidate) and Agent-to-Agent (A2A, Google origin, 150+ adopting organisations as of April 2026, native in Azure AI Foundry, AWS Bedrock AgentCore, and Google Cloud) are the two interoperability protocols that matter in 2026. MCP connects agents to tools; A2A connects agents to other agents across frameworks and vendors. Framework choice now matters less than protocol coverage. Framework choice used to lock a team's toolchain for years. In 2026 the lock-in has shifted to protocol coverage: any framework that ships native MCP can call any MCP tool server without a custom adapter, and any framework that ships native A2A can delegate to any other A2A agent without a custom bridge. That is a bigger portability win than picking the "right" framework, and it is why Alice Labs treats MCP + A2A support as a hard requirement in every current agent evaluation. ### Model Context Protocol (MCP) Origin: Anthropic (open standard, no vendor lock-in). Current spec: 2026-07-28 release candidate (final locks 28 July 2026 window per [blog.modelcontextprotocol.io](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/)). The 2026-07-28 spec is the largest MCP revision to date — the protocol layer is now stateless, remote MCP servers can run behind plain round-robin load balancers, clients can cache tools/list responses (with ttlMs and cacheScope on list and resource-read results), and the release ships with the Extensions framework, refined Tasks primitive, MCP Apps, authorization hardening, and a formal deprecation policy. All 10 frameworks in our ranking now support MCP; 7 support it natively (Claude Agent SDK, Microsoft Agent Framework 1.0, LangGraph 1.x, CrewAI 1.14.7, LlamaIndex Workflows 1.0, Pydantic AI 2.0, OpenAI Agents SDK). The deepest MCP integration is in the Claude Agent SDK, where MCP is the primary tool contract. ### Agent-to-Agent (A2A) Origin: Google (open standard, hosted at [a2a-mcp.org](https://a2a-mcp.org)). As of April 2026 the protocol has crossed 150+ adopting organisations, is natively integrated in Azure AI Foundry, AWS Bedrock AgentCore, and Google Cloud, and ships production SDKs in Python, JavaScript, Java, Go, and .NET ([PR Newswire announcement](https://www.prnewswire.com/news-releases/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year-302737641.html)). Adopters include Google, Microsoft, AWS, Salesforce, SAP, ServiceNow, Workday, and IBM. Native A2A in an SDK today: Microsoft Agent Framework 1.0 and Google ADK 2.0. Everyone else uses community adapters. MCP and A2A complement each other — MCP connects agents to tools; A2A connects agents to other agents across frameworks and vendors. ### Alice Labs rule of thumb If interoperability is a hard requirement (your agent must call another team's MCP server, or delegate to a partner agent behind an A2A endpoint) the safest August-2026 picks are Microsoft Agent Framework 1.0 (both protocols native), Google ADK 2.0 (native A2A + strong MCP), or a pairing of the Claude Agent SDK (deepest MCP) with a small A2A shim service. For teams already delivering on LangGraph, the community A2A bridges are production-ready but you should read them before adopting. 07 / 11 Context ## AI Agent Architecture 2026: Design Patterns from LangGraph, CrewAI, and Multi-Agent Systems In short The named agent architecture patterns you should know in 2026: ReAct, Reflexion, Plan-and-Execute, Supervisor, Swarm, Hierarchical, Event-Driven, Human-in-the-Loop, and Magentic-One. Every major framework implements a subset natively — pattern-framework fit is often more decision-critical than framework choice alone. Across Alice Labs' 100+ production AI implementations, the same nine architecture patterns show up again and again. Pattern selection is upstream of framework selection: once you know the shape of the agent, the framework choice usually narrows to two or three. The catalog below is the internal decision tree we use. Pattern One-line shape Native in Best for ReAct Reason → Act → Observe loop Every framework Single-agent tool use, the default starting point Plan-and-Execute Generate plan → execute steps → replan on failure LangGraph, MSAF, CrewAI (Flow) Long-horizon tasks with intermediate checkpoints Reflexion Self-critique + retry with reflection memory LangGraph, Pydantic AI (hooks) Code generation, evals, quality-sensitive outputs Supervisor One orchestrator delegates to worker agents LangGraph, MSAF, CrewAI (hierarchical process), Claude Agent SDK (subagents) Complex tasks needing role decomposition + audit trail Swarm Peer agents with dynamic handoffs MSAF (handoff pattern), OpenAI Agents SDK (evolved from Swarm) Customer service routing, expert triage Hierarchical Nested supervisors (agents spawn subagents) Claude Agent SDK (5 levels, 200-spawn ceiling), LangGraph subgraphs, MSAF Layered task decomposition (research → subresearch → synthesis) Event-Driven Steps composed via typed events LlamaIndex Workflows 1.0, MSAF Long-running async pipelines, document processing Human-in-the-Loop Interrupt for human approval, then resume LangGraph, MSAF, Google ADK Regulated actions, high-risk tool calls, approvals Magentic-One Orchestrator + specialised agents (WebSurfer, Coder, ComputerTerminal, FileSurfer) Microsoft Agent Framework 1.0 General-purpose agent teams (Microsoft's canonical example) **Anti-patterns we've seen fail:** (1) using Swarm for regulated actions — dynamic handoffs make audit trails hard; use Supervisor + HITL instead. (2) Using Reflexion without an eval harness — the self-critique loop turns into unbounded token spend. (3) Hierarchical spawn without a depth or spawn-count ceiling — this is exactly why the Claude Agent SDK ships 5-level and 200-spawn defaults. (4) Plan-and-Execute without persisted intermediate state — the first failure kills the whole run. 08 / 11 Context ## Emerging AI Agent Frameworks 2026: New Entrants and Ones to Watch In short The three most credible new agent frameworks that broke through in 2026 outside the ten in our main ranking: Hermes Agent (Nous Research, ~220K GitHub stars by late July), Strands Agents (AWS open-source), and BeeAI (IBM Research). None replaces the top-10 for enterprise production yet, but all three are on the Alice Labs 'evaluate every quarter' watchlist. We evaluate one new framework per month at Alice Labs. Most fall out of contention because they are LangGraph clones with a lighter dependency footprint (interesting, not differentiated). The three below solve genuinely new problems and are worth an evaluation cycle in Q4 2026: - **Hermes Agent (Nous Research)** — Launched 25 February 2026 under MIT with persistent memory + auto-generated skills as the headline. Crossed ~140K GitHub stars in under three months and ~220K by late July per [startupfortune.com](https://startupfortune.com/hermes-agent-crosses-214000-github-stars-as-developers-abandon-commercial-ai-agent-frameworks/). Solves a real problem (agent skill generation without a human in the loop). Risk: community governance and enterprise support story is still forming; not yet an Alice Labs production pick. - **Strands Agents (AWS open-source)** — AWS's open-source agent SDK, framework-agnostic and designed to feed into Bedrock AgentCore. Best if you are AWS-native and want vendor-blessed patterns without lock-in. - **BeeAI (IBM Research)** — IBM's open-source agent framework built around interoperable agent protocols. Complements watsonx Orchestrate and is where IBM's agent research is landing. Best for regulated / hybrid IBM shops that want an open-source path. **On "openclaw"** — this query appears in our GSC data but does not correspond to a real framework as of August 2026 and appears to be a typo (most likely of "OpenClaude" / "OpenAI Claude" / the Claude Agent SDK). If you were searching for it, the closest real project is the Claude Agent SDK (rank 3 above). 09 / 11 Context ## Most Popular AI Agent Frameworks 2026: Ranked by GitHub Stars, PyPI Downloads, and Ecosystem Signals In short Popularity and production-readiness are not the same axis. As of August 2026 the popularity leaders (by GitHub stars) are Hermes Agent (~220K), LangGraph, CrewAI, and Mastra (~22-24K, dominant on the TypeScript side). But Alice Labs' ranking above is by production-readiness across our 100+ implementations — a different question that only partially overlaps. Popularity is a leading indicator, not a decision criterion. A framework can be popular because it is easy to get started with (CrewAI, Mastra), because it is the vendor default (OpenAI Agents SDK, Claude Agent SDK), or because it went viral on a specific technical claim (Hermes Agent's auto-generated skills). None of those signals guarantee production-readiness. The table below separates the two axes: Framework Popularity signal (Aug 2026) Alice Labs Production Score Quadrant LangGraph 1.x High GitHub activity + LangChain ecosystem gravity 9/10 Popular AND production-ready Microsoft Agent Framework 1.0 Microsoft-scale enterprise adoption + BUILD 2026 launch 8/10 Popular AND production-ready Claude Agent SDK High — Anthropic's own agents run on it (Claude Code) 9/10 Popular AND production-ready (Anthropic-native) CrewAI 1.14.7 High GitHub stars + weekly release cadence 7/10 Popular; production-ready with caveats Mastra ~22-24K GitHub stars, ~300K weekly npm downloads (TS-first leader) 7/10 Popular in TS; production-ready with fewer enterprise refs Hermes Agent ~220K GitHub stars by late July 2026 (fastest riser) Not yet scored (evaluating) Popular; production-readiness unproven Pydantic AI 2.0 Moderate — high in FastAPI / typing crowd 8/10 Less popular; production-ready for typed Python teams AG2 0.12.2 Long-tail — inherited from AutoGen 0.2 users 6/10 Declining relative to MSAF; production-ready but shrinking gravity "Popularity" figures are directional and drawn from public GitHub, PyPI/npm, and vendor announcements as of August 2026. Star counts move quickly; the ordinal ranking of production-readiness moves slowly, and it is the one we underwrite. 10 / 11 Context ## What Changed in 2025 (historical context) In short 2025 set up 2026's consolidation. The AutoGen / AG2 split, LangGraph 1.0 GA (October 22), Claude Code SDK launch (later renamed to Claude Agent SDK), CrewAI commercialization, and Pydantic AI V1 (September) were the year's five biggest movers. Q2 2026 releases are covered in the dedicated section above. For the current Q2 2026 shipping list see [What Changed in Q2 2026 — AI Agent Framework Releases](#q2-2026-framework-releases) above. The 2025 story remains important context because it set up the 2026 consolidation. The biggest movers were on the open-source side, tracked in detail in our [open-source AI agent frameworks comparison](/en/insights/open-source-ai-agent-frameworks-comparison-2026). The notable 2025 shifts: - **AutoGen / AG2 split.** Microsoft renamed and rewrote AutoGen as v0.4+ with a different API. The original v0.2 community continued under the AG2 name (ag2.ai). Both lineages were subsequently unified when Microsoft folded AutoGen into Microsoft Agent Framework 1.0 in April 2026. - **LangGraph 1.0 GA (22 October 2025).** Production patterns (checkpointing, durable execution, HITL approvals) became first-class primitives rather than community recipes. Powers agents at Uber, LinkedIn, and Klarna. - **Claude Code SDK public launch.** Anthropic released the SDK behind Claude Code as a public library. Renamed to Claude Agent SDK in early 2026 to reflect the broader agent scope beyond code. - **CrewAI commercialization.** The open-source core stays free; enterprise tooling (UI, RBAC, deployments) is paid. - **Pydantic AI V1 (September 2025).** Committed to API stability. The V2 redesign released nine months later (June 2026) collected the breaking changes that the V1 stability guarantee didn't allow. 11 / 11 Context ## Production Considerations Beyond the Framework In short Framework choice is necessary but insufficient. Production agents also need observability (LangSmith / Langfuse / Arize), guardrails, evaluation harnesses, and a deployment story. Underestimating these is the most common reason agent projects stall after a successful demo. Across our client engagements — see the deployment patterns behind our [enterprise AI agents](/en/ai-agents) practice and the outcome math in [enterprise AI agent ROI](/en/insights/ai-agents-enterprise-roi) — the non-framework choices that determine production success are: - **Observability.** LangSmith (LangChain ecosystem), Langfuse, or Arize for traces, evaluations, and prompt versioning. Without traces, you cannot debug agent regressions. - **Evaluation harness.** A regression test suite for the agent — task-level success, latency, cost. Run on every prompt change. - **Guardrails.** Input filtering, output validation, and tool-use approvals for high-risk actions. Pydantic AI does this natively; others use NeMo Guardrails or Guardrails AI. - **Deployment surface.** Streaming, sessions, retries, idempotency. LangGraph Platform and CrewAI+ provide these out of the box; rolling your own is a multi-week project. Choose framework + observability together We recommend pairing LangGraph with LangSmith, CrewAI / Pydantic AI with Langfuse, and Semantic Kernel with Azure Application Insights. Picking observability after the fact creates rework. ## Methodology Selection is based on (a) hands-on usage in Alice Labs client engagements across our 100+ production AI implementations since 2023, (b) public GitHub activity (release cadence, issue response, contributor count), (c) ecosystem signals — integrations, observability tooling, deployment maturity, and (d) protocol maturity (MCP client / server, A2A cards). Frameworks are ordered by general-purpose suitability for new production projects starting in Q3/Q4 2026, not by absolute quality — every framework in this list has a use case where it is the best pick. ## About the Authors & Reviewers Published April 15, 2026 · Updated August 2, 2026 Written by ![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) Reviewed by August 2, 2026 ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) Eric Lundberg 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 [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Published April 15, 2026 · Updated August 2, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is the official full name of AutoGen? AutoGen is Microsoft Research's Automated Generation of Multi-Agent Conversation framework (github.com/microsoft/autogen), first released September 2023. The v0.2 lineage lives on as the community fork AG2 (ag2.ai), while Microsoft's v0.4+ AutoGen is now succeeded by Microsoft Agent Framework 1.0, which shipped on 3 April 2026 and merges AutoGen with Semantic Kernel into a single production SDK. In Alice Labs' agent engagements we default new Microsoft-stack projects to Microsoft Agent Framework 1.0 and only touch AutoGen / AG2 for existing v0.2 codebases. ### What are the best open source AI agent frameworks in 2026? Alice Labs' ranking of open-source-only AI agent frameworks for 2026, drawn from our 100+ production AI implementations: (1) LangGraph 1.0 (MIT) — best overall for stateful production workflows; (2) Claude Agent SDK (MIT) — best Anthropic-native primitives; (3) CrewAI 1.14 (MIT) — fastest path to a role-based multi-agent prototype; (4) Microsoft Agent Framework 1.0 (MIT) — best for .NET / Python enterprise stacks; (5) LlamaIndex Workflows 1.0 (MIT) — best RAG-grounded agents; (6) Pydantic AI V2 (MIT) — best type-safe Python DX; (7) AutoGen / AG2 (Apache 2.0) — legacy option for existing v0.2 code. All seven now ship native or first-class MCP support. ### What are the best frameworks for building AI agents in 2026? If you are building AI agents from scratch in 2026, Alice Labs recommends starting with LangGraph 1.0 for stateful production workflows that need explicit branching and human-in-the-loop, or Claude Agent SDK if you are Anthropic-native and want the same architecture that powers Claude Code. Choose CrewAI 1.14 when the work decomposes into role-based tasks (researcher, writer, reviewer) and you need a working prototype in days, not weeks. Choose Microsoft Agent Framework 1.0 if your dominant stack is .NET / Azure / M365. All four ship native MCP, durable state, and production observability hooks. ### Which enterprise AI agent platform is best for 2026? There is no single best — pick by your dominant stack. Microsoft 365 / Azure → Microsoft Copilot Studio + Microsoft 365 Agents. AWS / Bedrock → AWS Bedrock AgentCore. Google Cloud → Vertex AI Agent Builder. OpenAI-first → OpenAI Agent Platform (AgentKit). Salesforce CRM → Agentforce 360. ServiceNow → ServiceNow AI Agents (Now Assist). Regulated / hybrid → IBM watsonx Orchestrate. Large UiPath estate → UiPath Agentic Automation. All eight align to NIST AI RMF, ISO/IEC 42001 and the EU AI Act. ### What's the difference between an enterprise agent platform and an open-source agent framework? Enterprise platforms (Microsoft Copilot Studio, AWS Bedrock AgentCore, Vertex AI Agent Builder, OpenAI Agent Platform, Agentforce 360, ServiceNow AI Agents, watsonx Orchestrate, UiPath) are managed, governed, and procurement-friendly — agents inherit identity, audit, data residency, and SLAs from a vendor tenant. Open-source frameworks (LangGraph, Claude Agent SDK, CrewAI, AutoGen/AG2, Semantic Kernel, LlamaIndex, Pydantic AI) are libraries — your engineering team owns deployment, observability, and governance. Most large programmes run both: platform for breadth, framework for depth. ### Which AI agent framework is best in 2026? There is no single best framework — they target different problems. For most new production projects with explicit control needs, LangGraph is the safest pick. For fast multi-agent prototypes, CrewAI is the fastest. Match framework to your dominant constraint. ### Is LangGraph the same as LangChain? LangGraph is built by the LangChain team but is a separate library. LangChain provides the underlying primitives (LLM wrappers, tools, prompts); LangGraph is the orchestration layer that models agents as state graphs. You can use LangGraph without using LangChain agents directly. ### What's the difference between AutoGen and AG2? AG2 is the community continuation of the original Microsoft AutoGen v0.2 lineage, hosted at ag2.ai. Microsoft has continued the AutoGen name with a v0.4+ rewrite that uses a different API. Both are open-source. Choose based on which API you started with and where the community you depend on lives. ### Should I use Semantic Kernel or LangChain? If your stack is .NET / C# or Microsoft / Azure-centric, Semantic Kernel is the right pick — it has first-class C# support and tight Azure integration. For Python-first stacks not tied to Microsoft, LangChain (with LangGraph) has a larger ecosystem and more community resources. ### Is Pydantic AI production-ready? Yes, but with the caveat that it's newer than LangGraph or LangChain. Teams that prioritize type safety, structured responses, and FastAPI-style ergonomics report excellent developer experience. Production references are growing but the ecosystem is smaller than LangChain's. ### Can I switch frameworks later? Partially. The LLM-facing prompts and tool definitions tend to be portable. The orchestration layer (state, control flow, multi-agent patterns) is framework-specific and requires rewrite. Plan to commit to one framework for at least the first year of a production system. ### Do I need a framework at all, or can I use the OpenAI Assistants API directly? For very simple single-agent tools you can use the OpenAI Assistants API or Anthropic's tool use directly. As soon as you need multi-step control flow, multi-agent patterns, model-agnostic deployment, or production-grade observability, a framework saves significant engineering time. ### What changed in AI agent frameworks in Q2 2026 (April-July 2026)? The biggest release was Microsoft Agent Framework 1.0 on April 3, 2026 — the unified successor to Semantic Kernel and AutoGen, shipping with native MCP and A2A protocol support for both .NET and Python. LangGraph added per-node timeouts, DeltaChannel, and a v2 typed streaming API. Anthropic's Claude Agent SDK shipped hierarchical subagent spawning (up to 3 levels deep), fallback model chains, and a community MCP tool marketplace. CrewAI 1.14.6 (May 28) plus a June 11 release introduced pluggable memory/knowledge/RAG/flow backends, a Chat API, and native Snowflake Cortex. Pydantic AI V2 (June 23) shipped a harness-first redesign with capabilities as a core primitive. LlamaIndex Workflows 1.0 landed on June 22. The MCP 2026-07-28 spec release candidate reworks the protocol to be stateless at the layer. ### Which frameworks support the Model Context Protocol (MCP) natively? As of July 2026 the deepest native MCP integration is in the Claude Agent SDK (MCP is the primary tool contract, with a community tool marketplace) and Microsoft Agent Framework 1.0 (native, not bolt-on). LangGraph 1.0 supports MCP tools as first-class graph nodes with full streaming. CrewAI 1.14, LlamaIndex Workflows 1.0, and Pydantic AI V2 all ship native MCP support. AutoGen v0.4 and AG2 use community adapters — new Microsoft-stack builds should use Microsoft Agent Framework 1.0 for native MCP + A2A. ### What is the Agent-to-Agent (A2A) protocol and which frameworks support it? A2A is Google's open protocol for direct communication between autonomous agents across organisations, frameworks, and vendors. As of April 2026, 150+ organisations support the standard (Google, Microsoft, AWS, Salesforce, SAP, ServiceNow, Workday, IBM among them), and A2A ships production SDKs in Python, JavaScript, Java, Go, and .NET. Native A2A integration is live in Azure AI Foundry, AWS Bedrock AgentCore, and Google Cloud. Microsoft Agent Framework 1.0 is the first open-source SDK with native A2A. Other frameworks use community adapters; if agent-to-agent interoperability is a hard requirement, prefer Microsoft Agent Framework 1.0 or pair another framework with a small A2A shim service. ### Should I migrate from Semantic Kernel or AutoGen to Microsoft Agent Framework 1.0? For new Microsoft-stack projects — yes, start on Microsoft Agent Framework 1.0. It merges the enterprise features of Semantic Kernel (session state, type safety, middleware, telemetry) with the multi-agent orchestration of AutoGen into one SDK, and ships native MCP + A2A. For existing production systems on Semantic Kernel, Microsoft has committed to critical bug fixes for at least one year after MAF GA (April 2026) — plan a migration but don't rush it. Microsoft has published a formal Semantic Kernel → Microsoft Agent Framework migration guide on Microsoft Learn. ### What is Semantic Kernel? (Microsoft's official definition) Semantic Kernel is Microsoft's open-source SDK for integrating LLMs with conventional programming languages (C#, Python, Java). It was Microsoft's primary agent SDK from 2023 through early 2026. In April 2026 Microsoft folded Semantic Kernel and AutoGen into a single successor product: Microsoft Agent Framework 1.0. Semantic Kernel remains supported with critical bug fixes for at least one year post-MAF-GA, but new Microsoft-stack projects should start on Microsoft Agent Framework 1.0. Official docs: learn.microsoft.com/semantic-kernel. ### Which AI agent frameworks shipped an update in August 2026? Two major August 2026 updates: (1) LangGraph shipped workflow updates for Python and JS — node caching (skip redundant computation on re-runs), deferred nodes (fan-in barriers that wait for all upstream branches), pre/post model hooks for context trimming / guardrails / PII redaction, and a content-block-centric streaming API replacing the legacy token stream (see changelog.langchain.com). (2) Microsoft previewed Agent Framework Agent Harness, Hosted Agents (managed runtime + sandbox execution), and CodeAct-style tool synthesis at BUILD 2026 (devblogs.microsoft.com/agent-framework). Claude Agent SDK continued rolling updates via Claude Code Week 24+ releases (5-level subagent hierarchy, 200-spawn per-session ceiling, @tool decorator with typing.Annotated). ### Which AI agent framework has the best TypeScript support in 2026? Mastra (@mastra/core 1.35) is the de-facto TypeScript-first default with ~300K weekly npm downloads and ~22-24K GitHub stars — it was designed from the ground up for TS teams shipping web-integrated agents (Next.js / Node). For frameworks with true Python and TypeScript parity, LlamaIndex Workflows 1.0 (June 22, 2026) and Google ADK 2.0 (TS + Python at parity) are the strongest picks. OpenAI Agents SDK ships TypeScript but the Python SDK still leads by 1-2 quarters on major features. LangGraph has a JS/TS SDK but Python-first ergonomics leak in. ### Which agent frameworks support Java or Go? As of August 2026, Google ADK is the only major agent framework with production-grade Java (ADK Java 1.0, March 2026) and Go (ADK Go 2.0, June 2026) SDKs at parity with its Python and TypeScript SDKs. Microsoft Agent Framework 1.0 supports Python and .NET (C#) — not Java or Go. LangGraph, CrewAI, LlamaIndex, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, and Mastra are Python and/or TypeScript only. If Java or Go is a hard requirement, Google ADK is the answer. ### Are there any new AI agent frameworks worth watching in 2026? Three new-in-2026 entrants worth an evaluation cycle: (1) Hermes Agent (Nous Research, launched Feb 25, 2026, MIT) — persistent memory + auto-generated skills; crossed ~220K GitHub stars by late July 2026 per startupfortune.com, though enterprise governance is still forming. (2) Strands Agents (AWS open-source) — framework-agnostic agent SDK designed to feed into Bedrock AgentCore. (3) BeeAI (IBM Research) — open-source agent framework built around interoperable protocols; complements watsonx Orchestrate. None replaces the top-10 for enterprise production yet, but all three are on Alice Labs' 'evaluate every quarter' watchlist. ### What is 'openclaw' AI agent framework? Is it real? 'Openclaw' does not correspond to a real AI agent framework as of August 2026. The query most likely stems from a typo or confusion with 'OpenClaude' / 'OpenAI Claude' / the Claude Agent SDK. If you were searching for an Anthropic-native agent framework, the correct answer is the Claude Agent SDK (github.com/anthropics/claude-agent-sdk, ranked #3 in Alice Labs' August 2026 ranking). If you were searching for an open-source AutoGen alternative, see AG2 (community fork) or Microsoft Agent Framework 1.0 (the official successor). ### What are the best AI agent orchestration frameworks in 2026? For multi-agent orchestration specifically (as opposed to single-agent tool use) the strongest 2026 picks are: (1) Microsoft Agent Framework 1.0 — supports sequential, concurrent, handoff, group chat, and Magentic-One patterns natively with C# + Python parity and native A2A cards; (2) LangGraph 1.x — supervisor and hierarchical patterns via subgraphs, plus deferred nodes (Aug 2026) for native fan-in barriers; (3) CrewAI 1.14.7 — hierarchical process + Flow DSL for role-based crews; (4) Claude Agent SDK — hierarchical subagents up to 5 levels deep with a 200-spawn ceiling; (5) Google ADK 2.0 — graph-based Workflow Runtime with a slider from dynamic to fully deterministic flows. ### Which frameworks support the Anthropic Claude models natively? The Claude Agent SDK is Anthropic's official first-party SDK and runs against Claude 5, Opus 4.8, and Haiku 4.5. Every other major framework in the top-10 (LangGraph, Microsoft Agent Framework 1.0, OpenAI Agents SDK, Google ADK 2.0, CrewAI 1.14.7, LlamaIndex Workflows 1.0, Pydantic AI 2.0, Mastra, AG2) supports Claude models via provider abstractions — Anthropic API keys work everywhere, but only the Claude Agent SDK is optimised specifically for Claude's tool-use and subagent behaviour. ### What's the difference between the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol? MCP (Model Context Protocol, Anthropic origin, 2026-07-28 spec release candidate) connects agents to tools — an MCP server exposes tools that any MCP-capable agent can call. A2A (Agent-to-Agent, Google origin, 150+ adopting organisations as of April 2026) connects agents to other agents across frameworks and vendors. They are complementary, not competing. In Alice Labs' current architecture calls: an agent uses MCP to call any tool it needs and A2A to delegate to any other agent it doesn't want to reimplement itself. Both are open standards, both ship in Microsoft Agent Framework 1.0 natively, and both are the interoperability layer that matters more than framework choice in 2026. ### Which AI agent framework did Alice Labs pick for its own production stack? Alice Labs runs a hybrid across our 100+ production AI implementations. For Nordic customer-service deployments we default to a supervisor + swarm hybrid on LangGraph 1.x (with LangSmith for observability). For Anthropic-native coding, research, and back-office agents we use the Claude Agent SDK with hierarchical subagents. For Microsoft-stack clients we use Microsoft Agent Framework 1.0. For our own internal Python type-safety-critical extraction agents we use Pydantic AI 2.0. For fast role-based prototypes handed to junior engineers we use CrewAI 1.14.7. Framework selection is per project — no single framework wins every axis. ### How does the Alice Labs Production Score work? The Alice Labs Production Score is a 1-10 rating we assign each framework based on four axes we've measured across our 100+ production AI implementations: (1) durable state and human-in-the-loop maturity, (2) native MCP + A2A protocol coverage, (3) observability and evaluation-harness integration, and (4) time-to-first-production-deploy for a new team. As of August 2026: LangGraph 1.x and Claude Agent SDK both score 9/10; Microsoft Agent Framework 1.0, OpenAI Agents SDK, Google ADK 2.0, and Pydantic AI 2.0 all score 8/10; CrewAI 1.14.7, LlamaIndex Workflows 1.0, and Mastra score 7/10; AG2 0.12.2 scores 6/10. Scores update at every quarterly review. ### What is Pydantic AI 2.0's 'capability' primitive? Pydantic AI 2.0 (shipped 23 June 2026) introduced a single 'capability' primitive that unifies instructions, tools, hooks, and settings into reusable composable units — one abstraction replaces four. Everything above the primitive lives in the separately versioned Pydantic AI Harness (which first shipped April 2026, ~2 months before v2 core) and includes 40+ features across 9 categories: memory, guardrails, sandbox, evals, and more. The clean separation means the stable core moves slowly while moving-target features (Harness) can iterate independently. Version policy tightened: no-breaking-changes window shrunk from 6 months to 3. ### Is CrewAI still relevant now that Microsoft Agent Framework 1.0 has shipped? Yes. CrewAI 1.14.7 (11 June 2026) sits in a different position than Microsoft Agent Framework 1.0: it is the fastest path from idea to a working role-based multi-agent prototype (researcher → writer → reviewer), independent of Microsoft's cloud gravity, with ergonomic Python, pluggable memory/knowledge/RAG/flow backends, native Snowflake Cortex + Bedrock + Databricks + OpenAI + Anthropic + Gemini providers, and a Chat API. Alice Labs still recommends CrewAI for early-stage prototypes and role-based multi-agent workloads that don't need Azure-specific integration. ### 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 ### What Is an AI Agent? Definition, Architecture & Examples ](/en/insights/what-is-an-ai-agent)[Next in AI Agents ### What Is Agentic AI? How It Differs from Generative AI ](/en/insights/what-is-agentic-ai) ## Further reading - [LangGraph documentation](https://langchain-ai.github.io/langgraph/)· langchain-ai.github.io - [CrewAI documentation](https://docs.crewai.com)· docs.crewai.com - [Semantic Kernel documentation](https://learn.microsoft.com/semantic-kernel)· learn.microsoft.com - [Pydantic AI documentation](https://ai.pydantic.dev)· ai.pydantic.dev - [Microsoft Copilot Studio](https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio)· microsoft.com - [AWS Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/)· aws.amazon.com - [Google Vertex AI Agent Builder](https://cloud.google.com/products/agent-builder)· cloud.google.com - [OpenAI Agent Platform](https://platform.openai.com/docs/guides/agents)· platform.openai.com - [Salesforce Agentforce 360](https://www.salesforce.com/agentforce/)· salesforce.com - [ServiceNow AI Agents](https://www.servicenow.com/products/ai-agents.html)· servicenow.com - [IBM watsonx Orchestrate](https://www.ibm.com/products/watsonx-orchestrate)· ibm.com - [UiPath Agentic Automation](https://www.uipath.com/product/agentic-automation)· uipath.com - [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)· nist.gov - [ISO/IEC 42001:2023](https://www.iso.org/standard/81230.html)· iso.org - [EU AI Act (European Commission)](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)· digital-strategy.ec.europa.eu - [Microsoft Agent Framework 1.0 announcement](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/)· devblogs.microsoft.com - [OpenAI Agents SDK — next evolution (April 2026)](https://openai.com/index/the-next-evolution-of-the-agents-sdk/)· openai.com - [Model Context Protocol — 2026-07-28 spec release candidate](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/)· blog.modelcontextprotocol.io - [A2A Protocol — 150+ organisations announcement](https://www.prnewswire.com/news-releases/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year-302737641.html)· prnewswire.com - [LlamaIndex Workflows 1.0 announcement](https://www.llamaindex.ai/blog/announcing-workflows-1-0-a-lightweight-framework-for-agentic-systems)· llamaindex.ai - [Pydantic AI — V1 and V2 releases](https://pydantic.dev/articles/pydantic-ai-v1)· pydantic.dev ## Related services [Production AI agents Alice Labs ships custom production agents on LangGraph, CrewAI, AutoGen, and Semantic Kernel. ](/en/ai-agents)[AI implementation consultant Enterprise AI consulting — from agent framework selection to production deployment in 90 days. ](/en/ai-consulting)[Enterprise AI training Hands-on AI training for engineering teams shipping agent systems in production. ](/en/ai-training)[AI strategy consulting AI roadmap consulting for boards making agent-architecture and build-vs-buy decisions. ](/en/ai-strategy) ## Related reading [quick take ### What Is an AI Agent? The definitional companion to this comparison. 6 min](/en/insights/what-is-an-ai-agent) [deep dive ### Enterprise AI Strategy: 6-Step Framework Where framework choice fits in the broader AI strategy. 12 min](/en/insights/enterprise-ai-strategy-framework) [deep dive ### Why AI Projects Fail: 7 Root Causes Failure patterns that no framework can fix on its own. 10 min ](/en/insights/why-ai-projects-fail) ## Sources 1. [LangGraph (langchain-ai/langgraph) — official repository](https://github.com/langchain-ai/langgraph)(accessed 2026-04-15) 2. [CrewAI (crewAIInc/crewAI) — official repository](https://github.com/crewAIInc/crewAI)(accessed 2026-04-15) 3. [AG2 (ag2ai/ag2) — community continuation of AutoGen v0.2 lineage](https://github.com/ag2ai/ag2)(accessed 2026-04-15) 4. [Microsoft AutoGen (microsoft/autogen) — v0.4+ rewrite](https://github.com/microsoft/autogen)(accessed 2026-04-15) 5. [Microsoft Semantic Kernel — official documentation](https://learn.microsoft.com/semantic-kernel/overview/)(accessed 2026-04-15) 6. [LlamaIndex (run-llama/llama\_index) — official repository](https://github.com/run-llama/llama_index)(accessed 2026-04-15) 7. [Pydantic AI (pydantic/pydantic-ai) — official repository](https://github.com/pydantic/pydantic-ai)(accessed 2026-04-15) 8. [Microsoft Copilot Studio — official product page](https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio)(accessed 2026-06-23) 9. [AWS Bedrock AgentCore — official product page](https://aws.amazon.com/bedrock/agentcore/)(accessed 2026-06-23) 10. [Google Vertex AI Agent Builder — official product page](https://cloud.google.com/products/agent-builder)(accessed 2026-06-23) 11. [OpenAI Agent Platform — official documentation](https://platform.openai.com/docs/guides/agents)(accessed 2026-06-23) 12. [Salesforce Agentforce 360 — official product page](https://www.salesforce.com/agentforce/)(accessed 2026-06-23) 13. [ServiceNow AI Agents (Now Assist) — official product page](https://www.servicenow.com/products/ai-agents.html)(accessed 2026-06-23) 14. [IBM watsonx Orchestrate — official product page](https://www.ibm.com/products/watsonx-orchestrate)(accessed 2026-06-23) 15. [UiPath Agentic Automation — official product page](https://www.uipath.com/product/agentic-automation)(accessed 2026-06-23) 16. [NIST AI Risk Management Framework (AI RMF 1.0)](https://www.nist.gov/itl/ai-risk-management-framework)(accessed 2026-06-23) 17. [ISO/IEC 42001:2023 — Artificial Intelligence Management System](https://www.iso.org/standard/81230.html)(accessed 2026-06-23) 18. [EU AI Act — Regulatory framework for AI (European Commission)](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)(accessed 2026-06-23) 19. [Microsoft Agent Framework 1.0 GA (April 3, 2026) — devblogs.microsoft.com](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/)(accessed 2026-07-05) 20. [Microsoft Agent Framework Overview — Microsoft Learn](https://learn.microsoft.com/en-us/agent-framework/overview/)(accessed 2026-07-05) 21. [OpenAI — The Next Evolution of the Agents SDK (April 15, 2026)](https://openai.com/index/the-next-evolution-of-the-agents-sdk/)(accessed 2026-07-05) 22. [LangGraph release notes — langchain-ai/langgraph GitHub releases](https://github.com/langchain-ai/langgraph/releases)(accessed 2026-07-05) 23. [LangChain — LangGraph 1.0 is now generally available (October 22, 2025)](https://changelog.langchain.com/announcements/langgraph-1-0-is-now-generally-available)(accessed 2026-07-05) 24. [CrewAI release notes — crewAIInc/crewAI GitHub releases](https://github.com/crewAIInc/crewAI/releases)(accessed 2026-07-05) 25. [Claude Agent SDK overview — code.claude.com docs](https://code.claude.com/docs/en/agent-sdk/overview)(accessed 2026-07-05) 26. [LlamaIndex — Announcing Workflows 1.0 (June 22, 2026)](https://www.llamaindex.ai/blog/announcing-workflows-1-0-a-lightweight-framework-for-agentic-systems)(accessed 2026-07-05) 27. [Pydantic AI V1 and V2 — pydantic.dev articles](https://pydantic.dev/articles/pydantic-ai-v1)(accessed 2026-07-05) 28. [Model Context Protocol — 2026-07-28 Specification Release Candidate](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/)(accessed 2026-07-05) 29. [Model Context Protocol — 2026 Roadmap](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/)(accessed 2026-07-05) 30. [A2A Protocol — 150+ organisations, native in Azure AI Foundry, Bedrock AgentCore, Google Cloud (PR Newswire, 2026)](https://www.prnewswire.com/news-releases/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year-302737641.html)(accessed 2026-07-05) 31. [LangChain — LangGraph & LangChain 1.0 announcement (October 22, 2025)](https://www.langchain.com/blog/langchain-langgraph-1dot0)(accessed 2026-08-02) 32. [LangChain changelog — LangGraph workflow updates (node caching, deferred nodes, hooks, streaming) — August 2026](https://changelog.langchain.com/announcements/langgraph-workflow-updates-python-js)(accessed 2026-08-02) 33. [CrewAI 1.14.7 release notes (June 11, 2026)](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7)(accessed 2026-08-02) 34. [Microsoft Agent Framework at BUILD 2026 announcement](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-at-build-2026-announce/)(accessed 2026-08-02) 35. [Microsoft Agent Framework RC migration guide (Semantic Kernel + AutoGen)](https://devblogs.microsoft.com/agent-framework/migrate-your-semantic-kernel-and-autogen-projects-to-microsoft-agent-framework-release-candidate/)(accessed 2026-08-02) 36. [Visual Studio Magazine — Microsoft ships production-ready Agent Framework 1.0 (April 2026)](https://visualstudiomagazine.com/articles/2026/04/06/microsoft-ships-production-ready-agent-framework-1-0-for-net-and-python.aspx)(accessed 2026-08-02) 37. [OpenAI Agents SDK harness + sandbox update (April 16, 2026)](https://www.helpnetsecurity.com/2026/04/16/openai-agents-sdk-harness-and-sandbox-update/)(accessed 2026-08-02) 38. [Google Developers Blog — ADK for Java 1.0 announcement](https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/)(accessed 2026-08-02) 39. [Google Cloud Blog — IO 2026 news for agent developers (ADK Tools & Integrations Ecosystem)](https://cloud.google.com/blog/topics/developers-practitioners/io26-news-for-agent-developers-on-google-cloud)(accessed 2026-08-02) 40. [Claude Agent SDK — What's New Week 24+ (2026)](https://code.claude.com/docs/en/whats-new/2026-w24)(accessed 2026-08-02) 41. [Pydantic AI V2 announcement (June 23, 2026)](https://pydantic.dev/articles/pydantic-ai-v2)(accessed 2026-08-02) 42. [Mastra — TypeScript-first agent framework](https://mastra.ai/)(accessed 2026-08-02) 43. [Mastra GitHub releases](https://github.com/mastra-ai/mastra/releases)(accessed 2026-08-02) 44. [AG2 (community AutoGen fork) — PyPI](https://pypi.org/project/autogen/)(accessed 2026-08-02) 45. [Startup Fortune — Hermes Agent crosses 214,000 GitHub stars](https://startupfortune.com/hermes-agent-crosses-214000-github-stars-as-developers-abandon-commercial-ai-agent-frameworks/)(accessed 2026-08-02) 46. [LlamaIndex Workflows — official announcement (June 22, 2026)](https://www.llamaindex.ai/blog/announcing-workflows-1-0-a-lightweight-framework-for-agentic-systems)(accessed 2026-08-02) 47. [AlphaSignal — Pydantic AI v2 ships a single primitive that rebuilds how agents work](https://alphasignal.ai/news/pydantic-ai-v2-ships-a-single-primitive-that-rebuilds-how-agents-work)(accessed 2026-08-02) Next scheduled review: 2026-11-02 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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