--- title: "Open Source vs Proprietary AI Agent Frameworks 2026" description: "Open source vs proprietary AI agent frameworks in 2026: license is no longer the axis. LangGraph, CrewAI, Claude Agent SDK, MAF, ADK — real lock-in, TCO, and MCP." lang: en json-ld: | [ { "@context": "https://schema.org", "@graph": [ { "@type": "Organization", "@id": "https://alicelabs.ai/#organization", "name": "Alice Labs", "alternateName": [ "Alice Labs AB", "AliceLabs" ], "legalName": "Alice Labs AB", "identifier": "559443-5470", "foundingLocation": { "@type": "Place", "name": "Stockholm, Sweden" }, "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "@id": "https://alicelabs.ai/#logo", "url": "https://alicelabs.ai/images/alice-logo.png", "contentUrl": "https://alicelabs.ai/images/alice-logo.png", "width": 2000, "height": 2027, "caption": "Alice Labs" }, "image": { "@id": "https://alicelabs.ai/#logo" }, "description": "Alice Labs är en svensk AI-byrå som hjälper företag implementera AI - från strategi till skalning.", "slogan": "From AI strategy to measurable results.", "foundingDate": "2023", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "address": { "@type": "PostalAddress", "streetAddress": "Hammarbybacken 27", "addressLocality": "Stockholm", "postalCode": "120 30", "addressCountry": "SE" }, "contactPoint": [ { "@type": "ContactPoint", "contactType": "customer service", "email": "hej@alicelabs.ai", "telephone": "+46734157476", "areaServed": [ "SE", "EU" ], "availableLanguage": [ "Swedish", "English" ] } ], "areaServed": [ { "@type": "Country", "name": "Sweden" }, { "@type": "Place", "name": "Europe" } ], "knowsAbout": [ "AI strategy", "AI implementation", "AI agents", "AI automation", "Generative AI", "AI governance", "AI training", "Machine learning", "Large language models", "RAG", "AI consulting", "Digital transformation", "AI search optimization", "LLMO", "AI for enterprise" ], "founder": [ { "@id": "https://alicelabs.ai/#linus" }, { "@id": "https://alicelabs.ai/#eric" } ], "sameAs": [ "https://www.linkedin.com/company/alicelabsai", "https://www.trustpilot.com/review/alicelabs.ai", "https://www.wikidata.org/wiki/Q140369570" ] }, { "@type": "Person", "@id": "https://alicelabs.ai/#linus", "name": "Linus Ingemarsson", "givenName": "Linus", "familyName": "Ingemarsson", "jobTitle": "Co-Founder", "description": "Co-founder of Alice Labs. 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decision is not free vs paid. Every major agent SDK is MIT or Apache 2.0 licensed. The real axis is whether the framework's runtime and orchestration state can operate against multiple model providers, or hard-binds to one vendor's inference API and hosted state. Alice Labs classifies frameworks on both model portability and orchestration portability — both must be portable to count as 'truly open source' for total cost of ownership." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "What Counts as 'Open Source' vs 'Proprietary' in Agent Frameworks", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#taxonomy", "description": "Two categories that matter: (1) open source pure play — LangGraph, CrewAI, LlamaIndex, Pydantic AI — MIT-licensed code, model-agnostic orchestration, no hosted runtime required; (2) vendor-anchored open source — Claude Agent SDK, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework — MIT or Apache 2.0 code, but the happy path targets one vendor's API and hosted runtime. Alice Labs' rule: if the happy path requires one vendor's API, treat it as proprietary for lock-in accounting regardless of license." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Open Source Contenders: LangGraph, CrewAI, LlamaIndex, Pydantic AI", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#open-source-contenders", "description": "Four production-serious open source frameworks compete in 2026. LangGraph (MIT, ~15k+ stars) — graph-based durable execution used in production by Klarna, Replit, and Elastic. CrewAI (MIT, 56.5k stars) — role-based multi-agent crews, claims 65% of Fortune 500 as Basic-tier users. LlamaIndex (MIT, 51.3k stars) — document agents and OCR-heavy retrieval. Pydantic AI (MIT, 19k+ stars) — type-safe agents with dependency injection and durable execution." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Proprietary (Vendor-Anchored) Contenders: OpenAI Agents SDK, Claude Agent SDK, MAF, Google ADK", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#vendor-anchored-contenders", "description": "Four vendor-shipped SDKs anchor the 'proprietary in practice' category despite MIT and Apache 2.0 licenses. OpenAI Agents SDK — Responses API by default, 100+ models via LiteLLM. Claude Agent SDK — Python and TypeScript only, same harness as Claude Code, Anthropic API required. Microsoft Agent Framework 1.0 (April 3, 2026) — direct successor to Semantic Kernel and AutoGen. Google ADK — code-first Python/TS/Go/Java/Kotlin, deployment biased to Google Cloud." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Vendor Lock-In: The Four Layers Every Buyer Underestimates", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#four-layers-lockin", "description": "Lock-in in 2026 agent frameworks accumulates in four distinct layers. Model lock-in — the API you call, swappable in hours when the framework abstracts it. Orchestration lock-in — the graph, handoff, and state semantics, a rewrite to swap. State and memory lock-in — session stores, checkpointers, vector stores tied to a hosted service. Governance and evidence lock-in — telemetry, audit trails, and eval fixtures that only exist inside one vendor's console. Kai Waehner (April 2026) calls the fourth the fastest-growing category of AI dependency risk." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "TCO Analysis: What Open Source Actually Costs", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#tco-analysis", "description": "Alice Labs' benchmark across 12 production Claude Agent SDK, LangGraph, and CrewAI deployments in 2026: framework license cost is under 2% of TCO. Model tokens are 55-70%, platform engineering 20-30%, observability and eval tooling 5-10%. Open source frameworks are usually paired with a paid platform: LangGraph + LangSmith, CrewAI + CrewAI Enterprise, LlamaIndex + LlamaCloud. Proprietary SDKs bundle observability and hosting into Anthropic Managed Agents, OpenAI's hosted agent runtime, Foundry, or Google Agent Platform." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "MCP as the Escape Hatch: Why the Protocol Layer Matters More Than the Framework", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#mcp-escape-hatch", "description": "Model Context Protocol is an open standard supported across Claude, ChatGPT, VS Code, Cursor, and MCPJam as of the 2026-07-28 spec. Every major framework in this comparison ships first-class MCP support — LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework, and Google ADK. MCP servers you own (data connectors, internal tools) are portable across frameworks: a Slack MCP server written for Claude Agent SDK works unchanged under LangGraph. Alice Labs recommends keeping proprietary business logic inside MCP servers and only orchestration inside the framework." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "When Open Source Wins: The Buyer Situations Where LangGraph, CrewAI, or Pydantic AI Beat Proprietary", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#when-open-source-wins", "description": "Open source wins in four buyer situations. Regulated workloads where model routing must be provable and auditable — LangGraph checkpointer plus LangSmith trace is admissible where a hosted vendor loop rarely is. Multi-model production where cost or quality routing matters — Pydantic AI or LangGraph can route Claude, Gemini, and GPT in one graph. On-prem or sovereign cloud deployment for EU AI Act, GDPR, and DORA obligations — CrewAI Enterprise or LangGraph in a customer-owned VPC beats any hosted proprietary runtime. Alice Labs uses LangGraph or CrewAI whenever the client's compliance officer will need to answer 'where does the state live?' with a customer-owned answer." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "When Proprietary Wins: The Buyer Situations Where Claude Agent SDK, OpenAI Agents SDK, MAF, or ADK Beat Open Source", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#when-proprietary-wins", "description": "Proprietary wins in four buyer situations. Coding and computer-use agents with deep OS access — Claude Agent SDK ships filesystem, bash, web, and subagent primitives that take months to replicate on LangGraph. Voice, realtime, and lightweight conversational handoffs — OpenAI Agents SDK's handoff and guardrail model is the shortest path to production. Existing Microsoft or Google shops with sunk cost in Azure Foundry or Vertex AI — MAF and ADK reduce integration cost more than the lock-in premium. Alice Labs uses Claude Agent SDK for internal engineering agents where Anthropic's coding-model quality is the differentiator." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Semantic Kernel / AutoGen Case Study: What Vendor-Anchored Open Source Looks Like When It Breaks", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#semantic-kernel-case-study", "description": "Microsoft moved Semantic Kernel and AutoGen into maintenance mode in October 2025; the last feature release was September 2025. Microsoft Agent Framework 1.0 shipped April 3, 2026 as the direct successor, requiring migration from both prior frameworks. AutoGen users must budget for Q3 2026 API deprecations; Semantic Kernel session and telemetry patterns carry over but the API surface is new. Lesson: MIT license did not prevent a forced migration when the vendor changed direction. Framework longevity is a governance question, not a license question." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Feature-by-Feature Comparison Matrix (2026)", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#feature-comparison", "description": "Durable execution and checkpointing ship in LangGraph, Pydantic AI, MAF, ADK, and Claude Agent SDK sessions. First-class multi-model support is native in LangGraph, CrewAI, Pydantic AI, MAF (5 providers), ADK (6+ providers), and OpenAI Agents SDK (100+ via adapters). MCP support is universal across all eight frameworks as of August 2026. Language coverage is widest in Google ADK (Python, TS, Go, Java, Kotlin), followed by MAF (Python, .NET, Go)." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Migration Cost: Moving Between Frameworks in Practice", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#migration-cost", "description": "Alice Labs measured three production migrations in 2026: AutoGen to MAF took ~120 engineering hours for a 12-agent system, with state and telemetry requiring rewrites. CrewAI to LangGraph took ~200 hours because role and task abstractions did not translate cleanly to graph nodes. OpenAI Agents SDK to Claude Agent SDK took ~80 hours if the agent was already OS-native, and larger if handoff chains had to be flattened. Migration cost is the number to price against lock-in — if migration is under 200 hours, layer-2 orchestration lock-in is manageable." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Alice Labs Recommendation Framework: Which Framework for Which Buyer", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#recommendation-framework", "description": "Regulated Nordic and EU enterprises with EU AI Act obligations: LangGraph or CrewAI, self-hosted, MCP-first. Fast-moving startups building coding or computer-use agents: Claude Agent SDK, accept the Anthropic dependency, extract business logic to MCP servers. Enterprise Microsoft shops: MAF, but keep orchestration graphs portable and treat Foundry as an execution target rather than a data lock-in. Consumer voice and chat: OpenAI Agents SDK for realtime; route to Claude via LiteLLM where reasoning matters. Alice Labs default for a greenfield agent build in 2026: LangGraph with Anthropic model, self-hosted checkpointer, MCP-first tool boundary — highest portability, lowest lock-in, production-proven at Klarna and Replit." } ], "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/open-source-vs-proprietary-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": "Open Source vs Proprietary AI Agent Frameworks 2026", "item": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-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 difference between open source and proprietary AI agent frameworks in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "In 2026 the distinction is not license — nearly all major SDKs are MIT or Apache 2.0. It is portability. Open source frameworks like LangGraph, CrewAI, LlamaIndex, and Pydantic AI let you swap model providers and self-host state. Vendor-anchored SDKs like Claude Agent SDK, OpenAI Agents SDK, Google ADK, and Microsoft Agent Framework are MIT-licensed but designed to run against one vendor's API and hosted runtime. Alice Labs treats the second category as proprietary for lock-in accounting." } }, { "@type": "Question", "name": "Is LangGraph open source and free to use commercially?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. LangGraph is distributed under the MIT license and is free for commercial use. It is used in production by Klarna, Replit, Elastic, Uber, LinkedIn, and NVIDIA. The paid layer is LangSmith (LangChain's hosted observability) and LangGraph Platform ($39/seat/month Plus tier) — both optional. Alice Labs runs LangGraph fully self-hosted for regulated Nordic clients where LangSmith would move state outside the customer's compliance boundary." } }, { "@type": "Question", "name": "Is the Claude Agent SDK open source?", "acceptedAnswer": { "@type": "Answer", "text": "The Claude Agent SDK code is MIT licensed, but usage is governed by Anthropic's Commercial Terms of Service and the SDK requires an Anthropic API key. It cannot be pointed at a non-Anthropic model without forking. Alice Labs classifies it as vendor-anchored: open source in source, proprietary in dependency. The trade-off is worth it for coding and computer-use agents where Claude's model quality is the differentiator." } }, { "@type": "Question", "name": "Is the OpenAI Agents SDK open source?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, the OpenAI Agents SDK is MIT-licensed and available at github.com/openai/openai-agents-python. Its Responses API is the default backend but it supports 100+ models through LiteLLM and any-llm adapters. Alice Labs uses it for lightweight handoff chains, voice, and realtime workloads, and routes reasoning-heavy nodes to Claude via LiteLLM when quality matters more than latency." } }, { "@type": "Question", "name": "Is Microsoft Agent Framework open source or proprietary?", "acceptedAnswer": { "@type": "Answer", "text": "Microsoft Agent Framework (MAF) is MIT-licensed and open source on github.com/microsoft/agent-framework. It shipped 1.0 on April 3, 2026 as the direct successor to Semantic Kernel and AutoGen, which entered maintenance mode in October 2025. It supports Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI, and Ollama. Alice Labs treats it as vendor-anchored because the happy path assumes Azure Foundry hosting." } }, { "@type": "Question", "name": "Is Google ADK open source?", "acceptedAnswer": { "@type": "Answer", "text": "Google ADK is Apache 2.0 licensed and available in Python, TypeScript, Go, Java, and Kotlin — the widest language coverage in the pack. It supports Gemini, Gemma, Claude, OpenAI, Ollama, vLLM, and LiteLLM. The framework itself has no lock-in on models, but deployment is optimized for Google Cloud Agent Platform and Vertex AI. Alice Labs uses ADK for clients already invested in Google Cloud and multilingual language coverage." } }, { "@type": "Question", "name": "Which is the best open source AI agent framework in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "There is no single best — it depends on workload. LangGraph wins for durable, stateful, human-in-the-loop workflows. CrewAI wins for role-based multi-agent teams. LlamaIndex wins for document and RAG-heavy agents. Pydantic AI wins for type-safe, structured-output-first agents. Alice Labs' default for a greenfield 2026 build is LangGraph with an Anthropic model, self-hosted checkpointer, and MCP-first tool boundaries." } }, { "@type": "Question", "name": "Does open source AI agent framework mean zero cost?", "acceptedAnswer": { "@type": "Answer", "text": "No. License cost is under 2% of total cost of ownership across the 12 production Alice Labs deployments benchmarked in 2026. Model tokens are 55-70% of TCO, platform engineering 20-30%, observability and eval tooling 5-10%. Open source frameworks are usually paired with a paid platform: LangGraph with LangSmith, CrewAI with CrewAI Enterprise, LlamaIndex with LlamaCloud. Free code is not free ownership." } }, { "@type": "Question", "name": "What is vendor lock-in in AI agent frameworks?", "acceptedAnswer": { "@type": "Answer", "text": "Vendor lock-in in agent frameworks accumulates in four layers: (1) the model API you call, (2) the orchestration graph or handoff model you built, (3) the state and memory stores you use, and (4) the governance evidence trail — telemetry, evals, audit logs. Kai Waehner's April 2026 landscape report calls the fourth the fastest-growing category of AI dependency risk. Alice Labs scores each layer separately in RFPs because they lock in independently." } }, { "@type": "Question", "name": "Can I use LangGraph with Claude, GPT-5, and Gemini in the same workflow?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. LangGraph's multi-model routing is a first-class pattern — each node can call a different provider via LangChain chat model wrappers. Alice Labs routinely routes Claude for reasoning, GPT for structured JSON extraction, Haiku for cheap fan-out, and open-weights models for embeddings — all in a single compiled graph. This is not possible inside the Claude Agent SDK without a parallel non-Anthropic code path." } }, { "@type": "Question", "name": "What is MCP and why does it matter for framework choice?", "acceptedAnswer": { "@type": "Answer", "text": "Model Context Protocol (MCP) is an open standard for exposing tools and data to LLM agents. As of the 2026-07-28 spec, MCP is supported across Claude, ChatGPT, VS Code, Cursor, and MCPJam. Every major framework in this comparison ships first-class MCP support — LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, MAF, and Google ADK. MCP servers you own are portable across frameworks, which makes MCP more strategic than framework choice for portability." } }, { "@type": "Question", "name": "What happened to Semantic Kernel and AutoGen in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Microsoft moved Semantic Kernel and AutoGen into maintenance mode in October 2025; the last feature release was September 2025. Microsoft Agent Framework 1.0 shipped on April 3, 2026 as the direct successor, and existing users must migrate. AutoGen users must budget for Q3 2026 API deprecations; Semantic Kernel session and telemetry patterns carry over conceptually but the API surface is new. The lesson: MIT license does not prevent a forced migration when the vendor changes direction." } }, { "@type": "Question", "name": "How much does it cost to migrate between agent frameworks?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs measured three production migrations in 2026. AutoGen to MAF: ~120 engineering hours for a 12-agent system (state and telemetry rewrites dominated). CrewAI to LangGraph: ~200 hours (role and task abstractions did not translate cleanly to graph nodes). OpenAI Agents SDK to Claude Agent SDK: ~80 hours if the agent was already OS-native. Our rule: if migration is under 200 hours, layer-2 orchestration lock-in is manageable and should not dominate the framework decision." } }, { "@type": "Question", "name": "Is CrewAI free to use in production?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. CrewAI is MIT-licensed and free to install and run in production — the framework claims 65% of Fortune 500 as users of the Basic tier. The paid layer is CrewAI Enterprise, which adds SSO, RBAC, PII redaction, and private VPC deployment for regulated enterprise environments. Alice Labs uses CrewAI open source for pilots and CrewAI Enterprise when a Nordic client needs single-tenant deployment inside their own VPC." } }, { "@type": "Question", "name": "Should I choose an open source framework or a proprietary SDK for an EU AI Act high-risk deployment?", "acceptedAnswer": { "@type": "Answer", "text": "Alice Labs' strong default for EU AI Act high-risk deployments is open source pure play — LangGraph or CrewAI, self-hosted on customer infrastructure, with an MCP-first tool boundary. The reason is layer-4 governance lock-in: your evidence for a high-risk audit is only as portable as the console it lives in. A LangGraph checkpointer plus trace you own is admissible; a hosted vendor loop is much harder to hand to an auditor. Always consult legal counsel for compliance determinations." } }, { "@type": "Question", "name": "Which framework has the most GitHub stars in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "CrewAI leads at 56.5k stars (crewAIInc/crewAI), followed by LlamaIndex at 51.3k (run-llama/llama_index), Pydantic AI at 19k+ (pydantic/pydantic-ai), and LangGraph at ~15k+ (langchain-ai/langgraph). Vendor-anchored SDKs are newer: Claude Agent SDK at 7.8k (anthropics/claude-agent-sdk-python). Stars are a useful proxy for ecosystem breadth but not a good proxy for production fit — Klarna and Replit run LangGraph, which is not the star leader." } }, { "@type": "Question", "name": "Can I keep proprietary business logic portable across agent frameworks?", "acceptedAnswer": { "@type": "Answer", "text": "Yes — keep it inside MCP servers, not inside framework-specific tool code. Alice Labs' portability recommendation: proprietary business logic goes into MCP servers, orchestration lives inside the framework, and the framework becomes a swappable layer. A Slack MCP server written for Claude Agent SDK runs unchanged under LangGraph, CrewAI, or Google ADK. This is the single most portable investment you can make in the 2026 agent stack." } }, { "@type": "Question", "name": "Do vendor-anchored SDKs like Claude Agent SDK cost more than open source frameworks?", "acceptedAnswer": { "@type": "Answer", "text": "Not at the license layer — Claude Agent SDK is MIT and free to install. The cost differences show up in tokens (you can only route to Claude), in optional managed services (Anthropic Managed Agents vs LangSmith), and in the four lock-in layers if you later want to leave. Across Alice Labs' 12 production benchmarks in 2026, model tokens dominated TCO at 55-70% — framework choice mostly changes which provider you spend those tokens with, not how many you spend." } }, { "@type": "Question", "name": "Which framework is best for coding agents in 2026, open source or proprietary?", "acceptedAnswer": { "@type": "Answer", "text": "For coding agents specifically, the Claude Agent SDK is Alice Labs' top pick despite being vendor-anchored. It ships filesystem, bash, web, and subagent primitives that would take months to replicate on LangGraph, and Claude's coding model quality is the differentiator. Extract business logic to MCP servers and accept the Anthropic dependency — the workload-vendor fit outweighs the portability cost for coding agents. Detailed head-to-head in our LangGraph vs Claude Agent SDK comparison." } }, { "@type": "Question", "name": "Can I mix open source and vendor-anchored agent frameworks in the same system?", "acceptedAnswer": { "@type": "Answer", "text": "Yes — this is Alice Labs' most common production pattern in Nordic enterprises. LangGraph acts as the outer supervisor (durable execution, human-in-the-loop, per-node model routing) while Claude Agent SDK subagents act as inner workers (deep MCP integration, filesystem tools, Claude-tuned prompt caching). MCP servers are the shared tool layer so any tool written under one framework runs under the other. Framework choice becomes workload-scoped rather than company-scoped." } }, { "@type": "Question", "name": "What is Alice Labs' default recommendation for a greenfield agent build in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph with an Anthropic model, self-hosted Postgres checkpointer, and MCP-first tool boundary. Highest portability across all four lock-in layers, lowest overall risk, production-proven at Klarna and Replit, and an escape hatch to any of the other seven major frameworks via MCP. This configuration wins more Nordic RFPs for us than any other, and it is the shape we recommend by default unless the workload pulls hard toward a specific vendor-anchored SDK (coding to Claude, voice to OpenAI, Foundry-committed shops to MAF)." } }, { "@type": "Question", "name": "How does the 2026 Zapier vendor dependency survey inform framework choice?", "acceptedAnswer": { "@type": "Answer", "text": "The Zapier survey found 81% of enterprise leaders concerned about AI vendor dependency and 74% saying loss of their AI vendor would disrupt or halt operations. The framework-choice implication: portability across the four lock-in layers (model, orchestration, state, evidence) is not a nice-to-have — it is what the majority of enterprise leaders now optimise against. Frameworks that keep all four layers customer-owned reduce enterprise dependency risk directly; frameworks that hard-bind at even one layer increase it." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "Open Source vs Proprietary AI Agent Frameworks 2026: Complete Guide", "description": "Open source vs proprietary AI agent frameworks in 2026: license is no longer the axis. LangGraph, CrewAI, Claude Agent SDK, MAF, ADK — real lock-in, TCO, and MCP.", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026", "datePublished": "2026-08-03", "dateModified": "2026-08-03", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "open source vs proprietary ai agent frameworks", "open source ai agent frameworks", "ai agent framework lock-in", "best open source agent framework 2026", "langgraph mit", "claude agent sdk license", "mcp portable agent", "ai agent tco 2026" ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026", "name": "Best AI Agent Frameworks 2026: The Complete Enterprise Guide" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/langgraph-vs-claude-agent-sdk-2026", "name": "LangGraph vs Claude Agent SDK 2026: The Head-to-Head Buyer's Guide" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/claude-agent-sdk-guide-2026", "name": "Claude Agent SDK Guide 2026: Production Anthropic Agents" }, { "@type": "ListItem", "position": 4, "url": "https://alicelabs.ai/en/insights/langgraph-guide-2026", "name": "LangGraph Guide 2026: From First Graph to Production" }, { "@type": "ListItem", "position": 5, "url": "https://alicelabs.ai/en/insights/eu-ai-act-compliance-checklist-2026", "name": "EU AI Act Compliance Checklist for 2026" }, { "@type": "ListItem", "position": 6, "url": "https://alicelabs.ai/en/insights/ai-agent-security-risks", "name": "AI Agent Security Risks and How to Mitigate Them" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 13, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Open Source vs Proprietary AI Agent Frameworks 2026: The Real Decision", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#the-real-decision" }, { "@type": "ListItem", "position": 2, "name": "What Counts as 'Open Source' vs 'Proprietary' in Agent Frameworks", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#taxonomy" }, { "@type": "ListItem", "position": 3, "name": "The Open Source Contenders: LangGraph, CrewAI, LlamaIndex, Pydantic AI", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#open-source-contenders" }, { "@type": "ListItem", "position": 4, "name": "The Proprietary (Vendor-Anchored) Contenders: OpenAI Agents SDK, Claude Agent SDK, MAF, Google ADK", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#vendor-anchored-contenders" }, { "@type": "ListItem", "position": 5, "name": "Vendor Lock-In: The Four Layers Every Buyer Underestimates", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#four-layers-lockin" }, { "@type": "ListItem", "position": 6, "name": "TCO Analysis: What Open Source Actually Costs", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#tco-analysis" }, { "@type": "ListItem", "position": 7, "name": "MCP as the Escape Hatch: Why the Protocol Layer Matters More Than the Framework", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#mcp-escape-hatch" }, { "@type": "ListItem", "position": 8, "name": "When Open Source Wins: The Buyer Situations Where LangGraph, CrewAI, or Pydantic AI Beat Proprietary", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#when-open-source-wins" }, { "@type": "ListItem", "position": 9, "name": "When Proprietary Wins: The Buyer Situations Where Claude Agent SDK, OpenAI Agents SDK, MAF, or ADK Beat Open Source", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#when-proprietary-wins" }, { "@type": "ListItem", "position": 10, "name": "The Semantic Kernel / AutoGen Case Study: What Vendor-Anchored Open Source Looks Like When It Breaks", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#semantic-kernel-case-study" }, { "@type": "ListItem", "position": 11, "name": "Feature-by-Feature Comparison Matrix (2026)", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#feature-comparison" }, { "@type": "ListItem", "position": 12, "name": "Migration Cost: Moving Between Frameworks in Practice", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#migration-cost" }, { "@type": "ListItem", "position": 13, "name": "Alice Labs Recommendation Framework: Which Framework for Which Buyer", "url": "https://alicelabs.ai/en/insights/open-source-vs-proprietary-ai-agent-frameworks-2026#recommendation-framework" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Agents", "item": "https://alicelabs.ai/en/insights/ai-agents" }, { "@type": "ListItem", "position": 4, "name": "Open Source vs Proprietary AI Agent Frameworks 2026: Complete Guide" } ] } ] --- [Alice Labs](/en/) Services [ What we do ](/#welcome)[ About Alice ](/#who-we-are)[ Case ](/en/case)[ Insights 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[Home](/en) [Insights](/en/insights) [AI Agents](/en/insights/ai-agents) Open Source vs Proprietary AI Agent Frameworks 2026: Complete Guide AI Agents Comparison Fresh · Last reviewed: 3 August 2026 · 43d ago # Open Source vs Proprietary AI Agent Frameworks 2026: Complete Guide ## TL;DR Quick Answer Cited by AI > In 2026 every major AI agent SDK is MIT or Apache 2.0. The real split is portability: LangGraph, CrewAI, LlamaIndex, and Pydantic AI are model-agnostic and self-hostable; Claude Agent SDK, OpenAI Agents SDK, Microsoft Agent Framework, and Google ADK are MIT-licensed but designed to run against one vendor's API and hosted runtime. Alice Labs treats the second group as proprietary for lock-in accounting. Every major agent SDK shipping in 2026 is nominally MIT or Apache 2.0. The real decision is portability, not license — model portability, orchestration portability, state portability, and evidence portability. This is the Alice Labs 2026 buyer's guide, drawn from 100+ production agent implementations and 12 benchmarked framework deployments this year. Overall winner ## Open Source Pure Play LangGraph, CrewAI, LlamaIndex, Pydantic AI — model-agnostic, self-hostable License MIT Model portability Any provider State ownership Customer-owned Best-in-class fit Regulated EU workloads Dimensions won 7 VS ## Vendor-Anchored Open Source Claude Agent SDK, OpenAI Agents SDK, MAF, Google ADK — MIT/Apache but vendor-runtime License MIT / Apache 2.0 Model portability Vendor-tuned happy path State ownership Often vendor-hosted Best-in-class fit Coding, realtime, single-vendor shops Dimensions won 4 In 2026 AI agent frameworks split along a portability axis, not a license axis. Open source frameworks (LangGraph, CrewAI, LlamaIndex, Pydantic AI) run model-agnostic on customer infrastructure. Vendor-anchored frameworks (Claude Agent SDK, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK) ship MIT or Apache 2.0 code whose happy path hard-binds to one vendor's inference API and hosted runtime. ![Eric Lundberg - Author at Alice Labs](/images/eric-lundberg.png) Written by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) ![Linus Ingemarsson - Reviewer at Alice Labs](/images/linus-ingemarsson.png) Reviewed by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) Published August 3, 2026 16 min read ## Head-to-head scorecard Dimension Open Source Pure Play Vendor-Anchored Open Source Winner License MIT MIT or Apache 2.0 \= Model portability Native — any provider per node Vendor-optimized happy path (except OpenAI SDK + ADK) A Orchestration portability Fully customer-owned graph / crew / workflow Vendor-shaped loop or handoff model A State + memory portability In-memory / SQLite / Postgres / customer VPC Often hosted (Managed Agents, Foundry, AgentCore) A Governance + evidence (EU AI Act audit) Customer-owned trace and checkpointed state Vendor console — portability at layer 4 A Time to first useful agent ~1 day (LangGraph) to hours (CrewAI, Pydantic AI) ~2 hours (Claude Agent SDK, OpenAI Agents SDK) B Built-in tools (filesystem, bash, web) None — bring your own MCP servers 8 tools ship with Claude Agent SDK; sandboxed exec in OpenAI SDK B MCP support depth Native (CrewAI, LlamaIndex, Pydantic AI) / adapter (LangGraph) First-class (Claude Agent SDK — Anthropic authored MCP) B Multi-model routing per node First-class in LangGraph, Pydantic AI, CrewAI Native in OpenAI Agents SDK, MAF, ADK; forced-parallel for Claude SDK A Language coverage Python + JS/TS (mostly) Python, TS, .NET, Go, Java, Kotlin across the pack (ADK widest) B Community + independent contributors 295+ (LangGraph), broad OSS communities Vendor-led, single-org roadmap A Enterprise production references Klarna, Replit, Elastic, Uber, LinkedIn, Cloudflare, NVIDIA Anthropic (Claude Code), Microsoft, Google, OpenAI enterprise base \= Cost of leaving (measured migration hours) 80-200 hrs typical 80-200 hrs typical (MCP-first designs) \= Best fit for EU AI Act high-risk deployments First choice — customer-owned everything Possible but layer-4 governance harder to hand to auditor A Total 7 wins 4 wins 3 ties ## Key Takeaways - 01 Every major agent SDK in 2026 (LangGraph, CrewAI, LlamaIndex, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, MAF, Google ADK) uses MIT or Apache 2.0. License is table stakes. - 02 A 2026 Zapier survey found 81% of enterprise leaders concerned about AI vendor dependency and 74% saying loss of their AI vendor would disrupt or halt operations. - 03 Alice Labs classifies frameworks on two axes: model portability and orchestration portability. Both must be portable for us to treat a framework as 'truly open source' in TCO terms. - 04 Vendor-anchored open source is a real category: Claude Agent SDK is MIT-licensed but its runtime cannot be pointed at a non-Anthropic model without a fork. - 05 Framework license cost is under 2% of TCO across the 12 production deployments Alice Labs benchmarked in 2026. Model tokens are 55-70%, platform engineering 20-30%, observability 5-10%. - 06 MCP crossed a critical threshold in 2026: every major framework in this comparison ships first-class Model Context Protocol support, making MCP servers portable across frameworks. - 07 Microsoft moved Semantic Kernel and AutoGen into maintenance mode in October 2025; Microsoft Agent Framework 1.0 shipped April 3, 2026 as the direct successor. MIT license did not prevent forced migration. - 08 Alice Labs default for a greenfield 2026 build: LangGraph with Anthropic model, self-hosted checkpointer, MCP-first tool boundary — highest portability, lowest lock-in, production-proven at Klarna and Replit. ### Contents 16 min left - [01 Open Source vs Proprietary AI Agent Frameworks 2026: The Real Decision ](#the-real-decision) - [02 What Counts as 'Open Source' vs 'Proprietary' in Agent Frameworks ](#taxonomy) - [03 The Open Source Contenders: LangGraph, CrewAI, LlamaIndex, Pydantic AI ](#open-source-contenders) - [04 The Proprietary (Vendor-Anchored) Contenders: OpenAI Agents SDK, Claude Agent SDK, MAF, Google ADK ](#vendor-anchored-contenders) - [05 Vendor Lock-In: The Four Layers Every Buyer Underestimates ](#four-layers-lockin) - [06 TCO Analysis: What Open Source Actually Costs ](#tco-analysis) - [07 MCP as the Escape Hatch: Why the Protocol Layer Matters More Than the Framework ](#mcp-escape-hatch) - [08 When Open Source Wins: The Buyer Situations Where LangGraph, CrewAI, or Pydantic AI Beat Proprietary ](#when-open-source-wins) - [09 When Proprietary Wins: The Buyer Situations Where Claude Agent SDK, OpenAI Agents SDK, MAF, or ADK Beat Open Source ](#when-proprietary-wins) - [10 The Semantic Kernel / AutoGen Case Study: What Vendor-Anchored Open Source Looks Like When It Breaks ](#semantic-kernel-case-study) - [11 Feature-by-Feature Comparison Matrix (2026) ](#feature-comparison) - [12 Migration Cost: Moving Between Frameworks in Practice ](#migration-cost) - [13 Alice Labs Recommendation Framework: Which Framework for Which Buyer ](#recommendation-framework) Part of [Best AI Agent Frameworks 2026](/en/insights/best-ai-agent-frameworks-2026) 01 / 13 Dimension ## Open Source vs Proprietary AI Agent Frameworks 2026: The Real Decision In short The 2026 decision is not free vs paid. Every major agent SDK is MIT or Apache 2.0 licensed. The real axis is whether the framework's runtime and orchestration state can operate against multiple model providers, or hard-binds to one vendor's inference API and hosted state. Alice Labs classifies frameworks on both model portability and orchestration portability — both must be portable to count as 'truly open source' for total cost of ownership. The framing most 2026 buyers still walk into the meeting with — _"open source is free and safe, proprietary is paid and risky"_ — is wrong twice over. Every major agent SDK shipping in 2026 (LangGraph, CrewAI, LlamaIndex, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework, Google ADK) uses MIT or Apache 2.0 for its code. License is table stakes. The real distinction is whether the framework's runtime and orchestration state can operate against multiple model providers, or whether the happy path hard-binds to one vendor's inference API and hosted state. Kai Waehner's April 2026 enterprise agentic AI landscape report calls this out explicitly: framework license and framework portability are now separate discussions, and buyers who conflate them make expensive single-vendor bets that a permissive license does nothing to reverse. The stakes are real. A 2026 Zapier survey of enterprise AI leaders found **81% concerned about AI vendor dependency** and **74% saying loss of their AI vendor would disrupt or halt operations**. Those numbers do not fall because your framework ships under MIT — they fall when your state, telemetry, and orchestration can be re-pointed at a different runtime without a rewrite. Alice Labs classifies frameworks along two axes when we advise Nordic and EU buyers on 2026 shortlists. **Model portability**: can the framework route to any provider, or does its abstraction assume one? **Orchestration portability**: is the graph, handoff, or state model reproducible on another runtime, or is the state store proprietary to the vendor? Both must be portable for us to treat a framework as genuinely open source for TCO accounting. This is the frame we use throughout the rest of this article, and it lives inside the broader shortlist covered in our [best AI agent frameworks 2026 guide](/en/insights/best-ai-agent-frameworks-2026). License permissiveness is not portability OpenAI Agents SDK is MIT and supports 100+ models via LiteLLM. Claude Agent SDK is MIT and cannot be pointed at a non-Anthropic model without forking. Same license, opposite portability. If your buyer thinks 'open source' is a license class, you are one contract renewal away from a discovery you would rather have made in the RFP. 02 / 13 Dimension ## What Counts as 'Open Source' vs 'Proprietary' in Agent Frameworks In short Two categories that matter: (1) open source pure play — LangGraph, CrewAI, LlamaIndex, Pydantic AI — MIT-licensed code, model-agnostic orchestration, no hosted runtime required; (2) vendor-anchored open source — Claude Agent SDK, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework — MIT or Apache 2.0 code, but the happy path targets one vendor's API and hosted runtime. Alice Labs' rule: if the happy path requires one vendor's API, treat it as proprietary for lock-in accounting regardless of license. The taxonomy that matters in 2026 has two categories, not one. Once you use the wrong category as the top-of-funnel filter, every downstream trade-off analysis drifts. ### Open source pure play **LangGraph** (MIT, langchain-ai/langgraph), **CrewAI** (MIT, crewAIInc/crewAI), **LlamaIndex** (MIT, run-llama/llama\_index), and **Pydantic AI** (MIT, pydantic/pydantic-ai). Code is source-available, orchestration is model-agnostic, and no hosted runtime is required — you can install, run, and observe entirely on customer infrastructure without contacting any vendor. The paid layer (LangSmith, CrewAI Enterprise, LlamaCloud) is optional and outside the framework itself. ### Vendor-anchored open source **Claude Agent SDK** (MIT code, but use is governed by Anthropic's Commercial Terms of Service and the SDK calls Anthropic's API by default), **OpenAI Agents SDK** (MIT, but Responses API is the default backend), **Google ADK** (Apache 2.0, first-class for Gemini and Vertex AI), and **Microsoft Agent Framework** (MIT, first-class for Azure Foundry). The code carries a permissive license; the runtime carries a vendor. The gap between these categories is not academic. OpenAI Agents SDK supports 100+ models via LiteLLM and any-llm adapters — the license and the runtime both give you options. Claude Agent SDK cannot be pointed at a non-Anthropic model without forking — the license is permissive, the runtime is not. Same MIT-licensed source; opposite lock-in profile. **Alice Labs decision rule:** if the framework's happy path requires one vendor's API, treat it as proprietary for lock-in accounting regardless of license. This is the rule we use with every buyer, and it is the rule that keeps procurement conversations honest. Read the docs' 'getting started', not the LICENSE file The first ten pages of Anthropic's Claude Agent SDK docs assume Anthropic auth. The first ten pages of Google ADK docs assume Vertex AI. The LICENSE file is a legal instrument; the getting-started page is where the lock-in lives. Use the latter for classification, not the former. 03 / 13 Dimension ## The Open Source Contenders: LangGraph, CrewAI, LlamaIndex, Pydantic AI In short Four production-serious open source frameworks compete in 2026. LangGraph (MIT, ~15k+ stars) — graph-based durable execution used in production by Klarna, Replit, and Elastic. CrewAI (MIT, 56.5k stars) — role-based multi-agent crews, claims 65% of Fortune 500 as Basic-tier users. LlamaIndex (MIT, 51.3k stars) — document agents and OCR-heavy retrieval. Pydantic AI (MIT, 19k+ stars) — type-safe agents with dependency injection and durable execution. These four are the frameworks we most often see reach production in Nordic and EU enterprise deployments in 2026. The community-metrics differences are real, but the technical positioning matters more. ### LangGraph MIT, langchain-ai/langgraph, ~15k+ GitHub stars, 295+ contributors. Graph-based durable execution: nodes are functions or LLM calls, edges are typed transitions, state is a checkpointed TypedDict. First-class human-in-the-loop via `interrupt()` and `Command(resume=...)`. Production references: Klarna, Replit, Elastic, Uber, LinkedIn, Rippling, Coinbase, NVIDIA. Optional paid layer: LangSmith (observability) and LangGraph Platform ($39/seat/month on the Plus tier). ### CrewAI MIT, crewAIInc/crewAI, 56.5k GitHub stars — the largest agent-framework repo in 2026 by stars. Role-based multi-agent crews plus event-driven flows. CrewAI claims **65% of Fortune 500 as users of the Basic tier**. Optional paid layer: CrewAI Enterprise (SSO, RBAC, PII redaction, private VPC deployment). Deep coverage in our [CrewAI implementation guide](/en/insights/crewai-guide-2026). ### LlamaIndex MIT, run-llama/llama\_index, 51.3k stars. Document agents and OCR-heavy retrieval are the sweet spot; the Workflows API extends the framework into agentic orchestration for RAG-shaped agents. Optional paid layer: LlamaCloud for hosted indexing and OCR. ### Pydantic AI MIT, pydantic/pydantic-ai, 19k+ stars. Type-safe agents with dependency injection, structured outputs, MCP support, and durable execution. Positioning: the FastAPI of agent frameworks — Python-idiomatic, testable, and comfortable for teams already invested in Pydantic elsewhere in the stack. All four are trivially self-hostable. All four are MIT. And all four are portable across model providers — a LangGraph node can call Claude, Gemini, GPT, or an open-weights model on the same graph without a rewrite. This is the property that justifies the "pure play open source" label. 56.5k stars is not a proxy for production fit CrewAI leads the pack on GitHub stars, but production references at Klarna and Replit sit with LangGraph. Use stars to gauge ecosystem breadth; use case studies to gauge production readiness. Alice Labs looks at both, weighted 20/80 toward case studies. 04 / 13 Dimension ## The Proprietary (Vendor-Anchored) Contenders: OpenAI Agents SDK, Claude Agent SDK, MAF, Google ADK In short Four vendor-shipped SDKs anchor the 'proprietary in practice' category despite MIT and Apache 2.0 licenses. OpenAI Agents SDK — Responses API by default, 100+ models via LiteLLM. Claude Agent SDK — Python and TypeScript only, same harness as Claude Code, Anthropic API required. Microsoft Agent Framework 1.0 (April 3, 2026) — direct successor to Semantic Kernel and AutoGen. Google ADK — code-first Python/TS/Go/Java/Kotlin, deployment biased to Google Cloud. These four are the SDKs shipped by the model vendors themselves. All four are MIT or Apache 2.0. All four ship a happy path that assumes their vendor's API and hosted runtime. The differences among them matter because they change which lock-in you accept and which you buy your way out of. ### OpenAI Agents SDK MIT, openai/openai-agents-python. Responses API is the default backend; handoffs, guardrails, sandboxed execution, and MCP support ship in the box. Supports 100+ models via LiteLLM and any-llm adapters — the framework's abstraction is genuinely provider-agnostic even though the docs assume OpenAI first. If OpenAI is your default but you want the escape hatch to Claude for reasoning or Gemini for long context, this SDK gives you the shortest path. ### Claude Agent SDK MIT code plus Anthropic Commercial Terms, anthropics/claude-agent-sdk-python (7.8k stars). Python and TypeScript only. Same agent loop as Claude Code, deepest MCP integration in the market (Anthropic authored MCP), filesystem, bash, web, and subagent primitives out of the box. API-key auth to Anthropic is required — you cannot point the SDK at a non-Anthropic model without forking. Deep coverage in our [Claude Agent SDK guide 2026](/en/insights/claude-agent-sdk-guide-2026). ### Microsoft Agent Framework MIT, microsoft/agent-framework. Shipped 1.0 on **April 3, 2026** as the direct successor to Semantic Kernel and AutoGen, which entered maintenance mode October 2025. Supports Foundry, Anthropic, Azure OpenAI, OpenAI, and Ollama. Python, .NET, and Go bindings. Happy path is Azure Foundry — deploying to non-Azure infrastructure is possible but not the golden path in the docs. ### Google ADK Apache 2.0, google/adk-python. Code-first Python, TypeScript, Go, Java, and Kotlin toolkit. Supports Gemini, Gemma, Claude, OpenAI, Ollama, vLLM, and LiteLLM. The framework itself has no lock-in axis on models, but deployment is optimized for Google Cloud Agent Platform and Vertex AI — the observability and evaluation surfaces assume the Google Cloud console. Language coverage is a lock-in too Google ADK's five-language coverage (Python, TS, Go, Java, Kotlin) is the widest in the pack. Claude Agent SDK is Python + TS only. If your service is on the JVM or in Go, ADK's Apache 2.0 license and language coverage together beat the Claude Agent SDK regardless of model preference — unless you drive the Claude Code CLI as a subprocess. 05 / 13 Dimension ## Vendor Lock-In: The Four Layers Every Buyer Underestimates In short Lock-in in 2026 agent frameworks accumulates in four distinct layers. Model lock-in — the API you call, swappable in hours when the framework abstracts it. Orchestration lock-in — the graph, handoff, and state semantics, a rewrite to swap. State and memory lock-in — session stores, checkpointers, vector stores tied to a hosted service. Governance and evidence lock-in — telemetry, audit trails, and eval fixtures that only exist inside one vendor's console. Kai Waehner (April 2026) calls the fourth the fastest-growing category of AI dependency risk. Lock-in in agent frameworks is not one thing. It is four layers that accumulate independently, and buyers who account for only the first layer routinely find themselves locked in through the fourth. ### 1\. Model lock-in The API you call. Swappable in hours if the framework abstracts it (LangGraph, CrewAI, Pydantic AI, MAF, ADK, OpenAI Agents SDK via LiteLLM). Impossible without a fork for Claude Agent SDK. This is the layer buyers look at and often the only layer they weigh. ### 2\. Orchestration lock-in The graph, handoff, and state semantics your code is written against. Reimplementing 40 nodes with typed edges on a different framework is a rewrite, not a swap. This is the layer buyers under-model most. ### 3\. State and memory lock-in Session stores, checkpointers, and vector stores tied to a hosted service (LangSmith, AgentCore, Foundry) create durable dependency independent of the framework. Migrating state out is often technically possible but operationally hard — mid-flight sessions do not port cleanly. ### 4\. Governance and evidence lock-in Telemetry, audit trails, and eval fixtures that only exist inside one vendor's console. Kai Waehner's April 2026 landscape report calls this **the fastest-growing category of AI dependency risk**, and it is the layer Alice Labs sees regulated EU clients hit last and most painfully. Your evidence for an EU AI Act audit is only as portable as the console it lives in. The Alice Labs pattern for scoring a framework in an RFP: score each of the four layers separately, weight by how expensive migration would be at that layer, and only then compute a lock-in cost. A framework that is portable at layers 1 and 2 but proprietary at layers 3 and 4 is still locking you in — the money just leaves the room later. Layer 4 is the one buyers under-model most Model lock-in gets talked about because it is visible. Governance and evidence lock-in gets talked about after the fact, because it becomes visible only when you try to leave. If your framework's audit trail lives in LangSmith or Foundry, migrating means recreating six months of compliance evidence somewhere else. 06 / 13 Dimension ## TCO Analysis: What Open Source Actually Costs In short Alice Labs' benchmark across 12 production Claude Agent SDK, LangGraph, and CrewAI deployments in 2026: framework license cost is under 2% of TCO. Model tokens are 55-70%, platform engineering 20-30%, observability and eval tooling 5-10%. Open source frameworks are usually paired with a paid platform: LangGraph + LangSmith, CrewAI + CrewAI Enterprise, LlamaIndex + LlamaCloud. Proprietary SDKs bundle observability and hosting into Anthropic Managed Agents, OpenAI's hosted agent runtime, Foundry, or Google Agent Platform. The most persistent misconception we see in 2026 procurement is that open source frameworks are free. They are free to install. They are not free to own. Open source frameworks are nearly always paired with a paid platform. LangGraph is almost always deployed with LangSmith for observability and, in bigger teams, LangGraph Platform for hosted runs. CrewAI's enterprise adopters typically take CrewAI Enterprise for SSO, RBAC, PII redaction, and private VPC deployment. LlamaIndex users routinely adopt LlamaCloud for hosted OCR and indexing. Proprietary SDKs bundle observability and hosting: Anthropic Managed Agents, OpenAI's hosted agent runtime, Microsoft Foundry, and Google Agent Platform each replace some portion of the platform-engineering spend you would otherwise carry. Alice Labs benchmarked total cost of ownership across 12 production framework deployments in 2026 — a mix of Claude Agent SDK, LangGraph, and CrewAI, spanning regulated Nordic clients and higher-volume EU deployments. The composition: TCO composition — Alice Labs 12-deployment benchmark, 2026 Cost category Share of TCO Notes Framework license <2% All frameworks MIT or Apache 2.0 — near-zero Model tokens 55-70% Highest lever — tier-by-role routing cuts 40-60% Platform engineering 20-30% Self-hosted infra, deployment, on-call Observability + eval 5-10% LangSmith, Datadog, Grafana, Braintrust Multi-vendor deployments above ~5,000 users are typically cost-justified vs single-vendor once negotiated token rates are factored in — but only if your framework is portable enough to route different workloads to different vendors without a rewrite. This is where model portability (layer 1 lock-in) starts to matter for real money, not just architectural preference. Free is not the same as free to own Across 12 Alice Labs production deployments in 2026, license cost was under 2% of TCO — the difference between MIT and a commercial licence is a rounding error. The 55-70% you spend on tokens and the 20-30% you spend on platform engineering is where the actual decision lives. 07 / 13 Dimension ## MCP as the Escape Hatch: Why the Protocol Layer Matters More Than the Framework In short Model Context Protocol is an open standard supported across Claude, ChatGPT, VS Code, Cursor, and MCPJam as of the 2026-07-28 spec. Every major framework in this comparison ships first-class MCP support — LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework, and Google ADK. MCP servers you own (data connectors, internal tools) are portable across frameworks: a Slack MCP server written for Claude Agent SDK works unchanged under LangGraph. Alice Labs recommends keeping proprietary business logic inside MCP servers and only orchestration inside the framework. MCP is the highest-leverage portability investment a buyer can make in the 2026 agent stack. It is more portable than the framework, it is more stable than the framework, and it survives framework migrations without a rewrite. Model Context Protocol is an open standard. As of the 2026-07-28 spec, it is supported in Claude, ChatGPT, VS Code, Cursor, MCPJam, and a growing list of enterprise agent surfaces. Every major framework in this comparison — LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework, and Google ADK — ships first-class MCP support. This is a genuine 8-out-of-8 crossover. The practical consequence is under-appreciated. MCP servers you own — the Slack connector, the Postgres tool, the internal API adapter you wrote for your product — are portable across frameworks. A Slack MCP server written for Claude Agent SDK runs unchanged under LangGraph via `langchain-mcp-adapters`, unchanged under CrewAI's MCP integration, and unchanged under Google ADK. **Alice Labs' portability recommendation:** keep proprietary business logic inside MCP servers, keep only orchestration inside the framework. This way, the framework becomes a swappable layer — you can migrate LangGraph to CrewAI, or Claude Agent SDK to Pydantic AI, without touching your tool integrations. In a stack where layer-1 model lock-in is variable and layer-2 orchestration lock-in is expensive, MCP neutralises much of the risk at the tool boundary. The one caveat: MCP support depth still varies. Claude Agent SDK has the deepest native MCP surface because Anthropic authored the protocol — runtime `add_mcp_server` and `get_mcp_status` APIs, per-subagent tool scoping, and one-line stdio server attach. LangGraph consumes MCP through an adapter. Both work in production; the ceremony delta is a real engineering cost when you are wiring 8+ servers. The Alice Labs MCP-first pattern Every integration you write goes into an MCP server, not into framework-specific tool code. This costs slightly more up front and pays back the first time you swap frameworks or add a second one. In our production pattern of LangGraph outer supervisor + Claude Agent SDK inner workers, MCP is what makes the whole thing coherent. 08 / 13 Dimension ## When Open Source Wins: The Buyer Situations Where LangGraph, CrewAI, or Pydantic AI Beat Proprietary In short Open source wins in four buyer situations. Regulated workloads where model routing must be provable and auditable — LangGraph checkpointer plus LangSmith trace is admissible where a hosted vendor loop rarely is. Multi-model production where cost or quality routing matters — Pydantic AI or LangGraph can route Claude, Gemini, and GPT in one graph. On-prem or sovereign cloud deployment for EU AI Act, GDPR, and DORA obligations — CrewAI Enterprise or LangGraph in a customer-owned VPC beats any hosted proprietary runtime. Alice Labs uses LangGraph or CrewAI whenever the client's compliance officer will need to answer 'where does the state live?' with a customer-owned answer. There are four buyer situations where the open source frameworks reliably win, in our experience across 100+ Alice Labs implementations. The decision framework here is prescriptive, not preference. **Regulated workloads where model routing must be provable and auditable.** A LangGraph checkpointer plus a LangSmith trace is admissible in front of an EU AI Act auditor because you can hand over the state and the trace as artefacts you own. A hosted vendor loop is rarely admissible because the auditor's chain-of-custody question leads back to a vendor console you do not control. **Multi-model production where cost or quality routing matters.** Pydantic AI or LangGraph can route to Claude for reasoning, Gemini for long context, and GPT for tool use in one graph. This routing is where the 40-60% cost reduction from tier-by-role model choice actually lives — and it is not available inside a single-vendor SDK without operating a parallel non-vendor code path. **On-prem or sovereign cloud deployment for EU AI Act, GDPR, and DORA compliance.** CrewAI Enterprise deployed in a customer-owned VPC or LangGraph deployed on customer infrastructure beats any hosted proprietary runtime for regulated workloads where data residency and processor definitions matter. **Nordic and EU enterprises where Alice Labs works.** Our rule of thumb: we use LangGraph or CrewAI whenever the client's compliance officer will need to answer _"where does the state live?"_ with a customer-owned answer. That single question resolves probably 70% of framework choices for regulated buyers. For deeper context on how compliance shapes framework choice, our [EU AI Act compliance checklist for 2026](/en/insights/eu-ai-act-compliance-checklist-2026) walks the full audit surface for high-risk deployments. 'Where does the state live?' is the fastest sorter Ask the compliance officer that one question in the first RFP meeting. If the answer needs to be 'customer-owned', open source pure-play frameworks win the RFP before you have compared feature matrices. If the answer can be 'vendor-owned', the vendor-anchored SDKs stay in scope. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Choosing between open source and vendor-anchored agent frameworks for a production build? Alice Labs is an Anthropic Partner headquartered in Stockholm with 100+ production AI implementations since 2023 — across LangGraph, CrewAI, LlamaIndex, Pydantic AI, Claude Agent SDK, OpenAI Agents SDK, Microsoft Agent Framework, and Google ADK. We map buyer situations to frameworks against the four lock-in layers before you commit. [Get a Framework Recommendation](#contact) 09 / 13 Dimension ## When Proprietary Wins: The Buyer Situations Where Claude Agent SDK, OpenAI Agents SDK, MAF, or ADK Beat Open Source In short Proprietary wins in four buyer situations. Coding and computer-use agents with deep OS access — Claude Agent SDK ships filesystem, bash, web, and subagent primitives that take months to replicate on LangGraph. Voice, realtime, and lightweight conversational handoffs — OpenAI Agents SDK's handoff and guardrail model is the shortest path to production. Existing Microsoft or Google shops with sunk cost in Azure Foundry or Vertex AI — MAF and ADK reduce integration cost more than the lock-in premium. Alice Labs uses Claude Agent SDK for internal engineering agents where Anthropic's coding-model quality is the differentiator. The comparison is only real if it recognises where proprietary wins. Four buyer situations where the vendor-anchored SDKs reliably beat open source pure play — this is not open source advocacy in disguise. **Coding and computer-use agents with deep OS access.** Claude Agent SDK ships with filesystem, bash, web, and subagent primitives that would take months to replicate on LangGraph. The same harness that powers Claude Code powers your production agent — feature parity is effectively guaranteed on each release. For a coding agent with a Claude commitment already made at procurement, the SDK is the shortest path and it is not close. **Voice, realtime, and lightweight conversational handoffs.** OpenAI Agents SDK's handoff plus guardrail model is the shortest path to production for a realtime conversational agent. If the workload is voice or chat and the vendor is already OpenAI, the ceremony saving over LangGraph is real. **Existing Microsoft or Google shops with sunk cost in Azure Foundry or Vertex AI.** MAF and ADK reduce integration cost more than the lock-in premium for buyers who already procured the vendor. In these situations, lock-in is not a technical decision — it is already made at the procurement layer, and the framework choice is about developer velocity, not portability. **Alice Labs' internal engineering agents.** We use Claude Agent SDK for our own internal engineering agents where Anthropic's model quality on code is the differentiator and portability is not a stated requirement. Different rule for different buyer — the same firm can (and should) use different frameworks for different workloads. For the head-to-head between the two most common vendor-anchored contenders and LangGraph, see our [LangGraph vs Claude Agent SDK 2026 comparison](/en/insights/langgraph-vs-claude-agent-sdk-2026). Different framework for different workload is fine Alice Labs' 100+ implementations regularly include multiple frameworks in the same enterprise. Coding agents on Claude Agent SDK, regulated finance workflows on LangGraph, RAG-heavy document agents on LlamaIndex. Framework choice is workload-scoped, not company-scoped, and buyers who force one framework across every workload usually regret it within a year. 10 / 13 Dimension ## The Semantic Kernel / AutoGen Case Study: What Vendor-Anchored Open Source Looks Like When It Breaks In short Microsoft moved Semantic Kernel and AutoGen into maintenance mode in October 2025; the last feature release was September 2025. Microsoft Agent Framework 1.0 shipped April 3, 2026 as the direct successor, requiring migration from both prior frameworks. AutoGen users must budget for Q3 2026 API deprecations; Semantic Kernel session and telemetry patterns carry over but the API surface is new. Lesson: MIT license did not prevent a forced migration when the vendor changed direction. Framework longevity is a governance question, not a license question. The Semantic Kernel and AutoGen story is the cautionary tale for anyone who thinks a permissive license inoculates them against vendor decisions. Both frameworks were MIT licensed. Both are now on a forced migration path. Microsoft moved Semantic Kernel and AutoGen into maintenance mode in **October 2025**. The last feature release was September 2025. In practice, both frameworks became closed to new capability development while the vendor invested elsewhere. **Microsoft Agent Framework 1.0 shipped April 3, 2026** as the direct successor to both prior frameworks. Existing users of Semantic Kernel and AutoGen must migrate — the successor is MIT-licensed and open source, but the API surface is different and the migration is real work. AutoGen users must budget for Q3 2026 API deprecations. Semantic Kernel session and telemetry patterns carry over conceptually, but the API surface is new. The lesson is not that Microsoft did anything wrong — the migration path is reasonable and the successor is a better framework. The lesson is that **MIT license did not prevent a forced migration when the vendor changed direction**. Framework longevity is a governance question — how the vendor manages the project, how open the roadmap is, how many independent contributors carry it forward — not a license question. For frameworks anchored to a single vendor, this pattern will recur. It is not a statement about Microsoft; it is a statement about vendor-anchored open source as a category. LangGraph 1.0 shipping in October 2025 was a similar kind of milestone with similar migration pressure — but LangGraph's independent-contributor base (295+ contributors) provides a governance signal that is different in kind from single-vendor project management. License is not a substitute for governance MIT is table stakes in 2026. What matters for framework longevity is roadmap transparency, independent-contributor breadth, and downstream production references you can call. Ask for all three in your RFP, not just for the license file. 11 / 13 Dimension ## Feature-by-Feature Comparison Matrix (2026) In short Durable execution and checkpointing ship in LangGraph, Pydantic AI, MAF, ADK, and Claude Agent SDK sessions. First-class multi-model support is native in LangGraph, CrewAI, Pydantic AI, MAF (5 providers), ADK (6+ providers), and OpenAI Agents SDK (100+ via adapters). MCP support is universal across all eight frameworks as of August 2026. Language coverage is widest in Google ADK (Python, TS, Go, Java, Kotlin), followed by MAF (Python, .NET, Go). The compact scan across all eight frameworks. This is the table Alice Labs starts every RFP shortlist with, and the one we recommend buyers reproduce in their own spreadsheets. Feature matrix — 8 major agent frameworks, August 2026 Framework License Multi-model Durable exec MCP Languages LangGraph MIT Native First-class Adapter Python, JS/TS CrewAI MIT Native Flow-level Native Python, JS/TS LlamaIndex MIT Native Workflows Native Python, TS Pydantic AI MIT Native First-class Native Python OpenAI Agents SDK MIT 100+ via LiteLLM Session-level Native Python, JS, Go Claude Agent SDK MIT + Anthropic ToS Anthropic only Sessions First-class (author) Python, TS Microsoft Agent Framework MIT 5 providers First-class Native Python, .NET, Go Google ADK Apache 2.0 6+ providers First-class Native Python, TS, Go, Java, Kotlin Three signals worth flagging inside the matrix. Durable execution now ships in five of eight frameworks — this used to be LangGraph's differentiator and no longer is. Multi-model is native in six of eight, and the two exceptions (Claude Agent SDK, OpenAI's default backend) are exactly where the vendor lock-in lives. MCP is universal — this is the biggest cross-framework standardisation event of 2026 and the reason MCP portability outranks framework portability in our recommendation. Durable execution is no longer a moat Two years ago, checkpointed durable execution was LangGraph's differentiator. In 2026 it ships in Pydantic AI, MAF, ADK, and effectively via sessions in Claude Agent SDK. Feature parity has caught up faster than most 2024-era comparison articles suggest — verify current-state features per framework, not lore. 12 / 13 Dimension ## Migration Cost: Moving Between Frameworks in Practice In short Alice Labs measured three production migrations in 2026: AutoGen to MAF took ~120 engineering hours for a 12-agent system, with state and telemetry requiring rewrites. CrewAI to LangGraph took ~200 hours because role and task abstractions did not translate cleanly to graph nodes. OpenAI Agents SDK to Claude Agent SDK took ~80 hours if the agent was already OS-native, and larger if handoff chains had to be flattened. Migration cost is the number to price against lock-in — if migration is under 200 hours, layer-2 orchestration lock-in is manageable. Lock-in is real only in proportion to the cost of leaving. Alice Labs measured three production migrations in 2026, and the numbers are worth quoting because they substantially outperform what most 2024-era comparison articles suggest. Alice Labs 2026 framework migrations — measured engineering hours Migration System size Hours Main cost driver AutoGen → MAF 12-agent system ~120 hrs State and telemetry rewrites CrewAI → LangGraph Mid-size workflow ~200 hrs Roles and tasks → graph nodes OpenAI Agents SDK → Claude Agent SDK OS-native handoff chain ~80 hrs Handoff chain flattening The most expensive migration was CrewAI to LangGraph, because CrewAI's role and task abstractions did not translate cleanly to graph nodes — the mental model change was as much of the cost as the code change. The cheapest was OpenAI Agents SDK to Claude Agent SDK, because both share an agent-loop mental model and MCP was already the tool boundary. **Rule we use in RFPs:** if the projected migration cost is under 200 engineering hours, layer-2 orchestration lock-in is manageable and should not dominate the framework decision. Above 200 hours, orchestration lock-in becomes a first-class procurement variable and should be scored explicitly. For a step-by-step example of one such migration, our [LangGraph guide 2026](/en/insights/langgraph-guide-2026) and the [LangGraph vs Claude Agent SDK head-to-head](/en/insights/langgraph-vs-claude-agent-sdk-2026) walk the migration patterns we use in production. 80-200 hours, not 2000 The 'six-month rewrite' framing that circulates in vendor sales decks is largely obsolete in 2026 for MCP-first agents. Migration cost dropped once MCP became universal — tool integrations are the biggest surface, and MCP made them portable. Framework-only migrations are now firmly under 200 hours in our production measurements. 13 / 13 Dimension ## Alice Labs Recommendation Framework: Which Framework for Which Buyer In short Regulated Nordic and EU enterprises with EU AI Act obligations: LangGraph or CrewAI, self-hosted, MCP-first. Fast-moving startups building coding or computer-use agents: Claude Agent SDK, accept the Anthropic dependency, extract business logic to MCP servers. Enterprise Microsoft shops: MAF, but keep orchestration graphs portable and treat Foundry as an execution target rather than a data lock-in. Consumer voice and chat: OpenAI Agents SDK for realtime; route to Claude via LiteLLM where reasoning matters. Alice Labs default for a greenfield agent build in 2026: LangGraph with Anthropic model, self-hosted checkpointer, MCP-first tool boundary — highest portability, lowest lock-in, production-proven at Klarna and Replit. This is the recommendation matrix Alice Labs uses in Nordic and EU RFPs. It is not a preference — it is the pattern we have arrived at after 100+ implementations, and it maps buyer situations to framework choices with high accuracy. Alice Labs framework recommendation by buyer situation, 2026 Buyer situation Recommended framework Regulated Nordic / EU enterprise, EU AI Act high-risk LangGraph or CrewAI, self-hosted, MCP-first Fast-moving startup, coding or computer-use agent Claude Agent SDK, extract business logic to MCP servers Enterprise Microsoft shop with Azure Foundry Microsoft Agent Framework, keep graphs portable Google Cloud shop with Vertex AI investment Google ADK, MCP-first tool boundary Consumer voice / realtime chat agent OpenAI Agents SDK, route to Claude via LiteLLM for reasoning RAG-heavy document/OCR agent LlamaIndex Workflows, MCP-first tool boundary Greenfield 2026 build, no committed vendor LangGraph + Anthropic model + self-hosted checkpointer + MCP **Alice Labs default recommendation for a greenfield agent build in 2026:** LangGraph with an Anthropic model, self-hosted checkpointer, and MCP-first tool boundary. Highest portability, lowest lock-in, production-proven at Klarna and Replit, with an escape hatch to any of the other seven frameworks via MCP. This is the configuration we recommend more often than any other in Nordic RFPs. A minimal example of the shape: \# LangGraph + Anthropic + MCP — the Alice Labs 2026 default from typing import TypedDict from langgraph.graph import StateGraph, START, END from langgraph.checkpoint.postgres import PostgresSaver from langchain\_anthropic import ChatAnthropic from langchain\_mcp\_adapters.client import MultiServerMCPClient class State(TypedDict): query: str plan: str result: str # MCP servers own the tool boundary — portable across frameworks mcp = MultiServerMCPClient({ "github": {"command": "npx", "args": \["-y", "@modelcontextprotocol/server-github"\]}, "postgres": {"command": "npx", "args": \["-y", "@modelcontextprotocol/server-postgres"\]}, }) tools = mcp.get\_tools() model = ChatAnthropic(model="claude-sonnet-5").bind\_tools(tools) def plan(state: State) -> State: return {\*\*state, "plan": model.invoke(state\["query"\]).content} def execute(state: State) -> State: return {\*\*state, "result": model.invoke(state\["plan"\]).content} graph = StateGraph(State) graph.add\_node("plan", plan) graph.add\_node("execute", execute) graph.add\_edge(START, "plan") graph.add\_edge("plan", "execute") graph.add\_edge("execute", END) # Self-hosted Postgres checkpointer — state stays inside customer VPC app = graph.compile(checkpointer=PostgresSaver.from\_conn\_string(POSTGRES\_URL)) Every property that matters in a regulated Nordic RFP is customer-owned: the graph, the state store, and the tool boundary. The model is swappable via `ChatAnthropic` → `ChatOpenAI` or `ChatGoogleGenerativeAI` with no other code changes. If the vendor changes direction, the migration cost is hours, not weeks. The MCP-first tool boundary is the highest-leverage decision Regardless of which framework you pick, keep proprietary business logic inside MCP servers. This is the single most portable investment you can make in the 2026 agent stack — it survives every future framework migration in our production data. ## Which should you choose? ### Choose Open Source Pure Play if… - You are a regulated Nordic or EU enterprise with EU AI Act high-risk obligations - Your compliance officer needs to answer 'where does the state live?' with 'customer-owned' - You need multi-model routing (Claude for reasoning, GPT for tool use, Haiku for fan-out) - You want to maximise portability across the four lock-in layers, not just at the license - You are building for the long term (3-5+ year deployment) and want vendor optionality - You already run Kubernetes and want the agent to sit alongside the rest of your stack ### Choose Vendor-Anchored Open Source if… - You are shipping a coding, refactor, or computer-use agent and want the shortest path - You are already committed to Claude, OpenAI, Azure Foundry, or Vertex AI at procurement - You need a first useful agent this week and can defer graph-level ceremony to sprint 2 - You are building consumer voice or realtime chat and want native handoff / guardrail primitives - Your buyer's lock-in tolerance is high because the vendor is the strategic bet - Portability across the four layers is not a stated requirement in this deployment ## Our verdict In 2026 the honest answer is: use open source pure play (LangGraph, CrewAI, LlamaIndex, Pydantic AI) as the default for regulated and multi-model deployments; use vendor-anchored SDKs (Claude Agent SDK, OpenAI Agents SDK, MAF, Google ADK) when the workload maps to a vendor's differentiated capabilities and portability is not a hard requirement. Alice Labs' 100+ production implementations converge on one prescriptive default for greenfield builds: LangGraph with an Anthropic model, self-hosted Postgres checkpointer, and an MCP-first tool boundary. Highest portability, lowest lock-in, production-proven at Klarna and Replit. ## About the Authors & Reviewers Published August 3, 2026 Written by ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Reviewed by August 3, 2026 ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Published August 3, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is the difference between open source and proprietary AI agent frameworks in 2026? ▾ In 2026 the distinction is not license — nearly all major SDKs are MIT or Apache 2.0. It is portability. Open source frameworks like LangGraph, CrewAI, LlamaIndex, and Pydantic AI let you swap model providers and self-host state. Vendor-anchored SDKs like Claude Agent SDK, OpenAI Agents SDK, Google ADK, and Microsoft Agent Framework are MIT-licensed but designed to run against one vendor's API and hosted runtime. Alice Labs treats the second category as proprietary for lock-in accounting. ### Is LangGraph open source and free to use commercially? ▾ Yes. LangGraph is distributed under the MIT license and is free for commercial use. It is used in production by Klarna, Replit, Elastic, Uber, LinkedIn, and NVIDIA. The paid layer is LangSmith (LangChain's hosted observability) and LangGraph Platform ($39/seat/month Plus tier) — both optional. Alice Labs runs LangGraph fully self-hosted for regulated Nordic clients where LangSmith would move state outside the customer's compliance boundary. ### Is the Claude Agent SDK open source? ▾ The Claude Agent SDK code is MIT licensed, but usage is governed by Anthropic's Commercial Terms of Service and the SDK requires an Anthropic API key. It cannot be pointed at a non-Anthropic model without forking. Alice Labs classifies it as vendor-anchored: open source in source, proprietary in dependency. The trade-off is worth it for coding and computer-use agents where Claude's model quality is the differentiator. ### Is the OpenAI Agents SDK open source? ▾ Yes, the OpenAI Agents SDK is MIT-licensed and available at github.com/openai/openai-agents-python. Its Responses API is the default backend but it supports 100+ models through LiteLLM and any-llm adapters. Alice Labs uses it for lightweight handoff chains, voice, and realtime workloads, and routes reasoning-heavy nodes to Claude via LiteLLM when quality matters more than latency. ### Is Microsoft Agent Framework open source or proprietary? ▾ Microsoft Agent Framework (MAF) is MIT-licensed and open source on github.com/microsoft/agent-framework. It shipped 1.0 on April 3, 2026 as the direct successor to Semantic Kernel and AutoGen, which entered maintenance mode in October 2025. It supports Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI, and Ollama. Alice Labs treats it as vendor-anchored because the happy path assumes Azure Foundry hosting. ### Is Google ADK open source? ▾ Google ADK is Apache 2.0 licensed and available in Python, TypeScript, Go, Java, and Kotlin — the widest language coverage in the pack. It supports Gemini, Gemma, Claude, OpenAI, Ollama, vLLM, and LiteLLM. The framework itself has no lock-in on models, but deployment is optimized for Google Cloud Agent Platform and Vertex AI. Alice Labs uses ADK for clients already invested in Google Cloud and multilingual language coverage. ### Which is the best open source AI agent framework in 2026? ▾ There is no single best — it depends on workload. LangGraph wins for durable, stateful, human-in-the-loop workflows. CrewAI wins for role-based multi-agent teams. LlamaIndex wins for document and RAG-heavy agents. Pydantic AI wins for type-safe, structured-output-first agents. Alice Labs' default for a greenfield 2026 build is LangGraph with an Anthropic model, self-hosted checkpointer, and MCP-first tool boundaries. ### Does open source AI agent framework mean zero cost? ▾ No. License cost is under 2% of total cost of ownership across the 12 production Alice Labs deployments benchmarked in 2026. Model tokens are 55-70% of TCO, platform engineering 20-30%, observability and eval tooling 5-10%. Open source frameworks are usually paired with a paid platform: LangGraph with LangSmith, CrewAI with CrewAI Enterprise, LlamaIndex with LlamaCloud. Free code is not free ownership. ### What is vendor lock-in in AI agent frameworks? ▾ Vendor lock-in in agent frameworks accumulates in four layers: (1) the model API you call, (2) the orchestration graph or handoff model you built, (3) the state and memory stores you use, and (4) the governance evidence trail — telemetry, evals, audit logs. Kai Waehner's April 2026 landscape report calls the fourth the fastest-growing category of AI dependency risk. Alice Labs scores each layer separately in RFPs because they lock in independently. ### Can I use LangGraph with Claude, GPT-5, and Gemini in the same workflow? ▾ Yes. LangGraph's multi-model routing is a first-class pattern — each node can call a different provider via LangChain chat model wrappers. Alice Labs routinely routes Claude for reasoning, GPT for structured JSON extraction, Haiku for cheap fan-out, and open-weights models for embeddings — all in a single compiled graph. This is not possible inside the Claude Agent SDK without a parallel non-Anthropic code path. ### What is MCP and why does it matter for framework choice? ▾ Model Context Protocol (MCP) is an open standard for exposing tools and data to LLM agents. As of the 2026-07-28 spec, MCP is supported across Claude, ChatGPT, VS Code, Cursor, and MCPJam. Every major framework in this comparison ships first-class MCP support — LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, MAF, and Google ADK. MCP servers you own are portable across frameworks, which makes MCP more strategic than framework choice for portability. ### What happened to Semantic Kernel and AutoGen in 2026? ▾ Microsoft moved Semantic Kernel and AutoGen into maintenance mode in October 2025; the last feature release was September 2025. Microsoft Agent Framework 1.0 shipped on April 3, 2026 as the direct successor, and existing users must migrate. AutoGen users must budget for Q3 2026 API deprecations; Semantic Kernel session and telemetry patterns carry over conceptually but the API surface is new. The lesson: MIT license does not prevent a forced migration when the vendor changes direction. ### How much does it cost to migrate between agent frameworks? ▾ Alice Labs measured three production migrations in 2026. AutoGen to MAF: ~120 engineering hours for a 12-agent system (state and telemetry rewrites dominated). CrewAI to LangGraph: ~200 hours (role and task abstractions did not translate cleanly to graph nodes). OpenAI Agents SDK to Claude Agent SDK: ~80 hours if the agent was already OS-native. Our rule: if migration is under 200 hours, layer-2 orchestration lock-in is manageable and should not dominate the framework decision. ### Is CrewAI free to use in production? ▾ Yes. CrewAI is MIT-licensed and free to install and run in production — the framework claims 65% of Fortune 500 as users of the Basic tier. The paid layer is CrewAI Enterprise, which adds SSO, RBAC, PII redaction, and private VPC deployment for regulated enterprise environments. Alice Labs uses CrewAI open source for pilots and CrewAI Enterprise when a Nordic client needs single-tenant deployment inside their own VPC. ### Should I choose an open source framework or a proprietary SDK for an EU AI Act high-risk deployment? ▾ Alice Labs' strong default for EU AI Act high-risk deployments is open source pure play — LangGraph or CrewAI, self-hosted on customer infrastructure, with an MCP-first tool boundary. The reason is layer-4 governance lock-in: your evidence for a high-risk audit is only as portable as the console it lives in. A LangGraph checkpointer plus trace you own is admissible; a hosted vendor loop is much harder to hand to an auditor. Always consult legal counsel for compliance determinations. ### Which framework has the most GitHub stars in 2026? ▾ CrewAI leads at 56.5k stars (crewAIInc/crewAI), followed by LlamaIndex at 51.3k (run-llama/llama\_index), Pydantic AI at 19k+ (pydantic/pydantic-ai), and LangGraph at ~15k+ (langchain-ai/langgraph). Vendor-anchored SDKs are newer: Claude Agent SDK at 7.8k (anthropics/claude-agent-sdk-python). Stars are a useful proxy for ecosystem breadth but not a good proxy for production fit — Klarna and Replit run LangGraph, which is not the star leader. ### Can I keep proprietary business logic portable across agent frameworks? ▾ Yes — keep it inside MCP servers, not inside framework-specific tool code. Alice Labs' portability recommendation: proprietary business logic goes into MCP servers, orchestration lives inside the framework, and the framework becomes a swappable layer. A Slack MCP server written for Claude Agent SDK runs unchanged under LangGraph, CrewAI, or Google ADK. This is the single most portable investment you can make in the 2026 agent stack. ### Do vendor-anchored SDKs like Claude Agent SDK cost more than open source frameworks? ▾ Not at the license layer — Claude Agent SDK is MIT and free to install. The cost differences show up in tokens (you can only route to Claude), in optional managed services (Anthropic Managed Agents vs LangSmith), and in the four lock-in layers if you later want to leave. Across Alice Labs' 12 production benchmarks in 2026, model tokens dominated TCO at 55-70% — framework choice mostly changes which provider you spend those tokens with, not how many you spend. ### Which framework is best for coding agents in 2026, open source or proprietary? ▾ For coding agents specifically, the Claude Agent SDK is Alice Labs' top pick despite being vendor-anchored. It ships filesystem, bash, web, and subagent primitives that would take months to replicate on LangGraph, and Claude's coding model quality is the differentiator. Extract business logic to MCP servers and accept the Anthropic dependency — the workload-vendor fit outweighs the portability cost for coding agents. Detailed head-to-head in our LangGraph vs Claude Agent SDK comparison. ### Can I mix open source and vendor-anchored agent frameworks in the same system? ▾ Yes — this is Alice Labs' most common production pattern in Nordic enterprises. LangGraph acts as the outer supervisor (durable execution, human-in-the-loop, per-node model routing) while Claude Agent SDK subagents act as inner workers (deep MCP integration, filesystem tools, Claude-tuned prompt caching). MCP servers are the shared tool layer so any tool written under one framework runs under the other. Framework choice becomes workload-scoped rather than company-scoped. ### What is Alice Labs' default recommendation for a greenfield agent build in 2026? ▾ LangGraph with an Anthropic model, self-hosted Postgres checkpointer, and MCP-first tool boundary. Highest portability across all four lock-in layers, lowest overall risk, production-proven at Klarna and Replit, and an escape hatch to any of the other seven major frameworks via MCP. This configuration wins more Nordic RFPs for us than any other, and it is the shape we recommend by default unless the workload pulls hard toward a specific vendor-anchored SDK (coding to Claude, voice to OpenAI, Foundry-committed shops to MAF). ### How does the 2026 Zapier vendor dependency survey inform framework choice? ▾ The Zapier survey found 81% of enterprise leaders concerned about AI vendor dependency and 74% saying loss of their AI vendor would disrupt or halt operations. The framework-choice implication: portability across the four lock-in layers (model, orchestration, state, evidence) is not a nice-to-have — it is what the majority of enterprise leaders now optimise against. Frameworks that keep all four layers customer-owned reduce enterprise dependency risk directly; frameworks that hard-bind at even one layer increase it. ### 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 ### LlamaIndex Workflows 1.0 Guide 2026: Event-Driven Agents ](/en/insights/llamaindex-workflows-guide-2026)[Next in AI Agents ### OpenAI Agents SDK vs CrewAI 2026: Cost + Decision Guide ](/en/insights/openai-agents-sdk-vs-crewai-2026) ## Further reading - [Kai Waehner — Enterprise Agentic AI Landscape 2026](https://www.kai-waehner.de/blog/2026/04/06/enterprise-agentic-ai-landscape-2026-trust-flexibility-and-vendor-lock-in/)· kai-waehner.de - [Anthropic — Claude Agent SDK Overview](https://code.claude.com/docs/en/agent-sdk/overview)· code.claude.com - [GitHub — crewAIInc/crewAI](https://github.com/crewAIInc/crewAI)· github.com - [GitHub — langchain-ai/langgraph](https://github.com/langchain-ai/langgraph)· github.com - [GitHub — openai/openai-agents-python](https://github.com/openai/openai-agents-python)· github.com - [Microsoft — Agent Framework Overview](https://learn.microsoft.com/en-us/agent-framework/overview/)· learn.microsoft.com - [Model Context Protocol Specification](https://modelcontextprotocol.io/)· modelcontextprotocol.io - [LangChain — LangGraph Product Page](https://www.langchain.com/langgraph)· langchain.com ## Related services [AI agents development and deployment ](/en/ai-agents) ## Related reading [pillar ### Best AI Agent Frameworks 2026: The Complete Enterprise Guide The pillar comparison across every serious agent framework in 2026 — LangGraph, Claude Agent SDK, CrewAI, LlamaIndex, Pydantic AI, OpenAI Agents SDK, Microsoft Agent Framework, and Google ADK. ](/en/insights/best-ai-agent-frameworks-2026)[comparison ### LangGraph vs Claude Agent SDK 2026: The Head-to-Head Buyer's Guide The two most-shortlisted frameworks in 2026 Nordic RFPs, compared across architecture, MCP, subagents, pricing, and production readiness. ](/en/insights/langgraph-vs-claude-agent-sdk-2026)[deepdive ### Claude Agent SDK Guide 2026: Production Anthropic Agents The Alice Labs implementation guide for the Claude Agent SDK — loop, tools, subagents, MCP, permissions, and Dynamic Workflows. ](/en/insights/claude-agent-sdk-guide-2026)[howto ### LangGraph Guide 2026: From First Graph to Production The typed-state, durable-execution, human-in-the-loop implementation guide for LangGraph 1.0 in regulated Nordic deployments. ](/en/insights/langgraph-guide-2026)[deepdive ### EU AI Act Compliance Checklist for 2026 The audit surface for high-risk AI systems in the EU — the compliance context that shapes framework portability decisions. ](/en/insights/eu-ai-act-compliance-checklist-2026)[deepdive ### AI Agent Security Risks and How to Mitigate Them Prompt injection, tool misuse, exfiltration paths, and the containment patterns you need before shipping any production agent framework. ](/en/insights/ai-agent-security-risks) ## Sources 1. [Enterprise Agentic AI Landscape 2026: Trust, Flexibility and Vendor Lock-In](https://www.kai-waehner.de/blog/2026/04/06/enterprise-agentic-ai-landscape-2026-trust-flexibility-and-vendor-lock-in/)Kai Waehner · kai-waehner.de “The 2026 buyer decision on agent frameworks is not license but portability. A 2026 Zapier survey found 81% of enterprise leaders concerned about AI vendor dependency and 74% saying loss of their AI vendor would disrupt or halt operations. Governance and evidence lock-in is the fastest-growing category of AI dependency risk.” (accessed 2026-08-03) 2. [Claude Agent SDK Overview](https://code.claude.com/docs/en/agent-sdk/overview)Anthropic · Anthropic “The Claude Agent SDK is MIT-licensed but governed by Anthropic's Commercial Terms of Service and requires an Anthropic API key. It cannot be pointed at a non-Anthropic model without forking. Deepest native MCP integration in the pack because Anthropic authored the Model Context Protocol.” (accessed 2026-08-03) 3. [crewAIInc/crewAI GitHub Repository](https://github.com/crewAIInc/crewAI)CrewAI · crewAIInc “CrewAI is MIT-licensed with 56.5k GitHub stars in 2026 — the largest agent-framework repo by stars. Role-based multi-agent crews plus event-driven flows. CrewAI claims 65% of Fortune 500 as users of the Basic tier.” (accessed 2026-08-03) 4. [langchain-ai/langgraph GitHub Repository](https://github.com/langchain-ai/langgraph)LangChain · LangChain “LangGraph is MIT-licensed with ~15k+ GitHub stars and 295+ contributors in 2026. Graph-based durable execution with checkpointing to in-memory, SQLite, or Postgres. Production references include Klarna, Replit, Elastic, Uber, LinkedIn, Cloudflare, and NVIDIA.” (accessed 2026-08-03) 5. [run-llama/llama\_index GitHub Repository](https://github.com/run-llama/llama_index)LlamaIndex · run-llama “LlamaIndex is MIT-licensed with 51.3k GitHub stars in 2026. Document agents and OCR-heavy retrieval sweet spot; Workflows API extends the framework into agentic orchestration for RAG-shaped agents.” (accessed 2026-08-03) 6. [pydantic/pydantic-ai GitHub Repository](https://github.com/pydantic/pydantic-ai)Pydantic · pydantic “Pydantic AI is MIT-licensed with 19k+ GitHub stars in 2026. Type-safe agents with dependency injection, structured outputs, MCP support, and first-class durable execution.” (accessed 2026-08-03) 7. [openai/openai-agents-python GitHub Repository](https://github.com/openai/openai-agents-python)OpenAI · OpenAI “The OpenAI Agents SDK is MIT-licensed. Responses API is the default backend; handoffs, guardrails, sandboxed execution, and MCP support ship in the box. Supports 100+ models via LiteLLM and any-llm adapters.” (accessed 2026-08-03) 8. [Microsoft Agent Framework Overview](https://learn.microsoft.com/en-us/agent-framework/overview/)Microsoft · Microsoft “Microsoft Agent Framework 1.0 shipped on April 3, 2026 as the direct successor to Semantic Kernel and AutoGen, which entered maintenance mode in October 2025. MIT-licensed. Supports Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI, and Ollama. Available in Python, .NET, and Go.” (accessed 2026-08-03) 9. [Google ADK — Agent Development Kit](https://google.github.io/adk-docs/)Google · Google “Google ADK is Apache 2.0 licensed and code-first. Available in Python, TypeScript, Go, Java, and Kotlin — widest language coverage in the pack. Supports Gemini, Gemma, Claude, OpenAI, Ollama, vLLM, and LiteLLM. Deployment optimized for Google Cloud Agent Platform and Vertex AI.” (accessed 2026-08-03) 10. [Model Context Protocol Specification](https://modelcontextprotocol.io/)Model Context Protocol · modelcontextprotocol.io “As of the 2026-07-28 spec, MCP is supported across Claude, ChatGPT, VS Code, Cursor, and MCPJam. Every major framework in this comparison ships first-class MCP support. MCP servers are portable across frameworks — a server written for one framework runs unchanged under another.” (accessed 2026-08-03) 11. [AI Procurement Playbook 2026](https://linesncircles.com/Blog/Enterprise/AI_Procurement_Playbook_2026)Lines and Circles · linesncircles.com “Framework license cost is a small fraction of AI agent TCO in 2026; model tokens, platform engineering, and observability dominate. Multi-vendor deployments above ~5,000 users are typically cost-justified vs single-vendor once negotiated token rates are factored in.” (accessed 2026-08-03) 12. [Enterprise Agent Framework Deployment Benchmarks](https://alicelabs.ai/en/ai-agents)Alice Labs · Alice Labs “Alice Labs benchmarked 12 production framework deployments in 2026 across Claude Agent SDK, LangGraph, and CrewAI. TCO composition: framework license <2%, model tokens 55-70%, platform engineering 20-30%, observability 5-10%. Measured migrations: AutoGen→MAF 120hrs, CrewAI→LangGraph 200hrs, OpenAI Agents SDK→Claude Agent SDK 80hrs.” (accessed 2026-08-03) Next scheduled review: 2026-11-03 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Ready to build a production AI agent on the right framework? Alice Labs has shipped 100+ enterprise AI agent systems across the Nordics and Europe. We help you pick between open source pure play (LangGraph, CrewAI, LlamaIndex, Pydantic AI) and vendor-anchored SDKs (Claude Agent SDK, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK) — and build it right the first time. [Book an AI Agent Consultation](#contact) Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fopen-source-vs-proprietary-ai-agent-frameworks-2026)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fopen-source-vs-proprietary-ai-agent-frameworks-2026&text=Open%20Source%20vs%20Proprietary%20AI%20Agent%20Frameworks%202026) ## Get in Touch! The lab usually responds within 24 hours. 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