--- title: "Open Source AI Agent Frameworks Comparison 2026" url: https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026 description: "We benchmarked 6 open source AI agent frameworks across 18 production deployments. LangGraph vs CrewAI head-to-head, plus AutoGen, LlamaIndex, Semantic Kernel, Pydantic AI scored." lang: en --- Alice Labs (https://alicelabs.ai/en/) Services What we do (https://alicelabs.ai/#welcome)About Alice (https://alicelabs.ai/#who-we-are)Case (https://alicelabs.ai/en/case)Insights (https://alicelabs.ai/en/insights)Contact (https://alicelabs.ai/#email-form) AI Agents Comparison Fresh Last reviewed: 17 September 2026 · 5d ago # Open Source AI Agent Frameworks Comparison 2026 ## TL;DR Quick Answer Cited by AI > In 2026, LangGraph (16K+ stars, MIT) and CrewAI (28K+ stars, MIT) lead the open source agent framework category. LangGraph wins on stateful control and observability via LangSmith. CrewAI wins on time-to-prototype for role-based crews. Both score 8.5+ on the Alice Labs Production Score across 18 deployments. An engineer's head-to-head of LangGraph vs CrewAI — the two open source agent frameworks dominating production in 2026 — plus a scored review of AutoGen, LlamaIndex, Semantic Kernel, and Pydantic AI. Overall winner ## LangGraph Graph-based state machines for stateful, branching agent flows GitHub stars 16K+ License MIT Alice Labs Score 8.9 / 10 Dimensions won 9 VS ## CrewAI Role-based multi-agent collaboration with intuitive API GitHub stars 28K+ License MIT Alice Labs Score 8.6 / 10 Dimensions won 4 Open source AI agent frameworks are MIT or Apache 2.0-licensed Python and TypeScript libraries that provide primitives for building LLM-powered agents. The leading frameworks in 2026 are LangGraph, CrewAI, AutoGen/AG2, Semantic Kernel, LlamaIndex agents, and Pydantic AI. All six are free to self-host and have active maintainers as of April 2026. Image: Linus Ingemarsson - Author at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Written by Linus Ingemarsson: https://www.linkedin.com/in/linus-ingemarsson/ Image: Eric Lundberg - Reviewer at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Reviewed by Eric Lundberg Published April 28, 2026 · Updated September 17, 2026 12 min read ## Head-to-head scorecard | Dimension | LangGraph | CrewAI | Winner | | --- | --- | --- | --- | | License | MIT (open source) | MIT (open source) | = | | GitHub stars (April 2026) | 16K+ | 28K+ | B | | Core abstraction | Stateful graph (nodes + edges) | Role-based crew (agents + tasks) | = | | Learning curve | Steeper — requires graph modelling | Gentler — declarative roles, fast onboarding | B | | State management | First-class — checkpoints, durable execution, time travel | Implicit — task outputs flow between agents | A | | Multi-agent support | Multi-agent via graph composition (supervisor, hierarchical) | Native — sequential, hierarchical, and consensus crews | B | | Tool calling | LangChain tool ecosystem (hundreds of pre-built tools) | Native tools + LangChain interop | A | | Observability | LangSmith — traces, evals, prompt versioning, replay | CrewAI built-in logging + AgentOps / Langfuse integrations | A | | Production deployment | LangGraph Platform (paid) or self-host with checkpointers | CrewAI Enterprise (paid) or self-host | A | | Cost (self-host) | Free — pay only for LLM tokens and infra | Free — pay only for LLM tokens and infra | = | | Python / TypeScript support | Python and JavaScript / TypeScript SDKs | Python only (as of April 2026) | A | | Schema enforcement | Pydantic-based state schemas, structured outputs | Pydantic outputs supported, less central to the API | A | | Ecosystem maturity | Inherits LangChain ecosystem (largest in agent space) | Independent ecosystem, growing fast — fewer integrations | A | | Average dev time to first agent (Alice Labs data) | ~5 days for a stateful single-agent workflow | ~2 days for a 3-role multi-agent crew | B | | Debugging tools | LangSmith time-travel, step replay, prompt diff | Verbose mode + logs; AgentOps for richer traces | A | | Alice Labs Production Score (out of 10) | 8.9 — tops on observability and state control | 8.6 — tops on developer experience and prototype velocity | A | | Total | 9 wins | 4 wins | 3 ties | ## Key Takeaways - LangGraph and CrewAI are the two open source agent frameworks we ship most often in production — every other framework is situational. - LangGraph's graph-based state machines are the safest pick for stateful, branching workflows that need explicit control and human-in-the-loop. - CrewAI's role-based API gets a multi-agent prototype to working state faster than any other framework — typically 2-3 days vs 1-2 weeks. - All six frameworks (LangGraph, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, Pydantic AI) are MIT or Apache 2.0 licensed and free to self-host. - The Alice Labs Production Score weights developer experience, production readiness, observability, scalability, and ecosystem maturity equally. - Across 18 Alice Labs deployments, LangGraph scored highest on observability and production readiness; CrewAI scored highest on developer experience. 01 / 05 Dimension ## The Open Source AI Agent Landscape in 2026 In short Six open source AI agent frameworks dominate production work in 2026: LangGraph, CrewAI, AutoGen / AG2, LlamaIndex, Semantic Kernel, and Pydantic AI. All are MIT or Apache 2.0 licensed. LangGraph and CrewAI are the two we ship most often. The open source agent space consolidated in 2025. By April 2026, six frameworks account for the overwhelming majority of production agent deployments we see. All six are free to self-host. All ship under permissive licenses (MIT or Apache 2.0). The differences that matter are architectural — not licensing. - **LangGraph** — graph-based state machines. 16K+ GitHub stars. MIT license. Built by the LangChain team. - **CrewAI** — role-based multi-agent collaboration. 28K+ GitHub stars. MIT license. Independent of LangChain. - **AutoGen / AG2** — Microsoft Research origin. Split into AutoGen v0.4+ (Microsoft) and AG2 (community fork) in late 2024. - **LlamaIndex agents** — agent primitives built on top of the LlamaIndex data framework. RAG-native by design. - **Semantic Kernel** — Microsoft's enterprise SDK. C#, Python, and Java parity. Strongest fit for .NET stacks. - **Pydantic AI** — type-safe Python agents from the Pydantic team. Released late 2024, gaining production traction in 2025-2026. The rest of this article focuses on LangGraph vs CrewAI — the two frameworks we recommend for most new open source agent projects in 2026. Compare implementation partners in our roundup of ai agent development companies (https://alicelabs.ai/en/insights/ai-agent-development-companies-2026). 02 / 05 Dimension ## The Alice Labs Production Score Methodology In short The Alice Labs Production Score is a proprietary 0-10 score combining developer experience, production readiness, observability, scalability, and ecosystem maturity. It is calibrated against 18 real production agent deployments shipped between 2024 and 2026. We needed a way to compare agent frameworks beyond GitHub stars and marketing claims. The Alice Labs Production Score is the result. The score weights five dimensions equally — each contributes up to 2.0 points to a 10.0 total. Every score is calibrated against actual deployment data, not vibes. - **Developer experience (DX) — 2.0 pts.** How fast can a competent engineer ship the first working agent? Measured in days from kickoff to demo. - **Production readiness — 2.0 pts.** Native support for retries, checkpoints, idempotency, durable execution, and graceful degradation. - **Observability — 2.0 pts.** Quality of traces, evals, prompt versioning, replay, and integration with tools like LangSmith and Langfuse. - **Scalability — 2.0 pts.** Performance under concurrent users, session isolation, multi-tenant safety, and horizontal scaling story. - **Ecosystem maturity — 2.0 pts.** Pre-built integrations, community size, release cadence, and long-tail of community recipes. The dataset behind the score: 18 production agent deployments shipped by Alice Labs between 2024 and 2026 across LangGraph, CrewAI, AutoGen, and Semantic Kernel. Industries covered include financial services, media, and the public sector. Frameworks we have not personally shipped to production (LlamaIndex agents, Pydantic AI) are scored conservatively from prototype work and public references — and we label that distinction explicitly in our scoring. Image: Linus Ingemarsson (https://alicelabs.ai/images/linus-ingemarsson.png) Image: Eric Lundberg (https://alicelabs.ai/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. Book a Discovery Call 03 / 05 Dimension ## Beyond LangGraph and CrewAI: The Other 4 Frameworks In short AutoGen / AG2, LlamaIndex agents, Semantic Kernel, and Pydantic AI each excel in narrower contexts: research-style conversations, RAG-native agents, .NET enterprise stacks, and type-safe Python respectively. They are situational picks rather than defaults. LangGraph and CrewAI cover the majority of production needs. The remaining four frameworks each shine in a specific context. ### AutoGen / AG2 Microsoft Research's multi-agent conversation framework. In late 2024 it split: Microsoft continued an AutoGen v0.4+ rewrite, while the community forked the proven v0.2 lineage as AG2 (ag2.ai). Best for research-style agent conversations, code-execution agents, and group chat patterns. Picking between AutoGen v0.4+ and AG2 should be deliberate — the APIs diverge. ### LlamaIndex (agents) LlamaIndex started as a data framework for LLMs and now ships first-class agent primitives. The strongest pick when the agent's primary value is reasoning over indexed private data. Best for RAG-first agents — agents whose job is to query and synthesize from your knowledge base, vector DB, or document store. ### Microsoft Semantic Kernel Microsoft's enterprise AI orchestration SDK with C#, Python, and Java parity. The best fit when your enterprise lives on .NET / Azure infrastructure. Best for enterprises on Microsoft / Azure that need first-class C# support, tight Azure OpenAI integration, and a plugin model that maps to enterprise governance. ### Pydantic AI Type-safe agent framework from the Pydantic team. Released late 2024, it brings FastAPI-style ergonomics — strict types, dependency injection, structured responses — to agent code. Best for Python teams that prioritize type safety and predictable IO. Newer than other frameworks, with a smaller ecosystem but cleanest DX in the type-safe niche. Alice Labs practitioner team ## Stuck between LangGraph and CrewAI? We've shipped 18 production agent deployments across LangGraph, CrewAI, AutoGen, and Semantic Kernel. Book a 30-minute architecture call and we'll help you pick the right open source stack for your use case. Book an architecture call 04 / 05 Dimension ## Production Deployment Patterns from Alice Labs Engagements In short Across 18 Alice Labs production agent deployments, three patterns dominate: LangGraph + LangSmith for stateful workflows with HITL, CrewAI for content and research crews, and Semantic Kernel for .NET-bound enterprises. Observability tooling is paired with the framework from day one. Looking at the 18 production agents Alice Labs has shipped between 2024 and 2026 — the same evidence base that shapes our enterprise AI agents (https://alicelabs.ai/en/ai-agents) practice — three deployment patterns recur often enough to be considered defaults. ### Pattern 1: LangGraph + LangSmith for stateful workflows Used in roughly 9 of 18 deployments — most often where the agent must survive crashes, escalate to humans, or resume across sessions. LangGraph models the workflow as a graph; LangSmith captures every trace; checkpoints persist state to Postgres. Postgres-backed checkpointers are the production default. ### Pattern 2: CrewAI for content and research crews Used in roughly 5 of 18 deployments — content generation, research synthesis, and coordinated drafting workflows. CrewAI defines researcher, writer, and reviewer agents declaratively. We pair it with Langfuse or AgentOps for observability — CrewAI's built-in logging is good enough for development, not for production debugging. ### Pattern 3: Semantic Kernel for .NET enterprises Used in roughly 3 of 18 deployments — all of them on Azure with C# back ends and enterprise governance requirements. Semantic Kernel slots into existing .NET service architectures and integrates natively with Azure OpenAI, Azure AI Search, and Application Insights. The plugin model maps cleanly to enterprise approval workflows. The remaining deployment used AutoGen for a research-style multi-agent prototype that we later rebuilt on LangGraph for production hardening. 05 / 05 Dimension ## When to Use Which: A Decision Tree In short Start from your dominant constraint. If you need explicit control and durable state, pick LangGraph. If you need a fast role-based prototype, pick CrewAI. If you're on .NET, pick Semantic Kernel. If you're RAG-first, pick LlamaIndex. If you prioritize Python type safety, pick Pydantic AI. We use a single decision rule across client engagements: identify the dominant constraint, then pick the framework whose core abstraction matches it. - **Need explicit control, durable state, observability?** LangGraph + LangSmith. The default for stateful production workflows. - **Need a working multi-agent prototype in days?** CrewAI. Define roles, assign tasks, ship. - **Building research-style conversational agents?** AutoGen v0.4+ or AG2 — pick deliberately based on which API your community uses. - **Stack is .NET / C# / Azure?** Semantic Kernel. C# parity, Azure integration, enterprise plugin model. - **Agent's main job is reasoning over private data?** LlamaIndex. Retrievers, indexes, and query engines are first-class. - **Python team that values strict types?** Pydantic AI. FastAPI ergonomics applied to agent code. One last rule: framework switching is expensive. Plan to commit to your pick for at least the first year of production. Prompts and tool definitions port between frameworks; orchestration code does not. Alice Labs practitioner team Book a Discovery Call ## Which should you choose? ### Choose LangGraph if… - Your workflow has explicit branching, retries, or human-in-the-loop approvals. - You need durable state and the ability to resume agents after a crash or restart. - Observability is non-negotiable — you need traces, evals, and replay (LangSmith). - Your team ships in TypeScript / JavaScript as well as Python. - You're building a long-running agent that must survive across sessions and tenants. ### Choose CrewAI if… - Your work decomposes cleanly into roles like researcher, writer, and reviewer. - You need a working multi-agent prototype in days, not weeks. - Your team prefers declarative agent definitions over graph modelling. - You're shipping a Python-only stack and want the lightest dependency footprint. - Your use case is content generation, research synthesis, or coordinated drafting. ## Our verdict Choose LangGraph when you need explicit control, durable state, and production-grade observability — it is the framework with the highest Alice Labs Production Score (8.9). Choose CrewAI when work decomposes naturally into roles and time-to-prototype is your dominant constraint — it scores 8.6 and ships demos faster than any other framework we use. ## About the Authors & Reviewers Written by Image: Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Linus Ingemarsson (https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & 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/ Linus Ingemarsson on LinkedIn: https://www.linkedin.com/in/linus-ingemarsson/ Email Linus Ingemarsson: linus@alicelabs.ai Reviewed by September 17, 2026 Image: Eric Lundberg - Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Eric Lundberg Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs Eric Lundberg on LinkedIn: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Email Eric Lundberg: eric@alicelabs.ai Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### Which is the best open source AI agent framework in 2026? There is no single best framework — they target different problems. For most new production projects on the open source side, LangGraph is the safest pick (Alice Labs Production Score 8.9). For fast multi-agent prototypes, CrewAI is the fastest (score 8.6). All six leading frameworks are MIT or Apache 2.0 licensed. ### Are all the frameworks in this comparison fully open source? Yes. LangGraph (MIT), CrewAI (MIT), AutoGen (CC-BY-4.0 / Apache 2.0 depending on fork), LlamaIndex (MIT), Semantic Kernel (MIT), and Pydantic AI (MIT) are all permissively licensed. Some offer paid managed platforms (LangGraph Platform, CrewAI Enterprise, LlamaCloud) on top of the open source core. ### LangGraph vs CrewAI — which should I pick? Pick LangGraph when you need explicit control, durable state, branching, or human-in-the-loop. Pick CrewAI when work decomposes into roles (researcher, writer, reviewer) and time-to-prototype matters. Across 18 Alice Labs deployments, LangGraph scored 8.9 on production readiness; CrewAI scored 8.6 with the highest developer experience score. ### What is the Alice Labs Production Score? The Alice Labs Production Score is a proprietary 0-10 score combining developer experience, production readiness, observability, scalability, and ecosystem maturity. Each dimension contributes up to 2.0 points. The score is calibrated against 18 production agent deployments Alice Labs shipped between 2024 and 2026. ### What's the difference between AutoGen and AG2? AG2 is the community continuation of the original Microsoft AutoGen v0.2 lineage, hosted at ag2.ai. Microsoft has continued the AutoGen name with a v0.4+ rewrite that uses a different API. Both are open source. Choose deliberately based on which API you started with and which community you depend on. ### Can I switch agent frameworks later? Partially. The LLM-facing prompts and tool definitions tend to be portable. The orchestration layer — state, control flow, multi-agent patterns — is framework-specific and requires rewrite. Plan to commit to one framework for at least the first year of production. ### Do open source frameworks support TypeScript / JavaScript? LangGraph ships first-class Python and JavaScript / TypeScript SDKs. Semantic Kernel ships C#, Python, and Java. CrewAI, LlamaIndex agents, AutoGen / AG2, and Pydantic AI are Python-only as of April 2026. If TypeScript is a hard requirement, LangGraph is the only mainstream open source choice. ### Which framework has the best observability story? LangGraph paired with LangSmith is the strongest production observability story we have shipped — traces, step replay, prompt versioning, and evals are all first-class. CrewAI's built-in logging is good for development; in production we pair it with Langfuse or AgentOps. Semantic Kernel integrates natively with Azure Application Insights. Previous in AI Agents ### What Is Agentic AI? How It Differs from Generative AI https://alicelabs.ai/en/insights/what-is-agentic-ai Next in AI Agents ### Best AI Coding Agents 2026: 7 Tools Developers Actually Use https://alicelabs.ai/en/insights/best-ai-coding-agents-2026 ## Further reading - LangGraph documentation (https://langchain-ai.github.io/langgraph/)· langchain-ai.github.io - CrewAI documentation (https://docs.crewai.com/)· docs.crewai.com - AG2 (community continuation of AutoGen v0.2) (https://ag2.ai/)· ag2.ai - Pydantic AI documentation (https://ai.pydantic.dev/)· ai.pydantic.dev - Semantic Kernel documentation (https://learn.microsoft.com/semantic-kernel)· learn.microsoft.com ## Related services AI Agent Development Services Alice Labs ships custom agents on LangGraph, CrewAI, AutoGen, and Semantic Kernel.: https://alicelabs.ai/en/ai-agents ## Related reading listicle ### Best AI Agent Frameworks 2026: 6 Compared The pillar comparison — wider scope including managed platforms. 11 min https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026 comparison ### LangGraph vs CrewAI vs AutoGen Direct comparison of the three most-adopted open-source agent frameworks. 10 min https://alicelabs.ai/en/insights/langgraph-vs-crewai-vs-autogen deep dive ### LangGraph Guide 2026 Graph-based agent workflows in LangGraph — official patterns and production use. 11 min https://alicelabs.ai/en/insights/langgraph-guide-2026 deep dive ### CrewAI Guide 2026 Role-based multi-agent collaboration with the CrewAI official multi-agent platform. 10 min https://alicelabs.ai/en/insights/crewai-guide-2026 quick take ### What Is an AI Agent? The definitional companion to this comparison. 6 min https://alicelabs.ai/en/insights/what-is-an-ai-agent deep dive ### Why AI Projects Fail: 7 Root Causes Failure patterns that no framework can fix on its own. 10 min https://alicelabs.ai/en/insights/why-ai-projects-fail ## Sources 1. LangGraph (langchain-ai/langgraph) — official repository (https://github.com/langchain-ai/langgraph)(accessed 2026-04-28) 2. LangGraph documentation — LangChain AI (https://langchain-ai.github.io/langgraph/)(accessed 2026-04-28) 3. CrewAI (crewAIInc/crewAI) — official repository (https://github.com/crewAIInc/crewAI)(accessed 2026-04-28) 4. CrewAI documentation (https://docs.crewai.com/)(accessed 2026-04-28) 5. AG2 (ag2ai/ag2) — community continuation of AutoGen v0.2 lineage (https://github.com/ag2ai/ag2)(accessed 2026-04-28) 6. Microsoft AutoGen (microsoft/autogen) — v0.4+ rewrite (https://github.com/microsoft/autogen)(accessed 2026-04-28) 7. LlamaIndex (run-llama/llama_index) — official repository (https://github.com/run-llama/llama_index)(accessed 2026-04-28) 8. Microsoft Semantic Kernel — official documentation (https://learn.microsoft.com/semantic-kernel/overview/)(accessed 2026-04-28) 9. Pydantic AI (pydantic/pydantic-ai) — official repository (https://github.com/pydantic/pydantic-ai)(accessed 2026-04-28) 10. Alice Labs internal production scoring — 18 agent deployments (2024-2026) (accessed 2026-04-28) Next scheduled review: 2026-12-16 Alice Labs practitioner team Book a Discovery Call Share Share on LinkedIn (https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fopen-source-ai-agent-frameworks-comparison-2026)Share on X (https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fopen-source-ai-agent-frameworks-comparison-2026&text=Open%20Source%20AI%20Agent%20Frameworks%20Comparison%202026) ## Get in Touch! The lab usually responds within 24 hours. Need help with AI? Get in touch (https://alicelabs.ai/en/ai-consulting-services#contact-form) ## Structured data ```json [ { "@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. Architects AI agent systems and automation in production for clients across financial services, media, and the public sector.", "url": "https://alicelabs.ai/en/linus-ingemarsson", "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" ], "knowsAbout": [ "AI agents", "agent orchestration", "AI implementation", "LangGraph", "RAG systems", "AI strategy", "enterprise AI", "AI search optimization", "LLMO", "Nordic AI ecosystem" ], "worksFor": { "@id": "https://alicelabs.ai/#organization" } }, { "@type": "Person", "@id": "https://alicelabs.ai/#eric", "name": "Eric Lundberg", "givenName": "Eric", "familyName": "Lundberg", "jobTitle": "Co-Founder", "description": "Co-founder of Alice Labs. Designs AI automation systems and agent workflows that remove repetitive work and make day-to-day operations more reliable.", "url": "https://alicelabs.ai/en/eric-lundberg", "sameAs": [ "https://www.linkedin.com/in/eric-lundberg-3530451bb/", "https://www.wikidata.org/wiki/Q140369978" ], "knowsAbout": [ "AI automation", "agent workflows", "AI integrations", "process automation", "knowledge systems", "AI engineering", "enterprise AI", "Nordic AI ecosystem" ], "worksFor": { "@id": "https://alicelabs.ai/#organization" } }, { "@type": "Person", "@id": "https://alicelabs.ai/#alice", "name": "Alice Holmgren", "givenName": "Alice", "familyName": "Holmgren", "jobTitle": "CEO", "description": "CEO of Alice Labs. Leads strategy and growth across the Nordic AI consulting market.", "url": "https://alicelabs.ai/en/alice-holmgren", "knowsAbout": [ "AI strategy", "AI consulting leadership", "business development", "Nordic AI ecosystem", "enterprise AI adoption", "AI program management" ], "worksFor": { "@id": "https://alicelabs.ai/#organization" } }, { "@type": [ "LocalBusiness", "ProfessionalService" ], "@id": "https://alicelabs.ai/#localbusiness", "name": "Alice Labs", "description": "AI-konsult i Stockholm. Vi hjälper företag implementera AI - från strategi till skalning. Boka möte för en kostnadsfri AI-genomgång.", "url": "https://alicelabs.ai", "logo": { "@id": "https://alicelabs.ai/#logo" }, "image": { "@id": "https://alicelabs.ai/#logo" }, "telephone": "+46734157476", "email": "hej@alicelabs.ai", "priceRange": "$$$", "currenciesAccepted": "SEK, EUR, USD", "paymentAccepted": "Invoice", "address": { "@type": "PostalAddress", "streetAddress": "Hammarbybacken 27", "addressLocality": "Stockholm", "postalCode": "120 30", "addressRegion": "Stockholms län", "addressCountry": "SE" }, "geo": { "@type": "GeoCoordinates", "latitude": 59.3018, "longitude": 18.1003 }, "areaServed": [ { "@type": "City", "name": "Stockholm" }, { "@type": "City", "name": "Göteborg" }, { "@type": "City", "name": "Malmö" }, { "@type": "City", "name": "Uppsala" }, { "@type": "Country", "name": "Sweden" } ], "openingHoursSpecification": [ { "@type": "OpeningHoursSpecification", "dayOfWeek": [ "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" ], "opens": "08:00", "closes": "18:00" } ], "hasOfferCatalog": { "@type": "OfferCatalog", "name": "AI-tjänster", "itemListElement": [ { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-konsult" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-strategi" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-implementation" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-utbildning" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-agenter" } }, { "@type": "Offer", "itemOffered": { "@type": "Service", "name": "AI-automation" } } ] }, "knowsAbout": [ "AI-konsult", "AI-strategi", "AI-implementation", "AI-utbildning", "AI-agenter", "AI-automation", "Generative AI", "Machine learning", "RAG", "Large language models", "AI governance" ], "parentOrganization": { "@id": "https://alicelabs.ai/#organization" }, "sameAs": [ "https://www.linkedin.com/company/alicelabsai" ] }, { "@type": "WebSite", "@id": "https://alicelabs.ai/#website", "url": "https://alicelabs.ai", "name": "Alice Labs", "alternateName": [ "Alice Labs AB" ], "description": "AI consulting, implementation and training for businesses.", "publisher": { "@id": "https://alicelabs.ai/#organization" }, "inLanguage": [ "sv-SE", "en-US" ], "potentialAction": { "@type": "SearchAction", "target": { "@type": "EntryPoint", "urlTemplate": "https://alicelabs.ai/?q={search_term_string}" }, "query-input": "required name=search_term_string" } } ] }, { "@context": "https://schema.org", "@graph": [ { "@type": "TechArticle", "@id": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#article", "headline": "Open Source AI Agent Frameworks Comparison 2026", "description": "We benchmarked 6 open source AI agent frameworks across 18 production deployments. LangGraph vs CrewAI head-to-head, plus AutoGen, LlamaIndex, Semantic Kernel, Pydantic AI scored.", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026", "datePublished": "2026-04-28", "dateModified": "2026-09-17", "expires": "2026-12-16", "author": { "@id": "https://alicelabs.ai/#linus" }, "reviewedBy": { "@id": "https://alicelabs.ai/#eric" }, "dateReviewed": "2026-09-17", "publisher": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai", "logo": { "@type": "ImageObject", "url": "https://alicelabs.ai/images/alice-logo.png" } }, "image": { "@type": "ImageObject", "@id": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#hero-image", "url": "https://alicelabs.ai/images/og/og-home.jpg", "contentUrl": "https://alicelabs.ai/images/og/og-home.jpg", "width": 1600, "height": 900, "caption": "Open Source AI Agent Frameworks Comparison 2026", "creator": { "@id": "https://alicelabs.ai/#organization" }, "representativeOfPage": true, "license": "https://alicelabs.ai/terms" }, "mainEntityOfPage": { "@type": "WebPage", "@id": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026" }, "inLanguage": "en", "articleSection": "ai-agents", "keywords": "open source ai agent frameworks comparison 2026, open source ai agents 2026, open source agent frameworks, langgraph vs crewai, autogen vs langgraph, open source ai agent framework comparison", "about": [ { "@type": "Thing", "name": "The Open Source AI Agent Landscape in 2026", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#open-source-agent-landscape-2026" }, { "@type": "Thing", "name": "The Alice Labs Production Score Methodology", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#alice-labs-production-score" }, { "@type": "Thing", "name": "Beyond LangGraph and CrewAI: The Other 4 Frameworks", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#beyond-langgraph-and-crewai" }, { "@type": "Thing", "name": "Production Deployment Patterns from Alice Labs Engagements", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#production-patterns" }, { "@type": "Thing", "name": "When to Use Which: A Decision Tree", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#decision-tree" } ], "mentions": [ { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, { "@type": "Organization", "name": "Microsoft", "url": "https://microsoft.com" }, { "@type": "Organization", "name": "OpenAI", "url": "https://openai.com" }, { "@type": "Organization", "name": "MIT", "url": "https://mit.edu" }, { "@type": "Person", "name": "Eric Lundberg", "url": "https://linkedin.com/in/eric-lundberg-3530451bb" }, { "@type": "Organization", "name": "Linus Ingemarsson" }, { "@type": "Organization", "name": "LangGraph (langchain-ai/langgraph)" }, { "@type": "Organization", "name": "LangGraph documentation" }, { "@type": "Organization", "name": "CrewAI (crewAIInc/crewAI)" }, { "@type": "Organization", "name": "CrewAI documentation" } ], "hasPart": [ { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Open Source AI Agent Landscape in 2026", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#open-source-agent-landscape-2026", "description": "Six open source AI agent frameworks dominate production work in 2026: LangGraph, CrewAI, AutoGen / AG2, LlamaIndex, Semantic Kernel, and Pydantic AI. All are MIT or Apache 2.0 licensed. LangGraph and CrewAI are the two we ship most often." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The Alice Labs Production Score Methodology", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#alice-labs-production-score", "description": "The Alice Labs Production Score is a proprietary 0-10 score combining developer experience, production readiness, observability, scalability, and ecosystem maturity. It is calibrated against 18 real production agent deployments shipped between 2024 and 2026." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Beyond LangGraph and CrewAI: The Other 4 Frameworks", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#beyond-langgraph-and-crewai", "description": "AutoGen / AG2, LlamaIndex agents, Semantic Kernel, and Pydantic AI each excel in narrower contexts: research-style conversations, RAG-native agents, .NET enterprise stacks, and type-safe Python respectively. They are situational picks rather than defaults." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Production Deployment Patterns from Alice Labs Engagements", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#production-patterns", "description": "Across 18 Alice Labs production agent deployments, three patterns dominate: LangGraph + LangSmith for stateful workflows with HITL, CrewAI for content and research crews, and Semantic Kernel for .NET-bound enterprises. Observability tooling is paired with the framework from day one." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "When to Use Which: A Decision Tree", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#decision-tree", "description": "Start from your dominant constraint. If you need explicit control and durable state, pick LangGraph. If you need a fast role-based prototype, pick CrewAI. If you're on .NET, pick Semantic Kernel. If you're RAG-first, pick LlamaIndex. If you prioritize Python type safety, pick Pydantic AI." } ], "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-ai-agent-frameworks-comparison-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 AI Agent Frameworks Comparison 2026", "item": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-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": "CEO & 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" }, { "@type": "DefinedTerm", "name": "AI consulting leadership" } ], "sameAs": [ "https://www.linkedin.com/in/linus-ingemarsson/", "https://www.wikidata.org/wiki/Q140369914" ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Which is the best open source AI agent framework in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "There is no single best framework — they target different problems. For most new production projects on the open source side, LangGraph is the safest pick (Alice Labs Production Score 8.9). For fast multi-agent prototypes, CrewAI is the fastest (score 8.6). All six leading frameworks are MIT or Apache 2.0 licensed." } }, { "@type": "Question", "name": "Are all the frameworks in this comparison fully open source?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. LangGraph (MIT), CrewAI (MIT), AutoGen (CC-BY-4.0 / Apache 2.0 depending on fork), LlamaIndex (MIT), Semantic Kernel (MIT), and Pydantic AI (MIT) are all permissively licensed. Some offer paid managed platforms (LangGraph Platform, CrewAI Enterprise, LlamaCloud) on top of the open source core." } }, { "@type": "Question", "name": "LangGraph vs CrewAI — which should I pick?", "acceptedAnswer": { "@type": "Answer", "text": "Pick LangGraph when you need explicit control, durable state, branching, or human-in-the-loop. Pick CrewAI when work decomposes into roles (researcher, writer, reviewer) and time-to-prototype matters. Across 18 Alice Labs deployments, LangGraph scored 8.9 on production readiness; CrewAI scored 8.6 with the highest developer experience score." } }, { "@type": "Question", "name": "What is the Alice Labs Production Score?", "acceptedAnswer": { "@type": "Answer", "text": "The Alice Labs Production Score is a proprietary 0-10 score combining developer experience, production readiness, observability, scalability, and ecosystem maturity. Each dimension contributes up to 2.0 points. The score is calibrated against 18 production agent deployments Alice Labs shipped between 2024 and 2026." } }, { "@type": "Question", "name": "What's the difference between AutoGen and AG2?", "acceptedAnswer": { "@type": "Answer", "text": "AG2 is the community continuation of the original Microsoft AutoGen v0.2 lineage, hosted at ag2.ai. Microsoft has continued the AutoGen name with a v0.4+ rewrite that uses a different API. Both are open source. Choose deliberately based on which API you started with and which community you depend on." } }, { "@type": "Question", "name": "Can I switch agent frameworks later?", "acceptedAnswer": { "@type": "Answer", "text": "Partially. The LLM-facing prompts and tool definitions tend to be portable. The orchestration layer — state, control flow, multi-agent patterns — is framework-specific and requires rewrite. Plan to commit to one framework for at least the first year of production." } }, { "@type": "Question", "name": "Do open source frameworks support TypeScript / JavaScript?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph ships first-class Python and JavaScript / TypeScript SDKs. Semantic Kernel ships C#, Python, and Java. CrewAI, LlamaIndex agents, AutoGen / AG2, and Pydantic AI are Python-only as of April 2026. If TypeScript is a hard requirement, LangGraph is the only mainstream open source choice." } }, { "@type": "Question", "name": "Which framework has the best observability story?", "acceptedAnswer": { "@type": "Answer", "text": "LangGraph paired with LangSmith is the strongest production observability story we have shipped — traces, step replay, prompt versioning, and evals are all first-class. CrewAI's built-in logging is good for development; in production we pair it with Langfuse or AgentOps. Semantic Kernel integrates natively with Azure Application Insights." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "Open Source AI Agent Frameworks Comparison 2026", "description": "We benchmarked 6 open source AI agent frameworks across 18 production deployments. LangGraph vs CrewAI head-to-head, plus AutoGen, LlamaIndex, Semantic Kernel, Pydantic AI scored.", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026", "datePublished": "2026-04-28", "dateModified": "2026-09-17", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "open source ai agent frameworks comparison 2026", "open source ai agents 2026", "open source agent frameworks", "langgraph vs crewai", "autogen vs langgraph", "open source ai agent framework comparison" ] }, { "@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: 6 Compared" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/langgraph-vs-crewai-vs-autogen", "name": "LangGraph vs CrewAI vs AutoGen" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/langgraph-guide-2026", "name": "LangGraph Guide 2026" }, { "@type": "ListItem", "position": 4, "url": "https://alicelabs.ai/en/insights/crewai-guide-2026", "name": "CrewAI Guide 2026" }, { "@type": "ListItem", "position": 5, "url": "https://alicelabs.ai/en/insights/what-is-an-ai-agent", "name": "What Is an AI Agent?" }, { "@type": "ListItem", "position": 6, "url": "https://alicelabs.ai/en/insights/why-ai-projects-fail", "name": "Why AI Projects Fail: 7 Root Causes" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 5, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "The Open Source AI Agent Landscape in 2026", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#open-source-agent-landscape-2026" }, { "@type": "ListItem", "position": 2, "name": "The Alice Labs Production Score Methodology", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#alice-labs-production-score" }, { "@type": "ListItem", "position": 3, "name": "Beyond LangGraph and CrewAI: The Other 4 Frameworks", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#beyond-langgraph-and-crewai" }, { "@type": "ListItem", "position": 4, "name": "Production Deployment Patterns from Alice Labs Engagements", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#production-patterns" }, { "@type": "ListItem", "position": 5, "name": "When to Use Which: A Decision Tree", "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026#decision-tree" } ] } ] }, { "@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 AI Agent Frameworks Comparison 2026" } ] } ] ```