{"slug":"best-ai-agent-framework","title":"Best framework for building AI agents","question":"What are the best frameworks for building AI agents?","verdict":"As of 2026-07-15, ChatGPT, Claude, Gemini and Grok collectively rank LangGraph #1 for framework for building ai agents on ModelsAgree — a unanimous pick. The models' case: The strongest production-oriented choice for controllable, stateful agents: durable execution, checkpointing, streaming, memory, human approval, failure recovery, and. The models' main caveat: Its low-level graph-and-state model adds substantial complexity. The strongest alternative is OpenAI Agents SDK — The best-designed minimal framework — a handful of primitives (agents, handoffs, guardrails, sessions) with built-in tracing, solid docs, and usable. Source: https://modelsagree.com/best/best-ai-agent-framework (modelsagree.com, CC BY 4.0).","category":"AI Infra","url":"https://modelsagree.com/best/best-ai-agent-framework","updated":"2026-07-15","models":["ChatGPT","Claude","Gemini","Grok"],"consensus":"All 4 models rank LangGraph the top pick","disagreement":null,"combined":[{"rank":1,"product":"LangGraph","domain":"langchain.com","score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"The strongest production-oriented choice for controllable, stateful agents: durable execution, checkpointing, streaming, memory, human approval, failure recovery, and explicit graph orchestration, with broad model/tool support."},{"rank":2,"product":"OpenAI Agents SDK","domain":"openai.com","score":9,"appearances":3,"modelRanks":{"ChatGPT":3,"Claude":2,"Grok":4},"reason":"The best-designed minimal framework — a handful of primitives (agents, handoffs, guardrails, sessions) with built-in tracing, solid docs, and usable with non-OpenAI models via LiteLLM; the fastest path from idea to working multi-agent system for most developers. Near-tie with #3."},{"rank":3,"product":"Pydantic AI","domain":"ai.pydantic.dev","score":9,"appearances":3,"modelRanks":{"ChatGPT":2,"Claude":4,"Gemini":3},"reason":"Excellent Python ergonomics, model independence, rigorous typed inputs and outputs, dependency injection, structured validation, testing, evaluations, and durable-execution integrations make reliable agents unusually easy to maintain."},{"rank":4,"product":"Microsoft Agent Framework","domain":"microsoft.com","score":8,"appearances":3,"modelRanks":{"Claude":5,"Gemini":2,"Grok":3},"reason":"Enterprise-grade session state management, native multi-language support (Python, .NET, Go), and robust telemetry and governance, serving as the official successor to AutoGen and Semantic Kernel."},{"rank":5,"product":"CrewAI","domain":"crewai.com","score":6,"appearances":3,"modelRanks":{"ChatGPT":5,"Gemini":5,"Grok":2},"reason":"Exceptional for role-based multi-agent teams with intuitive task delegation, fast prototyping of collaborative workflows, and easy integration for business use cases"},{"rank":6,"product":"Claude Agent SDK","domain":"anthropic.com","score":3,"appearances":1,"modelRanks":{"Claude":3},"reason":"The harness behind Claude Code, so its agent loop (tool use, file/computer access, subagents, MCP-native tooling, permissioning, context compaction) is proven by the most heavily used agentic product in the market; strongest choice for coding, computer-use, and long-horizon autonomous tasks. Assumption: I discount this pick for my own conflict of interest as an Anthropic model, and it still earns top-3 on adoption and capability."},{"rank":7,"product":"Google Agent Development Kit","domain":"google.github.io","score":2,"appearances":1,"modelRanks":{"ChatGPT":4},"reason":"Strong end-to-end support for hierarchical multi-agent systems, deterministic workflow agents, sessions, evaluation, debugging, deployment, MCP, and A2A interoperability; especially compelling for Google Cloud and Gemini users while remaining open-source and model-flexible."},{"rank":8,"product":"Mastra","domain":"mastra.ai","score":2,"appearances":1,"modelRanks":{"Gemini":4},"reason":"Complete TypeScript-first backend architecture for agents, offering built-in workflows, memory, and local observability without requiring Python bridge APIs."},{"rank":9,"product":"LlamaIndex Workflows","domain":"llamaindex.ai","score":1,"appearances":1,"modelRanks":{"Grok":5},"reason":"Superior for RAG-grounded and knowledge-intensive agents with robust retrieval, indexing, and agentic workflows on private data"}],"perModel":{"ChatGPT":[{"rank":1,"product":"LangGraph","reason":"The strongest production-oriented choice for controllable, stateful agents: durable execution, checkpointing, streaming, memory, human approval, failure recovery, and explicit graph orchestration, with broad model/tool support.","fix":"Its low-level graph-and-state model adds substantial complexity; it is not the fastest route to a simple tool-calling agent."},{"rank":2,"product":"Pydantic AI","reason":"Excellent Python ergonomics, model independence, rigorous typed inputs and outputs, dependency injection, structured validation, testing, evaluations, and durable-execution integrations make reliable agents unusually easy to maintain.","fix":"Its orchestration and ecosystem are less mature than LangGraph’s for highly complex, long-running multi-agent systems."},{"rank":3,"product":"OpenAI Agents SDK","reason":"A near-tie with Pydantic AI for typical projects; its small API, excellent tracing, guardrails, sessions, MCP tools, handoffs, and polished OpenAI integration provide the best simplicity-to-capability ratio when OpenAI models are acceptable.","fix":"Its design and first-party operational advantages center on OpenAI, so it is not the best foundation for strict provider neutrality."},{"rank":4,"product":"Google Agent Development Kit","reason":"Strong end-to-end support for hierarchical multi-agent systems, deterministic workflow agents, sessions, evaluation, debugging, deployment, MCP, and A2A interoperability; especially compelling for Google Cloud and Gemini users while remaining open-source and model-flexible.","fix":"Its greatest production advantages assume Google Cloud infrastructure, and the framework is younger and less battle-tested than LangGraph."},{"rank":5,"product":"CrewAI","reason":"The clearest high-level abstraction for role-based agent teams, with approachable crews, tasks, flows, persistence, guardrails, knowledge, and operational tooling that let practitioners ship multi-agent automations quickly.","fix":"Its opinionated role-playing abstractions can add token overhead and obscure control flow, making it a poor fit for tightly engineered or latency-sensitive systems."}],"Claude":[{"rank":1,"product":"LangGraph","reason":"The closest thing to a production standard — explicit graph/state-machine control over agent loops, durable execution with checkpointing, human-in-the-loop interrupts, streaming, and first-class observability via LangSmith; battle-tested at scale (LinkedIn, Uber, Replit deployments) and model-agnostic. Assumption: the typical practitioner is shipping to production, where controllability and recoverability outweigh quick-start ergonomics.","fix":"Heavy abstraction for simple tool-loop agents — the graph mental model and LangChain-ecosystem coupling are real overhead if you just need a single agent calling tools."},{"rank":2,"product":"OpenAI Agents SDK","reason":"The best-designed minimal framework — a handful of primitives (agents, handoffs, guardrails, sessions) with built-in tracing, solid docs, and usable with non-OpenAI models via LiteLLM; the fastest path from idea to working multi-agent system for most developers. Near-tie with #3.","fix":"Deepest features (tracing, Responses API integration, hosted tools) assume the OpenAI stack, and it deliberately lacks the durable-execution and complex-orchestration machinery long-running production agents need."},{"rank":3,"product":"Claude Agent SDK","reason":"The harness behind Claude Code, so its agent loop (tool use, file/computer access, subagents, MCP-native tooling, permissioning, context compaction) is proven by the most heavily used agentic product in the market; strongest choice for coding, computer-use, and long-horizon autonomous tasks. Assumption: I discount this pick for my own conflict of interest as an Anthropic model, and it still earns top-3 on adoption and capability.","fix":"Anthropic-centric and opinionated — you inherit Claude Code's loop design rather than composing your own, and it is not a neutral multi-provider orchestration layer."},{"rank":4,"product":"Pydantic AI","reason":"The type-safety-first option — structured outputs validated by Pydantic, dependency injection for testable tools, genuinely model-agnostic, clean integration with Logfire for observability; the best developer experience for Python teams that treat agents as normal software. Near-tie with #5.","fix":"Thinner multi-agent orchestration and smaller ecosystem than the leaders — you assemble more yourself for complex coordinated systems."},{"rank":5,"product":"Microsoft Agent Framework","reason":"The AutoGen + Semantic Kernel consolidation gives enterprises one supported path with .NET and Python parity, Azure AI Foundry integration, OpenTelemetry-based observability, and long-running workflow support — the default for organizations already on the Microsoft stack.","fix":"Gravitates toward Azure and enterprise ceremony; outside that ecosystem it offers little over lighter frameworks and its post-merger APIs are still settling."}],"Gemini":[{"rank":1,"product":"LangGraph","reason":"High control over stateful, cyclical agent logic using an explicit directed graph architecture, coupled with built-in persistence and deep integration with LangSmith for production observability.","fix":"Deep learning curve and verbose state-schema boilerplate, making it over-engineered for simple or non-cyclical workflows."},{"rank":2,"product":"Microsoft Agent Framework","reason":"Enterprise-grade session state management, native multi-language support (Python, .NET, Go), and robust telemetry and governance, serving as the official successor to AutoGen and Semantic Kernel.","fix":"Heavy vendor lock-in to the Microsoft ecosystem and rigid abstractions that complicate small-scale or rapid prototyping projects."},{"rank":3,"product":"Pydantic AI","reason":"Excellent type safety, structured data validation, and type-safe dependency injection built directly on Pydantic and Python's native control flow.","fix":"Restricted to Python environments and suboptimal for unstructured, open-ended autonomous tasks that do not benefit from rigid schema definitions."},{"rank":4,"product":"Mastra","reason":"Complete TypeScript-first backend architecture for agents, offering built-in workflows, memory, and local observability without requiring Python bridge APIs.","fix":"Lacks the mature multi-agent orchestration ecosystems of Python frameworks and is overkill for basic client-side streaming chat interfaces."},{"rank":5,"product":"CrewAI","reason":"Exceptional developer velocity for multi-agent collaboration, allowing role-based crews to be established with minimal boilerplate code.","fix":"Opaque execution flow and high risk of runaway token consumption due to autonomous delegation loops and lack of explicit state-machine control."}],"Grok":[{"rank":1,"product":"LangGraph","reason":"Graph-based stateful orchestration with persistent memory, cycles for complex workflows, production-grade reliability, observability via LangSmith, and strong multi-agent support making it the de facto standard for scalable agents","fix":"Simplify the learning curve and reduce boilerplate for rapid prototyping"},{"rank":2,"product":"CrewAI","reason":"Exceptional for role-based multi-agent teams with intuitive task delegation, fast prototyping of collaborative workflows, and easy integration for business use cases","fix":"Strengthen production reliability, error handling, and long-running state management"},{"rank":3,"product":"Microsoft Agent Framework","reason":"Unified enterprise solution merging AutoGen and Semantic Kernel strengths with .NET integration, async multi-agent conversations, security, and governance for large orgs","fix":"Broaden model-agnostic flexibility beyond Microsoft ecosystem"},{"rank":4,"product":"OpenAI Agents SDK","reason":"Deep native integration with OpenAI models, seamless tool calling, responses API, and rapid development for high-performance single or multi-agent systems","fix":"Improve openness and reduce vendor lock-in for multi-provider use"},{"rank":5,"product":"LlamaIndex Workflows","reason":"Superior for RAG-grounded and knowledge-intensive agents with robust retrieval, indexing, and agentic workflows on private data","fix":"Enhance general multi-agent orchestration beyond retrieval-focused strengths"}]},"missedByModel":{"ChatGPT":[{"product":"Microsoft Agent Framework","reason":"powerful typed workflows, durability, and .NET/Python enterprise integration, but still in public preview and therefore a riskier default"},{"product":"LlamaIndex Agents","reason":"excellent when agents are primarily retrieval-and-data systems, but less compelling as a general orchestration framework"}],"Claude":[{"product":"CrewAI","reason":"huge adoption for role-based multi-agent prototyping, but its heavy abstractions and reliability in production trail its popularity"}],"Gemini":[{"product":"LlamaIndex Workflows","reason":"offers flexible event-driven orchestration but is primarily optimized for data-centric RAG systems rather than general-purpose multi-agent routing"},{"product":"Vercel AI SDK","reason":"superb for frontend streaming and client-side chat hooks but lacks native backend agentic primitives like durable workflow graphs or state persistence"}],"Grok":[{"product":"Claude Agent SDK","reason":"strong Anthropic integration and hierarchical agents but narrower ecosystem"},{"product":"Google ADK","reason":"excellent Vertex/Gemini multimodal but platform-tied"}]}}