Best framework for building AI agents
4 models · updated 2026-07-15
The verdict
LangGraph leads — All 4 models rank LangGraph the top pick.
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).
Combined ranking
- 1GPT #1Claude #1Gemini #1Grok #1
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.
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GPT 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.
Claude 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.
Gemini 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.
Grok 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
Where it falls shortper GPT Its low-level graph-and-state model adds substantial complexity; it is not the fastest route to a simple tool-calling agent.
per Claude 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.
per Gemini Deep learning curve and verbose state-schema boilerplate, making it over-engineered for simple or non-cyclical workflows.
per Grok Simplify the learning curve and reduce boilerplate for rapid prototyping
- 2GPT #3Claude #2Gemini —Grok #4
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.
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Claude 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.
GPT 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.
Grok Deep native integration with OpenAI models, seamless tool calling, responses API, and rapid development for high-performance single or multi-agent systems
Where it falls shortper GPT Its design and first-party operational advantages center on OpenAI, so it is not the best foundation for strict provider neutrality.
per Claude 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.
per Grok Improve openness and reduce vendor lock-in for multi-provider use
- 3GPT #2Claude #4Gemini #3Grok —
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.
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GPT 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.
Gemini Excellent type safety, structured data validation, and type-safe dependency injection built directly on Pydantic and Python's native control flow.
Claude 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.
Where it falls shortper GPT Its orchestration and ecosystem are less mature than LangGraph’s for highly complex, long-running multi-agent systems.
per Claude Thinner multi-agent orchestration and smaller ecosystem than the leaders — you assemble more yourself for complex coordinated systems.
per Gemini Restricted to Python environments and suboptimal for unstructured, open-ended autonomous tasks that do not benefit from rigid schema definitions.
- 4GPT —Claude #5Gemini #2Grok #3
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.
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Gemini 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.
Grok Unified enterprise solution merging AutoGen and Semantic Kernel strengths with .NET integration, async multi-agent conversations, security, and governance for large orgs
Claude 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.
Where it falls shortper Claude Gravitates toward Azure and enterprise ceremony; outside that ecosystem it offers little over lighter frameworks and its post-merger APIs are still settling.
per Gemini Heavy vendor lock-in to the Microsoft ecosystem and rigid abstractions that complicate small-scale or rapid prototyping projects.
per Grok Broaden model-agnostic flexibility beyond Microsoft ecosystem
- 5GPT #5Claude —Gemini #5Grok #2
Exceptional for role-based multi-agent teams with intuitive task delegation, fast prototyping of collaborative workflows, and easy integration for business use cases
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Grok Exceptional for role-based multi-agent teams with intuitive task delegation, fast prototyping of collaborative workflows, and easy integration for business use cases
GPT 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.
Gemini Exceptional developer velocity for multi-agent collaboration, allowing role-based crews to be established with minimal boilerplate code.
Where it falls shortper GPT 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.
per Gemini Opaque execution flow and high risk of runaway token consumption due to autonomous delegation loops and lack of explicit state-machine control.
per Grok Strengthen production reliability, error handling, and long-running state management
- 6GPT —Claude #3Gemini —Grok —
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.
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Claude 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.
Where it falls shortper Claude 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.
- 7GPT #4Claude —Gemini —Grok —
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.
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GPT 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.
Where it falls shortper GPT Its greatest production advantages assume Google Cloud infrastructure, and the framework is younger and less battle-tested than LangGraph.
- 8GPT —Claude —Gemini #4Grok —
Complete TypeScript-first backend architecture for agents, offering built-in workflows, memory, and local observability without requiring Python bridge APIs.
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Gemini Complete TypeScript-first backend architecture for agents, offering built-in workflows, memory, and local observability without requiring Python bridge APIs.
Where it falls shortper Gemini Lacks the mature multi-agent orchestration ecosystems of Python frameworks and is overkill for basic client-side streaming chat interfaces.
- 9GPT —Claude —Gemini —Grok #5
Superior for RAG-grounded and knowledge-intensive agents with robust retrieval, indexing, and agentic workflows on private data
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Grok Superior for RAG-grounded and knowledge-intensive agents with robust retrieval, indexing, and agentic workflows on private data
Where it falls shortper Grok Enhance general multi-agent orchestration beyond retrieval-focused strengths
Rank history
Just missed the top 5
GPT Microsoft Agent Framework — powerful typed workflows, durability, and .NET/Python enterprise integration, but still in public preview and therefore a riskier default · LlamaIndex Agents — excellent when agents are primarily retrieval-and-data systems, but less compelling as a general orchestration framework
Claude CrewAI — huge adoption for role-based multi-agent prototyping, but its heavy abstractions and reliability in production trail its popularity
Gemini LlamaIndex Workflows — offers flexible event-driven orchestration but is primarily optimized for data-centric RAG systems rather than general-purpose multi-agent routing · Vercel AI SDK — superb for frontend streaming and client-side chat hooks but lacks native backend agentic primitives like durable workflow graphs or state persistence
Grok Claude Agent SDK — strong Anthropic integration and hierarchical agents but narrower ecosystem · Google ADK — excellent Vertex/Gemini multimodal but platform-tied
By model
ChatGPT
- 1.LangGraph
- 2.Pydantic AI
- 3.OpenAI Agents SDK
- 4.Google Agent Development Kit
- 5.CrewAI
Claude
- 1.LangGraph
- 2.OpenAI Agents SDK
- 3.Claude Agent SDK
- 4.Pydantic AI
- 5.Microsoft Agent Framework
Gemini
- 1.LangGraph
- 2.Microsoft Agent Framework
- 3.Pydantic AI
- 4.Mastra
- 5.CrewAI
Grok
- 1.LangGraph
- 2.CrewAI
- 3.Microsoft Agent Framework
- 4.OpenAI Agents SDK
- 5.LlamaIndex Workflows
Common questions
What is the best framework for building ai agents according to AI models?
LangGraph leads. All 4 models rank LangGraph the top pick. The current top 3: LangGraph, OpenAI Agents SDK, Pydantic AI. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-15. Source: modelsagree.com.
Which framework for building ai agents did each AI model pick first?
ChatGPT: LangGraph. Claude: LangGraph. Gemini: LangGraph. Grok: LangGraph.
What changed in the latest framework for building ai agents ranking?
In the latest poll (2026-07-15): CrewAI climbed 5 spots, Claude Agent SDK climbed 1 spot, LlamaIndex Workflows climbed 3 spots; Google Agent Development Kit dropped 2 spots. The models are re-polled on demand, so this ranking moves.
How is this framework for building ai agents ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best framework for building AI agents” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-15. https://modelsagree.com/best/best-ai-agent-framework (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand