Best framework for building AI agents
4 models · updated 2026-08-14
The verdict
LangGraph leads — 3 of 4 models rank LangGraph the top pick.
Not unanimous: ChatGPT picks Pydantic AI.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank LangGraph #1 for framework for building ai agents on ModelsAgree by aggregate score. The models' case: Graph-based, explicitly stateful orchestration with durable checkpointing, human-in-the-loop interrupts, and time-travel — the most battle-tested option for production. The models' main caveat: Steepest learning curve here — the low-level graph/state API is verbose and over-engineered for simple single-agent tool loops, and it drags in. The strongest alternative is Pydantic AI — Near-tie with LangGraph; its model-agnostic, type-safe Python API, dependency injection, validated tools and outputs, MCP, human approval, evals. Not unanimous: ChatGPT picks Pydantic AI. Source: https://modelsagree.com/best/best-ai-agent-framework (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #2Claude #1Gemini #1Grok #1
Graph-based, explicitly stateful orchestration with durable checkpointing, human-in-the-loop interrupts, and time-travel — the most battle-tested option for production agents that need controllable, inspectable control flow; large ecosystem, LangSmith observability, and framework-agnostic model support; near-tie with the OpenAI Agents SDK for the top slot for teams that value control over simplicity.
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Claude Graph-based, explicitly stateful orchestration with durable checkpointing, human-in-the-loop interrupts, and time-travel — the most battle-tested option for production agents that need controllable, inspectable control flow; large ecosystem, LangSmith observability, and framework-agnostic model support; near-tie with the OpenAI Agents SDK for the top slot for teams that value control over simplicity.
Gemini State-machine and cyclic graph model provides unmatched deterministic control over agent execution, state persistence, durable checkpointing, and native human-in-the-loop branching for complex enterprise workflows.
Grok Explicit graph-based control with durable checkpoints, time-travel debugging, and native human-in-the-loop make it the strongest for reliable long-running production agents; proven at scale (Uber, LinkedIn, Klarna and hundreds more) and fully model-agnostic with mature observability
GPT The strongest choice for complex stateful agents: explicit control flow, checkpointing, parallel branches, streaming, memory, human interrupts, and failure recovery make long-running behavior inspectable and reliable. It would rank first for orchestration-heavy systems.
Where it falls shortper GPT Its low-level graph and state machinery create unnecessary learning and maintenance overhead for straightforward tool-using agents.
per Claude Steepest learning curve here — the low-level graph/state API is verbose and over-engineered for simple single-agent tool loops, and it drags in LangChain-ecosystem abstraction weight many teams don't want.
per Gemini High architectural complexity and steep learning curve; poorly suited for simple, single-prompt tool-calling agents that do not need state machines.
per Grok Steep learning curve and overkill for simple single-agent tool loops
- 2GPT #1Claude #3Gemini #2Grok #4
Near-tie with LangGraph; its model-agnostic, type-safe Python API, dependency injection, validated tools and outputs, MCP, human approval, evals, OpenTelemetry, graphs, and durable-engine integrations make it the best maintainable default for typical Python teams.
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GPT Near-tie with LangGraph; its model-agnostic, type-safe Python API, dependency injection, validated tools and outputs, MCP, human approval, evals, OpenTelemetry, graphs, and durable-engine integrations make it the best maintainable default for typical Python teams.
Gemini Exceptional developer experience and minimal abstraction overhead, leveraging native Python type hints, Pydantic validation, and clean dependency injection to build reliable, type-safe agents with full execution transparency. Near-tie with LangGraph for single-agent systems.
Claude Brings genuine type safety, structured/validated outputs, and dependency injection to agents with a clean Python-native feel; model-agnostic, strong testing story, and Logfire observability; the best fit for engineers who want typed, maintainable, well-tested agent code rather than notebook glue.
Grok Native Pydantic validation, typed tools, dependency injection, and FastAPI-style ergonomics deliver the highest reliability and developer experience for structured-output Python agents; model-agnostic and production-oriented
Where it falls shortper GPT It lacks a self-contained durable runtime, so long-running production work requires Temporal, DBOS, Prefect, Restate, or equivalent infrastructure.
per Claude Younger and smaller ecosystem with fewer prebuilt integrations; multi-agent orchestration is more manual, and it's Python-only.
per Gemini Lacks native out-of-the-box multi-agent orchestration protocols and built-in durable checkpointing for long-running workflows.
per Grok Multi-agent coordination and long-running stateful workflows remain less battle-tested than graph runtimes
- 3GPT #4Claude #2Gemini —Grok #2
Minimal, production-oriented primitives (agents, handoffs, guardrails, sessions, tracing) that stay out of your way; works across providers via a Chat Completions/LiteLLM adapter despite the name; excellent built-in tracing and the smoothest path from prototype to shipped agent; the pragmatic default for most practitioners starting fresh.
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Claude Minimal, production-oriented primitives (agents, handoffs, guardrails, sessions, tracing) that stay out of your way; works across providers via a Chat Completions/LiteLLM adapter despite the name; excellent built-in tracing and the smoothest path from prototype to shipped agent; the pragmatic default for most practitioners starting fresh.
Grok Minimal primitives deliver clean handoffs, built-in tracing, and the fastest path to lean, production-quality single- or multi-agent systems with very low abstraction tax; near-tie with CrewAI for speed-to-value on straightforward workloads
GPT The cleanest lightweight option for Python or TypeScript, combining an excellent agent loop with typed tools, handoffs, guardrails, MCP, sessions, human approval, tracing, sandbox agents, and realtime voice support.
Where it falls shortper GPT Non-OpenAI models work, but the SDK’s highest-value hosted tools and operational integrations favor the OpenAI stack.
per Claude Thinnest orchestration layer — no durable execution or checkpointing out of the box, so long-running, resumable, or complex branching workflows require you to build persistence yourself; best-supported inside the OpenAI stack.
per Grok Orchestration power drops and convenience weakens outside the OpenAI ecosystem
- 4GPT —Claude —Gemini #4Grok #3
Role/goal/backstory mental model plus Flows give the lowest-friction route to working multi-agent collaboration; minimal boilerplate and strong early-production adoption for role-based teams
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Grok Role/goal/backstory mental model plus Flows give the lowest-friction route to working multi-agent collaboration; minimal boilerplate and strong early-production adoption for role-based teams
Gemini High-level role-based multi-agent abstractions enable rapid prototyping of collaborative teams with built-in task delegation, sequential and hierarchical execution, and accessible process flows.
Where it falls shortper Gemini Highly opinionated abstractions that become difficult to inspect, debug, or customize when fine-grained control or non-linear state transitions are required.
per Grok Opaque orchestration and weaker durability/checkpointing once flows grow complex
- 5GPT #3Claude —Gemini —Grok —
An unusually complete, model-flexible stack spanning Python, TypeScript, Go, Java, and Kotlin, with graph and multi-agent workflows, managed context, sessions, memory, evaluation, MCP, A2A, observability, and portable deployment.
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GPT An unusually complete, model-flexible stack spanning Python, TypeScript, Go, Java, and Kotlin, with graph and multi-agent workflows, managed context, sessions, memory, evaluation, MCP, A2A, observability, and portable deployment.
Where it falls shortper GPT Its most polished models, tools, runtime, and operational path remain Google Cloud and Gemini-centric.
- 6GPT —Claude —Gemini #3Grok —
Flexible event-driven async architecture provides clear control flow without rigid graph boilerplate, coupled with industry-standard data indexing, retrieval, and RAG capabilities built directly into the agent execution loop.
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Gemini Flexible event-driven async architecture provides clear control flow without rigid graph boilerplate, coupled with industry-standard data indexing, retrieval, and RAG capabilities built directly into the agent execution loop.
Where it falls shortper Gemini Heavily centered around data ingestion and retrieval paradigms; less ergonomic for pure task-automation agents that do not interface with complex knowledge bases.
- 7GPT —Claude #5Gemini —Grok #5
Consolidates AutoGen's multi-agent research patterns with Semantic Kernel's enterprise footing into one supported SDK; strong for complex multi-agent collaboration, .NET and Python support, and Azure/enterprise governance and identity needs.
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Claude Consolidates AutoGen's multi-agent research patterns with Semantic Kernel's enterprise footing into one supported SDK; strong for complex multi-agent collaboration, .NET and Python support, and Azure/enterprise governance and identity needs.
Grok Direct successor that unifies AutoGen multi-agent patterns with Semantic Kernel enterprise features (sessions, middleware, telemetry, type safety) for robust production systems, especially in Python/.NET
Where it falls shortper Claude Newer merged surface carries migration churn and heavier abstractions; real value concentrates in Microsoft/Azure-centric enterprises, and it's overkill for a single-agent app.
per Grok Strongest inside Microsoft/Azure environments; less natural fit for pure open-source polyglot stacks outside that ecosystem
- 8GPT —Claude #4Gemini —Grok —
The default for TypeScript/JavaScript agents — unified provider API, first-class streaming, tool calling, and tight React/Next.js UI integration make it unmatched for building agentic web products; excellent docs and huge adoption in the JS world.
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Claude The default for TypeScript/JavaScript agents — unified provider API, first-class streaming, tool calling, and tight React/Next.js UI integration make it unmatched for building agentic web products; excellent docs and huge adoption in the JS world.
Where it falls shortper Claude UI/app-layer focus over heavy backend orchestration — lacks durable multi-step workflow and complex multi-agent coordination primitives; not the right tool for Python data/ML shops or long-running server-side agent pipelines.
- 9GPT #5Claude —Gemini —Grok —
Near-tie with Microsoft Agent Framework; it wins for typical TypeScript teams through a coherent package of model routing, agents, memory, typed workflows, suspend/resume, MCP, evals, OpenTelemetry, local tooling, and flexible deployment.
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GPT Near-tie with Microsoft Agent Framework; it wins for typical TypeScript teams through a coherent package of model routing, agents, memory, typed workflows, suspend/resume, MCP, evals, OpenTelemetry, local tooling, and flexible deployment.
Where it falls shortper GPT It is TypeScript-only, making it a poor fit for Python- or .NET-centered AI teams.
- 10GPT —Claude —Gemini #5Grok —
Robust enterprise-grade framework with native polyglot support across C#, Python, and Java, strong memory management, and deep integration into enterprise software architectures.
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Gemini Robust enterprise-grade framework with native polyglot support across C#, Python, and Java, strong memory management, and deep integration into enterprise software architectures.
Where it falls shortper Gemini High enterprise boilerplate and ceremony; suboptimal for fast-iterating Python-first teams building autonomous experimental agents.
Rank history
Just missed the top 5
GPT Microsoft Agent Framework — excellent durable workflows, middleware, provider support, and enterprise integration, but its broad abstraction surface and Microsoft-stack bias make it a heavier default · CrewAI — fast for role-based multi-agent automation, but its crew abstractions provide less deterministic control and debuggability than the ranked workflow-oriented options
Claude CrewAI — fast, intuitive role-based multi-agent setup with real traction, but its opinionated crew abstraction limits fine-grained control and it leans toward demos over hardened production control
Gemini AutoGen — Powerful multi-agent conversational capabilities, but hindered by ecosystem fragmentation and less predictable state control in production compared to LangGraph · smolagents — Innovative and lightweight code-centric action design, but too minimal in state persistence, ecosystem tooling, and multi-agent coordination for broad enterprise deployment
Grok Google ADK — excellent multi-language and A2A support but still younger ecosystem and Google-leaning · LlamaIndex Workflows — best-in-class for RAG/document agents but not a general-purpose orchestration framework
By model
ChatGPT
- 1.Pydantic AI
- 2.LangGraph
- 3.Google ADK
- 4.OpenAI Agents SDK
- 5.Mastra
Claude
- 1.LangGraph
- 2.OpenAI Agents SDK
- 3.Pydantic AI
- 4.Vercel AI SDK
- 5.Microsoft Agent Framework
Gemini
- 1.LangGraph
- 2.Pydantic AI
- 3.LlamaIndex Workflows
- 4.CrewAI
- 5.Semantic Kernel
Grok
- 1.LangGraph
- 2.OpenAI Agents SDK
- 3.CrewAI
- 4.Pydantic AI
- 5.Microsoft Agent Framework
Common questions
What is the best framework for building ai agents according to AI models?
LangGraph leads. 3 of 4 models rank LangGraph the top pick. The current top 3: LangGraph, Pydantic AI, OpenAI Agents SDK. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which framework for building ai agents did each AI model pick first?
ChatGPT: Pydantic AI. Claude: LangGraph. Gemini: LangGraph. Grok: LangGraph.
Do the AI models agree on the best framework for building ai agents?
Not unanimous. ChatGPT picks Pydantic AI.
What changed in the latest framework for building ai agents ranking?
In the latest poll (2026-08-14): OpenAI Agents SDK climbed 1 spot, CrewAI climbed 2 spots; Microsoft Agent Framework dropped 4 spots, Mastra dropped 2 spots; Google ADK and LlamaIndex Workflows entered the ranking. 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 2026-08-14. 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