What is an AI agent framework?

An AI agent framework is a software library or platform that gives you the primitives to build LLM-powered agents — autonomous (or semi-autonomous) programs that use language models to reason, call tools, take actions, and decide what to do next. It is the layer above raw model APIs. Where calling OpenAI directly gives you "model returns text", an agent framework gives you "model picks a tool, calls it, gets the result, decides the next step, and loops until it is done". Examples: LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK on the code-first side; Dify, Flowise, Langflow, Lindy on the no-code / low-code side.

What is the best AI agent framework in 2026?

No single winner — it depends on the workload shape and team. For broad orchestration with the biggest ecosystem, LangChain. For opinionated role-based multi-agent crews, CrewAI. For conversational multi-agent with human-in-the-loop, AutoGen. For production single agents on OpenAI, the OpenAI Agents SDK. For production single agents on Claude, the Claude Agent SDK. For an AI product with RAG and your own data, Dify. For visual no-code flows, Flowise or Langflow. For no-code AI assistants for sales / ops / support roles, Lindy. Most code-first teams land on LangChain or CrewAI; most product teams land on Dify; most assistant-shaped workloads land on Lindy.

Code-first or no-code: which AI agent framework should I pick?

Pick by who owns the workflow long-term. Code-first (LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK) wins when engineers own the workflow and it lives inside a wider codebase — version control, code review, CI, and unit tests are non-negotiable. No-code (Dify, Flowise, Langflow, Lindy) wins when non-engineers need to read or tweak the flow, when speed-to-ship beats fine-grained control, and when the workflow is the product rather than a feature inside one.

Is LangChain still the best AI agent framework?

For the broadest workload coverage, yes — and the biggest ecosystem by a meaningful margin. LangChain has the largest integration catalog (vector stores, retrievers, model providers, tools), the most templates, the most Stack Overflow answers, and a steady evolution toward LangGraph for explicit state-graph control. The trade-off is exactly its strength: it is broad, so the abstraction is heavier than CrewAI / OpenAI Agents SDK for narrow workloads. For "we will build many agent flows over time and want one stack to rule them", LangChain. For "one tight agent for one product", a smaller SDK wins.

Is CrewAI a good alternative to LangChain?

For opinionated role-based multi-agent workflows, yes — and the friendliest on-ramp in the category. CrewAI thinks in roles, tasks, and tools (researcher → writer → reviewer); LangChain thinks in chains, agents, and tools. For "team of specialists doing a sequential job", CrewAI is the cleaner abstraction. For "we need vector retrieval, output parsing, complex routing, custom tools, and a long tail of integrations in one place", LangChain wins. Different abstraction levels; overlapping use cases.

What is the difference between AutoGen and CrewAI?

AutoGen thinks in conversations between agents — agents debate, refine, and converge. CrewAI thinks in roles handing off to roles in a fixed sequence. For "agents disagree, revise, and ask for human input", AutoGen wins. For "team of specialists doing a sequential job", CrewAI wins. AutoGen burns more tokens; CrewAI is friendlier on cost. Microsoft maintains AutoGen; CrewAI is independent and Apache 2.0. Both run on any model provider.

Should I use the OpenAI Agents SDK or LangChain?

Use the OpenAI Agents SDK when your workload is "one agent with tools on OpenAI models" and you want production batteries included — tracing, guardrails, handoffs, sessions, structured output — without third-party glue. Use LangChain when you need cross-provider portability, vector stores, retrievers, RAG, or any of the long tail of LangChain integrations. The OpenAI SDK is opinionated and OpenAI-coupled; LangChain is broad and provider-neutral. For "production agent on GPT-4 with tools", the SDK wins on ergonomics. For "agent that talks to Claude this quarter and Mistral next quarter", LangChain wins on portability.

Is Dify an AI agent framework or a platform?

Both. Dify is a self-hostable AI application platform — visual workflow and agent builders, RAG with datasets, team workspaces, ops console, multi-provider model support — and underneath it is a framework you can extend. Where LangChain is a library you compose, Dify is an app you run. For "build an AI product with our own data, give non-engineers a canvas, and self-host the result", Dify is the most production-shaped option in this list.

Is Flowise the same as Langflow?

Different projects, similar shape. Both are visual no-code builders for LangChain-style flows; both run on Docker; both target the "I want a canvas, not Python" audience. Flowise is more JavaScript-leaning and lighter; Langflow is Python-native and ships more LangChain-native components. For most teams, the choice is "whichever has the integrations you need today" — the abstraction gap is small. For "single Docker container, simplest deployment", Flowise. For "stay closest to upstream LangChain", Langflow.

What is Lindy and how does it compare to the rest?

Lindy is a closed-source SaaS platform for no-code AI agents — opinionated toward "AI assistant for a role" (SDR, EA, support agent). It wins on speed-to-ship and integration with inbox / CRM out of the box. Where Dify is a platform you own, Lindy is a platform you rent. Trade-offs: closed SaaS (no self-host), task-based pricing that scales aggressively, and limited fit outside assistant-shaped workloads. For "ship an AI SDR this week without engineering", Lindy. For "build an AI product we own", anything else on this list.

Which AI agent framework is best for production?

For a single-agent production workload on OpenAI: the OpenAI Agents SDK. On Claude: the Claude Agent SDK. For multi-agent with explicit state-graph control: LangGraph (part of LangChain). For opinionated multi-agent crews: CrewAI with discipline on token cost. For an AI product platform with RAG: Dify. The shared lesson: production needs tracing, guardrails, deterministic-enough behavior, and observability — every framework on this list can ship to production, but they reward different operational disciplines.

Pillar guide · Updated 2026-06-04

AI Agent Frameworks in 2026: The Complete Guide

The AI agent framework landscape in 2026 has converged into roughly nine names that actually ship production work. LangChain remains the broadest. CrewAI is the friendliest multi-agent on-ramp. AutoGen owns conversational multi-agent. OpenAI Agents SDK and Claude Agent SDK are the cleanest production single-agent runtimes. Dify is the AI product platform. Flowise and Langflow are the no-code canvases. Lindy is the rented SaaS assistant layer. Every other framework is either a re-skin of these or a thin abstraction on top.

This is the full 2026 buyer's guide — every framework we have actually shipped on, where each one wins, where each one loses, licensing trade-offs, cost realities, and the meta-pattern for picking. Fifteen minute read, no "30 best agent frameworks" filler.

Published 2026-06-04 · ~15 min read · Independent, no paid placements (disclosure)

The short answer

Best for broad orchestration and ecosystem reach: LangChain — biggest integration catalog, most templates.
Best for opinionated role-based crews: CrewAI — friendliest multi-agent syntax, Apache 2.0.
Best for conversational multi-agent: AutoGen — agents debate, refine, escalate to humans.
Best for production single agents on OpenAI: OpenAI Agents SDK — tracing, guardrails, handoffs included.
Best for production single agents on Claude: Claude Agent SDK — Anthropic-aligned, deep tool integration.
Best for an AI product with RAG and your own data: Dify — visual builder, datasets, self-host.
Best for visual no-code (lightweight): Flowise — single Docker container, JS-leaning.
Best for visual no-code (LangChain-native): Langflow — Python-native, closest to upstream.
Best for rented no-code AI assistants: Lindy — SaaS, opinionated toward SDR / EA / support roles.

Want head-to-heads? See Best LangChain alternatives · Best CrewAI alternatives · Best AutoGen alternatives.

What an AI agent framework actually does

An AI agent framework gives you the primitives to build LLM-powered programs that pick tools, call them, evaluate results, and decide what to do next. The layer above raw model APIs. Without one, you are writing your own agent loop, tool router, retry logic, memory store, and tracing. With one, you compose primitives.

The frameworks on this list cover three architectural shapes:

Library / SDK: code-first. You import it, you write the agent in Python / TypeScript, you own the runtime. LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK.
Self-hostable platform: visual builder + runtime + ops console. You run it on your infrastructure. Dify, Flowise, Langflow.
Hosted SaaS: opinionated product. You sign up and configure. Lindy.

The right pick depends entirely on which shape your team needs to operate, not on which framework looks shiniest this week.

Code-first frameworks (libraries / SDKs)

1. LangChain — broad orchestration, biggest ecosystem

LangChain is the default landing for "we will build many agent flows over time and want one stack". Biggest integration catalog (vector stores, retrievers, model providers, tools), most templates, broadest community. MIT licensed. Continues to evolve toward LangGraph for explicit state-graph control.

Strengths: largest ecosystem in the category; cross-provider portability (OpenAI, Anthropic, Mistral, Ollama, etc); vector stores, retrievers, RAG primitives all in one place; LangGraph for state-graph control; LangSmith for tracing.

Trade-offs: broad means heavier — more API surface than a narrow SDK; documentation has accumulated faster than it has been cleaned up; LangSmith is the natural observability tier and creates vendor coupling.

Best for: teams building many agent flows, anyone needing cross-provider portability or rich RAG primitives, organisations standardising one agent stack.

Read the full LangChain review · See Best LangChain alternatives

2. CrewAI — opinionated role-based multi-agent

CrewAI is the friendliest on-ramp to multi-agent code. Roles, tools, goals, tasks — 80 lines of Python and you have a working crew. Apache 2.0, independent, lighter than AutoGen, more opinionated than LangChain.

Strengths: friendliest multi-agent syntax in the category; reads like the team you are modeling (researcher → writer → reviewer); Apache 2.0 with no commercial restrictions; healthy community and growing templates; hosted Enterprise option available.

Trade-offs: token cost scales aggressively with crew size; determinism is thin (same input, different output); past "fixed sequence of roles" the abstraction stops fitting; less suited to free-form multi-agent debate than AutoGen.

Best for: sequential specialist pipelines (research, content production, multi-step analysis), teams who want the friendliest multi-agent syntax.

Read the full CrewAI review · See Best CrewAI alternatives · OpenAI Agents SDK vs CrewAI

3. AutoGen — conversational multi-agent

AutoGen owns conversational multi-agent. Agents that debate, escalate to humans, and refine their outputs through dialogue. MIT licensed, Microsoft- maintained, capable of running on any model provider.

Strengths: the strongest conversational multi-agent framework; human-in-the-loop is first-class; agents can disagree, revise, and converge; Microsoft-maintained means long-term stability; broad provider support.

Trade-offs: the 0.4 rewrite broke production codebases (reputation is sticky); token costs on long conversations get uncomfortable fast; for "one agent with tools", AutoGen is overkill; debugging multi-agent loops is hard.

Best for: conversational multi-agent workloads where agents need to debate and refine, human-in-the-loop pipelines, teams comfortable with token-cost discipline.

Read the full AutoGen review · See Best AutoGen alternatives

4. OpenAI Agents SDK — production single agents on OpenAI

The OpenAI Agents SDK is the answer when your workload is really "one agent with tools" and you want production batteries included. Tracing, guardrails, handoffs, sessions, structured output — all built in. Tightly coupled to OpenAI models.

Strengths: production batteries included without third-party glue; tool calling and structured output aligned with OpenAI model capabilities; clean handoffs between agents; built by OpenAI (tracks model API changes the same day); smaller surface area than LangChain.

Trade-offs: tightly coupled to OpenAI — cross-provider work is possible but loses the polish; younger ecosystem (fewer community templates); opinionated runtime (fight the SDK if you want a custom loop).

Best for: production single-agent workloads on OpenAI, small handoff workflows, teams who want batteries included and would rather not assemble LangChain pieces.

Read the full OpenAI Agents SDK review · See OpenAI Agents SDK vs CrewAI

5. Claude Agent SDK — production single agents on Claude

The Claude Agent SDK is the Anthropic-aligned equivalent. Same shape as the OpenAI Agents SDK — production batteries, tool use, structured output, computer-use ready — coupled to Claude models. The cleanest production runtime if Claude is your target model.

Strengths: deep tool integration with Claude's tool-use API; computer-use ready out of the box; production-grade ergonomics; Anthropic-maintained (model API parity); smaller surface area than LangChain.

Trade-offs: Claude-coupled — cross-provider work is awkward; younger ecosystem than LangChain; opinionated runtime; less mature than the OpenAI SDK by a small margin.

Best for: production single-agent workloads on Claude, computer-use agents, teams optimising for Anthropic-aligned ergonomics.

Read the full Claude Agent SDK review

Self-hostable platforms (visual + runtime)

6. Dify — AI product platform with RAG

Dify is the most production-shaped option for "build an AI product with our own data". Visual workflow and agent builders, RAG with datasets and team workspaces, ops console, multi-provider model support. Self-host on Docker, Apache 2.0 with a multi-tenant SaaS resale clause.

Strengths: visual workflow and agent builders for non-developers; RAG with datasets is first-class; multi-provider model support (OpenAI, Anthropic, open-source); self-host on Docker; team workspaces and ops console.

Trade-offs: heavier deployment than Flowise (Postgres, Redis, vector store); Apache 2.0 has a multi-tenant SaaS resale clause; less code-first than LangChain; more platform than library.

Best for: customer-facing AI products with their own data, teams that need a canvas non-engineers can tweak, organisations needing self-host with RAG.

Read the full Dify review · See Best Dify alternatives

7. Flowise — lightweight visual no-code canvas

Flowise is the simplest no-code AI agent canvas. Single Docker container, JavaScript-leaning, MIT-ish licensed. Where Dify is a platform, Flowise is a canvas — lighter and faster to stand up.

Strengths: single-Docker-container deployment; permissive licence; clean drag-and-drop canvas; growing component library; faster to stand up than Dify.

Trade-offs: thinner RAG / dataset layer than Dify; fewer team / workspace features; smaller community than Langflow on Python-heavy teams; less polished as a product platform.

Best for: small teams wanting a no-code canvas with minimum ops, prototypes, internal AI tools where the canvas is enough.

Read the full Flowise review · See Best Flowise alternatives

8. Langflow — Python-native LangChain canvas

Langflow is the Python-native visual builder closest to upstream LangChain. MIT licensed, Docker-deployable, ships LangChain-native components out of the box. The right pick when your team already lives in LangChain and wants a canvas on top.

Strengths: closest to upstream LangChain components; Python-native; MIT licensed; integrates naturally with existing LangChain codebases; healthy community.

Trade-offs: heavier than Flowise; more LangChain-coupled (lock-in story); the visual layer can lag behind LangChain code-side features.

Best for: teams already on LangChain who want a visual layer, Python-heavy organisations, internal tools that bridge engineering and non-engineering teams.

Read the full Langflow review · See Best Langflow alternatives

Hosted SaaS (rented platforms)

9. Lindy — rented no-code AI assistants for roles

Lindy is the closed-source SaaS for "AI assistant that lives in your inbox and CRM". Opinionated toward SDR / EA / support roles. Fastest speed-to-ship in this list for assistant-shaped work; trades off self-host and cost-at-scale.

Strengths: fastest speed-to-ship for inbox / CRM-shaped assistants; integrations with common SaaS out of the box; clean UX for non-technical operators; no infrastructure to own.

Trade-offs: closed SaaS (no self-host, limited export); task-based pricing scales aggressively; opinionated toward assistant-shaped roles (awkward outside that shape); model and tool wiring are abstracted away.

Best for: sales / customer success / executive teams who want an AI assistant in their inbox without engineering, prototyping role-based agents fast.

Read the full Lindy review · See Best Lindy alternatives

The meta-pattern: pick by workload shape

The pattern that survives every project we have shipped: pick by the actual shape of your workload, not by which framework's marketing page is loudest. Five questions resolve 90% of the choice.

Who owns the workflow long-term? Engineers → code-first SDK. Non- engineers → no-code platform. Both → self-hostable platform (Dify) with a code escape hatch.
Is it one agent or many? One agent with tools → OpenAI / Claude Agent SDK. Many agents in a fixed sequence → CrewAI. Many agents that debate → AutoGen. State-graph control → LangGraph.
What is the model strategy? Locked to OpenAI → OpenAI Agents SDK. Locked to Claude → Claude Agent SDK. Cross-provider → LangChain / Dify.
Is RAG central? Yes → Dify (platform) or LangChain (library). No → any other choice.
Self-host or hosted? Self-host → everything but Lindy. Hosted → Lindy (assistant-shaped) or any code-first SDK on your own cloud.

Most production stacks end up running 2–3 of these in parallel: a code-first SDK for the production agent runtime, a self-hostable platform for the AI product surface that non- engineers maintain, and sometimes a rented SaaS for a specific assistant role nobody wants to engineer.

Licensing in 2026

Every code-first framework on this list is permissively licensed: LangChain (MIT), CrewAI (Apache 2.0), AutoGen (MIT), OpenAI Agents SDK (open / OpenAI-aligned), Claude Agent SDK (open / Anthropic-aligned). Dify is Apache 2.0 with a multi-tenant SaaS resale clause — fine for internal commercial use, restricted only for "I want to resell Dify as a hosted product". Flowise is MIT-ish; Langflow is MIT. Lindy is closed-source SaaS only.

The honest read: licensing is not the binding constraint for most teams. Token cost, production ergonomics, and which model you are betting on matter more.

Cost: framework is rounding error, tokens are the bill

The framework itself is rarely the cost. Self-hosting Dify, Flowise, or Langflow runs $6–24/month on a small VPS. Code-first SDKs run wherever your code already runs. Lindy adds a platform tax on top of model cost.

The real bill is model tokens. A chatty multi-agent crew (CrewAI or AutoGen) on GPT-4- class models can burn $50–500/day at production volume without discipline; the same workload on smaller models, with prompt caching, or with model routing can be 10× cheaper. Token discipline matters more than framework choice once you are past prototype.

For hosting open-source agent platforms on a VPS, see our self-hosted workflow automation guide — same hosting math, different runtime.

Final verdict

For most teams in 2026:

Building many agent flows, one stack to standardise on: LangChain (LangGraph for state control).
Multi-agent role-based pipelines: CrewAI.
Conversational multi-agent with humans: AutoGen.
Production single agent on OpenAI: OpenAI Agents SDK.
Production single agent on Claude: Claude Agent SDK.
AI product with RAG and your own data: Dify.
Lightweight visual canvas: Flowise.
LangChain-native visual canvas: Langflow.
Rented no-code assistant for sales / ops / CS: Lindy.

Meta-recommendation: most engineering teams land on LangChain + one production SDK (OpenAI Agents SDK or Claude Agent SDK) for the runtime, plus Dify for the AI product surface. Most non-engineering teams land on Dify alone, or Lindy for assistant-shaped work. The frameworks compete in fewer places than the marketing suggests.

Next reads

FAQ

What is an AI agent framework?: An AI agent framework is a software library or platform that gives you the primitives to build LLM-powered agents — autonomous (or semi-autonomous) programs that use language models to reason, call tools, take actions, and decide what to do next. It is the layer above raw model APIs. Where calling OpenAI directly gives you "model returns text", an agent framework gives you "model picks a tool, calls it, gets the result, decides the next step, and loops until it is done". Examples: LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK on the code-first side; Dify, Flowise, Langflow, Lindy on the no-code / low-code side.
What is the best AI agent framework in 2026?: No single winner — it depends on the workload shape and team. For broad orchestration with the biggest ecosystem, LangChain. For opinionated role-based multi-agent crews, CrewAI. For conversational multi-agent with human-in-the-loop, AutoGen. For production single agents on OpenAI, the OpenAI Agents SDK. For production single agents on Claude, the Claude Agent SDK. For an AI product with RAG and your own data, Dify. For visual no-code flows, Flowise or Langflow. For no-code AI assistants for sales / ops / support roles, Lindy. Most code-first teams land on LangChain or CrewAI; most product teams land on Dify; most assistant-shaped workloads land on Lindy.
Code-first or no-code: which AI agent framework should I pick?: Pick by who owns the workflow long-term. Code-first (LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK) wins when engineers own the workflow and it lives inside a wider codebase — version control, code review, CI, and unit tests are non-negotiable. No-code (Dify, Flowise, Langflow, Lindy) wins when non-engineers need to read or tweak the flow, when speed-to-ship beats fine-grained control, and when the workflow is the product rather than a feature inside one.
Is LangChain still the best AI agent framework?: For the broadest workload coverage, yes — and the biggest ecosystem by a meaningful margin. LangChain has the largest integration catalog (vector stores, retrievers, model providers, tools), the most templates, the most Stack Overflow answers, and a steady evolution toward LangGraph for explicit state-graph control. The trade-off is exactly its strength: it is broad, so the abstraction is heavier than CrewAI / OpenAI Agents SDK for narrow workloads. For "we will build many agent flows over time and want one stack to rule them", LangChain. For "one tight agent for one product", a smaller SDK wins.
Is CrewAI a good alternative to LangChain?: For opinionated role-based multi-agent workflows, yes — and the friendliest on-ramp in the category. CrewAI thinks in roles, tasks, and tools (researcher → writer → reviewer); LangChain thinks in chains, agents, and tools. For "team of specialists doing a sequential job", CrewAI is the cleaner abstraction. For "we need vector retrieval, output parsing, complex routing, custom tools, and a long tail of integrations in one place", LangChain wins. Different abstraction levels; overlapping use cases.
What is the difference between AutoGen and CrewAI?: AutoGen thinks in conversations between agents — agents debate, refine, and converge. CrewAI thinks in roles handing off to roles in a fixed sequence. For "agents disagree, revise, and ask for human input", AutoGen wins. For "team of specialists doing a sequential job", CrewAI wins. AutoGen burns more tokens; CrewAI is friendlier on cost. Microsoft maintains AutoGen; CrewAI is independent and Apache 2.0. Both run on any model provider.
Should I use the OpenAI Agents SDK or LangChain?: Use the OpenAI Agents SDK when your workload is "one agent with tools on OpenAI models" and you want production batteries included — tracing, guardrails, handoffs, sessions, structured output — without third-party glue. Use LangChain when you need cross-provider portability, vector stores, retrievers, RAG, or any of the long tail of LangChain integrations. The OpenAI SDK is opinionated and OpenAI-coupled; LangChain is broad and provider-neutral. For "production agent on GPT-4 with tools", the SDK wins on ergonomics. For "agent that talks to Claude this quarter and Mistral next quarter", LangChain wins on portability.
Is Dify an AI agent framework or a platform?: Both. Dify is a self-hostable AI application platform — visual workflow and agent builders, RAG with datasets, team workspaces, ops console, multi-provider model support — and underneath it is a framework you can extend. Where LangChain is a library you compose, Dify is an app you run. For "build an AI product with our own data, give non-engineers a canvas, and self-host the result", Dify is the most production-shaped option in this list.
Is Flowise the same as Langflow?: Different projects, similar shape. Both are visual no-code builders for LangChain-style flows; both run on Docker; both target the "I want a canvas, not Python" audience. Flowise is more JavaScript-leaning and lighter; Langflow is Python-native and ships more LangChain-native components. For most teams, the choice is "whichever has the integrations you need today" — the abstraction gap is small. For "single Docker container, simplest deployment", Flowise. For "stay closest to upstream LangChain", Langflow.
What is Lindy and how does it compare to the rest?: Lindy is a closed-source SaaS platform for no-code AI agents — opinionated toward "AI assistant for a role" (SDR, EA, support agent). It wins on speed-to-ship and integration with inbox / CRM out of the box. Where Dify is a platform you own, Lindy is a platform you rent. Trade-offs: closed SaaS (no self-host), task-based pricing that scales aggressively, and limited fit outside assistant-shaped workloads. For "ship an AI SDR this week without engineering", Lindy. For "build an AI product we own", anything else on this list.
Which AI agent framework is best for production?: For a single-agent production workload on OpenAI: the OpenAI Agents SDK. On Claude: the Claude Agent SDK. For multi-agent with explicit state-graph control: LangGraph (part of LangChain). For opinionated multi-agent crews: CrewAI with discipline on token cost. For an AI product platform with RAG: Dify. The shared lesson: production needs tracing, guardrails, deterministic-enough behavior, and observability — every framework on this list can ship to production, but they reward different operational disciplines.
How much do AI agent frameworks cost to run?: The framework itself is rarely the cost — most are open-source (LangChain MIT, CrewAI Apache 2.0, AutoGen MIT, Dify Apache 2.0, Flowise MIT-ish, Langflow MIT, OpenAI Agents SDK open). The real bill is model tokens. A chatty multi-agent crew on GPT-4-class models can burn $50–500/day at production volume without discipline; the same workload on smaller models or with prompt caching can be 10× cheaper. Lindy adds a platform tax on top of model cost. Hosting the open-source platforms (Dify, Flowise, Langflow) runs $6–24/month on a small VPS. Pick by workload shape, not framework price.

Read the LangChain review → Read the CrewAI review → Read the Dify review →