Do the work for you directly
You set the goal, and within the permitted scope it finds materials, writes drafts, runs steps and operates the computer. Examples: Claude Code, Codex, OpenCode, Hermes Agent, OpenClaw.
AI tool guide · For people solving real problems
Don't start by asking "which AI is the strongest."
First ask: who do you want it to do what for?
The common AI tools today do not do the same thing. Some are like employees, looking things up, writing code and organizing files for you; some are like factories, helping you build an AI application for customers to use; and some are like project managers, assigning work to multiple AI employees, following up and accepting deliverables. Clarify the roles first, and your choices will not be a mess.
First look at the four roles
"Agent," "platform" and "framework" are not the same thing.
Calling them all AI tools is like calling employees, offices and production lines all "company supplies." This map below first helps you see what each one is responsible for.
Manage multiple AI employees
It is usually not the one doing the work itself, but is responsible for assigning tasks, tracking progress, keeping records, and having people accept the results. Examples: Multica, Paperclip.
Build apps for developers
It provides components and workflows so programmers can assemble models, data, tools and business rules into their own systems. Examples: LangChain, LangGraph.
Quickly build an AI app for others to use
Configure knowledge bases, processes and Bots through the page — publish with little coding. Examples: Dify, Coze (Kouzi).
Remember one thing first:Do you want AI to do the work for you, or do you want to build an AI application for customers, colleagues or fans to use? For the former, prioritize Agents; for the latter, prioritize Dify, Coze or custom development.
Choose by scenario
Start from your immediate work, not from the brand.
Here are common starting points. They are not fixed packages, just a way to help you narrow things down.
First look Claude Code、Codex、OpenCode、Pi、Reasonix. For a top-tier coding experience, look at Claude Code; if you value security and the OpenAI tool ecosystem, look at Codex; if you want to switch models freely and customize it yourself, look at OpenCode.
First look OpenClaw、Hermes Agent、WorkBuddy. Look at OpenClaw when you need multi-channel, long-term memory, and local automation; look at Hermes when you value "getting better the more you use it"; look at WorkBuddy when you want out-of-the-box support for domestic office work.
First look Dify and Coze. If you need self-hosting and control over materials and processes, prioritize Dify; if you need to build fastest and publish to domestic channels, prioritize Coze.
First look Multica、Paperclip. They act like the project management layer of an AI team, suited to situations with many tasks, many participants, and a need for auditing and budget control.
First look LangChain and LangGraph. The former is suited to quickly assembling an application, while the latter is suited to fine-grained control over steps, branches, pauses, and human confirmation.
Don't buy tools first. First clearly describe this work's inputs, steps, materials, human judgments, and results, then decide whether to use an off-the-shelf tool, a low-code platform, or build a small custom process. You can start from ServicesStart.
Category one · AI employees
What they have in common: once they receive a task, they actually do it.
"Employees" is just a metaphor. How much they can do depends on the model, permissions, materials and whether you keep a review step. Don't take "can execute" to mean "can skip checking."
The premium choice for the coding experience
More like a senior programmer who knows the project's rules: can read the codebase, work according to project conventions, and keep pushing forward on longer tasks.
Best for: professional development, results-driven. Note: closed-source, primarily tied to the Claude tool ecosystem.
In one sentence: you want to hand coding over to mature professionals.
A coding assistant with more complete safety guardrails
Suited to writing code and modifying projects, and also to people already using the ChatGPT tool ecosystem. It usually offers clearer control over which operations require prior consent.
Suitable for: teams that value security and already have an OpenAI subscription. Note: the model and tooling system are tied together.
In one sentence: you want a more cautious coding assistant with clearer rules.
A balanced choice: open source, swappable models
Like a family car where you can swap the engine and add accessories yourself: terminals, web and desktop entry points all work, suitable for gradually going deeper.
Suitable for: developers who want to control the model and configuration themselves. Note: security and some features still require your own understanding.
In one sentence: you want to get started first and gradually reshape it into your own tool later.
Minimal, free, but you are responsible for yourself
Default capabilities are few and the structure is simple, suited to those who like to build up from the basics. It won't prepare many management features for you.
Best for: developers who love to tinker. Note: fewer permission guardrails, so beginners should not grant it full permissions right away.
In one sentence: they want a clean blank sheet, not a product stuffed with features.
Multi-entry, programmable coding Agent
Beyond the command line, it also connects to the web, editors and messaging entry points — suited to people who need to plug coding tasks into their own workflow.
Suitable for: teams that want deep configuration and integration with existing processes. Note: requires understanding of configuration and plugins.
In one sentence: I want to integrate a coding assistant into my own way of working.
Category 2 · Personal and office assistants
When choosing a tool, first look at where it works, what it can remember, and how far it can go on your behalf.
Multi-channel, long-term companion assistant
It can connect to multiple messaging entry points such as Feishu, Telegram and Discord, and with memory, browser and scheduled tasks, continuously take on work.
Suitable for: those who want long-term use within a familiar chat app. Note: configuration is extensive, and permissions and data boundaries need to be set carefully.
In one sentence: you want an assistant that can catch work at any entry point.
An assistant that leaves behind a repeatable approach
After completing a task, it tries to capture the steps and revisions from this round for reference in similar work next time. Beyond the chat history, the steps and revisions from this work are also retained.
Best for: long-term repetitive automation, where you want it to get smoother the more you use it. Note: deployment and maintenance are relatively heavy.
In one sentence: you want the same type of task done more skillfully the second time than the first.
An out-of-the-box choice for domestic office scenarios
More like an office assistant installed on your computer: organizing files, making PPTs, editing spreadsheets, writing weekly reports, and also accepting tasks remotely via messaging.
Best for: individuals and small teams that prefer not to deploy and mainly work in Chinese. Note: check closed-source terms, quotas and cloud data boundaries carefully.
In one sentence: if you want less configuration, first let AI help you handle daily office work.
Category three · Orchestration layer
When there's more than one AI, someone needs to assign work and accept deliverables.
These tools are not meant to replace a single Agent, but to put multiple Agents into one task workflow. For small-scale individual use, there is usually no rush to adopt them.
Manage AI teammates like a kanban board
Assign tasks to different Agents such as Claude Code, Codex and OpenCode, track status, logs and consumption, and have a human do final acceptance.
Suitable for: multiple coding Agents working in parallel, requiring project-based management.
More like a company's management team
Emphasizes organizational structure, budget, permissions and audit; suited to managing AI as a long-term team member.
Suited to: multi-Agent teams that need governance, quotas and responsibility boundaries.
When tasks are few, management ends up adding burden instead
If you only have one job, getting the work running is more important than building a management platform. Once tasks, people and acceptance truly become more complex, then add an orchestration layer.
Category 4 · Low-Code AI Platforms
They are better suited to "building something for others to use."
For example, customer service bots, internal knowledge Q&A, and marketing assistants. It is mainly used to publish reusable AI applications, not to operate your computer for you.
A self-hostable, deeply customizable application platform
You can build knowledge bases, workflows and application interfaces; materials and deployment methods are more controllable, suitable for enterprises gradually building their own AI applications.
Best for: teams with technical support that value data autonomy and process control. Note: deployment, permissions and maintenance require investment.
In one sentence: they want to build AI applications somewhere they can control.
A Bot factory with a fast start and many publishing channels
Configure persona, knowledge and workflow through the page, then publish to channels such as Feishu, WeChat and Douyin — suited to quickly validating a Bot.
Suited to: non-technical people doing rapid experimentation and distribution. Note: cloud hosting, platform dependency and long-term costs need to be assessed.
In one sentence: I want to quickly build an AI Bot that others can use.
Coze is not WorkBuddy
Coze produces "bots for others to use"; WorkBuddy is more like "a computer assistant that does your own work." One is opening a shop to sell services, the other is hiring someone to do the housework.
Category 5 · Development frameworks
Only when off-the-shelf tools are not enough do you enter the "build it yourself" stage.
Parts library and high-level assembly tools
Encapsulate capabilities such as models, prompts, tools, and document retrieval into components that are easier to combine, suited to quickly building prototypes and common applications.
Suitable for: developers quickly building RAG, customer service and internal applications.
Flowchart engine
Draw every step, conditional branch, loop, pause and human intervention clearly—suited to complex processes that require stability, recoverability and auditability.
Suitable for: production-grade, multi-step Agent workflows that require strict control.
Don't treat "the lower the level, the more professional" as a selection criterion
If you just want your team to spend less time digging through materials and produce drafts faster, using an Agent or platform directly is usually more suitable. Only when the business process is already validated and off-the-shelf tools genuinely fall short is it worth building your own.
Four things not to miss when selecting a solution
Beyond tool names, what really determines the outcome is the working conditions.
Is the material allowed to be uploaded to the cloud? Does it contain customer, pricing, or personal information? Can it be isolated by customer and project?
Should it only give suggestions, let you confirm first, or directly edit files, send messages and execute commands? The degree of delegation should match the task risk.
AI can organize, draft, and check, but customer commitments, quotes, public releases, and key judgments must have a clearly assigned owner.
Materials change, rules change, models change. Without a maintainer and a review method, even the best demo quickly becomes useless.
My suggestion:Don't use tools to try to cover up a job that hasn't been clearly defined yet. Start with a small-scale trial run on a real task, leave checkable results, and then decide whether to keep using an off-the-shelf tool, switch tools, or build something custom. You can start by looking atHow Andy Ren Xu works, and you can also start fromSpecific work scenarios on the homepageStart.
Start from the problem, not the tool
You don't need to decide first whether to use Claude, OpenClaw, or Dify.
Bring one task that keeps recurring lately: proposals, client records, content production, document Q&A, handovers or website maintenance. First see where it gets stuck, then judge what is worth handing to tools and what must stay with people.
Look at the service approach; bring a piece of work to judge