DEV Community: AI-Hub-Admin

How I Shipped Production AI Agents in One Week — With Real Domains, Websites, Apps, APIs, and Skills Serving Online Traffic

AI-Hub-Admin — Mon, 18 May 2026 07:27:16 +0000

Hi, I’ve been busy working on two production-grade AI Agent projects, and both are now live online, released on Product Hunt (one reached #36 of the day rank among 200+ products), and actively serving real user traffic.

I’d like to share some of the experiences, technology stack, development process, deployment lessons, and the actual costs involved in building and serving these AI agents. More importantly, I want to show how ideas can quickly become working applications.

First of all, these are not “vibe-coded” rushed projects. Turning ideas into production-ready code, design, deployment, and debugging within one week was still much faster than I expected.

The first project is Craftsman-Agent(https://craftsman-agent.aiagenta2z.com), which turns prompts into buildable 3D assembly charts and step-by-step instructions for creations such as LEGO builds, Minecraft structures, Tesla car wraps, and more.

The platform is designed for creativity and play, targeting:

game hobbyists
parents and kids
3D design prototyping
creative builders

Example prompts include:

“How do I build a blue-and-white LEGO yacht?”
“Design a red LEGO F1 race car.”
“Create a Minecraft-style castle with medieval towers.”

The second project is CoachOwl Agent Timetable (https://coachowl.aiagenta2z.com/), an AI Agent timetable, calendar, and orchestration platform that helps users set objectives and assign tasks to both humans and AI agents (such as Codex, Claude, and Gemini) for collaborative work in:

fitness
career growth
learning
personal development
productivity planning

The stack I am using:

Server
Python/ Node.js / FastAPI / pnpm serve

LLMs & AI Models
Gemini/ OpenAI /Qwen

APIs(DeepNLP OneKey Gateway)
Google Search APIs
Tavily Search APIs
Nutrition calculation APIs
Gemini Nano Banana image recognition APIs
3D rendering APIs

Deployment & Hosting
AI Agent A2Z platform
One free domain and project per user account (*.aiagenta2z.com)

Coding agent:
Claude
Codex

As you can see, these are fully functioning applications with:

deployed websites and landing pages

AI Agent tool usage / pipelines
LLM/Agent Gateway Integration
External API Fast Integrations (3D/Food Nutrition/Search/Images)

The most important lesson I learned is that shipping production AI systems is far more about orchestration, deployment, debugging, UX, and infrastructure than prompt engineering alone.

1. Craftsman Agent

Design & Development

webpage: https://craftsman-agent.aiagenta2z.com
GitHub: https://github.com/AI-Hub-Admin/Craftsman-agent

I build the main website homepage using claude code/codex in a few minutes. And building the main AI Agent workflow and 3D text-3D instruction using 3 days,
which is more complicated and no free APIs available. The AI Agent workflow of turning text prompts to 3D instruction, for example

text -> 3D parts plans json list -> 3D rendering APIs calling

Agent Workflow: Text to 3D instruction

For example, a 3D lego boats will needs a inventory of less than 10 to 1000 pieces 3D parts of data in the following formats.
And converting these LLM generated 3D models data into rendering instructions will also involve calling 3D APIs multiple times.
And the raw LLM generated 3D files are actually of pure quality, so SFT and few shots are also needed to enhance the final design.

[
    {
        "color": "red",
        "size": [
            4.0,
            2.0,
            1
        ],
        "position": [
            2,
            1,
            0.5
        ],
        "part_id": 3020,
        "part_name": "Plate 2x4",
        "image": "M3020",
        "categories": "Basic, Architectural, Transportation, Space, Plate, Solid Studs"
    },
    {
        "color": "red",
        "size": [
            2.0,
            1.0,
            1
        ],
        "position": [
            1,
            2,
            1.5
        ],
        "part_id": 3023,
        "part_name": "Plate 1x2",
        "image": "M3023",
        "categories": "Basic, Architectural, Transportation, Space, Plate, Solid Studs"
    },
]

LLM and API Calling: DeepNLP OneKey Agent Gateway of Gemini LLM endpoint and 3D building APIs (https://deepnlp.org/doc/onekey_gateway).

Server/APIs/Skills/MCPs:
The server is developed using python FastAPI backend with endpoints serving both MCPs/Skills and CLIs APIs.
These codes are not open sourced but skills and clis are available to use and registered on OneKey Gateway,
And there are agent run payment paypal endpoint designed to charge per 3D assembly generated.

## endpoint
app = Starlette(
    routes=[
        Route("/chat", chat_endpoint, methods=["POST"]),   # New Chat Endpoint
        Mount("/static", app=StaticFiles(directory= STATIC_DIR.resolve() , html=True), name="static"),
        Mount("/assets", app=StaticFiles(directory= ASSETS_DIR.resolve(), html=True), name="assets"),
        Route("/api/v1/generate_minecraft_build_plan", api_generate_minecraft_build_plan, methods=["POST"]),
        Route("/api/v1/generate_lego_build_plan", api_generate_lego_build_plan, methods=["POST"]),
        Route("/api/v1/generate_tesla_wraps", api_generate_tesla_wraps_build_plan, methods=["POST"]),
        Route("/paypal/agent/purchase/callback", paypal_webhook, methods=["POST"]),
        Mount("/", app=mcp_app),  ## MCP Endpoint Always mounts /mcp
    ],
    lifespan=lifespan,
)

Website Domain & Hosting:
Website: Typescript JS based website serving the /static, /image gallery
https://github.com/aiagenta2z/agent-mcp-deployment-templates/tree/main/quickstart/website_typescript
Doc: https://deepnlp.org/doc/agent_mcp_deployment

Summary:
Cost of time turning the ideas into production AI Agents:

Phase	Cost
Development	Website (A Few Minutes) + AI Agent Workflow/APIs(3 days)
LLM Tokens & APIs	Gemini + 3D Rendering APIs (Not Free, Consume tokens/avg $67 dollars/1k gemini 3.1 image generation/OneKey Credit 5000 credits/1k images call ~ roughly $50/1k images)
Hosting and Domains	Free (https://craftsman-agent.aiagenta2z.com) for basic plan with limited mem and CPU usage.
Traffic Routing	Get Free Cold Start Traffic from DeepNLP Agent Router for hosted agents.

2. CoachOwl AI Agent Orchestration

Features:

Online Timetable & Calendar: CoachOwl Agent has the calendar & timeline features to allows AI Agent to connect, track, assign and schedule tasks for human and AI Agents to collaborative, competible with Google Calendar, Outlook for AI Agents.
AI Agent Orchestration: Humans can better assign repetitive & periodic tasks easily to your personal Agents by scheduling, tracking compared to sending messages. Scenario: Repetitively Sending Emails of competitor analysis of ProductHunt daily for 2 weeks. Anaylyzing food calories for 2 weeks. Prepare for SAT/CFA/GRE exams.
Add a task & objective: You can add task, objective (AI coach will plans several periodic tasks for you and you can always edit task contents.)
AI Coaches: AI Coaches with Special Skills assigns tasks to both human and agents, such as Fitness Coach, Relationship Coach, Career Coach, Relationship Coach, Fitness Coach: Food Calories Analysis, Image Recognition, Career Coach: Search Indutry info and send briefs to Emails. Prepare for Social Media Anouncement, write Blogs. Learning Coaches
Easy Voice Input, Habit Tracking, AI Agent Task Scheduling, Connect to Claude Code, Codex, OpenClaw and more.

Support Coaches and Agent Tools & Ability

The app is build on Onekey Agent Router APIs for Image processing, Food Calories Searching, Sending Emails, Deep Research Abilities.

You can always extends more skills to use CoachOwl as an AI Agent Orchestrator to assign tasks to your Local Agents (Codex/Claude/Gemini)

Category	Agent Tools & Features
BASE	`base_search` Deep Research of Google Search Tavily Search APIs, `send_email_with_attachments` Send summary reports to your Email accounts.
Fitness	`analyze_foods_nutrition_workflow` generate nutritions & calories reports from uploaded images or text input
Career	`track_competitor_launches_producthunt` Fetch ProductHunt releases, `job_search` APIs
Learning	Exam mock question generation, such as `CFA` `SAT`

Agents OneKey Gateway	Supported Agents
Default	Default, Server Web Agents on CoachOwl
Codex	Local Agents, `codex` CLIs
Claude Code	Local Agents, `claude` CLIs
Gemini	Local Agents, `gemini` CLIs
OpenClaw	Local Agents, `openclaw` CLIs

Design & Development

Website Homepage: https://craftsman-agent.aiagenta2z.com

Github(Fully OpenSourced): https://github.com/AI-Hub-Admin/CoachOwl-Agent-Timetable

Deployment: https://github.com/aiagenta2z/agent-mcp-deployment-templates

The AI Agent Orchestration

The Timeline (oneday todo list) and the calendar
The app is build using codex, which costs me one day to finish all the tables design, frontend web design, etc.
Agent Execution system
Agent Execution include setting up agent tasks (recursive tasks), such as "Deep Research the producthunt AI Agent releases in last 7days and send to my Emails at xxxx@gmail.com".
The agent execution related include
"""
/claim task -> /Execution -> Report Heartbeat -> Update Results
"""

The db design and implementation costs 4- 5 days. And for local agents running, use "onekey gateway coachowl/coachowl" to pull tasks from the web, which involves
another one day to complete.

Summary

Phase	Cost
Development	Website (A Few Minutes) + AI Agent Orchestration API DBs (3 days)
LLM Tokens & APIs	Gemini/OpenAI/Qwen
Hosting and Domains	Free (https://coachowl.aiagenta2z.com) for basic plan with limited mem and CPU usage.
Traffic Routing	Google Search with Domain Verification

Craftsman-Agent: https://craftsman-agent.aiagenta2z.com

CoachOwl AI Agent Timetable: https://coachowl.aiagenta2z.com

AI Agent A2Z Deployment & Hosting: Deployment Portal & Doc

OneKey Gateway API & LLM Usage: https://deepnlp.org/doc/onekey_gateway

Tired of All the Agent Access Formats And I Built a OneKey Gateway as omni converter to Ship APIs as MCPs Skills CLIs 10x faster

AI-Hub-Admin — Thu, 09 Apr 2026 04:55:31 +0000

Hey dev community 👋

As the agent ecosystem keeps growing, I’ve noticed a recurring problem:

You might have one solid business API or proprietary data, but to make it usable across different agent platforms, you end up rebuilding it in multiple formats:
MCPs, Skills ,CLIs , REST APIs , Even language-specific SDKs (Python / TypeScript). That is painful when you want to upgrade, you have to upgrade on all of it, which means duplicated effort, multiple codebases, and painful maintenance.

That’s why I started OneKey Gateway project:
GitHub OneKey Gateway

with internal gateway layer, central Agent API registry platform, onekey authentication, and omni format converter, you only need to maintain one API/data, and users/agents can use in all formats.

Introduction

OneKey Gateway Agent/Skills/MCP Conversion Matrix Roadmap

Register and maintain your APIs only once, allow agents/users to use your APIs in other formats 10x faster, supported CLIs/Skills/Rest API/MCPs

From \ To	API	CLI	Skills	Routed API	MCPs (StreamingHTTP)	MCPs (Stdio Local)
API	-	✅	✅	✅	✅	-
CLI	-	-	✅	-	-	✅
Skills	-	-	-	-	✅	✅
Routed API	-	-	-	-	-	-
MCPs (Streaming HTTP)	-	-	-	-	-	-
MCPs (Stdio Local)	-	-	-	-	-	-

So the question is: Today, most solutions only partially solve this. You’ll find tools like fastapi_mcp and similar converters—but they’re still code-level solutions:
You still need to write glue code. You still need to maintain multiple packages, publish and version each format
In reality, you might end up managing 6–10 different packages for a single API.
That’s not scalable.

The workflow of OneKey Agent Gateway works like

Register your API 
→ Store metadata (Agent meta, API meta OpenAPI, auth, configs)
→ Auto-generate & distribute as
   - CLI
   - Skills
   - MCP
   - REST Router
-> Agent Requests Routed to your APIs

Here is the roadmap of potential cases of conversion between various formats: https://github.com/aiagenta2z/onekey-gateway
Now I will introduce you how to convert a food calories API to various formats without publishing any more packages and use the OneKey Gateway.

OneKey Gateway Registered and Routed

Original API

Food Nutrition API from U.S. Department of Agriculture

Search the nutritions caloeries of Cheddar Cheese.

curl -XPOST -H "Content-Type:application/json" -d "{\"query\":\"Cheddar cheese\"}" https://api.nal.usda.gov/fdc/v1/foods/search?api_key=DEMO_KEY

After registration as in docs OneKey Gateway Doc. Agents/Users can use your APIs in various formats:

Convert API to CLI

npx onekey agent fdcnal/usda-fooddata-central-agent search_foods '{"query": "Cheddar Cheese","pageSize": 10}'

Convert API to Skills

npx agtm skills build fdcnal/usda-fooddata-central-agent

It will generate local ./skills folder with scripts and CLIs

Convert API to MCP

npx onekey mcp fdcnal/usda-fooddata-central-agent

It generates a mcp config to access your APIs

{
  "mcpServers": {
    "deepnlp-onekey-fdcnal-usda-fooddata-central-agent": {
      "url": "https://agent.deepnlp.org/mcp?server_name=fdcnal/usda-fooddata-central-agent&onekey=YOUR_KEY"
    }
  }
}

Convert API to REST Router in programming way

export DEEPNLP_ONEKEY_ROUTER_ACCESS=YOUR_ROUTER_KEY
curl -X POST "https://agent.deepnlp.org/agent_router" -H "Content-Type: application/json" -H "X-OneKey: $DEEPNLP_ONEKEY_ROUTER_ACCESS" -d '{"unique_id":"fdcnal/usda-fooddata-central-agent","api_id":"search_foods","data":{"query":"Cheddar Cheese","pageSize":10}}'

And for the remaining blog, I will introduce you how to register the API to DeepNLP x AI Agent A2Z agent registry.
and distribute in various formats.

2. Quickstart Example: USDA FoodData Central API

Let’s see how to register and convert a Food Nutrition API:

2.1 Prepare API Specs

Use the USDA OpenAPI specs (fdc_api.json or fdc_api.yaml) to define your agent metadata. If no OpenAPI or similar specs are available, provide the python/typescript definition as "def my_func(param1: str,...)" to coding agent/claude code/codex to help you generate the meta.

agent.yaml

api_list:
  - api_id: search_foods
    protocol: https
    description: Search for food items and calories
    endpoint: https://api.nal.usda.gov/fdc/v1/foods/search
    method: POST
    params:
      query: str
      pageSize: int
    auth:
      type: API_KEY
      header: "X-Api-Key"
      value: $YOUR_FDC_API_KEY

You can also generate this using Claude Code or Codex. I am using the prompt, in the Github repo run the prompt:

Generate agent.json and agent.yaml file following the format of ./examples/usda_food_api/agent.json and agent.yaml format,
fill in the api_list key, with meta of APIs: api_id,protocol,description,endpoint,method,params,auth, For params, you can read the necessary informations from the the OpenAPI specs of fdc_api.json.
Output to /agent.json or /agent.yaml formats

1.2 Register Your API

Get your OneKey Gateway registry key, the key will identify you as the owner of the registered API. Get Keys at Keys

export AI_AGENT_MARKETPLACE_ACCESS_KEY=YOUR_REGISTRY_KEY
npx agtm upload --config ./agent.yaml

Your agent’s unique ID is in user_id/repo_name format, e.g.:

fdcnal/usda-fooddata-central-agent

1.3. Usage Examples

API2REST

The request will be posted to central registry endpoint, https://agent.deepnlp.org/agent_router, authenticated once and routed
to your registered API endpoint with payload and headers for authentication. Note the access_key devs provide are encrypted safely and decrypted/routed to your registered API.

export AI_AGENT_MARKETPLACE_ACCESS_KEY=YOUR_REGISTRY_KEY

curl -X POST "https://agent.deepnlp.org/agent_router" \
-H "Content-Type: application/json" \
-H "X-OneKey: $DEEPNLP_ONEKEY_ROUTER_ACCESS" \
-d '{
  "unique_id": "fdcnal/usda-fooddata-central-agent",
  "api_id": "search_foods",
  "data": {"query": "Cheddar Cheese","pageSize": 10}
}'

API2CLI

Users can use your API in the common command line npx onekey agent <unique_id> <api_id> <payload>

npx onekey agent <unique_id> <api_id> <payload>
npx onekey agent fdcnal/usda-fooddata-central-agent search_foods '{"query": "Cheddar Cheese","pageSize": 10}'

API2Skills

If you readily convert your APIs to local skills under the folder ./skills/your-api-converted-skill/*

npx agtm skills build fdcnal/usda-fooddata-central-agent

Generates:

skills/fdcnal-usda-fooddata-central-agent/
  ├── SKILL.md
  ├── scripts/
  └── reference/

API2MCP Server

npx onekey mcp fdcnal/usda-fooddata-central-agent

Generates config for StreamingHTTP MCPs:

{
  "mcpServers": {
    "deepnlp-onekey-fdcnal-usda-fooddata-central-agent": {
      "url": "https://agent.deepnlp.org/mcp?server_name=fdcnal/usda-fooddata-central-agent&onekey=YOUR_KEY"
    }
  }
}

RoadMap

If you like the ideas of the onekey gateway and roadmap of converter.
You can also contribute more use cases, documents and your registered APIs at the Onekey Gateway GitHub and AI Agent Marketplace GitHub.

I Keep Forgetting CLIs So Does My AI Agent - Simplifying CLI Usage for Human and AI Agents with Hints and Completion

AI-Hub-Admin — Fri, 20 Mar 2026 08:07:52 +0000

As an LLM/AI Agent application developer, I frequently use tools like Claude Code, Codex, or OpenClaw to manage agent tasks via MCP, Skills, and especially command-line interfaces (CLIs).

One problem I face: I can never remember all the exact CLI commands, positional arguments, or options for tools like playwright-cli, claude, or codex. Searching through documentation or README.md files every time is tedious
and I am just too lazy to do it.

To solve this, I spend some time to built a CLI helper tool with hints and auto-completion capabilities.
This tool helps both humans and agents quickly discover and run CLI commands without memorizing them. And I would like to share with the community
agtm which means agent manager or agent marketplace.

How It Works

For example, if you want to test playwright-cli for web page reading but forget the exact commands:

Type a keyword, e.g., play, after agtm run:

npx agtm run play

Then you can follow the guidance and cli hints to finalize your cli.

Step 1. Choose the suggested Skill ID

Skill ID suggestions:
  1. microsoft/playwright-cli
  2. paul-gauthier/aider
  3. anthropic/claude-code
  4. openai/codex-cli
  5. googleworkspace/cli

Step 2. Choose the suggested CLI:

Command hints:
  1. playwright-cli goto <url> # navigate to a url
  2. playwright-cli open [url] # open browser, optionally navigate to url

Step 3.
Complete the final command by filling in parameters:

playwright-cli goto https://www.google.com
agtm run microsoft/playwright-cli playwright-cli goto https://www.google.com

Then it gives you exact cli running results

### Ran Playwright code

js
await page.goto('https://www.google.com');

### Page
- Page URL: https://www.google.com/
- Page Title: Google
### Snapshot
- [Snapshot](../aiagenta2z/agtm/.playwright-cli/page-2026-03-20T08-03-59-159Z.yml)

shell

Installation

npm install -g @aiagenta2z/agtm
npx agtm setup --hint  # Builds a local RAG of CLI hints
npx agtm run <provider_unique_id> <agent_cli>

provider_unique_id: identifies the project owner/repo (GitHub style)
agent_cli: the CLI commands in that repository

Agent Mode

Agents like Claude or Codex can also use the hint system to run commands automatically if there are documents mismatch and skills update. In agent mode, the tool searches relevant Skill IDs and CLI suggestions for non-interactive execution:

npx agtm run play --mode agent

Skill ID suggestions:
  1. microsoft/playwright-cli
     - playwright-cli open [url]  # open browser, optionally navigate to url
     - playwright-cli goto <url>   # navigate to a url

prompt: Help me visit producthunt.com and extract front-page items to a local text file.

Architecture

CLI RAG Local:
agtm setup builds a local RAG (trie) of CLI hints. It matches Skill IDs first, then provides CLI suggestions and hints for the selected Skill.

Agent Mode:
Enables agents to use CLI hints automatically. Instead of interactive selection, the tool provides relevant CLI suggestions from the local RAG.

What do you think of the cli hint tool that work for both human and agent?

Please welcome to contribute to github aiagenta2z/agtm of the clis repos.

DEV Community: AI-Hub-Admin

How I Shipped Production AI Agents in One Week — With Real Domains, Websites, Apps, APIs, and Skills Serving Online Traffic

1. Craftsman Agent

Design & Development

2. CoachOwl AI Agent Orchestration

Features:

Support Coaches and Agent Tools & Ability

Design & Development

The AI Agent Orchestration

Related

Tired of All the Agent Access Formats And I Built a OneKey Gateway as omni converter to Ship APIs as MCPs Skills CLIs 10x faster

Introduction

OneKey Gateway Agent/Skills/MCP Conversion Matrix Roadmap

OneKey Gateway Registered and Routed

Original API

Convert API to CLI

Convert API to Skills

Convert API to MCP

Convert API to REST Router in programming way

2. Quickstart Example: USDA FoodData Central API

2.1 Prepare API Specs

1.2 Register Your API

1.3. Usage Examples

API2REST

API2CLI

API2Skills

API2MCP Server

RoadMap

I Keep Forgetting CLIs So Does My AI Agent - Simplifying CLI Usage for Human and AI Agents with Hints and Completion

How It Works

Agent Mode

Architecture