semvec/pypi-packages huggingface dataset:
mindframe-scaffold 0.1.1
A powerful CLI-based project scaffolding tool for Generative AI and Agentic AI.
Requires Python: >=3.9
Keywords: genai, ai, scaffolding, agents, architecture

# 🧠 Mindframe

**"Structure first. Intelligence second."**

Mindframe is a powerful Python CLI-based project scaffolding tool designed specifically for Generative AI and Agentic AI developers. It enforces professional software architecture patterns from Day 1, ensuring your AI systems are scalable, maintainable, and swap-ready.

---

## 🚀 Why Mindframe?

Most GenAI projects start as "spaghetti code." Mindframe solves this by providing a standardized, industrial-grade project structure that separates concerns across 5 critical layers.

- **Zero Manual Work**: Initialize a full project structure with one command.
- **Clean Boundaries**: Dedicated layers for Domain logic, LLM interface, Knowledge/RAG, Orchestration, and Evaluation.
- **Agentic Ready**: Seamlessly extend standard GenAI projects into autonomous agent systems.
- **Swap-Ready**: Effortlessly switch between OpenAI, Anthropic, or Local models without rewriting your app logic.

---

## 📦 Installation

To use Mindframe globally on your system, install it directly from GitHub:

```bash
pip install git+https://github.com/Ahmedmalik414/mindframe.git
```

For local development/contributions:
```bash
git clone https://github.com/Ahmedmalik414/mindframe.git
cd mindframe
pip install -e .
```

---

## 🛠 Usage

### 1. Standard GenAI (RAG) Project
Perfect for knowledge-based chatbots and standard LLM applications.
```bash
mindframe init my_project
```

### 2. Agentic AI Project
Adds specialized layers for planning, tool execution, and memory management.
```bash
mindframe init my_agent --type agentic
```

---

## 📂 The "Authoritative" Structure
When you run `mindframe init`, it generates a clean, scalable skeleton:

```text
my_project/
├── config/             # Model & Logging settings
├── data/               # Cache, Embeddings, & Vector DB
├── src/                
│   ├── core/           # LLM Abstractions (GPT, Claude, etc.)
│   ├── prompts/        # Templates & Chaining
│   ├── rag/            # Retrieval logic & Search
│   ├── processing/     # Chunking & Cleaning
│   └── inference/      # Model execution engine
├── tests/              # Unit & Integration tests
├── scripts/            # Automation (env setup, ingestion)
├── Dockerfile          # Container config
└── requirements.txt    # Standard dependencies
```

*Note: The `agentic` type adds `src/orchestration/` containing `agents/` and `memory/` modules.*

---

## 🤝 Contributing
Mindframe is built for the community. If you have ideas for better AI architectural patterns, feel free to open a PR!

---

**Built by [Ahmed Malik](https://github.com/Ahmedmalik414)**  
*Architecture is the frame that supports the mind.*
aimailbox 1.0.4
Permissionless inbox for AI agents. Receive emails without registration.
Requires Python: >=3.8
Keywords: agent, ai, cli, email, inbox, temporary-email, verification

<p align="center">
  <img src="https://raw.githubusercontent.com/ted2048-maker/aimailbox/main/logo.svg" alt="AIMailbox Logo" width="120" height="120">
</p>

<h1 align="center">aimailbox</h1>

<p align="center"><strong>An inbox for your agent, no questions asked.</strong></p>

*Permissionless email for AI agents. No signup, no KYC, no wait.*

## Installation

```bash
pip install aimailbox
```

Or with pipx:

```bash
pipx install aimailbox
```

## Quick Start

```bash
# Create a new inbox
aimailbox create

# List messages
aimailbox list <inbox>

# Read a message
aimailbox read <inbox> 1

# Read the latest message
aimailbox read <inbox> --latest

# Get only the verification code
aimailbox read <inbox> 1 --code-only

# Output as JSON
aimailbox read <inbox> 1 --json

# Delete an inbox
aimailbox delete <inbox>
```

## Features

- **Permissionless**: No registration, no KYC, no account required
- **Receive-only**: Only receive emails, no sending (prevents spam abuse)
- **AI-friendly**: JSON output, automatic verification code extraction
- **Secure**: Token-based authentication, only you can read your inbox

## Commands

### `aimailbox create`

Create a new inbox. Returns the email address and a secret token.

```bash
$ aimailbox create

✓ Inbox created successfully!

  Email:    x7k2m@aimailbox.dev
  Inbox ID: x7k2m
  Token:    abc123...

Token has been saved locally. Keep it safe - it cannot be recovered!
```

### `aimailbox list <inbox>`

List messages in an inbox.

Options:
- `-l, --limit`: Number of messages to show (default: 20)
- `-t, --token`: Authentication token (uses stored token if not provided)
- `--json`: Output as JSON

### `aimailbox read <inbox> [index]`

Read a specific message.

Options:
- `--latest`: Read the latest message
- `-t, --token`: Authentication token
- `--json`: Output as JSON
- `--code-only`: Only output the verification code (useful for scripts)

### `aimailbox delete <inbox>`

Delete an inbox and all its messages.

Options:
- `-f, --force`: Skip confirmation
- `-t, --token`: Authentication token

## Environment Variables

- `AIMAILBOX_API_URL`: Override the default API endpoint

## Token Storage

Tokens are stored locally in `~/.aimailbox/tokens.json` with restricted permissions (600).

## License

MIT