semvec/pypi-packages huggingface dataset:
megatron-mcp 0.1.0
Semantic memory for Claude Code sessions
Requires Python: >=3.10
Keywords: claude, claude-code, mcp, memory, semantic-search

# Megatron

Semantic memory system for Claude Code. Indexes conversation transcripts into project-local vector databases for context persistence across sessions.

## Installation

```bash
claude mcp add megatron-mcp uvx megatron-mcp
```

That's it. Restart Claude Code and the memory tools are available.

### Alternative: Manual Setup

If you prefer manual configuration, add to your `.mcp.json`:

```json
{
  "mcpServers": {
    "megatron-mcp": {
      "command": "uvx",
      "args": ["megatron-mcp"]
    }
  }
}
```

### Available Tools

Once configured, Claude Code gains these MCP tools:

| Tool | Description |
|------|-------------|
| `memory_search` | Search past conversations semantically |
| `memory_resume` | Get context to continue previous work |
| `memory_remember` | Save important decisions/context |
| `memory_sync` | Index the current session |
| `memory_stats` | View memory index statistics |

### Skills (Slash Commands)

If you copied the skills, these commands become available:

- `/recall <topic>` - Search memory for past context
- `/remember` - Save something to memory
- `/resume` - Continue where you left off
- `/stats` - Show memory statistics
- `/sync` - Sync current session to memory

### CLI Commands

```bash
uv run megatron init                    # Initialize project memory
uv run megatron sync                    # Sync from latest transcript
uv run megatron project-search "query"  # Search project memory
uv run megatron resume                  # Get resume context
uv run megatron project-stats           # Show memory stats
```

## Project Structure

When used, each project gets a `.megatron/` directory containing:
- `index/` - ChromaDB vector database
- `state.json` - Session state snapshots
- `queue/` - Pending sync items

## How It Works

1. Parses Claude Code JSONL transcripts from `~/.claude/projects/`
2. Groups messages into user-assistant exchanges
3. Indexes chunks into ChromaDB with semantic embeddings
4. Exposes search/remember/sync tools via MCP protocol

See [CLAUDE.md](./CLAUDE.md) for detailed architecture.
toonet 0.0.1
TooNet: A non-profit open-world query initiative, part of the TOO project.
Requires Python: >=3.7

# TooNet

**TooNet** is a non-profit open-world query initiative. It is a foundational component of the **TOO (To Operate Open-world)** ecosystem.

## Vision
Our mission is to provide an open, transparent, and high-efficiency query infrastructure for autonomous agents and AI systems operating in the open world.

## Project Ecosystem
- **TooPort**: Commercial encapsulation layer and transaction assurance.
- **TooNet**: Open data and query initiative (Non-profit).

## Links
- **Official Website**: [toonet.ai](https://toonet.ai)
- **Organization**: [TOO Project](https://github.com/toonet)

---
*Note: This package is currently a placeholder for the TooNet initiative.*
omniprompt 1.0.0
A command-line utility for quickly testing and interacting with various LLM APIs.
Requires Python: >=3.9

# OmniPrompt

OmniPrompt is a command-line utility for quickly testing and interacting with various large language model (LLM) APIs from different providers. "Omni" reflects the tool's ability to connect to all different AI providers, and "Prompt" is the core action.

## Features

-   Test prompts against multiple AI providers from a single interface.
-   Support for major providers: Google, OpenAI, Anthropic, Groq, and more.
-   List available models for each provider.
-   Generate images using Google's Imagen model.
-   Run a single prompt against all configured providers simultaneously.
-   Secure and discoverable API key configuration using environment variables.

## Setup

1.  **Clone the repository:**
    ```bash
    git clone https://github.com/andkamau/omniprompt.git
    cd omniprompt
    ```

2.  **Install the package:**
    You can install OmniPrompt in editable mode for development:
    ```bash
    pip install -e .
    ```
    This will install all dependencies and create the `omniprompt` command.

3.  **Configure API Keys:**
    OmniPrompt reads API keys from environment variables. It looks for a `config.yaml` file in the following locations:
    -   `~/.config/omniprompt/config.yaml`
    -   `~/.omniprompt.yaml`
    -   The current directory (`./config.yaml`)

    **Step 1: Create your config file**
    Copy the provided `config.yaml` to one of the locations above.

    **Step 2: Set the Environment Variables**
    Set the environment variables with your actual API keys as defined in your `config.yaml`.

    *Example:*
    ```bash
    export OPENAI_API_KEY="sk-..."
    export GOOGLE_API_KEY="AIza..."
    ```

## Usage

Once installed, you can use the `omniprompt` command from anywhere.

### Basic Prompt
```bash
omniprompt -P openai -m gpt-4o -p "What are the three laws of thermodynamics?"
```

### List Available Models
```bash
omniprompt -l google
```

### Image Generation
```bash
omniprompt -P google -i "A futuristic cityscape at sunset, digital art."
```

### Test All Providers
```bash
python omniprompt.py -a -p "Write a haiku about a robot learning to paint."
```

### Arguments

| Full Argument      | Short Argument | Description                                           |
| ------------------ | -------------- | ----------------------------------------------------- |
| `--provider`       | `-P`           | The API provider (e.g., `google`, `openai`).          |
| `--model`          | `-m`           | The specific model to use (e.g., `gpt-4o`).           |
| `--prompt`         | `-p`           | The text prompt to send to the model.                 |
| `--list-models`    | `-l`           | List available models for a given provider.           |
| `--generate-image` | `-i`           | The prompt for image generation.                      |
| `--all-providers`  | `-a`           | A flag to send a prompt to all configured providers.  |