semvec/pypi-packages huggingface dataset:
lw2 0.1.0
Lightware LW2 API
Requires Python: >=3.10

# LW2

Lightware LW2 API

## Overview

This project provides the `lw2` Python package, a lightweight wrapper for the Lightware LW2 API.

---

## Prerequisites

Before you begin, ensure you have the following installed on your system:

- **Python**: Version 3.10 or higher
- **pip**: The Python package manager (comes with Python)
- **uv**: A CLI tool for running project-specific commands (see below)

---

## macOS

```bash
brew install uv
```

## Setting Up a Development Environment

Follow these steps to set up the project for development:

1. **Install environment**

   ```bash
   uv sync
   ```


## Running tests

The project uses `tox` to run `pytest` for testing and `ruff` for linting. To run the tasks:

```bash
uv run pytest
```

If you want to run manual integration tests you have to install and run:

```bash
npm -i -g mountebank
mb --configfile imposter.json
```

and in another terminal window:

```bash
uv run test.py
```

To fix linting errors you can run:

```bash
uv run ruff check . --fix
```

## Update dependencies

To update the lockfile run

```bash
uv sync
```

## Building the Package

To build the package (wheel and source distribution), run:

```bash
uv build
```

This will generate the following files in the dist/ directory:

Source distribution: `lw2-<version>.tar.gz`
Wheel file: `lw2-<version>-py3-none-any.whl`

## Publishing the Package

```bash
uv publish
```
ldk-lnprototest 1.0.1
Ldk-Sample Runner for Lnprototest
Requires Python: <4.0,>=3.8

## Lnprototest Runner for LDK-Sample

This is a runner script for LDK-Sample. It can be used to run **Lnprototest** tests against a **LDK-Sample** node.

### Usage

To run against Lnprototest BOLT tests:

1. **Clone Lnprototest:**

   ```bash
   git clone https://github.com/rustyrussell/lnprototest.git
   ```

2. **Clone LDK-Sample:**

   ```bash
   git clone https://github.com/Psycho-Pirate/ldk-sample.git
   ```

3. **Build LDK-Sample:**

   ```bash
   cd ldk-sample
   cargo build
   ```

4. **Set environment variables:**

   ```bash
   export LDK_SRC=[path to ldk-sample repo]
   export PYTHONPATH=$PYTHONPATH:[path to lnprototest repo]
   ```

5. **Install the runner:**

   ```bash
   pip install ldk-lnprototest
   ```

6. **Run the tests:**

   ```bash
   pytest [path to lnprototest repo]/tests --runner=ldk_lnprototest.Runner --log-cli-level=DEBUG
   ```
agent-flow-craft 2025.4.1.1
Framework para automação de fluxo de criação de features usando agentes de IA
Requires Python: >=3.9

# Agent Flow Craft

Framework para automação de fluxo de criação de features usando agentes de IA.

## Instalação

### Instalação Rápida

```bash
# Clonar o repositório
git clone https://github.com/seu-usuario/agent-flow-craft.git
cd agent-flow-craft

# Criar ambiente virtual e instalar dependências
make install

# Para desenvolvimento, instalar dependências adicionais
make setup
```

### Configuração

1. Copie o arquivo `.env.example` para `.env`:
```bash
cp .env.example .env
```

2. Configure as variáveis de ambiente no arquivo `.env`:
```env
# Chaves de API
OPENAI_API_KEY=sua-chave-openai
OPENROUTER_API_KEY=sua-chave-openrouter
GOOGLE_API_KEY=sua-chave-google
GITHUB_TOKEN=seu-token-github

# Configurações de Modelos
DEFAULT_MODEL=gpt-4-turbo-preview
ELEVATION_MODEL=gpt-4-turbo-preview
FALLBACK_MODEL=claude-3-sonnet
```

## Uso

O pacote oferece vários comandos para automação de tarefas:

```bash
# Criar uma nova feature
agent-flow-craft feature --name "nome-da-feature" --description "descrição da feature"

# Gerar conceitos
agent-flow-craft concept --name "nome-do-conceito"

# Validar plano
agent-flow-craft validate --plan "plano-a-validar"

# Integração com GitHub
agent-flow-craft github --repo "usuario/repositorio"

# Gerar critérios TDD
agent-flow-craft tdd --feature "nome-da-feature"

# Verificar status do sistema
agent-flow-craft status
```

## Desenvolvimento

Para contribuir com o projeto:

```bash
# Instalar dependências de desenvolvimento
make setup

# Formatar código
make format

# Verificar código
make lint

# Executar testes
make test

# Limpar arquivos gerados
make clean
```

## Modelos Suportados

O framework suporta os seguintes provedores de modelos:

- OpenAI (GPT-4, GPT-3.5)
- OpenRouter (Claude, Mistral, etc)
- Google (Gemini)

O sistema possui um mecanismo de fallback que alterna automaticamente entre modelos em caso de falhas ou limites de quota.

## Licença

Este projeto está licenciado sob a licença MIT - veja o arquivo [LICENSE](LICENSE) para detalhes.