semvec/pypi-packages huggingface dataset:
code-workspace-sync 0.1.1
synchronize a VS Code workspace with directories in a source repositories folder
Requires Python: >=3.12

# VS Code Workspace Synchronizer

A script to synchronize a VS Code workspace with directories in a source repositories folder.
It can add new directories and optionally remove directories that no longer exist.

```bash
uv tool install code-workspace-sync
```

For help and available commands, run: `code-workspace-sync --help`:

```text
usage: code-workspace-sync [-h] [--src-repos SRC_REPOS]
                           [--workspace WORKSPACE] [--remove-missing]
                           [--dry-run] [--verbose]

Synchronize VS Code workspace with src_repos directories

options:
  -h, --help            show this help message and exit
  --src-repos SRC_REPOS
                        Path to the source repositories directory (default:
                        src_repos)
  --workspace WORKSPACE
                        Path to the VS Code workspace file (default: auto-
                        detect in current directory)
  --remove-missing      Remove directories from workspace that no longer exist
                        in src_repos
  --dry-run             Show what would be changed without making any
                        modifications
  --verbose, -v         Enable verbose output

Examples:
  # Basic sync (add missing directories)
  python sync_workspace.py

  # Sync with custom paths
  python sync_workspace.py --src-repos ./repositories --workspace my-workspace.code-workspace

  # Sync and remove missing directories
  python sync_workspace.py --remove-missing

  # Dry run to see what would change
  python sync_workspace.py --dry-run --verbose

  # Full sync with verbose output
  python sync_workspace.py --remove-missing --verbose

```
restack_ai 0.0.115
Restack AI library
Requires Python: >=3.10

# Restack AI libraries

This is the Python Restack AI libraries.

## Installation

You can install the Restack AI libraries using pip:

### Using pip

```bash
pip install restack_ai
```

### Using Poetry

```bash
poetry add restack_ai
```

### Using uv

```bash
uv add restack_ai
```

## Prerequisites

- Python 3.8 or higher
- Poetry (for dependency management)

## Usage

To use the libraries in your project, you can import it like this:

```python
from restack_ai import Restack
```

## Initialize the Restack client

```python
client = Restack()
```

## Documentation

For detailed usage instructions, please visit our examples repository at [https://github.com/restackio/examples-python](https://github.com/restackio/examples-python).

## Development

If you want to contribute to the libraries development:

### Clone the repository and navigate to the `engine/libraries/python` directory.

### Install Poetry/uv if you haven't already: [https://python-poetry.org/docs/#installation](https://python-poetry.org/docs/#installation) or use pip

### Activate the virtual environment:

If using uv:

```bash
uv venv && source .venv/bin/activate
```

If using poetry:

```bash
poetry env use 3.12 && poetry shell
```

If using pip:

```bash
python -m venv .venv && source .venv/bin/activate
```

### Install the project dependencies:

If using uv:

```bash
uv sync
```

If using Poetry:

```bash
poetry install
```

If using pip:

```bash
pip install -e .
```
archer-api-client 1.0.0
A comprehensive Python library for Archer IRM API integration
Requires Python: >=3.8

# Archer API Client Library

A comprehensive Python library for seamless integration with Archer IRM APIs.

## Features

- 🔐 **Secure Authentication**: Built-in session management and token handling
- 🎯 **OOP Architecture**: Clean, intuitive class-based design
- 📦 **Multiple API Support**: RESTful, Web Services, and Content APIs
- 🔄 **Automatic Retries**: Configurable retry logic for failed requests
- 📝 **Type Hints**: Full type annotation support
- 📚 **Extensive Documentation**: Detailed guides and examples

## Installation

```bash
pip install -e .
```

## Quick Start

```python
from archer_api import ArcherClient

# Initialize the client
client = ArcherClient(
    base_url="https://your-archer-instance.com",
    instance_name="YourInstance",
    username="your_username",
    password="your_password"
)

# Get applications
applications = client.rest.applications.get_all()

# Create a record
new_record = client.rest.records.create(
    application_id=123,
    field_values={
        "Field Name": "Field Value",
        "Another Field": "Another Value"
    }
)
```

## Documentation

See the `docs/` directory for detailed documentation.

## License

MIT License
langgraph-s3-checkpointer 0.1.0
Langgraph S3 Checkpoint Persistant Memory
Requires Python: >=3.10

# LangGraph S3 Checkpoint

A Python package that provides S3 storage capabilities for LangGraph checkpoints. This package implements the `BaseCheckpointSaver` interface from LangGraph to allow storing and retrieving checkpoints from Amazon S3.

## Installation

You can install the package directly from PyPI:

```bash
pip install langgraph-s3-checkpointer
```

Or using uv:

```bash
uv pip install langgraph-s3-checkpointer
```

## Requirements

- Python 3.8+
- boto3
- langgraph

## Usage

### Basic Usage

```python
from s3checkpointer.s3saver import S3CheckpointSaver
from langgraph.graph import StateGraph

# Initialize the S3 checkpoint saver
s3_saver = S3CheckpointSaver(
    bucket_name="my-langgraph-checkpoints",
    prefix="my-app",  # Optional prefix for S3 keys
    # AWS credentials (optional if using environment variables or IAM roles)
    aws_access_key_id="YOUR_ACCESS_KEY",  
    aws_secret_access_key="YOUR_SECRET_KEY",
    region_name="us-east-1"
)

# Create a LangGraph with the S3 checkpoint saver
builder = StateGraph(checkpointer=s3_saver)

# ... define your graph nodes and edges ...

# Build the graph
graph = builder.compile()

# The graph will now use S3 for checkpoint storage
```

### Using with Environment Variables

You can also configure AWS credentials using environment variables:

```python
import os
from s3checkpointer.s3saver import S3CheckpointSaver

# Set AWS credentials in environment variables
os.environ["AWS_ACCESS_KEY_ID"] = "YOUR_ACCESS_KEY"
os.environ["AWS_SECRET_ACCESS_KEY"] = "YOUR_SECRET_KEY"
os.environ["AWS_REGION"] = "us-east-1"

# Initialize without explicit credentials
s3_saver = S3CheckpointSaver(
    bucket_name="my-langgraph-checkpoints",
    prefix="my-app"
)
```

## Configuration Options

The `S3CheckpointSaver` class accepts the following parameters:

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `bucket_name` | str | Yes | The name of the S3 bucket to store checkpoints |
| `prefix` | str | No | A prefix to use for all keys in the bucket (default: "") |
| `aws_access_key_id` | str | No | AWS access key ID (default: None, read from environment) |
| `aws_secret_access_key` | str | No | AWS secret access key (default: None, read from environment) |
| `region_name` | str | No | AWS region name (default: None, read from environment) |

## Features

- Store and retrieve LangGraph checkpoints in Amazon S3
- Automatic bucket creation if it doesn't exist
- Efficient batch deletion of checkpoints
- Support for checkpoint metadata
- Thread-based organization of checkpoints

## Testing

To run the tests:

```bash
# Install test dependencies
pip install pytest moto

# Run tests
pytest tests/
```

## License

[MIT License](LICENSE)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.