semvec/pypi-packages huggingface dataset:
pipen-dry 1.1.1
Dry runner for pipen pipelines
License: MIT
Requires Python: <4.0,>=3.9

# pipen-dry

Dry runner for [pipen][1]

It is useful to quickly check if there are misconfigurations for your pipeline without actually running it.

## Install

```shell
pip install -U pipen-dry
```

## Usage

- Use it for process

    ```python
    class P1(Proc):
        scheduler = "dry"
    ```

- Use it for pipeline

    ```python
    Pipen(scheduler="dry", ...)
    ```

[1]: https://github.com/pwwang/pipen
pipen-annotate 1.0.2
Use docstring to annotate pipen processes
License: MIT
Requires Python: <4.0,>=3.9

# pipen-annotate

Use docstring to annotate [pipen](https://github.com/pwwang/pipen) processes

## Installation

```shell
pip install -U pipen-annotate
```

## Usage

```python
from pprint import pprint
from pipen import Proc
from pipen_annotate import annotate


class Process(Proc):
    """Short description

    Long description

    Input:
        infile: An input file
        invar: An input variable

    Output:
        outfile: The output file

    Envs:
        ncores (type=int): Number of cores
    """
    input = "infile:file, invar"
    output = "outfile:file:output.txt"
    args = {'ncores': 1}

annotated = annotate(Process)
# returns:
{'Envs': {'ncores': {'attrs': {'type': 'int'},
                     'help': 'Number of cores',
                     'name': 'ncores',
                     'terms': {}}},
 'Input': {'infile': {'attrs': {'action': 'extend',
                                'itype': 'file',
                                'nargs': '+'},
                      'help': 'An input file',
                      'name': 'infile',
                      'terms': {}},
           'invar': {'attrs': {'action': 'extend',
                               'itype': 'var',
                               'nargs': '+'},
                     'help': 'An input variable',
                     'name': 'invar',
                     'terms': {}}},
 'Output': {'outfile': {'attrs': {'default': 'output.txt', 'otype': 'file'},
                        'help': 'The output file',
                        'name': 'outfile',
                        'terms': {}}},
 'Summary': {'long': 'Long description', 'short': 'Short description'}}
```
coloco 0.4.8
A kit for creating FastAPI + Svelte applications
License: MIT
Requires Python: >=3.12

# Coloco

A kit for creating full-stack apps with co-located code, built on FastAPI and Svelte. Bundle your front-end and back-end code and easily tie them together with codegen.

Example:

`hello/api.py`

```python
from coloco import api

@api
def test(name: str):
    return f"Hello {name}!"

```

`hello/index.svelte`

```svelte
<script lang="ts">
  import { test } from "./api";
</script>

{#await test({ name: "Coloco" })}
	Loading...
{:then result}
	The server says {result}
{/await}
```

Serves the page `myapp.com/hello`, which calls `myapp.com/hello/test?name=Coloco` and prints the message `Hello Coloco!`

# Getting Started

- `pip install coloco`
- `coloco createapp myapp`
- From `myapp` - `coloco dev`

# Running in Production

- `coloco build`
- Artifacts will be saved to `dist`
- From dist, run `coloco serve`

# Opinions

This framework is opinionated and combines the following excellent tools:

- FastAPI
- Svelte
- openapi-ts (codegen)
- svelte5-router (file-based routing)
- tortoise-orm (optional)

# TBD Required features for launch

- Config with secrets encryption
- Package/share modules with git
- Docs

# Features in development

- CRUD
- Caching
- User module
- Dev tools module
- Admin panel module

# Dreams

- Move `node_modules` into `+node`

# Other TODO

- Swap to `copier` for template creation
- `Textual` CLI app / installer / dev tools?
- Resolve duplicate types in openapi spec (due to model serializers)
fastloop 0.1.128
A Python package for deploying stateful loops
License: MIT
Requires Python: >=3.12
Keywords: agentic, ai, baml, workflow

# FastLoop

A Python package for building and deploying stateful loops. We use this at [beam.cloud](https://www.beam.cloud) to deploy agents.

## Installation

```bash
pip install fastloop
```

## Usage

### Basic Example

```python
from fastloop import FastLoop, LoopContext, LoopEvent

app = FastLoop(name="my-app")

@app.event("user_message")
class UserMessage(LoopEvent):
    user_id: str
    message: str

@app.loop(name="chat", start_event=UserMessage)
async def chat_loop(context: LoopContext):
    user_msg = await context.wait_for(UserMessage, timeout=5.0)
    print(f"User {user_msg.user_id} sent a message: {user_msg.message}")
    
    # Your loop logic here

    # If you want to stop the loop
    context.stop()

    # If you want to pause the loop
    context.pause()

    # By default, we just run it again

if __name__ == "__main__":
    app.run(port=8000)
```

## Development

This project uses `uv` for dependency management.

```bash
# Install dependencies
uv sync

# Run tests
uv run pytest

# Build package
uv build
```