semvec/pypi-packages huggingface dataset:
anshin 0.1a2.dev1
anshin is a Python/TOML-based system build tool.
Requires Python: >=3.14
Keywords: build, configuration, reproducible, system

# anshin

```
⠀⠀⠀⠀⠀⠀⠀⠀⢀⣤⣴⡆⢰⣶⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠚⠛⠛⠛⠃⠘⠛⠛⠛⠛⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⣠⣾⣿⠏⢠⣾⣿⣿⣿⣿⣿⣦⠈⢿⣿⣦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠐⠛⠛⠛⠀⠛⠛⠛⠛⠛⠛⠛⠛⠃⠘⠛⠛⠓⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⢰⠇⣾⣿⣿⣿⣿⣿⣿⡇⢸⣿⣿⣿⠏⣰⣿⣿⡏⢠⣾⣿⡟⠁⠸⣷⠀⠀
⠀⠀⣉⣀⣉⣉⣉⠉⣉⣉⣉⣁⣈⣉⣉⡉⠀⣉⣉⣉⠀⣈⣉⣉⠁⠀⠀⣿⡇⠀
⠀⠸⠿⠿⠿⠿⠿⠀⠿⠿⠿⠿⠿⠿⠿⠟⠀⠿⠿⠿⠀⠿⠿⠿⠁⠀⠀⠻⠇⠀
```


<h4>
    anshin is an early-stage Python/TOML tool for describing and building a
    system in a reproducible way (inspired by declarative OS configuration
    workflows).
</h4>

>>> [!important]
Concepts related to and including the following are explicitly deferred:

- perfect sandboxing
- full package ecosystem
- bootloader integration (later)
- full cross-compilation
- more init systems (yes!)

Only concepts related to and including the following are explicity supported:

- BUILD HOST: Ubuntu 24.04(-based)
- TARGET INIT BACKEND: `systemd`
- MODULES [SMALL SET]: (base, users, sshd, packages)
- RECIPES [SMALL SET]: (shell, utilities)

⚠️ YOU are FREE to LICENSE this project under the terms set forth via
either the `LICENSE_APACHE` OR `LICENSE_GPLv3` file available at the project
root ⚠️
>>>

This package is a minimal functional seed:
- `anshin init` creates starter TOML config files
- `anshin validate` validates an `anshin.system.toml`
- `anshin --version` prints the version

## Usage:

```bash
anshin
anshin init --dir .
anshin validate anshin.system.toml
anshin --version
```

---

## Status:

Pre-alpha. Expect changes.

---


###### Made with ❤️ (Be nice!)
brainmass 0.0.5
Whole-brain brain modeling for differentiable neural mass models.
Requires Python: >=3.10
Keywords: neural mass model, brain modeling, rate model, mean field model, neuroscience, computational neuroscience, brain simulation

# BrainMass: whole-brain modeling with differentiable neural mass models

<p align="center">
  	<img alt="Header image of braintrace." src="https://raw.githubusercontent.com/chaobrain/brainmass/main/docs/_static/brainmass.png" width=40%>
</p>

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![PyPI Version](https://img.shields.io/pypi/v/brainmass.svg)](https://pypi.org/project/brainmass/)
[![PyPI Downloads](https://img.shields.io/pypi/dm/brainmass.svg)](https://pypi.org/project/brainmass/)
[![CI](https://github.com/chaobrain/brainmass/actions/workflows/CI.yml/badge.svg)](https://github.com/chaobrain/brainmass/actions/workflows/CI.yml)
[![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/)
[![Docs](https://readthedocs.org/projects/brainmass/badge/?version=latest)](https://brainmass.readthedocs.io/)

BrainMass is a Python library for whole-brain computational modeling using differentiable neural mass models. Built on
JAX for high-performance computing, it provides tools for simulating brain dynamics, fitting neural signal data, and
training cognitive tasks.

## Installation

### From PyPI (recommended)

```bash
pip install brainmass
```

### From Source

```bash
git clone https://github.com/chaobrain/brainmass.git
cd brainmass
pip install -e .
```

### GPU Support

For CUDA support:

```bash
pip install brainmass[cuda12]
pip install brainmass[cuda13]
```

For TPU support:

```bash
pip install brainmass[tpu]
```

### Ecosystem

For whole brain modeling ecosystem:

```bash
pip install BrainX 

# GPU support
pip install BrainX[cuda12]
pip install BrainX[cuda13]

# TPU support
pip install BrainX[tpu]
```


## Citation

If you use BrainMass in your research, please cite:

```bibtex
@software{brainmass,
  title={BrainMass: Whole-brain modeling with differentiable neural mass models},
  author={BrainMass Developers},
  url={https://github.com/chaobrain/brainmass},
  version={0.0.4},
  year={2025}
}
```

## License

BrainMass is licensed under the Apache License 2.0. See [LICENSE](LICENSE) for details.

## See also the ecosystem

[BrainMass](https://github.com/chaobrain/brainmass) is one of our brain simulation ecosystem: https://brainmodeling.readthedocs.io/
artificer-agents 0.1.0a22
A library for creating agents
Requires Python: >=3.13

# artificer-agents

A lightweight Python library for running deterministic agent loops with MCP tools.

## Installation

```bash
pip install artificer-agents

# With OpenAI support
pip install artificer-agents[openai]
```

Requires Python 3.13+.

## Usage

Define agents as classes:

```python
import asyncio
from pydantic import BaseModel
from artificer.agents import Agent
from artificer.agents.mcp import MCPClient
from artificer.agents.models import OpenAIModel


class Input(BaseModel):
    query: str


class Output(BaseModel):
    result: str


class MyAgent(Agent):
    system_prompt = "You are a helpful assistant."
    model = OpenAIModel(model="gpt-4o-mini")
    mcp_client = MCPClient(["path/to/mcp-server.py"])  # optional
    _input_schema = Input
    _output_schema = Output


async def main():
    agent = MyAgent()

    async with agent.mcp_client:
        result = await agent.run(Input(query="Hello"), verbose=True)
        print(result.result)

asyncio.run(main())
```

## Simple String Agents

For simple str -> str agents, no schemas needed:

```python
class SimpleAgent(Agent):
    system_prompt = "Answer questions concisely."
    model = OpenAIModel(model="gpt-4o-mini")

result = await SimpleAgent().run("What is Python?")
print(result.output)  # StringOutput with .output field
```

## Subagents

Agents can spawn other agents:

```python
class ResearcherAgent(Agent):
    system_prompt = "Research the topic and return findings."
    model = model
    mcp_client = mcp_client
    _input_schema = ResearchInput
    _output_schema = ResearchOutput


class OrchestratorAgent(Agent):
    system_prompt = "Use the researcher subagent to gather info."
    model = model
    subagents = [ResearcherAgent()]
    _input_schema = Input
    _output_schema = Output
```

## Development

```bash
uv sync                    # Install dependencies
./scripts/check.sh         # Run all checks (lint, format, typecheck, tests)
./scripts/test.sh          # Run tests only
./scripts/format.sh        # Format code
```

## License

MIT
bolt-control-flow 0.4.0
Utilities and helpers for overriding control flow behaviour in Bolt
Requires Python: <4,>=3.14
Keywords: beet, bolt, minecraft, minecraft-commands, mcfunction

# bolt-control-flow

[![GitHub Actions](https://github.com/vdvman1/bolt-control-flow/workflows/CI/badge.svg)](https://github.com/vdvman1/bolt-control-flow/actions)

> Utilities and helpers for overriding control flow behaviour in Bolt

## Installation

```bash
pip install bolt_control_flow
```

---

License - [MIT](https://github.com/vdvman1/bolt-control-flow/blob/main/LICENSE)