semvec/pypi-packages huggingface dataset:
virsanghavi-axis 1.0.0
Python SDK for the Axis Context Protocol
Requires Python: >=3.7
Keywords: axis context ai agents governance

# Axis Python SDK

The official Python client for the Axis Context Protocol. 

## Installation

```bash
pip install virsanghavi-axis
```

## Quick Start

### 1. Initialize the Client

```python
from axis import Axis

# Reads AXIS_API_KEY from environment by default
axis = Axis() 
```

### 2. Get a Context Mirror

```python
mirror = axis.get_mirror(path="./src")

# Convert to text for your prompt
prompt_context = mirror.to_prompt()
print(prompt_context)
```

### 3. Check Governance

```python
is_allowed = axis.check_governance(
    agent_id="agent-001",
    file_path="src/auth/secrets.ts",
    action="read"
)

if not is_allowed:
    print("Access Denied by Axis Governance Law")
```

## Environment Variables

- `AXIS_API_KEY`: Your project's secret key.
- `AXIS_BASE_URL`: (Optional) Defaults to `https://api.axis.sh/v1`.
neuralqx 1.0.0.post1
A high-performance simulations toolkit for loop quantum gravity.
Requires Python: >=3.11
Keywords: Jax, Loop quantum gravity, Machine learning, Netket, Neural Quantum States, Neural networks, Quantum, Variational Monte Carlo

<div align="center">
  <img src="docs/_static/_logo.png" alt="neuraLQX logo" width="420" />
</div>

<div align="center">

# neuraLQX
**High-performance variational simulations for canonical Loop Quantum Gravity - built on NetKet & JAX.**

[![PyPI](https://img.shields.io/pypi/v/neuralqx.svg)](https://pypi.org/project/neuralqx/)
[![Python](https://img.shields.io/pypi/pyversions/neuralqx.svg)](https://pypi.org/project/neuralqx/)
[![License](https://img.shields.io/github/license/waleed-sh/neuraLQX.svg)](https://github.com/waleed-sh/neuraLQX/blob/main/LICENSE)
[![Documentation Status](https://readthedocs.org/projects/neuralqx/badge/?version=latest)](https://neuralqx.readthedocs.io/en/latest/?badge=latest)

</div>

---

neuraLQX is an open-source Python package for variational canonical Loop Quantum Gravity.
It lets you work directly with LQG-native building blocks, graphs, Hilbert spaces, gauge groups, constraints, and projectors,
while leveraging the battle-tested and state of the art variational backend of NetKet.

Under the hood, neuraLQX builds on NetKet and JAX, making fast Monte Carlo methods, automatic differentiation, and scalable optimisation
available in an API that speaks the language of LQG.



## Installation

neuraLQX requires **Python ≥ 3.11**.

### Stable release (PyPI)

```bash
pip install --upgrade neuralqx
```

### From source (editable)

```bash
git clone https://www.github.com/waleed-sh/neuralqx
cd neuralqx
pip install -e .
```

### Optional extras

#### Developer / contributor dependencies

```bash
pip install --upgrade "neuralqx[dev]"
```

#### MPI (local use only, requires an MPI toolchain)

```bash
mpicc --showme:link
pip install --upgrade "neuralqx[mpi]"
```

#### Docs

```bash
pip install --upgrade "neuralqx[docs]"
```

#### Profiling

```bash
pip install --upgrade "neuralqx[profile]"
```

## Getting help & contributing

- **Questions / ideas:** GitHub Discussions  
  https://github.com/waleed-sh/neuraLQX/discussions

- **Bug reports:** GitHub Issues  
  https://github.com/waleed-sh/neuraLQX/issues

- **Contributing:** contributor guide  
  https://neuralqx.readthedocs.io/en/latest/contribute.html


## License

This package is licensed under the [Apache License 2.0](https://github.com/waleed-sh/neuraLQX/blob/main/LICENSE).
relace-mcp-server 0.1.7
Relace MCP server
Requires Python: >=3.13

# relace-mcp-server

[![PyPI](https://img.shields.io/pypi/v/relace-mcp-server)](https://pypi.org/project/relace-mcp-server/)

Official Relace MCP server.

Currently supports [Fast Agentic Search](https://www.relace.ai/blog/fast-agentic-search) via the `relace_search` tool.

## Installation

See [Relace MCP Installation docs](https://docs.relace.ai/docs/fast-agentic-search/mcp-installation) for detailed instructions.

## Releasing

1. [Create a new release](https://github.com/squack-io/relace-mcp-server/releases/new)
2. Enter a new tag (e.g., `v0.1.4`)
3. Publish the release

The workflow will automatically build and publish to PyPI, with the package version derived from the git tag.