semvec/pypi-packages huggingface dataset:
darrxscale-workspace-agent 1.0.0
DarrxScale Workspace Agent — remote tool execution engine for the DarrxScale Agentic System
License: MIT
Requires Python: >=3.10
Keywords: agent, workspace, automation, tools, remote-execution

# DarrxScale Workspace Agent

[![PyPI](https://img.shields.io/pypi/v/darrxscale-workspace-agent)](https://pypi.org/project/darrxscale-workspace-agent/)
[![Python](https://img.shields.io/pypi/pyversions/darrxscale-workspace-agent)](https://pypi.org/project/darrxscale-workspace-agent/)

A standalone tool execution engine that runs on your machine. Receives tool commands from the DarrxScale backend via WebSocket, executes them locally, and returns results.

## Install

```bash
pip install darrxscale-workspace-agent
```

## Quick Start

```bash
# Get your token from the DarrxScale Workspace Hub
workspace-agent --server wss://your-server.com --token dt_ws_your_token --workspace ~/projects
```

Or with Python module:

```bash
python -m agent_runtime --server wss://your-server.com --token dt_ws_your_token --workspace ~/projects
```

## Docker

```bash
docker run -d \
  --name workspace-agent \
  -v ~/projects:/workspace \
  -e TOKEN=dt_ws_your_token \
  -e SERVER=wss://your-server.com \
  darrxscale/workspace-agent:latest
```

## What It Does

- **Executes tools locally**: filesystem, shell, browser, Android, web fetch, email
- **Service API calls**: receives credential-less specs from backend, resolves credentials from local encrypted vault
- **Credentials stay local**: entered in the frontend, synced to your machine, never stored on servers
- **Multi-agent**: one instance serves all your agents — configure routing from the frontend

## Architecture

```
Frontend (control plane)
    ↓ WSS
Backend (LLM + orchestration)
    ↓ WSS
Workspace Agent (your machine)
    → Local tools (file, shell, browser)
    → Service executor (vault → inject creds → HTTP)
    → Encrypted credential vault
```

## Configuration

| Env Var | Default | Description |
|---------|---------|-------------|
| `TOKEN` | (required) | Auth token from the frontend |
| `SERVER` | `wss://localhost:8000` | Backend WSS URL |
| `WORKSPACE_PATH` | `/workspace` | Working directory |
| `LOG_LEVEL` | `INFO` | Logging level |
| `AUDIT_LEVEL` | `full` | Audit detail: `full`, `summary`, `errors_only` |

## Optional Extras

```bash
# Browser automation (Playwright)
pip install darrxscale-workspace-agent[browser]

# Android automation
pip install darrxscale-workspace-agent[android]

# Everything
pip install darrxscale-workspace-agent[all]
```

## Self Test

```bash
workspace-agent --self-test
```

## Security

- **Encrypted vault**: credentials stored with AES-256-GCM, key derived from your token
- **Per-agent limits**: filesystem restrictions, command allowlists, rate limits
- **Audit trail**: every tool execution logged as structured JSON to stdout
cutehap 1.0.4
Haplotype-resolved genomic structural variation detection with cuteHap
License: MIT

<div align="center">
  <img src="logo.png" alt="Logo" />
</div>

cuteHap is a haplotype-resolved structural variant (SV) detector designed for phased long-read sequencing data. It takes phased alignments generated by tools such as WhatsHap and LongPhase and detects haplotype-aware SVs in several minutes.

[![Anaconda-Server Badge](https://anaconda.org/bioconda/cutehap/badges/version.svg)](https://anaconda.org/bioconda/cutehap)
[![PyPI version](https://badge.fury.io/py/cutehap.svg)](https://badge.fury.io/py/cutehap)
[![Anaconda-Server Badge](https://anaconda.org/bioconda/cutehap/badges/latest_release_date.svg)](https://anaconda.org/bioconda/cutehap)
[![Anaconda-Server Badge](https://anaconda.org/bioconda/cutehap/badges/platforms.svg)](https://anaconda.org/bioconda/cutehap)
[![Anaconda-Server Badge](https://anaconda.org/bioconda/cutehap/badges/license.svg)](https://anaconda.org/bioconda/cutehap)

---
### Installation
We recommand the installation via conda or pip:
```
    $ conda install -c bioconda cutehap
    or
    $ pip install cutehap
```
Alternatively, installing from source with:
```
    $ git clone https://github.com/Meltpinkg/cuteHap.git
    $ cd cuteHap
    $ python setup.py install
```

---	
### Introduction
Structural variations (SVs), as a major category of genomic rearrangements, are capable of altering millions of nucleotides within the human genome. The detection of germline SVs and somatic mosaicism has emerged as a critical frontier in genomic research. Long-read sequencing technologies have demonstrated transformative potential in characterizing these variants. cuteHap is designed to produce high-quality, phased call sets for germline SV detection while simultaneously identifying low-frequency somatic mosaic events. The method delivers high-performance, haplotype-resolved SV detection and comprehensive detection of low-frequency mosaicism. A detailed usage guide documentation is available at [here](https://github.com/Meltpinkg/cuteHap/blob/main/src/benchmarks/guidelines.md). A comprehensive demo and benchmarks are available at [here](https://github.com/Meltpinkg/cuteHap/blob/main/src/benchmarks/benchmarks.md).

---
### Dependence
    
    1. python3
    2. scipy
    2. pysam
    3. Biopython
    4. cigar
    5. numpy
    6. Cython

---
### Quick Start
```
cuteHap <phased.bam> <reference.fa> <output.vcf> <workdir>
```

---
### Changelog
    cuteHap (v1.0.4)
    1. update the logo and README.md
    2. improve the mosaic mode

    cuteHap (v1.0.3)
    1. update the searching algorithm
    
    cuteHap (v1.0.2)
    1. modify the setup instructions
    2. add installation via conda and pypi

    cuteHap (v1.0.1)
    1. update README.md
    2. add user guide documentation
    3. remove unused codes
    
    cuteHap (v1.0.0)
    1. the initial version of cuteHap

---
### Contact
For advising, bug reporting and requiring help, please post on [Github Issue](https://github.com/Meltpinkg/cuteHap/issues) or contact sqcao@stu.hit.edu.cn.
risicare 0.1.0
Risicare SDK - Observability and self-healing for AI agents
License: MIT
Requires Python: >=3.10
Keywords: agents, ai, llm, observability, self-healing, tracing

# Risicare

Self-healing observability for AI agents. Captures decision-level traces, diagnoses failures, and deploys fixes — automatically.

[![PyPI version](https://img.shields.io/pypi/v/risicare.svg)](https://pypi.org/project/risicare/)
[![Python](https://img.shields.io/pypi/pyversions/risicare.svg)](https://pypi.org/project/risicare/)

## Quick Start

```bash
pip install risicare
```

```python
import risicare

risicare.init(
    api_key="rsk-...",
    endpoint="https://app.risicare.ai"
)

# That's it. LLM calls are now traced automatically.
```

## Progressive Integration

| Tier | Effort | What You Get |
|------|--------|-------------|
| **Tier 0** | `RISICARE_TRACING=true` (env var) | Auto-instrument all LLM calls |
| **Tier 1** | `import risicare` (1 line) | Explicit config, custom endpoint |
| **Tier 2** | `@agent()` decorator | Agent identity and hierarchy |
| **Tier 3** | `@session` decorator | User session tracking |
| **Tier 4** | `@trace_think / @trace_decide / @trace_act` | Decision phase visibility |
| **Tier 5** | `@trace_message / @trace_delegate` | Multi-agent communication |

## Supported Providers (20)

Auto-instrumented with zero code changes:

| Provider | | Provider | |
|----------|---|----------|---|
| OpenAI | `openai` | Anthropic | `anthropic` |
| Google Gemini | `google-generativeai` | Mistral | `mistralai` |
| Cohere | `cohere` | Groq | `groq` |
| Together AI | `together` | Ollama | `ollama` |
| AWS Bedrock | `boto3` | Google Vertex AI | `google-cloud-aiplatform` |
| Cerebras | `cerebras-cloud-sdk` | HuggingFace | `huggingface-hub` |
| DeepSeek | via OpenAI `base_url` | xAI (Grok) | via OpenAI `base_url` |
| Fireworks | via OpenAI `base_url` | Baseten | via OpenAI `base_url` |
| Novita | via OpenAI `base_url` | BytePlus | via OpenAI `base_url` |
| vLLM | via OpenAI `base_url` | Any OpenAI-compatible | via `base_url` |

## Supported Frameworks (10)

```bash
pip install risicare[langchain]    # LangChain + LangGraph
pip install risicare[crewai]       # CrewAI
pip install risicare[autogen]      # AutoGen
pip install risicare[instructor]   # Instructor
pip install risicare[litellm]      # LiteLLM
pip install risicare[dspy]         # DSPy
pip install risicare[pydantic-ai]  # Pydantic AI
pip install risicare[llamaindex]   # LlamaIndex
pip install risicare[all]          # Everything
```

## OpenTelemetry

```bash
pip install risicare[otel]
```

```python
risicare.init(api_key="rsk-...", otel_bridge=True)
```

Compatible with any OTel-instrumented application. Export to Risicare alongside your existing OTel pipeline.

## Links

- [Documentation](https://risicare.ai/docs)
- [Dashboard](https://app.risicare.ai)
risicare-core 0.1.0
Core types and taxonomy for Risicare Agent Self-Healing Infrastructure
License: MIT
Requires Python: >=3.10

# risicare-core

Shared types, context propagation, and error taxonomy for [Risicare](https://risicare.ai) — self-healing infrastructure for AI agents.

This package is a dependency of the [`risicare`](https://pypi.org/project/risicare/) SDK. You typically don't install it directly.

```bash
pip install risicare
```

## What's Inside

- **Type system** — `Span`, `SpanKind`, `SpanStatus`, `LLMAttributes`, ID generation
- **Context propagation** — Thread-safe and async-safe session/agent/phase tracking via `contextvars`
- **Error taxonomy** — 154 error codes across 10 modules (Perception, Reasoning, Tool, Memory, Output, Coordination, Communication, Orchestration, Consensus, Resources)
- **Observability** — Prometheus-compatible metrics and context health reporting

Zero external dependencies. Pure Python 3.9+.

## Links

- [Risicare Documentation](https://risicare.ai/docs)
- [Risicare SDK](https://pypi.org/project/risicare/)
Anisearch 2.1.0
Declarative GraphQL query builder for the AniList API.
License: MIT
Requires Python: >=3.8
Keywords: anime, manga, anilist, graphql

# Anisearch

Declarative GraphQL query builder for the [AniList](https://anilist.co) API.

## Installation

```bash
pip install Anisearch

# For async support
pip install Anisearch[async]
```

## Quick Start

```python
from Anisearch import Anilist

anilist = Anilist()

# Get anime by ID
result = anilist.media(id=13601, type="ANIME") \
    .id().title("romaji", "english").episodes().status() \
    .execute()
print(result)
```

## Builder API

Every query starts from an `Anilist` instance. Call `.media()`, `.character()`, `.staff()`, or `.studio()` to get a builder, chain the fields you want, then `.execute()`.

### Media

```python
result = anilist.media(search="Psycho-Pass", type="ANIME") \
    .id().title("romaji", "english").genres().episodes().status() \
    .execute()
```

### Character

```python
result = anilist.character(search="Saber") \
    .id().name().image() \
    .execute()
```

### Staff

```python
result = anilist.staff(id=113803) \
    .id().name().image() \
    .execute()
```

### Studio

```python
result = anilist.studio(search="J.C. Staff") \
    .id().name() \
    .execute()
```

## Pagination

Wrap any query with `.paginate()` to get paginated results:

```python
result = anilist.media(search="Sword", type="ANIME") \
    .id().title("romaji") \
    .paginate(page=1, per_page=10) \
    .execute()
```

## Nested Fields

Some fields accept sub-field selections:

```python
result = anilist.media(id=13601) \
    .title("romaji", "english", "native") \
    .cover_image("large", "medium") \
    .characters(sort="FAVOURITES_DESC", per_page=5)(lambda c: c.id().name().image()) \
    .execute()
```

## Fragments

Reuse field selections across queries with `Fragment`:

```python
from Anisearch import Fragment

basic_info = Fragment.media(lambda m: m.id().title("romaji", "english").genres())

result = anilist.media(id=13601).use(basic_info).episodes().execute()
```

## Retry Configuration

Customize retry behavior with `RetryStrategy`:

```python
from Anisearch import RetryStrategy

retry = RetryStrategy(
    max_retries=5,
    on_rate_limit="wait",       # "wait" or "raise"
    on_server_error="backoff",  # "backoff" or "raise"
    max_wait=60,
)
anilist = Anilist(retry=retry)
```

## Raw Query

For queries the builder doesn't cover, use `raw_query`:

```python
query = """
query ($id: Int) {
  Media(id: $id) {
    id
    title { romaji }
  }
}
"""
result = anilist.raw_query({"id": 13601}, query)
```

## Async Usage

All builders support async execution (requires `aiohttp`):

```python
import asyncio
from Anisearch import Anilist

async def main():
    anilist = Anilist()
    result = await anilist.media(id=13601) \
        .id().title("romaji") \
        .execute_async()
    print(result)

asyncio.run(main())
```

## License

MIT
geobox 2.4.2
Python SDK for Geobox's APIs
License: MIT
Requires Python: >=3.7

[<img width="200" alt="Geobox logo" src="https://www.geobox.ir/wp-content/uploads/2022/05/geologo-slider.png">](https://www.geobox.ir/)


Geobox® is a cloud-based GIS platform that enables users (local governments, companies and individuals) to easily upload their geo-spatial data, publish them as geo-services, visualize and analyze their geo-content (geo-data or -services) and share them with others. Geobox is a modern, world-class and cloud-ready geo-spatial platform that provides standard, safe, efficient and easy to use GI-Services.

Geobox python SDK provides seamless integration with the Geobox API, enabling developers to work with geospatial data and services programmatically. This comprehensive toolkit empowers applications to leverage advanced geospatial capabilities including data management and analysis.

[Here](https://geobox.readthedocs.io) you can find the official documentation for Geobox Python SDK.

Installation
============

Enable Virtualenv and Install Dependencies:

```
pip install geobox
```

Install with Geometry Dependencies

```
pip install geobox[geometry]
```
```
from geobox import GeoboxClient

client = GeoboxClient()

layer = client.get_vectors(search='tehran')[0]
feature = layer.get_feature(feature_id=1)
geom = feature.geometry
```

Install with Progress Bar Support

```
pip install geobox[tqdm]
```
```
from geobox import GeoboxClient

client = GeoboxClient()

task = client.get_tasks()[0]
task.wait() # shows progress bar by default. use progress_bar=False to disable it.
```

Install with Async Support

```
pip install geobox[async]
```
```
from geobox.aio import AsyncGeoboxClient

async with AsyncGeoboxClient() as client:
    files = await client.get_files()
    downloads = [file.download() for file in files]
    await asyncio.gather(*downloads) # downloads multiple file asynchronously
```

Install with All Available Dependencies

```
pip install geobox[all]
```


Example
=======

```
from geobox import GeoboxClient

client = GeoboxClient()

layer = client.get_vectors(search='tehran')[0]
features = layer.get_features(out_srid=4326, bbox_srid=4326)
fields = layer.get_fields()
```