semvec/pypi-packages huggingface dataset:
aievalkit 0.1.0
Core library for EvalKit AI Evaluation.
Requires Python: >=3.9

# EvalKit Core Library

This directory contains the core Python library for EvalKit.

## Installation

To install the EvalKit core library:

```bash
pip install evalkit
```

For development, you can install from this directory:

```bash
cd core
pip install -e .
```

## Usage

Import the necessary components from the `core` package (after installing `evalkit`):

```python
from core.kit import EvalKit
from core.models import Span, Task # etc.
# You might also import storage modules if needed directly
# from core.storage import MemoryStorage, HttpStorage

eval_kit = EvalKit() # Uses MemoryStorage by default

@eval_kit.trace_interaction(
        agent_name="agentX",
        prompt_template_arg_name="prompt_template"
)
def agent_function(prompt_template):
    pass

# Example using tracing:
with eval_kit.trace_task(task_name="My Task") as task_ctx:
    # Your agent code here, using @eval_kit.trace_interaction
    pass

# Example using models (if needed separately):
# my_span = Span(...)
# my_task = Task(...)
```

Refer to the main project README for more detailed examples and context within the full EvalKit application.
winmemory 0.1.0
Memory manipulation using python and ctypes

# winmemory

Open progress

```python
from winmemory import *
game=winmemory.winmemory('game.exe')
```
Write and read data of an address
```python
game.write_int(0xE2CA7FF85C,10)
data=game.read_int(0xE2CA7FF85C)
print(data)
#--->Address a is assigned the value 10
#Can also read and write to many other data types, such as string, byte, long, double, float,...

```
Get base address of module
```python
module_addr=game.get_module_base_address('game.exe')
```
Find_pointer_address
```python
pointer=game.find_pointer_address(base,offset)
```
Get pip of progress
```python
pid=getpid('ganme.exe')
```
Scan all addresses with the value you want
```python
pid=getpid('ganme.exe')
scaned=scan_all_addresses_with_value(pid,10)
#scan all addresses with value 10
```
Thanks from Phan Huynh Thien Phu
tomato-example-counter 2.1.1
tomato example counter driver
Requires Python: >=3.10

# tomato-example-counter
An example driver for `tomato`, used for testing purposes.

This driver is developed by the [ConCat lab at TU Berlin](https://tu.berlin/en/concat).

## Supported functions

### Capabilities
- `count`: for counting up every second
- `random`: for returning a random number between `min` and `max` every query

### Attributes
- `max`: the upper limit to `random`, `float`
- `min`: the lower limit to `random`, `float`
- `param`: test attribute for unit validation, `param > pint.Quantity("0.1 seconds")`
- `choice`: test attribute for `options` validation, `choice ∈ {"red", "blue", "green"}`

## Contributors
- Peter Kraus
polyflux-logging 0.0.1a0
Zero-overhead structured logging for Polyflux using loguru
Requires Python: >=3.12

# polyflux-logging

Zero-overhead structured logging for Polyflux using loguru as the backend.

## Features

- Zero performance impact on client operations through lazy evaluation
- Async-first design with non-blocking I/O
- Structured logging with context propagation
- Optional integration with other Polyflux packages
- Level-based short-circuiting for minimal overhead
- Protocol-based interfaces for extensibility

## Installation

```bash
uv pip install polyflux[logging]
```

## Usage

```python
from polyflux.logging import PolyfluxLogger

# Create a logger with zero-overhead configuration
logger = PolyfluxLogger(lazy_init=True, async_emit=True)

# Configure with custom settings
logger = PolyfluxLogger(
    lazy_init=True,      # Delay initialization until first use
    async_emit=True,     # Use async background emission
    min_level="INFO",    # Minimum level to process
    queue_size=10000,    # Size of async emission queue
    batch_size=100,      # Number of logs to batch process
    flush_timeout=0.1    # Timeout for batch flushing
)

# Log messages with automatic level filtering
logger.log("INFO", "Application started")
logger.log("DEBUG", "Debug information", user_id=123)

# Async logging for non-blocking operations
await logger.alog("ERROR", "An error occurred", exception=str(e))

# Gracefully shutdown the logger when done
logger.shutdown()
```

## Performance

The logging module is designed with zero performance impact as the primary goal:
- Lazy initialization delays setup until first use
- Level checks short-circuit before any processing
- Message formatting only occurs for emitted logs
- All I/O operations are asynchronous and non-blocking
- Queue-based batch processing minimizes syscalls

## Resource Management

To ensure proper cleanup of async resources:
- Always call `logger.shutdown()` when you're done with the logger
- The shutdown process gracefully cancels background tasks without blocking
- In test environments, loggers will be automatically cleaned up between tests
naramarket-mcp 0.1.2
나라장터 입찰 공고 검색을 위한 MCP 서버
Requires Python: >=3.11

# naramarket-mcp

## Overview

조달청의 조달데이터허브가 제공하는 API를 연동해 나라장터 입찰공고목록을 검색하는 MCP 서버입니다.

### Tools

1. `search_bids`
    - 나라장터에서 입찰 공고를 검색합니다.
    - Input:
        - keyword: 검색할 키워드 (입찰공고명에 포함될 단어) [required]
        - bid_type: 입찰 종류 (공사, 용역, 외자, 물품) [기본값: 물품]
        - page: 페이지 번호 [기본값: 1]
        - rows: 한 페이지 결과 수 [기본값: 10]
        - start_date: 조회 시작일 (YYYYMMDD 형식) [optional]
        - end_date: 조회 종료일 (YYYYMMDD 형식) [optional]

2. `get_bid_details` 
    - 특정 입찰 공고의 상세 정보를 조회합니다.
    - Input:
        - bid_notice_no: 입찰공고번호 [required]
        - bid_notice_ord: 입찰공고차수 [기본값: 01]

3. `search_by_organization` 
    - 특정 기관이 등록한 입찰 공고를 검색합니다.
    - Input:
        - organization_name: 기관명 [required]
        - bid_type: 입찰 종류 (공사, 용역, 외자, 물품) [기본값: 물품]
        - is_demand_org: True면 수요기관, False면 공고기관으로 검색 [기본값: false]
        - page: 페이지 번호 [기본값: 1]
        - rows: 한 페이지 결과 수 [기본값: 10]

4. `search_by_price_range` 
    - 특정 가격 범위의 입찰 공고를 검색합니다.
    - Input:
        - min_price: 최소 추정가격 (원) [required]
        - max_price: 최대 추정가격 (원) [required]
        - bid_type: 입찰 종류 (공사, 용역, 외자, 물품) [기본값: 물품]
        - page: 페이지 번호 [기본값: 1]
        - rows: 한 페이지 결과 수 [기본값: 10]

## Installation

### Using uv (recommended)

[`uv`](https://docs.astral.sh/uv/)를 사용하면 별도의 설치가 필요하지 않습니다.

[`uvx`](https://docs.astral.sh/uv/guides/tools/)로 *naramarket-mcp*를 곧바로 사용합니다.


## Configuration

### Usage with Claude Desktop

`claude_desktop_config.json`에 다음을 추가하세요.

<details>
<summary>Using uvx</summary>

```json
"mcpServers": {
  "naramarket": {
    "command": "uvx",
    "args": [
    "naramarket-mcp",
    "--service-key",
    "<YOUR_SERVICE_KEY>"
    ]
  }
}
```
</details>



## Debugging

You can use the MCP inspector to debug the server. For uvx installations:

```
npx @modelcontextprotocol/inspector uvx naramarket-mcp --service-key YOUR_SERVICE_KEY
```


## LICENSE

MIT