semvec/pypi-packages huggingface dataset:
sentient-cli 0.1.0a1
Sentient Space CLI - deployment and evals for AI agents and MCP servers
License: MIT
Requires Python: >=3.8

# Sentient CLI

The official CLI tool for the Sentient Web Platform - Deploy AI agents and MCP servers with zero friction.

## Installation

```bash
pip install sentient-cli
```

## Quick Start

1. **Authenticate with the platform:**
   ```bash
   sen auth login
   ```

2. **Initialize your project:**
   ```bash
   sen init
   ```

3. **Deploy your agent or MCP server:**
   ```bash
   sen push
   ```

## Commands (core)

- `sen auth login` / `sen auth logout` / `sen auth status` - Manage authentication
- `sen init` - Initialize project for deployment
- `sen push [--entry <file>] [--visibility public|private] [--wait]` - Deploy project (default Lambda path)
- `sen redeploy <deployment-id> [--wait]` - Redeploy an existing deployment
- `sen list` - List your deployments
- `sen logs [--live] [--build|--live]` - View deployment logs (live or build)
- `sen metrics` - View deployment metrics
- `sen delete <deployment-id>` - Delete a deployment

## Configuration

The CLI uses a `sentient.config.json` file in your project root to configure deployments:

```json
{
  "name": "my-agent",
  "description": "My AI agent",
  "type": "agent",
  "framework": "langchain",
  "visibility": "public",
  "runtime": "python",
  "buildCommand": "pip install -r requirements.txt",
  "startCommand": "python main.py",
  "port": 8000,
  "environment": {
    "PYTHONPATH": "."
  }
}
```
jb3 0.1.4
JB3 package – unified access to LJSE, FRED and NASDAQ financial data
License: MIT
Requires Python: >=3.8
Keywords: finance, stocks, nasdaq, ljse, fred, macro, economics, time-series, data

# JB3

JB3 is a Python package providing unified access to LJSE, NASDAQ, and FRED (Federal Reserve Economic Data).
It enables unified downloading, cleaning, transforming, and pivoting of financial and macroeconomic time-series data
for analysis, trading strategies, or machine-learning pipelines.

## Features

### NASDAQ & LJSE Stock Market Data
- Download historical NASDAQ and LJSE stock data
- Clean and normalize numeric columns (open, high, low, close, volume)
- Pivot data by ticker for analysis-ready DataFrames
- Screener with flexible filtering:
  - Exchange
  - Market cap
  - Analyst rating
  - Sector
  - Region
  - Country

### FRED Macroeconomic Data
- Search FRED series by keyword (e.g., GDP, CPI, M2, UNRATE)
- Download multiple macroeconomic series at once
- Automatic detection of the lowest valid FRED frequency (Daily → Weekly → Monthly → Quarterly → Annual)
- Optional server-side unit transformations:
  - pch = percent change
  - pc1 = percent change from year ago (YoY)
  - chg = change from previous period
  - log = natural log
- Clean, aligned DataFrames with datetime index
- Built-in retry logic for stable API access

## Installation

pip install jb3

## Usage Examples

### 1. Download NASDAQ Historical Data

from jb3 import nasdaq

df = nasdaq.download_ticker_data("AAPL", clean=True, pivot_col="close")
print(df.head())

### 2. NASDAQ Screener Example

from jb3 import nasdaq

screener = nasdaq.get_nasdaq_screener(
    exchange="NASDAQ",
    marketcap="Large",
    sector="Technology",
    limit=50
)

print(screener.head())

### 3. FRED Example — GDP & CPI (auto frequency detection)

from jb3 import fred

df = fred.get_fred_series(
    ["GDP", "CPIAUCSL"],
    api_key="YOUR_FRED_API_KEY"
)
print(df.tail())

### 4. FRED Example — YoY Inflation (pc1 transformation)

df = fred.get_fred_series(
    "CPIAUCSL",
    api_key="YOUR_FRED_API_KEY",
    units="pc1"
)
print(df.head())

### 5. Search FRED Tickers (GDP-related)

tickers = fred.get_fred_tickers("GDP", api_key="YOUR_FRED_API_KEY")
print(tickers[["id", "title", "frequency"]].head())
  
## LJSE Documentation

https://pypi.org/project/LJSE/