strot-ai 0.1.1

Build tools, agents, and pipelines for your STROT instance

STROT

Build tools, agents, pipelines, and dashboards for your STROT instance — in Python.

Installation

pip install strot-ai

Quick Start

strot login                         # Authenticate
strot init tool my-calculator       # Scaffold a project
cd my-calculator                    # Edit main.py with any editor
strot test                          # Validate locally
strot deploy                        # Ship to your STROT instance

SDK Reference

Tools (@function)

from strot_sdk import function, llm

@function(
    name='calculate_roi',
    description='Calculate return on investment',
    category='finance',
    parameters=[
        {'name': 'cost', 'type': 'number', 'description': 'Total cost'},
        {'name': 'revenue', 'type': 'number', 'description': 'Total revenue'},
    ],
    returns={'type': 'number', 'description': 'ROI percentage'}
)
class CalculateROI:
    def run(self, cost: float, revenue: float) -> float:
        return ((revenue - cost) / cost) * 100

Agents (@agent)

from strot_sdk import agent

@agent(
    name='sales_analyst',
    description='Analyzes sales data and provides insights',
    tools=['calculate_roi', 'top_n'],
    model='gpt-4o',
    temperature=0.1,
)
class SalesAnalyst:
    system_prompt = """You are a sales analyst.
    Analyze data and provide actionable recommendations."""

Cortex Pipelines (@cortex)

from strot_sdk import cortex
from strot_sdk.cortex import Flow

@cortex(name='daily_etl', description='Daily ETL pipeline')
class DailyETL:
    def build(self, flow: Flow):
        data = flow.data_connector('load_sales', query_id=42)
        cleaned = flow.transform(data, prompt='Clean and normalize the data')
        flow.publish(cleaned, name='daily_report', destination='slack', channel='#data')

Pages / Dashboards (@page)

from strot_sdk import page
from strot_sdk.pages import Dashboard, Row, KPI, Chart, Table

@page(name='sales_dashboard', description='Sales overview', type='dashboard')
class SalesDashboard:
    def layout(self):
        return Dashboard(
            Row(
                KPI(query_id=1, label='Revenue', format='currency'),
                KPI(query_id=2, label='Orders'),
                KPI(query_id=3, label='Customers'),
            ),
            Row(
                Chart(query_id=5, type='line', title='Revenue Trend', span=8),
                Chart(query_id=6, type='donut', title='By Region', span=4),
            ),
            Row(
                Table(query_id=7, title='Recent Orders', sortable=True),
            ),
        )

LLM

All LLM calls go through your STROT instance — no API keys needed in your code.

from strot_sdk import llm

result = llm.complete("Summarize this: " + text)
result = llm.chat([{"role": "user", "content": "What is 2+2?"}])
category = llm.classify("Great product!", ["positive", "negative", "neutral"])
data = llm.extract("John is 30 years old", {"name": "string", "age": "number"})

Data Access

from strot_sdk import strot, query, query_one

rows = strot.queries['monthly_sales'].execute()
rows = query("SELECT * FROM users", data_source_id=1)
row = query_one("SELECT * FROM users WHERE id = 1", data_source_id=1)

Destinations

from strot_sdk import email, slack, webhook

email.send(to="[e-mail]", subject="Report Ready", body="Done.")
slack.send(channel="#alerts", message="New alert!")
webhook.post(url="https://api.example.com/hook", data={"event": "deploy"})

CLI Reference

strot login                              # Authenticate
strot login --token sk_live_abc123       # Direct API key
strot whoami                             # Show current user/org
strot logout                             # Clear credentials

strot init tool my-calculator            # Scaffold tool
strot init agent my-analyst              # Scaffold agent
strot init cortex my-pipeline            # Scaffold pipeline
strot init page my-dashboard             # Scaffold dashboard

strot test                               # Validate locally
strot deploy                             # Deploy to STROT
strot deploy --dry-run                   # Validate without deploying

strot resources                          # List all resources
strot resources queries                  # List saved queries
strot resources datasources              # List data sources

Configuration

Credentials stored in ~/.strot/credentials:

version: 1
current_profile: default
profiles:
  default:
    url: https://app.strot.ai
    api_key: sk_live_abc123
    org: 98bf9a0a-c9cd-42a8-9ea4-4f7fee9a4535

Priority: Constructor args > Environment variables (STROT_URL, STROT_API_KEY) > Credentials file

License

MIT