flyto-core

A debuggable automation engine. Trace every step. Replay from any point.

flyto2.com · Desktop App · Documentation · YouTube

Try in 30 seconds

pip install flyto-core[browser] && playwright install chromium
flyto recipe competitor-intel --url https://github.com/pricing

  Step  1/12  browser.launch         ✓      420ms
  Step  2/12  browser.goto           ✓    1,203ms
  Step  3/12  browser.evaluate       ✓       89ms
  Step  4/12  browser.screenshot     ✓    1,847ms  → saved intel-desktop.png
  Step  5/12  browser.viewport       ✓       12ms  → 390×844
  Step  6/12  browser.screenshot     ✓    1,621ms  → saved intel-mobile.png
  Step  7/12  browser.viewport       ✓        8ms  → 1280×720
  Step  8/12  browser.performance    ✓    5,012ms  → Web Vitals captured
  Step  9/12  browser.evaluate       ✓       45ms
  Step 10/12  browser.evaluate       ✓       11ms
  Step 11/12  file.write             ✓        3ms  → saved intel-report.json
  Step 12/12  browser.close          ✓       67ms

  ✓ Done in 10.3s — 12/12 steps passed

Screenshots captured. Performance metrics extracted. JSON report saved. Every step traced.

What happens when step 8 fails?

With a shell script you re-run the whole thing. With flyto-core:

flyto replay --from-step 8

Steps 1–7 are instant. Only step 8 re-executes. Full context preserved.

3 recipes to try now

# Competitive pricing: screenshots + Web Vitals + JSON report
flyto recipe competitor-intel --url https://competitor.com/pricing

# Full site audit: SEO + accessibility + performance
flyto recipe full-audit --url https://your-site.com

# Web scraping → CSV export
flyto recipe scrape-to-csv --url https://news.ycombinator.com --selector ".titleline a"

Every recipe is traced. Every run is replayable. See all 32 recipes →

Install

pip install flyto-core            # Core engine + CLI + MCP server
pip install flyto-core[browser]   # + browser automation (Playwright)
playwright install chromium        # one-time browser setup

The 85-line problem

Here's what competitive pricing analysis looks like in Python:

Python — 85 lines

import asyncio, json, time
from playwright.async_api import async_playwright

async def main():
    async with async_playwright() as p:
        browser = await p.chromium.launch()
        page = await browser.new_page()
        await page.goto("https://competitor.com/pricing")

        # Extract pricing
        prices = await page.evaluate("""() => {
            const cards = document.querySelectorAll(
              '[class*="price"]'
            );
            return Array.from(cards).map(
              c => c.textContent.trim()
            );
        }""")

        # Desktop screenshot
        await page.screenshot(
            path="desktop.png", full_page=True
        )

        # Mobile
        await page.set_viewport_size(
            {"width": 390, "height": 844}
        )
        await page.screenshot(
            path="mobile.png", full_page=True
        )

        # Performance
        perf = await page.evaluate("""() => {
            const nav = performance
              .getEntriesByType('navigation')[0];
            return {
              ttfb: nav.responseStart,
              loaded: nav.loadEventEnd
            };
        }""")

        # Save report
        report = {
            "prices": prices,
            "performance": perf,
        }
        with open("report.json", "w") as f:
            json.dump(report, f, indent=2)

        await browser.close()

asyncio.run(main())

flyto-core — 12 steps

name: Competitor Intel
steps:
  - id: launch
    module: browser.launch
  - id: navigate
    module: browser.goto
    params: { url: "{{url}}" }
  - id: prices
    module: browser.evaluate
    params:
      script: |
        JSON.stringify([
          ...document.querySelectorAll(
            '[class*="price"]'
          )
        ].map(e => e.textContent.trim()))
  - id: desktop_shot
    module: browser.screenshot
    params: { path: desktop.png, full_page: true }
  - id: mobile
    module: browser.viewport
    params: { width: 390, height: 844 }
  - id: mobile_shot
    module: browser.screenshot
    params: { path: mobile.png, full_page: true }
  - id: perf
    module: browser.performance
  - id: save
    module: file.write
    params:
      path: report.json
      content: "${prices.result}"
  - id: close
    module: browser.close

No trace. No replay. No timing. If step 5 fails, re-run everything.

Full trace. Replay from any step. Per-step timing. Every run is debuggable.

What's New in v2.19.0

• Smart validate_params — auto-corrects wrong field names via alias mapping (e.g., site to url) and suggests alternatives when a non-existent module is requested
• Enhanced search_modules — word-level and tag matching scoring replaces the old substring-only search, producing more relevant results for natural language queries
• browser.extract default text mode — when no fields are specified, browser.extract now returns the text content of matched elements by default (previously returned empty objects)
• Browser channel support — pass channel: 'chrome' to browser.launch to use the system-installed Chrome instead of bundled Chromium, useful for bypassing anti-bot detection on sites that fingerprint headless browsers

Engine Features

• Execution Trace — structured record of every step: input, output, timing, status
• Replay — re-execute from any step with the original (or modified) context
• Breakpoints — pause execution at any step, inspect state, resume
• Evidence Snapshots — full state before and after each step boundary
• Data Lineage — track data flow across steps, build dependency graphs
• Timeout Guard — configurable workflow-level and per-step timeout protection

450 Modules, 83 Categories

See the Full Module Catalog for every module, parameter, and description.

How is this different?

How to Use

# Run a built-in recipe
flyto recipe site-audit --url https://example.com

# Run your own YAML workflow
flyto run my-workflow.yaml

# List all recipes
flyto recipes

pip install flyto-core
claude mcp add flyto-core -- python -m core.mcp_server

Or add to your MCP config:

{
  "mcpServers": {
    "flyto-core": {
      "command": "python",
      "args": ["-m", "core.mcp_server"]
    }
  }
}

Your AI gets all modules as tools.