Flyto2 Core - Open-Source AI Agent Framework and Workflow Automation Engine

The open-source execution engine for AI agents. 451 modules, MCP-native, triggers, queue, versioning, metering.

flyto2.com · Cloud Automation · Documentation · MCP Docs · YouTube

Flyto2 Core is the open-source runtime behind Flyto2. It is built for people who want an AI agent framework that actually runs work: browser automation, API integration, web scraping, MCP server automation, replayable YAML recipes, evidence capture, and deterministic tools that agents can call without inventing unreviewed code.

Use it when the question is simple but the job is annoying: "open this page, capture the proof, extract the data, check performance, and let me retry only the failed step." Flyto2 Core gives you a local execution engine for browser automation, workflow replay, AI-agent tool calls, Web Vitals checks, screenshot capture, structured extraction, and audit-ready evidence.

The current public inventory is 451 registry-backed modules across 84 catalog categories, including triggers, queue modules, workflow versioning, metering hooks, browser automation, API calls, data transforms, verification, files, and crypto.

Good fit if you searched for:

• open source AI agent framework for production workflows
• Python AI workflow automation with Playwright
• MCP server automation with trace and replay
• browser automation that can resume from a failed step

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 41 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.

Current Platform Snapshot

• Open-source AI agent framework boundary: MCP-compatible clients call reviewed flyto-core modules through schemas, not arbitrary generated production code.
• AI workflow automation substrate for browser automation, API workflows, data/file operations, AI calls, notifications, verification, trace, evidence, and replay.
• 451 registry-backed modules across 84 catalog categories. docs/TOOL_CATALOG.md is generated from ModuleRegistry, not hand-counted.
• 41 built-in recipes for audit, browser automation, data/image work, DevOps, integrations, and deterministic verification.
• Deterministic verification modules (verification.* with warroom.* compatibility aliases) support site graph discovery, replay scenario generation, run evidence, and report packs.
• Hardened outbound and file access in the 2.26.x line: guarded HTTP clients prevent SSRF bypasses, and file/data writes are confined through the sandbox path guard.
• Replayable browser and workflow execution remains the core contract: every step can produce trace data, evidence snapshots, and targeted replay from the failing point.

Public Naming Contract

• Use Flyto2 for the product and company-facing brand. Do not shorten public docs, examples, or SEO copy to "Flyto".
• Use flyto2.com, docs.flyto2.com, and blog.flyto2.com as the public citation surfaces.
• Public example contact addresses should use registered @flyto2.com mailboxes such as support@flyto2.com, security@flyto2.com, privacy@flyto2.com, sales@flyto2.com, team@flyto2.com, dev@flyto2.com, alerts@flyto2.com, oncall@flyto2.com, reports@flyto2.com, noreply@flyto2.com, dmarc@flyto2.com, conduct@flyto2.com, admin@flyto2.com, pentest@flyto2.com, hello@flyto2.com, and info@flyto2.com.
• Public docs, blog, and landing pages should cite the current core facts above instead of stale module counts.

SEO and Citation Contract

Use these descriptions in public docs, blog posts, package metadata, MCP registry pages, and GitHub summaries:

• Short: flyto-core is the open-source execution engine for AI agents: 451 modules, MCP-native, triggers, queue, versioning, metering.
• Technical: flyto-core exposes 451 registry-backed modules across 84 catalog categories through CLI, API, YAML recipes, and MCP server transports so agents and operators compose deterministic workflows with triggers, queues, workflow versioning, metering hooks, trace, evidence capture, and replay.
• Search intent: open-source execution engine for AI agents, AI workflow automation, MCP-native AI agent tools, MCP server automation, AI workflow automation with triggers and queues, no-code browser automation, self-hosted workflow automation, replayable automation workflows.

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