Flyto2 Core

AI said it finished. Flyto2 shows the proof.

A Python execution engine for AI agents. It runs browser and API work as explicit steps, records what every step did, and replays from the step that failed — instead of re-running the whole job.

The current public inventory is 480 registry-backed modules across 88 catalog categories, including triggers, queue modules, workflow versioning, metering hooks, browser automation, API calls, data transforms, verification, files, and crypto.

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

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Try it 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.

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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.

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How is this different?

	Playwright / Selenium	Shell scripts	flyto-core
Step 8 fails	Re-run everything	Re-run everything	flyto replay --from-step 8
What happened at step 3?	Add print(), re-run	Add echo, re-run	Full trace: input, output, timing
Browser + API + file I/O	Write glue code	3 languages	All built-in
Share with team	"Clone my repo"	"Clone my repo"	pip install flyto-core
Run in CI	Wrap in pytest/bash	Fragile	flyto run workflow.yaml

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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 ->

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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

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Write Your Own Workflows

Recipes are just YAML files. Write your own:

name: price-monitor
steps:
  - id: open
    module: browser.launch
    params: { headless: true }

  - id: page
    module: browser.goto
    params: { url: "https://competitor.com/pricing" }

  - id: prices
    module: browser.evaluate
    params:
      script: |
        JSON.stringify([...document.querySelectorAll('.price')].map(e => e.textContent))

  - id: save
    module: file.write
    params: { path: "prices.json", content: "${prices.result}" }

  - id: close
    module: browser.close

flyto run price-monitor.yaml

Every run produces an execution trace and state snapshots. If step 3 fails, replay from step 3 — no re-running the whole thing.

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Usage

# 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 480 modules as tools.

pip install flyto-core[api]
flyto serve
# ✓ flyto-core running on 127.0.0.1:8333

Endpoint	Purpose
POST /v1/workflow/run	Execute workflow with evidence + trace
POST /v1/workflow/{id}/replay/{step}	Replay from any step
POST /v1/execute	Execute a single module
GET /v1/modules	Discover all modules
POST /mcp	MCP Streamable HTTP transport

import asyncio
from core.modules.registry import ModuleRegistry

async def main():
    result = await ModuleRegistry.execute(
        "string.reverse",
        params={"text": "Hello"},
        context={}
    )
    print(result)  # {"ok": True, "data": {"result": "olleH"}}

asyncio.run(main())

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480 Modules, 88 Catalog Categories

Category	Count	Examples
browser.*	54	launch, goto, click, evaluate, screenshot, performance, challenge
flow.*	24	switch, loop, branch, parallel, retry, circuit breaker, rate limit
array.*	15	filter, sort, map, reduce, unique, chunk, flatten
api.*	13	OpenAI, Anthropic, Gemini, Notion, Slack, Telegram
data.*	13	JSON, YAML, CSV, XML parse/generate/convert
string.*	11	reverse, uppercase, split, replace, trim, slugify, template
ai.*	10	chat, model calls, vision, embeddings, moderation
object.*	10	keys, values, merge, pick, omit, get, set, flatten
testing.*	10	assertions, scenarios, E2E steps, reports
image.*	9	resize, convert, crop, rotate, watermark, OCR, compress
verify.*	9	evidence, visual diff, rulesets, annotations
file.*	8	read, write, copy, move, delete, exists, edit, diff
stats.*	8	mean, median, percentile, correlation, standard deviation
test.*	8	API, browser, and visual checks
check.*	7	validation and guard checks
crypto.*	7	AES encrypt/decrypt, JWT create/verify, hashes
http.*	7	get, request, batch, paginate, session
validate.*	7	email, url, json, phone, credit card
66 more prefixes	221	Docker, archive, math, k8s, network, PDF, AWS, cache, git

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

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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

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Architecture

CLI, MCP, HTTP, Python, and packaged recipes converge on the same workflow engine, module registry, policy, trace, evidence, and replay boundaries. Start with the Technical Whitepaper, then use the Architecture Map and exhaustive source reference for implementation detail.

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Where to go next

You want to	Go to
Run one of the other built-in recipes	docs/RECIPES.md
Browse every module and parameter	docs/TOOL_CATALOG.md
See the module categories at a glance	480 Modules, 88 Catalog Categories
Configure network, filesystem, auth, and permission switches	docs/CONFIGURATION.md
Install a module pack or plugin	docs/PLUGIN_SDK.md
Write your own module	docs/MODULE_SPECIFICATION.md
Understand why the engine is shaped this way	docs/WHY.md
Read the product boundary between the three packages	ARCHITECTURE.md

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

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Testing

python -m pytest
python -m ruff check .
flyto recipe full-audit --url https://example.com

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Security

Report security vulnerabilities via security@flyto2.com. See SECURITY.md for the security policy and the environment variables that define the filesystem and outbound-network boundaries.

SECURITY_STATUS.md lists every published advisory with its severity, affected range, fixed-in version, and the regression test that covers it. Two boundaries are enforced registry-wide by tests that fail the build — every module taking a caller-supplied path must reach the filesystem sandbox helper, and every module taking a caller-supplied URL or host must reach an SSRF guard — so coverage is a CI property rather than a convention.

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License

Apache License 2.0 — free for personal and commercial use.

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Cloud Automation · Pricing · flyto2.com

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Hosted deployment

A hosted deployment is available on Frontier AI.

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Also known as: 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