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  3. Flyto Core
Flyto Core logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 5:16:48 PM

Flyto Core

User RatingsBe the first to rate and review this MCP server!
View Repository480 GitHub StarsTotal stargazers on GitHub for the source repository (480 stars).Visit Website
pythonworkflow-automationbrowser-automationai-agentmcp

Open-source deterministic AI agent execution engine with 468 modules for browser automation, API calls, workflow replay, and evidence capture.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "flytohub-flyto-core": {
      "command": "uvx",
      "args": [
        "flyto-core"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives☁️ More in Cloud Platforms

Overview

Flyto-core is an open-source AI agent framework and workflow automation engine designed for deterministic execution of complex tasks like browser automation, API integration, web scraping, and MCP server automation. It supports traceable, replayable workflows with evidence snapshots and step-level retry, enabling reliable automation with audit-ready outputs. The engine includes a large registry of modules across diverse categories to build and run production-grade AI workflows locally or in automation pipelines.

Use cases

•Automate browser interactions with replay and evidence capture
•Integrate APIs and perform data extraction in workflows
•Run MCP server automation with trace and step replay
•Perform web scraping and export structured data
•Audit web performance and accessibility with automated checks

Key features

•468 registry-backed modules across 85 categories
•Deterministic execution with step tracing and replay
•Supports browser automation via Playwright
•Evidence capture including screenshots and performance metrics
•Workflow versioning and metering hooks
•Supports STDIO and streamable HTTP transport

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Flyto Core.

Extracted Tool Capabilities
468 registry-backed modules across 85 categories
Deterministic execution with step tracing and replay
Supports browser automation via Playwright
Evidence capture including screenshots and performance metrics
Workflow versioning and metering hooks
Supports STDIO and streamable HTTP transport

Documentation Overview

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.

PyPI version License Python 3.10+

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

flyto-core demo: API pipeline → replay → browser automation


Try it in 30 seconds

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

bash
flyto replay --from-step 8

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


How is this different?

Playwright / SeleniumShell scriptsflyto-core
Step 8 failsRe-run everythingRe-run everythingflyto replay --from-step 8
What happened at step 3?Add print(), re-runAdd echo, re-runFull trace: input, output, timing
Browser + API + file I/OWrite glue code3 languagesAll built-in
Share with team"Clone my repo""Clone my repo"pip install flyto-core
Run in CIWrap in pytest/bashFragileflyto run workflow.yaml

3 recipes to try now

bash
# 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

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

Write Your Own Workflows

Recipes are just YAML files. Write your own:

yaml
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
bash
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.


Usage

CLI — run workflows from the terminal
bash
# 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
MCP Server — for Claude Code, Cursor, Windsurf
Terminal
pip install flyto-core
claude mcp add flyto-core -- python -m core.mcp_server

Or add to your MCP config:

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

Your AI gets all 480 modules as tools.

HTTP API — for integrations and remote execution
Terminal
pip install flyto-core[api]
flyto serve
# ✓ flyto-core running on 127.0.0.1:8333
EndpointPurpose
POST /v1/workflow/runExecute workflow with evidence + trace
POST /v1/workflow/{id}/replay/{step}Replay from any step
POST /v1/executeExecute a single module
GET /v1/modulesDiscover all modules
POST /mcpMCP Streamable HTTP transport
Python API — for programmatic use
server.ts
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())

480 Modules, 88 Catalog Categories

CategoryCountExamples
browser.*54launch, goto, click, evaluate, screenshot, performance, challenge
flow.*24switch, loop, branch, parallel, retry, circuit breaker, rate limit
array.*15filter, sort, map, reduce, unique, chunk, flatten
api.*13OpenAI, Anthropic, Gemini, Notion, Slack, Telegram
data.*13JSON, YAML, CSV, XML parse/generate/convert
string.*11reverse, uppercase, split, replace, trim, slugify, template
ai.*10chat, model calls, vision, embeddings, moderation
object.*10keys, values, merge, pick, omit, get, set, flatten
testing.*10assertions, scenarios, E2E steps, reports
image.*9resize, convert, crop, rotate, watermark, OCR, compress
verify.*9evidence, visual diff, rulesets, annotations
file.*8read, write, copy, move, delete, exists, edit, diff
stats.*8mean, median, percentile, correlation, standard deviation
test.*8API, browser, and visual checks
check.*7validation and guard checks
crypto.*7AES encrypt/decrypt, JWT create/verify, hashes
http.*7get, request, batch, paginate, session
validate.*7email, url, json, phone, credit card
66 more prefixes221Docker, archive, math, k8s, network, PDF, AWS, cache, git

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


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

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.


Where to go next

You want toGo to
Run one of the other built-in recipesdocs/RECIPES.md
Browse every module and parameterdocs/TOOL_CATALOG.md
See the module categories at a glance480 Modules, 88 Catalog Categories
Configure network, filesystem, auth, and permission switchesdocs/CONFIGURATION.md
Install a module pack or plugindocs/PLUGIN_SDK.md
Write your own moduledocs/MODULE_SPECIFICATION.md
Understand why the engine is shaped this waydocs/WHY.md
Read the product boundary between the three packagesARCHITECTURE.md

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.


Testing

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

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.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
480
Stargazers on the source repository.
Last commit
4d ago
Most recent push to the default branch.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Flyto Core

Flyto-core is implemented in Python and requires Python 3.9 or higher.

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Technical Specs & Signals

Category☁️Cloud Platforms
PricingFree
More technical detailsExpand ▾
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseApache-2.0
Last updatedSep 7, 2026
11/11 checks healthy over the last 33d
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars480
GitHub Star CountTotal stargazers on GitHub representing community popularity (480 stars).
Last commit4d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 7, 2026
49Quality signal: Fair · 49/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership4/20
Documentation & tools24/30
Adoption & activity9/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

8 high-severity advisories on record for this package. Most advisories affect transitive dependencies and may not be exploitable in this server's actual usage — this is a directional signal, not a security audit.

Critical 2High 6Medium 0Low 8

Scanned 23d ago via OSV.dev · flyto-core (PyPI)

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