FlyMy.AI is the agentic cloud - a hosted runtime for autonomous AI agents that fuses model inference, MCP tool integrations, scheduling, and the agent loop into a single API. You write a goal, the cloud runs the agent, and you pay per execution.

How is FlyMy.AI different from CrewAI, LangGraph, or AutoGen?

CrewAI, LangGraph, and AutoGen are self-hosted Python or JavaScript frameworks: you bring your own servers, queues, schedulers, retry logic, and observability. FlyMy.AI is the cloud itself - agents run on managed infrastructure with auto-scale, scheduling, MCP catalog, and a workspace UI out of the box.

Can FlyMy.AI run neural networks for image, video, or LLM inference?

Yes. The Models API at https://api.flymy.ai exposes hundreds of neural networks (SDXL Turbo, Flux, ByteDance Seedream and Seedance, Google Veo 3, Imagen 4, nano-banana, Qwen-VL, Wan 2.2, and more) through one synchronous or asynchronous API. The same API key works for both the Models API and the Agents API.

How do I get an API key for FlyMy.AI?

Sign up at https://app.flymy.ai/profile. The same key authenticates both the Agents API (https://backend.flymy.ai) and the Models API (https://api.flymy.ai) via the X-API-KEY header.

What is freeze in FlyMy.AI?

Freeze takes a successful agent chat and distils the canonical input → output pipeline into a deterministic Markdown instruction. Off-path chat steps (exploratory questions, abandoned branches) are dropped. The frozen instruction runs ~10× faster than re-exploration and can be re-executed on demand or on a cron with fresh input variables.

Does FlyMy.AI support MCP (Model Context Protocol) tools?

Yes - FlyMy.AI is MCP-native. There are 1000+ MCP integrations (Slack, GitHub, Jira, Tavily, Stripe, Notion, Drive, Calendar, Telegram, Apollo.io, Linear, and more) configurable from the workspace UI. Custom MCP servers can be added by URL.

FlyMy.AI: Cloud That Can Think

A new way to ship agents that think.

Teach it in text, not code.

Describe what it should do in plain prose - the way you'd brief a new hire. No SDK, no YAML, no graphs.

Plug in tools, safely.

Any MCP, any model - connected through an encrypted vault. Keys never reach the LLM, every run is sandboxed.

Ship it. Embed it.

Use it yourself, or embed it in your product through one API. Auto-scales with live traces and cost caps.

Safe by default.

Open frameworks leak API keys into prompts and run tools on your host. FlyMy.AI keeps credentials in a vault, isolates every run, and never lets your LLM touch your secrets.

Encrypted key vault

API keys and OAuth tokens live in an encrypted vault. Never injected into prompts, never written to logs.

Sandboxed runtime

Each agent run executes in its own isolated container. Filesystem, network, and memory boundaries by default.

Scoped tool access

Every tool gets the minimum permissions it needs. Audit every call, revoke in one click.

Platform

MCP agents that
actually ship.

Use any of 1000+ MCP integrations. One prompt, and the agent picks models, calls APIs, reasons through steps, and delivers the result. Same SDK, same infra, scales from a chatbot to a full pipeline.

Ship agents, not infrastructure

Describe what you need in a prompt. The agent picks models, calls tools, reasons through steps, and delivers the result. One run, and the whole cycle happens automatically.

Prompt → run Auto-routing Multi-model MCP tools

workflow.py

from flymy import AsyncFlyMy, FlyMyRunner client = AsyncFlyMy() runner = FlyMyRunner(client) response = await runner.run( input="Review PR #42, fix issues, deploy to prod", model=["claude-opus-4.6", "gpt-4o"], mcp_servers=["github", "vercel", "slack"], ) # Agent: review → fix 3 issues → deploy → notify #eng # 4 tools, 2 models, 12 steps, 38s total

Every tool your agent needs

Gmail, Slack, GitHub, HubSpot, Notion, Jira, every major MCP tool with managed OAuth out of the box. Describe the workflow in a prompt. The agent figures out which tools to call and when.

OAuth managed MCP tools Event triggers Custom tools

lead_scorer.py

from flymy import AsyncFlyMy, FlyMyRunner client = AsyncFlyMy() runner = FlyMyRunner(client) response = await runner.run( input="""For each new inbound lead: 1. Read email from Gmail 2. Enrich contact in HubSpot 3. Score and qualify 4. Notify sales team in Slack""", model="claude-sonnet-4.5", tools=["gmail", "hubspot", "slack"], auth="managed", ) # 47 leads → 12 qualified → Slack notified

Catches regressions before your team does

New PR opens, and the agent reads the diff, runs analysis, searches for related past issues, posts a structured review, and files a ticket if it finds something critical. Fully autonomous.

GitHub MCP Linear MCP Slack MCP Auto-triage

code_review.py

from flymy import AsyncFlyMy, FlyMyRunner client = AsyncFlyMy() runner = FlyMyRunner(client) response = await runner.run( input="""On every new PR in repo acme/backend: 1. Read the diff via GitHub 2. Analyze for bugs, security issues, style 3. Search past issues for related regressions 4. Post a structured review comment 5. If critical, file a Linear ticket, ping #eng""", model="claude-opus-4.6", mcp_servers=["github", "linear", "slack"], trigger="github.pull_request.opened", ) # 142 PRs reviewed, 23 bugs caught, 0 false positives

Resolves tickets before a human sees them

New ticket in Zendesk, and the agent classifies urgency, searches the knowledge base in Notion, drafts a resolution for routine issues, routes complex ones to the right team with full context.

Zendesk MCP Notion MCP Slack MCP Auto-resolve

support_triage.py

from flymy import AsyncFlyMy, FlyMyRunner client = AsyncFlyMy() runner = FlyMyRunner(client) response = await runner.run( input="""For each new Zendesk ticket: 1. Classify urgency and category 2. Search Notion knowledge base for solution 3. If routine, draft reply, send to customer 4. If complex, route to team in Slack with context 5. Update ticket status and tags""", model="claude-sonnet-4.6", mcp_servers=["zendesk", "notion", "slack"], trigger="zendesk.ticket.created", ) # 847 tickets/week, 62% auto-resolved, CSAT 4.7

Ships release notes while the code ships

PR merged to main, and the agent summarizes commits, checks deploy status, writes release notes to Notion, announces in Slack, sends a customer-facing digest via Gmail. Zero manual steps.

GitHub MCP Notion MCP Slack MCP Gmail MCP

release_ops.py

from flymy import AsyncFlyMy, FlyMyRunner client = AsyncFlyMy() runner = FlyMyRunner(client) response = await runner.run( input="""When PR merges to main: 1. Summarize all commits since last tag 2. Check deploy status on Vercel 3. Write release notes → Notion docs 4. Announce in #releases on Slack 5. Email digest to customers via Gmail""", model="claude-sonnet-4.6", mcp_servers=["github", "vercel", "notion", "slack", "gmail"], trigger="github.push.main", ) # v2.14.0 shipped → notes → Slack → 1.2k customers emailed

Infrastructure

Infrastructure for agents
with reflexes.

Serverless GPU fleet built for agentic workloads. Sub-second cold starts, auto-scaling from zero to thousands, and pay-per-second pricing, so your agents think fast and your invoices stay small.

<200ms

Cold start on GPU

0→N

Auto-scale to zero & back

$/sec

Pay per second, not per hour

99.9%

Uptime SLA

H100 A100 L40S L4 T4 CPU

from flymy import AsyncFlyMy, FlyMyRunner

client = AsyncFlyMy()
runner = FlyMyRunner(client)

response = await runner.run(
  input="Ship a release and notify the team",
  model=["claude-opus-4.6", "gpt-4o"],
  mcp_servers=["github", "slack"],
  tools=['search_files', 'run_tests', 'deploy'],
)

# Agent reasons, selects tools, and acts
print(response.reasoning)  # full chain-of-thought
print(response.actions)    # tools used + results

Developer experience

Three lines to
a thinking agent.

No prompt engineering. No chain management. No tool wiring. Just describe what you need, and FlyMy.AI handles the reasoning.

TypeScript SDK with full type safety

Streaming responses with reasoning steps

Works with Claude Code, Cursor, and any IDE

Ecosystem

Everything you need,
unified.

Every model, 1000+ MCP integrations, and growing. One API key to access the entire AI stack.

Claude Opus 4.8

Anthropic: frontier reasoning, analysis

GPT-5.6

OpenAI: next-gen multimodal, agents

Gemini 3.1 Pro

Google: long context, vision and video

Llama 4 Maverick

Meta: open-source, customizable

Mistral Large 3

Mistral: multilingual, efficient

DeepSeek V4

DeepSeek: reasoning, math, code

Nano Banana Pro

Google: image model for media agents

Claude Sonnet 5

Anthropic: fast, balanced, reliable

Veo 3.1

Google: SOTA video, audio & effects

Web Search

Real-time search across the web

Code Execution

Sandboxed Python, JS, shell runtime

File Operations

Read, write, parse any file format

Web Browser

Headless Chrome, screenshots, DOM

Database Query

SQL, NoSQL, vector DB access

API Calls

HTTP requests, webhooks, REST/GraphQL

Image Analysis

Vision, OCR, image generation

Email & Messaging

Send emails, Slack, Teams messages

Auth & Security

OAuth, JWT, secrets management

GitHub

Repos, PRs, issues, actions

Google Workspace

Drive, Docs, Sheets, Calendar

Slack

Channels, messages, workflows

HubSpot

CRM, contacts, deals, marketing

Notion

Pages, databases, knowledge base

Jira

Issues, sprints, project tracking

Salesforce

CRM, leads, opportunities, reports

Zapier

Workflow automation via Zapier MCP

Custom MCP

Build your own server in minutes

Any

Foundation models

Any

MCP tools

<100ms

Routing latency

Thinking engine

Watch it
reason.

MCP agents reason step-by-step, calling every MCP tool as needed. One prompt, and the agent figures out which APIs to call and when. Watch it work.

MCP agents with managed MCP tools

Automatic tool selection and execution

Step-by-step reasoning transparency

$ flymy.agent.think("Analyze and deploy")

├─ Routing → Claude Opus 4.6

├─ Loading → [search, code, deploy]

├─ Reasoning → 12 steps, 3 tool calls

├─ Synthesizing → merging results...

└─ Done in 2.4s → deployed to production

Cloud that can think...