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Cloud that can think

The all-in-one cloud for AI agents. Every model, every MCP tool, one safe serverless runtime.

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A new way to ship agents that thinks.

01

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.

02

Plug in tools, safely.

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

03

Ship it. Let it think.

One click to production. Auto-scale from zero, live traces, cost caps — and the agent reasons through each step on its own.

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 credential vault
01

Encrypted key vault

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

Sandboxed runtime per run
02

Sandboxed runtime

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

Scoped tool permissions
03

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 50+ MCP tools. 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, 50+ MCP tools 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 50+ 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
MCP agents → 50+ MCP tools → GPU/CPU auto-scaling | One SDK, any model | Event triggers
AI agents and automation workflow illustration
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.

100+ models, 50+ MCP tools, and growing. One API key to access the entire AI stack.

Claude Opus 4.6
Anthropic: frontier reasoning, analysis
GPT-5.2
OpenAI: next-gen multimodal, agents
Gemini 3 Pro
Google: long context, vision and video
Llama 4 Maverick
Meta: open-source, customizable
Mistral Large
Mistral: multilingual, efficient
DeepSeek R1
DeepSeek: reasoning, math, code
Nano Banana Pro
Google: image model for media agents
Claude Sonnet 4.5
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
100+
Foundation models
50+
MCP tools
<100ms
Routing latency
Thinking engine

Watch it
reason.

MCP agents reason step-by-step, calling 50+ MCP tools as needed. One prompt, and the agent figures out which APIs to call and when. Watch it work.

MCP agents with 50+ managed MCP tools
Automatic tool selection and execution
Step-by-step reasoning transparency
Abstract visualization of AI reasoning
$ 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
Get started

From prompts to
production.

MCP agents, 50+ MCP tools, any model. Start building with FlyMy.AI today. Free during beta.

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Agents and cloud illustration