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Cloud that can think...

Build thinking agents that reason, react, and run in real time. Every model, every tool, every skill — from MCP chatbots to real-time robot brains.

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Core primitives

Everything agents need.
Nothing they don't.

Models, tools, skills, and reasoning infrastructure — all in one cloud. Stable, fast, scalable, and secure.

Model Access Layer

GPT-5.2, Claude, Gemini, Veo-3.1, Nano Banana Pro, Llama, Mistral — every foundation model through one endpoint. Switch models with a parameter, not a migration.

Agent tools & skills

Pre-built tools for search, code execution, file operations, and web browsing. Composable skills that agents combine to solve complex tasks.

Single secure key

One API key for everything. End-to-end encryption, rate limiting, usage tracking, and audit logs. Built for production from day one.

Infrastructure

Infrastructure for agents
with reflexes.

Serverless GPU fleet built for real-time AI agents. 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
Platform

From MCP agents to
real-time AI pipelines.

MCP tool calls for agentic workflows. Frozen graphs for real-time streaming. Same SDK, same infra — GPU and CPU clusters that scale from a chatbot to a robot brain.

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 — 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 — 800+ 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 800+ 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

Freeze a graph. Stream in real-time.

Define a pipeline of tools — ASR, LLM, TTS — describe the graph in a prompt, freeze it. The frozen graph scales as a single streaming unit. Sub-200ms, 40+ languages.

Frozen graph <200ms 40+ languages Auto-scale
translate.py
from flymy import AsyncFlyMy, RealtimeGraph client = AsyncFlyMy() graph = RealtimeGraph(client) graph.define( prompt="""Realtime translation pipeline: 1. whisper-v3 → transcribe incoming audio 2. claude-sonnet-4.5 → translate JP to EN 3. eleven-flash-v2 → synthesize speech""", tools=["whisper-v3", "claude-sonnet-4.5", "eleven-flash-v2"], ) endpoint = await graph.freeze() # frozen → ready to stream async for chunk in endpoint.stream(audio_input): yield chunk # JP→EN, 180ms e2e

Voice agents that close tickets

Freeze a voice graph with ASR, LLM, TTS, and CRM tools. The agent handles calls in real-time — reads CRM history, resolves issues, escalates when needed. Scales to thousands.

Frozen graph CRM tools Sentiment Auto-escalate
callcenter.py
from flymy import AsyncFlyMy, RealtimeGraph client = AsyncFlyMy() graph = RealtimeGraph(client) graph.define( prompt="""Voice support agent pipeline: 1. whisper-v3 → transcribe caller speech 2. claude-sonnet-4.5 → reason with CRM context 3. salesforce → lookup customer, create ticket 4. eleven-flash-v2 → respond to caller Escalate to human if sentiment negative 3x.""", tools=["whisper-v3", "claude-sonnet-4.5", "salesforce", "eleven-flash-v2"], ) endpoint = await graph.freeze() endpoint.serve() # ready for calls # 2.4k calls/day, 87% resolved, CSAT 4.6

Eyes, brain, hands — one graph

Freeze a VLA pipeline: YOLO spots the target, VLM understands the scene, and a Vision-Language-Action model outputs joint trajectories for the robot arm directly. No manual motion planning.

Frozen graph VLA actions YOLO + VLM <100ms loop
robot_arm.py
from flymy import AsyncFlyMy, RealtimeGraph client = AsyncFlyMy() graph = RealtimeGraph(client) graph.define( prompt="""Warehouse bin-picking arm: 1. yolov11 → detect items, output bboxes 2. qwen2.5-vl-72b → identify item, check orientation 3. pi0-base → VLA: frame + bbox → arm trajectory Output 6-DOF joint positions for UR5 arm.""", tools=["yolov11", "qwen2.5-vl-72b", "pi0-base"], ) endpoint = await graph.freeze() async for step in endpoint.stream(video_feed): await arm.move(step.joints) # 6-DOF # see → understand → grasp, 83ms loop, 1200 picks/hr
MCP agents → Frozen real-time graphs → GPU/CPU auto-scaling | <100ms streaming loop | One SDK
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 — 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.

200+ models, 50+ 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 & 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
5,000+ app integrations via MCP
Custom MCP
Build your own server in minutes
50+
AI models available
200+
Pre-built tools
<100ms
Routing latency
Ship it

Deploy and distribute.

From prototype to production in minutes. No Docker, no YAML, no infra teams. FlyMy.AI handles hosting, scaling, and model hand-offs.

01

Build

Write your agent with the SDK. Define models, tools, and skills. Test locally with hot reload.

$ flymy dev --watch
02

Deploy

Push to FlyMy.AI Cloud with a single command. Auto-scaling, zero-downtime deployments built in.

$ flymy deploy --prod
03

Distribute

Share via API, embed in apps, or publish to the marketplace. Usage analytics and billing included.

$ flymy publish --public
Thinking engine

Watch it
reason.

MCP agents reason step-by-step, calling tools as needed. Frozen graphs stream at wire speed — same engine, different mode. Watch both work in real time.

MCP agents & frozen real-time graphs
Automatic tool selection and execution
Real-time 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
Get started

From chatbots to
robot brains.

Thinking agents, real-time pipelines, frozen graphs — start building with FlyMy.AI today. Free during beta.

Try for free Book a demo