
chat
An open-source SDK for agentic workflows based on MCPs. Integrated LLM cost management and one-click deploy to Cloudflare.
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deco.chat is an open-source foundation for building AI-native software, providing developers, engineers, and AI enthusiasts with robust tools to rapidly prototype, develop, and deploy AI-powered applications. It empowers Vibecoders to prototype ideas and Agentic engineers to deploy scalable, secure, and sustainable production systems. The core capabilities include an open-source runtime for composing tools and workflows, MCP Mesh for secure integration of models and APIs, a unified TypeScript stack for backend logic and custom frontends, global modular infrastructure built on Cloudflare, and a visual workspace for building agents and orchestrating everything in code.
README:
deco.chat is an open-source foundation for building AI-native software.
We equip developers, engineers, and AI enthusiasts with robust tools to rapidly
prototype, develop, and deploy AI-powered applications.
Official docs: https://docs.deco.page
[!TIP] If you have questions or want to learn more, please join our discord community: https://deco.chat/discord
- Vibecoders prototyping ideas
- Agentic engineers deploying scalable, secure, and sustainable production systems
Our goal is simple: empower teams with Generative AI by giving builders the tools to create AI applications that scale beyond the initial demo and into the thousands of users, securely and cost-effectively.
- Open-source Runtime – Easily compose tools, workflows, and views within a single codebase
- MCP Mesh (Model Context Protocol) – Securely integrate models, data sources, and APIs, with observability and cost control
- Unified TypeScript Stack – Combine backend logic and custom React/Tailwind frontends seamlessly using typed RPC
- Global, Modular Infrastructure – Built on Cloudflare for low-latency, infinitely scalable deployments. Self-host with your Cloudflare API Key
- Visual Workspace – Build agents, connect tools, manage permissions, and orchestrate everything built in code
A Deco project extends a standard Cloudflare Worker with our building blocks and
defaults for MCP servers.
It runs a type-safe API out of the box and can also serve views — front-end apps
deployed alongside the server.
Currently, views can be any Vite app that outputs a static build. Soon, they’ll
support components declared as tools, callable by app logic or LLMs.
Views can call server-side tools via typed RPC.
- Install the CLI
npm i -g deco-cli
or
bun i -g deco-cli
- Log in to deco.chat. Don’t have an account? Sign up first.
deco login
- Create a new project
deco create # create new project, select workspace and choose template
cd my-project
npm install # or bun, deno, pnpm
- Start the dev server
npm run dev # → http://localhost:8787 (hot reload)
Need pre‑built MCP integrations? Explore deco-cx/apps.
my-project/
├── server/ # MCP tools & workflows (Cloudflare Workers)
│ ├── main.ts
│ ├── deco.gen.ts # Typed bindings (auto-generated)
│ └── wrangler.toml
├── view/ # React + Tailwind UI (optional)
│ └── src/
├── package.json # Root workspace scripts
└── README.md
Skip
view/
if you don’t need a frontend.
Command | Purpose |
---|---|
deco dev |
Run server & UI with hot reload |
deco deploy |
Deploy to Cloudflare Workers |
deco gen |
Generate types for external integrations |
deco gen:self |
Generate types for your own tools |
For full command list:
deco --help
or see the CLI README
A Deco project is built using tools and workflows — the core primitives for connecting integrations, APIs, models, and business logic.
Atomic functions that call external APIs, databases, or AI models. All templates include the necessary imports from the Deco Workers runtime.
import { createTool, Env, z } from "deco/mod.ts";
const createMyTool = (env: Env) =>
createTool({
id: "MY_TOOL",
description: "Describe what it does",
inputSchema: z.object({ query: z.string() }),
outputSchema: z.object({ answer: z.string() }),
execute: async ({ context }) => {
const res = await env.OPENAI.CHAT_COMPLETIONS({
model: "gpt-4o",
messages: [{ role: "user", content: context.query }],
});
return { answer: res.choices[0].message.content };
},
});
Tools can be used independently or within workflows. Golden rule: one tool call per step — keep logic in the workflow.
Orchestrate tools using Mastra operators like .then
, .parallel
,
.branch
, and .dountil
.
Tip: Add Mastra docs to your AI code assistant for autocomplete and examples.
import { createStepFromTool, createWorkflow } from "deco/mod.ts";
return createWorkflow({
id: "HELLO_WORLD",
inputSchema: z.object({ name: z.string() }),
outputSchema: z.object({ greeting: z.string() }),
})
.then(createStepFromTool(createMyTool(env)))
.map(({ inputData }) => ({ greeting: `Hello, ${inputData.answer}!` }))
.commit();
Build React + Tailwind frontends served by the same Cloudflare Worker.
- Routing with TanStack Router
- Typed RPC via
@deco/workers-runtime/client
- Preconfigured with
shadcn/ui
andlucide-react
-
Add an integration via the deco.chat dashboard (improved UX coming soon)
-
Run
npm run gen
→ updatesdeco.gen.ts
with typed clients -
Write tools in
server/main.ts
-
Compose workflows using
.map
,.branch
,.parallel
, etc. -
(Optional) Run
npm run gen:self
→ typed RPC clients for your tools -
Build views in
/view
and call workflows via the typed client -
Run locally
npm run dev # → http://localhost:8787
-
Deploy to Cloudflare
npm run deploy
We welcome contributions! Check out CONTRIBUTING.md
for
guidelines and tips.
Made with ❤️ by the Deco community — helping teams build AI-native systems that scale.
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