AI that builds AI.

Describe the agent you want. camelAI builds a working AI app, deploys it to a live URL, and handles everything.

No API keys. No provider config. Just describe what you want.
camelAI Chat
Build me a support chatbot that answers questions about our docs and cites the relevant page
Building
├─Scaffolded chat UI with streaming
├─Built RAG pipeline for doc indexing
├─Added source citations to responses
├─Wired up Workers AI binding
└─Published to support-bot--acme.camelai.app

Your app is live:

support-bot--acme.camelai.app
Chat interface with streaming responses
RAG over your documentation
Source citations in every answer
Describe what you want to build...

Every app gets AI built in.

The platform handles models, authentication, and infrastructure. You just describe what your AI should do.

AI Models

Workers AI powers every deployed app natively. Your AI agent gets access to language models without any setup.

No API Keys

Zero configuration. The platform virtualizes AI bindings at deploy time — your code never touches credentials or provider config.

Streaming Responses

Real-time streaming out of the box. Your users see AI responses as they generate, just like ChatGPT.

Persistent Conversations

Conversation history is stored automatically using Durable Objects. Your agent remembers every interaction.

Custom Tools

Define tools your AI agent can call using Zod schemas. Weather lookups, database queries, API calls — anything.

One-Click Deploy

One command to a live URL. Your AI app goes from code to *.camelai.app instantly. Share it with anyone.

This is what camelAI builds for you.

Real production code. Cloudflare's AI chat framework, the Vercel AI SDK, streaming responses, persistent conversations. Not a prototype.

workers/chat.ts
import { AIChatAgent } from "@cloudflare/ai-chat";
import { createWorkersAI } from "workers-ai-provider";
import { streamText, createUIMessageStream,
  createUIMessageStreamResponse, convertToModelMessages
} from "ai";

export class Chat extends AIChatAgent<Env> {
  async onChatMessage(onFinish, options) {
    const workersai = createWorkersAI({
      binding: this.env.AI
    });

    const stream = createUIMessageStream({
      execute: async ({ writer }) => {
        const result = streamText({
          model: workersai("auto", {}),
          messages: await convertToModelMessages(
            this.messages
          ),
          system: "You are a helpful assistant.",
        });
        writer.merge(result.toUIMessageStream());
      ,
    });

    return createUIMessageStreamResponse({ stream });
  }
}

This is the entire chat agent. The platform handles AI model routing, authentication, and message persistence. Your code stays clean.

Your agent is more than a chatbot.

AI apps built on camelAI receive inputs, reason about them, use tools, and take action — all autonomously.

Input

Trigger

A user asks a question. A webhook fires. A cron job runs. An email arrives.

Receive

Your AI App

Your deployed app receives the request at its live URL. The full conversation context is already loaded.

Process

Reason with AI

The agent thinks, calls tools, queries databases, fetches external data. All powered by Workers AI.

Act

Take Action

Respond to the user. Update a database. Send a Slack message. Generate a report. Trigger the next step.

Persistent context — your agent remembers everything across every interaction

Agent runtime

AI agents that do real work.

Give the agent a schedule, an inbox, memory, and live systems to work with. This is where camelAI stops being a prompt box and starts behaving like a persistent operator.

Core loop

Persistent computer + deployed app

The agent keeps context across threads, uses real tools, and can act through the channels your team already works in. Not another static chatbot shell.

Cron jobs50+ integrationsWorkspace emailPersistent storage

Scheduled runtime

AI + Cron

+Runs without you

An agent that wakes up on a schedule, analyzes fresh data, and acts on the results. Combine AI reasoning with automated triggers.

Trigger

8:00am schedule fires

Reason

Analyze yesterday's ticket volume

Outcome

Email a trend summary

"Every morning at 8am, analyze our support tickets from yesterday and email me a summary with trends."

Connected systems

AI + Integrations

+50+ integrations

Pull data from Notion, Salesforce, or HubSpot. Analyze it with AI. Push results to Slack, email, or a dashboard.

Trigger

HubSpot marks a deal closed

Reason

Draft onboarding from account context

Outcome

Send the email automatically

"When a new deal closes in HubSpot, use AI to draft a personalized onboarding email and send it."

Persistent memory

AI + Database

+Stateful by default

Store conversation history, build knowledge bases, track user interactions over time. Your agent gets smarter with every conversation.

Trigger

Log each unresolved question

Reason

Spot patterns across conversations

Outcome

Flag knowledge gaps for the team

"Build a FAQ bot that learns from every question it can't answer and flags gaps for our team."

Native inbox

AI + Email

+Reply in thread

Your agent has its own inbox. Email it tasks, questions, or data — it processes everything with AI and replies right to your thread.

Trigger

Receive a CSV over email

Reason

Categorize and summarize the data

Outcome

Reply inline with the report

"Email my agent a CSV of last month's expenses. Have it categorize and reply with a summary."

Describe the agent. We'll build it.

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