Claude Chatbot: What It Is and How to Build One

What people mean by "Claude chatbot"

"Claude chatbot" usually refers to one of two things. The first is Claude.ai, Anthropic's own consumer chat interface — the web and mobile app where you type messages and Claude answers, similar to ChatGPT. The second, and the more common intent for developers landing on this page, is building your own custom chatbot that uses Claude as its underlying model — for a website widget, a Slack bot, a support tool, or an internal assistant.

If you just want to chat with Claude, go to claude.ai or the mobile app and sign up — no coding required. If you want to embed Claude's conversational ability into your own product, you need programmatic access to the model, which means either Anthropic's direct API or a wrapper service that turns your existing Claude access into an HTTPS API you can call from code. The rest of this article covers the second case: what building a Claude chatbot actually involves, what decisions you'll make along the way, and how to get from zero to a working bot quickly.

Claude.ai vs. a custom Claude chatbot

These solve different problems:

• Claude.ai is a finished product. You get a chat UI, file uploads, projects, and memory across a conversation, all managed by Anthropic. There's no API access bundled with a standard consumer plan — it's built for humans typing into a browser.
• A custom Claude chatbot is something you build: your own UI (web widget, chat app, Discord bot, internal tool) that sends user messages to Claude and streams the response back. This requires an API key, request/response handling, and usually some prompt engineering specific to your use case.

If your goal is "I want my app to have a chat feature powered by Claude," you're in custom chatbot territory, and the core building block is the same everywhere: a POST request with a system prompt, conversation history, and the latest user message.

The core anatomy of a Claude chatbot

Regardless of which API layer you use, every Claude chatbot has the same moving parts:

1. System prompt — defines the bot's persona, tone, and boundaries ("You are a support assistant for Acme Software. Only answer questions about our product.").
2. Conversation history — an array of user/assistant messages so Claude has context across turns. You manage this array in your own backend or database.
3. The current user message — appended to history before each call.
4. Streaming — for a chat UI to feel responsive, you want tokens streamed back as they're generated rather than waiting for the full response.
5. Tool use (optional) — if your bot needs to look things up (order status, a knowledge base, a calendar), you define tools Claude can call mid-conversation.

Here's a minimal example of a chatbot turn using SubToAPI, which exposes Claude through a standard sub_live_... API key so you don't need a separate Anthropic developer account to build against:

const res = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-sonnet-4",
    system: "You are a friendly support assistant for a SaaS product. Keep answers under 4 sentences.",
    messages: [
      { role: "user", content: "How do I reset my password?" }
    ],
    max_tokens: 500
  })
});

const data = await res.json();
console.log(data.content[0].text);

Each new turn, you push the previous assistant reply and the new user message onto the messages array and send it again. That's the entire loop — the complexity in a real chatbot comes from history management, streaming, and tools, not from the request shape itself.

Adding streaming for a real chat feel

A chatbot that returns one big blob of text after a five-second wait feels broken. Streaming fixes that by sending the response token-by-token as server-sent events:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "stream": true,
    "messages": [{"role": "user", "content": "Explain webhooks in one paragraph."}],
    "max_tokens": 400
  }'

On the frontend, you read the stream and append text as it arrives, which is what makes a chatbot feel like ChatGPT or Claude.ai rather than a slow form submission. Full details on event formats are in the docs at /docs/streaming.

Giving your chatbot memory and tools

Basic conversation memory is just storing the message array per user or session — nothing exotic, but easy to get wrong (truncate too aggressively and the bot forgets context; keep everything and you burn tokens and hit context limits). A common pattern is to keep the last N turns verbatim and periodically summarize older history into the system prompt.

For bots that need to do things — check an order, query a database, hit an internal API — tool use lets Claude request a function call, your code executes it, and you send the result back for Claude to incorporate into its reply. This is what turns a chatbot from "answers questions from training data" into "answers questions using your live data." See /docs/tools for the request/response pattern.

Getting from idea to a working chatbot

If you already have Claude access through a subscription and want an API to build against without setting up separate billing and infrastructure, SubToAPI turns that access into application keys you can drop into a backend, with streaming, tool use, and usage metadata included. Plans start at €9/month for solo builders, with team seats at €19 and €49 for larger usage tiers — see /pricing. A free trial is available at /signup, and the /docs/quickstart page walks through your first authenticated request in a few minutes. The /docs/messages reference covers the full request schema if you're building something more than a basic Q&A bot.

questions

Is Claude.ai itself a chatbot I can use for free? Yes — claude.ai offers a free tier for chatting directly with Claude in a browser or app. That's separate from building a custom chatbot in your own product, which requires API access.

What's the difference between Claude and ChatGPT for building a chatbot? Both are large language models accessible via API with similar request/response patterns (system prompt, message history, streaming). The choice usually comes down to response quality for your use case, pricing, and which ecosystem you're already using.

Do I need to manage conversation history myself? Yes. Claude's API is stateless per request — you send the full relevant message history each time. Your application is responsible for storing and trimming that history per user or session.