Building Your First Claude Chatbot

Building Your First Claude Chatbot: A Step-by-Step Guide

Claude is a powerful large language model (LLM) developed by Anthropic, known for its advanced capabilities and large context window (it can handle up to 200k tokens of context, roughly 160,000 words). This makes Claude excellent for building chatbots that can maintain long conversations and provide detailed answers.

In this guide, we’ll walk through building your first Claude-powered chatbot from scratch. We’ll cover everything from obtaining API access and writing the backend in Python to creating a React frontend and deploying the finished app. This is a technical, hands-on guide aimed at developers and product managers who want to integrate Claude into real products (this is not a no-code tutorial).

By the end, you’ll have a fully functioning Q&A chatbot with features like context awareness, short-term memory per session, and even the ability to call external tools or functions. Let’s dive in!

Preliminary: Test Claude on the Web (Optional)

Before coding, you might want to get a feel for Claude’s behavior using Anthropic’s web interface. Anthropic provides an official chat interface (for example, via the Claude.ai platform) where you can chat with Claude interactively. This step is optional but recommended for quick prompt testing – it helps you understand how Claude responds to questions, how it follows instructions, and what kind of output to expect. Use it to prototype a few typical user questions and see how Claude replies. This will inform how you design prompts and handle responses in your own app. Once you’re comfortable with Claude’s basic Q&A in the web UI, you’re ready to start building your own chatbot using the API.

Planning Your Claude-Powered Chatbot

Before coding, let’s outline the key features and requirements of our chatbot and how we’ll implement them:

Context Awareness & Memory: Our chatbot should remember what was said earlier in the conversation so it can handle follow-up questions. We’ll achieve this by storing the conversation history for each user session and sending that context with every API call. Claude’s API supports multi-turn conversations by accepting a list of messages (alternating user and assistant roles) as input. By preserving previous turns in the messages payload, Claude will generate responses that take into account the prior dialogue.

User Session Tracking: In a multi-user environment (like a SaaS app), each user’s conversation should be kept separate. We’ll assign each user a unique session ID (or user ID) and use it to track that user’s message history on the backend. This way, User A’s chat history won’t mix with User B’s. In practice, the frontend can generate or fetch a user ID (for example, storing a random ID in local storage on first visit) and include it with each request to the backend, which uses it to fetch the correct conversation context.

Claude API Integration: We’ll use the Claude API as the core AI engine. Anthropic offers a cloud API that lets you send prompts (messages) to Claude and receive its responses. We need to obtain an API key, and then our backend will call Claude’s API (likely via the official Anthropic SDK or HTTP requests). The API is pay-as-you-go, so keep an eye on token usage and costs, especially as conversation length grows.

Tool Calling (Optional): To make our chatbot truly powerful, we can enable it to perform actions like fetching external data or executing functions. For example, if a user asks for the weather, the bot could call a weather API; if asked to perform a calculation or database query, it could trigger code to do so. Claude has an advanced tool-use interface where you can define tools (with a name, description, and input schema) in the API request. Claude will then respond with structured JSON when it decides to use a tool, which our backend can parse and act on. We’ll touch on how to set up a simple tool and handle these structured outputs. (This is an optional, advanced feature – you can skip it on first pass, but it’s good to know it’s possible.)

Structured Outputs: Even when not using tools, you might sometimes want Claude to return information in a structured format (e.g. JSON) for easier processing. We’ll see how to ask Claude for structured output and validate it. For instance, if building a chatbot that interfaces with other systems, you could prompt Claude to output its answer as a JSON object under certain conditions, then have your code parse that. Ensuring the JSON is well-formed (and handling errors if not) will be part of our validation strategy.

Safety & Validation: Anthropic designed Claude with safety in mind – by default Claude aims to be helpful, harmless, and honest. However, as developers we should still implement guardrails. We’ll use system instructions (or prompts) to guide Claude’s behavior if needed, and double-check outputs especially for tool calls or critical actions. For example, if we expect a JSON from Claude for a tool, our code will verify it’s valid JSON before executing anything. We’ll also handle cases where Claude’s answer might be off-track or if the API returns an error, ensuring the user gets a safe and user-friendly response even in those scenarios.

Now that the blueprint is ready, let’s get our hands dirty. We’ll start by setting up access to Claude’s API.

Setting Up Claude API Access

Before coding the chatbot, you need access to the Claude API:

1. Get an Anthropic API Key: Sign up for an Anthropic developer account on the Claude console and obtain an API key. In the Anthropic console, go to Settings > API Keys and create a new key. Copy this key somewhere secure; you won’t be able to see it again after you close that page. You may need to add billing info or credits to your account as well, since the API is a paid service (it charges based on token usage).

2. Secure the API Key: Never hard-code your API key in your frontend code or commit it to a repository. Treat it like a password. The best practice is to store it in an environment variable on your backend server or in a .env file that your code loads (and make sure .env is in your .gitignore so it’s not checked into version control). For example, you might create a .env file in your project with a line like:

CLAUDE_API_KEY="xoxp-YourClaudeApiKeyHere"

And then load it in your Python code (using python-dotenv or similar) so that the key becomes available as an environment variable. This way, your secret is not exposed in code. Remember: if someone gets hold of your API key, they could use your credits or incur charges on your account, so keep it safe!

3. Install Anthropic’s SDK (optional): Anthropic provides an official Python library (anthropic on PyPI) that makes calling the API easier. You can install it with pip:

pip install anthropic

We will use this SDK in our backend, as it abstracts away the HTTP details and provides a convenient method to call Claude. (Alternatively, you could call the HTTP API directly with requests, but using the SDK is simpler and less error-prone.)

With our API key ready and environment set up, we can proceed to build the backend of our chatbot.

Building the Backend with FastAPI (Python)

Our backend’s role is to mediate between the frontend (user interface) and the Claude API. It will receive user messages, add context (conversation history), call Claude, and return the assistant’s response. We’ll use FastAPI, a fast Python web framework, to build a simple REST API for our chatbot. FastAPI makes it easy to define HTTP endpoints and comes with automatic JSON handling and data validation.

1. Set up the FastAPI project: Create a new directory for your backend (e.g., claude-chatbot-backend). Inside, set up a Python virtual environment and install the needed packages:

mkdir claude-chatbot-backend && cd claude-chatbot-backend
python3 -m venv venv
source venv/bin/activate   # (for Windows, use "venv\\Scripts\\activate")
pip install fastapi \"uvicorn[standard]\" python-dotenv anthropic

Here we installed FastAPI, Uvicorn (an ASGI server to run FastAPI), python-dotenv (to load the .env file), and the anthropic SDK.

2. Initialize FastAPI app: Create a file main.py and start by importing FastAPI and setting up the app and configuration:

# main.py
import os
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from anthropic import Anthropic

# Load environment variables from .env
from dotenv import load_dotenv
load_dotenv()

app = FastAPI()

# Initialize the Anthropic client with your API key
client = Anthropic(api_key=os.environ.get("CLAUDE_API_KEY"))

In the above, we created a FastAPI instance and also initialized the Anthropic client for Claude using our API key loaded from the environment. This client will be used to send messages to Claude.

3. Configure CORS: If your frontend will be served from a different origin (domain or port) than this backend, you need to enable CORS (Cross-Origin Resource Sharing) so that the browser can call your API. FastAPI provides a middleware for this:

# Allow the React frontend (running on localhost:3000 or 5173 in dev, for example) to call this API
origins = [
    "http://localhost:3000",
    "http://localhost:5173",
    # you can add your production frontend URL here later
]
app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

This configuration permits the specified origins to make requests. It’s crucial for local testing and later when deploying (you’ll include your actual domain).

4. Define data models: We’ll define a Pydantic model for the request payload our API will accept. The client (frontend) will send a JSON with the user’s message (and possibly a user/session ID). For example:

class ChatRequest(BaseModel):
    user_id: str
    message: str

This means our /chat endpoint will expect a JSON body like {"user_id": "...", "message": "..."}.

5. Implement conversation storage: To handle context, we need to store each conversation’s messages. For simplicity, we can use an in-memory store (a dictionary) in our FastAPI app, where the key is the user_id and the value is a list of message dicts (role/content) representing the conversation so far. For example:

# In-memory storage for conversations: {user_id: [ {"role": ..., "content": ...}, ... ]}
sessions = {}

In a production app, you might use a database or cache (like Redis) for this, especially to persist across server restarts or scale to multiple servers. But an in-memory Python dict works for a basic implementation (just note it will reset if the server restarts).

6. Create the chat endpoint: Now, we write the core logic. We’ll make a POST endpoint /chat that takes a ChatRequest, updates the session history, calls Claude, and returns the bot’s answer:

@app.post("/chat")
async def chat(request: ChatRequest):
    user_id = request.user_id
    user_message = request.message

    # Initialize session history if not exists
    if user_id not in sessions:
        sessions[user_id] = []
        # (Optional) Add a system message at the start to set the assistant's behavior or context
        # sessions[user_id].append({"role": "system", "content": "You are a helpful AI assistant named Claude."})

    # Append the user's message to the history
    sessions[user_id].append({"role": "user", "content": user_message})

    try:
        # Call Claude API with the conversation history
        response = client.messages.create(
            model="claude-4",  # or use the specific model version you have access to
            max_tokens=1000,
            messages=sessions[user_id]
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Claude API error: {e}")

    # Extract Claude's reply from the response
    assistant_reply = response.content[0].text   # using the anthropic SDK response format:contentReference[oaicite:15]{index=15}

    # Save the assistant's reply in the history for context
    sessions[user_id].append({"role": "assistant", "content": assistant_reply})

    # Return the assistant's reply as JSON
    return {"reply": assistant_reply}

A few things to note in this code:

  • We append the new user message to the sessions[user_id] list. Then we send all messages in the session to Claude’s API. Claude is trained to handle a list of messages (with alternating roles) and continue the conversation appropriately. By providing the full history, we give Claude the context it needs to generate a relevant answer that takes into account the previous dialogue.
  • We used model="claude-4" as a placeholder; you should use the model name you have access to. Anthropic has various models (Claude Instant, Claude 2, Claude 4, etc.). Check their documentation for model IDs (for example, a model ID might look like "claude-2.0" or a specific version like in the example "claude-3-haiku-20240307"). Use one of the available model names from your account.
  • We set max_tokens=1000 which is the maximum length of Claude’s answer in tokens. Adjust this based on how long you want responses (and note it impacts cost). Claude can handle very long outputs, but capping it is usually wise.
  • The client.messages.create(...) call comes from the Anthropic SDK. It returns a response object; in the snippet above, response.content[0].text contains Claude’s reply text. We then append that to the history and return it. (If you’re not using the SDK, the raw HTTP API would return a JSON where the assistant message is embedded – the SDK abstracts that away for us.)
  • We wrap the API call in a try/except and convert any error into an HTTP 500 error. This ensures if something goes wrong (e.g., network issue or an invalid request), our API doesn’t just crash silently – it will respond with an error status that our frontend can handle.

At this point, we have a basic backend that can handle chat messages with context memory. You can run the server locally to test it:

uvicorn main:app --reload

The --reload flag will auto-restart the server on code changes, useful during development. The API should now be running at http://127.0.0.1:8000. Try a quick test with curl or a tool like Postman:

curl -X POST "http://127.0.0.1:8000/chat" \
     -H "Content-Type: application/json" \
     -d '{"user_id": "testuser123", "message": "Hello, Claude!"}'

You should get back a JSON with Claude’s reply. If you send another message with the same user_id, the backend will include the previous context, and Claude’s response should reflect the conversation continuity.

Session memory: Thanks to our session tracking, if you use a consistent user_id for a series of requests, the sessions dictionary grows with each turn. This gives short-term memory on a per-user basis. For a more production-ready approach, you could implement expiration of old sessions or trimming of very long histories (though Claude’s 100k+ token context means you have a lot of breathing room). But always be mindful of cost: long histories mean more tokens sent each time, which could increase latency and expense.

Next, let’s build the frontend so users can interact with our chatbot in a nice interface.

Building the Frontend with React

We will create a simple web interface in React for users to chat with Claude. The frontend will present a chat box where the user can type messages and see Claude’s responses, much like a typical messaging app. We’ll use the browser’s Fetch API to send user messages to our FastAPI backend and display the results.

1. Set up a React project: You can use Create React App or Vite to scaffold a new React app. For a modern and fast setup, Vite is great. Run the following in a separate directory (outside the backend):

npm create vite@latest claude-chatbot-frontend --template react
cd claude-chatbot-frontend
npm install
npm run dev   # to start the dev server (usually at http://localhost:5173)

This will create a basic React app. We’ll primarily work in the src/App.jsx (or App.tsx if using TypeScript) file to implement the chat UI.

2. Design the chat UI: Our UI needs to show a conversation history and an input box. We’ll maintain state for messages and user input. Each message can have a role (or type): either “user” or “assistant”, and some text. We’ll also show a loading indicator when waiting for Claude’s reply.

In App.jsx, let’s outline the component:

import React, { useState, useEffect } from 'react';

function App() {
  const [userInput, setUserInput] = useState('');
  const [chatLog, setChatLog] = useState([]);
  const [loading, setLoading] = useState(false);
  const [sessionId, setSessionId] = useState('');

  // On first load, set or retrieve a session ID for the user
  useEffect(() => {
    let storedId = localStorage.getItem('sessionId');
    if (!storedId) {
      storedId = Date.now().toString();  // simple unique ID (timestamp)
      localStorage.setItem('sessionId', storedId);
    }
    setSessionId(storedId);

    // Optionally, restore past chat from localStorage
    const storedChat = localStorage.getItem('chatLog');
    if (storedChat) {
      setChatLog(JSON.parse(storedChat));
    }
  }, []);

  const handleSubmit = async (e) => {
    e.preventDefault();
    if (!userInput.trim()) return;  // ignore empty messages

    // Append the user's message to chat log (for immediate feedback)
    const newUserMessage = { role: 'user', text: userInput };
    setChatLog(prev => [...prev, newUserMessage]);
    setUserInput('');
    setLoading(true);

    try {
      // Send the message to our backend API
      const response = await fetch('http://localhost:8000/chat', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ user_id: sessionId, message: userInput })
      });
      if (!response.ok) {
        throw new Error(`HTTP error! status: ${response.status}`);
      }
      const data = await response.json();
      const botMessage = { role: 'assistant', text: data.reply };
      // Update the chat log with Claude's response
      setChatLog(prev => [...prev, botMessage]);
    } catch (error) {
      console.error('Error fetching chat response:', error);
      const errorMsg = { role: 'assistant', text: "Sorry, something went wrong." };
      setChatLog(prev => [...prev, errorMsg]);
    } finally {
      setLoading(false);
    }
  };

  return (
    <div className="chat-container">
      <div className="messages">
        {chatLog.map((msg, index) => (
          <div key={index} className={`message ${msg.role}`}>
            {msg.text}
          </div>
        ))}
        {loading && <div className="message assistant">Claude is typing...</div>}
      </div>
      <form onSubmit={handleSubmit}>
        <input 
          type="text" 
          value={userInput}
          onChange={e => setUserInput(e.target.value)}
          placeholder="Type your message..."
          disabled={loading}
        />
        <button type="submit" disabled={loading}>Send</button>
      </form>
    </div>
  );
}

export default App;

Let’s break down a few important parts of this React code, some of which mirror what we saw in a similar OpenAI chatbot example:

  • We use React state (chatLog) to keep track of all messages in the conversation so far. On initial load, we try to restore any previous chat from localStorage so the conversation persists if the user refreshes the page. This is optional but enhances user experience.
  • We generate or retrieve a sessionId for the user and store it in localStorage. Here, for simplicity, we use the current timestamp as an ID. In a real app, you might use a more robust unique ID (or have the backend assign one, or use the user’s login ID if accounts are involved). The key is that this sessionId will be sent with each message to allow the backend to know which session’s history to use.
  • The handleSubmit function is called when the user sends a message. We prevent the default form submission, and ignore if the input is empty. We then optimistically add the user’s message to the UI (chatLog) so the user sees it immediately. We set loading to true to indicate we’re waiting for a response (which could trigger a “Claude is typing…” message or disable the input to prevent multiple sends).
  • We make a fetch POST request to our FastAPI backend at http://localhost:8000/chat. We include user_id: sessionId and the message text in the JSON body. This matches what our backend expects. We also check for HTTP errors (non-200 response) and throw an error to be caught if so.
  • On success, we parse the JSON and get data.reply (recall our backend returns {"reply": "some text"}). We then append a new message to chatLog with role: 'assistant' and the text from Claude. This causes the UI to update and show Claude’s answer.
  • On error, we catch it, log it, and also append a message indicating something went wrong. This way the user isn’t left hanging if the backend fails. For example, if our backend returned a 500 error (maybe Claude API failed), the user will see “Sorry, something went wrong.” as a response.
  • We always set loading to false at the end to re-enable the input and possibly remove any typing indicator.
  • We included a simple loading indicator in the UI: if loading is true, we render a div saying “Claude is typing…” as an assistant message. This gives immediate feedback that the bot is working on a response. You could style this to be italic or a lighter color to differentiate from actual messages.
  • Basic styling: You would add CSS to style the chat container, messages, etc. For brevity we didn’t include CSS here, but you can imagine styling .message.user and .message.assistant differently (e.g., user on right side blue bubble, assistant on left side gray bubble, etc.).

Now, with npm run dev, your React app should be up. When you type a message and hit send, it will call the backend and display Claude’s response once it arrives. You have a functioning Claude chatbot! 🎉

A quick note on conversation memory: Our frontend is not actually sending the whole history every time – only the latest user message. The memory is handled on the backend (which keeps sessions[user_id]). This is efficient: it means less data sent over the network for each message. The backend’s job is to combine the history with the new query for Claude. We also chose to store chat history in the browser (localStorage) just for UX, so if the user refreshes, they still see the conversation. However, the real source of truth for memory is the backend store. If you wanted the conversation to persist across multiple client devices or long periods, you’d need to use a database on the backend instead.

Implementing Tool Calling (Advanced, Optional)

Up to now, our chatbot can have a coherent Q&A with the user. But what if the user asks something that requires external knowledge or an action? For example: “What’s the weather in Paris right now?” Claude by itself doesn’t have live data access. Or “Can you translate this text and save it to my account?” – something requiring a custom action. To handle such requests, we introduce tools.

A tool is essentially a function (internal or external API) that the chatbot can invoke via your backend. Anthropic’s Claude API supports tool usage by allowing you to define tools in the API call. Each tool has a name, a description of what it does, and a JSON schema for its inputs. Claude can decide, based on the conversation, to use a tool and will output a structured JSON invocation of that tool instead of a normal answer. Your backend can detect this and perform the action, then return the result (often by inserting it back into the conversation for Claude to use in formulating a final answer).

How to define a tool: When calling client.messages.create, you can include a parameter tools=[ ... ] defining each tool. For example, suppose we want a simple “Weather” tool:

tools = [
    {
      "name": "get_weather",
      "description": "Fetches the current weather for a given city. Use when the user asks for weather. Input should be {'city': 'City Name'}.",
      "input_schema": {
          "type": "object",
          "properties": {
              "city": {"type": "string"}
          },
          "required": ["city"]
      }
    }
]
response = client.messages.create(
    model="claude-4",
    messages=sessions[user_id],
    tools=tools
)

Here we defined a tool get_weather with an input schema that expects a JSON object with a “city” field. We also gave a description that tells Claude when and how to use it. Anthropic’s system will incorporate this into Claude’s prompting under the hood such that Claude knows this tool exists.

Handling tool responses: If the user asks, “What’s the weather in Paris?”, Claude might choose to invoke our tool. Instead of answering directly, Claude could output a message that essentially says (in JSON): “I want to call get_weather with {"city": "Paris"}”. The exact format of Claude’s output when using tools is a JSON structure that matches the schema. For example, Claude’s response might come back as:

{"tool": "get_weather", "input": {"city": "Paris"}}

Our backend should detect this. We can check if response.content[0].text looks like a JSON string indicating a tool usage (it might even be delivered in a structured way via the SDK – we’d need to parse it). If we find that Claude is requesting a tool, our backend would then pause generating a final answer, execute the tool, and then continue the conversation. One way to continue is to insert the tool result into the messages and call Claude again. For example:

  1. Claude responds with {"tool": "get_weather", "input": {"city": "Paris"}}.
  2. Our backend sees this and recognizes a tool call. It calls an actual weather API (like OpenWeatherMap) with the city “Paris” and gets, say, “15°C, clear”.
  3. We then append a message to the conversation history like: {"role": "assistant", "content": "TOOL:get_weather result: It is 15°C and clear in Paris."} or some agreed format.
  4. Finally, we call Claude again, now with the updated history (which includes the fact that the tool provided that info), and this time Claude can output a natural language answer to the user incorporating the tool result.

This turn-by-turn tool use is essentially implementing an agent loop. Anthropic’s documentation covers programmatic tool calling in detail, which allows the model to request a function and your code to fulfill it.

For our guide, it’s enough to know that this is possible and how you might integrate it. Implementing a full tool pipeline requires careful design (parsing JSON, error handling if Claude’s JSON is malformed, deciding how to feed results back, etc.). If you prefer a simpler approach for specific needs, you can also manually intercept queries: e.g., if the user’s message matches a pattern like “weather in X”, call the weather API before sending anything to Claude, and then include the result in Claude’s prompt or even bypass Claude and answer directly. However, the built-in tool calling is more general and powerful since Claude can decide when to use the tool.

Structured JSON outputs: Even outside the context of tools, you might instruct Claude to output a result in JSON format for certain queries (for easier downstream processing). When doing so, always verify the response. Claude (like any LLM) might occasionally produce invalid JSON if the prompt isn’t strict. Anthropic provides guidance on prompting for structured outputs and even features to improve JSON compliance. A common strategy is to have Claude respond with a code block or a triple-backtick fenced JSON, which you then extract. Always use a JSON parser in your code to validate the output; if parsing fails, you can either retry or fall back to a plain text answer.

In summary, tool usage and structured outputs allow your chatbot to go from just a Q&A system to an agent that can take actions. It involves more complex prompt engineering and backend logic, so consider it an advanced enhancement. You can gradually add tools as needed (for example, a calculator tool, a database lookup tool, etc.) to increase your chatbot’s capabilities.

Ensuring Safety and Validating Responses

Building a chatbot comes with responsibility to ensure it behaves correctly and safely:

Claude’s built-in safety: Claude was designed with a “Constitutional AI” approach to be helpful, honest, and harmless. By default, it refuses or safe-completes requests that violate its guidelines (e.g., instructions to produce disallowed content). You can reinforce this by including a system message at the start of the conversation specifying any additional rules or context. For example, you might set the system role with something like: “You are a company assistant. Follow the company guidelines and don’t answer questions about XYZ.” Claude will generally follow these instructions.

Validate critical outputs: When Claude is used to perform actions (like our tool calls or any operation that could affect user data), never blindly trust the output. Always validate and sanitize. For instance, if Claude is supposed to output JSON for a tool, wrap the parsing in a try/except and handle failures gracefully (perhaps by asking Claude again or falling back to a safe response). If Claude returns code to execute (in some advanced use case), scrutinize it or run in a sandbox. It’s good to insert checks – e.g., limit what commands can run, or if using something like a shell command via a tool, ensure it’s from an allowed list.

User input validation: Similarly, be mindful of user input. In our case, we pass user messages straight to Claude. This is generally fine (Claude can handle various inputs safely), but if you integrate with tools like a database or an API, avoid directly injecting user input without sanitization (to prevent things like SQL injection in a DB tool, etc.). Essentially, treat anything that will be executed in your system with the same caution as you would in any web app.

Rate limiting and abuse prevention: As a production consideration, you might want to rate limit the API usage per user to avoid abuse (someone spamming your chatbot could rack up token use costs). Anthropic’s API doesn’t inherently limit per key beyond your quota, so implement application-level limits if needed.

Logging and monitoring: Keep logs of the questions asked and answers given (within the bounds of privacy policies). This helps in reviewing the chatbot’s performance and detecting if it ever says something it shouldn’t. Anthropic likely logs at least at their end for safety, but as a developer, you should keep an eye on your specific domain use. For instance, if your chatbot is for a specific product support, you want to ensure it’s giving correct and safe advice about your product.

By combining Claude’s built-in safeguards with your own validation layer, you can create a chatbot that is both powerful and reliable for users.

Deployment: Bringing Your Chatbot to the World

With a working backend and frontend on your local machine, the final step is to deploy them so real users can access the chatbot. We’ll outline deploying the frontend and backend separately (which is common in a modern web stack).

Deploying the Frontend (React) on Vercel: Vercel is a great platform for hosting React applications (among others) and has a seamless integration with frontend frameworks.

  1. Push your code to a Git repository (GitHub, GitLab, etc.). Ensure the production backend URL is configured in your React app. For example, if you deployed your backend to some URL, update the fetch call in React to use that. You can make this configurable via an environment variable in React (Vercel allows setting those).
  2. Create a Vercel account and import your repository as a new project. Vercel will automatically detect the React project (especially if it’s a standard CRA or Vite project) and configure a build. Typically, for Vite, it runs npm run build and serves the dist directory.
  3. Configure environment variables on Vercel if needed (for instance, if you have a VITE_API_URL variable for the backend URL). In our simple case, we might not need any for the frontend, since we can hardcode the backend URL for now. But using env vars is more flexible.
  4. Deploy. Vercel will give you a domain (something like your-project.vercel.app). Visit it and your React app should load. It likely won’t work yet with the backend until the backend is up on a publicly reachable URL – so let’s do that next.

Deploying the Backend on Render or AWS Lambda: The backend is a Python FastAPI app. You have multiple options to host it:

  • Render.com: Render is a Platform-as-a-Service that can easily deploy web services from a Git repo. Create an account on Render, choose to add a new Web Service, and connect your repository. It will autodetect a Python app if you have a requirements.txt or pyproject.toml. You’ll need to specify a Start Command, which would be uvicorn main:app --port $PORT --host 0.0.0.0. Render will handle building a Docker image for you and running it. In the settings, add an environment variable CLAUDE_API_KEY with the value of your key (so that the key is available to the app). Once deployed, Render will provide a URL like https://claude-chatbot-backend.onrender.com. Use that as your API base URL in the frontend (you might configure it in React accordingly). Render has a free tier for small services, which might be sufficient for testing.
  • AWS Lambda (with API Gateway): This is more involved, but essentially you can deploy FastAPI on AWS Lambda using frameworks like Zappa or AWS API Gateway’s proxy integration. The idea is to run your FastAPI app as a serverless function. If going this route, you’d package your app, upload to Lambda, and configure an API Gateway to route HTTP requests to it. The benefit is you pay per request and it can scale automatically. However, keep in mind cold starts and the stateless nature of Lambda: since it can spin down, you would not want to rely on in-memory session storage. You’d use an external store (like DynamoDB or Redis) to persist conversation history if using Lambda. For simplicity, if you’re new to deployment, Render (or another PaaS) might be easier to start with.
  • Other options: You could also use services like Heroku (though their free tier is gone), fly.io, or even containerize the app and run it on Kubernetes or a VM in the cloud. The choice depends on your familiarity and the scale needed.

CORS and domains: When your frontend is on a domain (say frontend.vercel.app) and backend on another (say backend.onrender.com), update the origins list in the backend CORS middleware to include the frontend’s URL. For example, allow_origins=["https://your-frontend.vercel.app"]. It’s okay to include the temporary domain or use a wildcard in development, but for production, it’s best to be specific for security. FastAPI also allows allow_origins=["*"] for a quick fix, but that’s not recommended for production.

Environment variables: On the backend, you must set your CLAUDE_API_KEY env var in the hosting platform’s config (never store it in code or push it to Git). On the frontend, if you use any env vars (like REACT_APP_... or Vite’s VITE_...), set those in Vercel’s project settings. For example, if you had VITE_BACKEND_URL for the API endpoint, set it to your backend’s URL.

Monitoring and Logging: Once deployed, test the live site. Check the logs in your backend platform (Render provides live logs) to see incoming requests and any errors. It’s good practice to add some logging in your FastAPI app – e.g., log each request, or at least log errors in the exception handler. This will help diagnose issues in production. You might also use an application performance monitoring (APM) tool or error tracking service (like Sentry) to catch exceptions.

Anthropic’s console can show your API usage and any errors from their side. Monitor your usage especially if the app is in public use – you don’t want an unexpectedly high bill. You can set up auto-reload on Anthropic billing or alerts for usage.

Scaling considerations: Our simple architecture should work for a small-scale launch. However, note that our backend in-memory session store will not share state across multiple instances. If you need to scale out horizontally (multiple server instances handling requests), you’d want to move session storage to a centralized datastore (Redis, database, etc.), or use sticky sessions (ensure the same user always hits the same server).

Similarly, if using serverless (Lambda), you must persist session data in a database, as Lambdas don’t share memory. For initial deployments, you can probably run a single instance which can handle quite a few concurrent chats (depending on hardware and Claude’s response time). As load grows, plan for a more robust state management.

Finally, after deploying both components, test the full flow: open your deployed React app, send a message, and verify you get a response from Claude via the deployed backend. If everything is configured correctly, you now have a live Claude-powered chatbot!

Conclusion

Congratulations – you’ve built a fully functional Claude chatbot from scratch! 🎉 In this guide, we covered the entire journey:

  • Getting access to the Claude API and understanding its capabilities.
  • Building a Python FastAPI backend to handle chat logic, maintain context, and securely call the Claude API.
  • Creating a React frontend for a smooth chat experience for users.
  • Implementing conversation memory (context awareness) so the chatbot can handle follow-up questions intelligently.
  • Discussing advanced features like tool calling for extending the bot’s functionality beyond just answering questions.
  • Ensuring safety and reliability through validations and Claude’s own alignment strengths.
  • Deploying the application in a production-like environment on modern cloud platforms.

This is a strong foundation for a real-world chatbot that could be integrated into a SaaS product or a website.

From here, you can continue to enhance your chatbot: add more tools (perhaps integrate your internal APIs so Claude can pull user-specific data securely), refine the prompting to suit your domain (customer support, education, etc.), add user authentication if needed, and scale up as your user base grows.

Keep in mind that AI development is iterative – monitor how users interact with the bot and refine its prompts and tools over time. Claude is a cutting-edge model, and Anthropic is continuously improving it, so stay updated with their latest releases and best practices.

With your first Claude chatbot up and running, you’re well-equipped to build even more sophisticated AI-powered applications. Happy coding, and enjoy your new Claude chatbot!

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