Anthropic cuts Claude subscribers off from OpenClaw in cost crackdown

Introduction

In this tutorial, you'll learn how to build a simple AI agent that can interact with Claude, the AI assistant from Anthropic. This tutorial will help you understand how to work with AI agent frameworks like OpenClaw, which were recently restricted by Anthropic for Claude Pro and Max subscribers. We'll create a basic AI assistant that can answer questions and perform simple tasks using Claude's API.

Prerequisites

• A basic understanding of Python programming
• An Anthropic Claude API key (you'll need to sign up at Anthropic's website)
• Python 3.7 or higher installed on your computer
• Basic knowledge of command line tools

Step-by-Step Instructions

Step 1: Set Up Your Development Environment

Install Python and Required Packages

First, ensure you have Python installed on your computer. You can check by running:

python --version

If Python is not installed, download and install it from python.org.

Next, create a new folder for your project and open a terminal in that folder:

mkdir claude-agent-tutorial
 cd claude-agent-tutorial

Create a Virtual Environment

It's good practice to use a virtual environment to manage dependencies:

python -m venv claude_env
source claude_env/bin/activate  # On Windows: claude_env\Scripts\activate

Step 2: Install Required Libraries

Install the Anthropic Python Library

We'll use the official Anthropic Python library to interact with Claude:

pip install anthropic

Install Additional Helpful Libraries

For this tutorial, we'll also install requests for HTTP operations:

pip install requests

Step 3: Get Your Anthropic API Key

Sign Up and Get Your Key

Visit Anthropic's Console and create an account if you don't have one. Navigate to the API section to generate a new API key. Copy this key as you'll need it in the next step.

Step 4: Create Your First Claude Agent

Create the Main Python File

Create a file named claude_agent.py and add the following code:

import os
from anthropic import Anthropic

# Initialize the Anthropic client
client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

def ask_claude(question):
    response = client.messages.create(
        model="claude-3-haiku-20240307",
        max_tokens=1000,
        messages=[
            {
                "role": "user",
                "content": question
            }
        ]
    )
    return response.content[0].text

# Test the agent
if __name__ == "__main__":
    print("Claude Agent is ready!")
    question = input("Ask Claude a question: ")
    answer = ask_claude(question)
    print(f"Claude's response: {answer}")

Set Your API Key as an Environment Variable

Before running the code, set your API key as an environment variable:

export ANTHROPIC_API_KEY='your_api_key_here'

On Windows, use:

set ANTHROPIC_API_KEY=your_api_key_here

Step 5: Run Your Claude Agent

Execute the Script

Run your script in the terminal:

python claude_agent.py

You should see a prompt asking you to ask Claude a question. Try asking something like:

• "Explain quantum computing in simple terms"">
• "What are the benefits of renewable energy?"">

Step 6: Enhance Your Agent with More Features

Add Conversation History

Update your claude_agent.py to maintain conversation history:

import os
from anthropic import Anthropic

client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

# Store conversation history
conversation_history = []

def ask_claude(question):
    # Add user question to history
    conversation_history.append({"role": "user", "content": question})
    
    response = client.messages.create(
        model="claude-3-haiku-20240307",
        max_tokens=1000,
        messages=conversation_history
    )
    
    # Add Claude's response to history
    assistant_response = response.content[0].text
    conversation_history.append({"role": "assistant", "content": assistant_response})
    
    return assistant_response

# Test the enhanced agent
if __name__ == "__main__":
    print("Enhanced Claude Agent is ready!")
    print("Type 'quit' to exit.")
    
    while True:
        question = input("\nAsk Claude a question: ")
        if question.lower() == 'quit':
            break
        answer = ask_claude(question)
        print(f"Claude's response: {answer}")

Step 7: Understand the Cost Implications

Why This Matters

As mentioned in the news article, Anthropic has restricted access to their API for third-party frameworks like OpenClaw. This means that if you're building AI agents that run continuously or make many API calls, you'll need to understand how costs are calculated. The pay-as-you-go model means each API call costs money, so it's important to optimize your agent's usage.

Monitor Your API Usage

Keep track of how many tokens you're using. Each API call costs based on the number of input and output tokens. You can monitor this in your Anthropic console or by adding logging to your code:

import time

def ask_claude_with_logging(question):
    start_time = time.time()
    response = client.messages.create(
        model="claude-3-haiku-20240307",
        max_tokens=1000,
        messages=[{"role": "user", "content": question}]
    )
    end_time = time.time()
    
    print(f"API call took {end_time - start_time:.2f} seconds")
    print(f"Input tokens: {response.usage.input_tokens}")
    print(f"Output tokens: {response.usage.output_tokens}")
    
    return response.content[0].text

Summary

In this tutorial, you've learned how to create a basic AI agent using Claude's API. You've set up your development environment, installed the required libraries, and created a simple question-answering agent. You've also learned about the cost implications of using AI APIs and how to monitor your usage. This foundation will help you build more complex AI agents in the future, even with the restrictions that Anthropic has implemented for third-party frameworks.

Remember that as AI technology evolves, so do the rules around usage and costs. Always stay updated with the latest API guidelines and pricing models from providers like Anthropic.