API Keys and Configuration

1import anthropic
2
3# Initialize the client (reads ANTHROPIC_API_KEY from environment)
4client = anthropic.Anthropic()
5
6# Test with a simple message
7response = client.messages.create(
8    model="claude-sonnet-4-20250514",
9    max_tokens=100,
10    messages=[
11        {"role": "user", "content": "Say 'Hello, Agent Builder!' in exactly 3 words."}
12    ]
13)
14
15print(response.content[0].text)
16# Expected output: "Hello, Agent Builder!"

1from openai import OpenAI
2
3# Initialize the client (reads OPENAI_API_KEY from environment)
4client = OpenAI()
5
6# Test with a simple completion
7response = client.chat.completions.create(
8    model="gpt-4o",
9    max_tokens=100,
10    messages=[
11        {"role": "user", "content": "Say 'Hello, Agent Builder!' in exactly 3 words."}
12    ]
13)
14
15print(response.choices[0].message.content)
16# Expected output: "Hello, Agent Builder!"

1import google.generativeai as genai
2
3# Configure with your API key
4genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
5
6# Initialize the model
7model = genai.GenerativeModel("gemini-2.0-flash")
8
9# Test with a simple prompt
10response = model.generate_content("Say 'Hello, Agent Builder!' in exactly 3 words.")
11
12print(response.text)
13# Expected output: "Hello, Agent Builder!"

1# LLM API Keys
2ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
3OPENAI_API_KEY=sk-proj-your-key-here
4GOOGLE_API_KEY=AIzaSy-your-key-here
5
6# Optional: Default model preferences
7DEFAULT_MODEL=claude-sonnet-4-20250514
8DEFAULT_MAX_TOKENS=4096
9
10# Optional: Logging level
11LOG_LEVEL=INFO

1import os
2from dotenv import load_dotenv
3
4# Load .env file at the start of your application
5load_dotenv()
6
7# Access keys (with validation)
8def get_api_key(provider: str) -> str:
9    key_map = {
10        "anthropic": "ANTHROPIC_API_KEY",
11        "openai": "OPENAI_API_KEY",
12        "google": "GOOGLE_API_KEY",
13    }
14
15    env_var = key_map.get(provider.lower())
16    if not env_var:
17        raise ValueError(f"Unknown provider: {provider}")
18
19    key = os.environ.get(env_var)
20    if not key:
21        raise ValueError(f"{env_var} not set in environment")
22
23    return key
24
25# Usage
26anthropic_key = get_api_key("anthropic")
27openai_key = get_api_key("openai")

1# Copy this file to .env and fill in your API keys
2# Do NOT commit .env to version control
3
4# Required: At least one LLM API key
5ANTHROPIC_API_KEY=
6OPENAI_API_KEY=
7GOOGLE_API_KEY=
8
9# Optional settings
10DEFAULT_MODEL=claude-sonnet-4-20250514
11LOG_LEVEL=INFO

1import time
2from typing import Callable, TypeVar
3import anthropic
4
5T = TypeVar("T")
6
7def with_retry(
8    func: Callable[[], T],
9    max_retries: int = 3,
10    base_delay: float = 1.0,
11) -> T:
12    """Execute a function with exponential backoff retry."""
13    for attempt in range(max_retries):
14        try:
15            return func()
16        except anthropic.RateLimitError:
17            if attempt == max_retries - 1:
18                raise
19            delay = base_delay * (2 ** attempt)
20            print(f"Rate limited. Retrying in {delay}s...")
21            time.sleep(delay)
22        except anthropic.APIError as e:
23            if attempt == max_retries - 1:
24                raise
25            print(f"API error: {e}. Retrying...")
26            time.sleep(base_delay)
27    raise RuntimeError("Max retries exceeded")
28
29# Usage
30def call_claude():
31    client = anthropic.Anthropic()
32    return client.messages.create(
33        model="claude-sonnet-4-20250514",
34        max_tokens=100,
35        messages=[{"role": "user", "content": "Hello"}]
36    )
37
38response = with_retry(call_claude)