How do I handle errors and exceptions when using the Anthropic Python SDK

Overview of Error Handling in Anthropic Python SDK

The Anthropic Python SDK raises specific exceptions derived from a base error class to handle different failure scenarios when interacting with the API. All error classes inherit from `anthropic.APIError`, a base class for all SDK exceptions. This hierarchy allows developers to catch specific errors or handle all SDK-related errors generically.

There are two broad categories of errors:

1. Connection errors: When the library cannot connect to the API, typically due to network failures or timeouts, it raises a subclass of `anthropic.APIConnectionError`.
2. API status errors: When the API returns a non-success HTTP status code (4xx or 5xx), the SDK raises a subclass of `anthropic.APIStatusError`. This error contains useful properties like `status_code` and `response` to understand the response details.

Specific exception subclasses correspond to particular HTTP status codes. Below are key errors mapped to their HTTP status codes:

- 400: `BadRequestError`
- 401: `AuthenticationError`
- 403: `PermissionDeniedError`
- 404: `NotFoundError`
- 422: `UnprocessableEntityError`
- 429: `RateLimitError`
- 500 or greater: `InternalServerError`
- No HTTP status (connection issues): `APIConnectionError`

Additionally, `RateLimitError` is important for handling situations where the API usage exceeds allowed thresholds.

How to Catch and Handle Errors

To handle errors, use try-except blocks around the SDK API calls. Here is an example structure:

python
import anthropic

client = anthropic.Anthropic()

try:
    response = client.completions.create(
        prompt=f"{anthropic.HUMAN_PROMPT} Your prompt here {anthropic.AI_PROMPT}",
        max_tokens_to_sample=300,
        model="claude-2",
    )
except anthropic.APIConnectionError as e:
    print("The server could not be reached.")
    print(e.__cause__)  # underlying httpx exception, e.g., network error
except anthropic.RateLimitError as e:
    print("Rate limit exceeded. Backing off...")
except anthropic.APIStatusError as e:
    print(f"Error: Received status code {e.status_code}")
    print(f"Response: {e.response}")
    # Additional handling based on e.status_code if needed
except anthropic.APIError as e:
    print(f"An unexpected Anthropic API error occurred: {e}")

This structure catches connection errors, rate limiting, general API response errors, and any other API errors. The SDK includes detailed error classes to enable precise handling.

Properties of API Exceptions

- `APIStatusError` instances include:
- `status_code` — the HTTP status code of the error response.
- `response` — the raw response object or content from the API call.
- All error instances have messages describing the problem.
- Connection errors may have an underlying cause accessible via the exception's `__cause__` attribute, revealing lower-level HTTP or network exceptions.

Automatic Retries and Configurations

The SDK automatically retries certain transient errors twice with exponential backoff. These include:

- Network connection errors (connection drops, timeouts)
- HTTP 408 (Request Timeout)
- HTTP 409 (Conflict)
- HTTP 429 (Rate Limit)
- HTTP 500 and above (Internal server errors)

Developers can customize retry behavior globally or per request. For example:

python
client = anthropic.Anthropic(max_retries=0)  # disable retries globally

# Or per request:
client.with_options(max_retries=5).messages.create(
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello, Claude"}],
    model="claude-sonnet-4-20250514",
)

Handling Specific Error Types

- Connection errors (`APIConnectionError`): Usually network or DNS problems. Handle by retrying manually, alerting users, or logging for diagnostics.
- Rate limit errors (`RateLimitError`): These indicate the usage quota is exceeded. Implement exponential backoff and retry logic with increasing delay.
- Authentication errors (`AuthenticationError`): Occur with invalid or expired API keys. Require token refresh or configuration updates.
- Permission errors (`PermissionDeniedError`): API key lacks authorization for the requested resource. Require checking API key scopes or permissions.
- Bad request errors (`BadRequestError`): Typically due to malformed inputs or invalid request parameters; check request payload correctness.
- Not found errors (`NotFoundError`): Requested resource or endpoint does not exist; confirm correctness of resource identifiers.
- Internal server errors (`InternalServerError`): Server-side issues at Anthropic. Usually transient; retries often successful.

Understanding Silent Errors

Certain errors from Anthropic's servers might come as successful HTTP 200 responses but carry error information in the response body, such as `"overloaded_error"`. These do not raise exceptions automatically but must be handled explicitly by inspecting the response content. This requires:

- Parsing the response JSON body.
- Detecting error fields such as `{"type":"error","error":{"type":"overloaded_error","message":"Overloaded">`.
- Applying fallback logic or retrying with different parameters or models.

Request IDs for Debugging

Every API response object includes a `_request_id` property extracted from the `request-id` response header. This ID uniquely identifies the API request and helps with:

- Debugging failed requests with Anthropic support.
- Logging and tracing requests for audit purposes.

Example:

python
response = client.messages.create(
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello, Claude"}],
    model="claude-opus-4-1-20250805",
)
print(f"Request ID: {response._request_id}")

Best Practices for Production

- Always use try-except blocks around API calls.
- Handle specific errors where possible for graceful degradation.
- Incorporate retry logic with exponential backoff for transient errors.
- Use the `_request_id` for logging and support diagnostics.
- Implement checks for silent content errors like “overloaded_error” during streaming or raw response analysis.
- Adjust `max_retries` based on application needs.
- Monitor and alert on rate limiting and authentication errors.

Example Extended Usage with Detailed Exception Handling

python
import anthropic

client = anthropic.Anthropic()

def call_api_with_handling():
    try:
        result = client.completions.create(
            prompt=f"{anthropic.HUMAN_PROMPT} Summarize this document{anthropic.AI_PROMPT}",
            max_tokens_to_sample=500,
            model="claude-2",
        )
        # Inspect for silent error messages in result if applicable
        if isinstance(result, dict) and "error" in result:
            error_type = result["error"].get("type", "")
            if error_type == "overloaded_error":
                print("Model overloaded. Consider fallback.")
                # fallback logic here
        return result
    except anthropic.APIConnectionError as e:
        print("Network error: Could not connect to Anthropic API.")
        # Additional retry or alert logic here
    except anthropic.RateLimitError:
        print("Rate limit hit. Backing off and retrying later.")
        # Implement backoff logic
    except anthropic.AuthenticationError:
        print("Authentication failed. Check API key.")
        # Alert or re-authenticate
    except anthropic.PermissionDeniedError:
        print("Permission denied. Verify API key permissions.")
    except anthropic.BadRequestError:
        print("Bad request. Check request parameters.")
    except anthropic.NotFoundError:
        print("Resource not found. Check resource ID or endpoint.")
    except anthropic.InternalServerError:
        print("Internal server error. Retry may help.")
    except anthropic.APIStatusError as e:
        print(f"API returned error status {e.status_code}: {e.response}")
    except anthropic.APIError as e:
        print(f"Unknown API error occurred: {e}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

response = call_api_with_handling()

This comprehensive error handling method ensures the program responds specifically to each error type, aiding in debugging, recovery, and user notification.

Summary of Error Handling Concepts

- Use the Anthropic SDK's exception classes to catch and handle errors clearly.
- Differentiate between network-related, API response-related, and usage quota errors.
- Leverage exception properties for diagnostics and logging.
- Respect rate limits and handle retries gracefully.
- Check response content for hidden error signals, particularly in streaming or long-running responses.
- Utilize request IDs from responses for troubleshooting and support.
- Adjust retry policies based on operation criticality and error types.

These guidelines provide robust strategies for managing errors and exceptions effectively when using the Anthropic Python SDK. They ensure resilience, better user experience, and easier debugging during integration with Anthropic services.