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If you already have a small command-line chatbot working with the Anthropic SDK, a useful next step is to make its behavior more deliberate: send only the history the conversation needs, inspect responses by content-block type, and handle blank input and API failures without letting the loop stop unexpectedly. These are practical refinements, not proof that a script is production-ready.
Keep only useful conversation history
A chatbot’s message list is part of what you send to the model. Include the user and assistant turns that provide context for the next answer; omit an opening assistant greeting if it does not help the model respond. The point is not to erase history, but to make the history intentional.
For a multi-turn conversation, removing earlier turns indiscriminately can make later requests harder to understand. Decide what context your bot needs, then preserve those turns in the message list you submit.
Inspect response blocks instead of assuming plain text
A response can contain structured content blocks, so inspect each block’s type rather than assuming there is one text value to print. The example below shows the basic branching pattern; adapt the field access to the response shape in the Anthropic SDK version you have installed.
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for block in response.content:
if block.type == "text":
messages.append({"role": "assistant", "content": block.text})
print(block.text)
elif block.type == "thinking":
# Keep this for controlled debugging if appropriate;
# do not display it as ordinary chatbot output.
print("[thinking block received]")Keep user-facing output separate from diagnostic inspection. A block that is useful to recognize while debugging is not automatically appropriate to show to the person using the chatbot. If you need to inspect the response, consider logging only the information needed for debugging and handle it according to your application’s privacy and access requirements.
Other response metadata, such as the model identifier and token-use fields, may help you understand what the API returned. Treat those as metadata to inspect deliberately; do not assume every response has the same content or that metadata alone proves how well the chatbot is working.
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Handle API errors at the request boundary
Network requests can fail for reasons the loop may be able to recover from, such as a timeout, rate limit, or temporary service overload. Catch relevant SDK exceptions around the API call so the user can be told what happened and the application can decide whether to continue, retry, or stop. Do not catch every exception and silently continue: programming errors should remain visible during development.
Anthropic’s API error reference describes typed SDK exceptions and HTTP error categories, and recommends handling specific exception classes rather than matching error-message text. It lists 400 for invalid requests, 401 for authentication problems, 429 for rate limits, 500 for internal errors, 504 for timeouts, and 529 for temporary overload. Those status categories do not guarantee that every failure is recoverable: an invalid request or authentication problem usually calls for fixing configuration or code, not retrying the same request.
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Use the exception names and handling pattern documented for the SDK version installed in your project. Avoid copying a fixed class hierarchy without checking it against that version’s documentation. Keep retries bounded and intentional; a loop that retries indefinitely can make a temporary problem worse.
Reject blank input before calling the API
Pressing Enter without typing, or entering only spaces, should not trigger a request. Check the stripped input before adding it to the message history or calling the API:
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user_input = input("You: ")
if not user_input.strip():
print("Please enter a question.")
continueThe strip() check catches whitespace-only input while leaving the original text available if you want to preserve the user’s spacing. Place the guard inside the conversation loop and before the API request.
What this second pass improves
These changes make the script’s intent easier to see: relevant turns provide context, response blocks are handled according to type, expected request failures have a deliberate path, and empty submissions are stopped locally. They are incremental maintenance improvements, not measured gains in reliability, latency, or cost. A production service would need broader testing and operational safeguards beyond these edits.
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