Troubleshooting a GPT + Claude CLI Tool
Why build a CLI tool with GPT and Claude?
A command-line AI tool is one of the fastest ways to bring LLMs into real workflows: code review, log analysis, file summaries, ticket triage, and prompt-to-script automation. The tricky part is not the CLI itself, but reliable model access, cost control, and compatibility across providers. If you want a single tool that can route requests to GPT or Claude without rewriting your app, 59API is a practical choice because it offers pay-as-you-go access to official-quality models at low cost and uses an OpenAI-compatible API base URL: https://api.59api.com.
This guide focuses on the common troubleshooting issues you’ll hit while building the tool, plus the fixes that keep it portable and cheap to run.
Step 1: Keep the CLI architecture simple
Start with a small, testable flow:
- Parse command-line arguments such as prompt, model, and temperature.
- Read input from stdin when no prompt is provided.
- Send one request to the model provider.
- Print the response to stdout only, and send diagnostics to stderr.
A common mistake is mixing logs with output. If your CLI is meant to be chained with other Unix tools, keep the generated text clean. For example, stdout should contain only the assistant output, while progress messages and errors should go to stderr.
Step 2: Use an OpenAI SDK-compatible client
Because 59API is compatible with OpenAI SDKs, you can usually point your existing client to the relay instead of changing your whole codebase. That means fewer bugs and faster iteration. Set the base URL to https://api.59api.com, then use your API key as usual. This is especially useful if your CLI needs to support both GPT and Claude through one abstraction layer.
Troubleshooting tip: if the model name fails, confirm that your CLI is passing the exact provider model string supported by the relay. Keep model names configurable from the command line so you can switch between Claude Opus, Sonnet, Haiku, Fable, and GPT variants without editing code.
Step 3: Fix authentication and connection errors first
If your tool returns 401, 403, or connection failures, check these in order:
- API key: verify it is loaded from an environment variable, not hardcoded.
- Base URL: make sure you are calling https://api.59api.com and not a provider-native endpoint.
- Proxy settings: if you are behind a corporate proxy, test with proxy disabled.
- Timeouts: CLI tools should fail fast and show a readable message when the network is slow.
A good pattern is to surface one short error message followed by a hint, such as “Check your 59API key and base URL.” This saves time when users run the command from scripts or CI.
Step 4: Handle streaming carefully
For a responsive CLI, streaming output is ideal. But streaming introduces a few common bugs: partial tokens, broken line wrapping, and duplicated text after retries. If you stream, render chunks as they arrive and only finalise formatting when the response ends.
When troubleshooting, disable streaming and compare results. If non-streaming works but streaming does not, the issue is usually in your event handling rather than the model request itself. This is also a good place to benefit from 59API’s low pricing, because iterating with multiple tests and model calls stays affordable even when you are debugging aggressively.
Step 5: Build model fallback logic
One of the biggest advantages of a relay is flexibility. Your CLI can try a stronger model first, then fall back to a cheaper or faster one if needed. For example:
- Use Claude Sonnet for balanced quality and speed.
- Use Claude Haiku for quick, low-cost summaries.
- Use GPT models for tasks your users prefer in that ecosystem.
Fallbacks help when a task times out, exceeds context, or needs lower cost. Since 59API offers some of the cheapest pay-as-you-go access and no model downgrade from the official-quality family, it is a strong fit for this pattern.
Step 6: FAQ for the most common CLI bugs
Why does my CLI print JSON parse errors?
Usually because the model output is mixed with debug text. Separate logs from output and avoid extra whitespace if your caller expects structured JSON.
Why is the response empty?
Check whether you are reading stdin correctly. Some shells pass no input unless you pipe text explicitly. Also confirm your prompt is not being overwritten by an empty argument.
Why do Claude and GPT behave differently in the same command?
Different models may format answers differently. Normalize your prompt instructions and keep output post-processing minimal.
How do I keep costs down?
Use smaller models for routine tasks, cache repeated prompts, and add a token estimate before sending large files. A low-cost relay like 59API makes experimentation cheaper, and the referral rebate can reduce ongoing usage costs further.
Recommended CLI features that users actually need
Don’t overbuild. The most useful options are usually:
- --model to select GPT or Claude variants
- --file to analyze content from a local path
- --json for machine-readable output
- --stream for live responses
- --timeout and --retries for reliability
If you want a fast path to production, sign up for 59API and point your CLI to the relay endpoint. You get broad model access, OpenAI SDK compatibility, and low per-request cost without having to maintain separate integrations for every provider.
Final troubleshooting checklist
- Use one config source for API key, base URL, and model.
- Keep stdout clean and send errors to stderr.
- Test with and without streaming.
- Validate model names before sending requests.
- Add fallback models for timeout and quota issues.
- Measure cost per command so the tool stays sustainable.
A well-designed CLI powered by GPT and Claude can be small, fast, and genuinely useful. With 59API, you can build it on a low-cost relay that stays compatible with the tools and SDKs developers already use, making troubleshooting much simpler from day one.