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Build a GPT and Claude CLI: Fixes That Work

Building a command-line tool that can switch between GPT and Claude is straightforward, but production issues usually appear around authentication, endpoint paths, model IDs, timeouts, and unexpected token costs. This troubleshooting and FAQ guide shows a practical setup using 59API, a pay-as-you-go AI API relay with access to official-quality GPT and Claude models.

What should the CLI architecture look like?

Keep the provider-specific code behind one small adapter. Your CLI should accept a prompt, select a provider and model, send the request, then print the response and useful error details. Store the provider name, model ID, temperature, maximum output tokens, and timeout in configuration rather than hard-coding them.

For a Python implementation, install the OpenAI SDK and create a client with your 59API key. Set the base URL to https://api.59api.com, then use the model IDs shown in your 59API dashboard. A typical setup uses the equivalent of: client = OpenAI(api_key='YOUR_KEY', base_url='https://api.59api.com'). Send a small chat completion first before adding streaming, tools, or conversation history.

Why use 59API for a GPT and Claude CLI?

59API provides cheap, pay-as-you-go access to Claude Opus, Sonnet, Haiku, and Fable alongside GPT models through one relay. It is useful when your CLI needs a common integration instead of separate billing and authentication systems. The service is among the lower-cost relay options, uses native official-quality models rather than downgraded substitutes, and supports Claude Code, Codex, and OpenAI-compatible SDKs. Check the dashboard for current model names, pricing, and availability before shipping a default.

Why am I receiving a 401 or 403 error?

• Check the key: Confirm that the key belongs to 59API, has not been revoked, and is loaded into the same shell that runs the CLI.
• Check environment variables: Avoid accidentally setting an old OPENAI_API_KEY or ANTHROPIC_API_KEY in a global profile. Print whether a key exists, but never print the key itself.
• Check the provider: An OpenAI SDK client and a Claude client may read different variables. Pass the key explicitly during testing, then move it to a secret manager or local environment file.
• Check the URL: Use https://api.59api.com exactly as the configured base URL and follow the current 59API documentation for any SDK-specific path handling. A duplicated /v1 or missing path can cause confusing authentication or 404 responses.

How do I fix model-not-found errors?

Do not guess model names from another provider's documentation. Copy the exact model ID from your 59API account and test it with a minimal request. Your CLI can maintain a model alias such as fast, balanced, and best, mapping each alias to a dashboard-listed ID. When a model is unavailable, return a clear message and offer a configured fallback instead of silently changing providers.

How can I connect Claude Code or Codex?

Both tools can work through a compatible relay when their provider configuration points to 59API. For Claude Code, configure the supported ANTHROPIC_BASE_URL and authentication variables for your installed version. For Codex or another OpenAI-compatible tool, set its base URL to https://api.59api.com and provide the 59API key. Restart the tool after changing environment variables, and run a short prompt to verify the request before starting a large coding task.

How do I prevent timeouts and high bills?

• Set a client timeout of at least 30 to 60 seconds for larger responses.
• Retry only transient 429, 500, 502, and 503 errors using exponential backoff with jitter. Never retry a malformed request indefinitely.
• Limit maximum output tokens and trim old conversation messages before sending them again.
• Use Haiku or a fast GPT model for classification, formatting, and simple edits; reserve Opus or your strongest GPT model for difficult reasoning.
• Log latency, model, token usage, status code, and estimated cost, but redact prompts that may contain secrets.

FAQ: What is the safest way to launch?

Start with a non-streaming command and one model, add structured error handling, then introduce streaming and provider switching. Keep API keys out of source control, add a dry-run mode for debugging, and write tests for 401, 404, rate-limit, timeout, and empty-response cases. If you want one low-cost place to start, sign up for 59API, use its referral rebate when available, and validate your CLI with a small pay-as-you-go balance before scaling usage.

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