API Request Builder — cURL, Python & JS
Build API requests for OpenAI, Anthropic, and Google AI. Generate ready-to-use code in cURL, Python, and JavaScript. Free tool.
Query Parameters
Headers
curl -X GET \
'https://api.example.com/data' \
-H 'Content-Type: application/json'This tool generates code snippets only. No actual HTTP requests are sent.
About API Request Builder
Building API requests for AI providers can be complex, with different authentication methods, endpoint URLs, and parameter formats. This tool generates ready-to-use code for OpenAI, Anthropic, and Google AI APIs.
Configure your request visually — select the model, set temperature, max tokens, system prompt, and messages. Then copy the generated code in cURL, Python, or JavaScript format.
Each AI provider has distinct API conventions. OpenAI uses Bearer token authentication with a JSON body containing a messages array. Anthropic requires an x-api-key header and a different message format. Google AI uses API keys as URL parameters. This builder handles these differences automatically — select a provider and the correct headers, endpoint, and body structure are generated.
The generated code is available in three formats: cURL for terminal testing, Python using the requests library, and JavaScript using the fetch API. Each output is a complete, runnable snippet — paste it into your terminal or code editor and it works immediately with your API key.
When testing APIs, start with a simple request and add complexity incrementally. Set temperature to 0 for deterministic responses during development, then adjust for production. Monitor token usage in the response headers to understand costs. Use the Context Window Visualizer to estimate costs before making expensive API calls.
How the API Request Builder Works
- Enter the API endpoint URL and select the HTTP method (GET, POST, PUT, DELETE)
- Add headers, query parameters, and request body as needed
- Send the request and inspect the response status, headers, and body
- Copy the request as cURL, JavaScript fetch, or Python requests code
API Testing Best Practices
Always start by testing API endpoints with simple GET requests before moving to POST or PUT. Include proper Content-Type headers (application/json for JSON payloads). When debugging, check the response status code first: 2xx means success, 4xx means client error (check your request), and 5xx means server error. Save frequently used requests as templates to speed up your workflow.
When to Use the API Request Builder
Use this tool when you need to quickly prototype API calls to AI providers without writing code from scratch. It is especially useful for developers learning a new API, for testing different model parameters before implementing them in production code, and for generating code snippets in cURL, Python, or JavaScript that can be directly pasted into projects.
Common Use Cases
- •Prototyping API calls to OpenAI, Anthropic, and Google AI endpoints
- •Generating ready-to-use code snippets in cURL, Python, or JavaScript
- •Testing different model parameters (temperature, max_tokens) before production implementation
- •Learning API conventions for different AI providers side by side AI Model Comparison — 50+ Models Side by Side
Expert Tips
- ✱Start with temperature 0 during development for deterministic responses, then increase for production once you've validated the output quality.
- ✱Set max_tokens to a reasonable value for your use case — leaving it unlimited can result in unexpectedly long (and expensive) responses.
- ✱Test with cURL first for quick debugging, then convert to Python or JavaScript for integration into your application.
Frequently Asked Questions
- The builder generates code that includes a placeholder for your API key. You need your own API key from the respective provider (OpenAI, Anthropic, Google) to actually execute the requests. The tool never stores or transmits your key — you add it after copying the generated code.
- Temperature controls the randomness of the model's responses. At 0, responses are nearly deterministic (same input = same output). At 1.0, the model takes more creative risks. Use 0-0.3 for factual tasks, 0.5-0.7 for balanced responses, and 0.8-1.0 for creative writing. Most production applications use 0.2-0.5.
- The context window is the total capacity (input + output). max_tokens limits only the output length. If you set max_tokens to 1000, the model will stop generating after 1000 output tokens regardless of the context window size. Setting it too low truncates responses; too high wastes budget on unused capacity.
- This tool generates code rather than executing requests, since it runs in your browser and cannot securely store API keys. Copy the generated cURL command to your terminal or the Python/JavaScript code to your development environment and run it there with your API key.
Do I need an API key to use this tool?▾
What is the temperature parameter?▾
What is the difference between max_tokens and context window?▾
Can I test the API request directly from this tool?▾
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