// extract variables from prompt templates
Extract placeholder variables like {{name}} or {topic} from AI prompt templates instantly. Find, list, and export all template fields in one click.
{{double}}, {single}, <angle>, and [bracket] styles
Ready to extract
Paste a prompt template and click ExtractDrop any prompt template into the input box. Supports {{double}}, {single}, <angle>, and [bracket] placeholder styles.
Toggle which placeholder formats to detect using the checkboxes below the input.
Click Extract. Review your variables, fill in values, then copy the completed prompt.
Prompt Variable Extractor parses AI prompt templates and identifies every placeholder field — whether wrapped in {{double braces}}, {single braces}, <angle brackets>, or [square brackets]. It deduplicates results, counts occurrences, and lets you fill in values to preview the final prompt.
The tool detects four common styles: {{double braces}} (used by LangChain and many LLM frameworks), {single braces} (Python f-string style), <angle brackets> (common in Anthropic-style prompts), and [square brackets] (used in some template engines). You can toggle each style independently.
When deduplication is enabled, if a variable like {{name}} appears three times in your prompt, it will only appear once in the output list — but the occurrence count is shown next to it. Disable deduplication to see every instance listed separately.
Yes. After extracting variables, a "Fill In Values" section appears below the variable list. Type a value for each variable and the preview updates in real time. You can then copy the fully filled-out prompt with one click.
No. All processing happens entirely in your browser using JavaScript. Your prompt content never leaves your device. This makes it safe to use with sensitive or proprietary prompt templates.
Use the JSON button to copy variables as a JSON array — useful for developers building dynamic forms. Use the CSV button to copy as comma-separated values — handy for spreadsheets or documentation. The "Copy All" button copies the plain list.
The extractor captures whatever is inside the delimiters, including spaces and underscores. For example, {{user name}} and {{user_name}} are treated as distinct variables. Names with special characters like . or - are also captured as-is.
A Prompt Variable Extractor is a tool that scans AI prompt templates and identifies every placeholder variable embedded within them. In modern AI development, prompts are rarely static strings — they are parameterized templates containing placeholders like {{user_name}}, {topic}, or <context> that get replaced with real values at runtime. Keeping track of these variables across large prompt libraries can quickly become unwieldy without dedicated tooling.
This tool solves that problem by parsing your template and returning a clean, deduplicated list of every variable — along with occurrence counts, export options, and a live fill-and-preview mode.
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As large language models become central to software products, prompt engineering has evolved from an informal skill into a formal discipline. Teams now maintain libraries of hundreds of prompt templates — for customer support bots, content generators, code assistants, and data extraction pipelines. Each template may contain dozens of variables that need to be populated dynamically.
Without a systematic way to track these variables, problems emerge fast: a developer renames a variable in code but forgets to update it in the prompt; a QA tester doesn't know which fields to fill in when testing; documentation becomes out of sync. A prompt variable extractor eliminates these issues by making the structure of your templates explicit and machine-readable.
Different AI frameworks and prompt libraries use different placeholder conventions. This tool supports all four of the most common styles:
{{variable}} — Used by LangChain, LlamaIndex, Jinja2, and many prompt management platforms. This is the most explicit format and has the lowest chance of false-positive matches.{variable} — Common in Python f-string–style templates, OpenAI cookbook examples, and many custom prompt frameworks.<variable> — Popularized by Anthropic's prompt engineering guides and Claude's XML-friendly formatting conventions.[variable] — Found in some prompt template engines, markdown-based workflows, and legacy systems.You can toggle each format independently so the extractor only picks up the styles relevant to your codebase, reducing noise and false positives.
The extractor runs entirely in your browser using JavaScript regular expressions. When you click Extract, it scans your template text for each enabled placeholder pattern and builds a list of all matched variable names. If deduplication is enabled, identical variable names are merged into a single entry with an occurrence count — so if {{user}} appears five times, you see one entry labeled "user × 5" rather than five separate lines.
This occurrence count is useful for understanding how central a variable is to the prompt's logic. Variables with high counts are critical — changing or removing them will have broad impact across the template. Variables with a count of one may be candidates for simplification or inlining.
Beyond extraction, the tool provides a fill-and-preview mode. After variables are extracted, a form automatically generates one input field per unique variable. As you type values into these fields, the filled prompt preview updates in real time — replacing every occurrence of each placeholder with the corresponding value. This lets you verify that a prompt template behaves correctly before plugging it into your application.
The "Copy Filled Prompt" button then gives you the complete, interpolated prompt text ready to paste directly into your LLM API call, playground, or testing environment.
Once variables are extracted, you can export them in three formats:
["variable1", "variable2"]. Ideal for developers who need to programmatically generate form fields or validation schemas based on the prompt's structure.Teams use this tool throughout the AI product development lifecycle:
Well-structured prompt templates use consistent variable naming conventions throughout. Prefer snake_case names like {{user_name}} over ambiguous single-word names like {{u}}. Group related variables by prefix — for example, {{company_name}}, {{company_industry}}, {{company_size}} — to make the template self-documenting.
Avoid reusing variable names with different intended meanings across prompts in the same library. If you have two prompts that both use {{context}} but expect different data types, rename them to {{support_context}} and {{product_context}} to prevent confusion when sharing templates.
Finally, keep the number of variables per prompt manageable. If a single template requires more than eight or ten distinct inputs, consider whether it should be split into smaller, composable prompt components — each with their own focused set of variables.