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is a tool that allows you to create functions in JavaScript and TypeScript using AI.","maintainers":[{"email":"rntgspr@gmail.com","name":"rntgspr"},{"email":"iamvladimirtrunov@gmail.com","name":"vtrunov"}],"readme":"# Cumaru\n\nCumaru is a powerful TypeScript/JavaScript tool that leverages Large Language Models (LLMs) to dynamically generate and manage functions based on natural language descriptions.\n\n## Features\n\n- 🤖 **AI-Powered Function Generation**: Create functions using natural language descriptions\n- 💾 **Smart Caching System**: Optimize performance with intelligent response caching\n- 🔄 **Dynamic Function Creation**: Generate and save functions on-the-fly\n- 📝 **Template String Support**: Use template literals for flexible prompt creation\n- 🔒 **Type Safety**: Full TypeScript support with type inference\n- 🚀 **Multiple LLM Support**: Compatible with various LLM providers (Anthropic, Google AI)\n\n## Installation\n\n```bash\nnpm install @cumaru/cumaru\n```\n\n## Usage\n\n```typescript\nimport { cumaru } from '@cumaru/cumaru';\n\n// math super-simple example\ntype SumTwoNumbers = (numA: number, numB: number) => number;\nconst numA = 1;\nconst numB = 2;\nconst sumTwoNumbers = await cumaru<SumTwoNumbers>`\n  You are given two numbers ${numA} and ${numB}.\n  Sum the two numbers and return the result.\n`;\nconst resultSumTwoNumbers = sumTwoNumbers?.(3, 2);\nconsole.log(\"resultSumTwoNumbers\", resultSumTwoNumbers); // 5\n\n// string merge example, leetcode 75\ntype MergeWords = (wordA: string, wordB: string) => string;\nconst wordA = \"abracadabra\";\nconst wordB = \"emme\";\nconst mergeWords = await cumaru<MergeWords>`\n  You are given two strings ${wordA} and ${wordB}.\n  Merge the strings by adding letters in alternating order, starting with first parameter.\n  If a string is longer than the other, append the additional letters onto the end of the merged string.\n`;\nconst resultMergeWords = mergeWords?.(\"abc\", \"pqr\");\nconsole.log(\"resultMergeWords\", resultMergeWords); // apbqcr\n\n// dynamic programming example, leetcode 75\nconst s = \"subsequence\";\nconst t = \"sub\";\nconst subsequence = await cumaru<(a: string, b: string) => boolean>`\n  Given two strings ${s} and ${t}, return true if s is a subsequence of t, or false otherwise.\n  A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters\n  without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n`;\nconst resultSubsequence = subsequence?.(\"abc\", \"ahbgdc\");\nconsole.log(\"resultSubsequence\", resultSubsequence); // true\n```\n\n## How It Works\n\n1. **Prompt Creation**: Takes a template string with values\n2. **Cache Check**: Verifies if a similar function already exists\n3. **LLM Processing**: If no cache exists, sends the prompt to the LLM\n4. **Function Generation**: Creates a new function based on the LLM response\n5. **Storage**: Saves the generated function to disk\n6. **Caching**: Updates the cache for future use\n7. **Execution**: Returns the logical output or function\n\n## Configuration\n\nCreate a `.env` file in your project root with the following variables:\n\n```env\n# API Keys\nCUMARU_KEYS_ANTHROPIC_API_KEY=your_anthropic_api_key\nCUMARU_KEYS_GEMINI_API_KEY=your_gemini_api_key\n\n# LLM Configuration\nCUMARU_LLM_CHOICE=gemini  # or anthropic\nCUMARU_LLM_MODEL=gemini-2.0-flash  # optional, defaults based on choice\n\n# Cache Configuration\nCUMARU_CACHE_DIR=./cumaru  # optional, defaults to ./cumaru\nCUMARU_CACHE_FILE=cumaru.json  # optional, defaults to cumaru.json\nCUMARU_FILE_PREFIX=cmr  # optional, defaults to cmr\n\n# Logging\nCUMARU_LOG_LEVEL=debug  # optional, defaults to debug\n\n# Prompt Customization\nCUMARU_LLM_PRE_PROMPT_REPLACE=  # optional, replaces the current pre-prompt\nCUMARU_LLM_POST_PROMPT_REPLACE=  # optional, replaces the current post-prompt\nCUMARU_LLM_PRE_PROMPT_APPEND=  # optional, appends the current pre-prompt\nCUMARU_LLM_POST_PROMPT_APPEND=  # optional, appends the current post-prompt\n```\n\n## Default Prompts\n\n### Pre-prompt\n```\nCreate a function that follows the parameters below. Respond with only the function.\n\nBuild a JSON with the parameters of: \"name\" as string, \"body\" as a string and \"parameters\", as an array of strings, example:\n\n{\n  \"name\": \"substractNumbers\",\n  \"body\": \"return a - b;\",\n  \"parameters\": [\"a\", \"b\"]\n}\n\nThis JSON will create a standard paramters for the \"new Function()\" class locally to parse it as a real function.\n```\n\n### Post-prompt\n```\nDo not output nothing else, no explanations, no video. No console logs or tests are necessary.\n\nDo not use restricted values.\n\nThe variables expressed in these prompts are just examples; Only commit to maintaining their type, not their actual value.\n\nFollow best practices for function construction for Typesceript and Javascript, consider strict linter rules.\n\nKeep in mind, the focus is to use this output as a separate TypeScript file/module.\n\nDo not use blocks of code.\n\nDo not use comments.\n\nDo not use triple quotes.\n\nRESPECT THE JSON STRUCTURE.\n```\n\n## Development\n\n```bash\n# Install dependencies\nnpm install\n\n# Build the project\nnpm run build\n\n# Watch mode for development\nnpm run watch\n\n# Start the development server\nnpm start\n```\n\n## License\n\nMIT\n\n## Author\n\nRenato Gaspar\n\n","readmeFilename":"README.md"}