This content originally appeared on DEV Community and was authored by GitHubOpenSource
Quick Summary: 
Code2prompt is a CLI tool and SDK that transforms codebases into structured prompts for Large Language Models. It offers features like automatic code processing, smart filtering using glob patterns and .gitignore, flexible templating with Handlebars, token tracking, and Git integration. It streamlines the process of creating LLM prompts for code analysis, generation, and automation.
Key Takeaways: 
Automate LLM prompt creation from your codebase.- Save time and effort by eliminating manual prompt formatting.
- Improve LLM interaction accuracy with structured prompts.
- Customize prompts with Handlebars templates.
- Integrate seamlessly with existing workflows via CLI, SDK, or MCP server.
Project Statistics: 
Stars: 5926
Forks: 331
Open Issues: 11
Tech Stack: 
- MDX
Tired of manually crafting prompts for your Large Language Models (LLMs)? Wish there was a simpler way to feed your entire codebase into an AI for analysis or generation? Then get ready to meet Code2Prompt – your new best friend for interacting with LLMs and your code! This amazing tool automates the often tedious process of preparing code for LLM consumption, saving you valuable time and effort. Imagine this: you’re working on a complex project, and you need to get insights from an LLM about a specific module. Instead of manually copying and pasting code snippets, carefully formatting them, and hoping you haven’t missed anything crucial, Code2Prompt does all the heavy lifting for you. It intelligently traverses your project’s directory structure, identifies relevant files, and generates a well-structured prompt that’s ready to be fed directly into your favorite LLM. It’s like having a super-efficient code butler! The tool supports various formats, ensuring compatibility with your existing workflow, and respects your .gitignore file, so you don’t have to worry about accidentally including sensitive information in your prompts. This is a game changer for anyone working with LLMs and large codebases. Code2Prompt isn’t just about convenience; it also improves the accuracy and consistency of your LLM interactions. By providing a structured and comprehensive context, you increase the likelihood of receiving relevant and insightful responses. No more guessing games or frustration over poorly formatted prompts! Furthermore, Code2Prompt offers flexibility through Handlebars templating, allowing you to fine-tune the generated prompts to match the specific requirements of your LLM and task. This level of customization ensures that you’re always getting the best possible results. The project also provides a handy CLI tool for quick prompt generation, with the added bonus of automatically copying the generated prompt to your clipboard. Need to integrate Code2Prompt into your existing workflows? No problem! It also offers a robust SDK with Python bindings, making it seamless to integrate into your AI agents or automation scripts. This means that you can automate the entire process of code analysis and generation, further streamlining your development workflow. For those who prefer a server-based approach, Code2Prompt also comes as a Model Context Protocol (MCP) server, allowing you to run it as a local service and provide your LLMs with on-demand access to your codebase. This is especially powerful when working with LLMs that require extensive context. Code2Prompt truly shines in its ability to handle codebases of any size, automatically processing and transforming them into digestible prompts for LLMs. It’s smart enough to filter files based on glob patterns, track token usage to stay within LLM context limits, and even integrate with Git to include diffs, logs, and branch comparisons in your prompts. This level of sophistication ensures that you have complete control and visibility over the process. In short, Code2Prompt is a must-have tool for any developer who wants to leverage the power of LLMs for code analysis, generation, or any other code-related task. It’s efficient, customizable, and incredibly user-friendly. Give it a try, and experience the future of LLM-assisted coding!
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This content originally appeared on DEV Community and was authored by GitHubOpenSource