Manually copying files and formatting them for a chat window is a massive waste of time. When you are trying to debug complex logic or explain an entire architecture to an LLM, the tedious task of gathering context often breaks your creative flow.
Code2Prompt solves this by ingesting entire codebases and transforming them into perfectly structured prompts designed specifically for Large Language Models. Whether you are working with ChatGPT manually or building sophisticated AI agents, this tool automates the heavy lifting of context engineering.
A Complete Ecosystem for Codebase Context
Code2Prompt is not just a simple CLI utility; it is a multi-layered ecosystem designed to fit into any development workflow. From high-speed Rust internals to easy-to-use Python interfaces, it provides the tools needed for modern AI-driven development.
| Component | Best Use Case | Key Benefit |
|---|---|---|
| CLI Tool | Manual prompting & quick tasks | Interactive TUI and clipboard support |
| Python SDK | Automation & RAG pipelines | Fast Rust-based bindings for Python |
| MCP Server | Agentic applications | Enables agents to read local codebases |
The Core Components
At its heart is a high-speed Rust Core responsible for secure file traversal and respecting your .gitignore rules. This core ensures that you don’t accidentally bloat your context window with unnecessary files like node_modules or build artifacts.
For developers building automated workflows, the Python SDK provides a seamless way to integrate codebase ingestion into larger scripts. If you are looking for more advanced integration, you can use it as an MCP server to give agents real power by allowing them to read your local files efficiently.
Powerful Features for Precision Prompting
Code2Prompt goes far beyond simple text concatenation. It provides intelligent features that ensure the LLM receives high-signal, low-noise information.
- Smart Filtering: Use glob patterns and
.gitignoreto include or exclude specific files. - Flexible Templating: Customize your output using Handlebars templates for different LLM personas.
- Token Estimation: Get accurate estimates of prompt size, including line numbers and template overhead.
- Git Integration: Include diffs, logs, and branch comparisons to provide historical context.
- Smart File Reading: Seamlessly process CSV, JSONL, Notebooks, and more.
Getting Started with Code2Prompt
Setting up the tool is incredibly straightforward. You can follow the official installation guide to get started in minutes.
The easiest way to begin is by installing it via pip:
pip install code2prompt-rsOnce installed, you can generate a prompt from your current directory with a single command. If you want to quickly paste the result into ChatGPT, use the -c flag to copy it directly to your clipboard:
code2prompt . -c
For more complex workflows, you can save the output to a specific file using the --output-file flag:
code2prompt path/to/project --output-file prompt.txt
Optimizing Your AI Agent Workflows
As we move toward more autonomous development, the ability to feed high-quality context into models is paramount. Tools like Code2Prompt are essential for building what many call a Codebase Memory MCP, ensuring your agents have the exact information they need without wasting precious tokens on noise.
By automating the preparation of codebase context, you can focus on what actually matters: solving problems and building features rather than formatting text files.
Ready to supercharge your AI workflow? Check out the official Code2Prompt repository on GitHub and start generating better prompts today!
