Code2prompt
Convert codebases into structured AI-optimized prompts in seconds
A precise, lightweight CLI for developers who want full control over how code context is fed to LLMs. Lacks a GUI and cloud API, but excels in offline, scriptable workflows. Ideal for power users.
- Developers using LLMs for code generation or review
- AI engineers needing structured code context for prompts
- Technical writers creating AI-assisted documentation
- Open-source contributors preparing codebases for AI analysis
- Non-technical users comfortable only with GUI tools
- Projects requiring real-time collaborative prompt editing
- Use cases needing cloud-hosted API access or web dashboard
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In short
Code2prompt — Convert codebases into structured AI-optimized prompts in seconds. Best for Developers using LLMs for code generation or review, AI engineers needing structured code context for prompts, Technical writers creating AI-assisted documentation. Free to use.
Viability Score
How likely is Code2prompt to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Convert entire codebase into a single LLM prompt
- Source tree generation with full directory structure
- File content inclusion with syntax formatting
- Token counting for prompt size estimation
- Prompt templating with customizable Handlebars templates
- Recursive directory traversal
- Support for multiple file extensions via glob patterns
- Goal + Format + Context framework integration
- Open-source and self-hostable (MIT license)
- No external API dependencies for core functionality
- Git integration for diff and log extraction
- Multiple output formats: JSON, Markdown, XML
- Built in Rust for high performance
- Include/exclude file filtering with glob patterns
- CLI, Python SDK, and MCP (Model Context Protocol) support
About Code2prompt
Code2Prompt is a context engineering tool that ingests your codebase and transforms it into a single, structured LLM prompt. It follows the Goal + Format + Context framework to produce AI-ready prompts that include source tree, file contents, and metadata, enabling more accurate and context-aware interactions with language models. The tool is designed for developers, AI engineers, and anyone working with LLMs who needs to provide rich code context without manual copy-pasting. By automating prompt construction, it reduces token waste and ensures the AI has the necessary information to understand and act on the codebase. Under the hood, Code2Prompt recursively traverses directories, reads file contents, and assembles them into a templated prompt. It also counts tokens to help users stay within context limits. The output can be customized with prompt templates, and the tool supports multiple output formats including JSON, Markdown, and XML. Open-source and built in Rust, it's lightweight and CLI-first, with a Python SDK and Model Context Protocol (MCP) support. Ideal for offline, scriptable workflows.
Behind the Verdict
Code2Prompt earns its keep if you routinely feed code context to LLMs and hate the manual copy-paste dance. Its CLI, Rust performance, and Handlebars templates give serious control — you define exactly what files go in and how the prompt looks. For teams adopting AI-assisted development, it slots well into CI/CD pipelines or local scripts where a browser-based tool would be awkward. Where it stumbles: no GUI, no cloud API, so non-developer team members may struggle. If your workflow lives entirely in an AI-assistant IDE (like Cursor or GitHub Copilot chat), Code2Prompt's extra step can feel redundant. Also, smart filtering is listed as 'soon', so for now you rely on glob patterns alone. Flipside: being MIT-licensed and self-hostable means zero vendor lock-in and full data privacy — important for regulated industries. The Python SDK and MCP support open the door to agent-based automation. Compared to tools like RepoPrompt or directory-to-prompt scripts, Code2Prompt is more structured (Goal+Format+Context) and performance-optimized (Rust). It's best for developers who want repeatable, scriptable context injection, not for those just wanting a quick web upload.
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Use Cases
- Generate a comprehensive prompt for code review tasks by including the entire project structure and relevant files.
- Create AI-optimized context for bug fixing by feeding the tool the source tree and error logs.
- Prepare codebase documentation summaries by templating prompts with project metadata.
- Build retrieval-augmented generation (RAG) inputs by converting code repositories into structured text.
- Improve LLM-assisted feature implementation by providing full context of existing code and desired changes.
Limitations
- Code2Prompt is a local CLI tool with no web interface or API, limiting integration into cloud-based workflows.
- It does not support real-time file watching or incremental updates, requiring full regeneration for each prompt.
- Token counting is approximate and may not match all model tokenizers exactly.
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