The Problem

When you ask an AI to help with code, quality depends almost entirely on what context you provide. Most approaches fail:

• Pasting random files gives the AI fragments without structure or relationships
• Dumping entire repositories overwhelms the AI with noise and irrelevant code
• Token limits force you to leave out important context, leading to incorrect suggestions
• AI doesn't know what it doesn't know — it can't ask for the files it needs
• Copy-paste workflows are slow, error-prone, and don't scale
• Every question requires re-gathering context from scratch

The result: AI gives generic answers, hallucinates function signatures, or misses critical dependencies — not because the AI is bad, but because the context is bad.

What Infiniloom Does

Infiniloom reads your codebase and produces a structured summary designed specifically for AI consumption.

Think of it like this: instead of handing someone a filing cabinet and saying "figure it out," you give them a well-organized briefing document that highlights what matters.

Here's how it works:

1. Analyzes structure — Infiniloom understands how your code is organized: which files exist, how they relate, what languages are used.

2. Extracts meaning — It identifies the important pieces: functions, classes, interfaces, types. Not just text, but semantic units that AI can reason about.

3. Ranks importance — Using techniques similar to how search engines rank web pages, it determines which code is central to your project and which is peripheral.

4. Filters noise — Generated files, build artifacts, vendored dependencies, and other distractions are automatically excluded.

5. Formats for AI — The output is structured in ways that different AI models understand best — XML for Claude, Markdown for GPT, YAML for Gemini.

The result is a context package that helps AI give you accurate, relevant answers about your actual code.

What You Can Do With It

For Developers

• AI-assisted code review — Give your AI the context to understand what a pull request actually changes
• Ask architectural questions — "How does authentication flow through this system?"
• Generate documentation — Let AI explain your code with full visibility into dependencies
• Onboard faster — Understand a new codebase in hours instead of weeks
• Debug complex issues — Provide AI with the relevant code paths, not just the error message

For AI / RAG / Agents

• Build better context — Prepare high-quality input for LLM applications
• Reduce token usage — Send what matters, not everything
• Improve answer accuracy — Relevant context produces relevant answers
• Enable code-aware agents — Give autonomous systems the context they need to act correctly

Quick Start

Install:

npm install -g infiniloom

Generate context for your repository:

infiniloom pack . --output context.xml

This produces an XML file containing your codebase's structure, key symbols, and content — ready to paste into Claude, GPT, or any other AI assistant.

Core Capabilities

CLI Overview

See the Command Reference for detailed documentation.

How This Is Different

Compared to "just paste the code":

Infiniloom understands code structure. It knows the difference between a core business function and a utility helper. It understands imports, dependencies, and relationships. Pasting files gives AI text; Infiniloom gives AI understanding.

Compared to generic RAG tools:

Most RAG systems treat code as documents. They chunk by character count, embed text, and retrieve by similarity. This misses the structure that makes code meaningful. Infiniloom preserves semantic boundaries — functions stay whole, relationships stay intact.

Compared to embedding-based approaches:

Embeddings are useful for "find code similar to X." They're less useful for "understand how X works." Infiniloom focuses on comprehension: what exists, how it connects, what matters. This is about building complete context, not searching fragments.

Our philosophy:

Context quality beats context quantity. A smaller, well-structured context produces better AI responses than a larger, noisier one. Infiniloom prioritizes signal over volume.

Who This Is For

Good fit:

• Developers using AI assistants for code review, debugging, or documentation
• Teams building AI-powered developer tools or code analysis products
• Engineers working with large or unfamiliar codebases
• Anyone who needs AI to understand real production code, not toy examples

Probably not needed:

• Single-file scripts or small utilities (just paste them directly)
• Projects where you already have perfect context (rare, but possible)
• Use cases where code search is more important than code comprehension

Project Status

Infiniloom is stable and actively maintained.

What's solid today:

• Core packing workflow across 22 languages
• All output formats (XML, Markdown, YAML, JSON)
• Security scanning and secret redaction
• Git-aware diff context
• Python and Node.js bindings

Coming next:

• Improved call graph analysis
• IDE integrations
• Streaming output for very large repositories
• Enhanced support for monorepo structures

Installation Options

Contributing

We welcome contributions of all kinds: bug reports, feature requests, documentation improvements, and code.

• Found a bug? Open an issue
• Have an idea? Start a discussion
• Want to contribute code? See CONTRIBUTING.md

cargo test --workspace    # Run tests
cargo clippy --workspace  # Lint
cargo fmt --all           # Format

Documentation

• Cheat Sheet — Quick reference
• Command Reference — Detailed CLI documentation
• Configuration Guide — Config files and options
• FAQ — Common questions answered

License

MIT — see LICENSE.