CodeRLM: Teaching AI to Explore Codebases Like a Senior Developer

How tree-sitter indexing and the Recursive Language Model pattern could transform how AI understands code

What if AI agents could navigate your codebase the way an experienced developer does — not by reading everything, but by knowing exactly where to look?

That’s the promise of CodeRLM, a new tool that applies the Recursive Language Model (RLM) pattern to code exploration. Instead of dumping entire codebases into context windows, it gives AI agents precise, targeted access to symbols, implementations, and relationships.

The Core Insight

CodeRLM consists of two main components:

1. A Rust server that indexes projects using tree-sitter, building a comprehensive symbol table with cross-references
2. A Claude Code plugin that wraps the server with a structured workflow, enabling AI to query exactly what it needs

The key innovation is moving from “context stuffing” — loading entire files or repositories into the AI’s context — to “targeted retrieval” — asking specifically for what you need and getting precise answers.

How It Works

The RLM pattern treats a codebase as external data that a root language model can recursively examine:

1. Index — The server walks the project directory (respecting .gitignore), parses files with tree-sitter, and builds a symbol table
2. Query — The agent queries the index: search symbols, list functions, find callers, grep for patterns
3. Read — The server returns exactly the code requested — full implementations, variable lists, line ranges

This replaces the typical glob/grep/read cycle with precise, index-backed lookups.

Why This Matters

Current AI coding assistants face a fundamental limitation: context windows. Even with massive token limits, reading entire codebases is inefficient and often misses the forest for the trees.

CodeRLM addresses this in several ways:

• Precision over volume — Instead of “read this file and find the auth function,” ask “what functions handle authentication?” and get exact answers
• Cross-reference awareness — Know not just what a function does, but what calls it and what it calls
• Scale handling — Can handle large codebases by indexing rather than loading

Supported Languages

Currently supports Rust, Python, TypeScript, JavaScript, and Go. All file types appear in the file tree and are searchable, but only these languages produce parsed symbols.

Key Takeaways

• Tree-sitter is the secret weapon — Provides precise, parseable code structure
• Claude Code integration is first-class — The plugin automates the query workflow
• Zero Python dependencies — The CLI wrapper uses only standard library
• MIT licensed — Open and extensible

Looking Ahead

The RLM pattern represents a shift in how we think about AI and code: not as context windows to fill, but as databases to query. As AI agents become more capable, this structured approach to code understanding will likely become standard.

The future might see more tools adopting this pattern — code search engines, IDEs, and CI/CD systems all benefiting from AI that understands code structure rather than just content.

Based on analysis of JaredStewart/coderlm repository