CodeRLM – Tree-sitter-backed code indexing for LLM agents
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
In plain words
CodeRLM is a code indexing tool that helps LLM coding agents navigate and understand large codebases more efficiently. It uses tree-sitter to parse code and build a searchable symbol table with cross-references, letting AI agents query only the information they need rather than reading files blindly. Built as a Rust server with an API backend, it's designed for developers who want their LLM agents to work with code systematically, drilling deeper into specific areas on demand instead of exhausting context by exploring everything at once.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into context, the model gets a searchable environment. The model can then query just for what it needs and can drill deeper where needed. coderlm is my implementation of that idea for codebases. A Rust server indexes a project with tree-sitter, builds a symbol table with cross-references, and exposes an API. The agent queries for structure, symbols, implementations, callers, and grep results — getting back exactly the code it needs instead of scanning for it. The agent workflow looks like: 1. `init` — register the project, get the top-level structure 2. `structure` — drill into specific directories 3. `search` — find symbols by name across the codebase 4. `impl` — retrieve the exact source of a function or class 5. `callers` — find everything that calls a given symbol 6. `grep` — fall back to text search when you need it This replaces the glob/grep/read cycle with index-backed lookups. The server currently supports Rust, Python, TypeScript, JavaScript, and Go for symbol parsing, though all file types show up in the tree and are searchable via grep. It ships as a Claude Code plugin with hooks that guide the agent to use indexed lookups instead of native file tools, plus a Python CLI wrapper with zero dependencies. For anecdotal results, I ran the same prompt against a codebase to "explore and identify opportunities to clarify the existing structure". Using coderlm, claude was able to generate a plan in about 3 minutes. The coderlm enabled instance found a genuine bug (duplicated code with identical names), orphaned code for cleanup, mismatched naming conventions crossing module boundaries, and overlapping vocabulary. These are all semantic issues which clearly benefit from the tree-sitter centric approach. Using the native tools, claude was able to identify various file clutter in the root of the project, out of date references, and a migration timestamp collision. These findings are more consistent with methodical walks of the filesystem and took about 8 minutes to produce. The indexed approach did better at catching semantic issues than native tools and had a key benefit in being faster to resolve. I've spent some effort to streamline the installation process, but it isn't turnkey yet. You'll need the rust toolchain to build the server which runs as a separate process. Installing the plugin from a claude marketplace is possible, but the skill isn't being added to your .claude yet so there are some manual steps to just getting to a point where claude could use it. Claude continues to demonstrate significant resistance to using CodeRLM in exploration tasks. Typically to use you will need to explicitly direct claude to use it. --- Repo: github.com/JaredStewart/coderlm Paper: Recursive Language Models https://arxiv.org/abs/2512.24601 — Zhang, Kraska, Khattab (MIT CSAIL, 2025) Inspired by: https://github.com/brainqub3/claude_code_RLM
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