Skip to main content

llmcc brings multi-depth architecture graphs for code understanding and generation.

Project description

llmcc

llmcc brings multi-depth architecture graphs for code understanding and generation.

Our goal is to build a multi-depth, tree-like context / architecture view of a codebase, so a coding agent can walk up (zoom out) for structure and intent, then walk down (zoom in) to the exact crates/modules/files/symbols it needs—getting a highly comprehensive understanding of any codebase (any programming language).

Supported Languages

Language Status Notes
Rust ✅ Supported Full support for crates, modules, and symbols
TypeScript ✅ Supported Includes TSX, supports ES modules
C++ 🔜 Planned Coming soon
Python 🔜 Planned Coming soon

Why multi-depth graphs?

People (and coding agents) need to understand systems from different dimensions. Sometimes you need the high-level architecture to see boundaries, ownership, and how subsystems connect; other times you need the low-level implementation details to make a safe, precise change. llmcc provides multiple depths so you can choose the right “distance” from the code for the task.

Depth Perspective Best for
0 Project multi-workspace / repo-to-repo relationships
1 Library/Crate ownership boundaries, public API flow
2 Module subsystem structure, refactor planning
3 File + symbol implementation details, edit planning

Walkthrough: Codex (midterm size multi-crate rust project)

This repo includes many examples under sample. Download and open them in browser for the best viewing experience.

Depth 1: crate graph

Codex crate graph (depth 1)

Depth 2: module graph

Codex module graph (depth 2)

Depth 3: file + symbol graph

Codex file and symbol graph (depth 3)

Here's a small portion of the graph at depth 3, showing the core abstraction layer for prompt handling in Codex. Developers and AI agents can quickly grasp the architecture by examining this view.

codex core logic

Performance

llmcc is designed to be very fast, and we will try to make it faster.

The repo contains benchmark for many famous project output here: sample/benchmark_results_16.md.

Excerpt (PageRank timing, depth=3, top-200):

Project Files LoC Total
databend 3130 627K 2.53s
ruff 1661 418K 1.73s
codex 617 224K 0.46s

CLI: generate graphs

Build the binary:

cargo build --release

Generate a crate-level graph for Codex (DOT to stdout):

./target/release/llmcc \
	-d sample/repos/codex/codex-rs \
	--graph \
	--depth 1

Generate a PageRank-filtered file+symbol graph (write to a file):

./target/release/llmcc \
	-d sample/repos/codex/codex-rs \
	--graph \
	--depth 3 \
	--pagerank-top-k 200 \
	-o /tmp/codex_depth3_pagerank.dot

Render DOT to SVG (requires Graphviz):

dot -Tsvg /tmp/codex_depth3_pagerank.dot -o /tmp/codex_depth3_pagerank.svg

For generating sample graphs:

just gen rust

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llmcc-0.2.53.tar.gz (181.0 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

llmcc-0.2.53-cp38-abi3-win_amd64.whl (1.2 MB view details)

Uploaded CPython 3.8+Windows x86-64

llmcc-0.2.53-cp38-abi3-manylinux_2_34_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.8+manylinux: glibc 2.34+ x86-64

llmcc-0.2.53-cp38-abi3-macosx_11_0_arm64.whl (1.2 MB view details)

Uploaded CPython 3.8+macOS 11.0+ ARM64

File details

Details for the file llmcc-0.2.53.tar.gz.

File metadata

  • Download URL: llmcc-0.2.53.tar.gz
  • Upload date:
  • Size: 181.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for llmcc-0.2.53.tar.gz
Algorithm Hash digest
SHA256 57e812fb95b6155e10513835562aa5cb0a6ae874541a67f2fc5b1ad72688f42d
MD5 4794cac76f3e19551218110f2bbcd79a
BLAKE2b-256 46738c301a762f6d5448a93723d4168bbf74461e6a0038d31eb920108a85159e

See more details on using hashes here.

File details

Details for the file llmcc-0.2.53-cp38-abi3-win_amd64.whl.

File metadata

  • Download URL: llmcc-0.2.53-cp38-abi3-win_amd64.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: CPython 3.8+, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for llmcc-0.2.53-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 7c46129708dfce94e39f99e59f1a0eb15f186e74b3efaa726017b4c3be1af0ce
MD5 e0d9f7980e253740026951b3844deb75
BLAKE2b-256 987a0f0e2cd6918ddf98200474fb39cd9086c985011d6ddb2ba419c9ca3fe5a3

See more details on using hashes here.

File details

Details for the file llmcc-0.2.53-cp38-abi3-manylinux_2_34_x86_64.whl.

File metadata

File hashes

Hashes for llmcc-0.2.53-cp38-abi3-manylinux_2_34_x86_64.whl
Algorithm Hash digest
SHA256 499443116df65f0b5bc271c18525bfc7ad378f0ff5286412fc5e24c94e060d3a
MD5 5d87e4ee24fce3df40726eaad994e698
BLAKE2b-256 67aebebd95792fedce451e04a7fbe932b455095001d4504b1741981c015c2af0

See more details on using hashes here.

File details

Details for the file llmcc-0.2.53-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for llmcc-0.2.53-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 193b23ee3e68f70b1717dc59933e51fade10c5bec24bc75d5b54a767d99b9f29
MD5 cc8450eb3e93ccdb1791184bd67893e1
BLAKE2b-256 b0a2adcc311b53dada7cb4260288c82821040668527d14c75da336e2c47e92f1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page