hypergumbo 0.6.0

Local-first repo behavior map generator (MVP)

hypergumbo

Get a quick overview of any codebase, sized to fit your context window.

Requires Python 3.10+

pip install hypergumbo              # from PyPI (after release)
pip install git+https://codeberg.org/iterabloom/hypergumbo.git  # from source
hypergumbo .

Output:

# my-project

## Overview
Python (72%), TypeScript (18%), Markdown (10%) · 84 files · ~12,400 LOC

## Structure
- `src/` — Source code
- `tests/` — Tests
- `docs/` — Documentation

## Frameworks
- fastapi
- pytest

## Key Symbols
### `src/api/routes.py`
- `create_user` (function) ★
- `get_user` (function) ★
...

Use -t to control the token budget:

hypergumbo . -t 500   # concise overview
hypergumbo . -t 2000  # include symbols and entry points

CLI Commands

hypergumbo [path]            # default: generate Markdown sketch
hypergumbo . -t 1000         # sketch with 1000 token budget
hypergumbo . -x              # exclude test files (faster on large codebases)
hypergumbo run [path]        # full analysis → hypergumbo.results.json
hypergumbo slice --entry X   # extract subgraph from entry point
hypergumbo slice --entry X --reverse  # find all callers of X
hypergumbo routes [path]     # list HTTP routes (FastAPI, Flask, Express, etc.)
hypergumbo search <query>    # search symbols by name pattern
hypergumbo init [path]       # initialize .hypergumbo/ capsule
hypergumbo init --assistant llm  # use LLM to generate analysis plan
hypergumbo catalog           # list available analysis passes
hypergumbo export-capsule    # export shareable capsule tarball
hypergumbo build-grammars    # build Lean/Wolfram grammars from source

What It Does

Default mode (hypergumbo .) generates a Markdown sketch with:

• Language breakdown and LOC count
• Directory structure with labels
• Framework detection
• Key symbols ranked by graph centrality (★ = most called)
• Entry points (CLI, HTTP routes, etc.)

Full analysis (hypergumbo run) outputs a JSON behavior map with:

• Nodes: Functions, classes, methods, interfaces with location and stable IDs
• Edges: Relationships between symbols (calls, imports, instantiates, extends, implements)
• Cross-language edges: 12 linkers connect symbols across language boundaries (see table below)

LLM-assisted init (hypergumbo init --assistant llm) demonstrates LLM integration patterns but provides no practical advantage over the default template-based approach. Since analyzers are language-level (not framework-level), both methods select the same passes. This feature exists as a technical scaffold showing how to integrate OpenRouter, OpenAI, or local models via the llm package. It may be removed in a future release.

Supported Languages (51 Analyzers)

Category	Languages
Application	Python, JavaScript, TypeScript, Java, C#, Go, Rust, Ruby, PHP, Swift, Kotlin, Scala, Elixir, Lua, Haskell, OCaml, Julia, R, Dart
Systems	C, C++, Zig, Objective-C, CUDA, Fortran
Smart Contracts	Solidity
Hardware	Verilog, VHDL, GLSL, WGSL
Infrastructure	Terraform/HCL, Dockerfile, CMake, Make, Nix, Bash, YAML/Ansible
Data/Schema	SQL, GraphQL, JSON, TOML, XML, CSS
Frontend	Vue, Svelte, HTML
Proof/Formal	Agda, Lean*, Wolfram*
Legacy/Academic	COBOL, LaTeX

* Lean and Wolfram require building tree-sitter grammars from source (not yet on PyPI). Run hypergumbo build-grammars to enable these analyzers.

All analyzers detect symbols and edges (calls, imports, instantiates, extends, implements). See STATUS.md for details.

Cross-Language Linkers (12 Linkers)

Linkers run automatically during hypergumbo run to connect symbols across language boundaries:

Linker	Description
JNI	Java native methods ↔ C JNI implementations
IPC	Electron IPC, Web Workers, postMessage patterns
WebSocket	Socket.io, native WebSocket, Django Channels, FastAPI WebSocket
Phoenix	Phoenix Channels (broadcast!, push, handle_in) and LiveView
Swift/ObjC	@objc annotations, #selector(), bridging headers
gRPC	Protobuf services, stubs, and servicer implementations
HTTP	fetch(), axios, requests → route handlers (URL pattern matching)
GraphQL	gql queries/mutations → schema definitions
GraphQL Resolver	Resolver implementations → schema type definitions
Message Queue	Kafka, RabbitMQ, SQS, Redis Pub/Sub topic matching
Database Query	SQL in app code → table definitions in schema files
Event Sourcing	EventEmitter, Django signals, Spring events

Development

To contribute to hypergumbo:

git clone https://codeberg.org/iterabloom/hypergumbo.git
cd hypergumbo
python3 -m venv .venv
source .venv/bin/activate
pip install -e .[dev]
./scripts/install-hooks
hypergumbo build-grammars  # optional: enables Lean and Wolfram analyzers
pytest

All agent instructions live in AGENTS.md. Vendor-specific files (CLAUDE.md, GEMINI.md, etc.) are thin adapters that import the AGENTS.md canonical source.

See STATUS.md for implementation progress.

Security

To report a vulnerability, see SECURITY.md.

License

AGPL-3.0-or-later