CodeGraph - Knowledge Graph Visualization Tool

Visualize codebases as knowledge graphs to analyze dependencies, detect bottlenecks, and optimize projects.

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Project Structure

code-graph/
├── api/                  # Python backend (FastAPI)
│   ├── index.py          # FastAPI app, auth deps, API routes, SPA serving
│   ├── graph.py          # FalkorDB graph operations
│   ├── llm.py            # GraphRAG + LiteLLM chat integration
│   ├── project.py        # Repository cloning and analysis orchestration
│   ├── info.py           # Repository metadata stored in Redis/FalkorDB
│   ├── prompts.py        # LLM system and prompt templates
│   ├── auto_complete.py  # Prefix search helper
│   ├── analyzers/        # Source analyzers (Python, Java, C#)
│   ├── entities/         # Graph/entity models
│   ├── git_utils/        # Git history graph utilities
│   └── code_coverage/    # Coverage utilities
├── app/                  # React frontend (Vite)
│   ├── src/              # Frontend source code
│   ├── public/           # Static assets
│   ├── package.json      # Frontend dependencies and scripts
│   ├── vite.config.ts    # Vite config and /api proxy for dev mode
│   └── tsconfig*.json    # TypeScript config
├── tests/                # Backend/unit and endpoint tests
├── e2e/                  # End-to-end helpers and Playwright assets
├── Dockerfile            # Unified container image
├── docker-compose.yml    # Local FalkorDB + app stack
├── Makefile              # Common dev/build/test commands
├── start.sh              # Container entrypoint
├── pyproject.toml        # Python package and dependency config
└── .env.template         # Example environment variables

Running Locally

Prerequisites

• Python >=3.12,<3.14
• Node.js 20+
• uv
• A FalkorDB instance (local or cloud)

1. Start FalkorDB

Option A: Free cloud instance at app.falkordb.cloud

Option B: Run locally with Docker:

docker run -p 6379:6379 -it --rm falkordb/falkordb

2. Configure environment variables

Copy the template and adjust it for your setup:

cp .env.template .env

Variable	Description	Required	Default
FALKORDB_HOST	FalkorDB hostname	No	localhost
FALKORDB_PORT	FalkorDB port	No	6379
FALKORDB_USERNAME	Optional FalkorDB username	No	empty
FALKORDB_PASSWORD	Optional FalkorDB password	No	empty
SECRET_TOKEN	Token checked by protected endpoints	No	empty
CODE_GRAPH_PUBLIC	Set 1 to skip auth on read-only endpoints	No	0
ALLOWED_ANALYSIS_DIR	Root path allowed for /api/analyze_folder	No	repository root
MODEL_NAME	LiteLLM model used by /api/chat	No	gemini/gemini-flash-lite-latest
HOST	Optional Uvicorn bind host for start.sh/make run-*	No	0.0.0.0 or 127.0.0.1 depending on command
PORT	Optional Uvicorn bind port for start.sh/make run-*	No	5000

The chat endpoint also needs the provider credential expected by your chosen MODEL_NAME. The default model is Gemini, so set GEMINI_API_KEY unless you switch to a different LiteLLM provider/model.

Authentication behavior

• Send Authorization: Bearer <SECRET_TOKEN> (or the raw token string) when SECRET_TOKEN is configured.
• Read endpoints use the public_or_auth dependency.
• Mutating endpoints (/api/analyze_folder, /api/analyze_repo, /api/switch_commit) use the token_required dependency.
• If SECRET_TOKEN is unset, the current implementation accepts requests without an Authorization header.
• Setting CODE_GRAPH_PUBLIC=1 makes the read-only endpoints public even when SECRET_TOKEN is configured.

3. Install dependencies

# Install backend dependencies
uv sync --all-extras

# Install frontend dependencies
npm install --prefix ./app

# Optional: install Playwright dependencies from the repo root
npm install

If you do not use uv, pip install -e ".[test]" also installs the backend package and test dependencies.

4. Run the app

Backend API with auto-reload:

uv run uvicorn api.index:app --host 127.0.0.1 --port 5000 --reload

Frontend hot-reload with Vite:

# Terminal 1: backend API
uv run uvicorn api.index:app --host 127.0.0.1 --port 5000 --reload

# Terminal 2: Vite dev server
cd app && npm run dev

The Vite dev server runs on http://localhost:3000 and proxies /api/* requests to http://127.0.0.1:5000.

Single-process built frontend + backend:

npm --prefix ./app run build
uv run uvicorn api.index:app --host 0.0.0.0 --port 5000

In this mode, the FastAPI app serves the built React SPA from app/dist on http://localhost:5000.

Using Make

make install       # Install backend + frontend dependencies
make build-dev     # Build frontend in development mode
make build-prod    # Build frontend for production
make run-dev       # Build dev frontend + run Uvicorn with reload
make run-prod      # Build prod frontend + run Uvicorn
make test          # Run backend pytest suite
make lint          # Run Ruff + frontend type-check
make e2e           # Run Playwright tests from repo root
make clean         # Remove build/test artifacts

make test currently points at the right backend test entrypoint, but some legacy analyzer/git-history tests still need maintenance before the suite passes on a clean checkout.

Running with Docker

Using Docker Compose

docker compose up --build

This starts FalkorDB and the CodeGraph app together. The checked-in compose file sets CODE_GRAPH_PUBLIC=1 for the app service.

Using Docker directly

docker build -t code-graph .

docker run -p 5000:5000 \
  -e FALKORDB_HOST=host.docker.internal \
  -e FALKORDB_PORT=6379 \
  -e MODEL_NAME=gemini/gemini-flash-lite-latest \
  -e GEMINI_API_KEY=<YOUR_GEMINI_API_KEY> \
  -e SECRET_TOKEN=<YOUR_SECRET_TOKEN> \
  code-graph

Creating a Code Graph

Analyze a local folder

analyze_folder only accepts paths under ALLOWED_ANALYSIS_DIR (defaults to the repository root unless you override it).

curl -X POST http://127.0.0.1:5000/api/analyze_folder \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR_SECRET_TOKEN>" \
  -d '{"path": "<FULL_PATH_TO_FOLDER>", "ignore": [".github", ".git"]}'

Analyze a Git repository

curl -X POST http://127.0.0.1:5000/api/analyze_repo \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR_SECRET_TOKEN>" \
  -d '{"repo_url": "https://github.com/user/repo", "ignore": [".github", ".git"]}'

List indexed repositories

curl http://127.0.0.1:5000/api/list_repos

Supported Languages

api/analyzers/source_analyzer.py currently enables these analyzers:

• Python (.py)
• Java (.java)
• C# (.cs)

A C analyzer exists in the source tree, but it is commented out and is not currently registered.

API Endpoints

Read endpoints

Method	Endpoint	Description
GET	/api/list_repos	List all indexed repositories
GET	/api/graph_entities?repo=<name>	Fetch a subgraph for a repository
POST	/api/get_neighbors	Return neighboring nodes for the provided IDs
POST	/api/auto_complete	Prefix-search indexed entities
POST	/api/repo_info	Return repository stats and saved metadata
POST	/api/find_paths	Find paths between two graph nodes
POST	/api/chat	Ask questions over the code graph via GraphRAG
POST	/api/list_commits	List commits from the repository's git graph

Mutating endpoints

Method	Endpoint	Description
POST	/api/analyze_folder	Analyze a local source folder
POST	/api/analyze_repo	Clone and analyze a git repository
POST	/api/switch_commit	Switch the indexed repository to a specific commit

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright FalkorDB Ltd. 2025