A comprehensive static analysis tool for Python source code that provides symbol table generation, call graph analysis, and semantic analysis using Jedi, CodeQL, and Tree-sitter.
pip install codeanalyzer-python- Python 3.12 or higher
The tool creates virtual environments internally using Python's built-in venv module.
Ubuntu/Debian systems:
sudo apt update
sudo apt install python3.12-venv python3-dev build-essentialFedora/RHEL/CentOS systems:
sudo dnf group install "Development Tools"
sudo dnf install python3-pip python3-venv python3-develor on older versions:
sudo yum groupinstall "Development Tools"
sudo yum install python3-pip python3-venv python3-develmacOS systems:
# Install Xcode Command Line Tools (for compilation)
xcode-select --install
# If using Homebrew Python (recommended)
brew install [email protected]
# If using pyenv (popular Python version manager)
# First ensure pyenv is properly installed and configured
pyenv install 3.12.0 # or latest 3.12.x version
pyenv global 3.12.0 # or pyenv local 3.12.0 for project-specific
# If using system Python, you may need to install certificates
/Applications/Python\ 3.12/Install\ Certificates.commandNote: These packages are required as the tool uses Python's built-in
venvmodule to create isolated environments for analysis.
The codeanalyzer provides a command-line interface for performing static analysis on Python projects.
codeanalyzer --input /path/to/python/projectTo view the available options and commands, run codeanalyzer --help. You should see output similar to the following:
❯ codeanalyzer --help
Usage: codeanalyzer [OPTIONS] COMMAND [ARGS]...
Static Analysis on Python source code using Jedi, CodeQL and Tree sitter.
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --input -i PATH Path to the project root directory. [default: None] [required] │
│ --output -o PATH Output directory for artifacts. [default: None] │
│ --format -f [json|msgpack] Output format: json or msgpack. [default: json] │
│ --analysis-level -a INTEGER 1: symbol table, 2: call graph. [default: 1] │
│ --codeql --no-codeql Enable CodeQL-based analysis. [default: no-codeql] │
│ --eager --lazy Enable eager or lazy analysis. Defaults to lazy. [default: lazy] │
│ --cache-dir -c PATH Directory to store analysis cache. [default: None] │
│ --clear-cache --keep-cache Clear cache after analysis. [default: clear-cache] │
│ -v INTEGER Increase verbosity: -v, -vv, -vvv [default: 0] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯-
Basic analysis with symbol table:
codeanalyzer --input ./my-python-project
This will print the symbol table to stdout in JSON format to the standard output. If you want to save the output, you can use the
--outputoption.codeanalyzer --input ./my-python-project --output /path/to/analysis-results
Now, you can find the analysis results in
analysis.jsonin the specified directory. -
Change output format to msgpack:
codeanalyzer --input ./my-python-project --output /path/to/analysis-results --format msgpack
This will save the analysis results in
analysis.msgpackin the specified directory. -
Toggle analysis levels with
--analysis-level:codeanalyzer --input ./my-python-project --analysis-level 1 # Symbol table onlyCall graph analysis can be enabled by setting the level to
2:codeanalyzer --input ./my-python-project --analysis-level 2 # Symbol table + Call graphNote: The
--analysis-level=2is not yet implemented in this version. -
Analysis with CodeQL enabled:
codeanalyzer --input ./my-python-project --codeql
This will perform CodeQL-based analysis in addition to the standard symbol table generation.
Note: Not yet fully implemented. Please refrain from using this option until further notice.
-
Eager analysis with custom cache directory:
codeanalyzer --input ./my-python-project --eager --cache-dir /path/to/custom-cache
This will rebuild the analysis cache at every run and store it in
/path/to/custom-cache/.codeanalyzer. The cache will be cleared by default after analysis unless you specify--keep-cache.If you provide --cache-dir, the cache will be stored in that directory. If not specified, it defaults to
.codeanalyzerin the current working directory ($PWD). -
Quiet mode (minimal output):
codeanalyzer --input /path/to/my-python-project --quiet
By default, analysis results are printed to stdout in JSON format. When using the --output option, results are saved to analysis.json in the specified directory. If you use the --format=msgpack option, the results will be saved in analysis.msgpack, which is a binary format that can be more efficient for storage and transmission.
This project uses uv for dependency management during development.
- Install uv
A comprehensive static analysis tool for Python source code that provides symbol table generation, call graph analysis, and semantic analysis using Jedi, CodeQL, and Tree-sitter.
pip install codeanalyzer-python- Python 3.12 or higher
The tool creates virtual environments internally using Python's built-in venv module.
Ubuntu/Debian systems:
sudo apt update
sudo apt install python3.12-venv python3-dev build-essentialFedora/RHEL/CentOS systems:
sudo dnf group install "Development Tools"
sudo dnf install python3-pip python3-venv python3-develor on older versions:
sudo yum groupinstall "Development Tools"
sudo yum install python3-pip python3-venv python3-develmacOS systems:
# Install Xcode Command Line Tools (for compilation)
xcode-select --install
# If using Homebrew Python (recommended)
brew install [email protected]
# If using pyenv (popular Python version manager)
# First ensure pyenv is properly installed and configured
pyenv install 3.12.0 # or latest 3.12.x version
pyenv global 3.12.0 # or pyenv local 3.12.0 for project-specific
# If using system Python, you may need to install certificates
/Applications/Python\ 3.12/Install\ Certificates.commandNote: These packages are required as the tool uses Python's built-in
venvmodule to create isolated environments for analysis.
The codeanalyzer provides a command-line interface for performing static analysis on Python projects.
codeanalyzer --input /path/to/python/projectTo view the available options and commands, run codeanalyzer --help. You should see output similar to the following:
❯ codeanalyzer --help
Usage: codeanalyzer [OPTIONS] COMMAND [ARGS]...
Static Analysis on Python source code using Jedi, CodeQL and Tree sitter.
╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --input -i PATH Path to the project root directory. [default: None] [required] │
│ --output -o PATH Output directory for artifacts. [default: None] │
│ --format -f [json|msgpack] Output format: json or msgpack. [default: json]. │
│ --analysis-level -a INTEGER 1: symbol table, 2: call graph. [default: 1] │
│ --codeql --no-codeql Enable CodeQL-based analysis. [default: no-codeql] │
│ --eager --lazy Enable eager or lazy analysis. Defaults to lazy. [default: lazy] │
│ --cache-dir -c PATH Directory to store analysis cache. [default: None] │
│ --clear-cache --keep-cache Clear cache after analysis. [default: clear-cache] │
│ -v INTEGER Increase verbosity: -v, -vv, -vvv [default: 0] │
│ --help Show this message and exit. │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯-
Basic analysis with symbol table:
codeanalyzer --input ./my-python-project
This will print the symbol table to stdout in JSON format to the standard output. If you want to save the output, you can use the
--outputoption.codeanalyzer --input ./my-python-project --output /path/to/analysis-results
Now, you can find the analysis results in
analysis.jsonin the specified directory. -
Toggle analysis levels with
--analysis-level:codeanalyzer --input ./my-python-project --analysis-level 1 # Symbol table onlyCall graph analysis can be enabled by setting the level to
2:codeanalyzer --input ./my-python-project --analysis-level 2 # Symbol table + Call graphNote: The
--analysis-level=2is not yet implemented in this version. -
Analysis with CodeQL enabled:
codeanalyzer --input ./my-python-project --codeql
This will perform CodeQL-based analysis in addition to the standard symbol table generation.
Note: Not yet fully implemented. Please refrain from using this option until further notice.
-
Eager analysis with custom cache directory:
codeanalyzer --input ./my-python-project --eager --cache-dir /path/to/custom-cache
This will rebuild the analysis cache at every run and store it in
/path/to/custom-cache/.codeanalyzer. The cache will be cleared by default after analysis unless you specify--keep-cache.If you provide --cache-dir, the cache will be stored in that directory. If not specified, it defaults to
.codeanalyzerin the current working directory ($PWD). -
Save output in msgpack format:
codeanalyzer --input ./my-python-project --output /path/to/analysis-results --format msgpack
By default, analysis results are printed to stdout in JSON format. When using the --output option, results are saved to analysis.json in the specified directory.
This project uses uv for dependency management during development.
-
Install uv
-
Clone the repository:
git clone https://github.com/codellm-devkit/codeanalyzer-python cd codeanalyzer-python -
Install dependencies using uv:
uv sync --all-groups
This will install all dependencies including development and test dependencies.
When developing, you can run the tool directly from source:
uv run codeanalyzer --input /path/to/python/projectuv run pytest --pspec -sThe project includes additional dependency groups for development:
- test: pytest and related testing tools
- dev: development tools like ipdb
Install all groups with:
uv sync --all-groups-
Clone the repository:
git clone https://github.com/codellm-devkit/codeanalyzer-python cd codeanalyzer-python -
Install dependencies using uv:
uv sync --all-groups
This will install all dependencies including development and test dependencies.
When developing, you can run the tool directly from source:
uv run codeanalyzer --input /path/to/python/projectuv run pytest --pspec -sThe project includes additional dependency groups for development:
- test: pytest and related testing tools
- dev: development tools like ipdb
Install all groups with:
uv sync --all-groups