PyFlow: Static Analysis for Python

PyFlow is a program analysis framework for Python. It combines program analysis infrastructure, experimental optimization passes, and security checking in a single research-oriented toolkit.

Current status: alpha. The project already contains substantial analysis and testing infrastructure, but APIs, pass behavior, and CLI details are still evolving.

If you use pyflow in your research or work, please cite the following:

@misc{pyflow2026,
  title         = {PyFlow: An Inter-procedural Static Analysis Framework for Python},
  author        = {Gu, Zinan and Yan, Haoxiang and Yao, Peisen},
  year          = {2026},
  eprint        = {2608.07026},
  archivePrefix = {arXiv},
  doi           = {10.48550/arXiv.2608.07026},
  url           = {https://arxiv.org/abs/2608.07026}
}

What PyFlow includes

• Intermediate representations: seven-instruction MIR, CFG, CDG, DDG, PDG, CPG, lowered data flow IR, and a shared store graph model
• Program analysis: call graph, IFDS, alias, IPA, CPA, shape, lifetime, and type-analysis infrastructure
• Optimization pipeline: modular passes such as simplify, method-call optimization, cloning, argument normalization, and load/store elimination
• Security checking: ast pattern, ast-dataflow, cpg, and ifds security analysis
• Supply-chain analysis: local SBOM generation, distribution integrity auditing, and dependency metadata extraction
• CLI tooling: commands for optimization, call graph generation, IR dumps, security, supply-chain, alias analysis, and concolic test-input generation

Examples and test inputs

• examples/: runnable examples of using PyFlow's Python APIs and extending analyses.
• tests/fixtures/: Python inputs for analysis and module-discovery tests. Small test-specific snippets also live alongside their suites.

Evaluation results

Security engines evaluated on the 240-file PySASTBench microbenchmark (960 runs):

.venv/bin/python evaluation/pysastbench/bench_micro.py --timeout 45 --workers 8

Engine	Precision	Recall	F1	Accuracy	Partial / 240
ast-scanner	0.636	0.350	0.452	0.575	0
ast-dataflow	0.857	0.450	0.590	0.688	238
cpg	0.828	0.683	0.749	0.771	240
ifds	0.837	0.858	0.848	0.846	0

Installation and Usage

Prerequisites

• Python 3.10 or newer
• Graphviz (for visualization features)

Install from source

git clone https://github.com/ZJU-PL/pyflow.git
cd pyflow
pip install -e .

For development, install the dev extras:

pip install -e ".[dev]"

The native MIR-based PyCG algorithm is included in the standard installation. If you want the optional upstream PyCG-backed call-graph algorithm, install:

pip install -e ".[dev,callgraph]"

Usage

# Dump IR for a specific function
pyflow ir input.py --dump-ast function_name

# Lower source to the seven-instruction MIR without executing the source
pyflow ir input.py --dump-mir --dump-format json --dump-output out/

# Generate a call graph
pyflow callgraph input.py

# Generate a native PyCG-style call graph from MIR
pyflow callgraph input.py --algorithm pycg-mir

# Generate a call graph with the optional upstream PyCG package
pyflow callgraph input.py --algorithm pycg

# Run security analysis (fast AST scan)
pyflow security input.py

# Run AST-based interprocedural taint dataflow
pyflow security input.py --engine ast-dataflow

# Run IFDS-backed interprocedural security analysis
pyflow security input.py --engine ifds --sources input --sinks eval

# Run CPG-based security analysis
pyflow security input.py --engine cpg --framework flask

# Run alias analysis (flow-sensitive heap or k-CFA pointer)
pyflow alias input.py --verbose

# Generate branch-covering integer inputs for a pure function named main
pyflow concolic input.py --inputs '[0]' --json

# Start the LSP server (Content-Length framed JSON-RPC)
pyflow lsp --root . --mode full

# Start the MCP server (newline-delimited JSON-RPC over stdio)
pyflow mcp --root . --mode full

# Run a one-shot semantic query
pyflow query . --get-callers package.module.function --pretty

# Generate a CycloneDX SBOM from local package metadata
pyflow supply-chain sbom package/

See CLI.md for the command reference, docs/source/ir/mir.rst for MIR, the native PyCG API, and implementation limits, docs/source/lsp.rst for LSP, MCP, and semantic-query integration, and docs/ for broader project documentation.

Running Tests

# Run the default unit-focused test suite
pytest

# Run integration tests explicitly
pytest -m integration tests/integration

# Run focused areas
pytest tests/ir
pytest tests/frontend
pytest tests/api
pytest tests/checker

Publishing

See RELEASING.md for versioning, validation, and publishing to PyPI through GitHub Actions Trusted Publishing.