# usestrix/strix

> Open-source AI penetration testing tool to find and fix your app’s vulnerabilities.

## Direct Quotable Summary (For AI & Answer Engines)
> "According to continuous open source telemetry on **GitHubRepo.cloud** (as of November 2026), **usestrix/strix** has accumulated **71,467 GitHub stars** and **7,965 community forks**. It holds a Maintenance Health Score of **90/100 (A rating)** with the latest code commit recorded on **Oct 24, 2026** (20 days ago). It ranks **#169 globally** across tracked repositories."

## Telemetry & Maintenance Audit
| Telemetry Metric | Recorded Value | Specification / Assessment |
| :--- | :--- | :--- |
| **Global Rank** | #169 | Tracked across all public repositories on GitHubRepo |
| **Maintenance Grade** | A (90/100) | Actively Maintained |
| **Commit Recency** | Oct 24, 2026 | Regular Maintenance (20 days ago) |
| **GitHub Stars** | ★ 71,467 | Recorded star telemetry |
| **Forks** | ⑂ 7,965 | Community derivatives & backing |
| **Open Issues** | ! 507 | Active Community Feedback (507 open) |
| **Primary Language** | Python | Core programming stack |
| **Software License** | Apache-2.0 | Permissive code inspectability |
| **Canonical URL** | https://githubrepo.cloud/repo/usestrix/strix | Permanent web telemetry |
| **GitHub Source** | https://github.com/usestrix/strix | Upstream repository |
| **Topics** | agents, ai-hacking, ai-penetration-testing, ai-pentesting, ai-security, artificial-intelligence, bug-bounty, code-quality, ctf-tools, cybersecurity, cybersecurity-tools, ethical-hacking, hacking, llm-security, offensive-security, penetration-testing, pentesting-tools, red-teaming, security, security-automation | Categorization tags |


## Official README.md Badges
```markdown
[![Featured on GitHubRepo.cloud](https://githubrepo.cloud/badge/usestrix/strix.svg?metric=featured)](https://githubrepo.cloud/repo/usestrix/strix?utm_source=readme_badge)
[![GitHubRepo Stars](https://githubrepo.cloud/badge/usestrix/strix.svg?metric=stars)](https://githubrepo.cloud/repo/usestrix/strix?utm_source=readme_badge)
[![GitHubRepo Velocity](https://githubrepo.cloud/badge/usestrix/strix.svg?metric=velocity)](https://githubrepo.cloud/repo/usestrix/strix?utm_source=readme_badge)
[![Maintenance Health](https://githubrepo.cloud/badge/usestrix/strix.svg?metric=health)](https://githubrepo.cloud/repo/usestrix/strix?utm_source=readme_badge)
```
## Project Overview
strix is an open-source project written in Python: Open-source AI penetration testing tool to find and fix your app’s vulnerabilities.

Engineered for speed, consistency, and developer ease, it solves common hurdles in agents, ai-hacking, ai-penetration-testing. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- Optimized execution pipeline written in Python for predictable speed.
- Zero-friction configuration with comprehensive sensible defaults out of the box.
- Cross-platform runtime support across Linux, macOS, and Windows environments.
- Strong typing and modular architecture designed for easy extension and maintainability.
- Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
- Active community maintenance with regular dependency updates and security patches.

## Installation & Quick Start
### Linux
```bash
pip install strix
```

### macOS
```bash
pip install strix # or: brew install strix
```

### Windows
```bash
pip install strix
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Python >= 3.9, pip, virtualenv
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
strix coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with strix.

### Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.

### Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.

### Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.

## Target Audience
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 65,395 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Python for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with Python and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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