KeygraphHQ/shannon
shannon
Shannon Lite is an autonomous, white-box AI pentester for web applications and APIs. It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.
Usage guide
shannon is an open-source project around penetration-testing, pentesting, security-audit with 42,787 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating shannon for TypeScript AI workflows.
- Comparing a GitHub project with 42,787 stars and current repository activity.
Pros
- shannon has visible GitHub traction with 42,787 stars. Topics: penetration-testing, pentesting, security-audit.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the AGPL-3.0 terms fit your use case.
Production readiness
shannon should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
AGPL-3.0 is reported by GitHub; review the repository license before redistribution or commercial use.
shannon architecture preview
shannon's main path starts at the entry surface, runs through Coding agent runtime, combines LLM / model client, Repository context, GitHub, and returns Code changes / developer feedback.
Entry
CLI / terminal entry
shannon is primarily entered through a developer command or terminal workflow.
npx @keygraph/shannon setup
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Repository context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Code changes / developer feedback
The final result is code edits, explanations, repository actions, or developer-facing feedback.
coding output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
shannon uses a Node.js-style toolchain. Confirm the Node version and package manager before installing.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/KeygraphHQ/shannon.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npx @keygraph/shannon setupAdoption guidance and sources
Practical use cases
Shannon Lite is an autonomous, white-box AI pentester for web applicat
This is one of the documented reasons to evaluate shannon before choosing a stack.
Focus area: penetration-testing
This is one of the documented reasons to evaluate shannon before choosing a stack.
AI Coding project comparison
Compare shannon with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official shannon setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented shannon example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is shannon?
shannon is an open-source ai coding project. Shannon Lite is an autonomous, white-box AI pentester for web applications and APIs. It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.
How do I install shannon?
Start with the official README. The first detected setup step is: git clone https://github.com/KeygraphHQ/shannon.git.
Is shannon beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can shannon be used commercially?
GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does shannon need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt shannon?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.