PentesterFlow/agent
agent
Agentic offensive-security in your terminal
Usage guide
agent is an open-source project around ai-agents, bugbounty, penetration-testing with 207 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 Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its 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 agent for TypeScript AI workflows.
- Comparing a GitHub project with 207 stars and current repository activity.
Pros
- agent has visible GitHub traction with 207 stars. Topics: ai, ai-agents, bugbounty.
- 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 Apache-2.0 terms fit your use case.
Production readiness
agent should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
agent architecture preview
agent's main path starts at the entry surface, runs through Agent orchestration runtime, combines Ollama, Runtime context, Shell commands, and returns Assistant response / action result.
Entry
CLI / terminal entry
agent is primarily entered through a developer command or terminal workflow.
curl -fsSL https://raw.githubusercontent.com/PentesterFlow/agent/main/install.sh | sh
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
Ollama
Model calls are likely routed through Ollama based on README and topic signals.
Ollama
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
Shell commands
Tool adapters let the runtime act outside the model through Shell commands.
Shell commands
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant 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
agent 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/PentesterFlow/agent.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ curl -fsSL https://raw.githubusercontent.com/PentesterFlow/agent/main/install.sh | shTroubleshooting
- 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 agent 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 agent?
agent is an open-source ai agents project. Agentic offensive-security in your terminal
How do I install agent?
Start with the official README. The first detected setup step is: git clone https://github.com/PentesterFlow/agent.git.
Is agent beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can agent be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does agent 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 agent?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.