Pentagi — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Pentagi. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 25, 2026 • Sentiment updated: Jun 16, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As The Pentagon pushes for battlefield AI, some military leaders urge caution, as seen on HackerNews

Pros & Cons

What People Love

Innovative features, GitHub community support

Common Complaints

Security risks, Technical issues

Biggest Positive: Innovative tool

Biggest Negative: Security risks

Why Pentagi Stands Out

PentAGI stands out from alternative penetration testing tools by leveraging cutting-edge artificial intelligence technologies and providing a fully autonomous AI agent. Its smart memory system and knowledge graph integration enable advanced context understanding and semantic relationship tracking. Additionally, PentAGI's flexible authentication and support for multiple LLM providers make it a valuable solution for security professionals and researchers.

Built With

Build an autonomous penetration testing system — PentAGI enables this by providing a fully autonomous AI agent that determines and executes penetration testing steps, Build a research agent that gathers information from web sources — PentAGI's web intelligence feature and integration with external search systems enable this, Build a customizable vulnerability reporting system — PentAGI's detailed reporting feature and API access allow for tailored reporting, Build a scalable security testing infrastructure — PentAGI's microservices-based design and support for horizontal scaling enable this, Build an AI-powered pentesting tool with a user-friendly interface — PentAGI's modern interface and comprehensive APIs enable easy management and automation

Getting Started

  1. Run `docker-compose up` to start the PentAGI system
  2. Configure the environment variables in the `.env` file to set up the database and API connections
  3. Run `pentagi init` to initialize the system and create the necessary databases and tables
  4. Configure the LLM provider and search systems in the `config.yaml` file
  5. Try running a penetration test using the `pentagi test` command to verify that the system is working correctly

About

Fully autonomous AI Agents system capable of performing complex penetration testing tasks

Official site: https://pentagi.com

Category & Tags

Category: multi-agent

Tags: ai-agents, ai-security-tool, anthropic, autonomous-agents, golang, gpt, graphql, multi-agent-system, offensive-security, open-source, openai, penetration-testing, penetration-testing-tools, react, security-automation, security-testing, security-tools, self-hosted

Market Context

Competing with OpenAI and Anthropic