Fully autonomous AI Agents system capable of performing complex penetration testing tasks
Subagents14.9k stars2k forks● GoMITUpdated 2d ago
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Last scanned: 4/14/2026
Install in Claude Desktop
Method detected: Manual
{
"mcpServers": {
"pentagi": {
"command": "node",
"args": ["/path/to/pentagi/dist/index.js"]
}
}
}1. Copy the snippet above.
2. Paste into
~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows).3. Replace any
<placeholder> values with your API keys or paths.4. Restart Claude Desktop. The MCP server appears automatically.
💡 Clone https://github.com/vxcontrol/pentagi and follow its README for install instructions.
Use cases
🧠 AI / ML⚙️ DevOps🎨 Creative
About
Subagents overview
# PentAGI
<div align="center" style="font-size: 1.5em; margin: 20px 0;">
<strong>P</strong>enetration testing <strong>A</strong>rtificial <strong>G</strong>eneral <strong>I</strong>ntelligence
</div>
<br>
<div align="center">
> **Join the Community!** Connect with security researchers, AI enthusiasts, and fellow ethical hackers. Get support, share insights, and stay updated with the latest PentAGI developments.
[](https://discord.gg/2xrMh7qX6m)⠀[](https://t.me/+Ka9i6CNwe71hMWQy)
<a href="https://trendshift.io/repositories/15161" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15161" alt="vxcontrol%2Fpentagi | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Architecture](#architecture)
- [Agent Supervision](#advanced-agent-supervision)
- [Quick Start](#quick-start)
- [API Access](#api-access)
- [LLM Provider Configuration](#custom-llm-provider-configuration)
- [Ollama](#ollama-provider-configuration)
- [OpenAI](#openai-provider-configuration)
- [Anthropic](#anthropic-provider-configuration)
- [Google AI (Gemini)](#google-ai-gemini-provider-configuration)
- [AWS Bedrock](#aws-bedrock-provider-configuration)
- [DeepSeek](#deepseek-provider-configuration)
- [GLM](#glm-provider-configuration)
- [Kimi](#kimi-provider-configuration)
- [Qwen](#qwen-provider-configuration)
- [Advanced Setup](#advanced-setup)
- [Langfuse Integration](#langfuse-integration)
- [Monitoring and Observability](#monitoring-and-observability)
- [Knowledge Graph (Graphiti)](#knowledge-graph-integration-graphiti)
- [OAuth Integration](#github-and-google-oauth-integration)
- [Docker Image Configuration](#docker-image-configuration)
- [Development](#development)
- [Testing LLM Agents](#testing-llm-agents)
- [Embedding Configuration and Testing](#embedding-configuration-and-testing)
- [Function Testing with ftester](#function-testing-with-ftester)
- [Building](#building)
- [Credits](#credits)
- [License](#license)
## Overview
PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. The project is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests.
You can watch the video **PentAGI overview**:
[](https://youtu.be/R70x5Ddzs1o)
## Features
- Secure & Isolated. All operations are performed in a sandboxed Docker environment with complete isolation.
- Fully Autonomous. AI-powered agent that automatically determines and executes penetration testing steps with optional execution monitoring and intelligent task planning for enhanced reliability.
- Professional Pentesting Tools. Built-in suite of 20+ professional security tools including nmap, metasploit, sqlmap, and more.
- Smart Memory System. Long-term storage of research results and successful approaches for future use.
- Knowledge Graph Integration. Graphiti-powered knowledge graph using Neo4j for semantic relationship tracking and advanced context understanding.
- Web Intelligence. Built-in browser via [scraper](https://hub.docker.com/r/vxcontrol/scraper) for gathering latest information from web sources.
- External Search Systems. Integration with advanced search APIs including [Tavily](https://tavily.com), [Traversaal](https://traversaal.ai), [Perplexity](https://www.perplexity.ai), [DuckDuckGo](https://duckduckgo.com/), [Google Custom Search](https://programmablesearchengine.google.com/), [Sploitus Search](https://sploitus.com) and [Searxng](https://searxng.org) for comprehensive information gathering.
- Team of Specialists. Delegation system with specialized AI agents for research, development, and infrastructure tasks, enhanced with optional execution monitoring and intelligent task planning for optimal performance with smaller models.
- Comprehensive Monitoring. Detailed logging and integration with Grafana/Prometheus for real-time system observation.
- Detailed Reporting. Generation of thorough vulnerability reports with exploitation guides.
- Smart Container Management. Automatic Docker image selection based on specific task requirements.
- Modern Interface. Clean and intuitive web UI for system management and monitoring.
- Comprehensive APIs. Full-featured REST and GraphQL APIs with Bearer token authentication for automation and integration.
- Persistent Storage. All commands and outputs are stored in PostgreSQL with [pgvector](https://hub.docker.com/r/vxcontrol/pgvector) extension.
- Scalable Architecture. Microservices-based design supporting horizontal scaling.
- Self-Hosted Solution. Complete control over your deployment and data.
- Flexible Authentication. Support for 10+ LLM providers ([OpenAI](https://platform.openai.com/), [Anthropic](https://www.anthropic.com/), [Google AI/Gemini](https://ai.google.dev/), [AWS Bedrock](https://aws.amazon.com/bedrock/), [Ollama](https://ollama.com/), [DeepSeek](https://www.deepseek.com/en/), [GLM](https://z.ai/), [Kimi](https://platform.moonshot.ai/), [Qwen](https://www.alibabacloud.com/en/), Custom) plus aggregators ([OpenRouter](https://openrouter.ai/), [DeepInfra](https://deepinfra.com/)). For production local deployments, see our [vLLM + Qwen3.5-27B-FP8 guide](examples/guides/vllm-qwen35-27b-fp8.md).
- API Token Authentication. Secure Bearer token system for programmatic access to REST and GraphQL APIs.
- Quick Deployment. Easy setup through [Docker Compose](https://docs.docker.com/compose/) with comprehensive environment configuration.
## Architecture
### System Context
```mermaid
flowchart TB
classDef person fill:#08427B,stroke:#073B6F,color:#fff
classDef system fill:#1168BD,stroke:#0B4884,color:#fff
classDef external fill:#666666,stroke:#0B4884,color:#fff
pentester["👤 Security Engineer
(User of the system)"]
pentagi["✨ PentAGI
(Autonomous penetration testing system)"]
target["🎯 target-system
(System under test)"]
llm["🧠 llm-provider
(OpenAI/Anthropic/Ollama/Bedrock/Gemini/Custom)"]
search["🔍 search-systems
(Google/DuckDuckGo/Tavily/Traversaal/Perplexity/Sploitus/Searxng)"]
langfuse["📊 langfuse-ui
(LLM Observability Dashboard)"]
grafana["📈 grafana
(System Monitoring Dashboard)"]
pentester --> |Uses HTTPS| pentagi
pentester --> |Monitors AI HTTPS| langfuse
pentester --> |Monitors System HTTPS| grafana
pentagi --> |Tests Various protocols| target
pentagi --> |Queries HTTPS| llm
pentagi --> |Searches HTTPS| search
pentagi --> |Reports HTTPS| langfuse
pentagi --> |Reports HTTPS| grafana
class pentester person
class pentagi system
class target,llm,search,langfuse,grafana external
linkStyle default stroke:#ffffff,color:#ffffff
```
<details>
<summary><b>Container Architecture</b> (click to expand)</summary>
```mermaid
graph TB
subgraph Core Services
UI[Frontend UI<br/>React + TypeScript]
API[Backend API<br/>Go + GraphQL]
DB[(Vector Store<br/>PostgreSQL + pgvector)]
MQ[Task Queue<br/>Async Processing]
Agent[AI Agents<br/>Multi-Agent System]
end
subgraph Knowledge Graph
Graphiti[Graphiti<br/>Knowledge Graph API]
Neo4j[(Neo4j<br/>Graph Database)]
end
subgraph Monitoring
Grafana[Grafana<br/>Dashboards]
VictoriaMetrics[VictoriaMetrics<br/>Time-series DB]
Jaeger[Jaeger<br/>Distributed Tracing]
Loki[Loki<br/>Log Aggregation]
OTEL[OpenTelemetry<br/>Data Collection]
end
subgraph Analytics
Langfuse[Langfuse<br/>LLM Analytics]
ClickHouse[ClickHouse<br/>Analytics DB]
Redis[Redis<br/>Cache + Rate Limiter]
MinIO[MinIO<br/>S3 Storage]
end
subgraph Security Tools
Scraper[Web Scraper<br/>Isolated Browser]
PenTest[Security Tools<br/>20+ Pro Tools<br/>Sandboxed Execution]
end
UI --> |HTTP/WS| API
API --> |SQL| DB
API --> |Events| MQ
MQ --> |Tasks| Agent
Agent --> |Commands| PenTest
Agent --> |Queries| DB
Agent --> |Knowledge| Graphiti
Graphiti --> |Graph| Neo4j
API --> |Telemetry| OTEL
OTEL --> |Metrics| VictoriaMetrics
OTEL --> |Traces| Jaeger
OTEL --> |Logs| Loki
Grafana --> |Query| VictoriaMetrics
Grafana --> |Query| Jaeger
Grafana --> |Query| Loki
API --> |Analytics| Langfuse
Langfuse --> |Store| ClickHouse
Langfuse --> |Cache| Redis
Langfuse --> |Files| MinIO
classDef core fill:#f9f,stroke:#333,stroke-width:2px,color:#000
classDef knowledge fill:#ffa,stroke:#333,stroke-width:2px,color:#000
classDef monitoring fill:#bbf,stroke:#333,stroke-width:2px,color:#000
classDef analytics fill:#bfb,stroke:#333,stroke-width:2px,color:#000
classDef tools fill:#fbb,stroke:#333,stroke-width:2px,color:#000
class UI,API,DB,MQ,Agent core
class Graphiti,Neo4j knowledge
class Grafana,VictoriaMetrics,Jaeger,Loki,OTEL monitoring
class Langfuse,ClickHouse,Redis,MinIO analytics
class Scraper,PenTest tools
```
</details>
<details>
<summary><b>Entity Relationship</b> (click to expand)</summary>
```mermaid
erDiagram
Flow ||--o{ Task : contains
Task ||--o{ SubTask : contains
SubTask ||--o{ Action : contains
Action ||--o{ Artifact : produces
Action ||--o{ Memory : stores
Flow {
string id PK
string name "Flow name"
string description "Flow description"
string status "active/completed/failed"
json parameters "Flow parameters"
timestamp created_at
Topics
ai-agentsai-security-toolanthropicautonomous-agentsgolanggptgraphqlmulti-agent-systemoffensive-securityopen-sourceopenaipenetration-testingpenetration-testing-toolsreactsecurity-automationsecurity-testingsecurity-toolsself-hosted
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