pentagi README

The project's README from github.com/vxcontrol/pentagi. As one markdown file: README.md.

PentAGI

Penetration testing Artificial General Intelligence

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Table of Contents

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: PentAGI Overview Video

Features

Current Capability Boundaries

Architecture

System Context

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/Firecrawl/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
Container Architecture (click to expand) 
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
Entity Relationship (click to expand) 
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
        timestamp updated_at
    }

    Task {
        string id PK
        string flow_id FK
        string name "Task name"
        string description "Task description"
        string status "pending/running/done/failed"
        json result "Task results"
        timestamp created_at
        timestamp updated_at
    }

    SubTask {
        string id PK
        string task_id FK
        string name "Subtask name"
        string description "Subtask description"
        string status "queued/running/completed/failed"
        string agent_type "researcher/developer/executor"
        json context "Agent context"
        timestamp created_at
        timestamp updated_at
    }

    Action {
        string id PK
        string subtask_id FK
        string type "command/search/analyze/etc"
        string status "success/failure"
        json parameters "Action parameters"
        json result "Action results"
        timestamp created_at
    }

    Artifact {
        string id PK
        string action_id FK
        string type "file/report/log"
        string path "Storage path"
        json metadata "Additional info"
        timestamp created_at
    }

    Memory {
        string id PK
        string action_id FK
        string type "observation/conclusion"
        vector embedding "Vector representation"
        text content "Memory content"
        timestamp created_at
    }
Agent Interaction (click to expand) 
sequenceDiagram
    participant O as Orchestrator
    participant R as Researcher
    participant D as Developer
    participant E as Executor
    participant VS as Vector Store
    participant KB as Knowledge Base

    Note over O,KB: Flow Initialization
    O->>VS: Query similar tasks
    VS-->>O: Return experiences
    O->>KB: Load relevant knowledge
    KB-->>O: Return context

    Note over O,R: Research Phase
    O->>R: Analyze target
    R->>VS: Search similar cases
    VS-->>R: Return patterns
    R->>KB: Query vulnerabilities
    KB-->>R: Return known issues
    R->>VS: Store findings
    R-->>O: Research results

    Note over O,D: Planning Phase
    O->>D: Plan attack
    D->>VS: Query exploits
    VS-->>D: Return techniques
    D->>KB: Load tools info
    KB-->>D: Return capabilities
    D-->>O: Attack plan

    Note over O,E: Execution Phase
    O->>E: Execute plan
    E->>KB: Load tool guides
    KB-->>E: Return procedures
    E->>VS: Store results
    E-->>O: Execution status
Memory System (click to expand) 
graph TB
    subgraph "Long-term Memory"
        VS[(Vector Store<br/>Embeddings DB)]
        KB[Knowledge Base<br/>Domain Expertise]
        Tools[Tools Knowledge<br/>Usage Patterns]
    end

    subgraph "Working Memory"
        Context[Current Context<br/>Task State]
        Goals[Active Goals<br/>Objectives]
        State[System State<br/>Resources]
    end

    subgraph "Episodic Memory"
        Actions[Past Actions<br/>Commands History]
        Results[Action Results<br/>Outcomes]
        Patterns[Success Patterns<br/>Best Practices]
    end

    Context --> |Query| VS
    VS --> |Retrieve| Context

    Goals --> |Consult| KB
    KB --> |Guide| Goals

    State --> |Record| Actions
    Actions --> |Learn| Patterns
    Patterns --> |Store| VS

    Tools --> |Inform| State
    Results --> |Update| Tools

    VS --> |Enhance| KB
    KB --> |Index| VS

    classDef ltm fill:#f9f,stroke:#333,stroke-width:2px,color:#000
    classDef wm fill:#bbf,stroke:#333,stroke-width:2px,color:#000
    classDef em fill:#bfb,stroke:#333,stroke-width:2px,color:#000

    class VS,KB,Tools ltm
    class Context,Goals,State wm
    class Actions,Results,Patterns em
Chain Summarization (click to expand)

The chain summarization system manages conversation context growth by selectively summarizing older messages. This is critical for preventing token limits from being exceeded while maintaining conversation coherence.

flowchart TD
    A[Input Chain] --> B{Needs Summarization?}
    B -->|No| C[Return Original Chain]
    B -->|Yes| D[Convert to ChainAST]
    D --> E[Apply Section Summarization]
    E --> F[Process Oversized Pairs]
    F --> G[Manage Last Section Size]
    G --> H[Apply QA Summarization]
    H --> I[Rebuild Chain with Summaries]
    I --> J{Is New Chain Smaller?}
    J -->|Yes| K[Return Optimized Chain]
    J -->|No| C

    classDef process fill:#bbf,stroke:#333,stroke-width:2px,color:#000
    classDef decision fill:#bfb,stroke:#333,stroke-width:2px,color:#000
    classDef output fill:#fbb,stroke:#333,stroke-width:2px,color:#000

    class A,D,E,F,G,H,I process
    class B,J decision
    class C,K output

The algorithm operates on a structured representation of conversation chains (ChainAST) that preserves message types including tool calls and their responses. All summarization operations maintain critical conversation flow while reducing context size.

Global Summarizer Configuration Options

Parameter Environment Variable Default Description
Preserve Last SUMMARIZER_PRESERVE_LAST true Whether to keep all messages in the last section intact
Use QA Pairs SUMMARIZER_USE_QA true Whether to use QA pair summarization strategy
Summarize Human in QA SUMMARIZER_SUM_MSG_HUMAN_IN_QA false Whether to summarize human messages in QA pairs
Last Section Size SUMMARIZER_LAST_SEC_BYTES 51200 Maximum byte size for last section (50KB)
Max Body Pair Size SUMMARIZER_MAX_BP_BYTES 16384 Maximum byte size for a single body pair (16KB)
Max QA Sections SUMMARIZER_MAX_QA_SECTIONS 10 Maximum QA pair sections to preserve
Max QA Size SUMMARIZER_MAX_QA_BYTES 65536 Maximum byte size for QA pair sections (64KB)
Keep QA Sections SUMMARIZER_KEEP_QA_SECTIONS 1 Number of recent QA sections to keep without summarization

Assistant Summarizer Configuration Options

Assistant instances can use customized summarization settings to fine-tune context management behavior:

Parameter Environment Variable Default Description
Preserve Last ASSISTANT_SUMMARIZER_PRESERVE_LAST true Whether to preserve all messages in the assistant's last section
Last Section Size ASSISTANT_SUMMARIZER_LAST_SEC_BYTES 76800 Maximum byte size for assistant's last section (75KB)
Max Body Pair Size ASSISTANT_SUMMARIZER_MAX_BP_BYTES 16384 Maximum byte size for a single body pair in assistant context (16KB)
Max QA Sections ASSISTANT_SUMMARIZER_MAX_QA_SECTIONS 7 Maximum QA sections to preserve in assistant context
Max QA Size ASSISTANT_SUMMARIZER_MAX_QA_BYTES 76800 Maximum byte size for assistant's QA sections (75KB)
Keep QA Sections ASSISTANT_SUMMARIZER_KEEP_QA_SECTIONS 3 Number of recent QA sections to preserve without summarization

The assistant summarizer configuration provides more memory for context retention compared to the global settings, preserving more recent conversation history while still ensuring efficient token usage.

Summarizer Environment Configuration

# Default values for global summarizer logic
SUMMARIZER_PRESERVE_LAST=true
SUMMARIZER_USE_QA=true
SUMMARIZER_SUM_MSG_HUMAN_IN_QA=false
SUMMARIZER_LAST_SEC_BYTES=51200
SUMMARIZER_MAX_BP_BYTES=16384
SUMMARIZER_MAX_QA_SECTIONS=10
SUMMARIZER_MAX_QA_BYTES=65536
SUMMARIZER_KEEP_QA_SECTIONS=1

# Default values for assistant summarizer logic
ASSISTANT_SUMMARIZER_PRESERVE_LAST=true
ASSISTANT_SUMMARIZER_LAST_SEC_BYTES=76800
ASSISTANT_SUMMARIZER_MAX_BP_BYTES=16384
ASSISTANT_SUMMARIZER_MAX_QA_SECTIONS=7
ASSISTANT_SUMMARIZER_MAX_QA_BYTES=76800
ASSISTANT_SUMMARIZER_KEEP_QA_SECTIONS=3

Advanced Agent Supervision (click to expand)

PentAGI incl

pentagi (vxcontrol/pentagi)

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30.8K ·GitHub
Pentagi
What is this?PentAGI (Penetration testing Artificial General Intelligence) is a fully autonomous AI agent system that performs complex penetration testing tasks for you.
Is this a cloud service I sign up for, or something I run myself?Neither SaaS nor a desktop app: it is a fully self-hosted platform you deploy on your own infrastructure, with complete control over your deployment and data.
How do I get it running?Deploy it with Docker Compose after filling in the environment configuration for your chosen LLM provider, then manage everything from the built-in web UI.
Do I need an OpenAI or Anthropic API key to use it?No single vendor is mandatory: you configure one of 10+ supported LLM providers — OpenAI, Anthropic, Gemini, AWS Bedrock, Ollama, DeepSeek, GLM, Kimi, Qwen, MiniMax — or any custom OpenAI-compatible endpoint.
Does it follow predefined attack campaigns, or does it plan each test itself?It improvises rather than follows scripts: PentAGI is not a BAS or adversary-emulation product, so predefined campaigns and attack plans are not supported, and BAS-style agent-authored attack scripts are future work.
Is this aimed only at professional pentesters?It is designed for information security professionals, researchers, and enthusiasts who need a powerful, flexible way to run penetration tests — so dedicated hobbyists are welcome too.
Where does the data from my tests end up?It stays on your infrastructure: all commands and outputs are kept in your own PostgreSQL database with the pgvector extension; nothing else is described as leaving your deployment besides the LLM and search providers you configure.
Does it actually run hacking tools itself, or just suggest them?It runs them: a built-in suite of 20+ professional security tools — including nmap, metasploit and sqlmap — is available inside its isolated Docker environment, and the agent autonomously decides and executes the pentest steps.
Is it a single AI or a whole team of agents?A team: a delegation system hands tasks to specialized AI agents for research, development and infrastructure work, with optional execution monitoring and intelligent task planning that also helps smaller models perform.
What do I get at the end of a run?A thorough vulnerability report with exploitation guides. The flow report can be viewed in the web UI, copied to the clipboard, or downloaded as Markdown or PDF — JSON export is not supported today.
Does it remember what worked in previous tests?Yes — a smart memory system keeps long-term storage of research results and successful approaches for future use, and an optional Graphiti-powered knowledge graph on Neo4j adds semantic relationship tracking.
What are similar projects?redamon, ARTEX, Decepticon, Pentest-Swarm-AI, WeKnora, MemoryBear, evenfire, ruflo
Which topics is it in?penetration-testing-tools, ai-security-tool, security-testing, security-automation, neo4j, grafana, prometheus, offensive-security, graphql, knowledge-graph, vllm, penetration-testing
Creator
vxcontrol
Size
59.3 MB
Stars
30.8K
Updated
CommitOct 09Releasev2.2.0Oct 05
Age
1y
Commits · 90 Days