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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vxcontrol%2Fpentagi | Trendshift

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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Pentagi
这是什么？PentAGI（渗透测试人工通用智能）是一套完全自主的 AI 智能体系统，可以替你完成复杂的渗透测试任务。
这是需要注册使用的云服务，还是我自己搭建运行的东西？既不是 SaaS 也不是桌面应用：它是一个完全自托管的平台，部署在你自己的基础设施上，你对部署和数据拥有完全的控制权。
我怎么把它跑起来？在环境配置中填好你选定的 LLM 服务商信息后，用 Docker Compose 部署即可，之后所有操作都可以在内置的网页界面里完成。
使用它需要 OpenAI 或 Anthropic 的 API 密钥吗？不强制使用任何一家：你可以从 10 多家支持的 LLM 服务商中配置一家——包括 OpenAI、Anthropic、Gemini、AWS Bedrock、Ollama、DeepSeek、GLM、Kimi、Qwen、MiniMax——或者任何自定义的 OpenAI 兼容接口。
它是按预先定义好的攻击流程走，还是每次测试都自己规划？它是即兴发挥而不是照本宣科：PentAGI 不是 BAS 或攻击仿真类产品，因此不支持预定义的攻击战役和攻击计划；由智能体自行编写攻击脚本的 BAS 式功能属于未来的开发计划。
这个工具只面向专业渗透测试人员吗？它是为需要强大且灵活的渗透测试方式的信息安全从业者、研究人员和爱好者设计的——所以认真的业余爱好者也完全可以用。
我测试产生的数据最后会到哪里去？数据留在你自己的基础设施上：所有命令和输出都保存在你自己的 PostgreSQL 数据库中（使用 pgvector 扩展）；除了你配置的 LLM 和搜索服务商之外，没有其他数据被描述为会离开你的部署环境。
它是真的会自己运行黑客工具，还是只是给建议？它会真的运行：它隔离的 Docker 环境里内置了 20 多款专业安全工具——包括 nmap、metasploit 和 sqlmap——由智能体自主决策并执行渗透测试的各个步骤。
它是单个 AI，还是一整个智能体团队？是一个团队：任务委派系统会把工作分派给专门负责研究、开发和基础设施的 AI 智能体，还提供可选的执行监控和智能任务规划，这也能让较小的模型有更好的表现。
一次测试跑完我能得到什么？一份详尽的漏洞报告，附带利用方法说明。流程报告可以在网页界面中查看、复制到剪贴板，或者下载为 Markdown 或 PDF——目前不支持 JSON 导出。
它会记住之前测试中有效的做法吗？会——智能记忆系统会长期保存研究结果和成功的方法供以后使用；还可以选配基于 Graphiti、运行在 Neo4j 上的知识图谱，用于追踪语义层面的关联关系。
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
31.4K
Updated
CommitOct 09Releasev2.2.0Oct 05
Age
1y
Commits · 90 Days