PentAGI

Penetration testing Artificial General Intelligence

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📖 Table of Contents

Overview
Features
Quick Start
Advanced Setup
Development
Testing LLM Agents
Embedding Configuration and Testing
Function Testing with ftester
Building
Credits
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:

✨ 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.
🔬 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.
🔍 Web Intelligence. Built-in browser via scraper for gathering latest information from web sources.
🔎 External Search Systems. Integration with advanced search APIs including Tavily, Traversaal, Perplexity, DuckDuckGo, Google Custom Search, and Searxng for comprehensive information gathering.
👥 Team of Specialists. Delegation system with specialized AI agents for research, development, and infrastructure tasks.
📊 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.
🔌 API Integration. Support for REST and GraphQL APIs for seamless external system integration.
💾 Persistent Storage. All commands and outputs are stored in PostgreSQL with pgvector extension.
🎯 Scalable Architecture. Microservices-based design supporting horizontal scaling.
🏠 Self-Hosted Solution. Complete control over your deployment and data.
🔑 Flexible Authentication. Support for various LLM providers (OpenAI, Anthropic, Ollama, AWS Bedrock, Google AI/Gemini, Deep Infra, OpenRouter, DeepSeek) and custom configurations.
⚡ Quick Deployment. Easy setup through Docker Compose with comprehensive environment configuration.

🏗️ 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/Traversaal/Perplexity/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 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| Tools
    Agent --> |Queries| DB

    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 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 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

The architecture of PentAGI is designed to be modular, scalable, and secure. Here are the key components:

Core Services
- Frontend UI: React-based web interface with TypeScript for type safety
- Backend API: Go-based REST and GraphQL APIs for flexible integration
- Vector Store: PostgreSQL with pgvector for semantic search and memory storage
- Task Queue: Async task processing system for reliable operation
- AI Agent: Multi-agent system with specialized roles for efficient testing
Monitoring Stack
- OpenTelemetry: Unified observability data collection and correlation
- Grafana: Real-time visualization and alerting dashboards
- VictoriaMetrics: High-performance time-series metrics storage
- Jaeger: End-to-end distributed tracing for debugging
- Loki: Scalable log aggregation and analysis
Analytics Platform
- Langfuse: Advanced LLM observability and performance analytics
- ClickHouse: Column-oriented analytics data warehouse
- Redis: High-speed caching and rate limiting
- MinIO: S3-compatible object storage for artifacts
Security Tools
- Web Scraper: Isolated browser environment for safe web interaction
- Pentesting Tools: Comprehensive suite of 20+ professional security tools
- Sandboxed Execution: All operations run in isolated containers
Memory Systems
- Long-term Memory: Persistent storage of knowledge and experiences
- Working Memory: Active context and goals for current operations
- Episodic Memory: Historical actions and success patterns
- Knowledge Base: Structured domain expertise and tool capabilities
- Context Management: Intelligently manages growing LLM context windows using chain summarization

The system uses Docker containers for isolation and easy deployment, with separate networks for core services, monitoring, and analytics to ensure proper security boundaries. Each component is designed to scale horizontally and can be configured for high availability in production environments.

🚀 Quick Start

System Requirements

Docker and Docker Compose
Minimum 4GB RAM
10GB free disk space
Internet access for downloading images and updates

Using Installer (Recommended)

PentAGI provides an interactive installer with a terminal-based UI for streamlined configuration and deployment. The installer guides you through system checks, LLM provider setup, search engine configuration, and security hardening.

Supported Platforms:

Linux: amd64 (download) | arm64 (download)
Windows: amd64
macOS: amd64 (Intel) | arm64 (M-series)

Quick Installation (Linux amd64):

# Create installation directory
mkdir -p pentagi && cd pentagi

# Download installer
wget -O installer.zip https://pentagi.com/downloads/linux/amd64/installer-latest.zip

# Extract
unzip installer.zip

# Run interactive installer
./installer

The installer will:

System Checks: Verify Docker, network connectivity, and system requirements
Environment Setup: Create and configure .env file with optimal defaults
Provider Configuration: Set up LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Ollama, Custom)
Search Engines: Configure DuckDuckGo, Google, Tavily, Traversaal, Perplexity, Searxng
Security Hardening: Generate secure credentials and configure SSL certificates
Deployment: Start PentAGI with docker-compose

For Production & Enhanced Security:

For production deployments or security-sensitive environments, we strongly recommend using a distributed two-node architecture where worker operations are isolated on a separate server. This prevents untrusted code execution and network access issues on your main system.

👉 See detailed guide: Worker Node Setup

The two-node setup provides:

Isolated Execution: Worker containers run on dedicated hardware
Network Isolation: Separate network boundaries for penetration testing
Security Boundaries: Docker-in-Docker with TLS authentication
OOB Attack Support: Dedicated port ranges for out-of-band techniques

Manual Installation

Create a working directory or clone the repository:

mkdir pentagi && cd pentagi

Copy .env.example to .env or download it:

curl -o .env https://raw.githubusercontent.com/vxcontrol/pentagi/master/.env.example

Fill in the required API keys in .env file.

# Required: At least one of these LLM providers
OPEN_AI_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
GEMINI_API_KEY=your_gemini_key

# Optional: AWS Bedrock provider (enterprise-grade models)
BEDROCK_REGION=us-east-1
BEDROCK_ACCESS_KEY_ID=your_aws_access_key
BEDROCK_SECRET_ACCESS_KEY=your_aws_secret_key

# Optional: Local LLM provider (zero-cost inference)
OLLAMA_SERVER_URL=http://localhost:11434

# Optional: Additional search capabilities
DUCKDUCKGO_ENABLED=true
GOOGLE_API_KEY=your_google_key
GOOGLE_CX_KEY=your_google_cx
TAVILY_API_KEY=your_tavily_key
TRAVERSAAL_API_KEY=your_traversaal_key
PERPLEXITY_API_KEY=your_perplexity_key
PERPLEXITY_MODEL=sonar-pro
PERPLEXITY_CONTEXT_SIZE=medium

# Searxng meta search engine (aggregates results from multiple sources)
SEARXNG_URL=http://your-searxng-instance:8080
SEARXNG_CATEGORIES=general
SEARXNG_LANGUAGE=
SEARXNG_SAFESEARCH=0
SEARXNG_TIME_RANGE=

# Assistant configuration
ASSISTANT_USE_AGENTS=false         # Default value for agent usage when creating new assistants

Change all security related environment variables in .env file to improve security.

Security related environment variables

Main Security Settings

COOKIE_SIGNING_SALT - Salt for cookie signing, change to random value
PUBLIC_URL - Public URL of your server (eg. https://pentagi.example.com)
SERVER_SSL_CRT and SERVER_SSL_KEY - Custom paths to your existing SSL certificate and key for HTTPS (these paths should be used in the docker-compose.yml file to mount as volumes)

Scraper Access

SCRAPER_PUBLIC_URL - Public URL for scraper if you want to use different scraper server for public URLs
SCRAPER_PRIVATE_URL - Private URL for scraper (local scraper server in docker-compose.yml file to access it to local URLs)

Access Credentials

PENTAGI_POSTGRES_USER and PENTAGI_POSTGRES_PASSWORD - PostgreSQL credentials

Remove all inline comments from .env file if you want to use it in VSCode or other IDEs as a envFile option:

perl -i -pe 's/\s+#.*$//' .env

Run the PentAGI stack:

curl -O https://raw.githubusercontent.com/vxcontrol/pentagi/master/docker-compose.yml
docker compose up -d

Visit localhost:8443 to access PentAGI Web UI (default is [email protected] / admin)

Note

If you caught an error about pentagi-network or observability-network or langfuse-network you need to run docker-compose.yml firstly to create these networks and after that run docker-compose-langfuse.yml and docker-compose-observability.yml to use Langfuse and Observability services.

You have to set at least one Language Model provider (OpenAI, Anthropic, Gemini, AWS Bedrock, or Ollama) to use PentAGI. AWS Bedrock provides enterprise-grade access to multiple foundation models from leading AI companies, while Ollama provides zero-cost local inference if you have sufficient computational resources. Additional API keys for search engines are optional but recommended for better results.

LLM_SERVER_* environment variables are experimental feature and will be changed in the future. Right now you can use them to specify custom LLM server URL and one model for all agent types.

PROXY_URL is a global proxy URL for all LLM providers and external search systems. You can use it for isolation from external networks.

The docker-compose.yml file runs the PentAGI service as root user because it needs access to docker.sock for container management. If you're using TCP/IP network connection to Docker instead of socket file, you can remove root privileges and use the default pentagi user for better security.

Assistant Configuration

PentAGI allows you to configure default behavior for assistants:

Variable	Default	Description
`ASSISTANT_USE_AGENTS`	`false`	Controls the default value for agent usage when creating new assistants

The ASSISTANT_USE_AGENTS setting affects the initial state of the "Use Agents" toggle when creating a new assistant in the UI:

false (default): New assistants are created with agent delegation disabled by default
true: New assistants are created with agent delegation enabled by default

Note that users can always override this setting by toggling the "Use Agents" button in the UI when creating or editing an assistant. This environment variable only controls the initial default state.

Custom LLM Provider Configuration

When using custom LLM providers with the LLM_SERVER_* variables, you can fine-tune the reasoning format used in requests:

Variable	Default	Description
`LLM_SERVER_URL`		Base URL for the custom LLM API endpoint
`LLM_SERVER_KEY`		API key for the custom LLM provider
`LLM_SERVER_MODEL`		Default model to use (can be overridden in provider config)
`LLM_SERVER_CONFIG_PATH`		Path to the YAML configuration file for agent-specific models
`LLM_SERVER_LEGACY_REASONING`	`false`	Controls reasoning format in API requests

The LLM_SERVER_LEGACY_REASONING setting affects how reasoning parameters are sent to the LLM:

false (default): Uses modern format where reasoning is sent as a structured object with max_tokens parameter
true: Uses legacy format with string-based reasoning_effort parameter

This setting is important when working with different LLM providers as they may expect different reasoning formats in their API requests. If you encounter reasoning-related errors with custom providers, try changing this setting.

Local LLM Provider Configuration

PentAGI supports Ollama for local LLM inference, providing zero-cost operation and enhanced privacy:

Variable	Default	Description
`OLLAMA_SERVER_URL`		URL of your Ollama server
`OLLAMA_SERVER_CONFIG_PATH`		Path to custom agent configuration file

Configuration examples:

# Basic Ollama setup
OLLAMA_SERVER_URL=http://localhost:11434

# Remote Ollama server
OLLAMA_SERVER_URL=http://ollama-server:11434

# Custom configuration with agent-specific models
OLLAMA_SERVER_CONFIG_PATH=/path/to/ollama-config.yml

# Default configuration file inside the docker container (just for example)
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama-llama318b.provider.yml

The system automatically discovers available models from your Ollama server and provides zero-cost inference for penetration testing workflows.

Creating Custom Ollama Models with Extended Context

PentAGI requires models with larger context windows than the default Ollama configurations. You need to create custom models with increased num_ctx parameter through Modelfiles. While typical agent workflows consume around 64K tokens, PentAGI uses 110K context size for safety margin and handling complex penetration testing scenarios.

Important: The num_ctx parameter can only be set during model creation via Modelfile - it cannot be changed after model creation or overridden at runtime.

Example: Qwen3 32B FP16 with Extended Context

Create a Modelfile named Modelfile_qwen3_32b_fp16_tc:

FROM qwen3:32b-fp16
PARAMETER num_ctx 110000
PARAMETER temperature 0.3
PARAMETER top_p 0.8
PARAMETER min_p 0.0
PARAMETER top_k 20
PARAMETER repeat_penalty 1.1

Build the custom model:

ollama create qwen3:32b-fp16-tc -f Modelfile_qwen3_32b_fp16_tc

Example: QwQ 32B FP16 with Extended Context

Create a Modelfile named Modelfile_qwq_32b_fp16_tc:

FROM qwq:32b-fp16
PARAMETER num_ctx 110000
PARAMETER temperature 0.2
PARAMETER top_p 0.7
PARAMETER min_p 0.0
PARAMETER top_k 40
PARAMETER repeat_penalty 1.2

Build the custom model:

ollama create qwq:32b-fp16-tc -f Modelfile_qwq_32b_fp16_tc

Note: The QwQ 32B FP16 model requires approximately 71.3 GB VRAM for inference. Ensure your system has sufficient GPU memory before attempting to use this model.

These custom models are referenced in the pre-built provider configuration files (ollama-qwen332b-fp16-tc.provider.yml and ollama-qwq32b-fp16-tc.provider.yml) that are included in the Docker image at /opt/pentagi/conf/.

OpenAI Provider Configuration

PentAGI supports OpenAI's advanced language models, including the latest reasoning-capable o-series models designed for complex analytical tasks:

Variable	Default	Description
`OPEN_AI_KEY`		API key for OpenAI services
`OPEN_AI_SERVER_URL`	`https://api.openai.com/v1`	OpenAI API endpoint

Configuration examples:

# Basic OpenAI setup
OPEN_AI_KEY=your_openai_api_key
OPEN_AI_SERVER_URL=https://api.openai.com/v1

# Using with proxy for enhanced security
OPEN_AI_KEY=your_openai_api_key
PROXY_URL=http://your-proxy:8080

The OpenAI provider offers cutting-edge capabilities including:

Reasoning Models: Advanced o-series models (o1, o3, o4-mini) with step-by-step analytical thinking
Latest GPT-4.1 Series: Flagship models optimized for complex security research and exploit development
Cost-Effective Options: From nano models for high-volume scanning to powerful reasoning models for deep analysis
Versatile Performance: Fast, intelligent models perfect for multi-step security analysis and penetration testing
Proven Reliability: Industry-leading models with consistent performance across diverse security scenarios

The system automatically selects appropriate OpenAI models based on task complexity, optimizing for both performance and cost-effectiveness.

Anthropic Provider Configuration

PentAGI integrates with Anthropic's Claude models, known for their exceptional safety, reasoning capabilities, and sophisticated understanding of complex security contexts:

Variable	Default	Description
`ANTHROPIC_API_KEY`		API key for Anthropic services
`ANTHROPIC_SERVER_URL`	`https://api.anthropic.com/v1`	Anthropic API endpoint

Configuration examples:

# Basic Anthropic setup
ANTHROPIC_API_KEY=your_anthropic_api_key
ANTHROPIC_SERVER_URL=https://api.anthropic.com/v1

# Using with proxy for secure environments
ANTHROPIC_API_KEY=your_anthropic_api_key
PROXY_URL=http://your-proxy:8080

The Anthropic provider delivers superior capabilities including:

Advanced Reasoning: Claude 4 series with exceptional reasoning for sophisticated penetration testing
Extended Thinking: Claude 3.7 with step-by-step thinking capabilities for methodical security research
High-Speed Performance: Claude 3.5 Haiku for blazing-fast vulnerability scans and real-time monitoring
Comprehensive Analysis: Claude Sonnet models for complex security analysis and threat hunting
Safety-First Design: Built-in safety mechanisms ensuring responsible security testing practices

The system leverages Claude's advanced understanding of security contexts to provide thorough and responsible penetration testing guidance.

Google AI (Gemini) Provider Configuration

PentAGI supports Google's Gemini models through the Google AI API, offering state-of-the-art reasoning capabilities and multimodal features:

Variable	Default	Description
`GEMINI_API_KEY`		API key for Google AI services
`GEMINI_SERVER_URL`	`https://generativelanguage.googleapis.com`	Google AI API endpoint

Configuration examples:

# Basic Gemini setup
GEMINI_API_KEY=your_gemini_api_key
GEMINI_SERVER_URL=https://generativelanguage.googleapis.com

# Using with proxy
GEMINI_API_KEY=your_gemini_api_key
PROXY_URL=http://your-proxy:8080

The Gemini provider offers advanced features including:

Thinking Capabilities: Advanced reasoning models (Gemini 2.5 series) with step-by-step analysis
Multimodal Support: Text and image processing for comprehensive security assessments
Large Context Windows: Up to 2M tokens for analyzing extensive codebases and documentation
Cost-Effective Options: From high-performance pro models to economical flash variants
Security-Focused Models: Specialized configurations optimized for penetration testing workflows

The system automatically selects appropriate Gemini models based on agent requirements, balancing performance, capabilities, and cost-effectiveness.

AWS Bedrock Provider Configuration

PentAGI integrates with Amazon Bedrock, offering access to a wide range of foundation models from leading AI companies including Anthropic, AI21, Cohere, Meta, and Amazon's own models:

Variable	Default	Description
`BEDROCK_REGION`	`us-east-1`	AWS region for Bedrock service
`BEDROCK_ACCESS_KEY_ID`		AWS access key ID for authentication
`BEDROCK_SECRET_ACCESS_KEY`		AWS secret access key for authentication
`BEDROCK_SERVER_URL`		Optional custom Bedrock endpoint URL

Configuration examples:

# Basic AWS Bedrock setup with credentials
BEDROCK_REGION=us-east-1
BEDROCK_ACCESS_KEY_ID=your_aws_access_key
BEDROCK_SECRET_ACCESS_KEY=your_aws_secret_key

# Using with proxy for enhanced security
BEDROCK_REGION=us-east-1
BEDROCK_ACCESS_KEY_ID=your_aws_access_key
BEDROCK_SECRET_ACCESS_KEY=your_aws_secret_key
PROXY_URL=http://your-proxy:8080

# Using custom endpoint (for VPC endpoints or testing)
BEDROCK_REGION=us-east-1
BEDROCK_ACCESS_KEY_ID=your_aws_access_key
BEDROCK_SECRET_ACCESS_KEY=your_aws_secret_key
BEDROCK_SERVER_URL=https://bedrock-runtime.us-east-1.amazonaws.com

Important

AWS Bedrock Rate Limits Warning

The default PentAGI configuration for AWS Bedrock uses two primary models:

us.anthropic.claude-sonnet-4-20250514-v1:0 (for most agents) - 2 requests per minute for new AWS accounts
us.anthropic.claude-3-5-haiku-20241022-v1:0 (for simple tasks) - 20 requests per minute for new AWS accounts

These default rate limits are extremely restrictive for comfortable penetration testing scenarios and will significantly impact your workflow. We strongly recommend:

Request quota increases for your AWS Bedrock models through the AWS Service Quotas console
Use provisioned throughput models with hourly billing for higher throughput requirements
Switch to alternative models with higher default quotas (e.g., Amazon Nova series, Meta Llama models)
Consider using a different LLM provider (OpenAI, Anthropic, Gemini) if you need immediate high-throughput access

Without adequate rate limits, you may experience frequent delays, timeouts, and degraded testing performance.

The AWS Bedrock provider delivers comprehensive capabilities including:

Multi-Provider Access: Access to models from Anthropic (Claude), AI21 (Jamba), Cohere (Command), Meta (Llama), Amazon (Nova, Titan), and DeepSeek (R1) through a single interface
Advanced Reasoning: Support for Claude 4 and other reasoning-capable models with step-by-step thinking
Multimodal Models: Amazon Nova series supporting text, image, and video processing for comprehensive security analysis
Enterprise Security: AWS-native security controls, VPC integration, and compliance certifications
Cost Optimization: Wide range of model sizes and capabilities for cost-effective penetration testing
Regional Availability: Deploy models in your preferred AWS region for data residency and performance
High Performance: Low-latency inference through AWS's global infrastructure

The system automatically selects appropriate Bedrock models based on task complexity and requirements, leveraging the full spectrum of available foundation models for optimal security testing results.

Warning

Converse API Requirements

PentAGI uses the Amazon Bedrock Converse API for model interactions, which requires models to support the following features:

✅ Converse - Basic conversation API support
✅ ConverseStream - Streaming response support
✅ Tool use - Function calling capabilities for penetration testing tools
✅ Streaming tool use - Real-time tool execution feedback

Before selecting models, verify their feature support at: Supported models and model features

⚠️ Important: Some models like AI21 Jurassic-2 and Cohere Command (Text) have limited chat support and may not work properly with PentAGI's multi-turn conversation workflows.

Note: AWS credentials can also be provided through IAM roles, environment variables, or AWS credential files following standard AWS SDK authentication patterns. Ensure your AWS account has appropriate permissions for Amazon Bedrock service access.

For advanced configuration options and detailed setup instructions, please visit our documentation.

🔧 Advanced Setup

Langfuse Integration

Langfuse provides advanced capabilities for monitoring and analyzing AI agent operations.

Configure Langfuse environment variables in existing .env file.

Langfuse valuable environment variables

Database Credentials

LANGFUSE_POSTGRES_USER and LANGFUSE_POSTGRES_PASSWORD - Langfuse PostgreSQL credentials
LANGFUSE_CLICKHOUSE_USER and LANGFUSE_CLICKHOUSE_PASSWORD - ClickHouse credentials
LANGFUSE_REDIS_AUTH - Redis password

Encryption and Security Keys

LANGFUSE_SALT - Salt for hashing in Langfuse Web UI
LANGFUSE_ENCRYPTION_KEY - Encryption key (32 bytes in hex)
LANGFUSE_NEXTAUTH_SECRET - Secret key for NextAuth

Admin Credentials

LANGFUSE_INIT_USER_EMAIL - Admin email
LANGFUSE_INIT_USER_PASSWORD - Admin password
LANGFUSE_INIT_USER_NAME - Admin username

API Keys and Tokens

LANGFUSE_INIT_PROJECT_PUBLIC_KEY - Project public key (used from PentAGI side too)
LANGFUSE_INIT_PROJECT_SECRET_KEY - Project secret key (used from PentAGI side too)

S3 Storage

LANGFUSE_S3_ACCESS_KEY_ID - S3 access key ID
LANGFUSE_S3_SECRET_ACCESS_KEY - S3 secret access key

Enable integration with Langfuse for PentAGI service in .env file.

LANGFUSE_BASE_URL=http://langfuse-web:3000
LANGFUSE_PROJECT_ID= # default: value from ${LANGFUSE_INIT_PROJECT_ID}
LANGFUSE_PUBLIC_KEY= # default: value from ${LANGFUSE_INIT_PROJECT_PUBLIC_KEY}
LANGFUSE_SECRET_KEY= # default: value from ${LANGFUSE_INIT_PROJECT_SECRET_KEY}

Run the Langfuse stack:

curl -O https://raw.githubusercontent.com/vxcontrol/pentagi/master/docker-compose-langfuse.yml
docker compose -f docker-compose.yml -f docker-compose-langfuse.yml up -d

Visit localhost:4000 to access Langfuse Web UI with credentials from .env file:

LANGFUSE_INIT_USER_EMAIL - Admin email
LANGFUSE_INIT_USER_PASSWORD - Admin password

Monitoring and Observability

For detailed system operation tracking, integration with monitoring tools is available.

Enable integration with OpenTelemetry and all observability services for PentAGI in .env file.

OTEL_HOST=otelcol:8148

Run the observability stack:

curl -O https://raw.githubusercontent.com/vxcontrol/pentagi/master/docker-compose-observability.yml
docker compose -f docker-compose.yml -f docker-compose-observability.yml up -d

Visit localhost:3000 to access Grafana Web UI.

Note

If you want to use Observability stack with Langfuse, you need to enable integration in .env file to set LANGFUSE_OTEL_EXPORTER_OTLP_ENDPOINT to http://otelcol:4318.

And you need to run both stacks docker compose -f docker-compose.yml -f docker-compose-langfuse.yml -f docker-compose-observability.yml up -d to have all services running.

Also you can register aliases for these commands in your shell to run it faster:

alias pentagi="docker compose -f docker-compose.yml -f docker-compose-langfuse.yml -f docker-compose-observability.yml"
alias pentagi-up="docker compose -f docker-compose.yml -f docker-compose-langfuse.yml -f docker-compose-observability.yml up -d"
alias pentagi-down="docker compose -f docker-compose.yml -f docker-compose-langfuse.yml -f docker-compose-observability.yml down"

GitHub and Google OAuth Integration

OAuth integration with GitHub and Google allows users to authenticate using their existing accounts on these platforms. This provides several benefits:

Simplified login process without need to create separate credentials
Enhanced security through trusted identity providers
Access to user profile information from GitHub/Google accounts
Seamless integration with existing development workflows

For using GitHub OAuth you need to create a new OAuth application in your GitHub account and set the GITHUB_CLIENT_ID and GITHUB_CLIENT_SECRET in .env file.

For using Google OAuth you need to create a new OAuth application in your Google account and set the GOOGLE_CLIENT_ID and GOOGLE_CLIENT_SECRET in .env file.

Docker Image Configuration

PentAGI allows you to configure Docker image selection for executing various tasks. The system automatically chooses the most appropriate image based on the task type, but you can constrain this selection by specifying your preferred images:

Variable	Default	Description
`DOCKER_DEFAULT_IMAGE`	`debian:latest`	Default Docker image for general tasks and ambiguous cases
`DOCKER_DEFAULT_IMAGE_FOR_PENTEST`	`vxcontrol/kali-linux`	Default Docker image for security/penetration testing tasks

When these environment variables are set, AI agents will be limited to the image choices you specify. This is particularly useful for:

Security Enforcement: Restricting usage to only verified and trusted images
Environment Standardization: Using corporate or customized images across all operations
Performance Optimization: Utilizing pre-built images with necessary tools already installed

Configuration examples:

# Using a custom image for general tasks
DOCKER_DEFAULT_IMAGE=mycompany/custom-debian:latest

# Using a specialized image for penetration testing
DOCKER_DEFAULT_IMAGE_FOR_PENTEST=mycompany/pentest-tools:v2.0

Note

If a user explicitly specifies a particular Docker image in their task, the system will try to use that exact image, ignoring these settings. These variables only affect the system's automatic image selection process.

💻 Development

Development Requirements

golang
nodejs
docker
postgres
commitlint

Environment Setup

Backend Setup

Run once cd backend && go mod download to install needed packages.

For generating swagger files have to run

swag init -g ../../pkg/server/router.go -o pkg/server/docs/ --parseDependency --parseInternal --parseDepth 2 -d cmd/pentagi

before installing swag package via

go install github.com/swaggo/swag/cmd/[email protected]

For generating graphql resolver files have to run

go run github.com/99designs/gqlgen --config ./gqlgen/gqlgen.yml

after that you can see the generated files in pkg/graph folder.

For generating ORM methods (database package) from sqlc configuration

docker run --rm -v $(pwd):/src -w /src --network pentagi-network -e DATABASE_URL="{URL}" sqlc/sqlc generate -f sqlc/sqlc.yml

For generating Langfuse SDK from OpenAPI specification

fern generate --local

and to install fern-cli

npm install -g fern-api

Testing

For running tests cd backend && go test -v ./...

Frontend Setup

Run once cd frontend && npm install to install needed packages.

For generating graphql files have to run npm run graphql:generate which using graphql-codegen.ts file.

Be sure that you have graphql-codegen installed globally:

npm install -g graphql-codegen

After that you can run:

npm run prettier to check if your code is formatted correctly
npm run prettier:fix to fix it
npm run lint to check if your code is linted correctly
npm run lint:fix to fix it

For generating SSL certificates you need to run npm run ssl:generate which using generate-ssl.ts file or it will be generated automatically when you run npm run dev.

Backend Configuration

Edit the configuration for backend in .vscode/launch.json file:

DATABASE_URL - PostgreSQL database URL (eg. postgres://postgres:postgres@localhost:5432/pentagidb?sslmode=disable)
DOCKER_HOST - Docker SDK API (eg. for macOS DOCKER_HOST=unix:///Users/<my-user>/Library/Containers/com.docker.docker/Data/docker.raw.sock) more info

Optional:

SERVER_PORT - Port to run the server (default: 8443)
SERVER_USE_SSL - Enable SSL for the server (default: false)

Frontend Configuration

Edit the configuration for frontend in .vscode/launch.json file:

VITE_API_URL - Backend API URL. Omit the URL scheme (e.g., localhost:8080 NOT http://localhost:8080)
VITE_USE_HTTPS - Enable SSL for the server (default: false)
VITE_PORT - Port to run the server (default: 8000)
VITE_HOST - Host to run the server (default: 0.0.0.0)

Running the Application

Backend

Run the command(s) in backend folder:

Use .env file to set environment variables like a source .env
Run go run cmd/pentagi/main.go to start the server

Note

The first run can take a while as dependencies and docker images need to be downloaded to setup the backend environment.

Frontend

Run the command(s) in frontend folder:

Run npm install to install the dependencies
Run npm run dev to run the web app
Run npm run build to build the web app

Open your browser and visit the web app URL.

🧪 Testing LLM Agents

PentAGI includes a powerful utility called ctester for testing and validating LLM agent capabilities. This tool helps ensure your LLM provider configurations work correctly with different agent types, allowing you to optimize model selection for each specific agent role.

The utility features parallel testing of multiple agents, detailed reporting, and flexible configuration options.

Key Features

Parallel Testing: Tests multiple agents simultaneously for faster results
Comprehensive Test Suite: Evaluates basic completion, JSON responses, function calling, and penetration testing knowledge
Detailed Reporting: Generates markdown reports with success rates and performance metrics
Flexible Configuration: Test specific agents or test groups as needed
Specialized Test Groups: Includes domain-specific tests for cybersecurity and penetration testing scenarios

Usage Scenarios

For Developers (with local Go environment)

If you've cloned the repository and have Go installed:

# Default configuration with .env file
cd backend
go run cmd/ctester/*.go -verbose

# Custom provider configuration
go run cmd/ctester/*.go -config ../examples/configs/openrouter.provider.yml -verbose

# Generate a report file
go run cmd/ctester/*.go -config ../examples/configs/deepinfra.provider.yml -report ../test-report.md

# Test specific agent types only
go run cmd/ctester/*.go -agents simple,simple_json,primary_agent -verbose

# Test specific test groups only
go run cmd/ctester/*.go -groups basic,advanced -verbose

For Users (using Docker image)

If you prefer to use the pre-built Docker image without setting up a development environment:

# Using Docker to test with default environment
docker run --rm -v $(pwd)/.env:/opt/pentagi/.env vxcontrol/pentagi /opt/pentagi/bin/ctester -verbose

# Test with your custom provider configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  -v $(pwd)/my-config.yml:/opt/pentagi/config.yml \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/config.yml -agents simple,primary_agent,coder -verbose

# Generate a detailed report
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  -v $(pwd):/opt/pentagi/output \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -report /opt/pentagi/output/report.md

Using Pre-configured Providers

The Docker image comes with built-in support for major providers (OpenAI, Anthropic, Gemini, Ollama) and pre-configured provider files for additional services (OpenRouter, DeepInfra, DeepSeek):

# Test with OpenRouter configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/openrouter.provider.yml

# Test with DeepInfra configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/deepinfra.provider.yml

# Test with DeepSeek configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/deepseek.provider.yml

# Test with OpenAI configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -type openai

# Test with Anthropic configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -type anthropic

# Test with Gemini configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -type gemini

# Test with AWS Bedrock configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -type bedrock

# Test with Custom OpenAI configuration
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/custom-openai.provider.yml

# Test with Ollama configuration (local inference)
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/ollama-llama318b.provider.yml

# Test with Ollama Qwen3 32B configuration (requires custom model creation)
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/ollama-qwen332b-fp16-tc.provider.yml

# Test with Ollama QwQ 32B configuration (requires custom model creation and 71.3GB VRAM)
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/ollama-qwq32b-fp16-tc.provider.yml

To use these configurations, your .env file only needs to contain:

LLM_SERVER_URL=https://openrouter.ai/api/v1      # or https://api.deepinfra.com/v1/openai or https://api.deepseek.com or https://api.openai.com/v1
LLM_SERVER_KEY=your_api_key
LLM_SERVER_MODEL=                                # Leave empty, as models are specified in the config
LLM_SERVER_CONFIG_PATH=/opt/pentagi/conf/openrouter.provider.yml  # or deepinfra.provider.yml or deepseek.provider.yml or custom-openai.provider.yml
LLM_SERVER_LEGACY_REASONING=false                # Controls reasoning format, for OpenAI must be true (default: false)

# For OpenAI (official API)
OPEN_AI_KEY=your_openai_api_key                  # Your OpenAI API key
OPEN_AI_SERVER_URL=https://api.openai.com/v1     # OpenAI API endpoint

# For Anthropic (Claude models)
ANTHROPIC_API_KEY=your_anthropic_api_key         # Your Anthropic API key
ANTHROPIC_SERVER_URL=https://api.anthropic.com/v1  # Anthropic API endpoint

# For Gemini (Google AI)
GEMINI_API_KEY=your_gemini_api_key               # Your Google AI API key
GEMINI_SERVER_URL=https://generativelanguage.googleapis.com  # Google AI API endpoint

# For AWS Bedrock (enterprise foundation models)
BEDROCK_REGION=us-east-1                         # AWS region for Bedrock service
BEDROCK_ACCESS_KEY_ID=your_aws_access_key        # AWS access key ID
BEDROCK_SECRET_ACCESS_KEY=your_aws_secret_key    # AWS secret access key
BEDROCK_SERVER_URL=                              # Optional custom Bedrock endpoint

# For Ollama (local inference)
OLLAMA_SERVER_URL=http://localhost:11434         # Your Ollama server URL (only for test run)
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama-llama318b.provider.yml  # or ollama-qwen332b-fp16-tc.provider.yml or ollama-qwq32b-fp16-tc.provider.yml

Using OpenAI with Unverified Organizations

For OpenAI accounts with unverified organizations that don't have access to the latest reasoning models (o1, o3, o4-mini), you need to use a custom configuration.

To use OpenAI with unverified organization accounts, configure your .env file as follows:

LLM_SERVER_URL=https://api.openai.com/v1
LLM_SERVER_KEY=your_openai_api_key
LLM_SERVER_MODEL=                                # Leave empty, models are specified in config
LLM_SERVER_CONFIG_PATH=/opt/pentagi/conf/custom-openai.provider.yml
LLM_SERVER_LEGACY_REASONING=true                 # Required for OpenAI reasoning format

This configuration uses the pre-built custom-openai.provider.yml file that maps all agent types to models available for unverified organizations, using o3-mini instead of models like o1, o3, and o4-mini.

You can test this configuration using:

# Test with custom OpenAI configuration for unverified accounts
docker run --rm \
  -v $(pwd)/.env:/opt/pentagi/.env \
  vxcontrol/pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/custom-openai.provider.yml

Note

The LLM_SERVER_LEGACY_REASONING=true setting is crucial for OpenAI compatibility as it ensures reasoning parameters are sent in the format expected by OpenAI's API.

Running Tests in a Production Environment

If you already have a running PentAGI container and want to test the current configuration:

# Run ctester in an existing container using current environment variables
docker exec -it pentagi /opt/pentagi/bin/ctester -verbose

# Test specific agent types with deterministic ordering
docker exec -it pentagi /opt/pentagi/bin/ctester -agents simple,primary_agent,pentester -groups basic,knowledge -verbose

# Generate a report file inside the container
docker exec -it pentagi /opt/pentagi/bin/ctester -report /opt/pentagi/data/agent-test-report.md

# Access the report from the host
docker cp pentagi:/opt/pentagi/data/agent-test-report.md ./

Command-line Options

The utility accepts several options:

-env <path> - Path to environment file (default: .env)
-type <provider> - Provider type: custom, openai, anthropic, ollama, bedrock, gemini (default: custom)
-config <path> - Path to custom provider config (default: from LLM_SERVER_CONFIG_PATH env variable)
-tests <path> - Path to custom tests YAML file (optional)
-report <path> - Path to write the report file (optional)
-agents <list> - Comma-separated list of agent types to test (default: all)
-groups <list> - Comma-separated list of test groups to run (default: all)
-verbose - Enable verbose output with detailed test results for each agent

Available Agent Types

Agents are tested in the following deterministic order:

simple - Basic completion tasks
simple_json - JSON-structured responses
primary_agent - Main reasoning agent
assistant - Interactive assistant mode
generator - Content generation
refiner - Content refinement and improvement
adviser - Expert advice and consultation
reflector - Self-reflection and analysis
searcher - Information gathering and search
enricher - Data enrichment and expansion
coder - Code generation and analysis
installer - Installation and setup tasks
pentester - Penetration testing and security assessment

Available Test Groups

basic - Fundamental completion and prompt response tests
advanced - Complex reasoning and function calling tests
json - JSON format validation and structure tests (specifically designed for simple_json agent)
knowledge - Domain-specific cybersecurity and penetration testing knowledge tests

Note: The json test group is specifically designed for the simple_json agent type, while all other agents are tested with basic, advanced, and knowledge groups. This specialization ensures optimal testing coverage for each agent's intended purpose.

Example Provider Configuration

Provider configuration defines which models to use for different agent types:

simple:
  model: "provider/model-name"
  temperature: 0.7
  top_p: 0.95
  n: 1
  max_tokens: 4000

simple_json:
  model: "provider/model-name"
  temperature: 0.7
  top_p: 1.0
  n: 1
  max_tokens: 4000
  json: true

# ... other agent types ...

Optimization Workflow

Create a baseline: Run tests with default configuration to establish benchmark performance
Analyze agent-specific performance: Review the deterministic agent ordering to identify underperforming agents
Test specialized configurations: Experiment with different models for each agent type using provider-specific configs
Focus on domain knowledge: Pay special attention to knowledge group tests for cybersecurity expertise
Validate function calling: Ensure tool-based tests pass consistently for critical agent types
Compare results: Look for the best success rate and performance across all test groups
Deploy optimal configuration: Use in production with your optimized setup

This tool helps ensure your AI agents are using the most effective models for their specific tasks, improving reliability while optimizing costs.

🧮 Embedding Configuration and Testing

PentAGI uses vector embeddings for semantic search, knowledge storage, and memory management. The system supports multiple embedding providers that can be configured according to your needs and preferences.

Supported Embedding Providers

PentAGI supports the following embedding providers:

OpenAI (default): Uses OpenAI's text embedding models
Ollama: Local embedding model through Ollama
Mistral: Mistral AI's embedding models
Jina: Jina AI's embedding service
HuggingFace: Models from HuggingFace
GoogleAI: Google's embedding models
VoyageAI: VoyageAI's embedding models

Embedding Provider Configuration (click to expand)

Environment Variables

To configure the embedding provider, set the following environment variables in your .env file:

# Primary embedding configuration
EMBEDDING_PROVIDER=openai       # Provider type (openai, ollama, mistral, jina, huggingface, googleai, voyageai)
EMBEDDING_MODEL=text-embedding-3-small  # Model name to use
EMBEDDING_URL=                  # Optional custom API endpoint
EMBEDDING_KEY=                  # API key for the provider (if required)
EMBEDDING_BATCH_SIZE=100        # Number of documents to process in a batch
EMBEDDING_STRIP_NEW_LINES=true  # Whether to remove new lines from text before embedding

# Advanced settings
PROXY_URL=                      # Optional proxy for all API calls

Provider-Specific Limitations

Each provider has specific limitations and supported features:

OpenAI: Supports all configuration options
Ollama: Does not support EMBEDDING_KEY as it uses local models
Mistral: Does not support EMBEDDING_MODEL or custom HTTP client
Jina: Does not support custom HTTP client
HuggingFace: Requires EMBEDDING_KEY and supports all other options
GoogleAI: Does not support EMBEDDING_URL, requires EMBEDDING_KEY
VoyageAI: Supports all configuration options

If EMBEDDING_URL and EMBEDDING_KEY are not specified, the system will attempt to use the corresponding LLM provider settings (e.g., OPEN_AI_KEY when EMBEDDING_PROVIDER=openai).

Why Consistent Embedding Providers Matter

It's crucial to use the same embedding provider consistently because:

Vector Compatibility: Different providers produce vectors with different dimensions and mathematical properties
Semantic Consistency: Changing providers can break semantic similarity between previously embedded documents
Memory Corruption: Mixed embeddings can lead to poor search results and broken knowledge base functionality

If you change your embedding provider, you should flush and reindex your entire knowledge base (see etester utility below).

Embedding Tester Utility (etester)

PentAGI includes a specialized etester utility for testing, managing, and debugging embedding functionality. This tool is essential for diagnosing and resolving issues related to vector embeddings and knowledge storage.

Etester Commands (click to expand)

# Test embedding provider and database connection
cd backend
go run cmd/etester/main.go test -verbose

# Show statistics about the embedding database
go run cmd/etester/main.go info

# Delete all documents from the embedding database (use with caution!)
go run cmd/etester/main.go flush

# Recalculate embeddings for all documents (after changing provider)
go run cmd/etester/main.go reindex

# Search for documents in the embedding database
go run cmd/etester/main.go search -query "How to install PostgreSQL" -limit 5

Using Docker

If you're running PentAGI in Docker, you can use etester from within the container:

# Test embedding provider
docker exec -it pentagi /opt/pentagi/bin/etester test

# Show detailed database information
docker exec -it pentagi /opt/pentagi/bin/etester info -verbose

Advanced Search Options

The search command supports various filters to narrow down results:

# Filter by document type
docker exec -it pentagi /opt/pentagi/bin/etester search -query "Security vulnerability" -doc_type guide -threshold 0.8

# Filter by flow ID
docker exec -it pentagi /opt/pentagi/bin/etester search -query "Code examples" -doc_type code -flow_id 42

# All available search options
docker exec -it pentagi /opt/pentagi/bin/etester search -help

Available search parameters:

-query STRING: Search query text (required)
-doc_type STRING: Filter by document type (answer, memory, guide, code)
-flow_id NUMBER: Filter by flow ID (positive number)
-answer_type STRING: Filter by answer type (guide, vulnerability, code, tool, other)
-guide_type STRING: Filter by guide type (install, configure, use, pentest, development, other)
-limit NUMBER: Maximum number of results (default: 3)
-threshold NUMBER: Similarity threshold (0.0-1.0, default: 0.7)

Common Troubleshooting Scenarios

After changing embedding provider: Always run flush or reindex to ensure consistency
Poor search results: Try adjusting the similarity threshold or check if embeddings are correctly generated
Database connection issues: Verify PostgreSQL is running with pgvector extension installed
Missing API keys: Check environment variables for your chosen embedding provider

🔍 Function Testing with ftester

PentAGI includes a versatile utility called ftester for debugging, testing, and developing specific functions and AI agent behaviors. While ctester focuses on testing LLM model capabilities, ftester allows you to directly invoke individual system functions and AI agent components with precise control over execution context.

Key Features

Direct Function Access: Test individual functions without running the entire system
Mock Mode: Test functions without a live PentAGI deployment using built-in mocks
Interactive Input: Fill function arguments interactively for exploratory testing
Detailed Output: Color-coded terminal output with formatted responses and errors
Context-Aware Testing: Debug AI agents within the context of specific flows, tasks, and subtasks
Observability Integration: All function calls are logged to Langfuse and Observability stack

Usage Modes

Command Line Arguments

Run ftester with specific function and arguments directly from the command line:

# Basic usage with mock mode
cd backend
go run cmd/ftester/main.go [function_name] -[arg1] [value1] -[arg2] [value2]

# Example: Test terminal command in mock mode
go run cmd/ftester/main.go terminal -command "ls -la" -message "List files"

# Using a real flow context
go run cmd/ftester/main.go -flow 123 terminal -command "whoami" -message "Check user"

# Testing AI agent in specific task/subtask context
go run cmd/ftester/main.go -flow 123 -task 456 -subtask 789 pentester -message "Find vulnerabilities"

Interactive Mode

Run ftester without arguments for a guided interactive experience:

# Start interactive mode
go run cmd/ftester/main.go [function_name]

# For example, to interactively fill browser tool arguments
go run cmd/ftester/main.go browser

Available Functions (click to expand)

Environment Functions

terminal: Execute commands in a container and return the output
file: Perform file operations (read, write, list) in a container

Search Functions

browser: Access websites and capture screenshots
google: Search the web using Google Custom Search
duckduckgo: Search the web using DuckDuckGo
tavily: Search using Tavily AI search engine
traversaal: Search using Traversaal AI search engine
perplexity: Search using Perplexity AI
searxng: Search using Searxng meta search engine (aggregates results from multiple engines)

Vector Database Functions

search_in_memory: Search for information in vector database
search_guide: Find guidance documents in vector database
search_answer: Find answers to questions in vector database
search_code: Find code examples in vector database

AI Agent Functions

advice: Get expert advice from an AI agent
coder: Request code generation or modification
maintenance: Run system maintenance tasks
memorist: Store and organize information in vector database
pentester: Perform security tests and vulnerability analysis
search: Complex search across multiple sources

Utility Functions

describe: Show information about flows, tasks, and subtasks

Debugging Flow Context (click to expand)

The describe function provides detailed information about tasks and subtasks within a flow. This is particularly useful for diagnosing issues when PentAGI encounters problems or gets stuck.

# List all flows in the system
go run cmd/ftester/main.go describe

# Show all tasks and subtasks for a specific flow
go run cmd/ftester/main.go -flow 123 describe

# Show detailed information for a specific task
go run cmd/ftester/main.go -flow 123 -task 456 describe

# Show detailed information for a specific subtask
go run cmd/ftester/main.go -flow 123 -task 456 -subtask 789 describe

# Show verbose output with full descriptions and results
go run cmd/ftester/main.go -flow 123 describe -verbose

This function allows you to identify the exact point where a flow might be stuck and resume processing by directly invoking the appropriate agent function.

Function Help and Discovery (click to expand)

Each function has a help mode that shows available parameters:

# Get help for a specific function
go run cmd/ftester/main.go [function_name] -help

# Examples:
go run cmd/ftester/main.go terminal -help
go run cmd/ftester/main.go browser -help
go run cmd/ftester/main.go describe -help

You can also run ftester without arguments to see a list of all available functions:

go run cmd/ftester/main.go

Output Format (click to expand)

The ftester utility uses color-coded output to make interpretation easier:

Blue headers: Section titles and key names
Cyan [INFO]: General information messages
Green [SUCCESS]: Successful operations
Red [ERROR]: Error messages
Yellow [WARNING]: Warning messages
Yellow [MOCK]: Indicates mock mode operation
Magenta values: Function arguments and results

JSON and Markdown responses are automatically formatted for readability.

Advanced Usage Scenarios (click to expand)

Debugging Stuck AI Flows

When PentAGI gets stuck in a flow:

Pause the flow through the UI
Use describe to identify the current task and subtask
Directly invoke the agent function with the same task/subtask IDs
Examine the detailed output to identify the issue
Resume the flow or manually intervene as needed

Testing Environment Variables

Verify that API keys and external services are configured correctly:

# Test Google search API configuration
go run cmd/ftester/main.go google -query "pentesting tools"

# Test browser access to external websites
go run cmd/ftester/main.go browser -url "https://example.com"

Developing New AI Agent Behaviors

When developing new prompt templates or agent behaviors:

Create a test flow in the UI
Use ftester to directly invoke the agent with different prompts
Observe responses and adjust prompts accordingly
Check Langfuse for detailed traces of all function calls

Verifying Docker Container Setup

Ensure containers are properly configured:

go run cmd/ftester/main.go -flow 123 terminal -command "env | grep -i proxy" -message "Check proxy settings"

Docker Container Usage (click to expand)

If you have PentAGI running in Docker, you can use ftester from within the container:

# Run ftester inside the running PentAGI container
docker exec -it pentagi /opt/pentagi/bin/ftester [arguments]

# Examples:
docker exec -it pentagi /opt/pentagi/bin/ftester -flow 123 describe
docker exec -it pentagi /opt/pentagi/bin/ftester -flow 123 terminal -command "ps aux" -message "List processes"

This is particularly useful for production deployments where you don't have a local development environment.

Integration with Observability Tools (click to expand)

All function calls made through ftester are logged to:

Langfuse: Captures the entire AI agent interaction chain, including prompts, responses, and function calls
OpenTelemetry: Records metrics, traces, and logs for system performance analysis
Terminal Output: Provides immediate feedback on function execution

To access detailed logs:

Check Langfuse UI for AI agent traces (typically at http://localhost:4000)
Use Grafana dashboards for system metrics (typically at http://localhost:3000)
Examine terminal output for immediate function results and errors

Command-line Options

The main utility accepts several options:

-env <path> - Path to environment file (optional, default: .env)
-provider <type> - Provider type to use (default: custom, options: openai, anthropic, ollama, bedrock, gemini, custom)
-flow <id> - Flow ID for testing (0 means using mocks, default: 0)
-task <id> - Task ID for agent context (optional)
-subtask <id> - Subtask ID for agent context (optional)

Function-specific arguments are passed after the function name using -name value format.

🏗️ Building

Building Docker Image

docker build -t local/pentagi:latest .

Note

You can use docker buildx to build the image for different platforms like a docker buildx build --platform linux/amd64 -t local/pentagi:latest .

You need to change image name in docker-compose.yml file to local/pentagi:latest and run docker compose up -d to start the server or use build key option in docker-compose.yml file.

👏 Credits

This project is made possible thanks to the following research and developments:

📄 License

PentAGI Core License

PentAGI Core: Licensed under MIT License
Copyright (c) 2025 PentAGI Development Team

VXControl Cloud SDK Integration

VXControl Cloud SDK Integration: This repository integrates VXControl Cloud SDK under a special licensing exception that applies ONLY to the official PentAGI project.

✅ Official PentAGI Project

This official repository: https://github.com/vxcontrol/pentagi
Official releases distributed by VXControl LLC
Code used under direct authorization from VXControl LLC

⚠️ Important for Forks and Third-Party Use

If you fork this project or create derivative works, the VXControl SDK components are subject to AGPL-3.0 license terms. You must either:

Remove VXControl SDK integration
Open source your entire application (comply with AGPL-3.0 copyleft terms)
Obtain a commercial license from VXControl LLC

Commercial Licensing

For commercial use of VXControl Cloud SDK in proprietary applications, contact:

Email: [email protected]
Subject: "VXControl Cloud SDK Commercial License"

Name		Name	Last commit message	Last commit date
Latest commit History 135 Commits
.github		.github
.vscode		.vscode
backend		backend
build		build
examples		examples
frontend		frontend
observability		observability
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
Dockerfile		Dockerfile
EULA.md		EULA.md
LICENSE		LICENSE
NOTICE		NOTICE
README.md		README.md
docker-compose-langfuse.yml		docker-compose-langfuse.yml
docker-compose-observability.yml		docker-compose-observability.yml
docker-compose.yml		docker-compose.yml
entrypoint.sh		entrypoint.sh

License

vxcontrol/pentagi

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PentAGI

📖 Table of Contents

🎯 Overview

✨ Features

🏗️ Architecture

System Context

Global Summarizer Configuration Options

Assistant Summarizer Configuration Options

Summarizer Environment Configuration

🚀 Quick Start

System Requirements

Using Installer (Recommended)

Manual Installation

Main Security Settings

Scraper Access

Access Credentials

Assistant Configuration

Custom LLM Provider Configuration

Local LLM Provider Configuration

Creating Custom Ollama Models with Extended Context

Example: Qwen3 32B FP16 with Extended Context

Example: QwQ 32B FP16 with Extended Context

OpenAI Provider Configuration

Anthropic Provider Configuration

Google AI (Gemini) Provider Configuration

AWS Bedrock Provider Configuration

🔧 Advanced Setup

Langfuse Integration

Database Credentials

Encryption and Security Keys

Admin Credentials

API Keys and Tokens

S3 Storage

Monitoring and Observability

GitHub and Google OAuth Integration

Docker Image Configuration

💻 Development

Development Requirements

Environment Setup

Backend Setup

Testing

Frontend Setup

Backend Configuration

Frontend Configuration

Running the Application

Backend

Frontend

🧪 Testing LLM Agents

Key Features

Usage Scenarios

For Developers (with local Go environment)

For Users (using Docker image)

Using Pre-configured Providers

Using OpenAI with Unverified Organizations

Running Tests in a Production Environment

Command-line Options

Available Agent Types

Available Test Groups

Example Provider Configuration

Optimization Workflow

🧮 Embedding Configuration and Testing

Supported Embedding Providers

Environment Variables

Provider-Specific Limitations

Why Consistent Embedding Providers Matter

Embedding Tester Utility (etester)

Using Docker

Advanced Search Options

Common Troubleshooting Scenarios

🔍 Function Testing with ftester

Key Features

Usage Modes

Command Line Arguments

Interactive Mode