pentagi Documentation

Technical Documentation: vxcontrol/pentagi

ℹ️ Provenance: Multi-Tier Ingestion · CodeWiki Reference

Overview (README.md) Copy Section

PentAGI

Table of Contents Copy Chapter

• Overview
• Features
• Architecture
  • Agent Supervision
• Quick Start
  • Agent Docker Access
  • Running Several Instances
• How to Use PentAGI After Login
• API Access
  • LLM Provider Configuration
    • Azure OpenAI
    • Ollama
    • OpenAI
    • Anthropic
    • Google AI (Gemini)
    • AWS Bedrock
    • DeepSeek
    • GLM
    • Kimi
    • Qwen
    • MiniMax
    • Mistral
    • xAI (Grok)
• Advanced Setup
  • Langfuse Integration
  • Monitoring and Observability
  • Knowledge Graph (Graphiti)
  • OAuth Integration
  • Docker Image Configuration
• Development
• Testing LLM Agents
• Embedding Configuration and Testing
• Function Testing with ftester
• Building
• Credits
• License

Overview Copy Chapter

PentAGI is an open-source, self-hosted multi-agent system for autonomous penetration testing. You describe a target and a goal in plain language; a team of specialized AI agents plans the work, runs 20+ professional tools such as nmap, Metasploit and sqlmap inside an isolated Docker sandbox, remembers what it learned and hands you a report.

• Who it is for. Security engineers, penetration testers, researchers and red teams who want an autonomous assistant on their own infrastructure, for authorized testing only.
• What sets it apart. It runs on your own servers with the model of your choice: 10+ cloud LLM providers, your own OpenAI-compatible gateway, or local models through Ollama or vLLM. Results and successful approaches are kept in PostgreSQL with pgvector for later flows, an optional Neo4j knowledge graph tracks hosts, services and findings, execution monitoring and task planning help smaller open models, and REST and GraphQL APIs automate it. PentAGI Enterprise adds agent profiles, plugins and MCP servers, customer-ready reports and a real-time attack-chain view.
• How to install. An interactive installer for Linux, macOS and Windows sets PentAGI up with Docker Compose — see Quick Start.

PentAGI is developed by VXControl, a security engineering company based in Dubai.

You can watch the video PentAGI overview:

Features Copy Chapter

• Secure & Isolated. All operations are performed in a sandboxed Docker environment with complete isolation.
• Fully Autonomous. AI-powered agent that automatically determines and executes penetration testing steps with optional execution monitoring and intelligent task planning for enhanced reliability.
• Professional Pentesting Tools. Built-in suite of 20+ professional security tools including nmap, metasploit, sqlmap, and more.
• Smart Memory System. Long-term storage of research results and successful approaches for future use.
• Optional Knowledge Graph Integration. Graphiti-powered knowledge graph using Neo4j for semantic relationship tracking and advanced context understanding.
• Web Intelligence. Built-in browser via scraper for gathering latest information from web sources.
• External Search Systems. Integration with advanced search APIs including Tavily, Firecrawl, Traversaal, Perplexity, DuckDuckGo, Google Custom Search, Sploitus Search and SearXNG for comprehensive information gathering.
• Team of Specialists. Delegation system with specialized AI agents for research, development, and infrastructure tasks, enhanced with optional execution monitoring and intelligent task planning for optimal performance with smaller models.
• Comprehensive Monitoring. Detailed logging and integration with Grafana/Prometheus for real-time system observation.
• Detailed Reporting. Generation of thorough vulnerability reports with exploitation guides.
• Smart Container Management. Automatic Docker image selection based on specific task requirements.
• Modern Interface. Clean and intuitive web UI for system management and monitoring.
• Comprehensive APIs. Full-featured REST and GraphQL APIs with Bearer token authentication for automation and integration.
• Persistent Storage. All commands and outputs are stored in PostgreSQL with pgvector extension.
• Scalable Architecture. Microservices-based design supporting horizontal scaling.
• Self-Hosted Solution. Complete control over your deployment and data.
• Flexible Authentication. Support for 10+ LLM providers (OpenAI, Anthropic, Google AI/Gemini, AWS Bedrock, Ollama, DeepSeek, GLM, Kimi, Qwen, MiniMax, Mistral, xAI, Custom for any OpenAI-compatible endpoint including Azure OpenAI) plus aggregators (OpenRouter, DeepInfra, Atlas Cloud, OpenCode Go plan). For production local deployments, see our vLLM + Qwen3.5-27B-FP8 guide.
• API Token Authentication. Secure Bearer token system for programmatic access to REST and GraphQL APIs.
• Quick Deployment. Easy setup through Docker Compose with comprehensive environment configuration.

Current Capability Boundaries Copy Section

• PentAGI today is an autonomous and assistant-guided penetration testing platform, not a CALDERA-style Breach and Attack Simulation (BAS) or adversary emulation product with predefined campaigns or attack plans.
• BAS-like agent-authored attack scripts should be treated as conceptual or future work, not as a feature that is implemented today.
• The current flow report UI supports web view, copy to clipboard, Markdown download, and PDF download. JSON flow-report export is not documented as a supported output format today.
• Provider flexibility is available today through built-in providers and custom/OpenAI-compatible endpoints. See Custom LLM Provider Configuration and the vLLM + Qwen3.5-27B-FP8 guide.

Architecture Copy Chapter

System Context Copy Section

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)

/* Detailed source-code truncated for AI context efficiency. */

Entity Relationship (click to expand)

/* Detailed source-code truncated for AI context efficiency. */

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 Copy Section

Assistant Summarizer Configuration Options Copy Section

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

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 Copy Section

# 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 includes sophisticated multi-layered agent supervision mechanisms to ensure efficient task execution, prevent infinite loops, and provide intelligent recovery from stuck states:

Execution Monitoring (Beta) Copy Section

• Automatic Mentor Intervention: Adviser agent (mentor) is automatically invoked when execution patterns indicate potential issues
• Pattern Detection: Monitors identical tool calls (threshold: 5, configurable) and total tool calls (threshold: 10, configurable)
• Progress Analysis: Evaluates whether agent advances toward subtask objective, detects loops and inefficiencies
• Alternative Strategies: Recommends different approaches when current strategy fails
• Information Retrieval Guidance: Suggests searching for established solutions instead of reinventing
• Enhanced Response Format: Tool responses include both <original_result> and <mentor_analysis> sections
• Configurable: Enable via EXECUTION_MONITOR_ENABLED (default: false), customize thresholds with EXECUTION_MONITOR_SAME_TOOL_LIMIT and EXECUTION_MONITOR_TOTAL_TOOL_LIMIT

Best for: Smaller models (< 32B parameters), complex attack scenarios requiring continuous guidance, preventing agents from getting stuck on single approach

Performance Impact: 2-3x increase in execution time and token usage, but delivers 2x improvement in result quality based on testing with Qwen3.5-27B-FP8

Intelligent Task Planning (Beta) Copy Section

• Automated Decomposition: Planner (adviser in planning mode) generates 3-7 specific, actionable steps before specialist agents begin work
• Context-Aware Plans: Analyzes full execution context via enricher agent to create informed plans
• Structured Assignment: Original request wrapped in <task_assignment> structure with execution plan and instructions
• Scope Management: Prevents scope creep by keeping agents focused on current subtask only
• Enriched Instructions: Plans highlight critical actions, potential pitfalls, and verification points
• Configurable: Enable via AGENT_PLANNING_STEP_ENABLED (default: false)

Best for: Models < 32B parameters, complex penetration testing workflows, improving success rates on sophisticated tasks

Enhanced Adviser Configuration: Works exceptionally well when adviser agent uses stronger model or enhanced settings. Example: using same base model with maximum reasoning mode for adviser (see vllm-qwen3.5-27b-fp8.provider.yml) enables comprehensive task analysis and strategic planning from identical model architecture.

Performance Impact: Adds planning overhead but significantly improves completion rates and reduces redundant work

Tool Call Limits (Always Active) Copy Section

• Hard Limits: Prevent runaway executions regardless of supervision mode status
• Differentiated by Agent Type:
  • General agents (Assistant, Primary Agent, Pentester, Coder, Installer): MAX_GENERAL_AGENT_TOOL_CALLS (default: 100)
  • Limited agents (Searcher, Enricher, Memorist, Generator, Reporter, Adviser, Reflector, Planner): MAX_LIMITED_AGENT_TOOL_CALLS (default: 20)
• Graceful Termination: Reflector guides agents to proper completion when approaching limits
• Resource Protection: Ensures system stability and prevents resource exhaustion

Reflector Integration (Always Active) Copy Section

• Automatic Correction: Invoked when LLM fails to generate tool calls after 3 attempts
• Strategic Guidance: Analyzes failures and guides agents toward proper tool usage or barrier tools (done, ask)
• Recovery Mechanism: Provides contextual guidance based on specific failure patterns
• Limit Enforcement: Coordinates graceful termination when tool call limits are reached

Recommendations for Open Source Models Copy Section

Must-Have for Models < 32B Parameters:
Testing with Qwen3.5-27B-FP8 demonstrates that enabling both Execution Monitoring and Task Planning is essential for smaller open source models:

• Quality Improvement: 2x better results compared to baseline execution without supervision
• Loop Prevention: Significantly reduces infinite loops and redundant work
• Attack Diversity: Encourages exploration of multiple attack vectors instead of fixating on single approach
• Air-Gapped Deployments: Enables production-grade autonomous pentesting in closed network environments with local LLM inference

Trade-offs:

• Token consumption: 2-3x increase due to mentor/planner invocations
• Execution time: 2-3x longer due to analysis and planning steps
• Result quality: 2x improvement in completeness, accuracy, and attack coverage
• Model requirements: Works best when adviser uses enhanced configuration (higher reasoning parameters, stronger model variant, or different model)

Configuration Strategy:
For optimal performance with smaller models, configure adviser agent with enhanced settings:

• Use same model with maximum reasoning mode (example: vllm-qwen3.5-27b-fp8.provider.yml)
• Or use stronger model for adviser while keeping base model for other agents
• Adjust monitoring thresholds based on task complexity and model capabilities

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

1. Core Services

   • Frontend UI: React-based web interface with TypeScript for type safety
   • Backend API: Go-based REST and GraphQL APIs with Bearer token authentication for programmatic access
   • 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

2. Optional Knowledge Graph

   • Graphiti: Knowledge graph API for semantic relationship tracking and contextual understanding
   • Neo4j: Graph database for storing and querying relationships between entities, actions, and outcomes
   • When enabled, automatically captures agent responses and tool executions for a flow-scoped knowledge base

3. 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

4. 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

5. 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

6. 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 Copy Chapter

For a step-by-step walkthrough that connects installation, configuration, LLM and embedding provider testing, and your first login, see the Installing and Configuring PentAGI guide. The sections below remain the detailed reference for each step.

System Requirements Copy Section

• Docker and Docker Compose (or Podman - see Podman configuration)
• Minimum 2 vCPU
• Minimum 4GB RAM
• 20GB free disk space
• Internet access for downloading images and updates

Using Installer (Recommended) Copy Section

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

macOS security warning: If macOS flags a downloaded installer, use only the official PentAGI links above, choose the archive that matches your CPU architecture, verify the source before continuing, and follow the installer troubleshooting guide before allowing the app to run.

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

Prerequisites & Permissions:

The installer requires appropriate privileges to interact with the Docker API for proper operation. By default, it uses the Docker socket (/var/run/docker.sock) which requires either:

• Option 1 (Recommended for production): Run the installer as root:

  bash Copy

  sudo ./installer

• Option 2 (Development environments): Grant your user access to the Docker socket by adding them to the docker group:

  bash Copy

  # Add your user to the docker group
  sudo usermod -aG docker $USER
  
  # Log out and log back in, or activate the group immediately
  newgrp docker
  
  # Verify Docker access (should run without sudo)
  docker ps

  ⚠️ Security Note: Adding a user to the docker group grants root-equivalent privileges. Only do this for trusted users in controlled environments. For production deployments, consider using rootless Docker mode or running the installer with sudo.

The installer will:

1. System Checks: Verify Docker, network connectivity, and system requirements
2. Environment Setup: Create and configure .env file with optimal defaults
3. Provider Configuration: Set up LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Ollama, DeepSeek, GLM, Kimi, Qwen, MiniMax, Mistral, xAI, Custom)
4. Search Engines: Configure DuckDuckGo, Google, Tavily, Firecrawl, Traversaal, Perplexity, Sploitus, SearXNG, and the optional internal browser-analytics fallback engine
5. Security Hardening: Generate secure credentials and configure SSL certificates
6. Deployment: Start PentAGI with docker-compose

Current Web Settings Coverage Copy Section

The PentAGI web console already manages several settings areas after the server is up and running:

• Settings -> Providers: Create, edit, delete, and test user-defined provider profiles for supported provider types. These profiles control per-agent model selection, runtime parameters, reasoning options, and pricing metadata.
• Settings -> Prompts: Manage system, human, and tool prompt templates.
• Settings -> PentAGI API: Create and manage PentAGI Bearer tokens for REST and GraphQL access.
• Other UI-managed preferences: Favorite flows are stored as user preferences, and theme selection is handled from the main sidebar/profile controls rather than the Settings pages.

Still Server-Managed Copy Section

The following configuration areas still need to be set on the server through environment variables, compose files, or mounted config files:

• LLM credentials and connection details: API keys, endpoints, auth modes, and provider-specific connection settings for OpenAI, Anthropic, Bedrock, Ollama, custom providers, and similar backends; config-path settings apply only where supported, such as OLLAMA_SERVER_CONFIG_PATH, LLM_SERVER_CONFIG_PATH, and BEDROCK_CONFIG_PATH.
• Search provider credentials and options: Settings such as DUCKDUCKGO_*, GOOGLE_*, TAVILY_API_KEY, FIRECRAWL_API_*, TRAVERSAAL_API_KEY, PERPLEXITY_*, SEARXNG_*, SPLOITUS_ENABLED, and the optional WEB_SEARCH_INTERNAL_* browser-analytics fallback settings.
• Third-party integrations: Langfuse, Graphiti, and similar external services remain server-side configuration.

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

Giving Agents Docker Without Giving Away the Host

Many pentest workflows need docker inside the agent's sandbox. There are two ways to provide it, and they differ sharply in risk.

Recommended — point sandboxes at a hardened dind daemon over TLS. Set DOCKER_INSIDE=true, leave DOCKER_SOCKET empty, and configure the daemon the sandbox may talk to:

DOCKER_INSIDE=true
DOCKER_SOCKET=                                          # mount no socket
DOCKER_INSIDE_HOST=tcp://10.0.0.5:3376                  # hardened dind endpoint
DOCKER_INSIDE_TLS_VERIFY=1
DOCKER_INSIDE_CERT_PATH=/etc/docker/dind/certs/client   # path on the worker node
DOCKER_INSIDE_POLICY_TESTS=true                         # prove it on every worker; off by default

PentAGI injects these into every worker container as DOCKER_HOST, DOCKER_TLS_VERIFY and DOCKER_CERT_PATH (the _INSIDE_ segment is dropped) and bind-mounts the certificate directory read-only at the same path, so docker works inside the sandbox with no further setup.

Not recommended — bind-mounting a Docker socket (DOCKER_SOCKET). This has two failure modes:

• Boot-order race: a bind-mount source that does not exist yet is created by Docker as a directory. After a worker-node reboot, a worker container can start before dind has recreated its socket — Docker then puts a directory where the socket belongs, and dind cannot start until it is removed by hand.
• Blast radius: the race is only reliably avoided when the mounted socket is the host daemon's, since that one always exists first. But that grants an autonomous agent the host Docker API: it can start a privileged container, mount /, and compromise the entire node — PentAGI included.

Use DOCKER_SOCKET only on single-node development setups where the host daemon is already trusted.

See: Worker Node Setup for the full dind hardening and TLS configuration, and Worker Docker Access for the exact resolution algorithm.

Running Several Instances (`TENANT_ID`)

A single PentAGI installation needs none of this — leave TENANT_ID empty (the default) and nothing changes.

Set it when several PentAGI installations share external resources: one PostgreSQL server, one worker node, one Neo4j/Graphiti, one Langfuse. The typical case is a management backend per server with a common worker node and database. Because every instance numbers its flows from 1, they would otherwise collide on container names, database rows, knowledge-graph namespaces and session cookies. TENANT_ID namespaces all of it:

The value must match ^[a-z][a-z0-9_]{0,31}$ — an invalid one aborts startup rather than being silently normalised.

Some things stay yours to set per instance, because they are host resources rather than names: DATA_DIR (two instances sharing it will overwrite each other's flow data), DOCKER_PORTS_BASE, the published ports, and INSTALLATION_ID. The effective values are printed at startup under Instance identity.

The installer provisions one instance per server. Running several on one server is possible — for example behind a shared nginx — but the stock docker-compose.yml uses fixed container and network names, so it has to be adapted to your own network layout first.

See: Multi-Instance Deployment for validation rules, upgrade notes and the full list of operator responsibilities.

Manual Installation Copy Section

1. Create a working directory or clone the repository:

mkdir pentagi && cd pentagi

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

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

3. Touch examples files (example.custom.provider.yml, example.ollama.provider.yml, example.bedrock.provider.yml) or download it. docker-compose.yml mounts each of them into the container, and Docker creates a directory in place of a mount source that does not exist:

curl -o example.custom.provider.yml https://raw.githubusercontent.com/vxcontrol/pentagi/master/examples/configs/custom-openai.provider.yml
curl -o example.ollama.provider.yml https://raw.githubusercontent.com/vxcontrol/pentagi/master/examples/configs/ollama-llama318b.provider.yml
curl -o example.bedrock.provider.yml https://raw.githubusercontent.com/vxcontrol/pentagi/master/examples/configs/bedrock.provider.yml

4. Fill in the required API keys in .env file.

/* Detailed source-code truncated for AI context efficiency. */

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

   Security related environment variables

Main Security Settings Copy Section

• 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)
• TENANT_ID - Leave empty unless this instance shares external resources with another PentAGI installation. When set, it is mixed into the cookie and API token signing keys and renames the session cookie, so a session minted by one instance is rejected by the others even though they share the same COOKIE_SIGNING_SALT. See Running Several Instances

Scraper Access Copy Section

• 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 Copy Section

• PENTAGI_POSTGRES_USER and PENTAGI_POSTGRES_PASSWORD - PostgreSQL credentials
• NEO4J_USER and NEO4J_PASSWORD - Neo4j credentials (for Graphiti knowledge graph)

6. 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

7. 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)

Web UI Accounts

PentAGI does not expose public self-service sign-up from the login page. A fresh installation creates the default local administrator account:

• Email: [email protected]
• Password: admin

On first login, change the default password before using the instance for real work. If the administrator password is lost later, use the installer maintenance menu to reset the default [email protected] account password.

For multi-user setups, an authenticated administrator can manage local users through the Users REST API (/api/v1/users/). The OpenAPI UI is available at https://localhost:8443/api/v1/swagger/index.html after the instance is running.

[!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, docker-compose-graphiti.yml, and docker-compose-observability.yml to use Langfuse, Graphiti, 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.

For fully local deployment with advanced models: See our comprehensive guide on Running PentAGI with vLLM and Qwen3.5-27B-FP8 for a production-grade local LLM setup. This configuration achieves ~13,000 TPS for prompt processing and ~650 TPS for completion on 4× RTX 5090 GPUs, supporting 12+ concurrent flows with complete independence from cloud providers.

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 routes the backend's own HTTP calls through a proxy: every request to LLM and embedding providers and to search engines — self-hosted ones such as Ollama or SearXNG included, as there is no NO_PROXY exemption — and the update check. Sandbox containers (where agents run their commands), the scraper, the Graphiti service, OAuth sign-in, and Langfuse/OpenTelemetry export do not use it, so if you need isolation from external networks, restrict their outbound traffic at the network level.

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.

Accessing PentAGI from External Networks Copy Section

By default, the PentAGI web interface and the other compose services bind to 127.0.0.1 (localhost only) for security. To access PentAGI from other machines on your network, you need to configure external access.

[!IMPORTANT]
Flow sandboxes are not bound to localhost. Each flow publishes two TCP ports for out-of-band callbacks such as reverse shells, taken from the 2000-port window that starts at DOCKER_PORTS_BASE (28000–29999 by default), on DOCKER_PUBLIC_IP — 0.0.0.0 by default, that is every interface of the Docker host. Docker routes published ports around ufw rules and lets them through firewalld with its own docker zone, so restrict this range with a firewall in front of the host or, with Docker's default iptables backend, with rules in the DOCKER-USER chain. With DOCKER_NETWORK=host, sandboxes share the host's network stack, so their listeners are exposed without any port mapping.

Configuration Steps

1. Update .env file with your server's IP address:

# Network binding - allow external connections
PENTAGI_LISTEN_IP=0.0.0.0
PENTAGI_LISTEN_PORT=8443

# Public URL - use your actual server IP or hostname
# Replace 192.168.1.100 with your server's IP address
PUBLIC_URL=https://192.168.1.100:8443

# CORS origins - list all URLs that will access PentAGI
# Include localhost for local access AND your server IP for external access
CORS_ORIGINS=https://localhost:8443,https://192.168.1.100:8443

[!IMPORTANT]

• Replace 192.168.1.100 with your actual server's IP address
• Do NOT use 0.0.0.0 in PUBLIC_URL or CORS_ORIGINS - use the actual IP address
• Include both localhost and your server IP in CORS_ORIGINS for flexibility

2. Recreate containers to apply the changes:

docker compose down
docker compose up -d --force-recreate

3. Verify port binding:

docker ps | grep pentagi

You should see 0.0.0.0:8443->8443/tcp or :::8443->8443/tcp.

If you see 127.0.0.1:8443->8443/tcp, the environment variable wasn't picked up. In this case, directly edit docker-compose.yml line 31:

ports:
  - "0.0.0.0:8443:8443"

Then recreate containers again.

4. Configure firewall to allow incoming connections on port 8443:

# Ubuntu/Debian with UFW
sudo ufw allow 8443/tcp
sudo ufw reload

# CentOS/RHEL with firewalld
sudo firewall-cmd --permanent --add-port=8443/tcp
sudo firewall-cmd --reload

5. Access PentAGI:

• Local access: https://localhost:8443
• Network access: https://your-server-ip:8443

[!NOTE]
You'll need to accept the self-signed SSL certificate warning in your browser when accessing via IP address.

Running PentAGI with Podman Copy Section

PentAGI fully supports Podman as a Docker alternative. However, when using Podman in rootless mode, the scraper service requires special configuration because rootless containers cannot bind privileged ports (ports below 1024).

Podman Rootless Configuration

The default scraper configuration uses port 443 (HTTPS), which is a privileged port. For Podman rootless, reconfigure the scraper to use a non-privileged port:

1. Edit docker-compose.yml - modify the scraper service (around line 199):

scraper:
  image: vxcontrol/scraper:latest
  restart: unless-stopped
  container_name: scraper
  hostname: scraper
  expose:
    - 3000/tcp  # Changed from 443 to 3000
  ports:
    - "${SCRAPER_LISTEN_IP:-127.0.0.1}:${SCRAPER_LISTEN_PORT:-9443}:3000"  # Map to port 3000
  environment:
    - MAX_CONCURRENT_SESSIONS=${LOCAL_SCRAPER_MAX_CONCURRENT_SESSIONS:-10}
    - USERNAME=${LOCAL_SCRAPER_USERNAME:-someuser}
    - PASSWORD=${LOCAL_SCRAPER_PASSWORD:-somepass}
  logging:
    options:
      max-size: 50m
      max-file: "7"
  volumes:
    - scraper-ssl:/usr/src/app/ssl
  networks:
    - pentagi-network
  shm_size: 2g

2. Update .env file - change the scraper URL to use HTTP and port 3000:

# Scraper configuration for Podman rootless
SCRAPER_PRIVATE_URL=http://someuser:somepass@scraper:3000/
LOCAL_SCRAPER_USERNAME=someuser
LOCAL_SCRAPER_PASSWORD=somepass

[!IMPORTANT]
Key changes for Podman:

• Use HTTP instead of HTTPS for SCRAPER_PRIVATE_URL
• Use port 3000 instead of 443
• Change internal expose to 3000/tcp
• Update port mapping to target 3000 instead of 443

3. Recreate containers:

podman-compose down
podman-compose up -d --force-recreate

4. Test scraper connectivity:

# Test from within the pentagi container
podman exec -it pentagi wget -O- "http://someuser:somepass@scraper:3000/html?url=http://example.com"

If you see HTML output, the scraper is working correctly.

Podman Rootful Mode

If you're running Podman in rootful mode (with sudo), you can use the default configuration without modifications. The scraper will work on port 443 as intended.

Docker Compatibility

All Podman configurations remain fully compatible with Docker. The non-privileged port approach works identically on both container runtimes.

Assistant Configuration Copy Section

PentAGI allows you to configure default behavior for 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.

How to Use PentAGI After Login Copy Chapter

Once the stack is running and you can sign in to the web UI, the fastest way to start is through the Flows workflow.

1. Create your first flow Copy Section

1. Open Flows in the sidebar.
2. Click New Flow.
3. Choose the mode that fits your goal:
   • Automation: fully autonomous execution for a testing goal you want PentAGI to carry out end-to-end
   • Assistant: interactive back-and-forth help when you want to steer the investigation step by step. In this mode you can also enable the Use Agents toggle to let PentAGI delegate subtasks to specialized sub-agents for more complex investigations.
4. Select the LLM provider you want to use for this flow.
5. Describe the target and the objective in natural language in the message box.

Good first prompts usually include:

• the target system or URL
• the type of assessment you want
• any scope limitations or rules of engagement
• the result you expect, such as a vulnerability report or validation of a hypothesis

Example:

Assess https://target.example for common web application vulnerabilities. Focus on authentication, file handling, and injection issues. Stay within the provided target only and summarize confirmed findings with reproduction steps.

Only test systems you own or are explicitly authorized to assess. See EULA.md for the acceptable use requirements.

2. Use templates for repeatable workflows Copy Section

The new flow form includes a template picker, which can prefill the message box with a saved flow template. This is useful when you run similar assessments repeatedly.

• Use an existing template if you already have one saved in Templates
• Start from the example prompt in examples/prompts/base_web_pentest.md if you need a practical baseline for web testing
• Adjust the target, scope, and constraints before starting the flow

Templates are starting points. You do not need special syntax to use PentAGI: plain natural-language instructions work well as long as the target and goal are clear.

3. Monitor execution and review output Copy Section

After submitting the flow, PentAGI opens the flow page automatically.

• Use the main flow view to follow messages, agent activity, and task progress
• Inspect tool activity and terminal output as the flow runs
• Review generated tasks and subtasks to understand what PentAGI is doing

Once the flow has enough results, use the Report menu on the flow page to:

• open the report in a web view
• copy the generated report to the clipboard
• download the report as Markdown
• download the report as PDF

4. Use the Assistant view to steer an active flow Copy Section

Each flow also includes an Assistant view for interactive guidance. This is useful when the autonomous run uncovers something that needs human direction instead of a hard restart.

• Open the Assistant view for the same flow when you want to inspect the current state before changing anything.
• Use the assistant to check flow status, stop the current task, submit follow-up instructions, or patch the remaining planned subtasks before the next step runs.
• Treat this as an explicit control path for the current flow, not as an invisible background queue. If you want to change direction, say so clearly and keep the new instruction tied to the current engagement scope.
• This works best for clarifying scope, redirecting priorities after intermediate findings, or answering an automation checkpoint without losing the rest of the flow context.

5. Manage flow-scoped files Copy Section

Each flow has its own Files tab in the flow page. Files are scoped to the parent flow: they live in {dataDir}/flow-{id}-data/ on the host and never leak into other flows.

The tab exposes three sources of files:

• Uploads (uploads/): files you provide from the web UI. Use the Upload files action, or drag and drop directly onto the Files tab. While the agent container is running, uploaded files are also pushed into it at /work/uploads/ so the agent can read them with normal shell tools.
• Resources (resources/): files attached from your saved user resources library via Attach resources from library. Attached resources are copied into the flow and pushed into the running container at /work/resources/.
• Container (container/): snapshots pulled from the running agent container via Pull file or directory from container. These are read-only on the flow side and are never sent back to the container.

Per-file actions in the Files tab include Download, Copy path, Save as resource (promote a flow file into your reusable resources library), and Delete. The Pull action is disabled when the container is not running, with the tooltip "Container is not running".

Uploaded files and attached resources are listed automatically in the agent's system prompts via the {{.UserFiles}} template variable, which renders a compact <task_files> XML block (with nested `` and <resources> sections), so the assistant and automation agents can reference them by path without you pasting the contents into chat. Container snapshots are visible in the UI only and are not auto-injected back into the prompt.

Current limits and limitations to be aware of:

• Maximum upload file size is 300 MB; per upload request up to 1000 files and 2 GB total. File names are capped at 255 bytes (roughly 255 ASCII characters; non-ASCII names use multiple bytes per character).
• Uploads and resources are mirrored into the running container at the fixed paths /work/uploads/ and /work/resources/; files written to other container paths are not auto-mirrored back into the flow file model. Container snapshots can originate from any container path you pull (for example /etc/...) and are cached on the flow side under container/; they are not pushed back into the container.
• Container snapshots are point-in-time pulls. Editing a snapshot in the UI does not write back into the running container.
• Deleting a flow today removes the flow record and its long-term memory entries, but does not yet archive or remove the flow's flow-{id}-data/ directory on disk. Operators are still expected to clean up the data directory manually if they want to reclaim the space.

For early testing, start with a narrow target and a single clear objective. This makes the output easier to review and helps you refine your prompts before running larger assessments.

API Access Copy Chapter

PentAGI provides comprehensive programmatic access through both REST and GraphQL APIs, allowing you to integrate penetration testing workflows into your automation pipelines, CI/CD processes, and custom applications.

Generating API Tokens Copy Section

API tokens are managed through the PentAGI web interface:

1. Navigate to Settings → API Tokens in the web UI
2. Click Create Token to generate a new API token
3. Configure token properties:
   • Name (optional): A descriptive name for the token
   • Expiration Date: When the token will expire (minimum 1 minute, maximum 3 years)
4. Click Create and copy the token immediately - it will only be shown once for security reasons
5. Use the token as a Bearer token in your API requests

Each token is associated with your user account and inherits your role's permissions.

Using API Tokens Copy Section

Include the API token in the Authorization header of your HTTP requests:

# GraphQL API example
curl -X POST https://your-pentagi-instance:8443/api/v1/graphql \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ flows { id title status } }"}'

# REST API example
curl https://your-pentagi-instance:8443/api/v1/flows \
  -H "Authorization: Bearer YOUR_API_TOKEN"

API Exploration and Testing Copy Section

PentAGI provides interactive documentation for exploring and testing API endpoints:

GraphQL Playground

Access the GraphQL Playground at https://your-pentagi-instance:8443/api/v1/graphql/playground

1. Click the HTTP Headers tab at the bottom
2. Add your authorization header:
   json Copy

   {
     "Authorization": "Bearer YOUR_API_TOKEN"
   }

3. Explore the schema, run queries, and test mutations interactively

Swagger UI

Access the REST API documentation at https://your-pentagi-instance:8443/api/v1/swagger/index.html

1. Click the Authorize button
2. Enter your token in the format: Bearer YOUR_API_TOKEN
3. Click Authorize to apply
4. Test endpoints directly from the Swagger UI

Generating API Clients Copy Section

You can generate type-safe API clients for your preferred programming language using the schema files included with PentAGI:

GraphQL Clients

The GraphQL schema is available at:

• Web UI: Navigate to Settings to download schema.graphqls
• Direct file: backend/pkg/graph/schema.graphqls in the repository

Generate clients using tools like:

• GraphQL Code Generator (JavaScript/TypeScript): https://the-guild.dev/graphql/codegen
• genqlient (Go): https://github.com/Khan/genqlient
• Apollo iOS (Swift): https://www.apollographql.com/docs/ios

REST API Clients

The OpenAPI specification is available at:

• Swagger JSON: https://your-pentagi-instance:8443/api/v1/swagger/doc.json
• Swagger YAML: Available in backend/pkg/server/docs/swagger.yaml

Generate clients using:

• OpenAPI Generator: https://openapi-generator.tech

  bash Copy

  openapi-generator-cli generate \
    -i https://your-pentagi-instance:8443/api/v1/swagger/doc.json \
    -g python \
    -o ./pentagi-client

• Swagger Codegen: https://github.com/swagger-api/swagger-codegen

  bash Copy

  swagger-codegen generate \
    -i https://your-pentagi-instance:8443/api/v1/swagger/doc.json \
    -l typescript-axios \
    -o ./pentagi-client

• swagger-typescript-api (TypeScript): https://github.com/acacode/swagger-typescript-api

  bash Copy

  npx swagger-typescript-api \
    -p https://your-pentagi-instance:8443/api/v1/swagger/doc.json \
    -o ./src/api \
    -n pentagi-api.ts

API Usage Examples Copy Section

Creating a New Flow (GraphQL)

mutation CreateFlow {
  createFlow(
    modelProvider: "openai"
    input: "Test the security of https://example.com"
  ) {
    id
    title
    status
    createdAt
  }
}

Listing Flows (REST API)

curl https://your-pentagi-instance:8443/api/v1/flows \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  | jq '.flows[] | {id, title, status}'

Python Client Example

import requests

class PentAGIClient:
    def __init__(self, base_url, api_token):
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {api_token}",
            "Content-Type": "application/json"
        }
    
    def create_flow(self, provider, target):
        query = """
        mutation CreateFlow($provider: String!, $input: String!) {
          createFlow(modelProvider: $provider, input: $input) {
            id
            title
            status
          }
        }
        """
        response = requests.post(
            f"{self.base_url}/api/v1/graphql",
            json={
                "query": query,
                "variables": {
                    "provider": provider,
                    "input": target
                }
            },
            headers=self.headers
        )
        return response.json()
    
    def get_flows(self):
        response = requests.get(
            f"{self.base_url}/api/v1/flows",
            headers=self.headers
        )
        return response.json()

# Usage
client = PentAGIClient(
    "https://your-pentagi-instance:8443",
    "your_api_token_here"
)

# Create a new flow
flow = client.create_flow("openai", "Scan https://example.com for vulnerabilities")
print(f"Created flow: {flow}")

# List all flows
flows = client.get_flows()
print(f"Total flows: {len(flows['flows'])}")

TypeScript Client Example

/* Detailed source-code truncated for AI context efficiency. */

Security Best Practices Copy Section

When working with API tokens:

• Never commit tokens to version control - use environment variables or secrets management
• Rotate tokens regularly - set appropriate expiration dates and create new tokens periodically
• Use separate tokens for different applications - makes it easier to revoke access if needed
• Monitor token usage - review API token activity in the Settings page
• Revoke unused tokens - disable or delete tokens that are no longer needed
• Use HTTPS only - never send API tokens over unencrypted connections

Token Management Copy Section

• View tokens: See all your active tokens in Settings → API Tokens
• Edit tokens: Update token names or revoke tokens
• Delete tokens: Permanently remove tokens (this action cannot be undone)
• Token ID: Each token has a unique ID that can be copied for reference

The token list shows:

• Token name (if provided)
• Token ID (unique identifier)
• Status (active/revoked/expired)
• Creation date
• Expiration date

Custom LLM Provider Configuration Copy Section

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

[!TIP]
For production-grade local deployments, consider using vLLM with Qwen3.5-27B-FP8 for optimal performance. See our comprehensive deployment guide which includes hardware requirements, configuration templates (thinking mode and non-thinking mode), and performance benchmarks showing 13K TPS prompt processing on 4× RTX 5090 GPUs.

The LLM_SERVER_PROVIDER setting is particularly useful when using LiteLLM proxy, which adds a provider prefix to model names. For example, when connecting to Moonshot API through LiteLLM, models like kimi-2.5 become moonshot/kimi-2.5. By setting LLM_SERVER_PROVIDER=moonshot, you can use the same provider configuration file for both direct API access and LiteLLM proxy access without modifications.

The LLM_SERVER_PRESERVE_REASONING setting controls whether reasoning content is preserved in multi-turn conversations:

• false (default): Reasoning content is not preserved in conversation history
• true: Reasoning content is preserved and sent in subsequent API calls

This setting is required by some LLM providers (e.g., Moonshot) that return errors like "thinking is enabled but reasoning_content is missing in assistant tool call message" when reasoning content is not included in multi-turn conversations. Enable this setting if your provider requires reasoning content to be preserved.

Using Azure OpenAI

The custom provider calls Azure OpenAI deployments directly:

LLM_SERVER_URL=https://<resource>.openai.azure.com  # the Endpoint from the resource's Keys and Endpoint page, without an /openai path
LLM_SERVER_KEY=your_azure_openai_api_key            # KEY 1 or KEY 2 from the same page
LLM_SERVER_API_TYPE=azure                           # the api-key header and deployment URLs
LLM_SERVER_API_VERSION=2024-10-21                   # the default; a newer version works as well
LLM_SERVER_MODEL=                                   # Leave empty, models are specified in the config
LLM_SERVER_CONFIG_PATH=/opt/pentagi/conf/azure-openai.provider.yml

Azure addresses a deployment, not a model: PentAGI calls <LLM_SERVER_URL>/openai/deployments/<model>/chat/completions?api-version=<version>, so every model: in the provider config must be the name of a deployment in your resource, and LLM_SERVER_PROVIDER must stay empty, because its prefix would become part of the deployment name. The bundled azure-openai.provider.yml expects deployments named after their models: gpt-4.1, gpt-4.1-mini and o4-mini. If yours are named differently, copy the file, change the model: values, and mount your copy:

PENTAGI_LLM_SERVER_CONFIG_PATH=/path/on/host/my-azure.provider.yml  # mounted at /opt/pentagi/conf/custom.provider.yml
LLM_SERVER_CONFIG_PATH=/opt/pentagi/conf/custom.provider.yml

After docker compose up -d, check the deployments before running a flow. Without -config, ctester tests the file LLM_SERVER_CONFIG_PATH names, the bundled one or your copy; the bundled configuration's own run is in examples/tests/azure-openai-report.md:

docker exec -it pentagi /opt/pentagi/bin/ctester

• PentAGI does not read a model catalogue from Azure, so it knows no context window for a deployment and does not compact agent chains to fit one.
• LLM_SERVER_API_TYPE=azure_ad sends LLM_SERVER_KEY as a Microsoft Entra ID bearer token instead of an API key. PentAGI does not refresh it, and Entra tokens expire after about an hour, so use an API key for anything longer.
• Azure's OpenAI-compatible v1 API works as a plain endpoint too: set LLM_SERVER_URL=https://<resource>.openai.azure.com/openai/v1 and leave LLM_SERVER_API_TYPE empty; model: is still the deployment name.
• The installer's custom provider form has API Type and API Version fields for the same two settings.
• Embeddings are configured separately through EMBEDDING_* and have no Azure mode; LLM_SERVER_API_TYPE does not apply to them.

Troubleshooting: tool-call (function-call) parser errors

PentAGI drives its agents with tool calls (also called function calls), so any custom OpenAI-compatible backend configured through LLM_SERVER_* must return valid tool-call JSON in the format the OpenAI Chat Completions API defines. When the backend emits malformed, truncated, or non-conforming tool-call arguments, the agent chain cannot continue.

Self-hosted engines such as llama.cpp, SGLang, and vLLM usually require a specific tool-call parser and a matching chat template to produce correct tool-call output. If the parser is missing or mismatched for the model you are serving, tool-call arguments can come back corrupted. Compatibility therefore depends on the backend's tool-call/function-call behavior and configuration, not on PentAGI alone; not every llama.cpp or SGLang setup produces valid tool calls out of the box.

Typical symptoms:

• Backend or proxy errors such as Failed to parse tool call arguments as JSON (often surfaced through a LiteLLM proxy as an HTTP 500), or other unexpected 5xx/4xx responses from the LLM endpoint.
• A flow that runs for a few steps and then stops responding to new input in the UI.
• Repeated or looping tool calls that never converge.
• A flow that fails right at the start with failed to select primary docker image via llm call, because the first action in a flow is an LLM tool call to choose the container image; a backend that cannot return a valid tool call fails at this step too.

How to investigate:

1. Check both sides of the connection: the PentAGI logs (docker compose logs -f pentagi) and the inference backend or proxy logs (llama.cpp, SGLang, vLLM, or LiteLLM). The backend log usually shows the same parse error when it produced the malformed tool call.
2. Validate the provider before running a full flow with the ctester utility, which exercises tool-calling agent types directly. See Testing LLM Agents.
3. Confirm the backend's tool-call parser and chat template are the ones recommended for the model you are serving, and that the model itself supports tool calling.
4. Update PentAGI to the latest build. Recent versions sanitize malformed function-call arguments returned by the model so a single bad response no longer stalls the whole flow; older builds forwarded the corrupted arguments and could get stuck.

Ollama Provider Configuration Copy Section

PentAGI supports Ollama for both local LLM inference (zero-cost, enhanced privacy) and Ollama Cloud (managed service with free tier).

Configuration Variables

Ollama Cloud Configuration

Ollama Cloud provides managed inference with a free tier and paid plans. Paid usage is billed from included or purchased credits at each model's published per-token rate; off-peak discounts apply to selected DeepSeek models.

Free Tier Setup (Single Model)

# Free tier allows one model at a time
OLLAMA_SERVER_URL=https://ollama.com
OLLAMA_SERVER_API_KEY=your_ollama_cloud_api_key
OLLAMA_SERVER_MODEL=gpt-oss:120b  # Example: OpenAI OSS 120B model

Paid Tier Setup (Multi-Model with Pre-built Configuration)

For paid tiers supporting concurrent requests and pay-as-you-go credits, use the pre-built Ollama Cloud configuration:

# Using pre-built Ollama Cloud configuration (included in Docker image)
OLLAMA_SERVER_URL=https://ollama.com
OLLAMA_SERVER_API_KEY=your_ollama_cloud_api_key
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama-cloud.provider.yml

The pre-built ollama-cloud.provider.yml configuration includes optimized model assignments for all agent types:

• Simple/Reflector/Enricher: minimax-m2.7:cloud - Reliable low-latency utility model
• Simple JSON/Primary Agent/Assistant/Pentester/Searcher: deepseek-v4.1-flash:cloud - Efficient long-context model at the same input/output rate as MiniMax M2.7
• Generator/Adviser: kimi-k3:cloud - Flagship long-horizon planning and knowledge-work model
• Refiner: glm-5.3:cloud - Flagship coding and agentic model
• Coder/Installer: kimi-k2.7-code:cloud - Coding-specialized long-context model

Custom Configuration (Advanced)

To create your own agent configuration, mount a custom file from your host filesystem:

# Using custom provider configuration
OLLAMA_SERVER_URL=https://ollama.com
OLLAMA_SERVER_API_KEY=your_ollama_cloud_api_key
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama.provider.yml

# Mount custom configuration from host filesystem (in .env or docker-compose override)
PENTAGI_OLLAMA_SERVER_CONFIG_PATH=/path/on/host/my-ollama-config.yml

The PENTAGI_OLLAMA_SERVER_CONFIG_PATH environment variable maps your host configuration file to /opt/pentagi/conf/ollama.provider.yml inside the container.

Example custom configuration (my-ollama-config.yml):

primary_agent:
  model: "deepseek-v4-flash:cloud"
  temperature: 1.0
  top_p: 0.95
  max_tokens: 32768

coder:
  model: "kimi-k2.7-code:cloud"
  temperature: 1.0
  max_tokens: 20480

Local Ollama Configuration

For self-hosted Ollama instances:

# Basic local Ollama setup
OLLAMA_SERVER_URL=http://localhost:11434
OLLAMA_SERVER_MODEL=llama3.1:8b-instruct-q8_0

# Production setup with auto-pull and model discovery
OLLAMA_SERVER_URL=http://ollama-server:11434
OLLAMA_SERVER_PULL_MODELS_ENABLED=true
OLLAMA_SERVER_PULL_MODELS_TIMEOUT=900
OLLAMA_SERVER_LOAD_MODELS_ENABLED=true

# Using pre-built configurations from Docker image
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama-llama318b.provider.yml
# or
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama-qwen332b-fp16-tc.provider.yml
# or
OLLAMA_SERVER_CONFIG_PATH=/opt/pentagi/conf/ollama-qwq32b-fp16-tc.provider.yml

Performance Considerations:

• Model Discovery (OLLAMA_SERVER_LOAD_MODELS_ENABLED=true): Adds 1-2s startup latency querying Ollama API
• Auto-pull (OLLAMA_SERVER_PULL_MODELS_ENABLED=true): First startup may take several minutes downloading models
• Pull timeout (OLLAMA_SERVER_PULL_MODELS_TIMEOUT=900): 15 minutes in seconds
• Static Config: Disable both flags and specify models in config file for fastest startup

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 Copy Section

PentAGI integrates with OpenAI's comprehensive model lineup, featuring advanced reasoning capabilities with extended chain-of-thought, agentic models with enhanced tool integration, and specialized code models for security engineering.

Configuration Variables

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

Supported Models

PentAGI supports 32 OpenAI models with tool calling, streaming, reasoning modes, and prompt caching. Models marked with * are used in default configuration. Models marked ⚠️ are deprecated by OpenAI and kept only for backward compatibility with agent configs already pinned to those names — avoid them for new assignments.

GPT-5.6 Series - Latest Frontier (Feb 2026 knowledge cutoff, 1.05M context, 128K max output)

GPT-5.5 Series - Frontier (Dec 2025 knowledge cutoff, 1.05M context, 128K max output)

GPT-5.4 Series - Advanced Reasoning at Scale (1M context)

GPT-5.2 Series - Previous Flagship Agentic

GPT-5/5.1 Series - Advanced Agentic Models

GPT-4.1 Series - Enhanced Intelligence (Non-Reasoning)

GPT-4o Series - Multimodal (Non-Reasoning)

o-Series - Advanced Reasoning Models (Current)

Deprecated Models - Kept for Backward Compatibility ⚠️

These models were marked deprecated by OpenAI. PentAGI keeps them defined only so that pre-existing agent configs pinned to these names keep working; do not assign them to new agents.

Prices: Per 1M tokens. Reasoning models include thinking tokens in output pricing.

[!WARNING]
GPT-5/5.1/5.2 Models - Trusted Access Required

The original GPT-5, GPT-5.1, and GPT-5.2 models (gpt-5, gpt-5.1, gpt-5.2, gpt-5-pro, gpt-5.2-pro, and all deprecated Codex variants) work unstably with PentAGI and may trigger OpenAI's cybersecurity safety mechanisms without verified access. This does not affect the newer GPT-5.4/5.5/5.6 series used in PentAGI's default configuration below.

To use these models reliably:

1. Individual users: Verify your identity at chatgpt.com/cyber
2. Enterprise teams: Request trusted access through your OpenAI representative
3. Security researchers: Apply for the Cybersecurity Grant Program (includes $10M in API credits)

Recommended alternatives without verification:

• Use PentAGI's defaults — gpt-5.4-mini/gpt-5.4-nano/gpt-5.6-terra — which work out of the box
• Use o3/o3-pro for reasoning tasks
• Use gpt-4.1 series for general intelligence and function calling without reasoning

Reasoning Configuration:

• Reasoning forced off by default: every default agent assigned gpt-5.4-mini or gpt-5.6-terra (primary_agent, assistant, generator, refiner, adviser, reflector, coder, installer, pentester) sets reasoning: {mode: off} — this genuinely disables reasoning, it is not simply "low effort". PentAGI calls OpenAI exclusively through /v1/chat/completions (never /v1/responses), and this endpoint rejects requests that combine function tools with these models' default-on thinking; forcing thinking off is required for tool calls to work reliably (see the investigation notes in backend/pkg/providers/openai/config.yml).
• No override needed for gpt-5.4-nano: used for simple, simple_json, searcher, and enricher, this tier does not default to thinking on, so tools attach without conflict and no reasoning override is required.
• Manual tuning available: outside the default assignments, GPT-5.6/5.5/5.4/5.2 series models expose explicit reasoning effort levels (low/medium/high/xhigh, plus none on GPT-5.5) for custom agent configs that need variable reasoning depth with tool calling disabled or via /v1/responses.

Key Features:

• Extended Reasoning: GPT-5.4/5.5/5.6 and o-series models with chain-of-thought for complex security analysis
• Agentic Intelligence: GPT-5.4/5.5/5.6 series with enhanced tool integration, million-token context windows, and autonomous capabilities
• Prompt Caching: Cost reduction on repeated context (10-50% of input price)
• Code Specialization: Legacy Codex models remain available (deprecated) for vulnerability discovery and exploit development in pinned configs
• Multimodal Support: gpt-4o-mini for vision-based security assessments
• Tool Calling: Robust function calling across all models for pentesting tool orchestration
• Streaming: Real-time response streaming for interactive workflows
• Proven Track Record: Industry-leading models with CVE discoveries and real-world security applications

Anthropic Provider Configuration Copy Section

PentAGI integrates with Anthropic's Claude models, featuring advanced extended thinking capabilities, exceptional safety mechanisms, and sophisticated understanding of complex security contexts with prompt caching.

Configuration Variables

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

# Workload Identity Federation instead of a long-lived key (leave ANTHROPIC_API_KEY empty)
ANTHROPIC_FEDERATION_RULE_ID=fdrl_...
ANTHROPIC_ORGANIZATION_ID=00000000-0000-0000-0000-000000000000
ANTHROPIC_SERVICE_ACCOUNT_ID=svac_...
ANTHROPIC_WORKSPACE_ID=wrkspc_...
ANTHROPIC_IDENTITY_TOKEN_FILE=/var/run/secrets/anthropic.com/token

With Workload Identity Federation, PentAGI exchanges the identity token your platform issues (Kubernetes, GitHub Actions, cloud IAM, any OIDC issuer) for a short-lived Claude API token and refreshes it on its own. The token file is re-read on every exchange and must be mounted into the pentagi container. A token carrying a jti claim, as Kubernetes and GitHub Actions tokens do, can be exchanged only once, so the file must hold a new token before each refresh: rotate it well within the lifetime of the minted token. ANTHROPIC_IDENTITY_TOKEN takes the token itself where the platform injects it as a variable, but it is read once and cannot be rotated, so it suits only runs shorter than the identity token's lifetime. A set ANTHROPIC_API_KEY wins over federation. See backend/docs/config.md for every variable.

[!NOTE]
Google Vertex AI for Claude models

PentAGI does not currently expose a dedicated Google Vertex AI configuration path for Anthropic Claude in .env. There is no separate Vertex AI API key field at this time, and the existing Anthropic variables (ANTHROPIC_API_KEY, ANTHROPIC_SERVER_URL) target the direct Anthropic API. Supported routes for Claude are:

• Direct Anthropic API: ANTHROPIC_API_KEY and ANTHROPIC_SERVER_URL (see above).
• AWS Bedrock: BEDROCK_* variables (see AWS Bedrock Provider Configuration).

If you need to use Vertex AI today, the safest supported workaround is to expose Vertex AI through an OpenAI-compatible proxy or gateway that translates Vertex AI calls into the Chat Completions format while preserving the chat and tool-call behavior PentAGI relies on, then point the Custom LLM provider at that gateway via LLM_SERVER_URL, LLM_SERVER_KEY, and LLM_SERVER_MODEL. This path is only as reliable as the gateway you choose.

Supported Models

PentAGI supports 9 Claude models with tool calling, streaming, extended thinking, adaptive thinking, and prompt caching. Models marked with * are used in default configuration.

Claude 5 Series - Newest Models (2026)

Claude 4 Series

Legacy Models - Still Supported

Prices: Per 1M tokens. Cache pricing includes both Read and Write costs.

Extended Thinking Configuration (default agent config, see backend/pkg/providers/anthropic/config.yml):

• Generator / Refiner (claude-opus-4-8): adaptive reasoning at xhigh/high effort for maximum reasoning depth on complex exploit development
• Primary agent, assistant, coder, adviser, installer, pentester (claude-sonnet-5): adaptive reasoning (adviser at xhigh effort) for balanced code analysis and vulnerability research
• Reflector, searcher (claude-haiku-4-5): fixed reasoning budget of 1024 tokens for focused reasoning on specific tasks
• Simple, simple_json, enricher (claude-haiku-4-5): no thinking, optimized for speed

Key Features:

• Extended Thinking: All Claude 4.5+ models support configurable chain-of-thought reasoning depths for complex security analysis
• Adaptive Thinking: Claude 4.6 series (Opus/Sonnet) dynamically adjusts reasoning depth based on task complexity; Claude Opus 4.7/4.8 and the Claude 5 series (Sonnet/Fable) are adaptive-thinking-only (manual budget thinking and sampling parameters are rejected with HTTP 400)
• Prompt Caching: Significant cost reduction with separate read/write pricing (10% read, 125% write of input)
• Extended Context Window: 200K tokens standard, up to 1M tokens (beta) for Claude Opus/Sonnet 4.6 for comprehensive codebase analysis
• Tool Calling: Robust function calling with exceptional accuracy for security tool orchestration
• Streaming: Real-time response streaming for interactive penetration testing workflows
• Safety-First Design: Built-in safety mechanisms ensuring responsible security testing practices
• Multimodal Support: Vision capabilities in latest models for screenshot analysis and UI security assessment
• Constitutional AI: Advanced safety training providing reliable and ethical security guidance

Google AI (Gemini) Provider Configuration Copy Section

PentAGI integrates with Google's Gemini models through the Google AI API, offering state-of-the-art multimodal reasoning capabilities with extended thinking and context caching.

Configuration Variables

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

Supported Models

PentAGI lists 14 Gemini model IDs that responded through the tested API endpoint. Availability in the catalogue does not mean a model is suitable for every agent role: the saved-chain results below determine the bundled defaults. Models marked with * are used in config.yml; the full replay matrix describes role and outcome limits.

Prices: Per 1M tokens. Google limits Gemini 2.5 access for new projects, so PentAGI uses the tested 3.1 Flash-Lite as its fallback and simple-role default. Existing custom 2.5 configurations remain usable where the project has access.

Default Model Assignments (config.yml):

• gemini-3.1-flash-lite - simple, simple_json, reflector, searcher, enricher
• gemini-3.5-flash-lite - primary_agent, assistant, generator, refiner, adviser, coder, installer, pentester

Both default models passed a basic API/configuration smoke for every role. Google reports stronger agentic and coding performance for 3.5 Flash-Lite than 3.1 Flash-Lite; PentAGI's saved production chains cover generator and reflector only, so the smoke does not establish semantic quality or multi-turn reliability for other roles. On two saved generator chains, 3.5 Flash-Lite first chose the allowed memorist tool and called subtask_list after a synthetic tool response; no real tool was executed in that continuation.

Reasoning Effort Levels:

• High: Deeper reasoning for planning and review (primary_agent, generator, refiner, adviser)
• Medium: Balanced reasoning for execution roles (assistant, coder, installer, pentester)
• Not set: No thinking setting is sent and the model's own default applies (simple, simple_json, reflector, searcher, enricher)

AWS Bedrock Provider Configuration Copy Section

PentAGI integrates with Amazon Bedrock, offering access to 20+ foundation models from leading AI companies including Anthropic, Amazon, Cohere, DeepSeek, OpenAI, Qwen, Mistral, and Moonshot.

Configuration Variables

Authentication Priority: BEDROCK_DEFAULT_AUTH → BEDROCK_BEARER_TOKEN → BEDROCK_ACCESS_KEY_ID+BEDROCK_SECRET_ACCESS_KEY

Configuration Examples

# Recommended: Default AWS SDK authentication (EC2/ECS/Lambda roles)
BEDROCK_REGION=us-east-1
BEDROCK_DEFAULT_AUTH=true

# Bearer token authentication (AWS STS, custom auth)
BEDROCK_REGION=us-east-1
BEDROCK_BEARER_TOKEN=your_bearer_token

# Static credentials (development, testing)
BEDROCK_REGION=us-east-1
BEDROCK_ACCESS_KEY_ID=your_aws_access_key
BEDROCK_SECRET_ACCESS_KEY=your_aws_secret_key

# With proxy and custom endpoint
BEDROCK_REGION=us-east-1
BEDROCK_DEFAULT_AUTH=true
BEDROCK_SERVER_URL=https://bedrock-runtime.us-east-1.vpce-xxx.amazonaws.com
PROXY_URL=http://your-proxy:8080

Custom Provider Config and Models (advanced)

By default the Bedrock provider uses a per-agent config and model catalog compiled into the binary. One optional path overrides them without rebuilding:

• BEDROCK_CONFIG_PATH — a YAML file (same shape as the other provider configs) that replaces the built-in per-agent model/price assignments. See examples/configs/bedrock-glm-flash.provider.yml.

This is useful to expose a Bedrock model newer than the compiled-in catalog — for example Z.AI's zai.glm-4.7-flash. Use the exact Model ID from the model's AWS Bedrock detail page; add a us./eu./apac. inference-profile prefix only when that page marks the model as requiring cross-region inference (zai.glm-4.7-flash is In-Region, so it is used as-is, with no prefix).

With Docker Compose the file is mounted from the host at /opt/pentagi/conf/bedrock.provider.yml. The default source is ./example.bedrock.provider.yml beside docker-compose.yml, a copy of the built-in config (examples/configs/bedrock.provider.yml) that the installer extracts, so editing it and pointing the backend at the mounted path is enough:

# tell the backend to read the mounted file
BEDROCK_CONFIG_PATH=/opt/pentagi/conf/bedrock.provider.yml

# optional: mount another file from the host instead of ./example.bedrock.provider.yml
PENTAGI_BEDROCK_CONFIG_PATH=/path/on/host/my-bedrock.provider.yml

Supported Models

PentAGI supports 24 AWS Bedrock models with tool calling, streaming, and multimodal capabilities. Models marked with * are used in default configuration.

Prices: Per 1M tokens. Models with thinking/reasoning support additional compute costs during reasoning phase.

Tested but Incompatible Models

Some AWS Bedrock models were tested but are not supported due to technical limitations:

[!IMPORTANT]
Rate Limits & Quota Management

Default AWS Bedrock quotas for Claude models are extremely restrictive (2-20 requests/minute for new accounts). For production penetration testing:

1. Request quota increases through AWS Service Quotas console for models you plan to use
2. Use Amazon Nova models - higher default quotas and excellent performance
3. Enable provisioned throughput for consistent high-volume testing
4. Monitor usage - AWS throttles aggressively at quota limits

Without quota increases, expect frequent delays and workflow interruptions.

[!WARNING]
Converse API Requirements

PentAGI uses Amazon Bedrock Converse API for unified model access. All supported models require:

• ✅ Converse/ConverseStream API support
• ✅ Tool use (function calling) for penetration testing workflows
• ✅ Streaming tool use for real-time feedback

Verify model capabilities at: AWS Bedrock Model Features

Key Features:

• Automatic Prompt Caching: 40-70% cost reduction on repeated context (Claude 4.x models)
• Extended Thinking: Step-by-step reasoning for complex security analysis (Claude, DeepSeek R1, OpenAI GPT)
• Multimodal Analysis: Process screenshots, diagrams, video for comprehensive testing (Nova, Claude, Mistral, Kimi)
• Tool Calling: Seamless integration with 20+ pentesting tools via function calling
• Streaming: Real-time response streaming for interactive security assessment workflows

DeepSeek Provider Configuration Copy Section

PentAGI integrates with DeepSeek, providing access to advanced AI models with strong reasoning, coding capabilities, and context caching at competitive prices.

Configuration Variables

Configuration Examples

# Direct API usage
DEEPSEEK_API_KEY=your_deepseek_api_key
DEEPSEEK_SERVER_URL=https://api.deepseek.com

# With LiteLLM proxy
DEEPSEEK_API_KEY=your_litellm_key
DEEPSEEK_SERVER_URL=http://litellm-proxy:4000
DEEPSEEK_PROVIDER=deepseek  # Adds prefix to model names (deepseek/deepseek-v4-flash) for LiteLLM

Supported Models

PentAGI supports 2 DeepSeek V4 models with tool calling, streaming, hybrid thinking/non-thinking modes, and context caching. Both models think by default and switch to non-thinking mode with reasoning.mode: off. Models marked with * are used in default configuration.

Prices: Per 1M tokens. Cache pricing applies to prompt tokens served from cache (input cache hit, reduced to 1/10 of launch price since 2026-04-26). Both models support hybrid thinking — thinking mode is enabled by default; set reasoning.mode: off (Reasoning Mode Off in the UI) to switch to non-thinking mode for faster/cheaper responses.

Pricing Note (deepseek-v4-pro): The 75% promotional discount on deepseek-v4-pro officially ended on 2026-05-31 15:59 UTC. The prices above reflect the standard post-promotional pricing. If you have legacy configurations using the discounted prices ($0.435/$0.87/$0.003625), update them to the current rates for accurate cost tracking.

The legacy model names deepseek-chat and deepseek-reasoner are scheduled
for deprecation by DeepSeek on 2026-07-24. Existing user configurations
referencing the legacy names continue to work until then; the defaults above
use the current V4 names. deepseek-chat maps to deepseek-v4-flash
non-thinking mode; deepseek-reasoner maps to deepseek-v4-flash thinking mode.

Default Agent Configuration:

Strategy: prefer deepseek-v4-flash (12x cheaper input, 12x cheaper output) as the workhorse for utility/lightweight agents; reserve deepseek-v4-pro for complex multi-step reasoning. The installer agent runs on Flash with thinking enabled because environment setup tasks (shell commands, config edits) rarely require pro-level reasoning. Run A/B tests on your own workloads before promoting more agents to Pro.

Note: DeepSeek ignores presence_penalty and frequency_penalty on both models. While thinking runs, temperature has no effect and langchaingo drops it, but top_p is sent and applied (values below 0.95 are raised to 0.95); in non-thinking mode top_p is fixed at 1.0, so langchaingo drops it and sends temperature instead. The thinking agents leave both unset, shown as "(auto)" in the table above.

Key Features:

• Hybrid Thinking Modes: Switch between thinking (deep reasoning) and non-thinking (fast) modes via reasoning.mode
• Automatic Prompt Caching: Significant cost reduction on repeated context via cache-hit pricing (1/10 of launch price)
• Extended Thinking: Reinforcement learning CoT for complex security analysis (both V4 models)
• Strong Coding: Optimized for code generation and exploit development
• Long Context: 1M token context window with up to 384K output tokens
• Tool Calling: Seamless integration with 20+ pentesting tools via function calling
• Streaming: Real-time response streaming for interactive workflows
• Multilingual: Strong Chinese and English support
• Additional Features: JSON Output, Chat Prefix Completion (beta), FIM/Fill-in-the-Middle Completion (non-thinking mode only)

Concurrency Limits: deepseek-v4-flash: 2500 concurrent requests; deepseek-v4-pro: 500 concurrent requests.

LiteLLM Integration: Set DEEPSEEK_PROVIDER=deepseek to enable model name prefixing when using default PentAGI configurations with LiteLLM proxy. Leave empty for direct API usage.

GLM Provider Configuration Copy Section

PentAGI integrates with GLM from Zhipu AI (Z.AI), providing advanced language models with MoE architecture, strong reasoning, and agentic capabilities developed by Tsinghua University.

Configuration Variables

Configuration Examples

# Direct API usage (international endpoint)
GLM_API_KEY=your_glm_api_key
GLM_SERVER_URL=https://api.z.ai/api/paas/v4

# Alternative endpoints
GLM_SERVER_URL=https://open.bigmodel.cn/api/paas/v4  # China
GLM_SERVER_URL=https://api.z.ai/api/coding/paas/v4   # Coding-specific

# With LiteLLM proxy
GLM_API_KEY=your_litellm_key
GLM_SERVER_URL=http://litellm-proxy:4000
GLM_PROVIDER=zai  # Adds prefix to model names (zai/glm-4) for LiteLLM

Supported Models

PentAGI supports 16 GLM models with tool calling, streaming, reasoning controls, and prompt caching. Models marked with * are used in the default configuration. GLM-5.3, GLM-5.3-Flash, and GLM-5.3-FlashX always reason and accept low, high, or max effort.

GLM-5.x Series - Latest Generation

GLM-4.7 Series - Premium with Interleaved Thinking

GLM-4.6 Series - Balanced with Auto-Thinking

GLM-4.5 Series - Unified Reasoning/Coding/Agents

GLM-4 Legacy - Dense Architecture

Prices: Per 1M tokens. Cache pricing is for prompt cache hit; cache storage is currently free per Z.AI promotion. GLM-4-32B has no cache support.

Default Agent Configuration:

Strategy: glm-5.3 for critical reasoning and glm-5.3-flash for inexpensive utility and latency-sensitive work. glm-5.3-flashx remains selectable but is not a default because not every compatible gateway deploys it.

All default roles use Z.AI's recommended temperature: 1.0 and top_p: 0.95. The full provider replay passed 288 of 289 cases; the sole failure was a transient gateway timeout and its focused retry passed.

Thinking Modes:

• Always on (GLM-5.3, GLM-5.3-Flash, GLM-5.3-FlashX): Cannot be disabled; reasoning.effort accepts low, high, or max
• Hybrid (GLM-5.2 and earlier GLM-5.x, GLM-4.7): Explicit toggle via reasoning.mode
• Auto (GLM-4.6, GLM-4.5 series): Model automatically determines when reasoning is needed
• Preserved Thinking (Z.AI Coding capability): all thinking-enabled agents in PentAGI also pass extra_body.thinking.clear_thinking: false so that reasoning_content from previous assistant turns is retained across the conversation. This is required on the standard API endpoint (/api/paas/v4) — on the Coding Plan endpoint it would be enabled by default. Improves reasoning continuity and cache hit rates in multi-turn tool call chains.
• All thinking-enabled agents also pass extra_body.tool_choice: auto defensively

Key Features:

• Long-Horizon Tasks: GLM-5.1 supports 8-hour sustained autonomous execution, ideal for complex multi-stage agentic workflows
• OpenClaw-Native Orchestration: GLM-5-Turbo is specifically optimized for tool invocation, instruction following, and long-chain execution
• Prompt Caching: Significant cost reduction on repeated context (cached input pricing shown)
• Ultra-Long Context: 200K tokens for GLM-5.x/4.7/4.6 series
• MoE Architecture: Efficient 744B/40B active (GLM-5/5.1), 355B/32B (GLM-4.5), 106B/12B (GLM-4.5-Air)
• Tool Calling: Seamless integration with 20+ pentesting tools via function calling
• Streaming: Real-time streaming with streaming tool calls support (GLM-4.6+)
• Multilingual: Exceptional Chinese and English NLP capabilities
• Free Options: GLM-4.7-Flash and GLM-4.5-Flash for prototyping and experimentation

LiteLLM Integration: Set GLM_PROVIDER=zai to enable model name prefixing when using default PentAGI configurations with LiteLLM proxy. Leave empty for direct API usage.

Kimi Provider Configuration Copy Section

PentAGI integrates with Kimi from Moonshot AI, providing ultra-long context models with multimodal capabilities perfect for analyzing extensive codebases and documentation.

Configuration Variables

Configuration Examples

# Direct API usage (international endpoint)
KIMI_API_KEY=your_kimi_api_key
KIMI_SERVER_URL=https://api.moonshot.ai/v1

# Alternative endpoint
KIMI_SERVER_URL=https://api.moonshot.cn/v1  # China

# With LiteLLM proxy
KIMI_API_KEY=your_litellm_key
KIMI_SERVER_URL=http://litellm-proxy:4000
KIMI_PROVIDER=moonshot  # Adds prefix to model names (moonshot/kimi-k3) for LiteLLM

Supported Models

PentAGI supports the four current Kimi models with tool calling, streaming, context caching, and multimodal input. Kimi K2.5, the older K2 series, kimi-latest, kimi-thinking-preview, and all Moonshot V1 models are retired and not included. Models marked with * are used in the default configuration.

Kimi K3 - Flagship (Always Reasoning)

Kimi K2.7 Code Series - Coding-Focused

Kimi K2.6 - Multimodal Model

Prices: Per 1M tokens. K3 cache writes cost $3.00 with the default five-minute TTL or $6.00 with a one-hour TTL; cache hits cost $0.30. K2.7 and K2.6 table prices distinguish cache misses from cache hits.

CRITICAL — Kimi parameter constraints per model family: API returns invalid_request_error for any deviation:

• kimi-k3: always reasons, no thinking param at all; reasoning depth is set via the top-level reasoning_effort field (low/high/max, default max) — PentAGI pins it to max for all agents using this model. temperature MUST be 1.0, top_p MUST be 0.95, n MUST be 1, presence_penalty/frequency_penalty MUST be 0. Do not switch effort per call — it invalidates the prefix cache.
• kimi-k2.7-code / kimi-k2.7-code-highspeed: thinking may be omitted; if set explicitly, only {"type":"enabled","keep":"all"} is accepted (type: disabled is rejected). reasoning_effort is not supported. temperature MUST be 1.0, top_p MUST be 0.95, n MUST be 1; tool_choice: required is not supported (use auto).
• kimi-k2.6: thinking mode needs temperature=1.0, top_p=0.95, n=1, thinking.keep="all"; non-thinking mode needs temperature=0.6, top_p=0.95, n=1.
• All Kimi models: presence_penalty=0, frequency_penalty=0, tool_choice in {auto, none}.

Default Agent Configuration:

Strategy: kimi-k2.6 handles utility and installer work, kimi-k2.7-code-highspeed serves the primary/assistant loop, kimi-k2.7-code serves tool-heavy generator/refiner/coder/pentester roles, and kimi-k3 handles adviser reasoning. This configuration passed all 294 conformance cases.

Key Features:

• Always-On Reasoning Flagship: kimi-k3 never disables thinking and offers a 1M token context for the most demanding long-horizon coding and knowledge work
• Ultra-Long Context: Up to 256K tokens (K2.7/K2.x) or 1M tokens (K3) for comprehensive codebase/documentation analysis
• Native Multimodal: K3, K2.7, and K2.6 support text, image, and video input
• Hybrid Thinking: K2.6 can toggle between thinking and non-thinking via reasoning.mode; K3 and K2.7 always reason
• Preserved Thinking (K2.7, K2.6): thinking.keep: "all" preserves historical reasoning_content across turns — required for multi-turn tool call chains
• Automatic Context Caching: K3/K2.7/K2.x models cache repeated prefixes
• Tool Calling: Full function-calling support for K3, K2.7, K2.x, and Moonshot V1
• Coding-Optimized Variants: kimi-k2.7-code/kimi-k2.7-code-highspeed target higher success rates on long-context programming tasks, with the highspeed variant tuned for throughput
• Multilingual: Strong Chinese, English, and multi-language support

Multi-turn with thinking + tool calls: PentAGI's universal reasoning preservation pattern (TextPartWithReasoning + WithPreserveReasoningContent) automatically ensures reasoning_content is sent back in the required TextContent → ToolCall order, satisfying Moonshot's "thinking is enabled but reasoning_content is missing in assistant tool call message" requirement.

LiteLLM Integration: Set KIMI_PROVIDER=moonshot to enable model name prefixing when using default PentAGI configurations with LiteLLM proxy. Leave empty for direct API usage.

Qwen Provider Configuration Copy Section

PentAGI uses one OpenAI-compatible door for both Qwen Cloud and Alibaba Cloud Model Studio (DashScope). The bundled catalogue is the union of their agent-capable Chat Completions models, while the default role configuration uses only model IDs available on both platforms.

Configuration Variables

Configuration Examples

# Direct Alibaba Cloud Model Studio usage (Global/US endpoint)
QWEN_API_KEY=your_qwen_api_key
QWEN_SERVER_URL=https://dashscope-us.aliyuncs.com/compatible-mode/v1
QWEN_PROVIDER=

# Alternative endpoints
QWEN_SERVER_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1  # International (Singapore)
QWEN_SERVER_URL=https://dashscope.aliyuncs.com/compatible-mode/v1       # Chinese Mainland (Beijing)

# Qwen Cloud through LiteLLM
QWEN_API_KEY=your_litellm_key
QWEN_SERVER_URL=http://litellm-proxy:4000
QWEN_PROVIDER=qwen_cloud

# Alibaba Cloud through LiteLLM
QWEN_PROVIDER=dashscope

Supported Models

PentAGI ships 50 text, reasoning, coding, and vision-language catalogue entries available from at least one of the two platforms. Image generation, video, speech, realtime, translation-only, embedding, reranking, and decision APIs use different protocols or billing units and are not exposed as PentAGI chat models. Prices below are Alibaba Cloud international list rates per 1M tokens where Alibaba offers the model; Qwen Cloud can bill differently.

Cross-platform model IDs used for portable configuration

Flagship Models (Top-tier Reasoning)

Balanced Plus Models (Mid-tier)

Fast Flash Models (Cost-optimized)

Code-Specialized Models

Vision-Language Models (Browser & Screenshot Analysis)

Open-Source Qwen3.8 Series

Open-Source Qwen3.6 Series

Open-Source Qwen3.5 Series

Open-Source Qwen3 Coder Series

Open-Source Qwen3 Dense & MoE Series

Third-Party Models

These models share the OpenAI-compatible Qwen door. Availability differs by platform; the cross-platform table above is authoritative for portable role configuration.

Prices: Per 1M tokens. Cache pricing reflects implicit cache hit (when available); MoE/dense open-source models do not expose cache pricing. Tiered models (Max/Plus) show lowest-tier pricing (typically ≤32k or ≤256k input); larger contexts incur higher rates per Alibaba Cloud pricing. deepseek-v4-flash-0731 shows the busy-hours rate; idle hours bill half.

Region Availability:

• Intl (International): Singapore region (dashscope-intl.aliyuncs.com)
• Global/US: US Virginia region (dashscope-us.aliyuncs.com)
• China: Chinese Mainland Beijing region (dashscope.aliyuncs.com)
• —: availability in that region is not recorded here

Default Agent Configuration:

The role configuration must run unchanged behind qwen_cloud/ and dashscope/. Qwen 3.6 Flash handles utility calls, DeepSeek V4.1 Flash provides the lower-cost workhorse tier, and DeepSeek V4 Pro remains on planning and advice. Thinking controls are left at provider defaults because Qwen Cloud does not accept the same disable operation that DashScope supports.

Key Features:

• Agent-Centric Design: Qwen3.7-Max is purpose-built for long-horizon autonomous execution and tool invocation
• Automatic Context Caching: 30-50% cost reduction on repeated context with implicit cache
• Extended Thinking: Chain-of-thought reasoning for complex security analysis (Qwen3.7/3.6/3.5/3-Max, QVQ-Max)
• Code Specialization: Qwen3-Coder series with multi-turn tool interaction and repository-level understanding
• Vision-Language: Qwen3-VL series for browser screenshot triage, 2D/3D localization, OCR-level analysis
• Tool Calling: Seamless integration with 20+ pentesting tools via function calling
• Streaming: Real-time response streaming for interactive workflows
• Multilingual: Strong Chinese, English, and multi-language support
• Open-Source Variants: Dense and MoE models from 27B to 2.4T for on-premises/air-gapped deployments

LiteLLM Integration: Set QWEN_PROVIDER=qwen_cloud for Qwen Cloud routes or QWEN_PROVIDER=dashscope for Alibaba Cloud routes. Leave it empty for direct DashScope API usage.

Alternative Integrations

DashScope is fully OpenAI-compatible, so Qwen can also power two other PentAGI subsystems through the standard OpenAI client.

As embedding provider (text-embedding-v4, see Alibaba Cloud Model Studio pricing):

EMBEDDING_PROVIDER=openai
EMBEDDING_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1  # International (Singapore)
# EMBEDDING_URL=https://dashscope.aliyuncs.com/compatible-mode/v1     # Chinese Mainland
EMBEDDING_KEY=sk-*******
EMBEDDING_MODEL=text-embedding-v4
EMBEDDING_BATCH_SIZE=         # optional, default applies
EMBEDDING_STRIP_NEW_LINES=    # optional, default applies

Note: the Global/US DashScope endpoint (dashscope-us.aliyuncs.com) does not expose embedding APIs — use the International or China endpoints for text-embedding-v4.

As OpenAI-typed custom LLM provider: instead of the dedicated QWEN_* variables, you can wire any Qwen chat model through PentAGI's custom OpenAI-compatible provider by pointing OPENAI_SERVER_URL (or a custom provider entry) to the DashScope /compatible-mode/v1 endpoint and selecting the desired Qwen model name. Useful when you already manage all model traffic through a single OpenAI-shaped client (e.g. shared with LiteLLM/OneAPI proxies).

MiniMax Provider Configuration Copy Section

PentAGI integrates with MiniMax's M-series through the OpenAI-compatible https://api.minimax.io/v1 endpoint: large-context agentic models with tool calling, JSON output, and streaming.

Configuration Variables

Configuration Examples

# Direct API usage
MINIMAX_API_KEY=your_minimax_api_key
MINIMAX_SERVER_URL=https://api.minimax.io/v1

# With LiteLLM proxy
MINIMAX_API_KEY=your_litellm_key
MINIMAX_SERVER_URL=http://litellm-proxy:4000
MINIMAX_PROVIDER=minimax  # Adds prefix to model names (minimax/MiniMax-M3) for LiteLLM

Supported Models

PentAGI ships 3 MiniMax models with tool calling, JSON output, and streaming. MiniMax-M3 is the default catalogue model; the bundled agent configuration uses MiniMax-M2.7 for roles where conformance testing showed more reliable context retention and unified-diff generation.

LiteLLM Integration: Set MINIMAX_PROVIDER=minimax to enable model name prefixing when using default PentAGI configurations with LiteLLM proxy. Leave empty for direct API usage.

Mistral Provider Configuration Copy Section

PentAGI talks to Mistral through the OpenAI-compatible https://api.mistral.ai/v1 endpoint: the
Medium/Large/Small line, the Ministral small models, Codestral for code, and one Z.ai model Mistral
serves itself.

Configuration Variables

Configuration Examples

# Direct API usage
MISTRAL_API_KEY=your_mistral_api_key
MISTRAL_SERVER_URL=https://api.mistral.ai/v1

# With LiteLLM proxy
MISTRAL_API_KEY=your_litellm_key
MISTRAL_SERVER_URL=http://litellm-proxy:4000
MISTRAL_PROVIDER=mistral  # Adds prefix to model names (mistral/mistral-small-latest) for LiteLLM

Supported Models

PentAGI ships 6 Mistral models. The default configuration uses mistral-small-latest for utility and read-heavy agents, mistral-large-latest for primary and execution agents, and mistral-medium-latest for planning and advice. Small, Medium, and Large each have a 256K-token context window.

LiteLLM Integration: Set MISTRAL_PROVIDER=mistral to enable model name prefixing when using default PentAGI configurations with LiteLLM proxy. Leave empty for direct API usage.

xAI (Grok) Provider Configuration Copy Section

PentAGI talks to xAI through the OpenAI-compatible https://api.x.ai/v1 endpoint: the Grok 4.x line with large context windows, plus a coding model.

Configuration Variables

Configuration Examples

# Direct API usage
XAI_API_KEY=your_xai_api_key
XAI_SERVER_URL=https://api.x.ai/v1

# With LiteLLM proxy
XAI_API_KEY=your_litellm_key
XAI_SERVER_URL=http://litellm-proxy:4000
XAI_PROVIDER=xai  # Adds prefix to model names (xai/grok-4.3) for LiteLLM

Supported Models

PentAGI lists seven xAI Chat Completions model IDs that responded through the tested API. Catalogue availability does not imply suitability as an agent default. The bundled configuration uses grok-4.3 for tool-heavy roles, including searcher, and grok-4.20-0309-non-reasoning for simple, simple_json, reflector, and enricher; the exact bindings are in backend/pkg/providers/xai/config.yml. In saved generator chains, grok-4.3 called subtask_list directly on all nine, whereas grok-4.6 returned text without a tool call on all nine. The full replay matrix records outcomes and limitations. The Responses-only multi-agent model is not listed because PentAGI agents supply their own client-side tools.

Note: prices are for prompts under 200K tokens; at or above that threshold xAI applies the higher long-context rate to every token in the request.

LiteLLM Integration: Set XAI_PROVIDER=xai to enable model name prefixing when using default PentAGI configurations with LiteLLM proxy. Leave empty for direct API usage.

Advanced Setup Copy Chapter

Langfuse Integration Copy Section

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

1. Configure Langfuse environment variables in existing .env file.

   Langfuse valuable environment variables

Database Credentials Copy Section

• 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 Copy Section

• 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 Copy Section

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

API Keys and Tokens Copy Section

• 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 Copy Section

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

2. 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}

3. 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 Copy Section

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

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

OTEL_HOST=otelcol:8148

2. 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.

To run all available stacks together (Langfuse, Graphiti, and Observability):

bash Copy

docker compose -f docker-compose.yml -f docker-compose-langfuse.yml -f docker-compose-graphiti.yml -f docker-compose-observability.yml up -d

You can also register aliases for these commands in your shell to run it faster:

bash Copy

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

Knowledge Graph Integration (Graphiti) Copy Section

[!IMPORTANT]
Graphiti is an optional beta integration and is disabled by default. Review Limitations and Security before enabling it in production.

PentAGI integrates with Graphiti, a temporal knowledge graph system powered by Neo4j, to provide advanced semantic understanding and relationship tracking for AI agent operations. The vxcontrol fork provides custom entity and edge types that are specific to pentesting purposes.

What is Graphiti?

Graphiti asynchronously extracts structured knowledge from agent interactions and builds a graph of entities, relationships, evidence, and temporal context. PentAGI sends agent responses and tool executions to Graphiti and exposes the graphiti_search tool to enabled agents. Graphiti complements the primary pgvector memory; it does not replace it.

• Semantic Memory: Store and recall relationships between tools, targets, vulnerabilities, and techniques
• Contextual Understanding: Track how different pentesting actions relate to each other over time
• Flow-Scoped Recall: Reuse knowledge within the active flow without exposing data from other engagements by default
• Advanced Querying: Search temporal context, relationships, successful tools, recent episodes, and entities by type

When enabled, PentAGI captures agent responses, tool execution details, and flow/task/subtask context. Ingestion is asynchronous, so newly submitted events can take time to become searchable.

Deployment Modes and Enabling

Graphiti can run as the bundled Neo4j + Graphiti stack, as an external service, or remain disabled.

For the bundled stack, configure .env:

GRAPHITI_ENABLED=true
GRAPHITI_TIMEOUT=30
GRAPHITI_URL=http://graphiti:8000
GRAPHITI_LLM_CLIENT_TYPE=openai

# Reused by the Graphiti OpenAI preset
OPEN_AI_KEY=your_openai_api_key
OPEN_AI_SERVER_URL=https://api.openai.com/v1

# Bundled Neo4j
NEO4J_USER=neo4j
NEO4J_DATABASE=neo4j
NEO4J_PASSWORD=replace_with_a_strong_password
NEO4J_URI=bolt://neo4j:7687

Download the optional compose file and the Neo4j settings with the APOC plugin Graphiti needs when installing manually, then start both stacks:

curl -O https://raw.githubusercontent.com/vxcontrol/pentagi/master/docker-compose-graphiti.yml
mkdir -p neo4j/conf neo4j/plugins
for f in conf/neo4j.conf conf/apoc.conf plugins/apoc-5.26.19-core.jar; do
  curl -fsSL -o "neo4j/$f" "https://raw.githubusercontent.com/vxcontrol/pentagi/master/examples/neo4j/$f"
done
docker compose -f docker-compose.yml -f docker-compose-graphiti.yml up -d

The base stack must create the external pentagi-network before the Graphiti stack can start. The installer handles stack ordering automatically.

For an external Graphiti deployment, set GRAPHITI_ENABLED=true and point GRAPHITI_URL to its API. Do not start docker-compose-graphiti.yml; configure providers, embeddings, the graph database, and ingest tuning on the external service itself.

PentAGI enables its client only when both GRAPHITI_ENABLED=true and GRAPHITI_URL is non-empty. At startup it performs three health-check attempts with a two-second backoff. If they all fail, PentAGI logs a warning and continues with Graphiti disabled.

LLM Provider and Model Presets

GRAPHITI_LLM_CLIENT_TYPE selects one deployment-wide preset. Model names and call parameters are not environment variables; they live in graphiti/.yaml.

The Gemini preset uses Graphiti's LiteLLM/OpenAI-compatible client path. Point GEMINI_SERVER_URL at a compatible gateway if the native Gemini endpoint does not provide the required OpenAI-compatible API.

Each preset file must contain a matching provider plus MODEL_NAME and SMALL_MODEL_NAME mappings. The small model is used for reranking and lighter calls. Supported call settings include temperature, token limits, sampling and penalty parameters, JSON mode, reasoning effort, verbosity, pricing metadata, and provider-specific extra_body.

The installer copies examples/graphiti beside the installation as ./graphiti. The compose mount is controlled by:

GRAPHITI_CONFIG_PATH=./graphiti
GRAPHITI_CONFIG_DIR=llm_configs

GRAPHITI_CONFIG_PATH may point directly to ./examples/graphiti for development. GRAPHITI_CONFIG_DIR=llm_configs activates the mounted presets. If an older .env omits that variable, a newer compose file mounts an empty host directory at the unused configs path instead of hiding the presets built into the image.

[!NOTE]
GRAPHITI_MODEL_NAME is obsolete and ignored. Edit the active YAML preset instead, then restart the Graphiti container.

Graphiti Embedding Configuration

By default, Graphiti uses the active LLM preset's credentials and its default OpenAI embedding model. To use PentAGI's shared embedding endpoint explicitly:

GRAPHITI_SEPARATE_EMBEDDING=true
EMBEDDING_URL=https://embedding.example.com/v1
EMBEDDING_KEY=your_embedding_api_key
EMBEDDING_MODEL=openai/text-embedding-3-large

Graphiti's embedder is OpenAI-compatible. EMBEDDING_PROVIDER is used by PentAGI but is not passed to Graphiti, so a non-OpenAI-compatible embedding provider cannot be shared directly.

Ingestion and Extraction Tuning

The supplied defaults prioritize flow isolation and limit expensive extraction to useful events.

Ingest policy actions:

• REJECT: do not store the episode.
• SKIP_LLM: store the episode for retrieval but do not extract nodes or edges.
• PROCESS: store the episode and run full LLM extraction.

Extraction uses the following fallback order: full combined extraction (nodes, attributes, summaries, and edges in one call), regular combined extraction (nodes and edges), then separate node/edge extraction. Empty or failed combined results automatically fall back; these log messages are expected during normal operation.

Anchors connect entities across episodes and are used by the separate extraction path. Combined extraction has already produced its edges and does not perform this anchor lookup.

These flags are passed as process environment variables by the bundled compose file. This is important for combined extraction because Graphiti reads those flags when Python modules are imported.

Runtime, Logging, and Neo4j

The values below are PentAGI's recommended .env.example/compose defaults, not the raw Graphiti image fallbacks. Running a freshly pulled image behind an old compose file can instead enable telemetry and global search, use one shared-pool worker with PROCESS as the unmatched ingest action, enable the full taxonomy, and disable combined extraction. Keep the image, compose file, .env, and presets in sync.

NEO4J_USER, NEO4J_PASSWORD, NEO4J_URI, and NEO4J_DATABASE configure the bundled connection. Neo4j Community Edition supports only its default database; do not configure a separate database name that requires Enterprise multi-database support.

The installer copies examples/neo4j beside the installation as ./neo4j. It contains static, non-.env-tunable settings that don't have a NEO4J_* variable: conf/neo4j.conf and conf/apoc.conf, plus a version-pinned plugins/apoc-*-core.jar. The compose mount is controlled by:

NEO4J_DIR=./neo4j

NEO4J_DIR may point directly to ./examples/neo4j for development. Both conf/ and plugins/ are mounted read-only; the stack still starts on Neo4j's built-in defaults (without APOC) if the directory is absent, since Docker creates an empty one automatically — but Graphiti writes relationships through APOC, so its graph then stays empty while PentAGI logs no error. Do not duplicate any NEO4J_* variable from the table above inside conf/neo4j.conf — the Neo4j Docker entrypoint always strips a matching line from the mounted file and re-appends the environment variable's value, so a duplicated setting in the file would be silently ignored.

The bundled stack currently wires Neo4j only. The Graphiti image contains FalkorDB support, but using it requires a separately configured deployment because the stock compose file does not expose GRAPHITI_GRAPH_BACKEND or FALKORDB_*.

Verification and Troubleshooting

Check service health, queue state, and logs:

docker compose -f docker-compose.yml -f docker-compose-graphiti.yml ps graphiti neo4j
docker compose -f docker-compose.yml -f docker-compose-graphiti.yml logs -f graphiti
curl -fsS http://localhost:8000/healthcheck
curl -fsS http://localhost:8000/queue-size

Neo4j Browser is available at http://localhost:7474; the Graphiti OpenAPI UI is at http://localhost:8000/docs. Both are bound to localhost by the stock compose file.

Common failures:

• A missing API key or base URL for the selected preset, a missing YAML file, or a YAML provider mismatch causes the Graphiti container to fail startup validation.
• LLM_CLIENT_TYPE=openai rejects local/custom model prefixes; use the custom preset for an OpenAI-compatible local server.
• In flowid search mode, requests without a group ID are rejected. PentAGI supplies the flow-derived group ID automatically.
• A bounded full queue returns HTTP 429. /queue-size reports waiting, processing, active-group, and dropped counters.
• Invalid retry, timeout, or anchor ranges fail startup rather than being silently normalized.

Update .env, docker-compose-graphiti.yml, the Graphiti image, and the graphiti preset directory together. Pulling only a new image can retain older compose defaults and silently change extraction behavior.

Limitations and Security

• Graphiti is beta and has no in-app graph explorer.
• One provider preset is active for the entire Graphiti deployment; it is not selected per PentAGI agent or flow.
• Graphiti extraction, reranking, and embeddings incur billing independently of the model used by the main PentAGI flow.
• Search is flow-scoped by default. Cross-flow reuse requires an explicit global-search design and must not be enabled on shared or multi-tenant deployments without additional isolation.
• The Graphiti HTTP API has no authentication layer in the bundled service. The stock compose binds it and Neo4j to 127.0.0.1; secure external deployments with network controls and authentication at a trusted reverse proxy.
• Agent and tool output may contain credentials and exploitation evidence. Protect Neo4j data, logs, dead letters, diagnostics, and backups accordingly.
• If Graphiti is unavailable, PentAGI continues with its primary memory and vector store after logging the failed startup health check. Set GRAPHITI_ENABLED=false to disable the integration explicitly.

GitHub and Google OAuth Integration Copy Section

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

PentAGI uses PUBLIC_URL as the public origin/base URL for OAuth redirects. In the default deployment, both GitHub and Google callbacks are handled by:

${PUBLIC_URL}/api/v1/auth/login-callback

For GitHub OAuth:

1. Create a new OAuth App in your GitHub account.
2. Set Homepage URL to your PUBLIC_URL.
3. Set Authorization callback URL to ${PUBLIC_URL}/api/v1/auth/login-callback.
4. Add the client credentials to your .env file:

PUBLIC_URL=https://pentagi.example.com
OAUTH_GITHUB_CLIENT_ID=your_github_client_id
OAUTH_GITHUB_CLIENT_SECRET=your_github_client_secret

For Google OAuth:

1. Create OAuth credentials in your Google Cloud project.
2. Use the same callback endpoint: ${PUBLIC_URL}/api/v1/auth/login-callback.
3. Add the client credentials to your .env file:

PUBLIC_URL=https://pentagi.example.com
OAUTH_GOOGLE_CLIENT_ID=your_google_client_id
OAUTH_GOOGLE_CLIENT_SECRET=your_google_client_secret

Make sure PUBLIC_URL matches the externally accessible HTTPS address of your PentAGI instance and does not include the callback path itself. If the URL configured in the OAuth provider does not exactly match the callback generated by PentAGI, the provider will reject the login attempt with a redirect URI mismatch error.

Docker Image Configuration Copy Section

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:

PENTAGI_IMAGE changes the image used by the main pentagi service in docker-compose.yml. The DOCKER_DEFAULT_IMAGE and DOCKER_DEFAULT_IMAGE_FOR_PENTEST variables only affect automatic worker image selection for task execution inside PentAGI. They do not rewrite the rest of the Compose stack, so services such as pgvector, scraper, and the optional graphiti stack still use the image references defined in the compose files.

DOCKER_DEFAULT_IMAGE and DOCKER_DEFAULT_IMAGE_FOR_PENTEST are suggestions the model receives in the image selection prompt, not limits: on their own they do not stop a model from answering with some other image. Two settings turn them into limits:

• DOCKER_ALLOWED_IMAGES restricts the choice. An answer outside the list is discarded and the pentest image is used instead. An entry without a tag admits any tag of that repository, so vxcontrol/kali-linux also admits vxcontrol/kali-linux:2026.1. While the list is empty the model may pick any image, which is the default behaviour.
• DOCKER_IMAGE_SELECTION_MODE=fixed removes the choice altogether: the flow always runs in DOCKER_DEFAULT_IMAGE_FOR_PENTEST and no model call is made. Use it when weaker or local models answer with the wrong environment.

Regardless of these settings, an answer that is not a usable image reference never reaches Docker: the flow falls back to the pentest image and the rejected answer is logged.

Restricting the choice is 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 PentAGI application image
PENTAGI_IMAGE=registry.example.com/security/pentagi:latest

# 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.

For an advanced OpenVAS/GVM experiment that uses a custom pentest image, see OpenVAS via a Custom Pentest Image.

Restricted Networks, Docker Mirrors, and Proxies

If your environment cannot reach Docker Hub (docker.io) directly, changing PentAGI environment variables is usually not enough to fix image download failures. PentAGI still relies on Docker's own registry access for Compose-managed services, and the installer network checks also validate Docker Hub reachability.

For restricted networks:

1. Confirm that the host can resolve and reach docker.io.
2. If your environment requires an outbound proxy for PentAGI or installer HTTP traffic, set the PROXY_URL environment variable. To route Docker image pulls through a proxy, configure the Docker daemon or Docker Desktop proxy separately — Docker does not use PentAGI's PROXY_URL for registry access.
3. If Docker Hub is blocked or heavily rate-limited, configure an organization-approved registry mirror or registry proxy before running the installer or docker compose up.
4. Restart Docker after changing the daemon configuration, then rerun the installer checks or Compose startup.

Example Docker daemon mirror configuration:

{
  "registry-mirrors": ["https://mirror.example.com"]
}

On Linux, this is typically configured in /etc/docker/daemon.json. On Docker Desktop, use the equivalent Docker Engine or proxy settings. A Docker Hub mirror covers Docker Hub-hosted images such as vxcontrol/*, but the main Compose stack already includes quay.io/prometheuscommunity/postgres-exporter, the optional observability stack includes gcr.io/cadvisor/cadvisor, and the optional Langfuse stack includes cgr.dev/chainguard/minio. Those registries still need direct access or individually approved proxy/mirror paths.

See the official Docker documentation for registry mirrors and daemon proxy configuration.

Troubleshooting: "failed to select primary docker image via llm call"

A flow that fails immediately with failed to select primary docker image via llm call usually indicates a problem with the configured LLM backend, not with Docker or the image registry. Older PentAGI versions reported the same failure as failed to get primary docker image, which led users to debug Docker even though the registry was healthy.

When a flow starts, PentAGI makes its first LLM call to choose the primary Docker image for the task. This image-selection call runs through the simple agent type, so a failure here points at the model assigned to that agent type rather than at Docker. A message such as API returned unexpected status code: 502 or 404 in this context is returned by the LLM backend, not by Docker Hub.

This is distinct from the registry reachability problems described above: if Docker pulls succeed and the Compose stack starts, but flow creation still fails at image selection, investigate the LLM backend rather than Docker.

To diagnose:

1. Check PentAGI logs first: docker logs pentagi.
2. Check the logs of your configured LLM backend (the server behind your provider or LLM_SERVER_URL).
3. Verify that the base URL, API key, and model name in Custom LLM Provider Configuration are correct and reachable from the container. If you assign different models per agent type, check the model used by the simple agent type, since image selection runs through it.
4. For custom, OpenAI-compatible, vLLM, or SGLang backends, confirm that the model supports tool calling (function calling) and that the matching tool-call parser is enabled. A missing or mismatched tool-call parser is a known cause of this failure.

Development Copy Chapter

Development Requirements Copy Section

• golang
• nodejs
• docker
• postgres

Environment Setup Copy Section

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:1.27.0 generate -f sqlc/sqlc.yml

For generating Langfuse SDK from OpenAPI specification

fern generate --local

and to install fern-cli

pnpm add fern-api

Testing

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

Frontend Setup

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

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

Be sure that you have graphql-codegen installed globally:

pnpm add graphql-codegen

After that you can run:

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

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

PostgreSQL / pgvector connection pool sizing

PentAGI opens two independent connection pools to the same Postgres instance:

Additional tuning knob:

• DATABASE_MAX_IDLE_CONNS — maximum idle connections kept open in the sql.DB pool between requests (default: 5).

Budget for the stock vxcontrol/pgvector image (max_connections = 100, superuser_reserved_connections = 3):

Available for client connections  = 97
  pentagi sql.DB  (DATABASE_MAX_OPEN_CONNS)   = 25
  pentagi pgxpool (DATABASE_VECTOR_MAX_CONNS) = 10
  pgexporter                                  =  3
  autovacuum workers                          =  3
  ─────────────────────────────────────────
  Total consumed                              = 41
  Free buffer                                 = 56  (≈ 58 %)

The defaults are sized for 10 parallel flows with concurrent API requests. If you run more flows or deploy multiple PentAGI instances against the same Postgres, raise max_connections via the command override in docker-compose.yml and increase the pool sizes proportionally:

pgvector:
  image: vxcontrol/pgvector:latest
  command: postgres -c max_connections=200

To inspect the live connection budget on a running deployment:

# Postgres limits
docker exec pgvector sh -c 'psql -U "$POSTGRES_USER" -d "$POSTGRES_DB" -c \
  "SELECT name, setting FROM pg_settings
   WHERE name IN ('"'"'max_connections'"'"', '"'"'superuser_reserved_connections'"'"');"'

# Current usage vs. available
docker exec pgvector sh -c 'psql -U "$POSTGRES_USER" -d "$POSTGRES_DB" -c \
  "SELECT max_conn, used, max_conn - used AS available
   FROM (SELECT current_setting('"'"'max_connections'"'"')::int AS max_conn,
                count(*) AS used FROM pg_stat_activity) t;"'

# Breakdown by client
docker exec pgvector sh -c 'psql -U "$POSTGRES_USER" -d "$POSTGRES_DB" -c \
  "SELECT application_name, client_addr, state, count(*)
   FROM pg_stat_activity
   WHERE pid <> pg_backend_pid()
   GROUP BY 1, 2, 3 ORDER BY count DESC;"'

External PostgreSQL and schema handling

DATABASE_URL may point at any PostgreSQL instance, not only the bundled pgvector container. Two extra knobs apply when — and only when — TENANT_ID is set, because that is when PentAGI creates its own schema and rewrites the connection's search_path:

Supabase (cloud or self-hosted) needs both of them considered, and is the reason they exist:

• Supabase installs its bundled extensions into an extensions schema instead of public, so set DATABASE_EXTENSIONS_SCHEMA=extensions. Without it, startup aborts with an error naming the schema where vector was actually found — no need to move a provider-managed extension with ALTER EXTENSION.
• Supabase's pooler (Supavisor) does not reliably forward a bare search_path connection parameter. Prefer a direct PostgreSQL connection: self-hosted, expose the db service port and bypass the supavisor service; cloud, use the "Direct connection" string (or the IPv4 add-on on IPv4-only networks). If the pooler cannot be bypassed, use its session mode and try DATABASE_SEARCH_PATH_VIA_OPTIONS=true — PentAGI verifies the effective schema on boot and refuses to start if it did not take effect, so a silent cross-tenant data mix-up is not possible.

Both settings are managed by the installer under Server Settings, next to TENANT_ID — see Running Several Instances for that scenario, and Multi-Instance Deployment for the full matrix, including the PgBouncer recipe (pool_mode = session, ignore_startup_parameters, per-tenant connect_query).

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 Copy Section

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 pnpm install to install the dependencies
• Run pnpm run dev to run the web app
• Run pnpm run build to build the web app

Open your browser and visit the web app URL.

Testing LLM Agents Copy Chapter

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 Copy Section

• 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 Copy Section

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, OpenCode, Atlas, OrcaRouter, DeepInfra, DeepSeek, Moonshot, Novita, xAI):

/* Detailed source-code truncated for AI context efficiency. */

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

/* Detailed source-code truncated for AI context efficiency. */

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

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 exec -it pentagi /opt/pentagi/bin/ctester -config /opt/pentagi/conf/custom-openai.provider.yml

Using LiteLLM Proxy

When using LiteLLM proxy to access various LLM providers, model names are prefixed with the provider name (e.g., moonshot/kimi-2.5 instead of kimi-2.5). To use the same provider configuration files with both direct API access and LiteLLM proxy, set the LLM_SERVER_PROVIDER variable:

# Direct access to Moonshot API
LLM_SERVER_URL=https://api.moonshot.ai/v1
LLM_SERVER_KEY=your_moonshot_api_key
LLM_SERVER_CONFIG_PATH=/opt/pentagi/conf/moonshot.provider.yml
LLM_SERVER_PROVIDER=                             # Empty for direct access

# Access via LiteLLM proxy
LLM_SERVER_URL=http://litellm-proxy:4000
LLM_SERVER_KEY=your_litellm_api_key
LLM_SERVER_CONFIG_PATH=/opt/pentagi/conf/moonshot.provider.yml
LLM_SERVER_PROVIDER=moonshot                     # Provider prefix for LiteLLM

With LLM_SERVER_PROVIDER=moonshot, the system automatically prefixes all model names from the configuration file with moonshot/, making them compatible with LiteLLM's model naming convention.

LiteLLM Provider Name Mapping:

When using LiteLLM proxy, set the corresponding *_PROVIDER variable to enable model prefixing:

• deepseek - for DeepSeek models (DEEPSEEK_PROVIDER=deepseek → deepseek/deepseek-v4-flash)
• zai - for GLM models (GLM_PROVIDER=zai → zai/glm-4)
• moonshot - for Kimi models (KIMI_PROVIDER=moonshot → moonshot/kimi-k3)
• dashscope - for Qwen models (QWEN_PROVIDER=dashscope → dashscope/qwen-plus)
• openai, anthropic, gemini - for major cloud providers
• opencode - for OpenCode Go plan
• openrouter - for OpenRouter aggregator
• orcarouter - for OrcaRouter aggregator
• deepinfra - for DeepInfra hosting
• novita - for Novita AI
• xai - for xAI (grok-* models)
• Any other provider name configured in your LiteLLM instance

Example with LiteLLM:

# Use DeepSeek models via LiteLLM proxy with model prefixing
DEEPSEEK_API_KEY=your_litellm_proxy_key
DEEPSEEK_SERVER_URL=http://litellm-proxy:4000
DEEPSEEK_PROVIDER=deepseek  # Models become deepseek/deepseek-v4-flash, deepseek/deepseek-v4-pro for LiteLLM

# Direct DeepSeek API usage (no prefix needed)
DEEPSEEK_API_KEY=your_deepseek_api_key
DEEPSEEK_SERVER_URL=https://api.deepseek.com
# Leave DEEPSEEK_PROVIDER empty

This approach allows you to:

• Use the same configuration files for both direct and proxied access
• Switch between providers without modifying configuration files
• Easily test different routing strategies with LiteLLM

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 Copy Section

The utility accepts several options:

• -env - Path to environment file (default: .env)
• -type - Provider type: custom, openai, anthropic, ollama, bedrock, gemini (default: custom)
• -config - Path to custom provider config (default: from LLM_SERVER_CONFIG_PATH env variable)
• -tests - Path to custom tests YAML file (optional)
• -report - 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 Copy Section

Agents are tested in the following deterministic order:

1. simple - Basic completion tasks
2. simple_json - JSON-structured responses
3. primary_agent - Main reasoning agent
4. assistant - Interactive assistant mode
5. generator - Content generation
6. refiner - Content refinement and improvement
7. adviser - Expert advice and consultation
8. reflector - Self-reflection and analysis
9. searcher - Information gathering and search
10. enricher - Data enrichment and expansion
11. coder - Code generation and analysis
12. installer - Installation and setup tasks
13. pentester - Penetration testing and security assessment

Available Test Groups Copy Section

• 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 Copy Section

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 Copy Section

1. Create a baseline: Run tests with default configuration to establish benchmark performance
2. Analyze agent-specific performance: Review the deterministic agent ordering to identify underperforming agents
3. Test specialized configurations: Experiment with different models for each agent type using provider-specific configs
4. Focus on domain knowledge: Pay special attention to knowledge group tests for cybersecurity expertise
5. Validate function calling: Ensure tool-based tests pass consistently for critical agent types
6. Compare results: Look for the best success rate and performance across all test groups
7. 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 Copy Chapter

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 Copy Section

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

OpenAI-compatible third parties: any provider exposing OpenAI's /embeddings API can be plugged in via EMBEDDING_PROVIDER=openai with a custom EMBEDDING_URL. For example, Qwen DashScope offers text-embedding-v4 through the /compatible-mode/v1 endpoint (International and Chinese Mainland regions only — the US region does not expose embeddings). See the Qwen Alternative Integrations subsection for the full configuration snippet.

Embedding Provider Configuration (click to expand)

Environment Variables Copy Section

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
EMBEDDING_MAX_TEXT_BYTES=8192   # Max bytes of text sent to embedding model per document (byte proxy for token limit)

# Advanced settings
PROXY_URL=                      # Optional proxy for all backend LLM, embedding, search and update-check calls
HTTP_CLIENT_TIMEOUT=600         # Timeout in seconds for external API calls (default: 600, 0 = no timeout)
TERMINAL_TOOL_TIMEOUT=1200      # Default timeout in seconds for terminal tool commands when timeout=0 or negative (range: 1–10800; values <= 0 or above 10800 are clamped to 10800 = 3 hours)

# SSL/TLS Certificate Configuration (for external communication with LLM backends and tool servers)
EXTERNAL_SSL_CA_PATH=           # Path to custom CA certificate file (PEM format) inside the container
                                # Must point to /opt/pentagi/ssl/ directory (e.g., /opt/pentagi/ssl/ca-bundle.pem)
EXTERNAL_SSL_INSECURE=false     # Skip certificate verification (use only for testing)

How to Add Custom CA Certificates (click to expand)

If you see this error: tls: failed to verify certificate: x509: certificate signed by unknown authority

Step 1: Get your CA certificate bundle in PEM format (can contain multiple certificates)

Step 2: Place the file in the SSL directory on your host machine:

# Default location (if PENTAGI_SSL_DIR is not set)
cp ca-bundle.pem ./pentagi-ssl/

# Or custom location (if using PENTAGI_SSL_DIR in docker-compose.yml)
cp ca-bundle.pem /path/to/your/ssl/dir/

Step 3: Set the path in .env file (path must be inside the container):

# The volume pentagi-ssl is mounted to /opt/pentagi/ssl inside the container
EXTERNAL_SSL_CA_PATH=/opt/pentagi/ssl/ca-bundle.pem
EXTERNAL_SSL_INSECURE=false

Step 4: Restart PentAGI:

docker compose restart pentagi

Notes:

• The pentagi-ssl volume is mounted to /opt/pentagi/ssl inside the container
• You can change host directory using PENTAGI_SSL_DIR variable in docker-compose.yml
• File supports multiple certificates and intermediate CAs in one PEM file
• Use EXTERNAL_SSL_INSECURE=true only for testing (not recommended for production)

Provider-Specific Limitations Copy Section

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: Supports all configuration options
• Jina: Supports all configuration options
• 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 both left empty, the system uses the corresponding LLM provider's server and key together (e.g., OPEN_AI_SERVER_URL and OPEN_AI_KEY when EMBEDDING_PROVIDER=openai). A key set in EMBEDDING_KEY without EMBEDDING_URL goes to the provider's public endpoint, and an EMBEDDING_URL set without a key receives the LLM provider's key only when it is that provider's own server. So an OpenAI-compatible endpoint of another vendor needs its key in EMBEDDING_KEY, and a keyless local server (LocalAI, vLLM, TEI) takes any non-empty value there. Whenever the Mistral embedder uses MISTRAL_SERVER_URL, it also names the model the way that server expects: MISTRAL_PROVIDER=mistral turns mistral-embed into mistral/mistral-embed, and a model that already starts with mistral/ is sent as is.

Why Consistent Embedding Providers Matter Copy Section

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

1. Vector Compatibility: Different providers produce vectors with different dimensions and mathematical properties
2. Semantic Consistency: Changing providers can break semantic similarity between previously embedded documents
3. 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) Copy Section

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 Copy Section

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 Copy Section

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)

Memory Lifecycle Across Flows Copy Section

PentAGI stores several kinds of vector documents, and they serve different purposes:

• memory captures flow-specific execution history such as tool results and agent observations
• guide, answer, and code are intended for reusable knowledge that can help future runs

If you want to inspect what happened in one engagement, search the vector store with the related flow_id. If you want knowledge to survive beyond a single run, store the durable result explicitly as a guide, answer, or code document instead of relying on execution memory alone.

For example, if a target has recurring setup notes, authentication quirks, or target-specific testing methodology, instruct the agent to save that information as a guide and search for it at the beginning of the next engagement. This is the safest current workflow when you want a new flow to start with reusable context.

Flow deletion removes the flow from normal queries through PentAGI's soft-delete mechanism, so reusable knowledge should be treated as a separate concern from per-flow execution history. If you enable the optional Graphiti knowledge graph described earlier in this README, treat its current search context as scoped to the active flow or engagement unless you explicitly build a separate cross-flow reuse workflow.

Common Troubleshooting Scenarios Copy Section

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

Troubleshooting: Flow Stalls or Hangs Without Progress Copy Section

If a flow starts but then appears to wait indefinitely with no subtasks progressing, a common cause is an embedding provider that is misconfigured or unreachable. PentAGI uses the embedding provider to store and search vector memory while a flow runs, so embedding calls that fail or hang can leave a flow waiting instead of advancing.

1. Check the container logs first. Embedding errors surface in the PentAGI logs:

docker logs pentagi

Look for embedding-related failures such as authentication errors (401/403), wrong-model or not-found errors (404), connection timeouts, or TLS certificate errors. These point at the embedding provider configuration rather than at the flow itself.

2. Validate the provider with etester. The Embedding Tester Utility (etester) checks both the embedding provider and the database connection without starting a flow:

docker exec -it pentagi /opt/pentagi/bin/etester test -verbose

A failing test confirms the problem is in the embedding configuration rather than in the flow.

3. Verify the configuration. Check the following in your .env file against the Supported Embedding Providers list and each provider's documented limitations:

• EMBEDDING_PROVIDER is one of the supported providers (default openai).
• EMBEDDING_MODEL is a valid model name for that provider.
• EMBEDDING_URL and EMBEDDING_KEY are correct for the provider. If both are left empty, PentAGI falls back to the matching LLM provider settings (for example OPEN_AI_KEY and OPEN_AI_SERVER_URL when EMBEDDING_PROVIDER=openai), so a missing or wrong key there can break embeddings too.
• The endpoint is reachable from inside the container. If outbound calls go through a proxy, confirm PROXY_URL is set; if calls hang rather than fail quickly, HTTP_CLIENT_TIMEOUT controls how long PentAGI waits on the provider before giving up.

Changing provider? If you switch embedding providers after data has already been indexed, run flush or reindex with etester so old and new vectors are not mixed. See Why Consistent Embedding Providers Matter above.

Function Testing with ftester Copy Chapter

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 Copy Section

• 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 Copy Section

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 Copy Section

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

Search Functions Copy Section

• browser: Access websites and capture screenshots
• web_search: Unified search orchestrator that agents actually call — pass a query and a mode (links, answer, research, exploit) and it auto-selects, retries, and falls back across the engines below, so you never name an engine explicitly
• google: Search the web using Google Custom Search
• duckduckgo: Search the web using DuckDuckGo
• tavily: Search using Tavily AI search engine
• firecrawl: Search using Firecrawl with main-content markdown scraping
• traversaal: Search using Traversaal AI search engine
• perplexity: Search using Perplexity AI
• sploitus: Search for security exploits, vulnerabilities (CVEs), and pentesting tools
• searxng: Search using SearXNG meta search engine (aggregates results from multiple engines)
• internal (ftester-only debug function, not an agent tool): Opt-in browser-analytics fallback engine that discovers links, scrapes each page, and summarizes the result; requires WEB_SEARCH_INTERNAL_ENABLED=true, a configured scraper, and at least one available link engine

Vector Database Functions Copy Section

• 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 Copy Section

• 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 Copy Section

• 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 Copy Section

When PentAGI gets stuck in a flow:

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

Testing Environment Variables Copy Section

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 Copy Section

When developing new prompt templates or agent behaviors:

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

Verifying Docker Container Setup Copy Section

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:

1. Langfuse: Captures the entire AI agent interaction chain, including prompts, responses, and function calls
2. OpenTelemetry: Records metrics, traces, and logs for system performance analysis
3. 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 Copy Section

The main utility accepts several options:

• -env - Path to environment file (optional, default: .env)
• -provider <type> - Provider type to use (default: custom, options: openai, anthropic, gemini, bedrock, ollama, deepseek, glm, kimi, qwen, minimax, custom)
• -flow - Flow ID for testing functions that require it (0 means using mocks, default: 0)
• -user - User ID for testing functions that require it (default: 0; 1 is the default admin user)
• -task - Task ID for agent context (optional)
• -subtask - Subtask ID for agent context (optional)

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

Pentesting Prompt Methodology Copy Section

When refining prompts for offensive security work, give the agent a clear methodology instead of a flat list of payloads:

1. Start with explicit scope, authorization, and success criteria
2. Map the application first: roles, routes, parameters, uploads, integrations, and trust boundaries
3. Prioritize attack surfaces systematically instead of testing everything at once
4. Validate findings with reproducible evidence before escalating to deeper exploitation
5. Finish with report-ready notes that capture impact, prerequisites, and next steps

For PentAGI-specific prompt guidance, see backend/docs/prompt_engineering_pentagi.md. For a practical starting point, reuse and adapt examples/prompts/base_web_pentest.md to match the target application, technology stack, and engagement scope.

Building Copy Chapter

Building Docker Image Copy Section

The Docker build process automatically embeds version information from git tags. To properly version your build, use the provided scripts:

Linux/macOS

# Load version variables
source ./scripts/version.sh

# Standard build
docker build \
  --build-arg PACKAGE_VER=$PACKAGE_VER \
  --build-arg PACKAGE_REV=$PACKAGE_REV \
  -t pentagi:$PACKAGE_VERSION_FULL .

# Multi-platform build
docker buildx build \
  --platform linux/amd64,linux/arm64 \
  --build-arg PACKAGE_VER=$PACKAGE_VER \
  --build-arg PACKAGE_REV=$PACKAGE_REV \
  -t pentagi:$PACKAGE_VERSION_FULL .

# Build and push
docker buildx build \
  --platform linux/amd64,linux/arm64 \
  --build-arg PACKAGE_VER=$PACKAGE_VER \
  --build-arg PACKAGE_REV=$PACKAGE_REV \
  -t myregistry/pentagi:$PACKAGE_VERSION_FULL \
  --push .

Windows (PowerShell)

# Load version variables
. .\scripts\version.ps1

# Standard build
docker build `
  --build-arg PACKAGE_VER=$env:PACKAGE_VER `
  --build-arg PACKAGE_REV=$env:PACKAGE_REV `
  -t pentagi:$env:PACKAGE_VERSION_FULL .

# Multi-platform build
docker buildx build `
  --platform linux/amd64,linux/arm64 `
  --build-arg PACKAGE_VER=$env:PACKAGE_VER `
  --build-arg PACKAGE_REV=$env:PACKAGE_REV `
  -t pentagi:$env:PACKAGE_VERSION_FULL .

Quick build without version

For development builds without version tracking:

docker build -t pentagi:dev .

[!NOTE]

• The build scripts automatically determine version from git tags
• Release builds (on tag commit) have no revision suffix
• Development builds (after tag) include commit hash as revision (e.g., 1.1.0-ce.hbc6e800); the edition is ce for Community and ee for Enterprise, and a release build is 1.1.0-ce
• To use the built image locally, update the image name in docker-compose.yml or use the build option

Credits Copy Chapter

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

• Emerging Architectures for LLM Applications
• A Survey of Autonomous LLM Agents
• Codel by Andriy Semenets - initial architectural inspiration for agent-based automation

License Copy Chapter

PentAGI is licensed under the MIT License.

Copyright (c) 2025 PentAGI Development Team

Third-Party Dependencies Copy Section

All third-party dependencies use MIT-compatible licenses. See licenses/ directory for detailed license reports.

VXControl Cloud Services Copy Section

⚠️ Note: While the VXControl Cloud SDK code is MIT licensed, accessing VXControl Cloud Services (threat intelligence, AI support, premium features) requires a separate License Key and compliance with Terms of Service.

The SDK code itself is free to use - service access requires registration.

For questions contact: [email protected] or [email protected]

Backend Docs Installer Installer Overview (`backend/docs/installer/installer-overview.md`) Copy Section

PentAGI Installer Overview

Comprehensive guide to the PentAGI installer - a robust Terminal User Interface (TUI) for configuring and deploying PentAGI services.

🎯 **Project Overview** Copy Chapter

The PentAGI installer provides a modern, interactive Terminal User Interface for configuring and deploying the PentAGI autonomous penetration testing platform. Built using the Charm tech stack, it implements responsive design patterns optimized for terminal environments.

**Core Purpose** Copy Section

• Configuration Management: Interactive setup of LLM providers, monitoring, and security settings
• Environment Setup: Automated configuration of Docker services and environment variables
• User Experience: Professional TUI with intuitive navigation and real-time validation
• Production Ready: Robust error handling, state persistence, and graceful degradation

**Build Command** Copy Section

# From backend/ directory
go build -o ../build/installer ./cmd/installer/main.go

# Monitor debug output
tail -f log.json | jq '.'

🏗️ **Technology Stack** Copy Chapter

**Core Technologies** Copy Section

• TUI Framework: BubbleTea (Model-View-Update pattern)
• Styling: Lipgloss (CSS-like styling for terminals)
• Components: Bubbles (viewport, textinput, etc.)
• Markdown: Glamour (markdown rendering)
• Language: Go 1.21+

**Architecture Components** Copy Section

• Navigation: Type-safe screen routing with parameter passing
• State Management: Persistent configuration with environment variable integration
• Layout System: Responsive design with breakpoint-based layouts
• Form System: Dynamic forms with validation and auto-completion
• Controller Layer: Business logic abstraction from UI components

🎯 **Key Features** Copy Chapter

**Responsive Design** Copy Section

• Adaptive Layout: Automatically adjusts to terminal size
• Breakpoint System: Horizontal/vertical layouts based on terminal width
• Content Hiding: Graceful degradation when space is insufficient
• Dynamic Sizing: Form fields and panels resize automatically

**Interactive Configuration** Copy Section

• LLM Providers: Support for OpenAI, Anthropic, Gemini, Bedrock, DeepSeek, GLM, Kimi, Qwen, Ollama, Custom endpoints
• Monitoring Setup: Langfuse integration for LLM observability
• Observability: Complete monitoring stack with Grafana, VictoriaMetrics, Jaeger
• Summarization: Advanced context management for LLM interactions

**Professional UX** Copy Section

• Auto-Scrolling Forms: Fields automatically scroll into view when focused
• Tab Completion: Boolean fields offer true/false suggestions
• Real-time Validation: Immediate feedback with human-readable error messages
• Resource Estimation: Live calculation of token usage and memory requirements
• State Persistence: Navigation and form state preserved across sessions

🏗️ **Architecture Overview** Copy Chapter

**Directory Structure** Copy Section

backend/cmd/installer/
├── main.go                 # Application entry point
├── wizard/
│   ├── app.go             # Main application controller
│   ├── controller/        # Business logic layer
│   │   └── controller.go
│   ├── locale/            # Localization constants
│   │   └── locale.go
│   ├── logger/            # TUI-safe logging
│   │   └── logger.go
│   ├── models/            # Screen implementations
│   │   ├── welcome.go     # Welcome screen
│   │   ├── eula.go        # EULA acceptance
│   │   ├── main_menu.go   # Main navigation
│   │   ├── llm_providers.go
│   │   ├── llm_provider_form.go
│   │   ├── summarizer.go
│   │   ├── summarizer_form.go
│   │   ├── update_overview.go   # What an update changes (informational)
│   │   ├── installer_update.go  # Installer build download
│   │   └── types.go       # Shared types
│   ├── styles/            # Styling and layout
│   │   └── styles.go
│   └── window/            # Terminal size management
│       └── window.go

**Component Responsibilities** Copy Section

**App Layer** (`app.go`)

• Global navigation management
• Screen lifecycle (creation, initialization, cleanup)
• Unified header and footer rendering
• Window size distribution to models
• Global event handling (ESC, Ctrl+C, resize)

**Models Layer** (`models/`)

• Screen-specific logic and state
• User interaction handling
• Content rendering (content area only)
• Local state management

**Controller Layer** (`controller/`)

• Business logic abstraction
• Environment variable management
• Configuration persistence
• State validation

**Styles Layer** (`styles/`)

• Centralized styling and theming
• Dimension management (singleton pattern)
• Memoised glamour renderers: one per wrap width, plus the default 80-column one
• Responsive style calculations

**Window Layer** (`window/`)

• Terminal size management
• Content area size calculations
• Dimension change coordination

🎯 **Navigation System** Copy Chapter

**Composite ScreenID Architecture** Copy Section

The installer implements a sophisticated navigation system using composite screen IDs:

// Format: "screen§arg1§arg2§..."
type ScreenID string

// Examples:
"welcome"                    // Simple screen
"main_menu§llm_providers"    // Menu with selection
"llm_provider_form§openai"   // Form with provider type
"summarizer_form§general"    // Form with configuration type

**Navigation Features** Copy Section

• Parameter Preservation: Arguments maintained across navigation
• Stack Management: Proper back navigation without loops
• State Persistence: Complete navigation state restoration
• Universal ESC: Always returns to welcome screen
• Type Safety: Compile-time validation of screen IDs

**Navigation Flow Example** Copy Section

1. Start: ["welcome"]
2. Continue: ["welcome", "main_menu"]
3. LLM Providers: ["welcome", "main_menu§llm_providers", "llm_providers"]
4. OpenAI Form: [..., "llm_provider_form§openai"]
5. GoBack: [..., "llm_providers§openai"]
6. ESC: ["welcome"]

🎯 **Form System Architecture** Copy Chapter

**Advanced Form Patterns** Copy Section

• Boolean Fields: Tab completion with true/false suggestions
• Integer Fields: Range validation with human-readable formatting
• Environment Integration: Direct EnvVar integration with presence detection
• Smart Cleanup: Automatic removal of cleared environment variables
• Resource Estimation: Real-time calculation of token/memory usage

**Dynamic Field Generation** Copy Section

Forms adapt based on configuration type:

// Type-specific field generation
switch m.configType {
case "general":
    m.addBooleanField("use_qa", "Use QA Pairs", envVar)
    m.addIntegerField("max_sections", "Max Sections", envVar, 1, 50)
case "assistant":
    m.addIntegerField("keep_sections", "Keep Sections", envVar, 1, 10)
}

**Viewport-Based Scrolling** Copy Section

Forms automatically scroll to keep focused fields visible:

• Auto-scroll: Focused field automatically stays visible
• Smart positioning: Calculates field heights for precise scroll positioning
• No extra hotkeys: Uses existing navigation keys

🎯 **Configuration Management** Copy Chapter

**Supported Configurations** Copy Section

**LLM Providers**

• OpenAI: GPT-4, GPT-3.5-turbo with API key configuration
• Anthropic: Claude-3, Claude-2 with API key configuration
• Google Gemini: Gemini Pro, Ultra with API key configuration
• AWS Bedrock: Multi-model support with AWS credentials
• DeepSeek/GLM/Kimi/Qwen: Base URL + API Key + Provider Name (optional, for LiteLLM)
• Ollama: Local model server integration
• Custom: OpenAI-compatible endpoint configuration

**Monitoring & Observability**

• Langfuse: LLM observability (embedded or external)
• Observability Stack: Grafana, VictoriaMetrics, Jaeger, Loki
• Performance Monitoring: System metrics and health checks

**Summarization Settings**

• General: Global conversation context management
• Assistant: Specialized settings for AI assistant contexts
• Token Estimation: Real-time calculation of context size

🎯 **Update Screens** Copy Chapter

Two screens under Maintenance stand outside the generic operation form: neither edits configuration, and neither is an operation the form framework describes for itself. They sit at different distances from the form system — the overview is a plain viewport over a rendered document, while the installer update is still a BaseScreen, built with an empty field list, which is what gives it the help panel and the vertical/horizontal layout.

The two entries appear under different conditions. Update Installer shows only when a newer build is on offer and the update server answered. Update PentAGI shows whenever an installed stack is not known to be up to date — which includes a check that failed, because a failed check resets every per-stack verdict instead of leaving a stale one in place. So the overview is reachable with nothing yet known to need doing, and that is exactly why it has to be able to say the check did not complete.

**Update Overview** (`models/update_overview.go`) Copy Section

Sits between the "Update PentAGI" menu entry and the apply form, which the entry used to point at directly — so the first thing the user saw was "are you sure?" about something the interface had never described. The overview describes it, then continues to the same form, confirmation and all.

• Informational only: the document is built from the update check already in memory, so the screen makes no network call and cannot fail
• Per stack: the version move (current → target), a note when components come from different releases and the version shown is therefore the oldest of them, and the per-component plan
• Three component verdicts, spelled out in words: will be updated, unchanged, and cannot verify — nothing to compare against. The third is not a synonym for the second — a component with nothing to compare against is not a component that is current, and collapsing the two is how an installation gets told it is up to date on no evidence at all
• The release notes of every release the update crosses, oldest first, each with its date and a (preview) marker when it is not stable. An answer that carries no release list falls back to the target release's changelog and release notes
• Stacks with nothing to do are gathered in one list at the bottom rather than omitted: silence about a stack reads as "not installed"
• A check that did not complete says so instead of saying "up to date" — the screen is reachable from a menu drawn by an earlier check, and "up to date" there would be the opposite claim
• Keys: enter continues to the apply form; ↑/↓, pgup/pgdown, home/end scroll. Deliberately not the EULA's enter-pages-down: this screen asks for no consent, so the one unambiguous key belongs to the one action it offers
• Width-aware markdown: rendered through styles.GetRendererForWidth() (clamped to a readable band, memoised) instead of the fixed 80-column renderer, and re-rendered only when the wrap width actually changes — a drag-resize delivers a WindowSizeMsg per column and glamour is not cheap

**Installer Update** (`models/installer_update.go`) Copy Section

Its own screen rather than an operation form, because the operation is a single verified download and the generic form described it as replacing the running binary and exiting.

• Says what it will do before it does it: running version, offered version, platform (os/arch), download size and the full path the file will be written to. The package description is fetched on entry so the size is on screen before the user agrees to the download; the answer is reused by the download itself rather than asked for twice
• Download and verification are the whole operation. Length, sha256 and signature are checked in a single pass as the bytes arrive; anything that fails is deleted rather than left behind under a release-looking name
• The installer never replaces itself, and that is deliberate rather than unfinished: a process cannot reliably overwrite the binary it is executing — Linux refuses the write, Windows holds the file locked — and failing halfway leaves the user with no installer at all. Instead the screen prints the exact command: mv "<downloaded file>" "<running installer>", or move /Y on Windows plus a note to close the installer first. Both paths are quoted, so an installation under a path with a space does not become two arguments; when the running executable's path cannot be determined, no command is printed at all, because a command with a guessed destination is worse than none
• One confirmation, for one thing: a file already sitting under that exact name — a previous download, or one interrupted partway. Writing over empty space is not a question, so it is not asked. Declining still prints the move command, since the file the user chose to keep is the build they came for
• Where the file lands: beside the environment file, named installer_<version> (.exe on Windows), written directly under its final name — staging an executable through the system temp directory and moving it is a pattern antivirus and EDR products score heavily. A download that would land on the running binary is refused outright
• Progress is reported against the size from the package description rather than a Content-Length, which the encrypted response body does not carry, and printed once per 10% rather than once per read — a line every few kilobytes would bury everything around it in a panel that scrolls, and the path and the verification result are printed after the last of them

🎯 **Localization Architecture** Copy Chapter

**Centralized Constants** Copy Section

All user-visible text stored in locale/locale.go:

// Screen-specific constants
const (
    WelcomeTitle = "PentAGI Installer"
    WelcomeGreeting = "Welcome to PentAGI!"

    // Form help text with practical guidance
    LLMFormOpenAIHelp = `OpenAI provides access to GPT models...

Get your API key from:
https://platform.openai.com/api-keys`
)

**Multi-line Help Text** Copy Section

Detailed guidance integrated into forms:

• Provider-specific setup instructions
• Configuration recommendations
• Troubleshooting tips
• Best practices

🎯 **Error Handling & Recovery** Copy Chapter

**Graceful Degradation** Copy Section

• Dimension Fallbacks: Handles invalid terminal sizes
• Content Fallbacks: Shows loading states and error messages
• Network Resilience: Offline operation support
• State Recovery: Automatic restoration from corrupted state

**User-Friendly Error Messages** Copy Section

• Validation Errors: Real-time feedback with clear guidance
• System Errors: Plain English explanations with suggested fixes
• Network Errors: Offline alternatives and retry mechanisms

🎯 **Performance Considerations** Copy Chapter

**Optimizations** Copy Section

• Lazy Loading: Content loaded on-demand when screens accessed
• Memoised Renderers: one cached glamour instance per wrap width, built once instead of per render
• Efficient Scrolling: Viewport-based rendering for large content
• Memory Management: Proper cleanup and resource sharing

**Responsive Performance** Copy Section

• Breakpoint-Based: Layout decisions based on terminal capabilities
• Content Adaptation: Hide non-essential content on small screens
• Progressive Enhancement: Full features on capable terminals

🎯 **Development Workflow** Copy Chapter

**File Organization** Copy Section

• One Model Per File: Clear separation of screen logic
• Shared Constants: Type definitions in types.go
• Centralized Locale: All text in locale.go
• Clean Dependencies: Business logic isolated in controllers

**Code Style** Copy Section

• Compact Syntax: Where appropriate for readability
• Expanded Logic: For complex business rules
• Comments: Explain "why" and "how", not "what"
• Error Handling: Graceful degradation with user guidance

**Testing Strategy** Copy Section

• Build Testing: Successful compilation verification
• Manual Testing: Interactive validation on various terminal sizes
• Dimension Testing: Minimum (80x24) to large terminal support
• Navigation Testing: Complete flow validation

This overview provides the foundation for understanding the PentAGI installer's architecture, features, and development approach. The system prioritizes user experience, maintainability, and production reliability.

Backend Docs Analytics Api (`backend/docs/analytics_api.md`) Copy Section

Analytics API

GraphQL API for system analytics and statistics monitoring. This document covers three main use cases for developers building dashboards and analytics tools.

Important Data Notes Copy Chapter

Duration Storage and Calculation Copy Section

Database columns (as of migration 20260129_120000):

• toolcalls.duration_seconds: Pre-calculated duration for completed toolcalls

  • Automatically set during migration: EXTRACT(EPOCH FROM (updated_at - created_at))
  • Set to 0.0 for incomplete toolcalls (received, running)
  • Column is NOT NULL DEFAULT 0.0
  • Incremental updates: Backend calculates delta time and passes it to SQL
  • Formula: duration_seconds = duration_seconds + duration_delta (parameter from Go code)
  • Updated by SQL queries: UpdateToolcallStatus, UpdateToolcallFinishedResult, UpdateToolcallFailedResult

• msgchains.duration_seconds: Pre-calculated duration for message chains

  • Automatically set during migration: EXTRACT(EPOCH FROM (updated_at - created_at))
  • Set to 0.0 for incomplete msgchains
  • Column is NOT NULL DEFAULT 0.0
  • Incremental updates: Backend calculates delta time and passes it to SQL
  • Formula: duration_seconds = duration_seconds + duration_delta (parameter from Go code)
  • Updated by SQL queries: UpdateMsgChain, UpdateMsgChainUsage

Benefits of pre-calculated durations:

• Faster query performance (no real-time EXTRACT(EPOCH FROM ...) calculations)
• Consistent duration values (accumulated over multiple updates)
• Simpler SQL queries for analytics (just tc.duration_seconds instead of complex expressions)
• Reduced database load for dashboard queries
• Incremental tracking: accurately captures time even when records are updated multiple times
• All analytics SQL queries updated to use duration_seconds column directly

Incremental update logic:

• Backend code tracks execution time and calculates delta in seconds
• Delta is passed as parameter to SQL update queries
• SQL adds delta to existing duration_seconds value
• Prevents overwrites and ensures accurate cumulative duration
• Works correctly for long-running operations with multiple status updates
• Backend has full control over time measurement (can use high-precision timers)

Execution Time Metrics (`totalDurationSeconds`) Copy Section

What the data shows:

• Subtask duration: Linear wall-clock time from subtask start to finish (created_at → updated_at)

  • Excludes subtasks in created or waiting status (not yet started)
  • Includes only running, finished, failed subtasks
  • NOT the sum of toolcalls (avoids double-counting nested agent calls)

• Task duration: Total execution time including:

  • Generator Agent execution (runs before subtasks)
  • All subtasks execution (with overlap compensation for batch-created subtasks)
  • Refiner Agent execution(s) (runs between subtasks)

• Flow duration: Complete flow execution time including:

  • All tasks duration (generator + subtasks + refiner)
  • Flow-level toolcalls (e.g., Assistant toolcalls without task/subtask binding)
  • EXCLUDES Assistant msgchain lifetime (only active toolcall time counted)

Why not sum of toolcalls:

• Primary Agent calls Coder Agent → Coder calls Terminal
• Summing toolcalls would count Terminal execution twice (in Coder time AND separately)
• Linear time gives accurate wall-clock duration

Overlap compensation:

• Generator creates multiple subtasks simultaneously (same created_at)
• Subtasks execute sequentially, not in parallel
• System compensates for this overlap to show real execution time

Toolcalls Count Metrics (`totalCount`, `totalToolcallsCount`) Copy Section

What the data shows:

• Only completed toolcalls (status = finished or failed)
• Excludes received (not started) and running (in progress) toolcalls
• Represents actual completed operations, not attempted or pending

Use this for:

• Counting successful/failed operations
• Calculating success rate: finished_count / (finished_count + failed_count)
• Understanding actual work performed

Agent Type Breakdown Copy Section

Important distinction:

• primary_agent: Main orchestrator for subtasks
• generator: Creates subtask plans (runs once per task, before subtasks)
• refiner: Updates subtask plans (runs between subtasks, can run multiple times)
• reporter: Generates final task reports
• assistant: Interactive mode (can have long idle periods)
• Specialist agents: coder, pentester, installer, searcher, memorist, adviser

Usage interpretation:

• High generator usage = complex task decomposition
• High refiner usage = adaptive planning (many plan adjustments)
• High specialist usage = delegated work (Primary Agent using team members)

Agent vs Non-Agent Tools (`isAgent` field) Copy Section

The isAgent field in FunctionToolcallsStats categorizes tools into two types:

Agent Tools (isAgent: true):

• Delegation to AI agents (e.g., coder, pentester, searcher)
• Store results from agent execution (e.g., coder_result, hack_result)
• Execution time represents delegation overhead + agent decision time
• Does NOT include nested tool calls made by the agent
• Examples: coder, pentester, installer, searcher, memorist, adviser

Non-Agent Tools (isAgent: false):

• Direct tool execution (e.g., terminal, browser, file)
• Search engines (e.g., google, duckduckgo, tavily, firecrawl, sploitus, searxng)
• Vector database operations (e.g., search_in_memory, store_guide)
• Environment operations (e.g., terminal, file)
• Execution time represents actual operation duration
• Examples: terminal, browser, file, google, search_in_memory

Use this distinction for:

• Analyzing delegation overhead vs direct execution time
• Identifying which agents are most frequently used
• Optimizing agent selection strategies
• Understanding the balance between AI-driven and direct operations
• Dashboard visualizations: color-code or separate agent vs non-agent tools

Common Fragments Copy Chapter

Define these fragments once and reuse across queries:

fragment UsageStats on UsageStats {
  totalUsageIn
  totalUsageOut
  totalUsageCacheIn
  totalUsageCacheOut
  totalUsageCostIn
  totalUsageCostOut
}

fragment ToolcallsStats on ToolcallsStats {
  totalCount
  totalDurationSeconds
}

fragment FlowsStats on FlowsStats {
  totalFlowsCount
  totalTasksCount
  totalSubtasksCount
  totalAssistantsCount
}

fragment FlowStats on FlowStats {
  totalTasksCount
  totalSubtasksCount
  totalAssistantsCount
}

fragment FunctionToolcallsStats on FunctionToolcallsStats {
  functionName
  isAgent
  totalCount
  totalDurationSeconds
  avgDurationSeconds
}

fragment SubtaskExecutionStats on SubtaskExecutionStats {
  subtaskId
  subtaskTitle
  totalDurationSeconds
  totalToolcallsCount
}

fragment TaskExecutionStats on TaskExecutionStats {
  taskId
  taskTitle
  totalDurationSeconds
  totalToolcallsCount
  subtasks {
    ...SubtaskExecutionStats
  }
}

fragment FlowExecutionStats on FlowExecutionStats {
  flowId
  flowTitle
  totalDurationSeconds
  totalToolcallsCount
  totalAssistantsCount
  tasks {
    ...TaskExecutionStats
  }
}

Use Case 1: Time-Filtered Dashboard Copy Chapter

Purpose: Display system activity over time periods (week/month/quarter) with day-by-day breakdowns and execution metrics.

Query Copy Section

query TimeDashboard($period: UsageStatsPeriod!) {
  # LLM token usage over time
  usageStatsByPeriod(period: $period) {
    date
    stats { ...UsageStats }
  }
  
  # Toolcalls activity over time
  toolcallsStatsByPeriod(period: $period) {
    date
    stats { ...ToolcallsStats }
  }
  
  # Flows/tasks/subtasks created over time
  flowsStatsByPeriod(period: $period) {
    date
    stats { ...FlowsStats }
  }
  
  # Flow execution times with full hierarchy
  flowsExecutionStatsByPeriod(period: $period) {
    ...FlowExecutionStats
  }
}

Variables:

{ "period": "week" }  // or "month", "quarter"

Data Interpretation Copy Section

Daily Usage Trends:

• usageStatsByPeriod: Track token consumption patterns (input/output, cache hits, costs)
• Chart: Line graph showing daily token usage and costs
• Insights: Identify peak usage days, cost optimization opportunities

Toolcalls Performance:

• toolcallsStatsByPeriod: Monitor tool execution frequency and duration
• Count: Only completed operations (finished/failed), excludes pending/running
• Duration: Sum of individual toolcall execution times (created_at → updated_at)
• Chart: Dual-axis chart (count bars + duration line)
• Insights: Detect performance degradation, identify bottlenecks
• Note: Duration here IS sum of toolcalls (unlike execution stats which use wall-clock)

Flow Activity:

• flowsStatsByPeriod: Track system load (flows/tasks/subtasks/assistants created per day)
• Chart: Stacked bar chart showing hierarchy depth
• Insights: Understand workload distribution, capacity planning, assistant usage patterns

Execution Breakdown:

• flowsExecutionStatsByPeriod: Hierarchical view of execution times
• Chart: Treemap or sunburst showing time distribution across flows → tasks → subtasks
• Important: Duration is wall-clock time, not sum of toolcalls
  • Subtask: Linear time from start to finish (excludes created/waiting subtasks)
  • Task: Subtasks + Generator + Refiner agents
  • Flow: Tasks + Flow-level toolcalls (Assistant active time, NOT lifetime)
• Count: Only completed toolcalls (finished/failed status)
• Insights: Identify slow flows/tasks, optimize critical paths
• Note: Batch-created subtasks have overlap compensation applied

Cross-Correlations:

• Compare token usage vs execution time (efficiency metric)
• Correlate toolcall count with flow complexity
• Cost per flow: totalUsageCost / flowsCount
• Average toolcalls per task: totalToolcallsCount / totalTasksCount

Use Case 2: Overall System Statistics Copy Chapter

Purpose: Get comprehensive system-wide metrics without time filters.

Query Copy Section

query SystemOverview {
  # Total LLM usage
  usageStatsTotal { ...UsageStats }
  
  # Usage breakdown by provider
  usageStatsByProvider {
    provider
    stats { ...UsageStats }
  }
  
  # Usage breakdown by model
  usageStatsByModel {
    model
    provider
    stats { ...UsageStats }
  }
  
  # Usage breakdown by agent type
  usageStatsByAgentType {
    agentType
    stats { ...UsageStats }
  }
  
  # Total toolcalls stats
  toolcallsStatsTotal { ...ToolcallsStats }
  
  # Toolcalls breakdown by function
  toolcallsStatsByFunction {
    ...FunctionToolcallsStats
  }
  
  # Total flows/tasks/subtasks
  flowsStatsTotal { ...FlowsStats }
}

Data Interpretation Copy Section

Token Economics:

• usageStatsTotal: Overall system cost and token consumption
• Metrics: Total spend, cache efficiency (cacheIn / (cacheIn + usageIn))
• Dashboard KPI: Display as headline metrics

Provider Distribution:

• usageStatsByProvider: Cost and usage per LLM provider
• Chart: Pie chart showing provider share by cost
• Insights: Identify most/least cost-effective providers

Model Efficiency:

• usageStatsByModel: Granular per-model breakdown
• Chart: Table sorted by cost with usage metrics
• Metrics:
  • Cost per token: (costIn + costOut) / (usageIn + usageOut)
  • Cache hit rate per model
• Insights: Choose optimal models for different tasks

Agent Performance:

• usageStatsByAgentType: Resource consumption by agent role
• Chart: Horizontal bar chart showing usage by agent
• Insights: Understand which agents consume most resources

Tool Usage Patterns:

• toolcallsStatsByFunction: Top tool functions by usage and duration
• Agent Classification: The isAgent field indicates if the function is an agent delegation tool
  • Agent tools (e.g., coder, pentester) show their own execution time
  • Does NOT include time of nested calls they make
  • Example: coder toolcall = time for Coder Agent to decide + delegate, not terminal commands
  • Non-agent tools (e.g., terminal, browser) show direct execution time
• Chart: Table with sortable columns (count, duration, average, agent type)
• Metrics:
  • Slowest tools: Sort by avgDurationSeconds
  • Most used tools: Sort by totalCount (only completed)
  • Time sinks: Sort by totalDurationSeconds
  • Agent vs non-agent breakdown: Filter by isAgent
• Insights: Optimize frequently-used slow tools, identify unused tools, distinguish delegation overhead from direct execution

System Scale:

• flowsStatsTotal: Total entities in system
• Metrics:
  • Tasks per flow: totalTasksCount / totalFlowsCount
  • Subtasks per task: totalSubtasksCount / totalTasksCount
  • Assistants per flow: totalAssistantsCount / totalFlowsCount
• Insights: Understand average flow complexity and assistant usage