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Strix is an autonomous penetration testing framework that combines AI agents, specialized security knowledge, and runtime sandboxing to discover vulnerabilities in your applications.

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Architecture Overview

At its core, Strix orchestrates multiple components working together:

AI Agents

LLM-powered security experts that reason about targets and execute tests

Tools

Specialized capabilities for terminal, browser, proxy, file manipulation

Skills

Domain-specific security knowledge injected into agent context

Runtime

Isolated sandboxes providing safe execution environments

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Execution Flow

When you start a scan, Strix follows this workflow:

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1. Scan Initialization

Strix creates a root agent and initializes a sandbox environment: 
# From strix/agents/StrixAgent/strix_agent.py
async def execute_scan(self, scan_config: dict[str, Any]) -> dict[str, Any]:
    user_instructions = scan_config.get("user_instructions", "")
    targets = scan_config.get("targets", [])
    
    # Process different target types
    for target in targets:
        target_type = target["type"]
        if target_type == "repository":
            # Clone and analyze repositories
        elif target_type == "web_application":
            # Test web endpoints
The root agent receives:
  • Target information (URLs, repositories, IP addresses)
  • User instructions (custom testing requirements)
  • Sandbox workspace (isolated environment with tools)

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2. Agent Loop

Each agent operates in a continuous reasoning loop: The agent loop handles:
Each agent has a maximum iteration limit (default: 300) to prevent infinite loops. As the agent approaches the limit, it receives warnings to prioritize task completion.
# From strix/agents/base_agent.py
if self.state.is_approaching_max_iterations():
    warning_msg = (
        f"URGENT: You are approaching the maximum iteration limit. "
        f"Current: {self.state.iteration}/{self.state.max_iterations}"
    )
    self.state.add_message("user", warning_msg)
Agent state tracks the full execution context:
  • agent_id: Unique identifier
  • messages: Conversation history with LLM
  • actions_taken: Tool invocations performed
  • context: Custom data storage
  • errors: Any failures encountered
  • iteration: Current loop iteration
Tools execute either locally (in the Strix CLI) or remotely (in the sandbox):
# From strix/tools/executor.py
async def execute_tool(tool_name: str, agent_state, **kwargs):
    execute_in_sandbox = should_execute_in_sandbox(tool_name)
    
    if execute_in_sandbox and not sandbox_mode:
        return await _execute_tool_in_sandbox(tool_name, agent_state, **kwargs)
    
    return await _execute_tool_locally(tool_name, agent_state, **kwargs)

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3. Multi-Agent Coordination

Strix can spawn specialized sub-agents for complex tasks: 
# From strix/tools/agents_graph/agents_graph_actions.py
@register_tool
def create_agent(
    task: str,
    name: str,
    skills: str | None = None,
    inherit_messages: bool = False,
) -> dict[str, Any]:
    # Create new agent with specialized skills
    new_agent = StrixAgent(config={
        "state": new_state,
        "llm_config": llm_config,
    })
Sub-agents share the same workspace and proxy history but maintain independent conversation contexts. This enables parallel testing while building on previous discoveries.

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4. Vulnerability Reporting

When agents discover security issues, they create structured reports: 
# From strix/tools/reporting/reporting_actions.py
@register_tool
def create_vulnerability_report(
    title: str,
    description: str,
    impact: str,
    target: str,
    technical_analysis: str,
    poc_description: str,
    poc_script_code: str,  # Required: actual exploit code
    remediation_steps: str,
    cvss_breakdown: str,
    endpoint: str | None = None,
    cve: str | None = None,
    cwe: str | None = None,
):
    # Validates CVSS metrics and creates report
    # Automatically checks for duplicate findings
Reports include:
  • CVSS scoring (automatic calculation from metrics)
  • Proof-of-concept code (executable exploit)
  • Duplicate detection (prevents redundant findings)
  • Code locations (vulnerable files and line numbers)

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Sandbox Architecture

Strix sandboxes provide isolated environments where agents can safely execute commands, browse applications, and test for vulnerabilities without affecting your local system.
Each sandbox includes:
  • Tool server: HTTP API for executing tools (terminal, browser, file operations)
  • Caido proxy: Intercepts and logs all HTTP/HTTPS traffic
  • Workspace: Shared /workspace directory for code analysis
  • Isolated network: Contained environment with controlled internet access
# Sandbox initialization from strix/agents/base_agent.py
runtime = get_runtime()
sandbox_info = await runtime.create_sandbox(
    self.state.agent_id,
    self.state.sandbox_token,
    self.local_sources  # Upload local code to sandbox
)

self.state.sandbox_id = sandbox_info["workspace_id"]
self.state.sandbox_info = sandbox_info

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Agent Communication

Agents can send messages to each other for coordination: 
<inter_agent_message>
  <sender>
    <agent_name>Auth Specialist</agent_name>
    <agent_id>agent_abc123</agent_id>
  </sender>
  <message_metadata>
    <type>information</type>
    <priority>high</priority>
  </message_metadata>
  <content>
    Found JWT token in localStorage: eyJhbGc...
    You can use this for authenticated endpoint testing.
  </content>
</inter_agent_message>

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LLM Integration

Strix supports multiple LLM providers with structured output:
  • Anthropic Claude: Extended thinking, tool use
  • OpenAI GPT-4: Function calling, structured responses
  • Google Gemini: Multi-modal analysis
  • OpenRouter: Access to multiple models
# From strix/agents/base_agent.py
async for response in self.llm.generate(self.state.get_conversation_history()):
    if response.tool_invocations:
        # Execute tools requested by LLM
        await process_tool_invocations(
            response.tool_invocations,
            conversation_history,
            self.state
        )

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State Persistence

Agent state is tracked throughout execution: 
# From strix/agents/state.py
class AgentState(BaseModel):
    agent_id: str
    agent_name: str
    parent_id: str | None = None
    sandbox_id: str | None = None
    
    task: str
    iteration: int = 0
    max_iterations: int = 300
    completed: bool = False
    
    messages: list[dict[str, Any]]  # Full conversation
    actions_taken: list[dict[str, Any]]  # Tool executions
    errors: list[str]  # Failures encountered
This enables:
  • Resume from interruptions: Continue scans after pauses
  • Debugging: Review full execution history
  • Analytics: Track agent performance and behavior

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Next Steps

Agents

Learn about agent types and capabilities

Tools

Explore available tools and their usage

Skills

Understand the skills system

Vulnerability Detection

See how Strix finds security issues

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