Overview The Agents API provides the foundation for building autonomous security testing agents. The BaseAgent class handles the agent lifecycle, tool execution, and interaction with the LLM.
BaseAgent The base class for all Strix agents. Manages the agent loop, tool execution, and state management.
Constructor from strix.agents import BaseAgent agent = BaseAgent(config: dict[ str , Any])
Configuration dictionary for the agent. LLM configuration for the agent
Existing agent state to restore from
Maximum number of iterations before stopping
Name of the LLM configuration
local_sources
list[dict[str, str]]
default: "[]"
Local source directories to mount in the sandbox
Whether to run in non-interactive mode
Properties The name of the agent class
Maximum number of iterations allowed
Current agent state including messages and context
The LLM instance used by the agent
Agent configuration dictionary
Methods agent_loop async def agent_loop ( task : str ) -> dict[ str , Any]
Runs the main agent loop until completion or max iterations.
The task for the agent to accomplish
Final result containing success status and any output
Example: agent = BaseAgent({ "llm_config" : llm_config, "max_iterations" : 100 }) result = await agent.agent_loop( "Scan example.com for vulnerabilities" ) print (result) # {'success': True, ...}
cancel_current_execution def cancel_current_execution () -> None
Cancels the currently running execution task.
Example: agent.cancel_current_execution()
set_agent_identity def set_agent_identity ( agent_name : str | None , agent_id : str | None ) -> None
Sets the agent’s identity metadata.
Human-readable agent name
AgentState Manages the state of an agent including messages, context, and execution status.
Constructor from strix.agents.state import AgentState state = AgentState( agent_name: str = "Strix Agent" , max_iterations: int = 300 )
agent_name
str
default: "'Strix Agent'"
Name of the agent
Maximum iterations allowed
Properties Unique identifier (auto-generated)
Parent agent ID for sub-agents
Docker container ID for the sandbox
Whether the task is completed
Additional context storage
Methods add_message def add_message ( role : str , content : Any, thinking_blocks : list[dict[ str , Any]] | None = None ) -> None
Adds a message to the conversation history.
Message role: “user” or “assistant”
thinking_blocks
list[dict[str, Any]] | None
Optional thinking blocks from reasoning models
increment_iteration def increment_iteration () -> None
Increments the iteration counter.
set_completed def set_completed ( final_result : dict[ str , Any] | None = None ) -> None
Marks the agent as completed.
get_conversation_history def get_conversation_history () -> list[dict[ str , Any]]
Returns the full conversation history.
List of message dictionaries
get_execution_summary def get_execution_summary () -> dict[ str , Any]
Returns a summary of the agent’s execution.
Dictionary containing agent_id, task, iteration count, completion status, etc.
Example: state = AgentState( agent_name = "Scanner" , max_iterations = 50 ) state.add_message( "user" , "Find vulnerabilities in the API" ) state.increment_iteration() if state.iteration >= state.max_iterations: state.set_completed({ "success" : False , "reason" : "max_iterations" }) summary = state.get_execution_summary() print (summary[ "iteration" ]) # Current iteration count
Usage Example import asyncio from strix.agents import BaseAgent from strix.llm import LLMConfig async def main (): # Configure LLM llm_config = LLMConfig( model_name = "claude-3-5-sonnet-20241022" , scan_mode = "standard" ) # Create agent agent = BaseAgent({ "llm_config" : llm_config, "max_iterations" : 100 , "non_interactive" : True }) # Run agent result = await agent.agent_loop( "Scan the web application at https://example.com" ) print ( f "Success: { result.get( 'success' ) } " ) print ( f "Iterations: { agent.state.iteration } " ) if __name__ == "__main__" : asyncio.run(main())