Initialized Agents

Sources/FatherOfGeorge/BaseAgent.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+
+import threading
+import time
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from smolagents import LiteLLMModel, ToolCallingAgent
+
+
+class Agent(ABC):
+    """Base class for AI agents."""
+
+    def __init__(self, model: LiteLLMModel, api_key: str = None, anthropic_api_key: str = None):
+        self.model = model
+        self.api_key = api_key
+        self.anthropic_api_key = anthropic_api_key
+        self.agents = {}
+        self.setup_agents()
+
+        # Rate limiting configuration
+        self.min_request_interval = 1.0  # Minimum seconds between requests
+        self.last_request_time = None
+        self.request_lock = threading.Lock()
+
+    def wait_for_rate_limit(self):
+        """Ensure minimum time between requests."""
+        with self.request_lock:
+            if self.last_request_time:
+                elapsed = time.time() - self.last_request_time
+                if elapsed < self.min_request_interval:
+                    sleep_time = self.min_request_interval - elapsed
+                    print(f"Rate limiting: waiting {sleep_time:.2f} seconds...")
+                    time.sleep(sleep_time)
+            self.last_request_time = time.time()
+
+    def get_prompt(self, prompt_name: str) -> str:
+        """Load prompt template from file."""
+        prompt_path = Path(__file__).parent / "prompts" / prompt_name
+        if prompt_path.exists():
+            with open(prompt_path, 'r') as f:
+                return f.read()
+        return ""
+
+    @abstractmethod
+    def setup_agents(self):
+        """Setup agents specific to this agent."""
+        pass
+
+    def get_manager_agent(self) -> ToolCallingAgent: 
+        """Get the main manager agent for this system."""
+        # Try to find father_of_george first
+        if 'father_of_george' in self.agents:
+            return self.agents['father_of_george']
+
+        # Look for agents with 'Manager' in their description or name
+        for key, agent in self.agents.items():
+            if hasattr(agent, 'description') and 'Manager' in agent.description:
+                return agent
+            if hasattr(agent, 'name') and 'Manager' in agent.name:
+                return agent
+
+        # Fallback: return the first agent if no manager found
+        if self.agents:
+            return list(self.agents.values())[0]
+
+        return None
+
+    def get_all_agents(self) -> list:
+        """Get all agents in this system."""
+        return list(self.agents.values())
+
+    def run_task(self, task_description: str, context: dict = None) -> dict:
+        """Run a task using this system's agents."""
+        results = {
+            "task_description": task_description,
+            "completed": False,
+            "output": None,
+            "error": None,
+        }
+
+        try:
+            self.wait_for_rate_limit()
+
+            # Get the manager agent
+            manager_agent = self.get_manager_agent()
+            if not manager_agent:
+                results["error"] = "No manager agent found for this system"
+                return results
+
+            # Create task prompt
+            prompt = self._create_task_prompt(task_description, context)
+
+            print(f"Using {manager_agent.name} for task: {task_description}")
+
+            # Run the agent
+            agent_output = manager_agent.run(prompt)
+            results["output"] = str(agent_output)
+            results["completed"] = True
+
+        except Exception as e:
+            results["error"] = str(e)
+            print(f"Error running task: {e}")
+
+        return results
+
+    def _create_task_prompt(self, task_description: str, context: dict = None) -> str:
+        """Create a prompt for the given task."""
+        prompt = f"Task: {task_description}\n\n"
+
+        if context:
+            prompt += "Context:\n"
+            for key, value in context.items():
+                prompt += f"- {key}: {value}\n"
+            prompt += "\n"
+
+        return prompt
+
+
+# Tool definitions for different agent types
+def get_manager_tools():
+    """Tools available to manager agents."""
+    return [
+        # TODO: Add manager-specific tools
+        # - orchestrate_subagents
+        # - collect_results
+        # - make_decisions
+        # - validate_outputs
+    ]
+
+def get_code_analysis_tools():
+    """Tools available to code analysis agents."""
+    return [
+        # TODO: Add code analysis tools
+        # - analyze_syntax
+        # - analyze_semantics
+        # - extract_patterns
+        # - identify_vulnerabilities
+    ]
+
+def get_retrieval_tools():
+    """Tools available to retrieval agents."""
+    return [
+        # TODO: Add retrieval tools
+        # - query_rag_db
+        # - search_vector_db
+        # - retrieve_context
+        # - validate_information
+    ]
+
+def get_v8_search_tools():
+    """Tools available to V8 search agents."""
+    return [
+        # TODO: Add V8 search tools
+        # - fuzzy_find
+        # - regex_search
+        # - compile_with_clang
+        # - test_with_python
+        # - view_call_graph
+        # - web_search
+    ]
+
+def get_program_builder_tools():
+    """Tools available to program builder agents."""
+    return [
+        # TODO: Add program builder tools
+        # - query_postgres_db
+        # - generate_seed_program
+        # - combine_contexts
+        # - validate_syntax
+    ]
+
+def get_corpus_generation_tools():
+    """Tools available to corpus generation agents."""
+    return [
+        # TODO: Add corpus generation tools
+        # - validate_syntax
+        # - validate_semantics
+        # - test_program
+        # - evaluate_interestingness
+    ]
+
+def get_runtime_analysis_tools():
+    """Tools available to runtime analysis agents."""
+    return [
+        # TODO: Add runtime analysis tools
+        # - analyze_execution_state
+        # - check_coverage
+        # - evaluate_flags
+        # - determine_seed_quality
+    ]
+
+def get_validation_tools():
+    """Tools available to validation agents."""
+    return [
+        # TODO: Add validation tools
+        # - validate_corpus
+        # - check_db_integrity
+        # - verify_results
+        # - quality_assurance
+    ]

Sources/FatherOfGeorge/FoG.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""
+Father of George, and thus George the negative First.
+L0 Root Manager Agent - Orchestrates Code Analysis and Program Building
+"""
+
+from smolagents import LiteLLMModel, ToolCallingAgent
+from BaseAgent import Agent, get_manager_tools, get_code_analysis_tools, get_program_builder_tools, get_retrieval_tools, get_v8_search_tools
+from GeorgeForeman import George
+
+
+class Father(Agent):
+    """Init code analysis and program builder."""
+
+    def setup_agents(self):
+        # L2 Worker: George Foreman
+        self.agents['george_foreman'] = George(
+            model=self.model,
+            api_key=self.api_key,
+            anthropic_api_key=self.anthropic_api_key
+        )
+
+        # L2 Worker: Retriever of Code (under CodeAnalyzer)
+        self.agents['retriever_of_code'] = ToolCallingAgent(
+            name="RetrieverOfCode",
+            description="L2 Worker responsible for retrieving code from various sources using RAG database",
+            tools=get_retrieval_tools(),
+            model=LiteLLMModel(model_id="gpt-5", api_key=self.api_key),
+            managed_agents=[
+                self.agents['george_foreman']
+            ], 
+            max_steps=10,
+            planning_interval=None,
+        )
+
+        # L2 Worker: V8 Search (under CodeAnalyzer)
+        self.agents['v8_search'] = ToolCallingAgent(
+            name="V8Search",
+            description="L2 Worker responsible for searching V8 source code using fuzzy find, regex, and compilation tools",
+            tools=get_v8_search_tools(),
+            model=LiteLLMModel(model_id="gpt-5", api_key=self.api_key),
+            managed_agents=[],
+            max_steps=10,
+            planning_interval=None,
+        )
+
+        # L1 Manager: Code Analysis Agent
+        self.agents['code_analyzer'] = ToolCallingAgent(
+            name="CodeAnalyzer",
+            description="L1 Manager responsible for analyzing code and coordinating retrieval and V8 search operations",
+            tools=get_code_analysis_tools(),
+            model=LiteLLMModel(model_id="gpt-5", api_key=self.api_key),
+            managed_agents=[
+                self.agents['retriever_of_code'],
+                self.agents['v8_search']
+            ],
+            max_steps=15,
+            planning_interval=None,
+        )
+
+        # L1 Manager: Program Builder Agent  
+        self.agents['program_builder'] = ToolCallingAgent(
+            name="ProgramBuilder",
+            description="L1 Manager responsible for building program templates using corpus and context",
+            tools=get_program_builder_tools(),
+            model=LiteLLMModel(model_id="gpt-5", api_key=self.api_key),
+            managed_agents=[
+                self.agents['george_foreman']
+            ],
+            max_steps=15,
+            planning_interval=None,
+        )
+
+        # L0 Root Manager Agent
+        self.agents['father_of_george'] = ToolCallingAgent(
+            name="FatherOfGeorge",
+            description="L0 Root Manager responsible for orchestrating code analysis and program building operations",
+            tools=get_manager_tools(),
+            model=LiteLLMModel(model_id="gpt-5-mini", api_key=self.api_key),
+            managed_agents=[
+                self.agents['code_analyzer'],
+                self.agents['program_builder']
+            ],
+            max_steps=20,
+            planning_interval=None,
+        )
+
+
+    def run_task(self, task_description: str, context: dict = None) -> dict:
+        results = {
+            "task_description": task_description,
+            "completed": False,
+            "output": None,
+            "error": None,
+        }
+        return results
+
+
+def main():
+    # Init model
+    model = LiteLLMModel(
+        model_id="gpt-5-mini",
+        api_key="<key>"
+    )
+
+    system = Father(model)
+
+    # run task
+    result = system.run_task(
+        task_description="Initialize corpus generation for V8 fuzzing",
+        context={
+            "CodeAnalyzer": "Analyze V8 source code for patterns. vulnerabilities. specifc components, etc...",
+            "ProgramBuilder": "Build JavaScript programs using corpus and context"
+        }
+    )
+
+    print("Task Result:")
+    print(f"Completed: {result['completed']}")
+    print(f"Output: {result['output']}")
+    if result['error']:
+        print(f"Error: {result['error']}")
+
+
+if __name__ == "__main__":
+    main()