Developer of brain-inspired AI systems specializing in prefrontal cortex simulation and cognitive augmentation.

Key modules

  • main.py: CLI loop, feedback persistence (feedback_history.json), session logging (logs/)
  • workflow.py: LangGraph StateGraph definition and dynamic delegation logic
  • agents/: agent implementations + model provider factory
  • config/agents.yaml: per-agent model/provider configuration

Execution flow

  1. User enters a task
  2. workflow.create_workflow() runs task_delegation (DLPFC)
  3. DLPFC output is parsed into delegated_agents (stage names)
  4. Specialist stages run in sequence and store results in agent_responses
  5. value_assessment (MPFC) integrates the final response
  6. CLI optionally collects feedback and persists it for future runs