Audience: This repository is intended for AI researchers, engineers, technical founders, and venture/innovation professionals seeking a modular, agentic, and extensible platform for knowledge management and intelligent automation.

rolodexter is an agent-operable knowledge base and modular intelligence mesh. It is not a static wiki or simple chatbot, but a platform for building, orchestrating, and deploying advanced, executive-functioning AI agents with persistent memory, semantic search, and real system integration.

• Knowledge & Memory Systems: Modular, semantically structured knowledge base (details) and a persistent, context-aware memory system.
• Agent Orchestration: Multi-agent architecture (details) with specialized agents for DevOps, research, system automation, and more.
• Foundation Model Integration: Leverages LLMs, vector DBs, and RAG for context-rich reasoning and action.
• System Automation: Agents can operate on Windows, cloud, and API environments (rolodexterWIN).
• Continuous Learning: Empirical development and real-world validation in rolodexterLABS.

How is this accomplished?

• Modular file structure and semantic linking
• Integration with open-source and proprietary LLMs
• Persistent, agent-accessible memory and knowledge stores
• System-level hooks for automation and orchestration

• notes/ — Research notes, daily logs, drafts
• projects/ — Modular project subfolders (e.g., rolodexterLABS, rolodexterLARP)
• prompts/ — Agent prompts, onboarding, savepoints
• .gitkeep — Tracks empty directories in version control
• README.md — This landing page for the repository
• index.md — Homepage for the GitHub Pages site (Chirpy theme)

• Technical Feasibility: rolodexter builds on proven agent frameworks (see agent technologies note), foundation models, and modern memory systems.
• Differentiation: Executive-functioning agent orchestration, modular knowledge/memory integration, and system-level automation.
• Deep Dives:
  • Knowledge & Memory Systems
  • Agent Architecture
  • rolodexterLABS: Empirical R&D
• FAQ:
  • How does rolodexter achieve persistent, context-aware reasoning?
    → By combining semantic knowledge bases, agent memory, and LLM-powered retrieval.
  • Can agents operate autonomously on real systems?
    → Yes. See rolodexterWIN and system integration docs.
  • What frameworks or models are supported?
    → rolodexter is framework-agnostic, supporting integration with Python, LLM APIs, OS-level hooks, and more.

rolodexter draws inspiration from Donald Hoffman’s “conscious agent” theories, exploring whether similar principles of consciousness and agency can emerge in artificial systems.
Read more in the rolodexter project README →

This repository is under active development. Major updates and new features are coming soon.