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Think - Operating System for Thought

An AI-driven agent framework where conversations become workflows.

This project is an experiment in building a persistent, multi-agent system that acts as a true operating system for thought. Unlike traditional chatbots that reset every time, this framework allows you to interact with a single Orchestrator agent that can spin up, manage, and recall specialized Execution agents—each with its own memory, tools, and responsibilities.


🔑 Core Idea

  • You always talk to the Orchestrator.
  • The Orchestrator decides whether to reuse an existing agent or spawn a new one.
  • Execution agents persist, remember, and can be recalled at any time.
  • Tools and tasks give agents real capabilities (drafting emails, scheduling reminders, searching knowledge).
  • Memory layers (short-term, episodic, semantic) ensure the system scales without forgetting critical context.

Example:

User: Email Alice and Bob about lunch tomorrow
Orchestrator: Spawning two email agents...
Agent(Alice): Draft ready.
Agent(Bob): Draft ready.
Orchestrator: Both drafts are ready. Send?

Later…

User: Did they respond?
Orchestrator: Recalling the existing Alice and Bob agents…
Agent(Alice): No reply yet.
Agent(Bob): Confirmed availability at 12:30.

🎯 Project Goals

  • Persistent Agents: Execution agents with their own logs, memory, and context.

  • Orchestration Layer: A router that maintains UX consistency while delegating real work.

  • Memory Model:

    • Short-term: recent messages.
    • Episodic: rolling summaries with temporal anchors.
    • Semantic: embeddings for durable facts and artifacts.
  • Tooling Framework: Agents can call tools (atomic ops) or tasks (multi-step routines).

  • Interfaces: Start with a Next.js chat UI; later extend to RCS/iMessage for natural messaging.


🛠️ Tech Stack

  • Frontend: Next.js (React, streaming chat UI, Agent Dock, Memory Viewer).
  • Backend: Convex (serverless DB + scheduler + actions).
  • Auth: Clerk.
  • LLM Orchestration: OpenAI / Anthropic with function-calling.
  • Memory Store: Convex tables for short-term, episodic summaries, and semantic embeddings.
  • Tools: Email drafting/sending, reminders, search.

📌 Roadmap

  • MVP: Chat UI + Orchestrator + Email draft tool + Reminder scheduling.
  • Phase 2: Episodic & semantic memory layers, richer tools (notes, search).
  • Phase 3: Multi-interface (RCS, iMessage), project workspaces, self-healing agents.

Why it Matters

Most AI “assistants” today are stateless and forgetful. This project shows what’s possible when you treat AI as a system of agents with memory and roles, not just a single model call. The goal isn’t just to demo LLMs, but to build the foundation of an AI-native operating system for human thought and action.

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