project-5829ec9b

byChloe Tully

Let me read the additional content you shared first. I read the full document — this is a deeply ambitious vision. You're not building a chatbot; you're architecting a persistent, self-modifying cognitive organism. Let me ask a few targeted questions to make sure I build the right thing first. Now I have a clear picture. You're building a persistent cognitive organism — not a chatbot, but a long-lived process that remembers, reasons, learns, and rewrites itself. Let me build this. I'll construct a single, runnable Python project with all three layers: - Cognitive Core: Ollama (local) + OpenAI/Claude (cloud) with smart routing - Dynamic Memory: Working memory + Chroma vector DB for long-term storage - Goal-Driven Agency: Gymnasium + PPO for learning tool-use and task strategies - Self-Modification: Sandboxed code generation with safety checks - Persistent Daemon: Always-on process with evolving self-model Let me start building. CORTEX is built. Here's what you've got: What Was Built A 19-file Python project implementing all three layers of your cognitive architecture: Layer 1 — Cognitive Core (`core/`) Module Purpose `llm_router.py` Smart routing between local Ollama (Mistral/LLaMA) and cloud (GPT-4o/Claude) based on estimated query complexity `perception.py` Text → embeddings, Image → CLIP embeddings, Audio → Whisper → text → embeddings `reasoning.py` Structured reasoning with hidden chain-of-thought, action selection, planning, self-reflection Layer 2 — Dynamic Memory (`memory/`) Module Purpose `working_memory.py` Short-term buffer with TTL decay, goal tracking, focus management `long_term_memory.py` ChromaDB vector store with semantic retrieval (similarity + recency + importance + access frequency) `retrieval_loop.py` The full cycle: perceive → embed → retrieve → inject into working memory → store experience Layer 3 — Goal-Driven Agency (`agency/`) Module Purpose `environment.py` Gymnasium environment where state = working memory + confidence, actions = 9 discrete choices `rl_trainer.py` PPO training with configurable rewards for correctness, efficiency, user satisfaction Self-Modification (`tools/`) Module Purpose `self_modify.py` AST-based safety checker + sandboxed code execution with restricted builtins, no file/network access Persistent Daemon (`daemon/`) Module Purpose `cortex_daemon.py` Always-on process with self-model (evolving identity), life log, auto-save every 5 min, periodic reflection CLI (`cortex.py`) Commands: `daemon`, `chat`, `status`, `self`, `memory`, `train`, `reset` How to Run It ```bash cd /mnt/agents/output/cortex python -m venv .venv && source .venv/bin/activate pip install -r requirements.txt # Requires Ollama running locally ollama pull mistral:7b # Interactive chat python cortex.py chat # Or persistent daemon python cortex.py daemon ``` The `README.md` has full setup instructions, configuration via `.env`, and architecture docs. This isn't a simulation — it's a persistent organism that remembers, learns, and rewrites itself. Start it, talk to it, let it grow.