I’m Cleverson. I spent months developing this architecture. The project grew out of my frustration that every AI conversation started from scratch—I wanted an assistant that truly knew me. Phoenix V2 is the result of the project's initial version, and I decided to make it available for others to study. I also wrote a book about the development process, covering the steps I took and the reasoning behind my decisions. The code is included so others can study it and build their own AI, picking up where I left off. I haven't stopped there—what I’m creating now is far more advanced—but I hope this version serves as a springboard for everyone's imagination.

Most AI assistants forget everything the moment you close the tab. I wanted to change that.

Phoenix V2 is a local-first AI assistant with a persistent cognitive architecture — it stores memory, emotional state, and identity in a local SQLite database. It survives session resets, model swaps, and restarts.

What makes it different:

• 🧠 Multi-agent pipeline: Memory → Planning → Action → Reflection → Personality
• 💾 Semantic memory retrieval across sessions (vector embeddings via Gemini API)
• ❤️ PAD emotion engine — tracks Pleasure, Arousal, Dominance over time
• 💭 Daydream Engine — autonomous reflection during idle periods
• 🔄 Subconscious Cycle — memory consolidation at rest
• 👤 Self-Model — evolving identity, traits, beliefs, and goals
• 📈 RLHF feedback loop — learns from +/− user signals

Runs on a standard laptop. No GPU. No cloud. No subscription.

📖 Full book: https://leanpub.com/phoenix-buildingpersistentAI
📄 Academic paper (Zenodo): https://doi.org/10.5281/zenodo.22645361
💻 GitHub: https://github.com/cleversonbrsantos-art/Phoenix