Project O.L.I.V.I.A.
AI companion with persistent memory, voice, and dreaming
Role
Solo Developer & Researcher
Duration
Ongoing
Tech Stack
6 technologies
About This Project
Project O.L.I.V.I.A. started as a question: what would it take to build an AI companion that actually remembers you? Not just within a single conversation, but across weeks and months of interaction. The result is an experimental system that pushes the boundaries of personal AI by combining persistent memory, natural voice, and a unique "dreaming" mechanism.
At its core, O.L.I.V.I.A. uses vector embeddings to store and retrieve memories from past conversations. But raw retrieval isn't enough, so the "dreaming" system periodically consolidates memories, finding patterns and connections that inform future interactions. This gives the AI a sense of continuity that feels qualitatively different from typical chatbots. The personality layer sits on top, evolving subtly based on interaction patterns while maintaining a coherent identity.
The technical stack is built for experimentation, and it runs entirely on local hardware. There are no cloud APIs anywhere in the pipeline. FastAPI orchestrates the whole system around a fine-tuned 8B LLM served through Ollama. faster-whisper handles speech-to-text and ChatterBox Turbo handles speech synthesis, so voice never leaves the machine either. Long-term memory lives in a tiered ChromaDB store, with the dreaming process consolidating and reorganizing those tiers over time. The project remains actively developed, with the core architecture open-sourced to gather community feedback and contributions.
Key Features
- ◆Persistent memory across conversations using vector embeddings
- ◆Natural voice interaction with real-time speech synthesis
- ◆"Dreaming" system that consolidates and reflects on past interactions
- ◆Evolving personality that adapts based on interaction patterns
- ◆Context-aware responses using RAG pipeline
Challenges
Managing long-term memory without context window limitations
Creating natural-feeling personality evolution over time
Balancing response quality with inference latency
Outcomes
- ✓Successfully maintains coherent personality across 1000+ interactions
- ✓Memory consolidation reduces retrieval noise by 40%
- ✓Open-sourced core architecture for community feedback
Tech Stack
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