The central reasoning, memory, and tool-use core that every vtuber persona depends on for character consistency across hours of live streaming, with a plug-and-play skill ecosystem.
- Objective 1: Central reasoning, memory, and tool use core for character consistency across hours of streaming
- Objective 2: Plug-and-play skill ecosystem — adding a new capability equals registering a typed tool with the brain
vtuber-brain aims to solve the following problem:
vtuber-brain is the Director half of the Director/Performer split (ADR-001 in repo_plus.yml). It receives context (chat, audience signal, internal state), runs reasoning and memory retrieval, decides which persona to wear and which skill to invoke, then emits a ConversationDirective containing text_prompt and voice_prompt to vtuber-voice via gRPC. Skills (game/sing/policy/strategy) register as typed tools and brain dispatches via tool-use protocol. Long-term memory lives in Postgres with pgvector; character lore loads from vtuber-commons at startup. Mojo handles hot inference kernels (RAG re-rank, intent classification); Python serves the underlying LLM (Ollama or vLLM).
By leveraging Rust, Axum, tonic, Mojo, Python (LLM bridge), Ollama, vLLM, Postgres, pgvector, we ensure that our solution is not only functional but also future-proof and high-performing.