[Beta] membrane: A self-hosted orchestration layer that turns raw local LLM turns into a self-structuring, live entity field.

Hey r/selfhosted, Most self-hosted personal assistants and automation wrappers feel like rigid, hard-coded integrations disguised as "AI". You either have to manually map every API payload, or you have to dump endless conversation logs into a context window/Vector DB and hope the model filters out the noise. I wanted an architecture where the system autonomously acts as its own integration layer based on live state tracking. So I built membrane — a self-hosted layer running directly on top of Ollama. Instead of treating memory as a passive text scroll or a collection of third-party plugins, membrane forces the local model to build and maintain its own real-time field entity integrations. The Self-Structuring Memory Field: Dynamic Entity Integration: As you interact with the system or pipe data through it, the model doesn't just reply; it continuously extracts, types, and links observations into a unified field of entities (facts, states, watches, and long-term goals). The integration happens at the cognitive layer, meaning the system learns how properties and entities correlate on the fly. In-Place Updates with Provenance: When a state changes, the model updates that specific entity in-place within the embedded store, stamping it with provenance. The model reads a high-density, updated snapshot of the world at every turn—not an out-of-date top-k vector chunk pile or a massive chat replay history. Fewer Tokens, By Design: Because the system dynamically builds and prunes its own field context, it doesn't need to swallow massive conversation loops. It reuses written-back conclusions, making large models highly performant on modest local hardware. Reactive Watch Loops: The field entities themselves are reactive. When the model detects a shift in a monitored entity state, it can independently evaluate dependencies and trigger downstream actions or workflows without waiting for a user prompt. Environment: Runs 100% locally on your machine (Linux, macOS, or Android/Termux). Requires Python 3.11+; setup takes roughly 15 minutes. The project is currently in public beta. If you are interested in an orchestration layer that focuses on autonomous state synthesis rather than static API glue, check out docs/BETA.md in the repository. 👉 GitHub: github.com