m1k-rsch/corti

m1k-rsch★ 0PythonLast synced: 2026-08-15

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Persistent memory layer for AI agent swarms. Postgres-backed retrieval, Markdown as source of truth, sub-second cascade sync. Self-hosted.

README excerpt

🧠 Corti Cognitive Substrate for AI Agent Swarms Markdown Source of Truth · Sub-second Cascade Sync · Postgres Vector Search · Multi-Agent Adaptive Quick Start · Install & Integration · Storage Stack · Lifecycle · Design Philosophy Corti (corti): persistent, self-evolving memory layer for AI agents. Decouples runtime state from static weights. Stores state as diffable Markdown. Rebuilds high-performance Postgres/SQLite vector/BM25 indexes. Fully self-hosted. 1. Persona Agent vs. CLI Agent : Target audience = Persona Agents (e.g., Hermes Agent, OpenClaw Agent) ≠ CLI task-runners. Persona Agents require persistent background identity, preference awareness, context stability. 2. State-Weight Decoupling : Trillion-parameter static model weights cannot mutate for local projects. Runtime state must be decoupled from static weights. 3. Random Pre-Injection of Memory Fragments : Random pre-injection of memory fragments ➔ context. Matches human long-term memory: constant background context + targeted activation weights. Widens activation surface, prevents recall omission. 4. Runtime Epistemic Limits : Map vector spaces directly to context. Resolves runtime bottleneck: LLMs "do not know what…

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