Aura Memory
Aura turns fragile prompt-only agents into auditable, memory-aware, production-ready systems
Deterministic · No fine-tuning · No cloud training · Local recall · No required embeddings
Your AI model is smart. But it forgets everything after every conversation.
New: Aura for Windows — a free desktop app built on this library that gives Claude Code, Cursor, Codex, Copilot and other AI tools one shared local memory.
Aura is a local cognitive runtime that runs alongside any frozen model. It gives agents durable memory, explainability, governed correction, bounded recall reranking, and bounded self-adaptation through experience — all locally, without fine-tuning or cloud training.
pip install aura-memoryfrom aura import Aura, Level
brain = Aura("./agent_memory")
brain.enable_full_cognitive_stack() # activate all four bounded reranking overlays
# store what happens
brain.store("User always deploys to staging first", level=Level.Domain, tags=["workflow"])
brain.store("Staging deploy prevented 3 production incidents", level=Level.Domain, tags=["workflow"])
# recall — local retrieval with optional bounded cognitive reranking
context = brain.recall("deployment decision") # local retrieval, no API call
# advisory hints are derived during maintenance once enough evidence accumulates
brain.run_maintenance()
for hint in brain.get_surfaced_policy_hints():
print(hint.action_kind, hint.domain, hint.recommendation)
# → prefer workflow deploy to staging first (only after repeated, consistent evidence)
