The problem
Every time your agent starts a conversation, it starts from zero.
Sure, you can stuff a summary into the system prompt. You can use RAG. You can call Mem0 or Zep.
But all of these have the same problem: they need LLM calls to learn. To extract facts, to build a user profile, to understand what matters — you're paying per token, adding latency, and depending on a cloud service.
What if the learning happened locally, automatically, without any LLM involvement?
What AuraSDK does differently
AuraSDK is a cognitive layer that runs alongside any LLM. It observes interactions and — without any LLM calls — builds up a structured understanding of patterns, causes, and behavioral rules.
from aura import Aura, Level
brain = Aura("./agent_memory")
brain.enable_full_cognitive_stack()
# 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"])
# sub-millisecond recall — inject into any LLM prompt
context = brain.recall("deployment decision")
# after enough interactions, the system derives this on its own:
hints = brain.get_surfaced_policy_hints()
# [{"action": "Prefer", "domain": "workflow", "description": "deploy to staging first"}]
Nobody wrote that policy rule. The system derived it from the pattern of stored observations.
The cognitive pipeline
AuraSDK processes every stored record through 5 layers:
Record → Belief → Concept → Causal → Policy
Each layer is bounded and deterministic:
Belief: groups related observations, resolves contradictions
Concept: discovers stable topic clusters across beliefs
Causal: finds cause-effect patterns from temporal and explicit links
Policy: derives behavioral hints (Prefer / Avoid / Warn) from causal patterns
The entire pipeline runs in milliseconds. No LLM. No cloud. No embeddings required.
Try it in 60 seconds
pip install aura-memory
python examples/demo.py
Output:
Phase 4 - Recall in action
Query: "deployment decision" [0.29ms]
1. Staging deploy prevented database migration failure
2. Direct prod deploy skipped staging -- caused data loss
Query: "code review" [0.18ms]
1. Code review caught SQL injection before merge
2. Code review found performance regression early
5 learning cycles completed in 16ms. Recall at 0.29ms.
How it compares
| AuraSDK | Mem0 | Zep | Letta | |
|---|---|---|---|---|
| LLM required for learning | No | Yes | Yes | Yes |
| Works offline | Fully | Partial | No | With local LLM |
| Recall latency | <1ms | ~200ms+ | ~200ms | LLM-bound |
| Self-derives behavioral policies | Yes | No | No | No |
| Binary size | ~3MB | ~50MB+ | Cloud | Python pkg |
What's new in v1.5.3
- Full 5-layer cognitive pipeline active by default
enable_full_cognitive_stack()— one call to activate everything- Decay now driven by memory level, not manual type labels
- Policy hints now work with explicit causal links (
link_records())
demo.py— see it working in 60 seconds
Built in Rust, from Kyiv
Pure Rust core. No Python dependencies for the engine. Patent pending (US 63/969,703).
Open source: github.com/teolex2020/AuraSDK
Install: pip install aura-memory
Web: aurasdk.dev
If you're building AI agents and want deterministic, explainable, offline-capable memory — give it a try and tell me what you think.
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