Aurra gives your AI a memory you can actually trust. Every answer comes with a receipt: what it knew, when it knew it, and why it responded the way it did — for teams who can't just take the AI's word for it.
Python & JavaScript SDKs are live.pip install aurraView changelog →As agents start making real decisions, "the AI remembered it" isn't good enough. You need to know what it knew, when it knew it, and what it based its answer on — especially when something goes wrong.
From regulated enterprises to solo builders — wherever an agent's memory has to be trusted, traceable, or simply out of your way.
A clinical assistant surfaces a fact about a patient — a medication, an allergy, a past visit — and a clinician acts on it. When that fact turns out wrong or outdated, the question is brutal: where did the AI get this, and when? Most memory layers have no answer. They overwrite old facts, lose or fabricate timestamps, and return recall as a black box. “The model said so” does not survive a clinical review.
Aurra returns a proof tree with every answer — the exact memories used, a tamper-evident hash, and what was superseded. Bi-temporal versioning means you can see what was known and when, so an outdated fact shows up as outdated instead of being silently repeated. Every recall carries a full audit trail, built for the moment someone asks you to account for it.
An internal agent starts taking real actions — approving, routing, recommending — based on what it remembers about your business and customers. The first time one of those decisions is questioned, you need to reconstruct exactly what the agent knew at that moment and why it chose what it did. Most memory layers keep no decision trail: they cannot tell you which memories drove an answer, whether any were stale, or what changed. Every agent decision becomes an unauditable liability your risk team will not sign off on.
Aurra records the memory behind every answer as a verifiable evidence chain. Bi-temporal queries let you ask “what did the agent believe on this date?” and get a precise answer. Native, database-enforced tenant isolation keeps each business unit or customer cleanly separated. When a decision needs explaining, the record is already there.
You’re building an agent product and your users expect it to remember them across sessions. Building that yourself means a vector store, an extraction pipeline, tenant isolation, and the ops to run it all — weeks of infrastructure that isn’t your actual product. Rolling your own multi-tenant isolation is exactly the kind of thing that breaks quietly and bites the moment you land a B2B customer who asks how their data is separated.
Five lines and you have a working memory layer, in Python or TypeScript. Multi-tenant from day one, with isolation enforced at the database layer, so you can sell to B2B customers without rebuilding anything. Bring your own LLM key so your inference costs stay on your bill — not marked up on ours.
You’re one person shipping an agent, and “give it memory” keeps turning into a project of its own. Every hour spent standing up and maintaining a memory system is an hour not spent on the thing that makes your product yours — and once it’s running, it’s one more piece of infrastructure to keep alive at 2am.
Drop Aurra in and your agent remembers across every session — nothing to deploy, nothing to run. The audit trail and proof trees are already there if you ever need to explain a result, debug a bad answer, or show an enterprise customer how recall works. The hard parts are handled; you ship.
Built for developers shipping production agents. Verifiable, bi-temporal, multi-tenant. Bring your own LLM — your costs stay on your bill.
tenant_id for end-user scoping, enforced at the database layer. Build B2B agents without rolling your own isolation.Add trustworthy memory to your agent in three steps — Python or TypeScript, no infrastructure to provision.
Every benchmark we cite is reproducible. Raw hypothesis files, judge prompts, and methodology are open at github.com/aurra-memory/benchmarks.
Pre-built memory ingestion adapters for common sources. Use them as examples — or write your own with the SDK.
Free for prototypes. Pay when you ship. Enterprise when compliance asks.
All plans billed monthly in USD. See full plan details →
Five lines to a memory layer with a proof tree on every answer. Free to start — no infrastructure to run.
Start free →pip install aurra · npm install aurra