VAAS-X Cognitive Memory

vaas-x

Persistent, outcome-grounded memory for AI agents and connected devices.

pip install vaas-x gives any device or agent a persistent, queryable record of what it's seen — what happened, what it did, what the outcome was — and retrieves the closest matching past episode in milliseconds, on CPU, with no GPU and no vector database to stand up yourself.

Install

pip install vaas-x

Get a free-tier API key at vaasx.com — no card required.

Quickstart

from vaasx import Bootstrap

brain = Bootstrap(api_key="...", device_id="my_device")

Point it at a structured data stream (sensor readings, agent actions,

API events -- anything JSON-shaped) and it profiles the stream

automatically, no schema design required.

brain.connect("https://your-data-stream.com/events")

Query for the most similar past episodes, ranked by what actually

worked, not just what's nearest in vector space.

hits = brain.query("engine running hot", k=5, prefer_success=True)

An agent that already knows its own outcome the moment it acts can log a full state/action/outcome episode in one call instead of a separate ingest-then-tag-later round trip:

resp = brain.ingest([{

"state": {"observation": observation}, "action": {"taken": action}, "outcome": {"success": result.success}, }]) episode_id = resp"episode_ids"

Report back on a retrieved episode once you know how it went

brain.outcome(hits0, success=True, delta=0.12) `

Three calls — ingest, query, outcome — cover the whole loop. No schema to design, no vector DB to host, no embedding model to serve yourself.

What it does

• Profiles a raw data stream automatically — statistical profiling, schema classification, and anomaly detection all run locally before anything touches the network.
• Stores episodes as (state, action, outcome) triples, so retrieval can be weighted toward what worked last time in a similar situation, not just nearest by embedding distance.
• Runs on both a small edge device and a standard server — same codebase, no domain-specific build. Verified live on a £30 ESP32 microcontroller and on a standard EC2 instance.

Numbers, not adjectives

• 14ms mean retrieval across 1.18M stored episodes on a standard AWS m7i-flex.large CPU instance — no GPU, no AVX-512.
• Blind-tested on NASA's CMAPSS FD001 predictive-maintenance benchmark: given 24 sensor channels with every identifier stripped, correctly classified all 24 with zero prior domain knowledge.
• Consistent zero-config classification result across 8 independent subjects on a real wearable IMU dataset (UCI Daily and Sports Activities).

Every number above is independently reproducible — see below.

Reproduce the benchmarks yourself

Every result this SDK's marketing points to has a step-by-step guide against a real public dataset or environment, with your own API key, plus an independent local cross-check that doesn't depend on the SDK at all:

• CMAPSS predictive maintenance
• Room-occupancy detection
• Memory-augmented CartPole (zero download)
• Wearable activity classification
• Physics simulations (QEC, lattice field theory, Ising model)
• Live drone control loop

Full guide index: vaasx.com/guides

Tiers

Free gets you an API key instantly. Developer (£19/mo) and Professional (£99/mo) are self-serve paid tiers with higher episode limits and additional capabilities. Enterprise is contract-based, for teams wanting on-prem or air-gapped deployment. See vaasx.com/pricing for current details.

The honest caveat

This package ships as a compiled wheel, not source-available — the retrieval and indexing internals are proprietary and protected as a trade secret. What *is* open: full API documentation, working examples, and the wire format, so you can see exactly what goes in and comes back even without reading the internals.

Multimodal support (vision/audio/depth/thermal, via the optional ac extra) is real but still early — encoder models are in active training and not yet production-tuned; treat it as a preview, not a finished feature.

Issues & questions

This repo doesn't carry the source (see above), but it's the right place to file bugs against the published package, ask integration questions, or request a reproduction guide for a dataset/environment not covered above.

Links

• Documentation
• Whitepapers — technical deep dives, one per use case
• Homepage

License

Proprietary — see LICENSE.