VAAS-X Cognitive Memory

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)

resp = brain.ingest([{    "state": {"observation": observation},        "action": {"taken": action},            "outcome": {"success": result.success},            }])            episode_id = resp["episode_ids"][0]
            # Report back on a retrieved episode once you know how it went            brain.outcome(hits[0]["id"], 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](https://vaasx.com/guides/cmapss.html)                                  - [Room-occupancy detection](https://vaasx.com/guides/occupancy.html)                                  - [Memory-augmented CartPole](https://vaasx.com/guides/cartpole.html) (zero download)                                  - [Wearable activity classification](https://vaasx.com/guides/sports_wearable.html)                                  - [Physics simulations](https://vaasx.com/guides/scientific_computing.html) (QEC, lattice field theory, Ising model)                                  - [Live drone control loop](https://vaasx.com/guides/drone_control.html)
                                  Full guide index: [vaasx.com/guides](https://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](https://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](https://vaasx.com/docs)                                  - [Whitepapers](https://vaasx.com/whitepapers) — technical deep dives, one per use case                                  - [Homepage](https://vaasx.com)
                                  ## License
                                  Proprietary — see [LICENSE](LICENSE).