VAAS-X Cognitive Memory

vaas-x

AI エージェントと接続デバイスのための、結果に基づく永続的なメモリです。

pip install vaas-x を実行すると、どのデバイスやエージェントにも、これまでに見たもの、つまり何が起き、何を行い、その結果どうなったかを記録する永続的でクエリ可能な記録を持たせられます。さらに、GPU も独自に用意するベクトルデータベースも必要とせず、CPU 上で数ミリ秒以内に、最もよく一致する過去のエピソードを取得できます。

インストール

pip install vaas-x

vaasx.com で無料プランの API キーを取得できます。クレジットカードは不要です。

クイックスタート

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)

行動した時点ですでに自身の結果を把握しているエージェントは、取り込み後にタグ付けする別々の往復処理を行わず、1 回の呼び出しで状態／行動／結果のエピソード全体を記録できます。

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).