Metadata-Version: 2.4
Name: vaas-x
Version: 0.1.10
Summary: Persistent, outcome-grounded episodic memory for AI agents and connected devices -- zero-config ingestion, CPU-only retrieval, no vector database to run yourself.
Author-email: VAAS-X Ltd <hello@vaasx.com>
License: Proprietary -- see LICENSE
Project-URL: Homepage, https://vaasx.com
Project-URL: Documentation, https://vaasx.com/docs
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: httpx>=0.27
Provides-Extra: websocket
Requires-Dist: websockets>=12.0; extra == "websocket"
Provides-Extra: mqtt
Requires-Dist: paho-mqtt>=2.0; extra == "mqtt"
Provides-Extra: dataframe
Requires-Dist: pandas>=2.0; extra == "dataframe"
Provides-Extra: ac
Requires-Dist: torch>=2.0; extra == "ac"
Requires-Dist: torchvision>=0.18; extra == "ac"
Requires-Dist: torchaudio>=2.0; extra == "ac"
Requires-Dist: sentence-transformers>=3.0; extra == "ac"
Requires-Dist: pillow>=10.0; extra == "ac"

# 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 cloud round-trip required for the actual similarity search.

## Install

```bash
pip install vaas-x
```

Get a free-tier API key at [vaasx.com](https://vaasx.com) -- no card
required.

## Quickstart

```python
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:

```python
resp = brain.ingest([{
    "state": {"observation": observation},
    "action": {"taken": action},
    "outcome": {"success": result.success},
}])
episode_id = resp["episode_ids"][0]
```

## 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.

## Tiers

Free gets you an API key instantly. Developer and Professional are
self-serve paid tiers with higher episode/query limits. 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, not through patent filing. 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.

## Links

- [Documentation](https://vaasx.com/docs)
- [Homepage](https://vaasx.com)

## License

Proprietary -- see `LICENSE`.
