Why I built this
Every AI agent I worked with forgot everything between sessions, and every
"add memory to your agent" tool I found either needed a GPU, billed per
query, or both.
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.
What it does
- Point a structured data stream at it (sensor readings, agent actions, API events — anything JSON-shaped) and it profiles the stream automatically, no schema design.
- Query it and get back the most similar past episodes, ranked by what actually worked, not just what's nearest in vector space.
- Runs identically on a £30 ESP32 microcontroller and an EC2 instance — same codebase, no domain-specific build.
Wiring it into an agent loop
This is the part most people ask about, so here it is up front:
from vaasx import Bootstrap
brain = Bootstrap(api_key="...", device_id="my_agent")
def agent_step(observation, act_fn):
action = act_fn(observation)
result = execute(action)
# Agent already knows the outcome the moment it acts, so log the whole
# episode in one call instead of ingest-then-tag-later
resp = brain.ingest([{
"state": {"observation": observation},
"action": {"taken": action},
"outcome": {"success": result.success},
}])
episode_id = resp["episode_ids"][0] # server-assigned, no UUID plumbing needed
# Recall similar past situations before the *next* decision
past = brain.query(observation, k=3, prefer_success=True)
return action, episode_id, past
Three calls, no vector DB to stand up, no embedding model to host yourself.
prefer_success=True is doing the actual work — it's not just
nearest-neighbour, it's ranked toward what worked last time in a similar
situation.
The numbers
| Metric | Result |
|---|---|
| Mean retrieval, 1.18M episodes, AWS m7i-flex.large CPU | 14ms |
| Mean retrieval, desktop i5, same dataset | 1.1ms |
| NASA CMAPSS FD001 blind trial (24 channels, identifiers stripped) | 24/24 correctly classified, zero prior knowledge |
| Live ESP32 edge deployment | Offline shard, syncing IMU episodes in real time |
| Longest-running live agent deployment | 35,000+ episodes, continuous operation |
No GPU, no AVX-512, for any of the above.
Pricing
Free tier gets you an API key instantly, no card required. Developer is
£19/mo, Professional £99/mo, both self-serve. Enterprise is contract-based
for teams wanting on-prem/air-gapped deployment.
The honest caveat
The SDK ships as a compiled wheel, not source-available — the retrieval and
indexing internals are proprietary and staying closed while patent filing
is in progress. What is open: full API docs, working examples, and the
wire format, so you can see exactly what goes in and comes back even
without reading the internals. Multimodal (vision/audio/depth) support is
real but still early — encoder models are in active training, flagged as
such wherever it's mentioned.
Happy to answer anything about the benchmark methodology, the architecture
at a high level, or why CPU-only mattered enough to build around.
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