finally, $SYAI is now live.
CA: 0x52082a3D2ce553265593f42647aBBDe0F50febA3
a unique and advanced memory layer for ai agents in building @SYAImem
SYAI uses multiple memory layer parameters to combine the data from the swarm and rename the constants to a single instant.
a memory layer which makes your ai agents smarter everyday
tried running 50 agents with a shared chroma instance.
it crashed.
moved to weaviate with multi tenancy and it actually held.
swarm scale forces you to care about infra the solo agent crowd ignores.
if your agent memory is just mbeddings.similarity_search(query, k=5) you're leaving so much on the table.
add a rerank step with cohere or a cross encoder.
top 5 becomes actually relevant instead of vaguely related.
the trick with hybrid search is tuning the alpha between BM25 and vector scores per query type.
factual queries want more BM25.
conceptual queries want more vector.
most people hardcode 0.5 and call it a day.
leaves 20 percent recall on the table.
learned the hard way that asyncio.gather on 30 agents all hitting the same memory store will melt your connection pool.
connection pooling, backpressure, and rate limits are not ML problems.
they're the reason your swarm falls over at scale.