Catalyst Brain
SDK Open Catalyst-Q

O(1) memory for agent teams that outgrow vector search

Catalyst Brain is the O(1) memory substrate behind Catalyst-Q and a Vector DB / RAG memory replacement for long-running agents, code workers, and strategic decision-proof workflows.

Run the memory check Open Catalyst-Q
Recall model
Bounded activation
Runtime target
Cloudflare edge
Catalyst-Q path
Decision-proof layer

Memory Plane

Compare growth pressure before you add another vector database.

Checking edge
RAG pressure grows while Catalyst stays bounded A line chart comparing projected vector retrieval pressure against bounded Catalyst Brain memory. Vector DB / RAG Catalyst bounded recall
RAG pressure --
Catalyst pressure --
Est. savings --
Confidence score --
01

Replace vector memory

Keep agent memory as compact hypervector state with fixed-dimension recall instead of pushing every session back through an expanding vector index.

Model workload
02

Power Catalyst-Q Decision Records

Use Catalyst Brain as the memory substrate behind Catalyst-Q: route files become proof packets, proof packets become Route Decision Records, and agent context stays bounded.

Open Catalyst-Q
03

Ship on Cloudflare

Run memory calls through the Catalyst Brain edge Worker, route quotas through durable state, and keep SDK adoption simple for solo teams and enterprise pilots.

Use SDK
04

Prove provenance when required

Audit and provenance remain the differentiated second motion: Article 50 starts Aug. 2, 2026, while Dec. 2027 is a practical procurement and insurance planning horizon.

Read Article 50

Install the SDK. Keep the memory.

Start with one agent loop or connect it to Catalyst-Q. Store the state you already have, recall only the bounded working set, and let the API packet become the proof artifact later.

catalyst-brain 
python -m pip install --upgrade catalyst-brain
edge Waiting for Worker health