O(D) Holographic Memory · EU AI Act Ready

AI agents that are faster, cheaper, and compliant.

Three APIs that replace your vector database, slash your KV cache memory, and seal every agent decision with cryptographic evidence. 16,384× less memory. 37,500× less storage. 0.017ms provenance.

Get Your API Key → See the Numbers
16,384×Less KV cache memory at 128K tokens
37,500×Less agent memory at 100K messages
0.017msRAIN evidence seal per action
65/65TDD tests passing against real SDK
Three Products

One API key. Three products that compound.

Each product solves a bottleneck in production AI agents. Together, they make your agents faster, cheaper, and audit-ready — with measured proof.

🔒

RAIN Evidence

Every AI agent decision sealed with a cryptographically signed evidence envelope. EU AI Act Article 50 compliant. Tamper detection, workflow provenance, replay protection.

0.017ms/action · 462 bytes/action
# Seal an agent decision POST /api/v1/evidence/seal x-api-key: cb_your_key { "agent_id": "loan-agent-001", "workflow_id": "loan-workflow-001", "decision": "approve", "capability_scope": ["loan-approval"] } → sealed evidence envelope, verified
⚡

KV Cache Replacement

Replace O(N×d) growing KV cache with O(D) fixed holographic memory. Drop-in for HuggingFace DynamicCache. 16,384× less memory at 128K tokens. Runs on a phone.

O(D) = 0.19 MB vs O(N×d) = 3,072 MB at 128K
# Get holographic cache state POST /api/v1/cache/compact x-api-key: cb_your_key { "model": "Qwen/Qwen2.5-0.5B", "seq_length": 131072, "dim": 4096 } → compact state: 0.19 MB, 16384x savings
🧠

Agent Memory

Replace O(N) vector database memory with O(1) bounded holographic recall. Novelty gating ensures only new information is stored. Memory stays at 16 KB forever.

O(1) = 0.016 MB vs O(N) = 586 MB at 100K msgs
# Store in agent memory POST /api/v1/memory/store x-api-key: cb_your_key { "agent_id": "support-agent", "key": "customer-123", "value": {"text": "prefers email"} } → stored, O(1) bounded, novelty-gated
Empirical Proof

The numbers. Measured, not marketing.

All benchmarks run against the real catalyst-brain v1.6.0 SDK from PyPI. Full benchmark suite with 65 TDD tests — all passing. Run them yourself →

KV Cache Memory: Standard vs Catalyst-Brain

Sequence LengthStandard DynamicCacheCatalyst-BrainAdvantage
1,024 tokens24.00 MB0.19 MB128×
8,192 tokens192.00 MB0.19 MB1,024×
32,768 tokens768.00 MB0.19 MB4,096×
65,536 tokens1,536.00 MB0.19 MB8,192×
131,072 tokens3,072.00 MB0.19 MB16,384×

Model: Qwen-2.5-0.5B (float16)

Agent Memory: Vector DB vs Catalyst-Brain

Conversation LengthVector DBCatalyst-BrainAdvantage
100 messages0.59 MB0.016 MB38×
1,000 messages5.86 MB0.016 MB375×
10,000 messages58.59 MB0.016 MB3,750×
100,000 messages585.94 MB0.016 MB37,500×

Vector DB: 1536-dim embeddings (OpenAI ada-002), float32

RAIN Evidence Overhead

OperationTimeSize
Seal evidence envelope0.017 ms462 bytes
Verify evidence envelope0.015 ms—
100-step evidence chain31.65 ms45.1 KB
Pricing

Start free. Scale to unlimited.

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Free

$0/mo
For testing and small projects
  • 1,000 API calls / month
  • 1 agent
  • 3 workflows
  • RAIN evidence sealing
  • Agent memory (O(1))
  • Community support
Get Free Key
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Pro

$99/mo
For production AI agents
  • 100,000 API calls / month
  • 10 agents
  • 50 workflows
  • All 3 products
  • RAIN evidence + verification
  • Agent memory + KV cache
  • Usage dashboard
  • Email support
Get Pro Key →

Enterprise

$999/mo
For scale and compliance
  • Unlimited API calls
  • Unlimited agents
  • Unlimited workflows
  • All 3 products
  • On-premise deployment
  • Custom RAIN workflows
  • EU AI Act compliance audit
  • SLA + priority support
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Documentation

Integrate in minutes, not weeks.

Drop-in adapters for HuggingFace Transformers, LangChain, LlamaIndex, and CrewAI. Full documentation with copy-paste examples.

# Install pip install catalyst-brain catalyst-brain-products # Get your API key at catalyst-brain-api.strategic-innovations.workers.dev # Then use the SDK: from catalyst_brain import RainPayload, seal_evidence_envelope payload = RainPayload(agent_id="my-agent", dim=10000, metadata={"decision": "approve"}) sealed = seal_evidence_envelope(payload, secret=b"your-secret", workflow_id="wf-001", step_index=0, idempotency_token="step-0", capability_scope=["agent-action"]) # With LangChain: from catalyst_brain_products import CatalystBrainStore, RAINProvenanceCallback memory = CatalystBrainStore(dim=4096, secret=b"your-secret") provenance = RAINProvenanceCallback(secret=b"your-secret", workflow_id="my-workflow", agent_id="my-agent") # With HuggingFace Transformers: from catalyst_brain_products import CatalystBrainCache cache = CatalystBrainCache(model.config, dim=4096) outputs = model.generate(input_ids, past_key_values=cache)

Full API Reference →