KYBERNESIS
MEMORY_ORCHESTRATOR
kybernesis@neural-net
00:00:00
_
STATUS: OPERATIONAL
MODE: NEURAL_ORCHESTRATION
LATENCY: <100ms

Infinite Context: The Persistent Memory Layer Your AI Agents Have Been Missing

Kybernesis provides the neural memory layer that connects you, your tools, and your AI agents into a unified knowledge topology. Ingest everything, retrieve instantly, orchestrate infinitely.

MEMORIES
UNLIMITED
RETRIEVAL
<100MS
UPTIME
99.9%
// SYSTEM_ARCHITECTURE

Neural memory topology with edge-optimized retrieval

HYBRID_RETRIEVAL

Semantic vector search combined with structured metadata filtering. Sub-100ms p95 latency across billions of embeddings.

convex + chroma + kv_cache
SLEEP_AGENT

Background AI continuously processes your memory graph. Automatic tagging, relationship discovery, intelligent tiering, and summarization.

tag → link → tier → summarize
CONNECTOR_SYNC

OAuth-based connectors for Google Drive, Notion, and more. One unified memory layer across all your productivity tools.

drive | notion | github | slack
MCP_PROTOCOL

Native Model Context Protocol integration. Claude Desktop, custom agents, and any MCP client can query your memory directly.

mcp://kybernesis/search
// DATA_FLOW

How memory orchestration works

01

Ingestion Pipeline

Upload files, connect tools, or ingest via chat. Content is chunked, embedded with OpenAI text-embedding-3-small, and stored across hot/warm/archive tiers.

upload → r2 → queue → embed → convex + chroma
02

Graph Orchestration

Sleep agent runs every 60 minutes, analyzing memory graph for relationships, extracting semantic tags, and optimizing storage tiers based on access patterns.

scheduler → tag → link → tier → summarize
03

Retrieval Interface

Query via topology UI, MCP protocol, or direct API. Cloudflare Workers edge network ensures sub-100ms p95 latency globally.

query → kv_cache → convex + chroma → response
// READY_TO_DEPLOY

Start orchestrating your AI memory layer

Free tier includes unlimited memories, hybrid retrieval, and MCP access. Scale to billions of embeddings without compromising latency.

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