Skip to content
@Kenotic-Labs

Kenotic Labs

Building the continuity layer for AI systems. Infrastructure that preserves and reconstructs the living state of a situation across time.

Kenotic Labs

The continuity layer for AI systems.

Store from Claude. Retrieve from ChatGPT. Reconstruct from Cursor. Same memory, same device.


Install

pip install kenotic
from sdk import Kenotic

k = Kenotic(user_id=0)
k.ingest(text="I moved to Detroit in January to start Kenotic Labs.", speaker="Sam")
k.ingest(text="My dog Kobe is a 2-year-old golden retriever who loves swimming.", speaker="Sam")

result = k.retrieve(query="What is Sam's dog's name?")
print(result.text)  # "a golden retriever named Kobe"

Or connect any AI via MCP / REST:

python -m mcp.http_server --port 7130
curl -X POST http://localhost:7130/api/v1/retrieve \
  -H "Content-Type: application/json" \
  -d '{"query": "What is Sam building?"}'

What It Does

Every message is decomposed into 5 structured traces — episodic, emotional, temporal, relational, schematic — and stored in on-device SQLite.

Retrieval is reconstruction, not search. The system doesn't find similar chunks. It rebuilds the answer from converging traces using MINERVA 2 resonance dynamics. No LLM in the retrieval loop. Deterministic. Sub-second.


How It's Different

Vector RAG Mem0 Kenotic
Storage Chunks + embeddings Key-value facts 5 structured traces
Retrieval Cosine similarity Keyword lookup Multi-dimensional resonance
LLM in loop Yes (reranking) Yes (extraction) No
Update handling Overwrite Overwrite Supersession chain
Temporal reasoning None None Built-in
Emotional state None None Built-in
Model independence No Partial Full (store on Claude, read from GPT)
Runs on Cloud Cloud / local 100% on-device

Repos

Repo What
reconstruct DTCM engine + MCP server + REST API. The product.
ATANT Evaluation framework for AI continuity. 250 stories, 1835 questions.
continuity-layer Research paper. arXiv:2604.17273
kenoticlabs.com Website source.

The 7 Properties of Continuity

  1. Persistence — Survives restarts, app closes, device changes.
  2. Update Handling — Reality changes. Memory updates without breaking history.
  3. Temporal Ordering — When, in what sequence, with what current status.
  4. Disambiguation — Two people, two events, two feelings — correctly separated.
  5. Reconstruction — Not "search results." The current living state.
  6. Model Independence — One model writes. Another reads. The layer is below the model.
  7. Operational Usefulness — Works in clinics, libraries, robots — not just chat.

Links

kenoticlabs.com | Docs | API Quickstart | sam@kenoticlabs.com

LinkedIn | Reddit

Popular repositories Loading

  1. ATANT ATANT Public

    The first open evaluation framework for AI continuity. 250 narrative tests, 1835 verification questions, 10 checkpoints. Benchmark for AI memory systems, stateful agents, and long-term context pers…

    Python 4

  2. .github .github Public

    Kenotic Labs builds the continuity layer for AI systems. The missing infrastructure between AI memory and AI relationship.

  3. kenoticlabs.com kenoticlabs.com Public

    Marketing and research site for Kenotic Labs — the continuity layer for AI systems. Next.js 16 + React 19.

    TypeScript

  4. continuity-layer continuity-layer Public

    The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward. arXiv:2604.17273

    TeX

  5. reconstruct reconstruct Public

    The continuity layer for AI systems. DTCM engine + MCP server + REST API. Store from Claude, retrieve from ChatGPT, reconstruct from Cursor — same memory, same device.

    Python

Repositories

Showing 5 of 5 repositories

People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages

Loading…

Most used topics

Loading…