Kai is a new AI assistant that works only on your own device, never sending your info to the cloud. It uses a special memory system, so it remembers your files, notes, and projects without ever giving up your privacy. Kai quickly finds what you need, even from years ago, and understands how your work is connected. You can use Kai with other tools safely, and developers are already building cool new versions. Everything stays private, fast, and right where you need it.
What makes Kai different from other AI assistants?
Kai is an on-device AI assistant that never sends data to the cloud. It uses a graph-based memory engine to recall and reason over your files and notes – always locally – protecting privacy, ensuring fast retrieval, and supporting interoperability with other AI tools through the Model Context Protocol (MCP).
2025 has become the year when your personal AI finally stops phoning home. Meet Kai , an assistant that lives exclusively on your laptop or phone, never uploads a single byte, yet still remembers every meeting note, PDF and half-finished draft you touched three months ago.
How Kai turns your device into a private “cognitive OS”
Instead of shipping queries to a distant GPU cluster, Kai installs a graph-based memory engine that keeps three layers of information:
| Tier | What it holds | Retrieval speed |
|---|---|---|
| Hot | Last 48 h docs + current project notes | < 50 ms |
| Warm | Past 30 days relevance-ranked memories | ~200 ms |
| Cold | Everything else, summoned by “spreading activation” (think ACT-R cognitive models) | 1-2 s |
The result: open a decade-old contract and Kai surfaces the three e-mails that modified it, plus the clause you annotated, without ever contacting the cloud.
It’s not “just RAG” – here’s why that matters
Classic retrieval systems (RAG) match text chunks by vector similarity. Kai’s knowledge graph stores relationships – that a budget line belongs to Q3, was edited by Alice, and cites two spreadsheets – letting the system reason instead of just recall. Early access builds are already passing 321 automated tests that benchmark correctness under adversarial queries.
Privacy by design, not by promise
- Zero telemetry – confirmed in the open test suite.
- Offline vector indexes built on-device with quantized embedding models (≈ 180 MB RAM footprint).
- Optional encrypted snapshot you can park on a USB drive for air-gapped workflows.
Developers who tried the early-access build report 3-5 second cold-start times on M-series Macs and near-instant warm starts once the graph is cached.
Interoperability – MCP is the new USB-C for AI brains
Kai’s roadmap lists Model Context Protocol (MCP) support, the open standard now adopted by 90 % of AI vendors [1]. That means Kai memories can plug into any MCP-ready coding agent or CRM bot later, turning your private graph into a shared, permissioned workspace without leaving the device.
Community forks – what people are already building
- Kai Lite – a minimal version synced to Obsidian notes.
- *dsam_model_memory * – a query-based activation layer GitHub source that strengthens related graph paths every time you re-open a file.
The core engine is slated to go fully open-source once the maintainers tag v1.0-stable, inviting auditors and plug-in authors to extend the “second brain” beyond what any single startup could imagine.
What makes Kai different from other AI assistants like Siri or ChatGPT?
Kai stores and processes everything locally on your device. In 2025 tests 100 % of its 321 regression tests passed without ever touching a cloud server. The core difference is the graph-based memory layer that turns your emails, notes and documents into an interconnected knowledge graph. Instead of retrieving snippets like a traditional RAG system, Kai maintains a living “cognitive OS” that learns continuously from your workflow, giving you data sovereignty and zero latency for recall-heavy tasks.
How does Kai protect my data and privacy?
Zero-cloud policy, period. All computation and storage happen on your hardware – no uploads, no external APIs, no accounts. The engine uses tiered memory (hot / warm / cold) inspired by ACT-R cognitive models, which means even the spreading-activation retrieval algorithm runs entirely on device. The upcoming open-source release of the core engine will let security researchers audit every line of code, reinforcing user-controlled privacy.
Can Kai really remember everything I do on my computer?
It can remember what you allow it to index. After granting local file-system and email access, Kai builds a persistent, structured knowledge graph that links concepts, timestamps and documents. A 3D memory visualisation shows how ideas connect over months, and filters let you surface “important memories” while ignoring noise. Unlike vector-only systems, the graph structure retains episodic traces, so you can ask “what was that design idea I sketched last quarter?” and get accurate context.
What devices and operating systems does Kai support in 2025?
Early access builds are currently macOS-only, distributed through the oneeko.ai portal. The roadmap posted in August 2025 lists native Windows and Linux ports after the core stabilises and is open-sourced. Developers can already experiment with the graph-memory SDK packaged inside the DMG installer.
When will Kai be open-sourced and what will be included?
The author plans to release the graph-memory engine under an OSI-approved licence once 400 internal tests pass (target Q1 2026). Initial drop will contain:
– The graph-based storage layer
– Spreading-activation retrieval
– Local telemetry hooks for debugging
– MIT-licensed sample MCP server for third-party tool integration
Enterprise features (visual IDE, email connectors) will remain closed add-ons, allowing the core to stay lightweight for self-hosted deployments.
