Local Memory vs Hosted Memory for AI Agents: AIOS ContextDB and Vilix AI, Honestly Compared

Quick answer: AI agents forget everything between sessions because every run starts with an empty context window. The fix is a memory layer outside the model. Two honest options: AIOS ContextDB keeps decisions, memos, and checkpoints on your own disk inside each project; Vilix AI keeps your full conversation history in a hosted layer reachable from every AI tool over MCP. Pick local when your work lives in one project on one machine. Pick hosted when your agents run across tools, machines, and devices.

The forgetting problem is not a model problem

Every LLM call is stateless: prompt in, output out, nothing retained. The "memory" inside a chat is the context window, and it is destroyed the moment the session ends. So an agent that helped ship a feature yesterday re-discovers everything today: the architecture choices, the naming conventions, the bug that was being chased at 11pm. For scheduled agents it is worse. A nightly automation wakes up in a fresh execution with no trace of the previous run: which records it already processed, which decisions it made, what failed and why.

A bigger context window does not fix this. It delays the reset inside one session; it does nothing for persistence between sessions. Genuine memory is a layer you add: store what matters durably, then load the right pieces into each new run. The question is only where that layer lives.

Option 1: memory on your disk — AIOS ContextDB

AIOS, from rexleimo's harness-cli and aios projects, takes the local-first route. Its ContextDB stores project memory inside the project itself, under .aios/context-db/. The moving parts: memos (durable decisions saved with aios memo add "Keep auth tests strict" and found later with aios memo search "auth"), session checkpoints (so a resumed run continues where the last session stopped instead of starting from zero), and searchable context packs (related docs, plans, and decisions bundled into units the agent pulls on demand).

The design is pull-based. Nothing gets injected into every prompt; the agent searches or recalls the relevant material when the task needs it. That keeps prompt budgets small while the memory stays durable across sessions. Setup is an install script plus two commands: install from the aios releases page, run aios init --all in the project root, verify with aios doctor --native --verbose. It works alongside codex, claude, gemini, opencode, hermes, grok, and the other coding CLIs it lists. Everything stays on your machine; no data leaves it.

The honest tradeoffs: memory is per-project and per-machine. You operate it and you back it up. It will not follow you to your phone, your laptop, or the next tool you add, and if a required source is missing or outside the active project, the agent needs an explicit new pointer. Local memory is the right call when the project is the unit of work and everything happens on one machine you control.

Option 2: memory in the cloud — Vilix AI

Vilix AI is the hosted counterpart: a memory and work-state layer that lives outside any single tool and is reachable over MCP. Connect your AI tools — Claude, Codex, Cursor, OpenClaw, Hermes, and any MCP-compatible tool — to one Vilix AI account, and the same memory follows you across all of them. Plan in one tool, build in another; the context, rules, and tasks come with you.

What it stores is the part worth underlining: your full user/assistant exchanges, not just extracted facts, plus the derived memories, projects, tasks, and reusable skills around them. Retrieval is semantic plus keyword search, recency-aware, with last-write-wins when two tools save conflicting facts. You manage zero infrastructure. You can list, update, and delete memories from any connected tool, review everything on the dashboard, export all of your data in a portable format anytime, or wipe the account instantly. There is a free plan forever, a 7-day Pro trial of full Pro with no credit card, and paid plans (Starter at $10/mo) for serious use.

The honest tradeoff: your memories live on cloud infrastructure, not your disk. If privacy means nothing ever leaves your machine, that is a hard no, and the local option wins by default.

The real question: who operates the memory

Both fix the forgetting. The differentiator is who runs the memory layer.

• Local project memory (ContextDB): you operate it. It lives on your disk, inside your project. Best when the project is the unit of work.
• Hosted memory layer (Vilix AI): it is operated for you. It lives in the cloud, outside any tool. Best when the unit of work is you — across projects, tools, and devices — and you want full conversation history rather than per-project summaries.

This framing also answers the scheduled-automation question directly. A cron agent that wakes in a fresh container or a hosted workflow execution cannot reach memory stored on your laptop. A hosted layer reachable over MCP from anywhere is the honest fit there: last night's run notes, the client preferences, the "do not contact these three leads again" rule are all retrievable from whatever machine tonight's run lands on. Local project memory fits the agent that always runs on the same box, like a coding CLI inside a repo you open every day.

There is no universal winner. If one project on one machine is your whole world, local memory is simpler and costs you nothing but your own disk. If your agents run everywhere and you are tired of re-briefing every session, hosted memory is the fix. You can also run both: local memory for per-repo decisions, a hosted layer for everything that has to follow you across tools. What does not work is waiting for a bigger context window to remember for you. It will not. Memory is a layer you add.

FAQ

Why does my AI agent forget everything between sessions? Because LLMs are stateless. Each call takes a prompt and produces output with no persistence between calls. Everything inside one session is the context window, which is destroyed when the session ends.

Is a bigger context window the same as memory? No. A bigger window delays the reset inside one session but stores nothing between sessions. Real memory persists information across runs and retrieves the relevant pieces when a new run needs them.

Local memory or hosted memory — which should I pick? Local (ContextDB) if you want data on your disk, one project, and you operate everything. Hosted (Vilix AI) if you want one memory across many tools and devices, full conversation history, and zero infrastructure to manage.

Do I have to pick one? No. Many setups run both: local project memory for per-repo decisions, and a hosted layer for everything that follows the operator across tools.