MemoryRouter or Vilix AI: A Memory Layer You Build or One You Connect To, Honestly Compared

Agents forget everything between sessions for a simple reason. A model keeps nothing between calls. The context window holds one session, then it clears. A scheduled automation that ran well yesterday starts today with its prompt, its inputs, and nothing else, unless something outside the model saved the last run and loads it back.

That outside layer is the fix, and two products take it in different directions. MemoryRouter gives each user a private vault keyed by a Memory Key, with recall before a turn and capture after it. Vilix AI gives every connected tool and agent one shared memory over MCP, built for operators whose work is created across many tools and many scheduled runs. Both answer the same question. The useful comparison is how each one stores, recalls, and shares what an agent learns.

Why the forgetting happens

Three facts cause the problem, and no prompt trick removes them.

Sessions are isolated. A new session starts from a system prompt and a new message. Yesterday's decisions, corrections, and findings are not in there unless a system put them there.

Built-in memory stays inside one product. A chat app can remember preferences inside that app. A coding tool can keep notes inside one project. Move to a second tool, or run an automation somewhere else, and that memory stays behind.

Summaries lose detail. When a long session is compressed, the shape of a decision survives and the reasons often do not. The next run then repeats a question, redoes finished work, or reverses a choice it cannot see the reasoning behind.

A real memory layer therefore has to do four jobs: store the work outside the model, recall the relevant part before the next prompt, capture the useful part after the turn, and keep the store readable when detail matters.

What MemoryRouter does

MemoryRouter grew out of an operator running a small fleet of agents and getting tired of being their memory. Its published design has four parts.

First, a store keyed to the user. Memories live in a vault tied to a Memory Key, not to one app, and tools that use the same key point at the same vault.

Second, recall before the prompt. A search runs against the vault and injects what is relevant, so the agent starts with context instead of having to ask for it.

Third, capture after the turn. Decisions, corrections, constraints, and preferences get written back when a turn completes.

Fourth, tiered storage. Raw turns get consolidated into reflections, and reflections into higher-level ones, so a search can return the distilled version and still drill down to the raw source.

The numbers in its writeup come from daily use. For a typical vault, described as under 15,000 vectors, recall lands in the 300 to 500 millisecond range. Its own heavier vault, at 66,486 vectors, is slower, which is the expected shape for any retrieval store as it grows. MemoryRouter connects through MCP and through tool-specific routes, including hooks for Claude Code and plugins for other agents, and it is priced at $20 a month after a 14-day trial.

That is a coherent answer for a builder who wants one vault per user, recall and retain as explicit tools or nodes, and a store shaped around conversations with a single key holder.

What Vilix AI does

Vilix AI is a cloud-hosted memory layer at Vilix AI, so there is zero infrastructure to run. AI tools and agents, including Claude, Codex, Cursor, ChatGPT, and other MCP-compatible clients, connect to the same account over MCP. A connected tool loads relevant saved context before it acts and saves the exchange after it acts, so the same memory follows the work on every tool and every device.

What gets stored is full conversation history, not just extracted facts. The actual exchanges stay available to revisit, alongside projects, tasks, personal rules, and reusable agent skills. For a scheduled automation, that means the run does not only get a summary of last time. It can load the prior run's full context: what was checked, what was decided, which leads were already contacted, which refunds were already issued, and what the operator corrected last time.

That shared shape matters most when more than one agent touches the same work. A research agent, a writing agent, and a posting agent that all read and write the same memory do not need a handoff document pasted between them. The second agent starts where the first one stopped, because both are reading the same store.

Start with the free plan, which is free forever, or the 7-day Pro trial, which needs no credit card. Data stays portable: export everything or delete it at any time.

The honest difference

MemoryRouter centers on a vault per key holder, with recall and capture built around that vault and integrations that fit builders wiring memory into their own agents and products. It is a strong fit when the job is giving each end user of an agent a private, persistent vault.

Vilix AI centers on one operator's whole working memory shared across every tool that operator uses, including scheduled and recurring agents that wake up on a timer. It is a strong fit when context is created in one tool, needed in another an hour later, and needed again by an automation tonight, with nobody available to re-brief any of them.

Both designs agree on the core point. Memory has to live outside the model, load before the prompt, and save after the turn. The question to ask is not whether an agent needs a memory layer. A scheduled agent without one restarts from zero every run, by design. The question is which shape matches the work: a per-user vault wired into a product, or one shared memory that every tool and agent an operator runs can read and write from day one.