Continue.dev is endlessly configurable, but configuration is static knowledge — it captures what you knew when you wrote the config, not what you learn as you work. The decision you reached in yesterday's chat, the correction you made three times, the gotcha you discovered mid-task: none of it survives, because Continue has no durable memory, only context providers you maintain by hand. This guide shows what persistent memory for Continue looks like in practice — what to store, what it changes, and exactly how to wire it up. New to the idea? Start with the complete guide to persistent memory for AI.
Why Continue needs persistent memory
The problem isn't the model — it's statelessness. Continue is only as consistent as the context it's handed each session, and without a durable memory that context resets to zero every time. Persistent memory is a store outside the model that keeps the facts, decisions, and preferences you build up, and surfaces them by meaning when they're relevant again — so Continue stops re-learning your project and starts compounding what it knows.
What your Continue setup should remember
- Decisions and rationale that would otherwise rot in a config comment.
- Corrections you repeat — each one is a durable preference the AI should learn once.
- Conventions and banned patterns specific to this codebase.
- Operational facts about your build, deploy, and test setup.
What it changes, concretely
Before: you keep editing your Continue config to nudge the model toward your conventions, and it still drifts. After: you tell it to remember the rule once, and every future session recalls it by meaning — the config stays lean and the behavior sticks.
How to add persistent memory to Continue
- Add the Kit for AI MCP server to your Continue configuration with your API key.
- remember and recall become tools the model uses to persist and retrieve knowledge across sessions.
- Keep a namespace per project so each codebase's memory is isolated.
- Combine with a knowledge base of your docs for cited, grounded recall alongside memory.
One memory across every tool
Because the memory lives in the cloud, it isn't locked to Continue. A decision you save here is recalled by every other MCP-capable tool and by your own scripts over the API — switch models or editors and your memory follows you. That's the difference between a per-app memory silo and a real memory layer for AI, and it's why the same account also handles document ingestion and cited search.
FAQ
Does Continue have persistent memory built in?
Continue keeps context within a session and through static config files, but it has no durable, cross-session memory of the decisions and preferences you build up — that resets every time. Persistent memory adds a store outside the tool so those facts survive restarts and follow you across machines.
How do I give Continue long-term memory?
Add the Kit for AI MCP server to Continue with an API key — remember and recall become tools it calls on its own. It takes a couple of minutes, and the memory is shared with every other MCP-capable tool you use.
Related guides
Go deeper: add a memory MCP server · a knowledge base your AI can read — or read the pillar guide to persistent memory for AI.
Add persistent memory to Continue in minutes — Start free, drop in the MCP server with your API key, and remember/recall become tools Continue calls on its own.