Adaptive Recall: Adding Long-Term Memory to MCP

Most AI assistants suffer from a fundamental amnesia problem: once a session ends, the context vanishes. I've been looking into how we can bridge this gap without bloating the context window or manually feeding old logs back into the prompt.

Adaptive Recall acts as a persistent memory layer designed specifically to work over the Model Context Protocol (MCP). Instead of treating every interaction as a fresh start, it allows an assistant to store and retrieve specific facts or preferences from previous encounters. Think of it like a digital notebook that the AI can actually read from when it needs to remember who you are or what you were working on last week.

From a technical standpoint, it solves the issue of "context drift" where a model loses the thread of a complex project because the history became too long or messy. It uses a retrieval mechanism to pull in only the relevant bits of past information, which is much more efficient than dumping a massive history file into every single request. This keeps the latency down and the costs predictable.

If you are building tools using MCP, this is a significant piece of the puzzle. It moves the assistant from being a stateless calculator to something that feels more like a collaborator with a continuous history.

https://www.adaptiverecall.com/