I Gave Claude a Memory That Survives Between Conversations — Here’s the MCP Server That Does It
Last Updated on July 16, 2026 by Editorial Team
Author(s): Sai Insights
Originally published on Towards AI.
I Gave Claude a Memory That Survives Between Conversations — Here’s the MCP Server That Does It
A tested, running MCP server that gives any AI agent persistent long-term memory — it remembers facts and preferences, reinforces the ones you actually use, decays and forgets the ones you don’t, and merges duplicates — with every log in this article captured from a real execution over the real MCP protocol
After the introduction, the article frames long-term agent memory as a solution to the stateless-agent problem: agents need persistence across sessions and a principled forgetting/curation mechanism so memory stores don’t grow into noisy junk. It reviews how memory approaches evolved—from context stuffing to vector-store retrieval—then argues that a cleaner 2026 architecture is “memory as an MCP server.” The piece explains core concepts (MCP as a standard connector, importance scoring with reinforcement, exponential decay with pruning, and consolidation/deduplication) and follows with an implementation-focused walkthrough of an MCP server that exposes tools like remember, recall, forget, list, consolidate, and run_maintenance. It includes a test-heavy, code-oriented walkthrough covering both demo mode (rule-based extraction) and live mode (Claude-backed extraction), demonstrates consolidation and decay behavior with real protocol tooling, and discusses performance, limitations, best practices, and production considerations like security, scaling, debugging, monitoring, and evaluation. The conclusion highlights measurable outcomes from the logs—memories strengthening when used, fading when ignored, and merging duplicates—to show why this loop enables durable, high-signal long-term memory for agents.
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