Memory Custom

The Memory Custom MCP server extends the base Memory server by adding fine‑grained control over how an AI assistant stores and retrieves knowledge. Rather than relying on a single, monolithic memory file, this server lets developers define distinct storage paths for each project or user context. This solves a common pain point in long‑running AI workflows: the accidental bleed of unrelated data across sessions or teams. By isolating memory per project, developers can maintain clean, project‑specific knowledge graphs that remain consistent and easy to audit.

At its core, the server operates as a lightweight knowledge‑graph manager. Every interaction with an LLM is logged as an entity or relation, and the system automatically timestamps each entry. The timestamping feature provides temporal context that is crucial for tasks such as trend analysis, conflict resolution, or simply understanding the evolution of a conversation. Developers can query memories not only by content but also by time, enabling sophisticated “time‑travel” queries that can surface the state of a user’s preferences or goals at any point in history.

Key capabilities include:

• Custom memory paths: Configure a dedicated JSON file for each project or user, ensuring data isolation and simplifying backup strategies.
• Automatic timestamping: Every creation or update is time‑stamped, enabling chronological queries and audit trails.
• Rich entity categorization: The server distinguishes between identities, behaviors, preferences, goals, and relationships up to three degrees of separation, allowing structured reasoning over complex social graphs.
• Seamless integration: The MCP interface exposes a standard set of resources, tools, and prompts that Claude (or any other MCP‑compatible client) can consume without additional plumbing.

Real‑world scenarios that benefit from this server include:

• Personal assistants: A user’s daily routine, preferences, and goals can be stored in a personal memory graph that the assistant consults at every interaction.
• Team collaboration tools: Each project’s knowledge base can be isolated, preventing cross‑project contamination while still allowing the assistant to surface relevant historical decisions.
• Compliance and audit: Timestamped logs provide a clear record of how data was captured, updated, and used—essential for GDPR or other regulatory requirements.

Integrating Memory Custom into an AI workflow is straightforward: the MCP client loads the server, and system prompts instruct the LLM to always begin conversations with a “Remembering…” cue. The assistant then pulls relevant facts from the project‑specific graph, updates it with new observations, and continues the dialogue. Because all operations are performed via standard input/output streams, the server can run in any environment that supports Node.js, making it ideal for on‑premises deployments or cloud functions.

In summary, the Memory Custom MCP server empowers developers to build context‑aware AI assistants that maintain clean, timestamped knowledge graphs tailored to each project or user. Its combination of path isolation, temporal metadata, and rich entity handling delivers a robust foundation for building sophisticated, trustworthy AI experiences.