MemGPT MCP Server

Overview

MemGPT Sample is a proof‑of‑concept implementation of the MemGPT research paper that demonstrates how an AI agent can maintain long‑term memory, perform dynamic tool calls, and manage a structured conversation context. The project is built on Spring Boot and Spring AI, exposing the agent as both a simple REST service and an MCP (Model Context Protocol) server. This dual‑interface design allows developers to experiment with the agent in a familiar HTTP environment or integrate it directly into an AI assistant using MCP, which provides richer context handling and tool invocation capabilities.

The core of the system is the core‑agent module, which encapsulates all MemGPT functionality. It stores and updates a system prompt, an extensible list of tools, a short‑term core memory, and an optional archival memory backed by a vector store. The agent handles message compilation, LLM communication, tool execution, context‑window self‑editing, and message summarization. These features give the agent the ability to “remember” past interactions beyond the immediate prompt length, automatically condense older messages, and retrieve relevant information when needed. The agent exposes three key interfaces—, , and —so developers can programmatically create, configure, and interact with multiple agent instances.

For developers building AI‑powered applications, MemGPT’s MCP server is particularly valuable. By exposing the agent through MCP, an AI assistant can delegate context management and tool execution to the server while retaining control over higher‑level dialogue flow. This separation of concerns simplifies client logic, reduces latency for context updates, and ensures consistent memory handling across multiple assistants or sessions. The REST wrapper offers a quick way to prototype or integrate with existing HTTP‑based workflows, making the same agent logic available in a broader range of environments.

Typical use cases include personal knowledge bases, customer support bots that need to recall past interactions, or collaborative agents that aggregate information from external APIs and internal databases. In each scenario, the agent’s ability to archive memory in a vector store allows efficient retrieval of relevant past messages, while its tool‑calling framework lets the assistant perform actions—such as querying a database or calling an external service—without leaving the conversational context. The summarization capability further keeps conversations concise, preventing prompt overflow and improving LLM performance.

In summary, MemGPT Sample provides a robust, modular foundation for building AI assistants that require persistent memory and dynamic tool integration. Its dual REST/MCP exposure offers flexibility for rapid prototyping or production‑grade deployment, and its clear separation of core agent logic from interface layers makes it straightforward for developers to extend or customize the system for specific business needs.