Local memory for any LLM agent (github.com)

A new middleware called mumpu has been introduced to enhance large language models (LLMs) by integrating long-term memory capabilities. Acting as a transparent HTTP relay, mumpu allows users to connect their tools—whether from OpenAI, Anthropic, or Gemini—enabling these applications to remember information across sessions. This innovative system extracts knowledge from interactions, builds contextual connections, and automatically injects relevant data, significantly improving the coherence and continuity of conversations with AI agents. The significance of mumpu lies in its ability to provide universal memory persistence, allowing any LLM application to maintain contextual awareness over time. Unlike traditional memory systems, which often employ static storage, mumpu utilizes a graph-based approach for smart retrieval of information, addressing the critical need for intelligent context management in AI/ML applications. Additionally, the setup process is straightforward, requiring only a simple installation and configuration to get started. This development marks a notable advance in the usability and effectiveness of LLMs, paving the way for more sophisticated AI interactions that feel more natural and personalized.