memcode-in/memcode

What it solves

MemCode provides persistent memory infrastructure for AI agents, copilots, and workflows. It addresses the limitation of temporary context windows, ensuring that user preferences, previous interactions, and learned knowledge are not lost between sessions or when switching AI models.

How it works

It acts as a dedicated memory layer that allows AI systems to store and retrieve information selectively. This infrastructure is designed to be persistent, grounded in sources, and queryable through both semantic and structured retrieval, while remaining inspectable and governed by access controls.

Who it’s for

Developers building AI agents, copilots, and AI-powered applications that require long-term memory to maintain continuity across sessions.

Highlights

• Persistent Memory: Maintains information across different sessions and model changes.
• Selective Retention: Focuses on what information deserves to be remembered rather than just expanding the prompt.
• Semantic & Structured Retrieval: Supports multiple ways to query and retrieve stored memories.
• Governance: Includes access and retention controls to manage how memory is handled.