KMM v0.0.2: Portable Memory for AI Agents

In the current AI development ecosystem, autonomous agents face a recurring challenge: how to manage acquired knowledge without being tied to a specific machine or file path. Version 0.0.2 of Knowledge-and-Memory-Management (KMM) addresses precisely this hurdle by transforming memory management into a truly portable system. This release does not aim to revolutionize with flashy new features, but rather to consolidate a solid foundation so developers can build AI agents that learn, remember, and move between environments seamlessly. The fundamental change lies in adopting the $AGENT_HOME environment variable, which replaces fixed paths and allows the same configuration to work without modifications on a local laptop, a Docker container, or a cloud production environment. This is especially relevant for teams working with AWS and Azure cloud services, where portability and reproducibility are critical. At Q2BSTUDIO, we understand that flexibility in deploying AI solutions is as important as the agent's internal logic. Therefore, when developing custom applications for our clients, we prioritize architectures that separate knowledge extraction from its storage, just as KMM does. In this version, the collector module supports three main sources: web pages, video transcripts (such as those from YouTube), and structured articles (Atom/RSS or Markdown). Each source has a dedicated extractor that normalizes the content into a common schema, removing superfluous metadata and retaining only the title, date, and source URL. The memory manager, for its part, indexes each document using a content hash and timestamps, ensuring no information is duplicated even if the same URL is collected at two different times. Everything is organized into directories by source type and date, allowing data inspection with system tools or building custom indexing pipelines. The separation of responsibilities between collection and persistence is an architectural success: the collector only handles extraction, the memory manager only handles storage, and the $AGENT_HOME variable acts as a decoupled bridge. This opens the door to changing the storage backend (for example, to a database or cloud storage) without touching the rest of the system. For teams needing enterprise AI with high cybersecurity standards, this ability to replace components without affecting agent integrity is vital. Additionally, the removal of the old command-line options --data-dir and --cache-dir simplifies configuration: just define AGENT_HOME and move the data folder to that location. The system will issue a warning if the variable is not defined, guiding the developer. This predictive cleanup is the kind of improvement professionals value in continuous integration and multi-environment setups. If your company is exploring the development of AI agents that manage knowledge autonomously, at Q2BSTUDIO we offer artificial intelligence services for businesses that include implementing portable memory architectures like the one proposed by KMM. We also develop custom software to integrate these capabilities into business processes, and complement with business intelligence and Power BI services to visualize the collected knowledge. The next planned feature for KMM —semantic search over articles and videos— will be built on this foundation, demonstrating that a well-designed infrastructure is the best starting point for innovation. With KMM v0.0.2, developers gain predictability and portability, two qualities that in the world of artificial intelligence make the difference between a fragile prototype and a robust product.