Agent memory that retrieves 96.4% on LongMemEval without calling a model

I discovered a project that surpasses mem0, supermemory, and more, even though it's free. It seems like a fairly proven project that surpasses the 1K star mark. The Agentlas memory architecture operates with only a single SQLite file inside the device, instead of a separate memory server or a complex external database. Memory is strictly managed separately into four ownership units: a user identity area that contains the user's language preferences or style, a project area that contains the project's goals and code structure, individual agent learning records, and a team sharing area that contains team common facts. Instead of incurring costs and time by calling the LLM every time information is retrieved, a hybrid method that combines a lightweight embedding model running inside the device with keyword search is used. It pre-screens information that does not match keywords at all or does not meet the similarity criteria, preventing unnecessary information from being mixed up or hallucinations from occurring. In the recent update, we have revamped the system to grant each team member independent identifier memory to address the issue of missing individual work records from team-based agents. It also features a self-evolving function that allows the agent to propose improvements themselves if failures are repeated or user modification instructions continue, and to approve or recover with a single click depending on the level of risk. As a result of measuring only the performance of finding the necessary information without going through the model, it recorded an accuracy of 96.4% on the LongMemEval benchmark and 91.5% on the LoCoMo benchmark. Ultimately, the core of a memory system lies in extracting the precise context at the necessary moment rather than simply running a massive model, and Agentlas has efficiently implemented this at the local device level.