Semelbase — Shared memory for AI agents

Semelbase addresses the inefficiency of agents re-solving identical problems by introducing shared execution memory. Key benefits include: • Identifies and reuses previously computed agent work • Reduces operational costs by preventing redundant computations • Accelerates task completion by leveraging existing results • Maintains high quality by ensuring relevance and freshness of shared answers This platform analyzes existing agent logs from various observability stacks (Langfuse, LiteLLM, Helicone, LangSmith, Datadog, OTel) to pinpoint duplicated work. It provides a detailed report showing the redundancy rate, recoverable expenditures, and frequently repeating tasks. The system runs as a shadow audit on your side, ensuring your trace data remains within your environment without any changes to production. Semelbase is designed for organizations managing multiple independent agents across different teams—such as marketing, finance, and procurement—who frequently tackle overlapping research or data analysis tasks. It optimizes resource allocation and improves operational efficiency, ensuring that work done once is effectively reused across the entire agent ecosystem. This leads to significant savings in both time and budget.