The complete, unedited sequence of all an agent's past interactions, including conversation logs, observation records, and tool outputs, preserved in its original form. Unlike pre-processed memory layers, this data remains untouched, requiring the agent's core model to directly perform retrieval operations and extract relevant information as needed.
Relying on raw interaction history allows for maximal fidelity and eliminates potential information loss or bias introduced by pre-processing steps. This is particularly valuable in environments where the nuances of every detail might become critical later, enabling the agent to access the full evidentiary record without filtering.
The primary challenge with raw interaction history is the significant computational burden it places on the core model. Practitioners often underestimate the processing cost and latency associated with searching vast, unstructured data, potentially leading to performance bottlenecks and higher operational expenses compared to systems utilizing pre-digested memory.