Multimodal Filesystem Memory for Long-Horizon Embodied Agents

Long-horizon agents need memory to retain and reuse experience across long interactions, but how to represent that memory remains unclear. Retaining complete interaction histories makes relevant experience difficult to find, while compressing them into summaries can discard visual and spatial details needed later. We introduce **MFM**, a multimodal filesystem memory architecture that maintains complementary forms of agent memory. **Episodic memory** preserves observations, actions, and outcomes, while **spatial memory** connects experiences to locations. **Semantic and procedural memory** accumulate knowledge and reusable ways of acting, and **working memory** maintains ongoing plans. MFM uses searchable textual records to link these representations to the underlying multimodal episodes. Agents can locate past experiences through contextual cues and inspect original observations to recover details absent from their textual descriptions. Through consolidation, agents turn experience into higher-level knowledge and procedures while retaining references to the supporting episodes. Across long-horizon Minecraft and computer-use tasks, MFM improves performance across three base models. With GPT-6-Astra, MFM completes ***Mine Diamond from Scratch*** and reaches 16 of 24 milestones on the ***Ender Dragon*** task, compared with 12.7 for the strongest baseline. On CUABench-KiCad, it achieves a mean reward of 0.440, compared with 0.400 for the strongest baseline and 0.267 without persistent memory.