Research / specialised systems
10 systems in the research / specialised systems category of the AI Agent Infrastructure Landscape, grouped by maturity tier.
Tier 2 — production-ready (1)
- MemPalace Verbatim chunks + spatial metaphor
Spatial-metaphor memory (wings / rooms / halls) on top of verbatim chunked storage. Independent analysis suggests the score is driven by verbatim storage + ChromaDB defaults rather than the palace structure.
Tier 3 — emerging (5)
- A-MEM Atomic-note / Zettelkasten-style
Treats memories as atomic linkable notes — explicit nod to Zettelkasten knowledge management. Dynamic linking; retroactive memory revision.
- BAI-LAB MemoryOS Hierarchical: short / mid / long-term
Hierarchical "OS" with Storage / Updating / Retrieval / Generation modules. Short-term → mid-term via FIFO dialogue-chain; mid-term → long-term via segmented paging.
- EVOLVE-MEM Self-adaptive 3-level hierarchy
Dynamic Memory Network + Hierarchical Memory Manager + Self-Improvement Engine. L0 raw embeddings, L1 contextual summaries, L2 high-level principles. NeurIPS 2025 (Scaling Environments for Agents workshop).
- MemoRAG Global memory-enhanced RAG
RAG framework on top of a long-context memory model. Builds global memory once, generates contextual clues at query time. TheWebConf 2025.
- MemOS (MemTensor) "Memory operating system" + MemCubes
Treats memory as an OS-managed resource with explicit allocation and process-like scoping. MemCubes unify parametric, activation, and plaintext memory.
Tier 4 — early / experimental (4)
- EverMemOS Episodic + semantic + reconstructive
Self-organizing memory OS for structured long-horizon reasoning. Three-phase model: episodic, semantic, reconstructive.
- LiCoMemory CogniGraph + temporal/hierarchy-aware retrieval
Lightweight hierarchical graph (CogniGraph) with entities and relations as semantic indexing layers. Incremental graph construction, fast updates, low-latency inference. Nov 2025.
- TiMem Temporal Memory Tree (5-layer)
Temporal-Hierarchical Memory Consolidation. 5-layer Temporal Memory Tree (segments → profiles); semantic-guided consolidation without fine-tuning; complexity-aware recall planning + gating. Jan 2026.
- Titans (Google) Neural fast/slow + surprise gating
Neural long-term memory module that learns to memorise at test time. Uses gradient-of-loss as "surprise" signal; adaptive weight-decay forgetting. Three variants: MAC (memory-as-context), MAG (memory-as-gate), MAL (memory-as-layer).