New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — ...
InfoWorld

Four agentic AI memory systems for smarter LLMs

These third-party projects greatly expand the ways agents and LLMs can draw on facts, documents, and conversations to deliver ...
HackerNoon

The Race to Build AI’s Context Layer Is Really About Meaning

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
KMWorld

AI 100 Trailblazer: AllegroGraph - Agentic AI Needs Context Graphs Built on Knowledge Graphs

As AI systems evolve from assistants into autonomous collaborators, enterprises will need durable memory, explicit semantics, lineage, governance, and explainability. AllegroGraph and GraphTalker ...
Electronic Design

MoSys’ Graph Memory Engine Accelerator IP Targets Packet Classification on Intel’s Stratix 10 FPGAs

MoSys announced that its Graph Memory Engine (GME) Accelerator IP is now running on Intel Stratix 10 FPGAs. The GME is part of MoSys’ Packet Classification Platform, and is capable of performing ...

Microsoft unveils Memora to tackle AI agents’ memory problem

The research project promises more efficient long-term recall by organizing knowledge around abstractions and cue-based ...