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 — ...
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?
InfoWorld

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 ...
VentureBeat

Mem0's scalable memory promises more reliable AI agents that remembers context across lengthy conversations

Researchers at Mem0 have introduced two new memory architectures designed to enable Large Language Models (LLMs) to maintain coherent and consistent conversations over extended periods. Their ...
Columbus Dispatch

Evermind AI Launches EverMemOS to Transform Artificial Intelligence Through Foundational Memory Infrastructure

Modern artificial intelligence systems operate with a fundamental paradox: they demonstrate remarkable reasoning capabilities while simultaneously suffering from systematic amnesia. Large language ...
dbta

Memgraph Creates Toolkit for Non-Graph Users to Jumpstart the Journey to Full GraphRAG AI Capability

Memgraph, a leader in open-source in-memory graph databases purpose-built for dynamic, real-time enterprise applications, is releasing two new tools specifically architected to open up the power of ...
dbta

Memgraph’s Atomic GraphRAG Speeds Up the Use of Graph-Based RAG Across Multiple Data Sources

Memgraph, a leader in open-source, in-memory graph databases, is introducing a new capability designed to accelerate business adoption of graph-based retrieval-augmented generation (GraphRAG), Atomic ...