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 — ...
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 ...
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?
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 ...
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 ...