Memori hits 81.95% LoCoMo accuracy at just 1,294 tokens/query — 67% smaller prompts than Zep, 20x cheaper than full-context — with MCP server and multi-agent attribution model.
Skill-RAG predicts LLM failure via hidden-state probing, retrieves only when needed, and routes failure types to specialized skills — beating RAG benchmarks on efficiency and accuracy.
OpenAI is deprecating text-embedding-3-small, prompting calls to open-source the model so trillions of indexed tokens remain queryable after the closed-source model is retired.
An analysis of context engineering patterns emerging from 50 production AI deployments — covering RAG architectures, knowledge graph integration, multi-layer memory systems, and the shift from prompt engineering to structured context pipelines.
How leading organizations combine knowledge graphs with LLMs to build AI systems that reason over structured relationships — covering GraphRAG architectures, entity resolution, and the emerging graph-native context engineering paradigm.
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