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Active Graph: Event-Sourced Runtime for Agentic Systems
August 17, 2026 · 37 minActiveGraph is an event-sourced reactive graph runtime designed to build long-running, auditable AI agents. Unlike traditional frameworks that focus on the model call, this system treats an immutable event log as the primary source of…
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The Graph Engineering Guide 2026
July 26, 2026 · 44 minThese sources trace the technological shift from basic prompting toward sophisticated graph engineering for managing complex AI agent systems. The first two guides establish a hierarchy of development, moving from single-agent loops—where…
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The Context Tax: Rethinking Database Awareness
June 18, 2026 · 18 minRef: https://inferal.com/blog/databases-dont-know-why/ The provided text explores a fundamental limitation in modern database architecture, specifically how these systems operate in isolation without understanding the purpose of a query…
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Benchmarking and Techniques for LLM Text-to-SQL Systems
October 2, 2025 · 15 minThese sources provide an extensive overview of Large Language Model (LLM)-based Text-to-SQL (NL2SQL) systems, focusing on techniques like prompt engineering, supervised fine-tuning (SFT), and Retrieval-Augmented Generation (RAG) to enhance…
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Beyond RAG: Giving AI Agents Persistent Memory with Open Source Tools
August 30, 2025 · 6 minMem0, Graphiti, Cognee, and LangMem are open-source libraries that provide persistent memory for AI agents. Mem0 uses a hybrid database to optimize personalization and reduce token costs. Graphiti creates temporal knowledge graphs for…
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Large Language Models for Text-to-SQL: Challenges, Advancements, and Evaluation
July 26, 2025 · 23 minText-to-SQL, translating natural language to SQL, has seen significant advancements due to Large Language Models (LLMs). However, challenges remain in handling complex database schemas, diverse SQL operations beyond simple queries, and…
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LLM Agent Memory Systems: MemGPT, Zep, MEM1 and more...
July 4, 2025 · 19 minThis briefing document synthesizes information from several recent academic papers and a commercial announcement, highlighting cutting-edge developments in enhancing Large Language Models (LLMs) with robust memory and retrieval…
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MEM1: Synergizing Memory and Reasoning for Agents
June 24, 2025 · 11 minhttps://arxiv.org/abs/2506.15841 The research introduces MEM1, a novel reinforcement learning framework designed to enhance language agents' efficiency and performance in complex, multi-turn interactions. Unlike traditional models that…