GitHub repo: https://t.co/knIZDLAxPg ...

Core Functionality - Zep aims to build human-like memory for agents, addressing real-time knowledge updates and fast data retrieval challenges in agentic and RAG systems [1] - Zep organizes agent memories into episodes, extracts entities and relationships, and stores them in a knowledge graph [1] - The system features an Episode Subgraph for capturing raw, timestamped data, a Semantic Entity Subgraph for extracting and versioning entities and facts, and a Community Subgraph for grouping related entities [1][2] Performance Metrics - Zep delivers up to 1850% (18.5 times) higher accuracy with 90% lower latency compared to tools like MemGPT [2] Open Source Nature - Zep is fully open-source [2]

RAG Implementation - Enterprises are building RAG (Retrieval-Augmented Generation) systems over hundreds of data sources [1] - The industry is moving towards RAG implementations across 200+ data sources, emphasizing local processing [1] MCP-Powered RAG Adoption - Microsoft includes MCP-powered RAG in M365 products [1] - Google integrates it into Vertex AI Search [1] - AWS offers it through Amazon Q Business [1]

General Overview - The document is a brief post or update, likely from a social media platform, focusing on comparing GPT-5 and Grok 4 on reasoning tasks [1] Author Information - Avi Chawla shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1] - Avi Chawla can be found on social media platform @_avichawla [1]

Finally, here are 10 more evaluations I ran using DeepEval on logical reasoning tasks.- GPT-5 won in 2 cases.- Grok 4 won in 3 cases.- A Tie happended in 5 cases.Grok 4 was found to be better in terms of depth of analysis.Check this👇 https://t.co/4siD5PqJPQ ...

Let's compare GPT-5 and Grok 4 on reasoning tasks: ...

RAG技术应用 - 企业正在构建基于超过 100 个数据源的 RAG 系统 [1] - Microsoft 在 M365 产品中提供 RAG 技术 [1] - Google 在 Vertex AI Search 中提供 RAG 技术 [1] - AWS 在 Amazon Q Business 中提供 RAG 技术 [1] 技术趋势 - 行业正在构建基于 MCP 驱动的 RAG 系统，数据源超过 200 个，并且 100% 本地化 [1]

In this demo, we used mcp-use.It lets us connect LLMs to MCP servers & build local MCP clients in a few lines of code.- Compatible with Ollama & LangChain- Stream Agent output async- Built-in debugging mode, etcRepo: https://t.co/PWcuwMFvzi(don't forget to star ⭐) ...

RAG Implementation - Enterprises are building RAG (Retrieval-Augmented Generation) systems over hundreds of data sources, not just one [1] - The industry is building MCP (Most Capable Platform)-powered RAG over 200+ sources, with 100% local data processing [1] Platform Adoption - Microsoft includes it in M365 products [1] - Google includes it in its Vertex AI Search [1] - AWS includes it in its Amazon Q Business [1]

AI Agent Development - The industry is focusing on building AI Agents in production [1] - The industry provides hands-on tutorials for learning AI Agent development [1]