Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Cinematic wide shot of a mystical emerald-lit library with hundreds of floating book pages and documents, each outlined with bright green object segmentation boundaries and connected by glowing threads to an ornate central retrieval apparatus, dramatic theatrical lighting in the style of Wicked movie, moody atmosphere with deep shadows and emerald highlights, large movie title text overlay reading RAG
Integrating 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 Systems via 𝗠𝗖𝗣 👇 If you are building RAG systems and packing many data sources for retrieval, most likely there is some agency present at least at the data source selection for retrieval stage. This is how MCP enriches the evolution of https://x.com/Aurimas_Gr/status/1986793469822529640
Google released another whitepaper 🔥. it’s a masterclass on building intelligent optimized sessions and long-term memories for agents that actually work. It covers: > context engineering best practices > comparing memory and RAG > memory-as-a-tool pattern > agent-to-agent (A2A) https://x.com/Hesamation/status/1988750893957730396
Building RAG pipelines usually means wrestling with infrastructure for weeks. @dify_ai and Weaviate make it easy to build a production-ready system in under an hour. 𝗗𝗶𝗳𝘆 handles orchestration and LLM logic while 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 powers fast retrieval under the hood. https://x.com/weaviate_io/status/1991539631259591085
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