Works with #Python #JavaScript #TypeScript #Go supporting frameworks like #LangChain #LlamaIndex #Genkit & more
Github: https://github.com/googleapis/genai-toolbox
MCP Manual: https://cloud.google.com/sql/docs/mysql/pre-built-tools-with-mcp-toolbox
OpenSearch’s new MCP standard lets LLMs like Claude securely access + act on your data — no brittle glue code.
Dynamic tool discovery
Built-in auth + security
Unified JSON interface
Build smarter AI assistants + RAG apps →
https://opensearch.org/blog/introducing-mcp-in-opensearch/
Want to build AI that acts, not just chats?
Discover why OpenAI’s new Agents SDK is outperforming LangChain and AutoGen in 2025. From agent chaining to built-in guardrails, this SDK changes the game for devs.
Here’s your edge in the Agentic AI era:
#AgenticAI #OpenAI #LangChain #PythonDev
https://medium.com/@rogt.x1997/7-reasons-why-openais-agents-sdk-beats-langchain-and-autogen-in-2025-690b58007e54
LangWHAT?
You've seen names like LangChain, LangGraph, LangFlow or LangSmith – but what’s really behind them?
LangChain helps us build LLM apps via modular code.
LangGraph adds branching logic and multi-agent workflows.
LangFlow lets us create flows with drag & drop.
LangSmith monitors and evaluates our LLM stack.
LangChain, LangGraph and LangSmith come from the same ecosystem. LangFlow is a visual builder developed independently by DataStax.
Tried both LangChain and Langflow to build the same chatbot — Medium article coming shortly.
Microsoft and LangChain: Leading the Way in AI Security for Open Source on Azure.
Want to build a generative AI web app without the headache? Ed Izaguirre explores the full spectrum, from complex MERN stacks with #LangChain and Pinecone, to surprisingly capable single-file SQLite monoliths.
https://towardsdatascience.com/the-simplest-possible-ai-web-app/
Think you know how GenAI works?
It's not just prompts. It’s orchestration with 25 next-gen Python libraries powering the world’s smartest LLMs: agents, memory, RAG, grammar control & more 
Dive into the tools under the hood of AI in 2025 https://medium.com/@rogt.x1997/25-groundbreaking-python-libraries-powering-genai-workflows-in-2025-982add3d9301
#GenAI #PythonLibraries #LLM #AIstack #LangChain #AutoGen #AItools #ArtificialIntelligence
https://medium.com/@rogt.x1997/25-groundbreaking-python-libraries-powering-genai-workflows-in-2025-982add3d9301
Started building LLM apps with Rust + rig.rs, now diving into LangGraph and Python too. Been a super fun ride so far 
Built a RAG system with LangChain, OpenSearch, Google’s Gemini & OpenAI embeddings! Transform data into smart apps—optimize, deploy, and innovate. Your AI toolkit is ready. 
#AI #RAG #LangChain #GenerativeAI https://zilliz.com/tutorials/rag/langchain-and-opensearch-and-google-vertex-ai-gemini-2.0-pro-and-openai-text-embedding-ada-002
"Built a RAG system with LangChain, OpenSearch, Fireworks AI's Llama 3.1, and Azure embeddings! Optimize, calculate costs, and innovate. #GenerativeAI #RAG #LangChain #Llama3" https://zilliz.com/tutorials/rag/langchain-and-opensearch-and-fireworks-ai-llama-3.1-8b-instruct-and-azure-text-embedding-3-large
ONNX Runtime GenAI is the optimal choice! #AI #OnPrem #MachineLearning #LangChain
For more information check: https://devblogs.microsoft.com/ise/running-rag-onnxruntime-genai/.
Neo4j treibt mit GraphRAG, Vektor-Indizes & Agentic RAG die #KI-Entwicklung voran. Ob #LangChain, #LlamaIndex, #SpringAI oder #VertexAI – das neue Python-Paket und das Model Context Protocol (MCP) verknüpfen Graphdaten nahtlos mit #LLM-Anwendungen.
#Neo4j #GenAI #RAG #GraphQL #Cypher #VectorSearch #AgenticAI
https://www.bigdata-insider.de/leistungssprung-bei-graph-datenbanken-mit-ki-integration-cloud-skalierung-und-terabyte-graphen-a-2307ed20cfaf562a1a0094b712b5be95/
Understand RAG at Easter? 
Why not use the time to learn something new — and build your own local PDF chatbot?
Learn how chunking, embeddings and vector search work in practice - with LangChain, FAISS, Ollama and Mistral running entirely on your machine (no API key required).
Perfect for beginners - here's the full guide & GitHub repo
step-by-step guide: https://bit.ly/3EfOHB9 GitHub Repo: https://bit.ly/3EtqYgK
`langchain-mcp-adapters` を使用した LangChain と MCP サーバーの連携
https://qiita.com/nanami_bitwise/items/d04dedb0c276bb624d8a?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
Getting started with LangChain
Rerun your failed tests in PyTest Usefulness of Context Managers in Python Testing your Flask App
Latest Edition of My Voyage of Discovery: http://eepurl.com/jbSOOQ
Subscribe for more at: http://eepurl.com/iu6PFU
In questo test, in una SERP di #Google in cui compare #AI Overviews, ho preso i contenuti nelle prime 12 posizioni e ho creato un piccolo #RAG usando #LangChain, #Chroma DB e #GPT4o. Inviandolo la query al RAG, ottengo una risposta simile a quella proposta da AI Overviews. Chiaramente Google usa anche query correlate ("fan-out") e il Knowledge Graph per espandere i risultati.
What is an agent?
That’s what Day 3 of Kaggle’s Gen AI Challenge is all about.
An agent is a system that observes its environment, plans actions, uses tools like APIs, functions, or data stores, and acts autonomously to achieve a goal – often over multiple steps (see whitepaper from Google below).
The cognitive architecture of an agent consists of three essential components: a model (like a language model),
tools (like APIs or functions), and
an orchestration layer that coordinates reasoning and action.
You can build such agents using tools like LangChain and LangGraph.
The full whitepaper from course day 3: https://www.kaggle.com/whitepaper-agents
La Grosse Conf 2025 - #LangChain : #OpenSource, compléxité et adaptation permanente
https://blog.octo.com/la-grosse-conf-2025-langchain--opensource-complexite-et-adaptation-permanente
sneak peek: we'll have an #elastic developer event in mountain view in may — single track and just engineering. and I'll make sure to keep it *very* technical: besides developers from #LangChain and #github with more to come, we'll have shay (elasticsearch creator), costin (who most recently worked on JOINs for ES|QL), and dinesh (currently researching on agentic search) from elastic 1/2
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