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Introducing our new agentic RAG framework. A collab with Google Cloud, our multi-agent workflow goes beyond standard RAG by breaking down complex enterprise queries & iteratively searching for sufficient context before generating dependable responses. 📜→

Google Research

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506,553 views • 1 month ago •via X (Twitter)

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OpenAI Developers

322,553 views • 5 months ago

I’m excited to announce a new course with DeepLearning.AI - Building Agentic RAG 💫 In this course, you’ll learn how to build a research assistant that can reason over multiple documents and answer complex questions. You’ll also learn how to step through the execution of the agent and steer it with human feedback. This represents a big step beyond any standard RAG pipeline, which is mostly good for simple questions over a small set of documents. Learn the layers first and then put them together: ✅ Routing ✅ Tool Use ✅ Multi-step reasoning with Memory ✅ Tool retrieval ✅ Debugging + user input Check it out!

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Jerry Liu

76,293 views • 2 years ago

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

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Andrew Ng

297,131 views • 2 years ago

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

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Andrew Ng

244,329 views • 2 years ago

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Andrew Ng

124,625 views • 1 year ago

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Tongyi Lab

280,542 views • 10 months ago

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660,002 views • 5 months ago

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71,710 views • 2 years ago

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Andrew Ng

105,343 views • 1 year ago

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🚀 Introducing LangGraph Cloud 🚀 LangGraph helps you build reliable agents that actually work. Today, we've launched LangGraph Cloud, our new infrastructure to run fault-tolerant LangGraph agents at scale. With LangGraph Cloud, you can: • Handle large workloads with horizontally-scaling servers, task queues, and built-in persistence • Debug agent failure modes and iterate quickly in a visual playground-like studio • Deploy in one-click and get integrated tracing & monitoring in LangSmith This builds on LangGraph v0.1, our latest stable release supporting diverse control flows - whether it's single- or multi-agent, hierarchal or sequential. You'll be able to create flexible agents with human-in-the-loop collaboration and first-class streaming support. We can't wait to see the agentic workflows you'll ship. LangGraph Cloud is available in closed beta today. 👉 Join the waitlist for LangGraph Cloud: ✍️ Read our blog post announcement: 📽️ Watch the video walkthrough: 🌀 Haven't tried LangGraph yet? Give it a spin here: 🙌 Check out the new LangGraph landing page:

LangChain

134,051 views • 2 years ago

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Kaustubh Sridhar

52,158 views • 11 months ago

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece

Victoria Slocum

45,497 views • 11 months ago

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

Factory

327,785 views • 1 year ago

We partnered with artists, designers, and builders to create new AI tools that solve real problems in their creative workflows. Here’s what’s new: — Introducing Google Pics in Google Workspace: A brand-new image creation & editing tool. Move and resize objects, add text, and translate just by hovering and clicking — Big updates to صافي النيه😉: 1) You can now create with Gemini Omni Flash in Google Flow 2) Google Flow Agent is a multi-step creative partner that reasons and plans complex tasks with you. 3) Google Flow tools are custom tools you can “vibe code” for animations, video effects, text layering & more — Design live with Stitch by Google: Now, you can use text or voice prompts to edit layouts in real time then export those designs straight to code — More creative control in صافي النيه😉Music: Edit songs section by section, remix the style of full songs, and create music videos with our new Gemini Omni Flash model

Google AI

13,953,239 views • 2 months ago