How @11xAIbuild built Alice, the AI SDR 🚀 Onboarding SDRs can take a long time, 11x shrunk this time to days by solving a critical challenge: getting AI to understand complex company materials like humans do. 𝗧𝗵𝗲 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵: Multi-modal document ingestion and parsing using LlamaParse ✅ PDFs, PowerPoints, and all sorts of documents - all parsed automatically and made legible to LLMs ✅ Fine-grained parsing control for tables, images, and unstructured data LlamaParse's ability to handle diverse file types with accuracy, plus developer-first tools that let 11x focus on building their agent, not parsing infrastructure. Want the full technical breakdown? 📖 Read the case study: 🎥 Watch the technical deep dive from the team at the latest @aiDotEngineer :

Using @claudeai you now have search results as content blocks, bringing citations to agent applications - no more document workarounds needed! 𝙎𝙚𝙖𝙧𝙘𝙝 𝙧𝙚𝙨𝙪𝙡𝙩𝙨 𝙖𝙨 𝙘𝙤𝙣𝙩𝙚𝙣𝙩 𝙗𝙡𝙤𝙘𝙠𝙨 by @AnthropicAI enables proper source attribution for results from tool calls, matching the citation quality you get from web search functionality: 🔗 Natural citations with source and title attribution linked back to specific tool calls ⚡ Available on Claude Opus 4.1, Claude Sonnet 4, and other latest models via Anthropic and Google Vertex AI 🛠️ We've already integrated this into LlamaIndex with full support for citable tool results and agent workflows Read the official docs: Get started with LlamaIndex integration:

Improve retrieval accuracy by reranking your LlamaParse PDF results with @ZeroEntropy_AI rerankers 📊 Learn how to enhance your document search pipeline with reranking techniques that significantly boost relevance scores for better AI responses. 🎯 Combine LlamaParse's advanced PDF extraction with reranking models to surface the most relevant chunks 📈 Implement semantic reranking with 𝙯𝙚𝙧𝙖𝙣𝙠-1 to improve retrieval quality beyond basic similarity search ⚡ Compare results before and after reranking to see measurable improvements in answer quality LlamaParse handles complex PDF structures while reranking ensures your users get the most relevant information every time. Check out the complete tutorial:

Build realtime AI agents that can process live voice data from @Zoom meetings using RTMS and LlamaIndex 🎙️🤖 Join us for a hands-on technical workshop on August 14th where you'll learn to create production-grade AI systems that work with streaming audio: 🔗 Set up Zoom RTMS to capture live audio 📊 Use transcript chunks for as LLM context 🧠 Build intelligent, event-driven agents that can summarize conversations, detect intent, create action items and meeting notes Join @ojusave and @tuanacelik for a complete blueprint for LLM orchestration with live voice data. Register for the workshop: Thu, Aug 14, 2025 6:00 PM CEST

Hello GPT-5! @OpenAI just announced their latest model 🔥 We have day-0 support: 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 -𝗨 𝗹𝗹𝗮𝗺𝗮-𝗶𝗻𝗱𝗲𝘅-𝗹𝗹𝗺𝘀-𝗼𝗽𝗲𝗻𝗮𝗶 Can GPT-5 find treasure in a maze? Try it out with Agent Maze: an agent with minimal tools, tasked to solve a maze (that we generate). We then test it for time and the number of tool calls. Try out Agent Maze: And get started with LlamaIndex & GPT-5:

Build enterprise AI applications with LlamaCloud Index and connect them to intelligent tool calling agents that can handle complex, multi-step queries. This tutorial by @seldo walks you through creating your first LlamaCloud Index, using JP Morgan Chase banking documents and building an agent that can reason across multiple data sources: 🏦 Set up LlamaCloud Index to parse and index dense PDF documents like banking agreements and fee schedules 🤖 Create multi-tool agents using our Workflows abstraction that can query your indexed data alongside other functions 💰 Handle complex scenarios like calculating banking fees across multiple transactions and timeframes 📊 Stream agent reasoning in real-time to see exactly how your AI system processes multi-step problems The agent successfully processes a complex banking scenario involving overdraft calculations, fee assessments, and timing - demonstrating how LlamaCloud Index integrates seamlessly with agentic workflows built on our open source framework. 📹 Watch the full walkthrough: 📖 Get started with the tutorial:

🤖 @AnthropicAI just dropped Claude Opus 4.1 and we have day-zero support for it, you just have to run: 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 -𝘜 𝘭𝘭𝘢𝘮𝘢-𝘪𝘯𝘥𝘦𝘹-𝘭𝘭𝘮𝘴-𝘢𝘯𝘵𝘩𝘳𝘰𝘱𝘪𝘤 Curious of trying it out with LlamaIndex? Check out our example notebook:

Ready to see how Document Agents handle messy financial documents? Our upcoming webinar in just 1️⃣ week shows you exactly how to build systems that work with the complex, multimodal documents financial teams deal with every day: 📊 Build document agents using LlamaCloud's enterprise-grade parsing engine that handles nested tables, charts, and inconsistent formats in 10-Ks and earnings reports 🤖 Set up end-to-end automated workflows with LlamaIndex agentic orchestration for seamless document processing 💼 Implement real use cases like SEC filing analysis, portfolio risk assessment, and compliance reporting with intelligent pipelines ⚡ Move beyond traditional OCR limitations to extract actionable data from unstructured financial documents Join @tuanacelik and the LlamaIndex team, August 12th at 9 AM PST:

Whether you want to chat with your terminal or add a voice assistant to your web-app, we got you covered with our Gemini Live integration, now available in TypeScript! 👇 Check out the demo below, where @itsclelia shows you how to set up and run a simple terminal chat - but if you're very eager to try it you can just run 𝘯𝘱𝘹 @𝘤𝘭𝘦-𝘥𝘰𝘦𝘴-𝘵𝘩𝘪𝘯𝘨𝘴/𝘭𝘪𝘷𝘦-𝘤𝘩𝘢𝘵 🏃 📚 Learn about LlamaIndex TS: ⭐ Star the demo code on GitHub:

The biggest barrier to deploying autonomous AI agents in production isn't capability, it's reliability. While demos showcase impressive autonomous behavior, most organizations struggle when agents hit the messy realities of enterprise environments. Token-driven loops drift unpredictably, context windows get polluted, and state management fails across sessions. 🏭 @MongoDB's persistent state management ensures agents retain complete context across system restarts and failures 🧠 Our intelligent retrieval systems eliminate context pollution by learning which historical information proves valuable ⚙️ LlamaIndex Workflows enable deterministic control within autonomous operation, providing auditability without sacrificing adaptability 📈 Real validation from @cemex shows development cycles dropping from three weeks to less than one day The future isn't about choosing between intelligence and reliability: it's about building autonomous agents on infrastructure reliable enough to support truly independent operation. When persistent state management converges with intelligent agent frameworks, organizations can finally deploy agents that work consistently in production. Read how @MongoDB and LlamaIndex are solving the reliability crisis in autonomous agents: