A RAG engine for deep document understanding! RAGFlow lets... you build enterprise-grade RAG workflows on complex docs with well-founded citations. Supports multimodal data understanding, web search, deep research, etc. 100% local & open-source with 55k+ stars!show more

Building RAG is easy. Parsing real, unstructured data is... the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓show more

OpenAI's Deep Research is getting a run for its... money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!show more

You can now access Box and Dropbox, in addition... to Google Drive, OneDrive, and SharePoint with Perplexity Enterprise Pro. Get comprehensive answers by running Deep Research on internal files and the web, all with enterprise-grade security.show more

Check this!! A 100% open-source toolkit to work with... LLMs. Transformer Lab is an app to experiment with LLMs: - Train, fine-tune, or chat. - One-click LLM download (DeepSeek, Gemma, etc.) - Drag-n-drop UI for RAG. - Built-in logging, and more. 100% local!show more

Drag-and-drop UI to build AI agents! Langflow is a... powerful visual tool for building and deploying AI-powered agents and workflows—without writing any code. Supports all major LLMs, vector DBs, etc. 100% open-source, 62k+ stars 🌟show more

introducing deep research for flights : book complex trips... with easeshow more

Introducing Firecrawl Workflows, installable skills for repeatable web tasks.... 10+ workflows are live now, including deep research, SEO audits, and website design cloning, all via our CLI. Here's the design cloning workflow pulling a site's visual style from a single URL with Grok Build!show more

Turn complex docs into clean, LLM-ready data! Every AI... company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!show more

Build powerful workflows (no code required). With Agent Designer... in Gemini Enterprise, you can orchestrate complex, specialized automations using a simple drag-and-drop interface.show more

🔥 We also open-source a UE5-based data engine, which... help reproduce research works in 3D/4D reconstruction. 4D Data Engine:show more

OpenClaw, but built for normal people. Sim is an... open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.show more

Enjoy Balaji’s “The Network State?” Use OpenAI to search... & chat with the book at Code & data used is 100% open-source. GitHub:show more

this is pure f*cking treasure A Stanford AI research... group finally drew the perfect RAG system: retrieval, Jev and agents in one loop, and it fixes the 3 things that break every RAG app: > the LLM reads 20 passages when only 3 matter > it answers questions your docs can't answer > it trusts whatever text it retrieves here's how it runs: > a lead agent sends the query > hybrid search pulls the top 20 candidates, dense + keyword > ONE Jev request scores all 20 + 2 gates: answerable? injection? > only passages above 0.6 reach the writer agent > a second Jev call checks every claim against its source > grounded answer, with citations and when the docs don't have it: > answerable fails, the writer never runs > a researcher agent rewrites the query and retries once > still nothing? "not in the docs". zero tokens spent on a guess retrieval casts the net. Jev decides what's real. agents do the work save this before you build your next RAGshow more

Claude Cowork now has a free, 100% local competitor... 🤯 Rowboat connects to your Gmail and meeting notes, builds a knowledge graph of your work, and acts on it. → Voice-enabled → Works with any LLM (local or hosted) → MCP tool extensibility → Background agents + web search → Obsidian-compatible Markdown vault 14.3k GitHub stars. 100% open-source.show more

Turn any PDF, image, DOCX, or PPTX into clean... Markdown! Production workloads contain millions of pages, mixed formats, inconsistent layouts, low-quality scans, and documents written in dozens of languages. Datalab Marker v2 (open-source with 40k stars) handles that workload at scale through one open-source document parsing pipeline. It recovers: - Reading order - Complex equations - Images and captions - Tables and nested tables - Text from scanned pages - Headings and page structure It can return Markdown, JSON, or HTML for RAG pipelines, search indexes, data extraction systems, document agents, and archive migrations. Marker v2 runs at up to 23.7 pages per second on a single B200 GPU and supports more than 90 languages. GitHub repo: (don’t forget to star it ⭐) I recently used Datalab to build an agent that scans an identity document, extracts the required fields, and fills a tax form automatically. The article covers the full workflow, including document parsing, structured extraction, field mapping, and form completion. Read it below.show more