{

{
"title": "A Practical Knowledge Pipeline for AI Agents: Web, Video, Docs, and Cloud Sync",
"body": "When I started building AI agents, I quickly realized that memory is the bottleneck. Most RAG examples stop at vector search over a few PDFs. But what about ingesting entire YouTube playlists, bookmarking web pages, reading DOCX files, and syncing everything to the cloud so your agent can recall it anytime, anywhere?\n\nI faced this problem and built an open-source solution: Knowledge & Memory Management (KMM). It's an extension layer for the hermes-memory-installer that adds a complete knowledge ingestion and management pipeline. Here's what it does and how it works.\n\n## The Problem\n\nStandard memory tools store embeddings and conversation history. They don't answer: how do you get structured knowledge into that memory? You either manually write notes or hack together a scraper that spews raw text into a vector DB. Neither scales.\n\nI wanted a system where I could give my agent a YouTube channel URL or a list of articles, and have it extract structured notes, build a knowledge graph, and store everything in a tiered memory system. And I wanted that memory to sync across machines.\n\n## Architecture Overview\n\nKMM is structured as a pipeline:\n\n*Collection Layer* → Analysis Layer → Storage Layer → Sync Layer\n\n### Collection: 40+ Tools\n\nThe collection layer uses well-known open-source tools for different sources:\n- Web pages: BeautifulSoup, requests, Playwright for JS-rendered pages, and more.\n- Video/Audio: yt-dlp (YouTube, Bilibili, etc.) to download transcripts and metadata.\n- Documents: MarkItDown for unified conversion, plus a SenseNova-powered document engine for PDF/PPT/Word with table extraction.\n- OCR: Tesseract for images and scanned PDFs.\n- Articles: Feedparser for RSS, newspaper3k for article extraction.\n\nAll tools are wrapped in a consistent interface and can be orchestrated via the collection pipeline.\n\n### Analysis: AI-Powered Processing\n\nOnce raw data is collected, it goes through analysis:\n- Note Generation: Sends content to a configurable LLM (OpenAI, Anthropic, or local) to generate a structured note: summary, key points, tags, related topics.\n- Knowledge Graph Extraction: Extracts entities and relationships to connect notes.\n- Fact Checking: Uses NLI models to verify claims against known sources.\n- Book Summarization: Automatically generates chapter summaries and key ideas.\n\nThe output is a JSON note with metadata and embeddings stored in the memory system.\n\n### Storage: Three-Tier Memory\n\nKMM works with the memory sidecar's storage, but decides where each note belongs:\n- Hot: Active memory (memory tool functions). For recent or frequently used items.\n- Warm: A knowledge graph (Hindsight) that holds up to 10,000 nodes.\n- Cold: A vector store (gbrain) with capacity for 11,000+ pages.\n\nRetrieval searches across all three tiers, ranking by relevance and recency.\n\n### Sync: 12+ Cloud Drives\n\nThe cloud_sync module wraps rclone to sync all notes