SourceWeft is an AI workstation you host yourself, where agents, your documents, and your model keys work in one place instead of scattering across chat tabs you do not control. Describe a job and a lead agent breaks it down, handing research, exploration, and execution to focused sub-agents that share the same working context and files. Agents draw on a skill library covering presentations, HTML pages, images, meeting summaries, and study guides, and you can add your own skills so good workflows become repeatable. MCP servers, web search, and web extraction connect agents to outside tools, while approved commands run inside a sandboxed environment that cannot touch the rest of your server. Feed it PDFs, web pages, notes, YouTube videos, Notion, Drive, Gmail, or Slack, then follow citations from any answer back to the source material it came from. Bring your own keys for the chat and indexing models you prefer, organize the team into workspaces with roles and access controls, and pick up any conversation from the web app or desktop clients. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

Instead of burying your documents in a vector index you cannot inspect, LLM Wiki v2 files them into a Markdown wiki you can read, browse, and question. Drop text, Markdown, or PDF files anywhere on the page and each one is catalogued automatically, filed into a folder that says what it holds, and linked to the notes it relates to. Ask a question and the answer cites the exact notes it used, with clickable sources you can open, verify, and correct. New documents update the note on their subject instead of piling up as disconnected chunks, so what you teach it stays taught. A small classifier routes filing and questions through the folder tree, keeping the wiki quick and cheap as it grows, and you can browse the tree, follow see-also links, move or delete notes, and watch the tokens spent by both the model and the classifier. Connect OpenRouter, OpenAI, Gemini, a local Ollama, or any compatible endpoint, and expose the same wiki to Claude Code or Codex as long-term memory they can recall from and write to. Because every note is plain Markdown in a folder you own, the whole knowledge base can live in git beside your other files. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Positioned as the open-source NotebookLM alternative for AI agents, SurfSense delivers a live web research platform where your agents access structured data from Reddit, YouTube, Instagram, TikTok, Amazon, Walmart, Google Maps, Google Search, Indeed, and any page on the open web through one REST API or MCP server. Scheduled and event-triggered agents transform findings into briefs, alerts, podcasts, and presentations, while a built-in knowledge base keeps every discovery searchable with Perplexity-style cited answers using hybrid semantic and full-text search powered by PostgreSQL with pgvector. Upload PDFs, Office documents, images, and audio files, or sync Google Drive, OneDrive, and Dropbox — 50+ file formats supported with AI file sorting that auto-organizes documents by source, date, and topic. The MCP server exposes scrapers, knowledge base, and workspaces as native tools for Claude, Cursor, and any MCP-compatible agent. Cross-country proxy rotation handles Reddit, TikTok, and Google Search scraping with geo-aware sticky sessions and captcha-aware anti-bot handling. The platform features collaborative chats, multi-format document export, git-native knowledge base with Open Knowledge Format export, and a desktop quick-ask panel with global shortcut. Docker Compose deployment manages nine services including Caddy proxy, PostgreSQL, Redis, FastAPI backend, Celery workers, zero-cache real-time sync, and Next.js frontend with automatic Watchtower updates. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

WeKnora turns scattered corporate documents into a searchable, reasoning-capable knowledge asset that your team can query in plain language and receive cited, sourced answers. Upload PDFs, Word files, web pages, Feishu wikis, Notion databases, Yuque docs, GitLab repositories, or RSS feeds into structured knowledge bases, and three distinct modes make the content actionable: RAG Quick Q&A retrieves relevant chunks and generates answers with source citations; the ReAct Agent autonomously orchestrates multi-step reasoning across knowledge retrieval, MCP tool calls, web search, and sandboxed code execution to produce comprehensive research reports; and Wiki Mode deploys LLM agents to distill raw documents into an interlinked markdown knowledge base with an interactive knowledge graph, revision history, and one-click rollback. Connect 20+ LLM providers including OpenAI, DeepSeek, Qwen, Claude, and local Ollama models without vendor lock-in, and choose from seven vector database backends (Qdrant, Milvus, Weaviate, and more) for embedding storage. Enterprise features include four-tier RBAC with per-resource ownership and per-workspace audit logs, AES-256-GCM credential encryption, scoped API keys, Langfuse observability tracing for every agent loop and tool call, and a runtime task-queue dashboard for worker-pool governance. Cross-session long-term memory preserves conversational context across interactions. The Agent Skills catalog lets teams install and share sandboxed scripts executed in Docker or E2B containers. A Chrome Extension captures web content directly into knowledge bases. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Kotaemon is a document QA platform that combines advanced RAG techniques with a clean Gradio-based web interface for chatting with your documents. Built by Cinnamon, the Python backend supports any LLM provider including OpenAI, Azure OpenAI, Cohere, Groq, and local models via Ollama and llama-cpp-python, with a model management panel for configuring LLM and embedding providers from the UI. The default hybrid RAG pipeline combines full-text keyword retrieval with vector similarity search and applies re-ranking to ensure optimal result quality, while multi-modal document parsing extracts content from tables and figures alongside text. Advanced citations link every answer to specific source passages with relevance scores, viewable directly in the built-in PDF viewer with highlighted text spans. GraphRAG indexing via NanoGraphRAG, LightRAG, or Microsoft GraphRAG builds knowledge graphs from document collections for relationship-aware retrieval. Agent-based reasoning supports question decomposition for multi-hop queries using ReAct and ReWOO strategies. Multi-user authentication organizes documents into private and public collections with sharing and collaboration features. The platform supports Docker deployment in lite, full, and Ollama-bundled variants, runs on port 7860, and stores application data in a persistent volume. MCP tool integration enables external system connections for extended retrieval capabilities. On RepoCloud, deploy Kotaemon on a dedicated VPS with Docker, root SSH access, and complete control over your document AI infrastructure, all under the Apache 2.0 license.

The most feature-complete open-source alternative to Google's NotebookLM — a self-hosted research platform where you upload PDFs, videos, audio files, and web pages into organized notebooks, then chat with your content, generate multi-speaker podcasts, and run semantic search across everything without sending a single byte to Google's servers. The podcast engine supports 1-4 fully customizable speakers with backstories, personalities, and expertise profiles, generating professional audio dialogue through OpenAI, ElevenLabs, Google TTS, or completely local text-to-speech via Kokoro for maximum privacy. Content processing uses token-based chunking with RAG-powered retrieval grounded in your uploaded sources, while both full-text keyword search and semantic vector search via SurrealDB enable conceptual discovery across all notebooks. The 18+ supported AI providers include OpenAI, Anthropic, Google Gemini, Groq, Ollama, LM Studio, and more — configurable per task so you can route cheap models to summarization and powerful models to analysis. Content transformations extract insights, generate summaries, create study guides, and produce structured outputs from any source material. The MCP integration connects Open Notebook to Claude Desktop, VS Code, and other MCP clients for seamless workflow integration. A full REST API on port 5055 enables complete automation of notebook management, source upload, and podcast generation. Deploy via Docker Compose with the application container, SurrealDB v2 on RocksDB, and optional TTS containers. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Insights LM gives you a NotebookLM-style research assistant that runs on your own infrastructure and answers strictly from the documents you feed it. Build a notebook for a project, drop in PDFs, pasted text, website links, and audio files, then ask questions in plain language and get answers grounded in those sources rather than the open internet. Every answer carries citations you can click to jump straight to the passage it came from, so verifying a claim takes seconds instead of a fresh search. Findings worth keeping become notes saved alongside the notebook, building a private knowledge base that grows with your research. When reading is too slow, generate an audio overview and listen to two hosts discuss your material like a podcast episode, perfect for absorbing a long report on a walk. Teams use it to interrogate internal documentation, onboard new hires against company knowledge, and brief themselves before meetings without uploading confidential files to a third-party service. Because the whole stack is yours, you choose which AI models do the reading, including local models for fully private work. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.