Formerly known as Danswer and now backed by over 31,000 GitHub stars with 253 releases, Onyx delivers a production-ready AI platform that turns any LLM into a context-aware enterprise assistant connected to your organization's actual knowledge. The agentic RAG pipeline combines BM-25 keyword search with prefix-aware embedding models in a hybrid index, then deploys AI agents to retrieve, verify, and synthesize answers with source citations from over 40 connected workplace tools including Google Drive, Confluence, Slack, Notion, Jira, SharePoint, GitHub, and Linear. Custom AI assistants with configurable prompts, backing knowledge sets, and document-level access control enable specialized agents for engineering, sales, support, and research workflows. The platform supports every major LLM provider — Anthropic Claude, OpenAI, Google Gemini, plus self-hosted options via Ollama, LiteLLM, and vLLM for fully air-gapped deployments. Beyond chat, Onyx provides web search with Serper, Google PSE, Brave, and SearXNG integration, an in-house web crawler, code execution, file creation, and multi-step deep research with report generation. Enterprise features include SSO via Google OAuth, OIDC, or SAML with SCIM provisioning, role-based access control, usage analytics by team and agent, query history auditing, PII removal through custom code hooks, and full whitelabeling. Deploy via Docker Compose on any infrastructure. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed (Community Edition).

Unify fragmented corporate knowledge across chat channels, documents, and cloud drives with PipesHub, an open-source workplace context platform providing permission-aware search and verifiable answers for teams and autonomous agents. Employees can query enterprise knowledge bases through a conversational interface that retrieves relevant information across Google Workspace, Microsoft 365, Slack, Confluence, Jira, and GitHub while strictly respecting individual user access permissions. Knowledge workers can verify every answer through clickable citation blocks that trace claims directly back to source documents, spreadsheet rows, or chat messages. Teams can assemble custom AI agents visually using a drag-and-drop builder, combining retrieval collections with interactive toolsets to automate multi-step operations like drafting customer responses or creating issue tickets. Autonomous agents connect via Model Context Protocol to inspect shared company knowledge and perform external actions across connected SaaS tools. Organizations can index scanned PDFs, slide decks, markdown pages, and spreadsheets with OCR and document parsing, maintaining synchronized records through automated schedules. Administrators can inspect synchronized records, monitor query history, and manage connector credentials across all integrated business applications. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache License 2.0 licensed.

Eliminate indexing blind spots across fragmented company networks with Fess, a distributed enterprise search engine that crawls internal document repositories, corporate intranets, and cloud services to provide employees with instant, permission-aware file discovery. Organizations can schedule automated crawlers across web pages, network shares, databases, and third-party storage platforms to extract text and generate visual thumbnails from PDFs, spreadsheets, presentations, and compressed archives. Users can search indexed records using natural language queries, filtering results by file metadata, creation dates, categories, and geographic coordinates while viewing contextual snippets with highlighted keyword matches. Granular role-based permissions mirror directory security structures from Active Directory and LDAP so staff only see search results for documents they have explicit authorization to inspect. The browser-based administrative console lets operations teams define path mapping rules, manage virtual hosts, monitor active crawl jobs, inspect error logs, and configure scheduled re-indexing workflows without modifying server configuration files. Security teams can establish single sign-on across the organization using SAML or OpenID Connect authentication providers to secure administrative workflows and search portals. 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.

Chat with your own documents: AnythingLLM, from Mintplex Labs, wraps retrieval-augmented generation (RAG) in an open-source application anyone can run. You organize content into workspaces, each an isolated namespace with its own documents, vector embeddings, chat history, and settings, so one instance can hold several separate knowledge bases. Upload PDFs, DOCX, TXT, and other formats, or scrape web pages; the built-in collector parses and chunks them into a vector database (LanceDB by default, with Pinecone, Chroma, Qdrant, and others supported). Answers cite their source documents. It works with both cloud LLMs (OpenAI, Anthropic, Gemini) and local ones via Ollama or LM Studio, and the embedding model is separately configurable. Beyond RAG chat, it includes AI agents that can browse the web and run tools, an embeddable chat widget for your website, a developer API, and multi-user mode with admin, manager, and default roles plus per-workspace access control. Context assembly is smarter than naive RAG: pinned documents, attached files, vector search hits, and recent chat history are combined under a token budget so the model's context window is filled efficiently, and each workspace supports multiple independent conversation threads against the same knowledge base. Because the embedding model, vector store, and chat LLM are all independently swappable, you can move between providers without re-ingesting a single document. The stack is Node.js with a React frontend, MIT-licensed.

RAGFlow has established itself as one of the most widely adopted open-source RAG engines available, powering production AI systems that demand traceable, hallucination-free answers from complex enterprise data. The platform processes PDF, DOCX, Excel, and PPT files through vision-based deep document understanding with layout analysis and OCR, extracting structured knowledge from tables, charts, and images that simpler parsers miss entirely. RAGFlow's hybrid retrieval pipeline combines vector search with BM25 keyword matching and multi-stage reranking across configurable document stores including Elasticsearch, InfiniFlow's Infinity engine, OpenSearch, and OceanBase. Developers connect any combination of LLM providers — OpenAI, DeepSeek, Anthropic Claude, Google Gemini, and locally-hosted models via Ollama — through a unified configuration layer. The visual agent workflow system enables multi-step reasoning chains with persistent memory, tool calling, and pre-built templates for common enterprise scenarios. RAGFlow synchronizes data from Confluence, S3, Notion, and Google Drive, and delivers answers through chat integrations with Feishu, Discord, Telegram, and Line. The Python SDK and RESTful API on port 9380 provide programmatic access to knowledge base management, document parsing, and conversational retrieval. The full stack deploys via Docker Compose with MySQL for metadata, Redis for task orchestration, and MinIO for object storage. 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.

Achieving 98.4% Recall@5 on LongMemEval-S without a reranker, AKB is the agent-native knowledge infrastructure giving Claude Code, Cursor, Windsurf, and any MCP-aware agent persistent organizational memory — a drop-in replacement for Confluence or Notion where AI agents read and write directly rather than scraping human-formatted pages. Documents are stored as Markdown with YAML frontmatter in Git bare repositories (full version history, branching, diff), while PostgreSQL 16 serves as source of truth for chunk text, metadata, and BM25 vocabulary, with dense embeddings handled by a pluggable vector store (pgvector default, Qdrant optional, or managed Seahorse). Agents interact through 20+ MCP tools — akb_put, akb_search, akb_browse, akb_relations, akb_sql, akb_edit, akb_history — performing hybrid dense plus BM25 search fused via Reciprocal Rank Fusion in one call. The URI graph (depends_on, related_to, implements relations) lets agents traverse knowledge connections without external graph databases. Multi-tenant vault isolation is enforced at the PostgreSQL ACL level with per-user roles and SET LOCAL ROLE for SQL sandboxing. Event fanout streams structured events to Redis Streams for external consumers (synthesis bots, digest agents, audit trails), and a hash-chained append-only audit log integrates with enterprise SIEMs. Ships with agent plugins for Claude Code and Codex (session capture, wiki ingest, lifecycle hooks), Kubernetes kustomize manifests, and Open Knowledge Format export. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. BSL 1.1 licensed (free under 100 seats, auto-converts to Apache 2.0 after four years).

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.

Navigating organizational knowledge, visualizing concept graphs, and orchestrating cooperative AI agents converge inside Yuxi, an open-source multi-tenant workspace built for enterprise research and deep archive exploration. Users can upload diverse business documents including scanned PDFs, spreadsheets, and slide presentations to automatically extract structured text blocks, tables, and entity relationships for semantic querying. Knowledge teams can inspect interactive topological graph views to explore connections between company entities, cross-referencing subgraphs against original file sources. Practitioners can configure autonomous agents with specialized subagents, assigning dedicated roles for concurrent research, deep data synthesis, and code execution. Human reviewers can inspect agent thought chains in real time and evaluate interactive confirmation cards before agents execute irreversible external actions or modify shared workspace files. Operators can execute sandboxed tasks that produce downloadable reports, interactive data charts, and rendered HTML pages stored within isolated workspace file systems. Administrators can govern model endpoints, set granular permission boundaries across departments, and monitor token consumption through live system analytics. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

With 1,200+ GitHub stars since its April 2026 launch, Arkon provides an enterprise-grade knowledge management layer that turns scattered organizational documentation into AI-accessible structured context. The platform runs as a centralized MCP server, compiling your SOPs, policies, technical docs, and institutional knowledge into a versioned wiki with draft-approval workflows, then serving that wiki to Claude Desktop, Claude.ai, Cursor, and any MCP-compatible client through a single permission-scoped endpoint. OAuth 2.1 with PKCE authentication eliminates manual token management — employees authenticate through a browser login while the system discovers endpoints automatically via RFC 8414. The RBAC v2 system supports custom roles with granular permissions, department-scoped AI Skills, workspace isolation, and comprehensive audit logging so every query and access event is traceable. RAG retrieval powered by pgvector embeddings enables AI clients to search across all organizational documents with source attribution, while the AI Skills system lets teams define reusable instruction sets scoped to specific departments or roles. The architecture runs seven Docker containers coordinated by Compose: PostgreSQL with pgvector for embeddings and metadata, Redis for caching, MinIO for document storage, a FastAPI backend, two ARQ async workers for embedding generation and document processing, and a Next.js frontend portal accessible on port 3119. API keys are encrypted at rest with Fernet, and no telemetry leaves the deployment. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. PolyForm Internal Use licensed.

A self-hosted "second brain": Khoj indexes your own files and answers questions from them, parsing Markdown (whole Obsidian vaults included), org-mode, PDF, Word, plain text, Notion pages, GitHub repositories, and images described by a vision model, then embedding everything with sentence-transformers into a vector index for semantic search and RAG with cited sources. Any LLM backend works: local models like Llama, Qwen, or Mistral via Ollama, or cloud models like GPT, Claude, and Gemini. You can build custom agents, each with its own persona, scoped knowledge base, chat model, and tools such as web search and code execution. Scheduled automations run recurring research and deliver newsletters or notifications to your inbox, and research mode performs multi-hop web searches with inline citations. Access it from a browser, the Obsidian plugin, Emacs, desktop, or WhatsApp - all clients connect to the same self-hosted instance, making Khoj one of the few AI assistants Emacs users can point at decades of org files. Semantic search means recall works without exact keywords: "that paper about forecasting with transformers" surfaces the right PDF even when you cannot remember its title. Switching LLM backends never requires re-indexing your documents, and with a local model via Ollama, even inference stays on hardware you control - journals, research, and private notes are never sent anywhere. Python/FastAPI stack, AGPL-licensed, with PostgreSQL storage.

Atomic transforms collections of personal Markdown notes into an AI-augmented knowledge graph by breaking documents down into semantically linked conceptual units. Users capture thoughts, research excerpts, and web clippings as modular notes that the system automatically chunks, embeds, and categorizes with hierarchical tags. An interactive force-directed canvas maps out the entire knowledge base, positioning related ideas closer together based on embedding cosine similarity rather than manual folder hierarchies. Built-in vector search queries across text embeddings directly within SQLite, retrieving precise contextual snippets in response to natural language prompts. The automated wiki engine synthesizes disparate notes into comprehensive reference articles equipped with verifiable inline citations back to original source materials. Integrated conversational agents leverage retrieved knowledge context to answer research questions, surface contradictions across past entries, and draft structured daily briefings. External AI tools can read, search, and update stored knowledge through an embedded Model Context Protocol server endpoint. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Large language models get a polished front end that can run fully offline: Open WebUI is the self-hosted front end of choice. It talks to local model runners, primarily Ollama, and to any OpenAI-compatible API, so LM Studio, vLLM, Groq, Mistral, OpenRouter, and cloud providers all plug into the same chat interface and can be mixed per conversation. RAG is built in: upload files to knowledge bases or reference them in chat with the # command, backed by a choice of nine vector databases (ChromaDB and PGVector officially maintained) and multiple extraction engines including Tika and Docling, with hybrid BM25-plus-vector search and cross-encoder reranking. Web search results from providers like SearXNG, Brave, and Tavily inject directly into conversations. Extensibility comes from Python tools and functions that run inside the chat, a Pipelines plugin framework, and native MCP support. Multi-user features include RBAC, SSO, and group permissions, and the instance itself exposes an OpenAI-compatible API your own apps can call.

Transform complex code repositories into searchable, interactive wikis with DeepWiki, an automated documentation platform that maps architectural relationships and provides instant answers to technical questions about any codebase. Software engineers can submit public or private repositories from GitHub, GitLab, or Bitbucket to generate comprehensive project overviews, component breakdowns, and interactive Mermaid diagrams illustrating data pipelines and class hierarchies. The built-in Ask interface allows developers to query system logic using retrieval-augmented answers grounded directly in repository source files. Teams can trigger iterative deep research sessions that perform multi-step codebase investigations to explain intricate execution paths, uncover hidden dependencies, and summarize complex algorithms across hundreds of files. Interactive Codemap tours provide guided walkthroughs of critical entry points, letting reviewers inspect synchronized code viewer panels with exact line references while onboarding to unfamiliar services. Developers can export complete wiki structures for offline sharing, customize documentation depth from high-level summaries to granular module analyses, and connect local or cloud model endpoints to process sensitive internal code securely. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Most wikis die of flat page-and-tag sprawl; BookStack's defining decision is enforced structure - an MIT-licensed PHP/Laravel platform (over MySQL) where content lives in a hierarchy of shelves, books, chapters, and pages, the way a physical library works. A shelf maps to a department, a book to "Engineering Runbooks," a chapter to "Database Procedures," a page to the actual document. That opinionation removes the "where does this go?" friction and keeps knowledge bases tidy as they grow. Editing works both ways: a clean WYSIWYG editor for most users, a Markdown editor with live preview for those who prefer it - switchable per page. Full-text search spans all books or scopes to one, with direct links to individual paragraphs, and include tags let you embed one page's content inside another so shared blocks update everywhere at once. Every edit creates a diffable, revertible revision. Page templates standardize recurring formats, tags add cross-cutting categorization, and built-in diagrams.net integration draws architecture diagrams in place. Authentication covers email/password plus OIDC, SAML2, LDAP, and social login; a full role and permission system locks content down per shelf, book, or page. Pages and books export to PDF, HTML, plain text, and Markdown, a REST API automates content, and the whole thing runs happily on the cheapest VPS you can find.

Confluence and Notion both want your team's documentation in their cloud; Docmost, an open-source collaborative wiki platform, keeps it on your server. The centerpiece is a Notion-style block editor with CRDT-based real-time collaboration: multiple people edit the same page simultaneously and changes merge without conflicts or overwrites. Content lives in spaces - per team, project, or department - with nested page trees, role-based permissions, groups, inline comments, page history with restore, and full-text search across everything. The editor covers tables, code blocks, callouts, KaTeX math, and file attachments, and diagramming is built in rather than bolted on: Mermaid, Draw.io, and Excalidraw all render inside pages, alongside embeds for Airtable, Loom, Miro, and more. Migration paths include Notion, Markdown, HTML, and ZIP archive imports (Confluence, PDF, and DOCX importers ship in the Enterprise edition, along with SSO via SAML/OIDC/LDAP and MFA). The stack is TypeScript with PostgreSQL and Redis, deploys via Docker Compose, runs in air-gapped environments with no external dependencies, and is translated into 10+ languages. The AGPL-3.0 community edition carries no per-seat fees; the project has passed 20,000 GitHub stars since its 2024 launch.