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.

Turn a plain business request into a finished workflow, with a human sign-off on anything risky. CUGA is an agent harness you run on your own infrastructure, with a browser workspace where you build agents, test them beside a draft chat, and publish a versioned configuration when it behaves. Connect tools through MCP servers or OpenAPI specs, then write five kinds of policy: intent guards, playbooks, tool guides, tool approvals, and output formatters. A supervisor agent can route work to specialists that each carry their own tools and instructions. The chat workspace keeps conversation threads, uploaded files, and a knowledge panel for cited answers from company documents. Event Studio runs watchers on cron schedules, polling, or push triggers, with run history showing tools used and results. Memory records task guidance and user preferences, shows where each came from, and lets users switch it off or forget items, and administrators set retention rules. OIDC sign-in separates chat users from managers. Point it at any OpenAI-compatible endpoint, including a private inference server. Operations teams, internal tooling groups, and platform engineers can automate CRM reports, document review, and recurring briefs without sending data outside their boundary. 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.

FastGPT lets you build production AI agents and knowledge base chatbots through a visual drag-and-drop workflow editor, connecting any LLM provider to your documents with retrieval-augmented generation that cites sources and reduces hallucination. The workflow canvas chains LLM calls, conditional branching, HTTP requests, code sandbox execution, and plugin nodes into complex conversation flows and agent skill pipelines without writing backend code. The knowledge base engine ingests documents in ten formats (TXT, Markdown, HTML, PDF, DOCX, PPTX, CSV, XLSX, URL scraping, and CSV batch import) then applies automatic chunking, hybrid vector retrieval with semantic reranking, and QA-pair splitting to deliver accurate, citation-backed answers. FastGPT connects to virtually any LLM provider through its AI Proxy aggregation layer: OpenAI GPT-4o, Anthropic Claude, Google Gemini, DeepSeek, Qwen, ERNIE Bot, and models hosted via Ollama all work through a unified OpenAI-compatible API. Bidirectional MCP support enables agents to call external tools and expose their own capabilities to other systems. Completed applications can be shared via login-free links, embedded as iframe widgets, or integrated with WeCom, Lark, DingTalk, and WeChat Official Accounts through the published REST API. Application operation logs, conversation annotation, and per-model usage analytics provide full lifecycle governance for compliance-sensitive deployments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. FastGPT Open Source License (Apache 2.0 based) 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.

Every major model provider behind one ChatGPT-style interface: LibreChat spans OpenAI, Anthropic, Google, Azure, AWS Bedrock, Vertex AI, Groq, Mistral, OpenRouter, DeepSeek, and any OpenAI-compatible endpoint including local Ollama. You can switch models mid-conversation and compare providers without changing tools. Its Agents framework builds no-code custom assistants with tool access via Model Context Protocol servers, file search over uploaded documents through an optional pgvector-backed RAG service, and a sandboxed Code Interpreter that executes Python, JavaScript, Go, C++, Java, PHP, and Rust. Artifacts render React components, HTML, and Mermaid diagrams directly in chat, and image generation works through DALL-E and other configured providers. Multi-user support is enterprise-grade, with OAuth, SAML, LDAP, and two-factor authentication, per-user conversation history in MongoDB, and Meilisearch-powered search across all messages and files, plus reusable presets, forkable threads, and persistent memory across conversations. The economics favor teams: instead of a ChatGPT Plus seat per person, everyone shares one instance billed per API token, with access to every provider rather than one - and providers see individual API calls, not your accumulated organizational knowledge. Deployment is Docker Compose; API keys and endpoints are configured through .env and librechat.yaml.

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.

Every AI project you build in RESTai becomes an endpoint that any app can call over plain HTTP. RESTai gives you a browser dashboard where each project gets its own model, system prompt, tools, and chat playground. Build knowledge-base assistants over uploaded files and synced web pages, ReAct-style agents that call MCP servers, or drag-and-drop block projects that need no model at all. Connect Ollama, OpenAI, Anthropic, Gemini, vLLM, Azure, Bedrock, or any OpenAI-compatible endpoint, and let teams share the ones you approve. Daily charts follow tokens, cost, and latency per project, with rate limits, guardrails, prompt version history, and evaluation runs for quality tracking. Teams, roles, two-factor sign-in, white-label branding, and an audit trail cover administration. A one-line chat widget embeds a project on any website, and connectors reach Telegram, Slack, and WhatsApp. One install can serve several branded assistants and bill each team separately. 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.

Casibase lets organizations build AI-powered knowledge bases that answer questions from their own documents, connecting to 30+ model providers through a unified admin interface with RAG retrieval and multi-agent orchestration via MCP and A2A protocols. The platform plugs into OpenAI GPT-4o, Anthropic Claude, Meta Llama, Google Gemini, DeepSeek, Ollama local models, HuggingFace, Azure OpenAI, and additional providers, while embedding APIs from OpenAI Ada and Baidu handle vector representation of ingested documents. Document ingestion parses TXT, Markdown, DOCX, PDF, CSV, XLSX, and PPTX files with intelligent chunking strategies for optimal retrieval accuracy. The built-in chat interface provides real-time AI conversations with manual session handover for human agent escalation, and comprehensive chat session logging enables audit trails for compliance. Enterprise identity management integrates Casdoor for Single Sign-On supporting GitHub, Google, WeChat, and OIDC providers with fine-grained access control via the Casbin permission engine. The multi-tenant architecture supports isolated knowledge bases per organization with role-based user management and configurable storage, model, and embedding providers per tenant. The React frontend with Ant Design v5 provides a polished admin dashboard for managing providers, knowledge stores, chat sessions, and user access, while the Go backend with Beego framework handles API logic with MySQL or MariaDB persistence. 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.

When a knowledge base gives a wrong answer, MimirQ shows which stage let it down. Documents move through a pipeline that teams can open at every step: parsing, cleaning, chunking, indexing, retrieval, reranking, and cited answers. A dataset catalog tracks documents, chunks, and ingestion status, and an execution monitor charts throughput, retries, parser queues, and run logs. The governance workbench previews each file, scores quality, and supports cleaning and labeling before chunks are cut by headings, records, or parent-child structure. Retrieval blends keyword and vector search with reranking, and every reply lists its source evidence with feedback scoring. A graph view maps entities, events, and relationships on one canvas for exploration. Golden-set evaluation stores standard questions and answers, runs them against the current pipeline, and reports recall and ranking metrics, so a release can be blocked when quality slips. Settings pages manage model providers, parsers, and retrieval options, and a Dify-compatible external knowledge API lets existing workflows call it. Role-based access and document permissions keep restricted material hidden from the wrong users. 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.

Turn scattered meeting notes, policies, postmortems, and documents into a readable wiki that your team can question in plain language. Paste text, upload a file, or point KaaS at a web page, and an AI pipeline pulls out the concepts, decisions, and people, then writes them up as Markdown articles sorted into decision, reference, guide, and concept folders. Ask a question in chat and the answer cites the exact articles it came from, so anyone can open the source and check it. Each article shows tags, a source count, and a table of contents, and every page is stored as a plain Markdown file you can read, edit by hand, or commit to version control. Adding one new document only recompiles that document, which keeps model costs predictable as the library grows. A Builds page lists every submission with its status and attempts, and a failed worker's job is picked up again instead of lost. Derive a topic knowledge base to carve a focused collection out of the wiki for one subject, and let coding agents query the same wiki through an MCP ask tool. Team handbooks, customer onboarding, incident history, and pricing rules are the knowledge that ends up scattered across a dozen files, and this is where it comes back together. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Document review, fixed-layout report generation, and multi-step research that ends in a PPT, Excel, or Word file all run inside BISHENG, a self-hosted workspace for enterprise AI applications. Review and report flows are drawn on a visual canvas where a circle becomes a loop, side-by-side nodes run in parallel, and a multi-item selection runs as a batch. A running flow can stop for a person to give input, confirm, or make changes, and chat and automation share one flow type. Research goes to Task Mode: give it a goal plus any files, knowledge bases, tools, and skills, and it writes a checklist, hands research to parallel sub-agents, and returns a report with clickable citation markers. Teams sharing documents use Knowledge Spaces, with folders, tags, keyword search, AI Q&A that shows its sources, and permissions down to a single file for people, departments, or user groups. For a daily news briefing, Channels turn WeChat official accounts and websites into topic feeds and save articles as knowledge in one click. Admins connect OpenAI-compatible APIs, Azure OpenAI, Anthropic, Qwen, DeepSeek, and local Ollama models, then govern access with approvals, audit logs, SSO, and LDAP. Private document parsing reads printed text, handwriting, tables, layouts, and seals, and a service-account Open API lets other systems call applications and knowledge bases. 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.

Dify turns the notoriously complex process of building production-grade AI applications into a visual drag-and-drop experience that teams can actually ship and maintain. With over 87,000 GitHub stars and backing from prominent investors, the platform has become the go-to open-source LLMOps solution for organizations that refuse to be locked into proprietary AI stacks. The visual workflow canvas lets developers wire together LLM calls, conditional logic, iteration loops, tool invocations, and human-in-the-loop checkpoints without writing boilerplate integration code. Its RAG pipeline engine handles the full document lifecycle from ingestion of PDFs, Word documents, and HTML through configurable chunking strategies, embedding with models from OpenAI or open-source alternatives, vector storage in Weaviate, Qdrant, Pinecone, or pgvector, and hybrid semantic-plus-keyword retrieval with citation tracking. Dify integrates with hundreds of model providers including OpenAI GPT-4o, Anthropic Claude, Google Gemini, Mistral, Llama, and any OpenAI-compatible endpoint like Ollama for fully local inference. The agent framework supports both ReAct and function-calling strategies with 50-plus built-in tools spanning Google Search, DALL-E, Stable Diffusion, WolframAlpha, and custom API definitions. Published apps can be deployed as hosted web interfaces, embedded chat widgets, REST API endpoints, or MCP-compatible tools. Enterprise features include role-based access control, SSO integration, and audit logging. A built-in marketplace enables teams to share and reuse model providers, tools, and workflow templates across projects. 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 with an open-source community edition.

Libre WebUI is a self-hosted AI workspace that keeps chat, notes, personas, and agent tasks in one place you control. Local models run through Ollama, and hosted ones come from OpenAI, Anthropic, Gemini, Groq, Mistral, OpenRouter, or any OpenAI-compatible endpoint added on the provider settings page. Pick a model per conversation. Replies stream, prompts queue, chats fork into branches, and several models can answer side by side. Notes keep Markdown documents with revision history. Personas bundle a system prompt, a model, and memory into a reusable assistant, and prompt templates with typed variables appear when you type a slash in the composer. Knowledge collections ground answers in uploaded documents and cite their sources. The Work area gives each task its own workspace with files, a terminal, diffs, and a live preview, and automations run scheduled prompts that arrive as normal chats. Administrators create users and groups, hand out roles, issue scoped API tokens, and share chats, notes, and personas through one grant model. A private team assistant, a research desk, or a sandbox where agents build small apps all fit here. 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.

A private ChatGPT built with Next.js: Lobe Chat is the open-source AI chat interface teams self-host instead. Its main advantage is provider breadth: one interface connects to 40+ model providers, including OpenAI, Anthropic Claude, Google Gemini, Mistral, Groq, AWS Bedrock, Azure, and local models served through Ollama, so you can switch models per conversation and compare outputs. It handles multi-modal work: image recognition, image generation, text-to-speech, and speech-to-text. A plugin system based on function calling and the Model Context Protocol (MCP) adds external tools like web search and code execution. Run it in standalone mode as a single container with settings in browser storage, or in database mode with PostgreSQL and S3-compatible storage for persistent history, multi-user auth, and RAG knowledge bases built from uploaded documents with pgvector retrieval. Because tools arrive through function calling and MCP rather than a proprietary plugin format, custom internal tools can be exposed to the assistant with a standard server over STDIO or HTTP. Hundreds of pre-configured assistant roles import from the community marketplace. For teams the cost model matters: provider API keys billed per token typically undercut a ChatGPT Plus seat per person, and self-hosting keeps API keys, uploaded files, embeddings, and conversation history entirely on your own server.

MateClaw delivers a multi-agent AI platform where digital employees run as persistent team members with roles, goals, and accumulated skills rather than stateless chat completions. The Spring Boot backend on Spring AI Alibaba provides ReAct iterative reasoning and Plan-and-Execute decomposition on a StateGraph runtime, with parallel delegation between employees and dynamic context pruning for multi-step tasks. Five career templates ship ready (Product Researcher, Customer Support, Knowledge Curator, Data Analyst, Executive Assistant) while custom employees inherit configurable backstories, pixel-art avatars, and dedicated tool bindings. The MCP integration supports stdio, SSE, and Streamable HTTP transports with per-employee tool isolation preventing capability bleed between agents. ACP bridges bring Claude Code, Codex, and other coding agents in as first-class employees. Workflow orchestration composes multiple employees and system actions into publishable linear DSL processes with seven step modes: sequential, fan_out, collect, conditional, await_approval, dispatch_channel, and write_memory. The trigger system wires cron schedules, webhooks, channel messages, employee lifecycle events, content matches, and workflow completions to automated flows. The Admin Runtime Console provides real-time visibility into running employees with token usage tracking and one-click force-recycle. Spring Boot Actuator monitoring, full audit trail, and per-channel error isolation deliver production-grade reliability. 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.

With over 7,700 GitHub stars and thousands of Cloudflare employees using it daily across every function, Cloudflare OS delivers an open-source AI workspace where every employee gets a personal agent grounded in company context, systems, and skills — not a generic chatbot but a programmable workspace that builds real applications, automates workflows, and connects to internal tools through governed access. The Code Mode agent writes and immediately executes code snippets to perform arbitrary tasks, build full-stack Gadgets with client code, server code, APIs, and durable SQLite state, debug errors, and test results within isolated sandboxes. Gadgets are private application instances running in separate sandboxes — each document, spreadsheet, or tool is its own secure runtime that cannot leak data even to attackers with access to other Gadgets. Blueprints enable sharing application code as templates that others instantiate with independent state, credentials, and resources. Gatekeepers provide security governance giving system owners precise control over what agents can see, change, and when human approval is required before actions execute. Built on Cloudflare Workers using Durable Objects for workspace persistence, Dynamic Workers for Gadget execution, and Facets for access management. Zero Trust security via Cloudflare Access verifies every user and request before granting access. Real-time collaboration lets colleagues use shared Gadgets. Deploy to your own Cloudflare account or self-host on workerd, the open-source Workers runtime, on your own servers. 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.

Give your team a shared chat workspace where AI bots sit next to people, answer questions, and pick up recurring work. Organize conversations into pods with channels and threads, and keep a private personal pod for each member. Mention a bot with @ to hand it a task, mention several and each one replies, or let a bot ask another bot for help. Every bot gets its own name, avatar, instructions, and model, picked from OpenRouter, Anthropic, OpenAI, Google Gemini, or any OpenAI-compatible endpoint you add. Connections link a pod to GitHub, Linear, Stripe, Notion, Sentry, Jira, any MCP server, or any HTTP API, and bots act through them with approvals where you want a human check. Routines run a bot on a schedule, a webhook, or a GitHub event, then post the result to the pod channel. Computers give bots an isolated machine for heavier jobs, and web search lets them look things up and cite pages. A Usage page breaks down what bots cost by bot, model, and pod. Use it for a support desk, an engineering ops room, a morning briefing, or a shared research assistant. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.