Ask for a recycling reminder from your phone on Sunday and have a research report waiting by Monday, without handing your data to a subscription service. PocketPaw is a personal agent you host yourself, reachable from the browser dashboard or the chat apps you already use, including Discord, Slack, WhatsApp, Telegram, Signal, Matrix, Teams, and Google Chat. Pick a backend for the work: Claude Agent SDK, OpenAI Agents, Google ADK, Codex CLI, OpenCode, or Copilot SDK, then point it at a cloud key or a local Ollama model. Over fifty tools cover browsing, web search, files, shell commands, OCR, voice, image generation, and delegation to helper agents. Reminders and scheduled intentions run on their own, and memory keeps long-term facts and compacts old sessions. Identity files shape the agent's tone and instructions, and a skills library adds new slash commands. A guardian model reviews tool calls, with injection scanning, plan mode approvals, tool profiles, and an audit log for oversight. Analytics show cost, latency, and traces against monthly budget caps. Memory, tool logs, and cost traces all live on your own server. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Your team's group chat could have a colleague who has read every email, ticket, and document, remembers all of it, and answers in seconds; MindRoom puts AI agents like that into your chat rooms as full participants. Each agent is a real user on the open Matrix protocol, so you talk to it in MindRoom Chat on web or mobile, in any other Matrix client, or through bridges in Slack, Telegram, WhatsApp, and Discord. Give every agent its own model: a local model for private rooms, a frontier model for hard coding problems, and switch per room or thread whenever the work changes. Agents connect to more than 100 tools, including Gmail, calendars, GitHub, Google Drive, Home Assistant, and any MCP server, and they search your own documents and knowledge bases for answers with sources. Memory persists across conversations and platforms, so an agent remembers people, preferences, and project context months later and can turn repeated work into reusable skills. Before an agent sends an email or runs a command, an approval card shows exactly what will happen; you tap once to allow it, and sandboxed workers keep executed code away from your credentials. Schedule morning briefings, silent inbox checks, and recurring digests in plain language, or wake an agent from any other system with a single HTTP request. 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.

CowAgent is a personal AI agent that plans a task, picks the right tool, and keeps working until the job is done, all from a browser console on your own server. Open the chat page to hand it a request, and watch it read files, run terminal commands, search the web, and report back. Build a team of agents, each with its own role, model, skills, and knowledge, then have them collaborate in one shared conversation. Memory is layered into context, daily notes, and core facts, so the agent gets to know you over time, and a knowledge base organizes saved articles and documents into a browsable wiki. Install skills from a hub, from GitHub, or by describing one in plain language, and connect MCP tools through a form or pasted JSON. Provider credentials for chat, vision, image generation, speech, and embeddings live in one Models page, so each capability can use a different vendor. Connect Telegram, Slack, Discord, WeChat, Feishu, DingTalk, or WeCom and schedule recurring tasks. An OpenAI-compatible Chat API and shareable guest pages put the agent in front of other people. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Meet an AI assistant that lives on your own server, remembers who you are, and keeps working after you close the tab. ShibaClaw opens with a four-step onboarding wizard for provider, credentials, and model, then drops you into a chat workspace with agent profiles for building, planning, reviewing, administering, and security work. Long-term memory lives in editable files, a dream diary records what the agent learned, and a Library view maps how profiles relate to each other. Agents call file, shell, and web tools, plug into external MCP servers, and hand work to isolated or forked sub-agents. The Automations page schedules one-shot, interval, or cron jobs like a weekday morning support brief, and a plugin catalog adds services like GitHub, Notion, PostgreSQL, and Google Workspace in one click. An optional knowledge base searches your own documents, and channel integrations reach the agent from Discord, Telegram, Slack, and Matrix. Provider choices span OpenRouter, Anthropic, OpenAI, Gemini, DeepSeek, Groq, Ollama, and any OpenAI-compatible endpoint. A credential vault encrypts keys, and file tools stay inside a restricted workspace. Anyone who wants research, drafting, and recurring chores handled by an assistant they host themselves will feel at home. 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.

Picture a small staff of AI agents clocking in on your own server, each with a name, a specialty, its own model, and a private Linux desktop it can click around in. Every agent works inside a disposable Docker workstation with a shell, persistent files, and a Chromium browser, so it can research the web, write and run code, and finish multi-step tasks without touching your own files. You watch an agent click and type in real time, and when a login, payment, or one-time code needs a human, you take over the desktop, then hand it back with a note and let it continue. Pair a Telegram bot by scanning a QR code and you can assign work, answer questions, and receive results from your phone, with no public URL or port forwarding. Routines turn recurring requests into schedules, so a morning briefing, a price watch, or a weekly report runs by itself and reports back when it is done. Each agent can use a different model, from OpenAI, Anthropic, Google, xAI, Groq, Mistral, or DeepSeek to a local Ollama endpoint, and every reply shows its cost while a monthly budget pauses paid models automatically. Agents remember what matters about you across every thread, and logins or cards you save sit in an encrypted vault the agents themselves never see. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

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.

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.

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.

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 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.

In seven wizard steps, PandaWiki sets up a product docs, manual, FAQ, or blog site whose built-in AI answers visitors using only your own content. Those visitors can chat with an assistant that cites its sources and accepts image questions, or switch to a docs-only search that lists ranked pages with summaries. Publishing stays under control: each document has three switches, visible in navigation, reachable by URL, and answerable by AI, and nothing appears or gets learned until it is published. Authors write in a Markdown and HTML editor, or import from URLs, RSS and Atom feeds, sitemaps, offline .txt, .md, .xlsx, .docx, and .pdf files, Notion, Yuque, SiYuan, Wiki.js, Feishu, Confluence, and DingTalk. Pages export to Word, PDF, or Markdown. The wizard covers models, domain, HTTP or HTTPS ports, import, publishing, answer testing, and page decoration. One knowledge base can also feed a web widget and DingTalk, Feishu, WeCom, and Discord bots. Administrators get a dashboard of visits, questions, visitor regions, hot documents, and referring domains. Chat models come from Baizhi Cloud, DeepSeek, OpenAI, or Ollama, while vector and reranking models are built in. The open-source edition covers one wiki site, 300 documents, and one admin, and its console and documentation are in Chinese. 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.

A personal AI assistant that remembers what it learns and reaches you wherever you are — OpenClaw is an open-source agent gateway built by the OpenClaw Foundation with 247,000+ GitHub stars. It connects to 200+ LLM models through providers like Anthropic, OpenRouter, and OpenAI, and meets you on 21+ messaging channels: Telegram, Slack, Discord, WhatsApp, Signal, iMessage, Matrix, and more. Persistent memory with full-text search lets the agent recall context across sessions, and a self-improving skills system means it gets more capable the longer it runs. Voice wake words and talk mode enable hands-free interaction on macOS, iOS, and Android. A live canvas provides an agent-driven visual workspace. Built-in browser automation, cron scheduling for unattended tasks, and subagent spawning for parallel workstreams round out the toolset. The gateway architecture keeps all sessions, credentials, and conversation history on your own server — nothing transits a third-party cloud unless you choose to connect one. The API key you provide for your chosen LLM provider powers the underlying calls; billing goes through your own account. Running on a dedicated VPS with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.