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.

Describe the task in plain words, pick the sources and model it may use, and an agent plans the workflow before running it. That is the core loop: agents build editable visual workflows from a task description, execute them on demand or on a schedule, and keep everything traceable. Prefer building by hand and the canvas offers 17 preconfigured steps, branching, undo and redo, JSON import and export, plus eight starter templates. Feed it real work by importing PDF, DOCX, XLSX, CSV, JSON, and text files, or by connecting folders, HTTP endpoints, and approved local programs. Upload a screen recording and it identifies the actions with timestamps, turning a demonstrated process into an editable workflow you can refine. Model calls go to the endpoint you choose: Ollama, LM Studio, vLLM, or any OpenAI-compatible service, local or cloud with your own credentials. Every run can be inspected step by step, cancelled mid-flight, and exported as JSON, CSV, or Markdown reports. Observations from completed runs land in a knowledge and learnings store, where you review them and promote the useful ones into reusable skills for the next task. The Docker release bundles the application with FFmpeg and local Whisper support, and your documents stay on your own machine. 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.

DopplerTask is the control layer where AI agents build your automations and you decide what goes live. You describe the outcome in plain language, and your agent assembles a task from a library of eighty-one ready-made steps spanning messaging, ticketing, sales, operations, documents, databases, queues, and AI models. The proposal lands in a visual editor that people and agents share, with new and changed steps marked and the old value of each field beside the new one. Nothing goes live until you approve it, and the previously approved version keeps running untouched in the meantime. Once a task is live it can start from a webhook, a schedule, a message queue, a manual click, or the agent itself, and every run is recorded with the output of each step, the person who started it, and the person who approved the version. Failed runs resume from the step that failed, with retries and continue-on-failure where you want them. Agents reference your credentials by name only; the values stay encrypted on your server, beyond the agent's reach, and separate tokens for people and agents plus single sign-on keep access tidy. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Nakama gives your whole team a shared home for AI agents, where each agent has its own identity, memory, tools, and permissions and meets people where they already talk. The dashboard feels like a familiar chat app: pick an agent from the rail, start a conversation, and watch it use tools, recall memory, and reply inside Telegram, WhatsApp, Discord, or Slack when the work lives there. Every agent is configured as a small personality you can read and edit, with prompt files for its soul, style, instructions, and memory, plus tabs for its channels, tools, MCP servers, and skills. One server hosts many organizations with invites and roles, and agents stay isolated from each other so the support agent never wanders into the finance agent's toolbox. The control center manages workspace settings, model providers, plugins, and integrations, and a connector layer opens the door to more than a thousand external apps. Daily encrypted backups and an in-app update button keep operations boring, and the entire platform idles in about two hundred megabytes inside a single container. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Backed by $50M+ from Benchmark, Y Combinator, and NVIDIA with 800+ contributors and 39,000+ GitHub stars, MindsHub Cowork is the unified AI workspace where open-source models handle entire projects — research, reporting, internal tools, scheduled operations — and return finished, shareable deliverables. The platform runs two interchangeable open-source agent harnesses, Anton and Hermes, swappable from a dropdown without losing context. A built-in Model Router pre-wires 25+ models spanning Anthropic Claude, OpenAI GPT, Google Gemini, DeepSeek, Qwen, Kimi, Grok, and MindsHub Air with automatic failover — no per-provider API keys required. A secure credentials vault connects BigQuery, PostgreSQL, Salesforce, HubSpot, Zendesk, Gong, Gmail, Google Drive, Notion, Linear, Stripe, and Slack, keeping secrets scoped per connection so agents never see raw keys. Agent output becomes publishable artifacts — documents, dashboards, apps, and code — each deployable to a live shareable URL. Cross-session persistent memory, a reusable skill library, and a background scheduler supporting hourly, daily, and weekly cadences enable autonomous recurring workflows. The architecture separates a React/Vite frontend (shipping as both Electron desktop app and web SPA) from a FastAPI backend with a versioned REST API at /api/v1 covering conversations, projects, artifacts, schedules, and connectors. Self-host via Docker Compose with nginx on port 3000 and the API on port 26866, or deploy on-prem, in a VPC, or air-gapped. 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 AI chats forget you by tomorrow; Hivekeep keeps a team of agents on your own server that remember everything, work together, and answer wherever you already talk. Each agent has a name, a role, and one continuous conversation that never resets, backed by long-term memory it can search. Agents hand tasks to one another, spawn helpers, and run scheduled jobs. Queenie, the built-in setup agent, connects providers and creates your first teammates through plain chat. They reach you on Telegram, Slack, Discord, WhatsApp, Signal, Matrix, or the installable web app, and a channel can move from one agent to another mid-conversation. Agents write custom tools, build mini-apps that open beside the chat, edit files in a workspace with a code editor, and add plugins from a marketplace. Credentials go straight into an AES-256-GCM vault that the model never reads. A context viewer and a token-usage dashboard show what each prompt contains and what each agent, model, and day costs. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Bridging local hardware and cloud intelligence, OwnPilot orchestrates autonomous multi-channel agent swarms and visual workflow pipelines while keeping sensitive personal records strictly on private infrastructure. Users can trigger autonomous task runs that browse websites using headless browser automation, extract data tables, and summarize research directly into encrypted markdown notebooks. The built-in workflow canvas allows individuals to construct branching automation sequences combining large language models, conditional logic gates, iterative data loops, and human-in-the-loop approvals. Users delegate calendar scheduling, task tracking, expense budgeting, and contact lookups through conversational chats conducted over web portals, Telegram bots, or WhatsApp numbers. Operators can dispatch coding agents to inspect local repositories, execute shell commands in sandboxed environments, and stream live terminal logs as automated patches develop. The platform also enables home automation enthusiasts to monitor MQTT telemetric feeds from microcontrollers, queue device commands, and receive instant alert notifications when sensor thresholds trigger. Advanced security layers inspect all incoming prompts for personal data patterns, enforce strict permission boundaries across file directories, and generate tamper-evident audit logs for every autonomous tool call. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

NextClaw is an AI workspace where a request for a report, a chart, or a small tool becomes organized research, files, and steps, with the result shown beside the conversation. Pick the engine for each task from Native, Claude Code, Codex, Hermes, or OpenCode, and the agent keeps its memory, skills, and workspace. A right-hand browser opens code, Markdown, HTML, Word, Excel, and PowerPoint files without losing the task that produced them. Connect providers like OpenRouter, OpenAI, Anthropic, Gemini, DeepSeek, or any OpenAI-compatible endpoint, and message the same agents from Telegram, Discord, Slack, WhatsApp, Feishu, or email. Scheduled tasks start from templates like a daily AI news brief or a competitor change tracker, and finished reports land in an AI Inbox you can read later or continue as a chat. A skill market installs new abilities, MCP servers extend tools, and Panel Apps keep the small pages an agent builds for you. Dedicated agents get their own role, home directory, and memory. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Modern engineering teams and busy households deploy Octop to run private, autonomous AI agents equipped with persistent memory workspaces, scheduled cron jobs, and direct browser automation. Users can orchestrate specialist agents tailored for software development, IT operations, content generation, and system diagnostics through an interactive React dashboard. The platform connects directly to Discord, Feishu, DingTalk, and WeCom, allowing team members to delegate complex tasks without leaving their everyday messaging apps. An integrated remote desktop and browser control engine lets agents navigate websites, capture screenshots, fill forms, and operate graphical software autonomously. Administrators can assign distinct MBTI personality profiles to agents, establish granular role-based permissions, configure scheduled cron workflows, and integrate custom Model Context Protocol servers for external tool access. Long-term memory persists across conversations through dedicated workspace files, ensuring contextual continuity whenever switching between underlying language models or team collaborators. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Chat, research, and everyday chores all run through OpenAgent, a private AI assistant hosted on a server you control. Pick from more than thirty model providers, including OpenAI, Anthropic, Gemini, DeepSeek, and local Ollama models, and switch models inside a conversation. Agents can drive a real browser, search the web, run shell commands, read and write Word, Excel, and PowerPoint files, and call any MCP server. Every tool call shows its arguments and result. Upload PDFs and spreadsheets to build isolated knowledge bases with semantic search, then attach them to a chat. A drag and drop workflow builder chains steps with branches, parallel tasks, and schedules. The admin console tracks token use and cost per user and model with charts and a message heatmap, lists every tool, records audit logs, and shows live CPU, memory, and API latency. Single sign-on through OIDC, OAuth2, LDAP, or SAML and separate workspaces keep teams apart, and a REST API with Swagger docs exposes every feature. 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.

Platypus gives a whole team always-on AI agents that reason, call tools, and keep working on your own servers while everyone is offline. Build an agent once by choosing a provider and model, writing its instructions, and capping its tool-calling loop, then reuse that preset across chats and scheduled runs. Agents load reusable Skills on demand and delegate specialist work to sub-agents that appear to the parent as tools. Connect local and remote data through Model Context Protocol servers, or expose an agent to outside agents and chat platforms through A2A endpoints with a token per client. Triggers run agents on a cron schedule, as one-off jobs, or when workspace events such as a card moving on a board occur, and every run records its result. Kanban boards give agents work they can move and update themselves, while notifications and HMAC-signed webhooks push board and workspace events into your own systems with filtering and retries. Facts and preferences are extracted from conversations in the background and injected into future chats, so returning users are remembered. Sandbox backends give agents shell and filesystem access inside an isolated per-workspace environment instead of the host. Organizations and workspaces keep each team's agents and data separate, and blueprints package shared agents, skills, MCP servers, and providers so a new workspace starts pre-equipped. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Give an AI agent a price list and it drafts the quote; hand it an invoice and it checks the numbers against purchase and receiving records before anyone pays. Atom is a self-hosted AI agent platform for business automation that wraps every agent action in governance: permissions, ordered policies authored in a canvas editor, human approval checkpoints, and durable audit records. Work starts from your files and instructions, with demonstrated workflows that map unfamiliar spreadsheet columns, calculate against source records, and produce cited Word or PowerPoint documents you can inspect line by line. Corrections become reusable teaching: saved field mappings, commercial terms, and contextual lessons carry into new work, and each job keeps the accepted snapshot it started with. Questions, approvals, and waits survive restarts, completed operations are retained, and uncertain effects must be reconciled before anything retries, so long-running business processes do not silently double-act. Results keep provenance back to the source records and operations that produced them, and unresolved requirements block completion instead of being invented away. You bring the model: configure a hosted provider or a local OpenAI-compatible endpoint, route by capability and cost, and run everything in a single-tenant workspace with user, role, and workspace boundaries. A starter sample runs with no model or cloud key at all. 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.

Webhooks, cron schedules, and app events trigger chains of nodes that fetch, transform, and route data: n8n is a workflow automation platform built around a visual, node-based editor. It ships with 400+ built-in integrations covering databases like Postgres, SaaS tools like Slack and HubSpot, and every major AI provider. When a pre-built node does not exist, the HTTP Request node calls any REST API, and the Code node runs JavaScript or Python inline, so you are never blocked by a missing connector. Workflows execute as directed graphs with branching, loops, error handling, and sub-workflows, and every run is logged for inspection and replay during debugging. It also includes LangChain-based nodes for building AI agents with tool calling and memory. Self-hosting on RepoCloud gives you unlimited workflow executions with no per-task pricing, and all data stays on your instance. Runs on Node.js with SQLite by default; add Postgres and Redis queue mode when you need to scale workers horizontally.

Zapier's job, on your own server: Activepieces is an open-source workflow automation platform built to be exactly that replacement. Flows are built in a visual no-code editor with triggers, actions, loops, conditional branches, auto-retries, raw HTTP steps, and code steps that run JavaScript or TypeScript with full npm package support. Integrations are "pieces" - type-safe TypeScript npm packages with hot reloading for local development - and the catalog spans 600+ services, with the large majority contributed by the community. The platform is AI-first in two directions: native AI pieces call OpenAI, Anthropic, Google, and Azure models inside flows, and every piece automatically doubles as an MCP server, so assistants like Claude Desktop and Cursor can invoke your integrations and workflows through natural language. A built-in MCP server also exposes 30 tools for building flows, managing tables, and running tests agentically. Flows are fully versioned with draft and locked states. The core is MIT-licensed and runs on TypeScript with PostgreSQL and Redis.

With over 600 GitHub stars and a featured Show HN launch, Mission Control is the agent-first command center that replaces the chaos of manually shepherding AI agents with structured delegation, approval workflows, and autonomous execution. The Next.js 15 web UI delivers an Eisenhower priority matrix with drag-and-drop quadrants, a Kanban board tracking tasks through Not Started, In Progress, and Done columns, and a goal hierarchy with milestone progress bars — powered by shadcn/ui, Radix UI, and @dnd-kit. Six built-in agent roles — Researcher, Developer, Marketer, Business Analyst, Tester, and You — receive tasks through a token-optimized API compressing context by 92 percent to approximately 50 tokens versus 5,400 unfiltered. The autonomous daemon polls task queues on cron schedules, spawns Claude Code sessions via the official CLI, enforces concurrency limits, and auto-retries with loop detection that escalates to human decisions after three failures. Field Ops extends execution to 64 external services across 16 categories with working X, Ethereum with MetaMask signing, and Reddit adapters, protected by AES-256-GCM encrypted vault with scrypt key derivation, per-service and global spend limits, a circuit breaker, and three autonomy levels. All data lives in local JSON files with Zod validation and async-mutex locking ensuring safe concurrent writes, backed by 193 automated Vitest tests. 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.

OverClick is the task board where your AI agents clock in: you write the assignments, they claim the work, and you stamp what actually got done. A card here is a contract, not a ticket: what should happen, why it matters, and a plain-language test script that says how to confirm it, all written before anyone starts. You tell your agent to grab the next task, and it claims the card over MCP, declares the CLI and model it really runs on, receives a self-contained briefing with the mission context and branch convention, and the card slides into progress. When it finishes, the handoff lands with a summary, evidence, branch and pull request links, and measured telemetry, the tokens per model and minutes the run actually took, read from the session transcript rather than guessed. Every card keeps three roles straight, who asked, who executed, and who reviews, and only a human can mark it validated; reopening with a comment puts your note in front of the agent's next claim. Big decisions can start as RFC cards whose approval spawns the execution cards, and insights roll up what each project, mission, and model consumed. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

A personal to-do list that scales to team project management: Vikunja is an open-source task manager that grows with the work. Every project can be viewed four ways - classic list, drag-and-drop Kanban board, Gantt chart for timeline planning, and a spreadsheet-style table - so a project manager watches the Gantt while developers work the board, on the same data. Tasks carry the full attribute set: labels, priorities, due dates and reminders, recurring schedules, subtasks and task relations, file attachments, assignees, and comments, and Quick Add Magic parses natural-language input like dates and labels typed inline. Projects share with team members or via links, and saved filters slice tasks across projects. Built-in CalDAV support (VTODO) syncs tasks bidirectionally with clients like Thunderbird, DAVx5, and iOS Reminders, and one-click importers migrate from Todoist, Trello, and Microsoft To-Do. Nothing is metered - unlimited projects, tasks, filters, and attachments. A single Go binary with a Vue frontend runs against SQLite, MySQL, or PostgreSQL, exposes a full REST API, and works as an installable PWA on mobile. AGPLv3-licensed.