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.

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.

SuperPlane is an open-source engineering automation platform that turns routine backlog tickets into verified, review-ready pull requests while orchestrating complex multi-system workflows across your developer tools. Engineers use the visual infinite canvas to assemble event-driven pipelines with trigger nodes, conditional routing, loops, and human-in-the-loop approval gates. You can delegate pull request code reviews, automated QA testing, documentation generation, and release management to coding agents operating within strictly defined policy boundaries. Custom operational consoles let teams build live monitoring dashboards displaying key delivery metrics, active incident states, and execution histories from connected tools. Integrations connect natively with GitHub, GitLab, Datadog, PagerDuty, Slack, AWS, and leading AI models through webhook events and bi-directional actions. Every automation run maintains durable state persistence across process restarts, ensuring failed steps resume with full input payloads and debugging context intact. Developers can define reusable work orders, configure canvas memory for long-term state retention, and execute fleet commands over SSH or containerized runners. 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.

Agent Zero equips large language models with a persistent virtual Linux desktop, letting autonomous AI agents write code, research the web, and execute multistep tasks without supervision. Operating within an isolated container, the system opens an interactive XFCE graphical desktop in your browser canvas where the agent interacts with GUI applications like Blender and terminal sessions directly. Its embedded browser features interactive annotation tools, letting you click web elements to inspect stylesheet hierarchies, extract component markup, or submit automated testing instructions. You can cowork on spreadsheets, presentations, and Markdown specifications in real time alongside the agent, preserving document revisions through snapshot time travel. A multi-tier memory architecture backed by FAISS vector indexing and SearXNG web discovery retains problem-solving context across projects, while hierarchical delegation spins up specialized subordinate agents to parallelize complex engineering audits. The platform connects seamlessly to commercial LLMs via OpenAI, Anthropic, or OpenRouter, runs private local models through Ollama, and integrates custom toolkits via the Model Context Protocol and community Plugin Hub. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Pinchy gives every team its own AI assistants without handing any of them the keys to the building. Create an agent per role, pick a personality preset like the Butler or the Professor, and choose a model from Anthropic, OpenAI, Google, any OpenAI-compatible endpoint, or a local Ollama. Each agent starts with nothing allowed. You switch on memory, file creation, web search, email, and other tools one by one, and point knowledge base agents at read-only folders so answers arrive with the source document and page cited. A new user meets Smithers, an onboarding assistant that interviews them to learn how they work. Colleagues reach agents in the web chat or in Telegram, with one bot per agent and the same permissions behind both. Email connects to Gmail, Microsoft 365, or any IMAP mailbox for reading, searching, and drafting, and Odoo templates handle sales and invoice chores inside your ERP. Admins invite members, and a tamper-evident audit trail records who did what, with integrity checks and CSV export. 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.

Open Walnut replaces your scattered terminals and notes with one self-hosted command center where a CEO agent and a lead for each project run your Claude Code sessions like a small company. You talk to the CEO; each project lead plans the work, spawns worker sessions, checks their output, and only brings you the questions that actually need a person. Every session sits in its own column with live output, permission controls, terminals, and files, and sessions survive browser disconnects, dropped SSH connections, and server restarts. Codex, Gemini, OpenCode, and Goose sessions join the same columns, on your laptop or on remote dev boxes reached over SSH. Finished work comes back as a per-session diff: comment on individual lines and send the review straight back into the session that wrote it. Around the agents sit your tasks with subtasks, dependencies, and due dates, Markdown notes with wiki links and backlinks, a calendar, and long-term memory that a background job keeps consolidated. Search covers notes, tasks, sessions, and memory together, mixing keyword matching with local embeddings, so nothing you told it last month gets lost. Routines run one-time, interval, and cron jobs; plugins add whole apps, tools, and skills; and paired phones reach the same console through device tokens. 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, files, editor, and shell share one browser window, so nothing the agent does happens out of sight. PiClaw streams replies from the model provider you configure, hosted or a local OpenAI-compatible server, and you can steer a running turn, queue follow-ups, or branch a side question with /btw. The workspace sidebar shows a file tree with folder sizes. Open a Markdown note for a live preview with checklists and tables, a CSV for a table view, or a script in the code editor with syntax highlighting and an optional Vim mode. A detachable terminal runs shell commands in the same workspace, and the agent's own file and shell tools work there too. Scheduled tasks run on cron timings, chat history stays searchable between visits, and nightly Dream memory consolidates notes into plain files. Settings pages cover providers, models, MCP servers, keychain secrets, budgets, and session recordings. Optional add-ons bring Draw.io, kanban boards, and Proxmox or Portainer tools. Browser sign-in supports authenticator codes and passkeys, and the layout adapts to phones. A homelab server, a laptop, and a phone can all open the same private agent workspace, which suits solo developers and small teams. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Cotal puts your AI agents in one shared room: Claude Code, Codex, and OpenCode discover each other, message channels, hand off work, and ask you to approve the result. Post to a channel and every subscriber hears it; message one peer and the durable inbox holds it until they are free; address a role like reviewer and one available reviewer claims the job. Presence shows who is actually there, and attention modes decide which channel traffic may interrupt an agent mid-task. A manager starts and stops agent processes in tmux or cmux, here or on remote hosts you authorize, without sitting between their messages. For work that waits and branches, Cotal Lang programs launch agents, pause at human checkpoints with deadlines, and resume from their recorded steps after an interruption. Services join the same space by publishing command contracts that authorized agents can discover and invoke; the bundled Linear adapter shows the pattern. Supervise from a terminal console with the live roster and activity feed, or from the read-only browser dashboard and its graph of agents and channels. Spaces stay separate, every message carries a checked identity, and per-agent grants decide who may delegate, spawn teammates, or call a service. 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.

PullMD turns any URL or file you point it at into clean Markdown your AI agent can actually afford to read. Paste a web page and get back tidy body text with metadata tucked into frontmatter instead of navigation soup and cookie banners. Send it a Reddit or Hacker News thread and it returns the full comment tree with depth and count under your control. Hand it a PDF, Word file, PowerPoint, spreadsheet, or EPUB and it hands back Markdown, with an optional OCR tier for stubborn table-heavy documents. YouTube links come back as title, description, and a transcript with clickable timecodes, no API key required. The query-scoped mode answers a question about a long page by returning only the relevant sections, typically cutting 70 to 95 percent of the tokens. Every conversion gets a short share link that re-fetches the source when it goes stale and serves the last good snapshot if the original dies. Agents connect through the built-in MCP server with OAuth 2.1, a plain REST API, or a downloadable Claude Code skill, while you keep history, an archive, and cache statistics in the web app. 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.

DB-GPT lets a team talk to its data: ask a business question in plain English, and an AI agent plans the work, writes the SQL, runs it, and hands back charts and a written report. Connect MySQL, PostgreSQL, SQL Server, ClickHouse, DuckDB, Doris, StarRocks, or MaxCompute, or drop in CSV and Excel files, including several at once. Every step appears in a live task view with its query, result table, code, and console output, and the agent pauses to ask clarifying questions, with answer options, when the data cannot settle the matter. Reusable skills package proven procedures, including CSV analysis, SQL analysis, and financial report analysis, and a skill creator helps write new ones. Code executes in a sandbox, parallel sub-agents split larger jobs, and an agentic knowledge-base search answers questions over uploaded documents. Scheduled tasks run saved conversations on a cron timetable with on/off switches and execution history, while MCP connectors link tools like Notion, Linear, Tavily, and GitHub. Pick OpenAI, DeepSeek, Qwen, SiliconFlow, Ollama, or vLLM for the model, then use the application and AWEL workflow tools to build data apps of your own. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Lightpanda is a headless browser built for AI agents and automation that loads and runs web pages without ever drawing them, so crawling, scraping, and agent browsing finish far faster and lighter than in Chrome. You drive it with the tooling you already have: Puppeteer, Playwright, and Chromedp connect over the Chrome DevTools Protocol, Selenium works over WebDriver BiDi, and existing Chrome automation scripts keep running against the new endpoint. Scripts can execute JavaScript on real pages, wait for network activity, read and mutate the DOM, submit forms, and pull structured data out of dynamic sites that plain HTTP clients cannot see. An MCP server mode hands the browser to AI applications as a callable tool, so a model can open pages, follow links, and read content in the middle of an agent workflow. The built-in agent accepts plain-language browsing tasks and can export what it did as PandaScript, a replayable JavaScript file that runs later with no model call at runtime. A fetch command loads one or more URLs and prints the result as HTML or Markdown, which turns page extraction into a single shell step inside data pipelines. In the project's published crawl benchmark it processed pages roughly nine times faster than headless Chrome at a fraction of the memory, so one server can hold many more concurrent browsing sessions. 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 single static binary is all it takes to put a tireless AI agent on your server, ready to read your codebase, run shell commands, edit files, and report back through a web UI, a terminal console, your editor, or a Telegram chat. Start it as a server and the embedded web interface gives you streaming chat sessions with visible tool calls, session history, plan documents, a background task board, and a visual map of connected nodes. Point any OpenAI-compatible client at its HTTP API, or drive the same sessions from Zed, VS Code, or Obsidian through the built-in ACP editor server. Delegate bounded work to subagents that run in isolated contexts with narrowed permissions, then resume them later in their own sessions if a run stops short. Teach it your project once: rules are picked up from .coddy, .cursor, .claude, and .codex folders, AGENTS.md files are honored as work proceeds, and installable skill packs turn repeatable procedures into slash commands. Schedule recurring jobs as plain Markdown files on a cron timetable, keep long-term memory across conversations, and connect external tool servers over MCP globally or per workspace. A Prometheus exporter reports provider calls, tokens, and task health for the whole fleet. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Containarium gives every AI agent a persistent Linux box of its own, reachable over SSH and driven through structured tools instead of a fragile terminal session. One command creates a named sandbox, syncs the SSH configuration, and wires the box into Claude Code, Codex, Gemini or Cursor through an MCP server that runs inside the container. From there the agent can execute shell commands, read and write files, install services, and expose a port on a public hostname with TLS terminated by a Caddy sentinel. Detached code runs keep working after a laptop sleeps or a connection drops, and the operator reattaches to the same byte-exact log later, checks status, or stops the run without losing output. An optional per-tenant eBPF egress policy limits outbound traffic per box, logs denied attempts for audit, and keeps provider API keys inside a model gateway rather than inside the agent environment. Boxes run on LXC or Kubernetes, keep state between agent sessions, support GPU passthrough for local model work, and stay separate from the host operating system and the cluster control plane. The dashboard presents containers, traffic, security events, audit records, GPU state and monitoring in one place. 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.

Aurora is an open-source AI SRE agent that investigates production incidents on its own, turning each incoming alert into a structured root cause analysis with evidence and a suggested fix. Alerts arrive from PagerDuty, Datadog, Grafana, New Relic, OpsGenie, incident.io, or CloudWatch, and every one triggers a background investigation without waiting for a human to pick it up. The agents choose from more than thirty tools, run cloud and cluster commands inside sandboxed pods, query logs, compare deployments, and search a knowledge base of past incidents for similar failures. An infrastructure dependency graph shows the blast radius across services and providers. When the root cause is found in code, Aurora can draft the remediation as a pull request instead of stopping at a diagnosis. Finished investigations become postmortems with a timeline, impact assessment, and remediation steps, exportable to Confluence, Notion, or SharePoint, and automated actions can open the fix, notify Slack, or update living documents as new evidence lands. Command policies and guardrails bound what the agents may run, and any major language model provider can power the reasoning, cloud-hosted or local. 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.

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.

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.

Instead of babysitting three coding agents in three terminal windows, you describe the work once and let Mikka run the team: its Coordinator bot plans the job, dispatches runs to Claude Code, Codex, or Cursor on your connected machines, and only interrupts you when a decision is actually yours. Every piece of work lives in a thread with its own conversation, so dispatched runs appear as cards that report progress, ask questions, and flag blocks while you watch, reply, or stop them from the web, desktop, or phone. Unclear requests come back as choice cards before anything runs, and finished work moves through review and verification runs, back to a fix run when a check fails, then to acceptance where the pull request merges as you. Runs use leases and checkpoints, so a machine that drops mid-task hands the work to another connected computer with the same repository. You can create your own bots beside the Coordinator, each with its own chat, memory, and threads, and answers can arrive as charts, tables, stat cards, and task chips with sources cited. Daemons connect over SSH or from the desktop app, other agents drive Mikka through MCP, and Telegram or Slack messages reach the Coordinator too. 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.

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.