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.

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.

With over 14,000 GitHub stars and 41 releases since its May 2026 launch, Prime Agent delivers a fundamentally different approach to AI coding agents by treating the agent's own operating environment as programmable state that improves through use. The Recursive Language Model architecture provides the model exactly one tool — a persistent IPython kernel — where file operations, shell commands, subagent delegation via rlm() function calls, and context management all happen through code rather than rigid tool-calling schemas. Subagents launch as independent sessions with their own model, kernel, and history, communicating results through agent_message.send() without blocking the parent. The Continual Harness stores supplemental prompts, memories, skill descriptions, and reusable subagent specifications as durable state that the /refine command updates through small, evidence-backed edits with full rollback by ID. Daemon-backed sessions keep running when the terminal disconnects, with automatic context compaction summarizing older messages while preserving recent state. The TUI provides an Agent View for monitoring, switching between, and steering multiple live sessions simultaneously. Autonomous mode operates within configurable turn, token, and time budgets with user-defined quality gates. Persistent goals, heartbeats, and scheduled prompts maintain continuity across terminal sessions. Compatible with Anthropic Claude, OpenAI, Google Gemini, local models via Ollama or vLLM, and Prime Inference endpoints. Install via a single curl command on Linux or macOS. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

OpenCode puts a capable coding agent in a browser tab, so a project on your server can be worked on from any device. Start a session, describe the change, and watch the agent read files, edit code, and explain what it did in plain language. A review panel lists every changed file with unified or split diffs, a file tree, and a git changes view, so nothing lands without a look. Sessions open as tabs, run in parallel, and keep a searchable history per project. The model picker spans more than 200 providers plus free hosted models, and any OpenAI compatible endpoint can be added from a config file, which keeps local and self-hosted models in reach. Settings offer top tabs, side tabs, or an inbox layout, optional permission auto-accept, steer or queue behavior for follow-up prompts, timeline detail levels, themes, and fonts. Extensions cover MCP servers, plugins, and skills, and custom agents and commands capture the team's habits. One-time pairing links connect a new browser or app to the server in seconds. 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 83,000+ GitHub stars and $18.8M in Series A funding, OpenHands delivers the leading open-source platform for AI coding agents that scored 68.4% on SWE-bench Verified with Claude Opus 4.6, outperforming Devin 2.0's publicly reported 45.8%. The Agent Canvas web UI organizes work into persistent conversations where agents edit files, run shell commands, browse the web, and execute multi-step development tasks inside isolated Docker sandbox containers. The observe-plan-act loop drives agent behavior: the Python controller manages LLM abstraction via LiteLLM routing to 100+ providers including OpenAI, Anthropic, Google, DeepSeek, Qwen, Llama, and local Ollama models. Built-in skills for code review, Docker management, PRD generation, repo-rules enforcement, release notes, and test running attach to conversations automatically via auto-discovery or trigger-based activation. The Automations system schedules recurring agent tasks with configurable templates for CI workflows, dependency updates, and documentation generation. MCP server integration enables agents to access external tools and data sources. The REST API powers an OpenAI-compatible endpoint for connecting agents to chat UIs, IDEs, and voice platforms. GitHub, GitLab, Slack, and Jira integrations enable pull request reviews, issue resolution, and team notifications. The SDK provides Python and REST APIs for embedding agents in custom tools with local or cloud execution, custom agent behaviors, and Kubernetes deployment. On RepoCloud, deploy OpenHands on a dedicated VPS with Docker socket access, persistent project storage, root SSH access, and complete control over your AI development infrastructure, all under the MIT license.

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.

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.

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.

Moltis gives individuals and teams a completely private personal AI agent server that bridges conversational assistants to everyday messaging platforms while executing real-world computing tasks inside secure execution environments. Users can interact with autonomous agents across Telegram, Discord, WhatsApp, Slack, Matrix, and browser interfaces to draft correspondence, automate research, and schedule recurring cron tasks. The system coordinates multi-agent delegation by spinning up specialized sub-agents with dedicated roles for code generation, pull request reviews, quality assurance testing, and technical documentation. Built-in sandboxing isolates tool operations within container boundaries or WebAssembly modules, ensuring shell commands, web scraping routines, and script executions cannot alter underlying operating system files without explicit permission. Native Model Context Protocol integration allows operators to attach external data endpoints, database connectors, and custom command-line utilities without rewriting core routing logic. Persistent memory preserves conversational context and document embeddings across multiple sessions, allowing assistants to recall prior discussions, user preferences, and project guidelines indefinitely. Administrators can configure granular tool approvals, manage WebAuthn passkey authentication, and review real-time WebSocket communication logs through an embedded control panel. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

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.

Pi Web is a browser interface for the Pi coding agent ecosystem, providing a visual workspace that reads the same local configuration and session files as the CLI. The session workspace groups conversations by project with running state indicators, context usage percentages, cost tracking, and compaction details, while two branching modes let users create independent session files from earlier messages or fork branches within existing sessions to explore alternative coding directions. Real-time streaming via Server-Sent Events delivers agent responses with structured Markdown rendering, thinking steps, tool call visualization, and image drag-and-drop input. The project file explorer browses working directories with syntax-highlighted source preview, Git diff inspection, and rendering for Markdown, images, audio, PDFs, and DOCX files with automatic refresh. Git worktree support switches checkouts from the sidebar while keeping sessions from the same repository grouped together. The Models panel manages provider authentication via OAuth and API keys, model selection, model smoke tests, and models.json configuration shared bidirectionally with the CLI agent. The Skills panel lists, searches, installs, and toggles agent skills without terminal access. The interface ships with English and Simplified Chinese translations, light and dark themes, a chat minimap, keyboard shortcuts, and completion sounds. Basic Auth protects remote access when binding to non-loopback addresses. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Codex LB Enhanced is a session-aware gateway that pools your ChatGPT accounts so a long Codex session survives quota limits, dropped connections, and paused accounts without losing the conversation. It serves Codex desktop, CLI, and IDE clients through the native Responses protocol and WebSocket transport, and answers any OpenAI-compatible client through the familiar /v1 endpoints. When an account runs out of quota, gets suspended, times out, or drops mid-stream, the gateway classifies the failure, removes that account from the current request, and selects the next one through your load-balancing policy. Verified request history, including interrupted tool-call context, is replayed across the account switch when it is safe, so the client sees a slower answer instead of a dead stream. Every API key carries its own policy: time windows from five hours to monthly, limits by credits, tokens, or cost, and model-specific rules. Usage appears in native Codex quota headers and in a /v1/usage view backed by the same data, so editors and automation agree on what is left. Diagnostics correlate each request with its upstream account, transport, failover stage, and retry count, and a web dashboard manages accounts, keys, usage windows, request logs, and recovery reports. 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.

OpenRouter's most-used application by token volume — over 17 trillion tokens processed — Hermes Agent is an open-source autonomous agent built by Nous Research that lives on your server and gets more capable every day. Define a goal in natural language and Hermes plans sub-tasks, executes them through tool integrations, observes results, handles errors, and refines until the job is done or it genuinely needs your input. Persistent memory with full-text search and LLM summarization lets it recall context across sessions, and an agent-created skills system self-improves after complex tasks. A messaging gateway connects Telegram, Discord, Slack, WhatsApp, Signal, and 16 more platforms with cross-channel conversation continuity. A built-in cron scheduler runs daily reports, nightly backups, and weekly audits unattended. Subagent spawning parallelizes workstreams, and six terminal backends — local, Docker, SSH, Singularity, Modal, and Daytona — fit any infrastructure. Works with any LLM provider: Nous Portal, OpenRouter for 400+ models from 70+ providers, OpenAI, Anthropic, or your own endpoint. The API key you supply powers all LLM 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.