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.

Describe the change in chat, and LoopForge opens a Docker sandbox, checks out your repositories, hands the work to specialist agent roles, and opens the pull request. That is the conversational path: a Main Bot coordinates planning, implementation, review, and testing roles, each with its own prompt, executor, model, rules, skills, and tools, and you can queue or steer messages mid-reply, then pause, resume, or take over a run at any point. When a way of working proves itself, you freeze it as an SOP: a visual canvas of agent steps, deterministic script steps, human gates, and sub-workflows that every requirement then follows in the same order with the same checks. Four built-in SOPs cover backend delivery, frontend delivery, product proposals, and bug fixes, and each one can be copied, edited, and versioned per project. A Needs review inbox collects every gate waiting on you, with per-node status, tool traces, logs, and artifact snapshots as the evidence behind each approval. Recurring AI tasks run on schedules with the same workspace and workflow access, and insights reports track engineering metrics and token costs per run and per conversation. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

PandaDOS is a self-hosted control plane that turns Claude Code and Codex into long-running coding agents you direct through an issue queue from any browser, instead of babysitting a terminal. You file an issue with the outcome and acceptance criteria, and PandaDOS clarifies missing requirements, plans the work, picks the agent, and drives implementation, verification, and delivery in stages. Three approval levels, cautious, balanced, and automatic, decide which actions proceed alone; safe reversible steps continue automatically while destructive, irreversible, production, or secret-related decisions always come back to you. The browser workspace shows the real agent conversation, a native terminal, project files, Git history and diffs, and an execution summary, so you can inspect or intervene at any moment from a phone or laptop. Agents run on local or SSH-connected executor machines inside tmux sessions, and work sharing one repository is serialized while independent projects proceed in parallel without conflicting edits. Modules keep one assigned agent and durable project knowledge, so context from finished issues carries into the next task instead of evaporating when a chat ends. Teams can sign in through Feishu, ask the project manager questions in chat, and receive subscription notifications and approval cards on their phones. 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 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.

Paca is a self-hosted project management platform and Scrumban workspace where autonomous artificial intelligence agents collaborate with human software engineers as first-class Scrum teammates instead of external chat assistants. Engineering teams manage sprints, backlogs, and task lifecycles through a shared real-time board powered by Socket.IO where both humans and agents pick up assignments and post progress updates. A native Model Context Protocol server connects external coding assistants such as Claude Desktop and Claude Code directly into projects, tasks, and sprint data without custom scraping scripts. Distributed agents execute in isolated runtime sandboxes powered by the OpenHands software development kit, co-authoring behavior-driven development Gherkin scenarios with product owners and maintaining living system design documents. Visual event-driven automation builders compose multi-branch trigger, condition, and action pipelines that evaluate task dependency gates and cron schedules. A sandboxed WebAssembly plugin runtime allows developers to write custom backend logic and custom board views using Go or Rust. 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.

Kandev provides a command center for orchestrating AI coding agents across parallel workstreams. The Go backend paired with a Next.js frontend delivers kanban boards with drag-and-drop columns, pipeline workflow definitions with per-step agent handoffs, and an IDE-like review workspace combining file editor, file tree, terminal, browser preview, and unified git diffs. Multi-provider support connects Claude Code, GitHub Copilot, Codex, Qoder, Grok, and custom agents through configurable profiles with per-agent prompts, runtimes, and review gates. Tasks execute in isolated git worktrees with multi-repository support, letting agents work on separate branches simultaneously while changes surface in a consolidated review interface. Native integrations with GitHub, GitLab, Jira, Linear, Sentry, and Slack pull external issues into the kanban and link tasks to pull requests. Kandev exposes streamable HTTP and SSE MCP endpoints, enabling external clients — Cursor, Claude Desktop, Augment — to create tasks and read workspace context programmatically. Workflow definitions export as portable YAML for sharing across installations. Agentic workflows chain multi-step pipelines mixing different models per step — Opus for architecture, Sonnet for implementation, with human review gates between stages. 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 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.

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.

Close the laptop; the agents keep shipping. Cezar is an open-source control center that runs Claude Code, Codex, OpenCode, Copilot, and eight other coding agents in parallel on your own server, around the clock. Every task gets an isolated git worktree, so a dozen agents edit the same repository at once without colliding, and overflow waits in a queue. You watch each run live: the agent's text, its tool calls, token counts, and reported cost stream into one cockpit covering all your projects. The workspace dashboard separates what needs your answer, what is running, and what finished, and the usage view totals spend and tokens per project so a runaway task shows up in dollars, not surprises. Repeatable playbooks are plain Markdown skill files, chained into short YAML workflows that can mix a different agent per step, add checks and gates, and open pull requests; nothing merges on its own. Hand it a GitHub issue in one click, connect Jira or Linear to launch from your tracker, and schedule recurring maintenance with automations. Flip Autonomous and a run stops asking questions and just finishes; run the same task two or three times and keep the best diff. State lives in plain files, no database, and the responsive interface works from your phone. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Hand a task to an AI teammate the way you would hand it to a person: describe the work, then follow along and step in when it matters. UseAgent runs Claude Code, Codex, OpenCode, and Pi inside isolated Linux workspaces, each with your repositories, a terminal, a real browser, and a desktop you can open and take over. Start work from the web app, by mentioning an agent in Slack, or through the API, and get pull requests, documents, spreadsheets, presentations, and research back as usable files. Four engines share one thread and one event format, so you can compare how Claude Code and Codex each handled the same bug, then switch engines on the next reply without losing history, files, or memory. Risky operations pause at approval gates, and a decision resumes the run with a one-shot, argument-bound capability instead of blanket trust. Every step, tool call, and approval is stored as an append-only event log in your Postgres, replayable after the fact and durable across restarts mid-task. Teach the workspace your procedures by importing versioned skills from GitHub and pinning playbooks to tasks, while knowledge pages and optional team memory stay beside the work, inspectable and correctable. Recurring tasks run on schedules with full history. Provider keys stay on your server and never enter an agent sandbox. 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.

Clay Studio is a self-hosted workspace where your team and its coding agents share projects, live sessions, and project knowledge in one browser interface instead of scattering them across terminals. You organize several repositories in one sidebar and run sessions in parallel while teammates watch live activity, enter the same session, hand work off, and answer permission requests from wherever they are. Ten agent runtimes plug into the same workspace, Claude Code, Codex, Grok Build, Kimi Code, GitHub Copilot CLI, Qwen Code, Junie CLI, Antigravity CLI, OpenCode, and Kiro CLI, each keeping its own authentication and models. Persistent Mates give AI collaborators an identity, instructions, memory, direct messages, and roles in structured debates that survive beyond a single chat. Worktrees, paired sessions, spawned workers, scheduled tasks, and Ralph Loops let background jobs proceed without losing sight of what is running. You browse and edit files, inspect diffs and history, open terminals, install skills, connect MCP servers, and manage project knowledge without leaving the workspace. The installable PWA works on mobile and pushes notifications for approvals, failures, and finished work. Sessions and knowledge stay on your disk as portable JSONL and Markdown, so switching providers never means migrating your workspace. 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-only licensed.

Your laptop, a desktop, and a build box each have a coding agent installed, and until now nothing has coordinated them. ADHDev is a self-hosted control plane that puts Claude Code, Codex, Kimi, Cursor CLI, Antigravity CLI, Grok CLI, and OpenCode behind one shared task queue and one web and mobile dashboard. Machines pair directly with an address and a one-time code on your own network, and a mesh binds to a single repository. Idle nodes claim tasks themselves. Dependencies order the queue, retries absorb failures, and missions group the work that belongs together. Every task gets an isolated git worktree the moment it starts. Agents build in parallel without touching each other's files, and the coordinator's prompt is rendered from live mesh state at launch, with events pushed in as they happen. Difficulty tags route easy work to cheap models and hard work to strong ones. The dashboard also reads how much of each subscription window remains, skips plans that are nearly empty, and shows the routing forecast before anything runs. Finished work enters the Refinery. Your repository's gates decide, the branch is rebased onto main, and the merge happens only when main has not moved, followed by automatic worktree cleanup. 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 15,500 GitHub stars and over 1,100 forks, Plandex delivers a terminal-based AI coding agent purpose-built for the complex, multi-file tasks that overwhelm single-file AI assistants. The Go-powered server maintains a cumulative diff review sandbox that quarantines all AI-generated changes from your project files until you explicitly approve them — enabling 20-file refactors where you cherry-pick good changes and reject bad ones without touching git. A 2M token effective context window loads only what each step requires, while tree-sitter project maps index repositories exceeding 20M tokens across 30+ programming languages, providing structural awareness of class hierarchies, function signatures, and import graphs without burning tokens on full file content. The configurable model pack system assigns different models to different roles — Claude for planning, GPT for coding, Gemini for summarization — supporting Anthropic, OpenAI, Google, OpenRouter, Azure OpenAI, AWS Bedrock, DeepSeek, Perplexity, and Ollama for local models. Full auto mode handles end-to-end autonomous workflows including high-level planning, context loading, implementation, terminal command execution, and automated debugging of both terminal and browser applications. The interactive REPL provides fuzzy auto-complete, version-controlled sandbox branching, rewind to any previous point, and Git integration for commit message generation. The Plandex Server exposes 60+ REST API endpoints for programmatic orchestration across organizations, projects, plans, and branches. Deploy via Docker Compose for self-hosted operation with your own API keys. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Inside your Git platform, AI-Git-Bot works as a self-hosted teammate that quietly handles the engineering chores everyone agrees matter and nobody has time for. Open a pull request and it reviews the diff with inline findings, re-reviews after a force-push, and answers questions when you mention it in the discussion, keeping the conversation context across updates. Assign it an issue and it can rewrite that issue with structure and acceptance criteria, route it to the right assignee with a stated reason, or implement it and open a pull request for you. It generates unit tests for new code, builds Playwright end-to-end suites and runs them against a preview environment, then posts the results back to the pull request. Documentation and locale files stop drifting, because README sync and translation coverage workflows update them as the code changes. Every run is observable through built-in metrics and signed outgoing webhooks, and you choose the brain behind it, from cloud models to a local model on your own hardware, so source code never has to leave your network. 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 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 Manager is a mobile-first command center for AI coding agents. Install the PWA on your phone or tablet and you get real-time streaming chat, multi-repository Git operations, and scheduled automation right in your pocket. Git integration handles SSH-authenticated repo cloning, worktree management, unified diffs, and branch operations across all your projects in one dashboard. Chat with coding agents through Server-Sent Events streaming that supports slash commands, @-mentions for files, Plan and Build modes, and Mermaid diagram rendering for architecture discussions. Schedule reusable prompts to run against any repository on intervals or cron expressions, with each run tracking history and linking to sessions so you can pick up exactly where automation left off. MCP server configuration adds local and remote HTTP servers with OAuth support, plugging into the broader Model Context Protocol ecosystem. A dedicated assistant workspace provides an isolated AI environment with auto-provisioned skills for managing schedules, notifications, and settings. Multiple AI providers are supported including Anthropic, GitHub Copilot, and OpenAI-compatible services, each configurable with custom system prompts and granular tool permissions. Push notifications alert you to session events, agent questions, errors, and task completions across all managed repositories.