# Paca | Artificialus > For the complete content index, see [llms.txt](https://artificialus.com/llms.txt). Markdown versions of all pages are available by appending `.md` to any URL. - Home - / - Agents - / - Paca PA # Paca AI-native project management where agents and humans collaborate as equals on the same Scrumban board — free, self-hosted, and open-source. Paca Open source Since 2026 Visit Website Repository Docs Share X Facebook Reddit Telegram Bluesky Email Paca is a free, open-source, self-hosted Scrum board where AI agents work as equal teammates — assigned to sprints, picking up tasks, and collaborating on BDD specs alongside humans. Built as an alternative to Jira and Linear, it treats AI agents as first-class Scrum members. ## + Pros - AI agents as first-class Scrum members — assigned to sprints, on the board, picking up tasks and updating status like human teammates - Built-in MCP server (@paca-ai/paca-mcp) connects any MCP-compatible AI agent (Claude Desktop, Claude Code, custom agents) directly to project data - Claude Code /paca skill set with 10 slash commands for managing epics, sprints, tasks, and docs in plain English without leaving the editor - WASM plugin system with sandboxed, capability-scoped permissions — extend safely in Go, Rust, or any language with WASM target - Free forever — no per-seat pricing, no vendor lock-in, fully self-hosted with a single Docker Compose command - BDD co-authoring with Gherkin scenarios written collaboratively by POs, BAs, and AI agents - Activity feed with visual diffs and one-click revert for every change — human or AI - AI agents run on OpenHands SDK inside isolated sandbox containers — host environment is never touched ## − Cons - Early-stage project (v0.4.3) with limited maturity — expect bugs, breaking changes, and incomplete features - Small community and ecosystem — 878 GitHub stars, limited plugins, fewer community resources than established tools - Self-hosted only — no managed cloud option, requires a Linux server with Docker and basic ops knowledge - Single primary maintainer (pikann) — bus factor is a genuine risk for long-term sustainability - Agent capabilities depend on external LLM APIs — requires API keys and incurs model usage costs beyond the platform itself ## Pricing ### Free $0 Self-hosted, Apache 2.0 licensed, all features included forever ## Introduction Paca is a free, open-source, self-hosted project management platform that reimagines Scrum boards for the age of AI agents. Where Jira gives you a backlog and ClickUp gives you automations, Paca gives your AI agents a seat at the table — they join sprint planning, pick up tasks from the board, write BDD specs alongside Product Owners, and contribute to system design documents, all on the same real-time Scrumban board as human teammates. Launched in April 2026 and built with Go (45.7%), TypeScript (42.9%), Gherkin (6.5%), and Python (2.5%), Paca hit 878 GitHub stars by mid-June 2026 after a Show HN post that gathered 165 points. Version 0.4.3 was released on June 14, 2026. It is distributed under an Apache 2.0 license — fully open-source, free forever, no per-seat pricing. AI agents should participate in the Scrum process, not just generate output in isolation. They are assigned to sprints, appear on the board next to humans, and collaborate on artifacts like BDD scenarios and architecture docs. ## Key Features ### AI Agents as First-Class Scrum Members This is Paca's core feature. AI agents are not chatbots bolted onto the side of a project management tool — they are genuine teammates on the board: - Assigned to sprints — agents appear in the sprint backlog like any human - Visible on the Scrumban board — agents' tasks move through To Do → In Progress → Done alongside human tasks - Pick up tasks — agents can claim work from the backlog and update status in real time - Collaborate on BDD specs — Product Owners, BAs, and AI agents co-write Gherkin scenarios - Contribute to System Design Documents — living architecture docs keep every agent contextually anchored > Paca structures collaboration around the P-A-C-A cycle: Plan (backlog refinement with BDD) → Act (sprint execution on the shared board) → Check (QA agents verify, humans review) → Adapt (sprint data informs the next cycle). ### In-App AI Chat (v0.4.0) The v0.4.0 release added a project-level AI chat that lets you plan work and manage the backlog in plain English. Chat with AI agents to create or update epics, stories, tasks, and documentation — all without leaving the board. The AI drafts items directly into the backlog with proper structure, ready for refinement. ### Activity Diff & Revert (v0.4.0) Every field change in Paca's activity pane shows a before/after diff. One click reverts any change to its previous value — whether the change was made by a human or an AI agent. This safety net means teams can let agents move fast without fear of unrecoverable changes. ### MCP Server — Connect Any AI Agent Paca ships an MCP (Model Context Protocol) server published as `@paca-ai/paca-mcp` on npm. It gives any MCP-compatible AI agent structured access to projects, tasks, sprints, documents, members, views, custom fields, attachments, activity, and comments. No scraping, no custom APIs. ``` `{ "mcpServers" : { "paca" : { "command" : "npx" , "args" : [ "-y" , " @paca -ai/paca-mcp" ], "env" : { "PACA_API_KEY" : "your-api-key" , "PACA_API_URL" : "http://localhost:8080" } } } }` ``` Connect Claude Desktop, Claude Code, or any MCP client and ask questions like "List all active sprints in project X" or "Create a task for implementing OAuth and assign it to sprint 3". ### Claude Code /paca Skill Set For teams using Claude Code, Paca provides a set of 10 slash commands that let you manage the entire workspace in plain English without leaving your editor: Command What it does `/paca ` General task, doc, and sprint operations `/paca-epic ` Turn requirements into an epic with stories and spec `/paca-clarify ` Identify ambiguities and update specs `/paca-breakdown ` Decompose tasks into estimable sub-tasks `/paca-sprint` Plan a sprint from the backlog against capacity `/paca-estimate ` Estimate story points `/paca-prioritize` Score and set priorities across the backlog `/paca-do ` Execute a task, update status, keep docs current `/paca-test ` Derive test cases, run them, record results `/paca-doc ` Write or update documentation Each command reads your Paca documentation first — so output matches how your project actually works. The skills use the Agent Skills open standard (`SKILL.md` format) and are installable via a one-liner script. ### WASM Plugin System Paca's plugin system uses WebAssembly for backend plugins and standard module bundles for frontend. Write backend plugins in Go, Rust, AssemblyScript — anything with a WASM target. Plugins run in a sandboxed environment with a capability-based permission model: they declare exactly which host functions they need, and nothing more. The Plugin Marketplace inside the Paca UI lets you browse and install community plugins without touching the command line. Go to Settings → Plugins → Marketplace, find a plugin, and click Install. ### Unified Scrumban Board Humans and AI agents share a single real-time board powered by Socket.IO . No separate "AI workspace" or "agent mode" — everyone sees changes the moment they happen, with live presence cursors showing who (or what) is working on what. ### OpenHands-Powered AI Agents Paca's AI agents run on the OpenHands SDK, each executing inside its own isolated sandbox container. The agent environment is completely separated from the host — agents can write code, run commands, and manipulate files without touching the Paca server or your infrastructure. ## Architecture Paca runs as a multi-service stack managed via Docker Compose : ``` `apps/web React + TanStack Start + shadcn/ui — user interface apps/mcp @paca-ai/paca-mcp — MCP server for AI agent integration services/api Go + Gin — core business logic and REST API services/realtime Node.js + Socket.IO — real-time event fan-out services/ai-agent Python + FastAPI + OpenHands SDK — AI agent orchestration apps/e2e Playwright — end-to-end test suite skills/ Agent Skills — /paca slash commands for Claude Code PostgreSQL Persistent store Valkey Cache + async event streams between services` ``` Key architectural decisions: - Go API with Gin handles the core domain logic — projects, tasks, sprints, documents, members. It's the single source of truth. - Valkey (the open-source fork of Redis) serves as cache and event stream between services, enabling real-time updates via Socket.IO from the Node.js realtime service. - The MCP server (`apps/mcp`) is a standalone npm package that translates MCP tool calls into REST calls against the Go API. Any MCP client can connect. - The AI agent service (`services/ai-agent`) runs Python with FastAPI and the OpenHands SDK. Each agent gets its own sandbox container — isolated from the host and from other agents. - PostgreSQL is the only persistent store. MinIO provides S3-compatible storage for attachments (optional — can use AWS S3 instead). - Nginx acts as the API gateway, routing traffic to the appropriate service. Services are independently scalable. Teams can scale down components they don't need: run without the AI agent service to reduce resource usage, or connect to an external PostgreSQL instead of the bundled one. ## Integration with AI Coding Agents Paca was designed for AI agent collaboration. The integration strategy operates at three levels: ### 1. MCP Server (Protocol Level) The `@paca-ai/paca-mcp` npm package implements the Model Context Protocol, giving any MCP-compatible agent structured CRUD access to the entire Paca data model. Tools are organized by domain: - Projects: list, get, create, update, delete - Tasks: list, get, create, update, delete, reorder, change status - Sprints: list, create, update, complete - Documents: list, get, create, update, delete - Members & Roles: list, add, manage permissions - Views & Custom Fields: create views, define custom fields - Activity & Comments: read activity, add comments Plugins can register additional MCP tools at runtime, extending what agents can do. ### 2. Claude Code Skills (Editor Level) The `/paca` skill set for Claude Code provides natural-language management of the entire workspace. A PO can say `/paca-epic "Implement OAuth login with PKCE flow"` and Claude Code creates the epic, generates child stories, writes a BDD spec, and posts everything to Paca — all without leaving the editor. ### 3. OpenHands Agent Runtime (Execution Level) Paca's built-in AI agents run on OpenHands, a generalist AI agent SDK. This means agents can execute code, run tests, generate documentation, and interact with the filesystem — all inside sandboxed containers. The results are posted back to the Paca board as task updates, comments, and document changes. This three-layer approach maps directly onto Paca's P-A-C-A cycle: the MCP layer provides structured data access for planning (Plan), Claude Code skills let agents execute tasks and produce artifacts (Act), and the OpenHands runtime generates results that the team reviews on the shared board (Check), feeding into the next sprint (Adapt). ## Use Cases ### AI-Augmented Scrum Teams A development team adopts Paca to bring their AI coding agents into the Sprint process. The PO collaborates with an AI agent to write BDD specs during sprint planning. The AI agent picks up implementation tasks from the board, works through them, and updates status — all visible to the team on the same Scrumban board. QA agents run verification tasks while humans review the output. The bot gains a full sprint retrospective with data from both human and AI contributors. ### Product Owners Who Want BDD Help A PO writes high-level requirements in Paca. An AI agent (connected via Claude Code) asks clarifying questions using `/paca-clarify`, then generates Gherkin scenarios that the PO refines. The resulting BDD specs are stored as documents in Paca, linked to the relevant stories, and accessible to the entire team. ### Self-Hosted AI-Native Team Collaboration A team replaces their $15/seat/month Jira instance with Paca, gaining AI-native collaboration that no cloud tool can match. They run it on their own infrastructure behind their own firewall — no data leaves their servers — while AI agents work alongside humans on the same board, assigned to sprints and collaborating on tasks. The self-hosted model means they control the AI agent runtime, choose their own LLM providers, and never pay per-seat for either humans or AI teammates. ## Pricing Paca is free forever — no subscriptions, no per-seat pricing, no feature gates. Plan Price What you get Free $0 Self-hosted, all features, Apache 2.0 license, unlimited users and projects The only costs are: - A Linux server with Docker (any cloud VM, VPS, or on-premise machine) - LLM API keys if you use Paca's built-in AI agents (you provide your own model access) No enterprise tiers, no premium plugins, no paid upgrades. The project is funded through community contributions and maintainer effort. ## Getting Started ### One-Command Install (Recommended) ``` `curl -fsSL https: //gi thub.com /Paca-AI/ paca /releases/ latest /download/i nstall.sh | bash` ``` The script walks you through configuration interactively and starts the full stack. Open `http://your-server-ip` when it finishes. ### Manual Docker Compose ``` ` # Create a directory and download the compose file mkdir paca && cd paca curl -fsSL https: //gi thub.com/Paca-AI/paca/releases/latest/download/docker-compose.yml -o docker-compose.yml mkdir -p nginx curl -fsSL https: //gi thub.com/Paca-AI/paca/releases/latest/download/gateway.conf -o nginx/gateway.conf # Create your environment file with secure credentials cat > .env << 'EOF' JWT_SECRET= ADMIN_PASSWORD= POSTGRES_PASSWORD= AGENT_API_KEY= INTERNAL_API_KEY= ENCRYPTION_KEY= PUBLIC_URL=http: //l ocalhost EOF # Start the stack docker compose --env-file .env up -d` ``` Open `http://localhost` — log in with `admin` and the password you set. ### Connect Claude Code ``` ` # Install the Paca skill set curl -fsSL https://raw.githubusercontent.com/Paca-AI/paca/master/scripts/install-claude-skill.sh | bash # Connect the MCP server claude mcp add paca \ -- env PACA_API_KEY= \ -- env PACA_API_URL= \ -- npx -y @paca-ai/paca-mcp # Run /paca-setup inside Claude Code for guided setup # Then start managing your workspace: # /paca-sprint # /paca-epic "Add rate limiting to API" ` ``` ### Upgrading ``` `docker compose pull docker compose -- env - file . env up -d` ``` Database migrations run automatically on API startup. ## Comparison Paca is the only project management tool designed specifically for human-AI collaboration on a Scrum board. Here's how it compares to alternatives: Dimension Paca Jira Trello Linear AI integration First-class Scrum members Chatbot add-ons, automations Power-Ups None AI agents on board Yes — same board, same sprints No No No MCP support Built-in server No No No Hosting Self-hosted Cloud only Cloud only Cloud only Cost Free $8.15+/seat/mo Free tier limited $8+/seat/mo Open source Apache 2.0 Proprietary Proprietary Proprietary Plugin system WASM sandboxed Atlassian Marketplace Power-Ups Integrations API BDD collaboration Built-in Gherkin editor 3rd party plugins No No Version v0.4.3 (early-stage) Mature (20+ years) Mature Mature Paca is not a replacement for Jira in large regulated enterprises — yet. It is an early-stage project (v0.4.3) with a small community and limited ecosystem. What it offers instead is a different approach to AI collaboration that no established tool provides. > Choose Paca when: you want AI agents as genuine participants in your Scrum process, you prefer self-hosted infrastructure, and you're comfortable with early-stage software. > Choose Jira/Linear when: you need enterprise maturity, compliance certifications, a large plugin ecosystem, and a managed cloud solution. ## Further Reading - Paca GitHub Repository — Main project with README, architecture docs, and getting started guides - Paca MCP Server Setup — Full MCP configuration reference and available tools - Claude Code Skill Documentation — All 10 /paca slash commands with examples - Paca Architecture Overview — System architecture, service responsibilities, and data flow - Paca Plugin System — WASM plugin development, capability permissions, and marketplace - Paca Roadmap — Upcoming features and project direction - OpenHands SDK — The AI agent runtime powering Paca's agent execution - Agent Skills Specification — The open standard used by Paca's Claude Code skills ## Version History v0.4.3 Jun 14, 2026 Bug fixes — MCP server improvements, username validation, upgrade instructions in docs v0.4.0 Jun 11, 2026 In-app AI chat for project-level planning, activity diff & revert functionality v0.3.5 Jun 9, 2026 Claude Code /paca skill set with interactive setup and 10 slash commands v0.3.0 Jun 4, 2026 AI agent implementation — agents run on OpenHands SDK in sandboxed containers Best for Scrum teams who want AI agents as first-class participants in sprint planning, task execution, and documentation Capability AI agents as first-class Scrum members on the same board · Built-in MCP server for connecting any AI agent · Claude Code /paca skill set with 10 slash commands · WASM-based plugin sandbox with capability-scoped permissions · Self-hosted with one Docker Compose command Runs on Web · Self-hosted Signature Snippet Copy ``` `# Install Paca on any Linux server with Docker curl -fsSL https://github.com/Paca-AI/paca/releases/latest/download/install.sh | bash # Open the board, log in as admin # Connect Claude Code to Paca in one line claude mcp add paca -- npx -y @paca-ai/paca-mcp # Inside Claude Code, manage the entire workspace: /paca-epic "Implement OAuth login with PKCE flow" # Agents appear on the Scrum board alongside human teammates, # assigned to sprints, picking up tasks, updating status in real time.` ``` ## More in this Space NA ### Nanobot Open source Nanobot is an ultra-lightweight, open-source (MIT) personal AI agent that ships with WebUI, multi-channel chat (Telegram, Discord, WeChat, Slack, Feishu, email), MCP support, memory, model routing with fallbacks, cron automation, and a plugin skill system — all pip-installable in seconds. Built on a deliberately small and readable Python core, it lets you truly own your AI agent stack. View profile AI ### AionUi Open source AionUi is a free, open-source Cowork desktop app that runs Claude Code, Codex, Gemini CLI, OpenClaw, Hermes Agent, and 20+ more coding agents side-by-side within a unified Electron interface. It auto-detects installed CLI agents, provides a built-in AI engine with 30+ provider support, and adds Team Mode for multi-agent orchestration — all from one desktop workspace. View profile CU ### Cua Open source Cua is an open-source (MIT) infrastructure platform for building, benchmarking, and deploying computer-use agents. It provides background desktop control via Cua Driver, ephemeral multi-OS sandboxes via Cua Sandbox, standardized benchmarks via Cua Bench, and macOS virtualization via Lume — all accessible through a unified Python/TypeScript SDK, MCP server, and CLI. View profile