What is Paca? Features, Pricing & Tutorial (2026 Guide)

The Problem Paca Solves

Modern software teams often struggle with the "chatbot gap." While you might use AI to generate code snippets or draft emails, these tasks usually happen in isolation, detached from your actual project management board. Your project tracking software—be it Jira, Trello, or ClickUp—usually remains a human-only domain where AI contributions are limited to peripheral, low-context automations or external chat widgets.

This creates a visibility problem: your AI agents lack context, and your human team lacks oversight on what the agents are actually doing. Project managers and developers are left manually syncing the work AI produces with the actual progress reflected on their boards. The result is fragmented information and a breakdown in the synchronization between planning and execution.

Paca fixes this by allowing AI agents to sit directly on your Scrumban board. They can pick up tasks, update statuses, collaborate on BDD scenarios, and participate in the P-A-C-A (Plan-Act-Check-Adapt) cycle just like a human developer. In this tutorial, you'll learn exactly how to use Paca — step by step.

How to Get Started with Paca in 5 Minutes

1. Clone the Repository: Visit the official Paca GitHub repository and clone the codebase to your local environment or your private cloud server.
2. Initialize the Infrastructure: Navigate to the project root and follow the quick-start documentation to spin up the core Paca container using Docker.
3. Configure Your First Project: Access the web UI, create a new project, and define your initial board layout and workflow statuses using the project-level configuration file.
4. Connect Your AI Agents: Utilize the integrated MCP (Model Context Protocol) Server to link your preferred AI agent models directly into the Paca environment.
5. Assign Tasks to Agents: Navigate to your sprint board and begin assigning stories or tasks to your connected agents to see them appear alongside your human team.

How to Use Paca: Complete Tutorial

Step 1: Setting Up Your First Project and Workflow

Once you have Paca running, the first step is configuring your project structure. Unlike traditional tools that force a rigid hierarchy, Paca uses configuration-driven workflows. You can define your own board columns, field definitions, and sprint rules by editing the YAML configuration file within your project directory.

This approach allows you to tailor the tool to your team's specific version of Scrum or Kanban without needing to navigate complex enterprise menus. Simply define the states your tasks move through—such as "Refinement," "In Progress," "QA," and "Review"—and ensure these are mapped in your project settings so agents understand how to transition tasks.

Step 2: Integrating AI Agents via the MCP Server

The core of Paca is its ability to treat AI as a peer. To do this, you must connect your agents through the MCP Server. Paca acts as the host, providing the agent with the necessary context about your project, tasks, and system design documents (SDD). By providing these documents as context, you ensure the AI agent is not hallucinating but is instead grounded in your current architectural reality.

Once connected, you can use the in-app chat to interact with these agents. You might ask an agent to look at the backlog, identify high-priority bugs, and move them into the current sprint. Because the agent is an authenticated member of the board, its actions are fully trackable and transparent.

Step 3: Managing the P-A-C-A Cycle

Paca is designed around the P-A-C-A cycle: Plan, Act, Check, and Adapt. In the "Plan" phase, use the in-app chat to collaborate with your agents to draft Gherkin scenarios for your BDD (Behavior Driven Development) needs. Once the sprint begins, move to the "Act" phase where agents pick up tasks and update their progress on the board.

The "Check" and "Adapt" phases are where Paca truly shines. Because every field change generates an activity log with a diff, you can easily review what an agent changed and revert it if it deviates from your expectations. During retrospectives, use this history to see where the collaboration worked well and where the agent might need more specific guidance in the next cycle.

Paca: Pros & Cons

Pros	Cons
100% free and open-source (Apache 2.0).	Requires self-hosting and ongoing maintenance.
True peer-to-peer collaboration with AI agents.	Lacks the vendor support of enterprise platforms.
Highly extensible via WASM plugin architecture.	Steeper learning curve for configuration and plugins.
Self-hosted architecture ensures data privacy.	Smaller community compared to established SaaS incumbents.

Paca Pricing: Free vs Paid

Paca is currently 100% free and distributed under the Apache 2.0 license. This means you have full access to the entire platform, the plugin marketplace, and the AI agent integration features without any hidden tiers or per-seat costs.

Because it is a self-hosted platform, your primary "costs" will be the infrastructure required to run your own servers and the time required for your team to maintain and configure the instance. For organizations that prioritize data sovereignty and extreme customization, this is often a significant financial advantage over proprietary SaaS models.

👉 Check the latest pricing on the official Paca website.

Who is Paca Best For?

For engineering-led teams: This tool is ideal if your team is already building with AI agents and needs a native way to manage their output. It removes the friction of maintaining separate silos for AI tasks and human tasks.

For privacy-focused organizations: If you are working in a regulated industry where your data cannot leave your infrastructure, the self-hosted nature of Paca provides the control you need. You retain full ownership of your board data, agent logs, and architectural documentation.

For developers who love customization: If you find proprietary tools like Jira too rigid, Paca's plugin system built on WebAssembly allows you to build exactly the board and workflow you want. It caters to developers who prefer to code their project management logic in Rust, Go, or AssemblyScript.

Alternatives to Paca

Established project management suites like Jira, Trello, and ClickUp provide extensive ecosystems but treat AI as an add-on rather than a peer. GitHub Projects offers a lightweight alternative for teams already deeply integrated into the GitHub ecosystem but lacks the specialized, native AI agent integration Paca provides.

Paca is the better choice for teams that want to move beyond simple automation. Its unique value is in its architecture: it is built specifically for a world where AI is a contributing member of your team, not just a service that completes a single, isolated prompt.

Final Verdict: Is Paca Worth It?

If you are serious about integrating AI agents into your development lifecycle, Paca is an essential tool to evaluate. While it demands more technical overhead than a managed SaaS product, the benefit of having an AI-native board is unmatched in the current market.

📋 Disclosure: This is an independent tutorial based on Paca's publicly available documentation and website content as of June 13, 2026. GitNeural is not affiliated with, sponsored by, or endorsed by Paca or github.com. Pricing and features may have changed — always verify on the official Paca website.