Compare Devin Desktop vs. Multica

Devin Desktop is an AI-native software development platform that serves as a central command center for managing coding agents, development workflows, and code execution. The platform combines a professional-grade IDE with agent orchestration capabilities, enabling developers to plan tasks, delegate work, review outputs, and collaborate with AI agents from a single interface. Developers can run local and cloud-based agents simultaneously, allowing multiple coding tasks to progress in parallel while maintaining shared context across projects. The platform includes features such as Spaces for shared worktrees, Fast Context for rapid codebase understanding, Supercomplete for predictive coding assistance, and comprehensive code review capabilities. Devin Desktop supports the Agent Client Protocol (ACP), enabling interoperability with different AI models and agent frameworks. The platform integrates with popular developer tools, including GitHub, Slack, Notion, Linear, Stripe, Datadog, Atlassian, and various language servers. Developers can inspect every change made by agents through built-in debugging, tracing, and review tools to ensure code quality and reliability. The platform is designed to streamline both individual and team-based software development workflows while reducing context switching. Devin Desktop enables engineering teams to increase development velocity by combining human oversight with autonomous AI execution.

Multica is an innovative open-source project management platform designed for collaboration between human teams and AI agents, transforming coding agents into collaborative partners instead of merely being viewed as separate tools. This platform offers a unified workspace where both humans and AI can interact seamlessly; agents are capable of taking on tasks, providing updates, engaging in discussions, addressing obstacles, delivering code, and showcasing their presence along with profiles, avatars, and issue queues. Users can delegate tasks to agents as casually as they would to a fellow teammate, or they can initiate a chat to request issue drafting, inquiries, or to manage one-off tasks. Furthermore, Multica's shared context layer ensures that comments, attachments, reports, task histories, and workspace knowledge remain readily available to both agents and users, while the implementation of skills serves as comprehensive playbooks that empower all agents to utilize consistent definitions and operational guidelines. This integration not only enhances productivity but also fosters a more cohesive working relationship between humans and AI in the project environment.