Trellis is an engineering framework for AI coding that persists project specifications, tasks, and memory into your repository so that coding agents maintain context across sessions.
The core problem Trellis addresses is that AI coding agents start fresh with each session, lacking knowledge of your project structure, coding conventions, and team requirements. Trellis solves this by storing specs, tasks, and memory directly in your repository. This allows any coding agent to work within your established engineering standards rather than reinventing approaches on each invocation. The framework provides an out-of-the-box structure that bridges the gap between fast AI code generation and the consistency needed in real engineering workflows.
Teams should adopt Trellis if they rely on AI agents for coding tasks and need those agents to respect project-specific conventions and maintain continuity across multiple sessions. It suits projects where consistency matters and where you want to avoid repeating context setup with each agent interaction. The tool is particularly valuable for teams using multiple coding agents, as it ensures all of them operate within the same framework and constraints rather than each agent developing its own approach.
The project maintains active engagement with its community through a Discord channel and tracks issues and pull requests openly. Development activity shows responsiveness to user feedback and ongoing refinement of the framework based on real-world usage patterns.
