An out-of-the-box engineering framework for AI coding.
AI writes code fast, but every session it starts from scratch — no memory of your project, your conventions, or your team's requirements. Trellis persists specs, tasks, and memory into your repo, so any coding agent works to your engineering standards.
简体中文 • Docs • Quick Start • Supported Platforms • Use Cases
Why Trellis?
| Capability | What it changes |
|---|---|
| Auto-injected specs | Write conventions once in .trellis/spec/, then let Trellis inject the relevant context into each session instead of repeating yourself. |
| Task-centered workflow | Keep PRDs, implementation context, review context, and task status in .trellis/tasks/ so AI work stays structured. |
| Project memory | Journals in .trellis/workspace/ preserve what happened last time, so each new session starts with real context. |
| Team-shared standards | Specs live in the repo, so one person's hard-won workflow or rule can benefit the whole team. |
| Multi-platform setup | Bring the same Trellis structure to 22 AI coding platforms instead of rebuilding your workflow per tool. |
Prerequisites:
- Node.js >= 18
- Python >= 3.9
Quick Start
# 1. Install Trellis
npm install -g @mindfoldhq/trellis@latest
# 2. Initialize in your repo
trellis init -u your-name
# 3. Or initialize with the platforms you actually use
trellis init --cursor --opencode --codex -u your-name
See the Quick Start and Supported Platforms guides for setup details.
How to Use
The workflow is simple:
- Describe what you want in natural language.
- Brainstorm with the AI one question at a time until the PRD is clear, then implementation begins.
- Let it run — the AI calls Trellis Implement and auto-checks the result against specs, lint, type-check, and tests.
- Type
/trellis:finish-workwhen the work is done or the session context fills up. Trellis archives the task and updates journals.
How It Works
Trellis runs a 4-phase loop with auto-invoked skills and sub-agents:
- Plan —
trellis-brainstormwalks through requirements one question at a time and writesprd.md. Research-heavy items go to atrellis-researchsub-agent. The result is curated specs + research files referenced fromimplement.jsonl/check.jsonl. - Implement — a
trellis-implementsub-agent writes code from the PRD with the curated context auto-injected, no git commit. - Verify — a
trellis-checksub-agent reviews the diff against specs and runs lint, type-check, and tests, self-fixing where it can. - Finish — a final check runs, then
trellis-update-specpromotes new learnings back into.trellis/spec/so the next session starts smarter.
Resources
| Need | Link |
|---|---|
| Install Trellis in a repo | Quick Start |
| Understand platform differences | Supported Platforms |
| See the workflow in practice | Real-World Scenarios |
| Start from spec templates | Spec Templates |
| Track releases | Changelog |
FAQ
How is Trellis different from CLAUDE.md, AGENTS.md, or .cursorrules?
Those files are useful entry points, but they tend to become monolithic. Trellis adds scoped specs, task PRDs, workflow gates, workspace memory, and platform-aware generated files around them.
Is Trellis only for Claude Code?
No. Trellis is a project layer that works across multiple coding agents and IDEs.
Is Trellis for solo developers or teams?
Both. Solo developers use it for memory and repeatable workflow. Teams get the larger benefit: shared standards, task boundaries, reviewable context, and platform portability.
Do I have to write every spec file manually?
No. Many teams start by letting AI draft specs from existing code and then tighten the important parts by hand. Trellis works best when you keep the high-signal rules explicit and versioned.
Can teams use this without constant conflicts?
Yes. Personal workspace journals stay separate per developer, while shared specs and tasks stay in the repo where they can be reviewed and improved like any other project artifact.
Star History
Community & Resources
Official Repository • AGPL-3.0 License • Built by Mindfold