mindfold-ai/TrellisPublic

The best agent harness.

AI summary: An engineering framework providing persistent project memory and state management for AI coding agents.

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TypeScriptAGPL-3.0Created Jan 26, 2026Last push 5d ago+79 stars this week+549 this month

Quick answers

What is Trellis?
An engineering framework providing persistent project memory and state management for AI coding agents.
What does Trellis do?
Trellis operates as a comprehensive, out-of-the-box harness that fundamentally transforms AI coding sessions by introducing persistent project memory and contextual awareness. Rather than forcing the AI to start from scratch every session, the framework meticulously stores architectural specs, active tasks, and engineering standards directly within the repository. This guarantees that any AI agent operating on the codebase strictly adheres to the team's specific conventions and operational requirements. By maintaining this continuous state, Trellis effectively bridges the gap between rapid AI code generation and reliable, long-term software engineering practices.
Who is Trellis for?
Trellis is engineered for software engineering teams and individual developers seeking to bring rigorous structure to AI-assisted coding workflows. It requires an existing codebase and foundational familiarity with operating AI coding agents.
How do I get started with Trellis?
npm install -g @mindfold-ai/trellis
How popular is Trellis on GitHub?
mindfold-ai/Trellis has 14,838 stars and 846 forks on GitHub, and gained 79 stars in the last 7 days.
What license does Trellis use?
mindfold-ai/Trellis is released under the AGPL-3.0 license.

Star history

since Jul 29, 2026
05K10KJul 2026Aug 2026Sep 2026Sep 2026
14.8K stars as of Sep 29, 2026. Measured daily since Jul 29, 2026; GitHub no longer exposes earlier star timestamps.

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Signals and awards

derived from tracked data
  • Widely adopted

    14,838 stars

  • Very active

    1,270 commits in 52 weeks

  • Continuous integration

    Automated checks passing

What Trellis does

Trellis operates as a comprehensive, out-of-the-box harness that fundamentally transforms AI coding sessions by introducing persistent project memory and contextual awareness. Rather than forcing the AI to start from scratch every session, the framework meticulously stores architectural specs, active tasks, and engineering standards directly within the repository. This guarantees that any AI agent operating on the codebase strictly adheres to the team's specific conventions and operational requirements. By maintaining this continuous state, Trellis effectively bridges the gap between rapid AI code generation and reliable, long-term software engineering practices.

Trellis is engineered for software engineering teams and individual developers seeking to bring rigorous structure to AI-assisted coding workflows. It requires an existing codebase and foundational familiarity with operating AI coding agents.

  • Persistent memory: Securely stores project context, tasks, and specifications directly in the repository to maintain state across coding sessions.
  • Convention enforcement: Actively ensures that AI agents strictly adhere to team-specific engineering standards and established coding styles.
  • State management: Continuously tracks the progress of complex development tasks to prevent the agent from losing context over time.
  • Agent agnostic: Specifically designed to function as a versatile harness for various AI coding tools rather than tying to a singular model.
  • Repo-native storage: Ensures all context and memory data remains fully version-controlled alongside the actual project codebase.

Where teams use it

Continuous AI development

Development teams can use Trellis to ensure AI agents retain critical architectural decisions across multiple disjointed coding sessions.

Standardized code generation

Engineering managers can rigorously enforce specific coding conventions on all AI-generated code committed within a repository.

Complex task breakdown

Developers can securely persist large feature specifications so the AI can tackle them incrementally without losing sight of the overall goal.

Onboarding AI assistants

Newly deployed AI agents can instantly comprehend a project's deep history and requirements by reading the natively stored Trellis state.

Getting started: npm install -g @mindfold-ai/trellis

README

main branch

Trellis Logo

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

npm version npm downloads license stars docs Discord open issues open PRs Ask DeepWiki Ask ChatGPT

Trellis workflow demo

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:

  1. Describe what you want in natural language.
  2. Brainstorm with the AI one question at a time until the PRD is clear, then implementation begins.
  3. Let it run — the AI calls Trellis Implement and auto-checks the result against specs, lint, type-check, and tests.
  4. Type /trellis:finish-work when 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:

  1. Plan — trellis-brainstorm walks through requirements one question at a time and writes prd.md. Research-heavy items go to a trellis-research sub-agent. The result is curated specs + research files referenced from implement.jsonl / check.jsonl.
  2. Implement — a trellis-implement sub-agent writes code from the PRD with the curated context auto-injected, no git commit.
  3. Verify — a trellis-check sub-agent reviews the diff against specs and runs lint, type-check, and tests, self-fixing where it can.
  4. Finish — a final check runs, then trellis-update-spec promotes 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.

Can I temporarily compare a project with and without Trellis?

Yes. trellis ablate temporarily removes all supported project-owned Trellis surfaces after creating a verified recovery transaction outside the project. Start a fresh agent session for the comparison, then run trellis restore to recover the exact prior state. Use --dry-run to preview either operation. The private recovery transaction includes exact .trellis task, spec, and workspace bytes, which may contain user-authored sensitive text, and is kept until restore verifies successfully.

This is different from trellis uninstall (permanent removal) and TRELLIS_HOOKS=0 (hooks only). Ablation does not launch agents, manage worktrees, hide Git changes, or remove the global CLI, channel logs, or host transcripts. If a managed path changes while ablated, restore refuses all writes until the conflict is resolved.

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Community & Resources

Official Repository • AGPL-3.0 License • Built by Mindfold

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When work happens

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SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 0 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 10 commitsSun 11:00 — 3 commitsSun 12:00 — 8 commitsSun 13:00 — 2 commitsSun 14:00 — 0 commitsSun 15:00 — 0 commitsSun 16:00 — 2 commitsSun 17:00 — 5 commitsSun 18:00 — 4 commitsSun 19:00 — 0 commitsSun 20:00 — 3 commitsSun 21:00 — 5 commitsSun 22:00 — 2 commitsSun 23:00 — 1 commitsMon 0:00 — 6 commitsMon 1:00 — 1 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 1 commitsMon 9:00 — 4 commitsMon 10:00 — 8 commitsMon 11:00 — 17 commitsMon 12:00 — 7 commitsMon 13:00 — 3 commitsMon 14:00 — 23 commitsMon 15:00 — 25 commitsMon 16:00 — 28 commitsMon 17:00 — 24 commitsMon 18:00 — 18 commitsMon 19:00 — 14 commitsMon 20:00 — 17 commitsMon 21:00 — 13 commitsMon 22:00 — 8 commitsMon 23:00 — 4 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 1 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 4 commitsTue 10:00 — 14 commitsTue 11:00 — 9 commitsTue 12:00 — 6 commitsTue 13:00 — 5 commitsTue 14:00 — 23 commitsTue 15:00 — 29 commitsTue 16:00 — 6 commitsTue 17:00 — 16 commitsTue 18:00 — 13 commitsTue 19:00 — 1 commitsTue 20:00 — 12 commitsTue 21:00 — 12 commitsTue 22:00 — 9 commitsTue 23:00 — 0 commitsWed 0:00 — 2 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 0 commitsWed 9:00 — 9 commitsWed 10:00 — 26 commitsWed 11:00 — 28 commitsWed 12:00 — 24 commitsWed 13:00 — 0 commitsWed 14:00 — 26 commitsWed 15:00 — 38 commitsWed 16:00 — 16 commitsWed 17:00 — 27 commitsWed 18:00 — 25 commitsWed 19:00 — 13 commitsWed 20:00 — 3 commitsWed 21:00 — 18 commitsWed 22:00 — 5 commitsWed 23:00 — 1 commitsThu 0:00 — 0 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 2 commitsThu 9:00 — 16 commitsThu 10:00 — 13 commitsThu 11:00 — 14 commitsThu 12:00 — 11 commitsThu 13:00 — 8 commitsThu 14:00 — 19 commitsThu 15:00 — 35 commitsThu 16:00 — 44 commitsThu 17:00 — 49 commitsThu 18:00 — 28 commitsThu 19:00 — 19 commitsThu 20:00 — 22 commitsThu 21:00 — 15 commitsThu 22:00 — 11 commitsThu 23:00 — 7 commitsFri 0:00 — 4 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 8 commitsFri 9:00 — 5 commitsFri 10:00 — 21 commitsFri 11:00 — 25 commitsFri 12:00 — 14 commitsFri 13:00 — 0 commitsFri 14:00 — 1 commitsFri 15:00 — 13 commitsFri 16:00 — 14 commitsFri 17:00 — 13 commitsFri 18:00 — 14 commitsFri 19:00 — 18 commitsFri 20:00 — 16 commitsFri 21:00 — 11 commitsFri 22:00 — 3 commitsFri 23:00 — 0 commitsSat 0:00 — 3 commitsSat 1:00 — 1 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 1 commitsSat 10:00 — 16 commitsSat 11:00 — 3 commitsSat 12:00 — 1 commitsSat 13:00 — 13 commitsSat 14:00 — 3 commitsSat 15:00 — 0 commitsSat 16:00 — 4 commitsSat 17:00 — 17 commitsSat 18:00 — 13 commitsSat 19:00 — 12 commitsSat 20:00 — 1 commitsSat 21:00 — 2 commitsSat 22:00 — 0 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Jan 30, 2026daily#16+149
Jan 29, 2026daily#14+165
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