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Lavra (/ˈla.vɾɐ/ — Portuguese for "harvest")

Lavra turns your AI coding agent into a development team that gets smarter with every task.

A plugin for coding agents that orchestrates the full development lifecycle -- from brainstorming to shipping -- while automatically capturing and recalling knowledge so each unit of work makes the next one easier.

Without Lavra

The agent forgets everything between sessions -- you re-explain context every time
Planning is shallow: it jumps to code before thinking through the problem
Review is inconsistent: sometimes thorough, sometimes a rubber stamp
Knowledge stays in your head. When a teammate hits the same bug, they start from zero
Shipping is manual: you run tests, create the PR, close tickets, push -- every time

Forgetful agent surrounded by scattered documents

With Lavra

Automatic memory. Knowledge is captured inline during work and recalled automatically at the start of every session. Hit the same OAuth bug next month? The agent already knows the fix.
Structured planning. Brainstorm with scope sharpening, research with domain-matched agents, adversarial plan review -- all before a single line of code is written.
Disciplined execution. Agents follow deviation rules (what to auto-fix vs. escalate), commit per task with traceable bead IDs, and verify every success criterion before marking work done.
Built-in quality gates. Every implementation runs through a review-fix-learn loop. Knowledge capture is mandatory, not optional.
Team-shareable knowledge. Memory lives in .lavra/memory/knowledge.jsonl, tracked by git. Your team compounds knowledge together.

Agent with memory climbing stairs of compounding knowledge

The workflow

Most of the time, you type three commands:

/lavra-design "I want users to upload photos for listings"

This runs the full planning pipeline as a single command: interactive brainstorm with scope sharpening, structured plan with phased beads, domain-matched research agents, plan revision, and adversarial review. The output is detailed enough that implementation is mechanical.

/lavra-work

Picks up the approved plan and implements it. Auto-routes between single and multi-bead parallel execution. Includes mandatory review, fix loop, and knowledge curation -- all automatic.

/lavra-ship

Rebases on main, runs tests, scans for secrets and debug leftovers, creates the PR, closes beads, and pushes the backup. One command to land the plane.

Add /lavra-qa between work and ship when building web apps -- it maps changed files to routes and runs browser-based verification with screenshots.

Who this is for

Anyone using coding agents who wants consistent, high-quality output instead of hoping the agent gets it right this time.

Lavra (/ˈla.vɾɐ/ — Portuguese for "harvest")

Lavra turns your AI coding agent into a development team that gets smarter with every task.

Without Lavra

The agent forgets everything between sessions -- you re-explain context every time
Planning is shallow: it jumps to code before thinking through the problem
Review is inconsistent: sometimes thorough, sometimes a rubber stamp
Knowledge stays in your head. When a teammate hits the same bug, they start from zero
Shipping is manual: you run tests, create the PR, close tickets, push -- every time

With Lavra

Automatic memory. Knowledge is captured inline during work and recalled automatically at the start of every session. Hit the same OAuth bug next month? The agent already knows the fix.
Structured planning. Brainstorm with scope sharpening, research with domain-matched agents, adversarial plan review -- all before a single line of code is written.
Disciplined execution. Agents follow deviation rules (what to auto-fix vs. escalate), commit per task with traceable bead IDs, and verify every success criterion before marking work done.
Built-in quality gates. Every implementation runs through a review-fix-learn loop. Knowledge capture is mandatory, not optional.
Team-shareable knowledge. Memory lives in .lavra/memory/knowledge.jsonl, tracked by git. Your team compounds knowledge together.

The workflow

Most of the time, you type three commands:

/lavra-design "I want users to upload photos for listings"

/lavra-work

Picks up the approved plan and implements it. Auto-routes between single and multi-bead parallel execution. Includes mandatory review, fix loop, and knowledge curation -- all automatic.

/lavra-ship

Rebases on main, runs tests, scans for secrets and debug leftovers, creates the PR, closes beads, and pushes the backup. One command to land the plane.

Add /lavra-qa between work and ship when building web apps -- it maps changed files to routes and runs browser-based verification with screenshots.

Who this is for

Anyone using coding agents who wants consistent, high-quality output instead of hoping the agent gets it right this time.

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Lavra (/ˈla.vɾɐ/ — Portuguese for "harvest")

Without Lavra

With Lavra

The workflow

Who this is for

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Help us improve

Lavra (/ˈla.vɾɐ/ — Portuguese for "harvest")

Without Lavra

With Lavra

The workflow

Who this is for