Autonomous Issue
Resolution
Security scanners find problems. Dependabot opens issues. Sugar reads the issue, writes the fix, runs the tests, and opens the PR.
The Problem
Your tools are great at finding problems. Nobody closes the loop.
Scanners Find, Humans Fix
Snyk, Dependabot, and GitHub's Copilot CLI are all excellent at surfacing issues - and terrible at resolving them. Every finding lands in a backlog someone has to manually work through.
Issue Overload
GitHub Copilot CLI just launched bulk security scanning - opening dozens of issues at once. More discovery tooling means more backlog, not fewer vulnerabilities in production.
Weeks Between Find and Fix
The average time from vulnerability discovery to patch is measured in weeks. The bottleneck is never detection - it's the human time required to read, understand, fix, test, and ship.
Repetitive, Low-Value Work
Dependency bumps, CVE patches, and routine bug fixes follow the same pattern every time: read issue, update code, run tests, open PR. This is exactly what automation is for.
More scanning without more resolution just grows the backlog.
The Solution
Sugar is the resolution layer.
Every other tool stops at the issue. Sugar reads it, fixes it, verifies it, and ships it.
How it compares
What Sugar Does
End-to-end autonomous issue resolution - from GitHub issue to merged PR.
Discovers
Watches GitHub repos for labeled issues. Configure which labels Sugar acts on - security, bug, dependabot, or your own.
Resolves
Reads each issue and implements a fix using Claude. Understands your codebase, your conventions, and what the issue is actually asking for.
Verifies
Runs your test suite and quality gates before committing. If tests fail, Sugar iterates until they pass - not "done" until it actually works.
Ships
Opens a PR referencing the original issue. Your team reviews and merges. Sugar handles the mechanical work - you handle the judgment calls.
Persistent Memory
Sugar remembers your decisions, patterns, and preferences across every session. No re-explaining your stack or conventions when the next issue arrives.
- •
decision- architecture and design choices - •
preference- how you like things done - •
error_pattern- bugs and their fixes - •
guideline- rules that apply everywhere
Global Knowledge
Cross-project memory at ~/.sugar/memory.db. Standards and best practices follow Sugar into every project automatically.
- • Store once with
--globalflag - • Available in every repo without setup
- • Team standards, not just personal prefs
- • Knowledge compounds across projects
MCP Integration
Works with Claude Code, Goose, OpenCode, and any MCP-compatible client. Use Sugar's memory tools from inside your AI assistant of choice.
- •
sugar opencode setup- one command - • 11 slash commands including
/sugar-remember - • Memory server works outside Sugar projects
- • Any MCP client can access your memory
Label Filtering
Configure exactly which GitHub issue labels Sugar acts on per repository.
Tool Restrictions
Task-type security controls which tools are available per task.
Self-Correction
Ralph Wiggum mode iterates until tests pass. Never marks "done" when tests are still failing.
Task Hooks
Pre/post execution hooks with variable substitution for custom automation pipelines.
How It Works
From open issue to merged PR - with no human in the loop for the mechanical parts.
Each issue resolved autonomously - your team reviews and merges
Connect your repos
Configure which GitHub repositories Sugar watches and which issue labels trigger resolution. Point Sugar at a repo and tell it what to act on.
sugar github connect --repo your-org/your-repo Issues get resolved automatically
When a matching issue appears, Sugar reads it, implements the fix using Claude, and runs your full test suite. If tests fail, it iterates until they pass.
sugar run --watch-github Review and merge
Sugar opens a PR referencing the original issue. Your team does what humans are good at - reviewing judgment calls. Sugar handles the mechanical work.
Memory compounds over time
Outcomes feed back into Sugar's memory. What worked, what failed, your conventions and standards - captured and available for every future issue in every repo.
Ralph Wiggum: Self-Correcting AI
Complex tasks need iteration. Ralph keeps going until tests actually pass.
Without Ralph
80% right isn't shipped code.
With Ralph
Iterates until it actually works.
The Iteration Loop
↻ Repeats until <promise>DONE</promise> or max iterations
When to Use Ralph
Complex Bugs
Iterates through debugging until the fix actually works
Refactoring
Self-corrects when changes break existing tests
TDD Features
Keeps implementing until all tests go green
# Enable iterative execution
sugar add "Implement rate limiting" --ralph --max-iterations 10
# Sugar iterates until tests pass
# Iteration 1: implement → test → 2/5 pass
# Iteration 2: fix → test → 4/5 pass
# Iteration 3: fix edge case → test → 5/5 pass
# Output: <promise>DONE</promise>
✓ Task completed after 3 iterationsIntelligent Triage
Not sure if your task needs Ralph? Let Sugar decide.
# Let Sugar analyze complexity and decide
sugar add "Refactor auth to use repository pattern" --triage
# Sugar analyzes:
# - Task complexity (keywords, scope, risk)
# - Codebase capabilities (pytest, eslint, etc.)
# - Generates completion criteria automatically
🔍 Triage: Ralph recommended (78% confidence)
Completion: "All tests pass, no lint errors"
✓ Task added with Ralph mode enabledTriage auto-enables Ralph when confidence is 60%+. Simple tasks run single-pass.
Who It's For
Any team that generates more issues than they can manually close.
Solo Developers
Security advisories and Dependabot alerts don't pile up waiting for your attention. Sugar resolves routine issues in the background while you work on what matters.
Small Teams
Stop burning engineering time on dependency bumps and CVE patches. Sugar handles the mechanical resolution - your team handles the architecture decisions.
Enterprises
At scale, the gap between issue discovery and resolution is a compliance risk. Sugar closes that gap systematically, across every repo, without adding headcount.
Stop Managing the Backlog. Close It.
Install Sugar in 30 seconds. Point it at a repo. Watch issues become pull requests.
pip install sugarai