Sugar

Autonomous issue resolution for AI-assisted development.

<!-- mcp-name: io.github.cdnsteve/sugar -->

Security scanners find vulnerabilities. Dependabot opens issues. Copilot flags problems. Sugar reads the issue, writes the fix, runs the tests, and opens the PR.

• Discovers - watches your GitHub repo for labeled issues (security, bug, dependabot)
• Resolves - reads each issue and implements a fix using Claude
• Verifies - runs your test suite and quality gates before committing
• Ships - opens a PR referencing the original issue, ready for your review

No issue left sitting in a backlog waiting for someone to have time.

How Sugar Compares

Most AI dev tools stop at the discovery layer:

GitHub Copilot CLI  ->  scan  ->  open issues
Snyk                ->  scan  ->  open issues
Dependabot          ->  scan  ->  open issues

Sugar is the resolution layer:

Labeled issue appears on GitHub
  -> Sugar picks it up (label filter: "security", "dependabot", "bug")
  -> AI agent reads the issue, analyzes the affected code
  -> Fix implemented, tests run locally
  -> PR opened - you review and merge

Configure which labels Sugar watches, point it at your repo, and run sugar run.

See workflow examples for security auto-fix, bug triage, test coverage, and more.

What Sugar Does

Sugar combines persistent memory with autonomous task execution:

• Project memory - Decisions, preferences, error patterns, and research stored per-project
• Global memory - Standards and guidelines shared across every project you work on
• GitHub integration - Watches for labeled issues and resolves them autonomously
• Semantic search - Retrieve relevant context by meaning, not just keywords
• MCP integration - Your AI agent reads and writes memory directly during sessions
• Task queue - Hand off work to run autonomously, powered by the same memory layer

Quick Start

# Install once, use in any project
pipx install sugarai

# Initialize in your project
cd ~/dev/my-app
sugar init

# Store what you know
sugar remember "We use async/await everywhere, never callbacks" --type preference
sugar remember "JWT tokens use RS256, expire in 15 min - see auth/tokens.py" --type decision
sugar remember "When tests fail with import errors, check __init__.py exports first" --type error_pattern

# Retrieve it later
sugar recall "authentication"
sugar recall "how do we handle async"

Your AI agent can also read and write memory directly - no copy-pasting required.

MCP Integration

Connect Sugar's memory to your AI agent so it can access project context automatically.

Claude Code - Memory server (primary):

claude mcp add sugar -- sugar mcp memory

Claude Code - Task server (optional):

claude mcp add sugar-tasks -- sugar mcp tasks

Once connected, Claude can call store_learning to save context mid-session and search_memories to pull relevant knowledge before starting work. The memory server works from any directory - global memory is always available even outside a Sugar project.

Other MCP clients (Goose, Claude Desktop):

# Goose
goose configure
# Select "Add Extension" -> "Command-line Extension"
# Name: sugar
# Command: sugar mcp memory

# OpenCode - one command setup
sugar opencode setup

Global Memory (New in 3.9)

Some knowledge belongs to you, not just one project. Coding standards, preferred patterns, security practices - these should follow you everywhere.

# Store a guideline that applies to all your projects
sugar remember "Always validate and sanitize user input before any DB query" \
  --type guideline --global

sugar remember "Use conventional commits: feat/fix/chore/docs/test" \
  --type guideline --global

# View your global guidelines
sugar recall "security" --global
sugar memories --global

# Search works project-first, but guidelines always surface
sugar recall "database queries"
# Returns: project-specific memories + relevant global guidelines

Global memory lives at ~/.sugar/memory.db. Project memory lives at .sugar/memory.db. When you search, project context wins - but guideline type memories from global always appear in results so your standards stay visible.

Via MCP, pass scope: "global" to store_learning to save cross-project knowledge directly from your AI session.

Memory types: decision, preference, file_context, error_pattern, research, outcome, guideline

Full docs: Memory System Guide

How Memory Works

Sugar uses two SQLite databases and a tiered search strategy.

Two stores:

• Project store (.sugar/memory.db) - context specific to one project
• Global store (~/.sugar/memory.db) - knowledge that applies everywhere

Seven memory types, each with different retrieval behavior:

Search strategy - project-first with reserved guideline slots:

1. Search the project store first (local context always wins)
2. Reserve slots for global guidelines (cross-project standards always surface)
3. Fill remaining slots with other global results
4. Deduplicate across both stores

This means a mature project's local context dominates results. A new project with no local memory gets global knowledge automatically. And your guidelines are always visible regardless.

Search engine: Semantic search via sentence-transformers (all-MiniLM-L6-v2, 384-dim vectors) with sqlite-vec. Falls back to SQLite FTS5 keyword search, then LIKE queries. No external API calls - everything runs locally.

# Install with semantic search (recommended)
pipx install 'sugarai[memory]'

# Works without it too - just uses keyword matching
pipx install sugarai

MCP tools available to your AI agent:

MCP resources:

• sugar://project/context - project summary
• sugar://preferences - coding preferences
• sugar://global/guidelines - cross-project standards

Task Queue

The task queue lets you hand off work and let it run autonomously. It reads from the same memory store, so Sugar already knows your preferences and patterns before it starts.

# Add tasks
sugar add "Fix authentication timeout" --type bug_fix --urgent
sugar add "Add user profile settings" --type feature

# Start the autonomous loop
sugar run

Sugar picks up tasks, executes them with your configured AI agent, runs tests, commits working code, and moves to the next task. It runs until the queue is empty or you stop it.

Delegate from Claude Code mid-session:

/sugar-task "Fix login timeout" --type bug_fix --urgent

Advanced task options:

# Orchestrated execution (research -> plan -> implement -> review)
sugar add "Add OAuth authentication" --type feature --orchestrate

# Iterative mode - loops until tests pass
sugar add "Implement rate limiting" --ralph --max-iterations 10

# Check queue status
sugar list
sugar status

Full docs: Task Orchestration

Supported AI Tools

Works with any CLI-based AI coding agent:

Installation

Recommended: pipx - installs once, available everywhere, no venv conflicts:

pipx install sugarai

Upgrade / Uninstall:

pipx upgrade sugarai
pipx uninstall sugarai

<details> <summary>Other installation methods</summary>

pip (requires venv activation each session)

pip install sugarai

uv

uv pip install sugarai

With semantic search (recommended for memory):

pipx install 'sugarai[memory]'

With GitHub integration:

pipx install 'sugarai[github]'

All features:

pipx install 'sugarai[all]'

</details>

Sugar is project-local by default. Each project gets its own .sugar/ folder with its own database and config. Global memory lives at ~/.sugar/. Like git - one installation, per-project state.

Project Structure

~/.sugar/
└── memory.db          # Global memory (guidelines, cross-project knowledge)

~/dev/my-app/
├── .sugar/
│   ├── sugar.db       # Project memory + task queue
│   ├── config.yaml    # Project settings
│   └── prompts/       # Custom agent prompts
└── src/

Recommended .gitignore:

.sugar/sugar.db
.sugar/sugar.log
.sugar/*.db-*

Commit .sugar/config.yaml and .sugar/prompts/ to share settings with your team.

Configuration

.sugar/config.yaml is created on sugar init:

sugar:
  dry_run: false
  loop_interval: 300
  max_concurrent_work: 3

claude:
  enable_agents: true

discovery:
  github:
    enabled: true
    repo: "user/repository"

Documentation

• Quick Start
• Memory System
• CLI Reference
• Task Orchestration
• Goose Integration
• OpenCode Integration
• GitHub Integration
• Configuration Guide
• Troubleshooting

Requirements

• Python 3.11+
• A CLI-based AI agent: Claude Code, OpenCode, Aider, or similar

Contributing

Contributions welcome. See CONTRIBUTING.md.

git clone https://github.com/roboticforce/sugar.git
cd sugar
uv pip install -e ".[dev,test,github]"
pytest tests/ -v

License

Dual License: AGPL-3.0 + Commercial

• Open Source (AGPL-3.0): Free for open source and personal use
• Commercial License: For proprietary use - sugar.roboticforce.io/licensing

Sugar is provided "AS IS" without warranty. Review all AI-generated code before use.