magic-beans 0.7.0

Graph-based issue tracker for AI agent coordination

🫘 Beans

Graph-based issue tracker for AI agent coordination.

Coding with AI makes developers absurdly productive. Coffee keeps them going. Beans keep the whole loop fed. ☕

Beans is a lightweight, embedded issue tracker designed for AI agents to coordinate work across tasks. It models issues as nodes in a dependency graph, backed by SQLite, with a CLI that both humans and agents can use.

$ beans create "Fix auth middleware"
task-a3f2dd1c  2025-06-15 10:42  Fix auth middleware

$ beans list
task-a3f2dd1c  2025-06-15 10:42  Fix auth middleware
task-7e2b9f01  2025-06-15 10:43  Add rate limiting

$ beans --json list
[{"id": "task-a3f2dd1c", "title": "Fix auth middleware", "status": "open", ...}]

Why Beans exists

Beans was born from analyzing beads, a graph-based issue tracker with a similar goal: giving AI agents a structured way to coordinate work. The idea is excellent. The execution, however, raised serious concerns.

The problem with beads

Beads is invasive software that assumes too much about the user's intentions:

• Curl-pipe-bash installer that silently escalates to sudo to install a ~200MB binary system-wide
• Strips macOS code signatures and applies ad-hoc signatures to bypass Gatekeeper — the same technique used by actual malware
• Installs 5 persistent git hooks (pre-commit, post-merge, pre-push, post-checkout, prepare-commit-msg) that run on nearly every git operation
• Runs a background database daemon (Dolt SQL server) that binds to TCP ports 3307/3308 — a MySQL-protocol server running on your machine
• Silently modifies commit messages by appending metadata trailers without asking
• Auto-pushes data to remote servers every 5 minutes
• Fingerprints your machine with unique UUIDs for the repo, clone, and project
• Injects system prompts into AI agent configuration files (e.g., .claude/settings.local.json) to force agents to use the tool
• Includes a "stealth mode" that hides its presence from collaborators
• Ships with telemetry that records every command and all arguments, including who ran them

The core data model is a 50+ field struct covering everything from agent heartbeats to ephemeral "wisps." It's not a simple tool — it's a complex system that assumes you want all of it.

What beans does differently

Beans keeps the good idea — a dependency graph for AI agent coordination — and throws away everything else:

	Beads	Beans
Storage	Dolt SQL server (~200MB, background daemon, TCP ports)	SQLite (zero-config, embedded, stdlib)
Installation	curl | bash + sudo + signature stripping	uv add beans or pip install beans
Git hooks	5 persistent hooks installed silently	None
Commit modification	Silent trailer injection	Never touches your commits
Background processes	Persistent MySQL-protocol daemon	None
Network	Opens TCP ports, auto-pushes every 5 min	Fully offline
Telemetry	OTel spans with actor identity + full args	None
Stealth mode	Yes, hides from collaborators	No — transparency is a feature
AI config injection	Writes to .claude/settings.local.json	Provides a skill you copy yourself
Data model	50+ fields	~10 fields + dependency edges
Agent integration	Forced via injected prompts	Opt-in via AGENTS.md instructions

Design principles

Polite software. Beans never modifies files without asking, never installs hooks, never runs background processes, and never phones home. It's a CLI tool that reads and writes to a local SQLite file. That's it.

Embedded storage. A single .beans/beans.db SQLite file with WAL mode. No servers, no ports, no daemons. Works offline, works in CI, works anywhere Python runs.

Graph-native. Issues (beans) are nodes. Dependencies are typed edges. "What's ready to work on?" is a graph query — show me all open beans with no open blockers.

Agent-friendly. The --json flag on every command makes beans machine-readable. Agents don't need to parse human output. The CLI is a thin wrapper around a clean Python API — agents can also use the library directly.

Minimal by default. A bean has an id, title, status, and timestamps. Everything else is optional. No 50-field structs, no agent heartbeat tracking, no ephemeral wisps.

Journal-based sync. Changes are recorded in an append-only JSONL journal that can be committed to git. The SQLite database is a materialized view that can be rebuilt from the journal at any time. Sync through git, not through custom protocols.

Installation

pip install magic-beans

Or with uv:

uv add magic-beans

Quick start

# Initialize a project
beans init

# Create some beans
beans create "Set up database schema"
beans create "Build API endpoints" --type task
beans create "Write integration tests" --body "Cover all CRUD operations"

# Create an epic with children
beans create "Launch v1" --type epic
beans create "Deploy to staging" --parent epic-<id>

# List all beans
beans list

# JSON output for agent consumption
beans --json list

CLI Reference

Global options

Option	Description
--json	Output as JSON (for agents)
--dry-run	Show what would happen without writing
--db PATH	Use a specific SQLite database file
--fields id,title,...	Limit output to specific fields
MAGIC_BEANS_DIR	Environment variable: override .beans/ directory discovery

Bean CRUD

# Create (default types: task, bug, epic — configurable via beans types)
beans create "Fix auth" --type bug --body "Detailed description" --parent epic-<id>

# Show
beans show task-a3f2dd1c

# Update
beans update task-a3f2dd1c --title "New title" --status in_progress --priority 0

# Close (sets status=closed and closed_at)
beans close task-a3f2dd1c --reason "Fixed in commit abc1234"

# Delete
beans delete task-a3f2dd1c

Querying

# List all beans
beans list

# List only unblocked beans (ready to work on)
beans ready

# Search by title and body
beans search "auth"

# Aggregate counts by status, type, assignee
beans stats

# Dependency tree visualization
beans graph

Dependencies

# A blocks B (B can't start until A is closed)
beans dep add task-aaaa1111 task-bbbb2222

# Remove a dependency
beans dep remove task-aaaa1111 task-bbbb2222

Agent coordination

# Claim a bean (sets assignee + status=in_progress)
beans claim task-a3f2dd1c --actor alice

# Release a claimed bean
beans release task-a3f2dd1c --actor alice

# Release all beans claimed by an actor
beans release --mine --actor alice

Journal & sync

# Export journal to JSONL (for git-based sync)
beans export-journal > journal.jsonl

# Rebuild database from journal
beans rebuild journal.jsonl

Custom types

Beans ships with three default types: task, bug, and epic. Add your own:

# List configured types
beans types

# Add a custom type
beans types add spike --description "Time-boxed investigation"

# Remove a type
beans types remove spike

Bean IDs are prefixed with their type: task-a3f2dd1c, epic-12345678, spike-deadbeef.

Configuration

# Show config path and settings
beans config

# Agent integration skill
beans skill

Introspection

# Output JSON schemas for all models
beans schema

# Field filtering (works with show, list, ready, search)
beans --fields id,title,status list
beans --json --fields id,title show task-a3f2dd1c

Architecture

src/beans/
├── models.py    # Pydantic models (pure, no I/O)
├── store.py     # SQLite storage (I/O boundary)
├── api.py       # Command API (composes store calls)
├── config.py    # Global config (~/.config/beans/)
├── project.py   # Project discovery (find .beans/)
└── cli.py       # Typer CLI (thin wiring layer)

• models.py — Pure data. Bean is a Pydantic model with validation. No I/O, no side effects, easy to test.
• store.py — The I/O boundary. Store wraps a SQLite connection and composes BeanStore, DepStore, and JournalStore. Accepts an injected connection for testing.
• api.py — Command API. Each function is one use case (create, close, claim, release, stats, graph). Composes store calls.
• cli.py — Thin wiring. Parses args, calls API functions, formats output. No business logic.

For AI agents

Beans is designed to be used by AI agents as a coordination mechanism. Add this to your project's AGENTS.md, or use beans skill for a ready-made integration:

beans skill              # output agent skill to terminal
beans skill > AGENTS.md  # save to file

Or add manually:

## Task tracking

This project uses `beans` for task tracking. Use `beans --json` for all commands.

- Check available work: `beans --json ready`
- Claim a task: `beans claim <id> --actor <name>`
- Show task details: `beans --json show <id>`
- Mark done: `beans close <id> --reason "Implemented in <sha>"`
- Create subtasks: `beans create "<title>" --parent <id>`
- Add dependencies: `beans dep add <blocker-id> <blocked-id>`

License

MIT

Contributing

Beans is built with Python 3.14, managed with uv.

git clone https://github.com/henriquebastos/beans.git
cd beans
uv sync
uv run pytest
uv run ruff check src/ tests/

Tests use real SQLite :memory: databases — no mocks. The test suite runs in under a second.

Releasing

1. Bump the version in pyproject.toml
2. Generate changelog:

   uv run git-cliff --tag v<VERSION> -o CHANGELOG.md
   

3. Commit and tag:

   git add pyproject.toml CHANGELOG.md
   git commit -m "chore: bump version to <VERSION>"
   git tag v<VERSION>
   git push origin main --tags
   

4. Create a GitHub Release:

   gh release create v<VERSION> --generate-notes
   

The release workflow runs tests, publishes to PyPI, and updates the Homebrew formula. The package is published as magic-beans on PyPI (pip install magic-beans), but the CLI command is beans and the import is import beans.