Spindle

MCP server for Claude Code to Claude Code delegation. Spawn background agents that run asynchronously, with optional git worktree isolation for safe parallel work.

Features

• Async agent spawning - Fire-and-forget pattern with spool IDs
• Optional blocking with gather/yield - Wait for all results at once, or stream them as agents complete. Alternatively, agent can continue other work, spins are nonblocking by default
• Permission profiles - Control what tools child agents can use (readonly, careful, full)
• Shard isolation - Run agents in sandboxed git worktrees to prevent conflicts
• Model selection - Route tasks to haiku, sonnet, or opus per-agent
• Session continuity - Resume conversations with child agents (auto-recovers expired sessions)
• Rich querying - Search, filter, peek at running output, export results

Requirements

• Python 3.10+
• Claude CLI installed and authenticated
• Git (for shard/worktree functionality)

Install

pip install spindle-mcp

Add to Claude Code's MCP config (~/.claude.json):

{
  "mcpServers": {
    "spindle": {
      "command": "spindle"
    }
  }
}

Usage

Basic: Spawn and collect

# Spawn an agent
spool_id = spin("Research the Python GIL")

# Do other work...

# Check result
result = unspool(spool_id)

Permission profiles

Control what tools the spawned agent can use:

# Read-only: Can only search and read
spin("Analyze the codebase", permission="readonly")

# Careful (default): Can read/write but limited bash
spin("Fix this bug", permission="careful")

# Full access: No restrictions
spin("Implement the feature", permission="full")

# Shard: Full access + auto-isolated worktree (common for risky work)
spin("Refactor the auth system", permission="shard")

# Careful + shard: Limited tools but isolated
spin("Update configs", permission="careful+shard")

Profiles:

• readonly: Read, Grep, Glob, safe bash (ls, cat, git status/log/diff)
• careful: Read, Write, Edit, Grep, Glob, bash for git/make/pytest/python/npm
• full: No restrictions
• shard: Full access + auto-creates isolated worktree
• careful+shard: Careful permissions + auto-creates isolated worktree

You can also pass explicit allowed_tools to override the profile.

Isolated workspaces with shards

Run agents in isolated git worktrees to prevent conflicts:

# Agent works in its own worktree
spool_id = spin("Refactor auth module", shard=True)

# Check shard status
shard_status(spool_id)

# Merge changes back when done
shard_merge(spool_id)

# Or discard if not needed
shard_abandon(spool_id)

Shards create a git worktree + branch. If SKEIN is available, uses skein shard spawn for richer tracking. Falls back to plain git worktree otherwise.

Wait for completion

# Spawn multiple agents
id1 = spin("Find all TODO comments")
id2 = spin("List unused imports")
id3 = spin("Check for type errors")

# Gather: block until all complete, get all results
results = spin_wait("id1,id2,id3", mode="gather")

# Yield: return as each completes
# Great when results are independent - process each as it lands
result = spin_wait("id1,id2,id3", mode="yield")  # Returns first to finish

# With timeout
results = spin_wait("id1,id2", mode="gather", timeout=300)

Yield mode keeps you responsive instead of blocking on the slowest agent.

Time-based waiting

Simple timed waiting with spin_sleep:

spin_sleep("90m")       # Sleep for 90 minutes
spin_sleep("2h")        # Sleep for 2 hours
spin_sleep("30s")       # Sleep for 30 seconds
spin_sleep("06:00")     # Wait until 6 AM

Or use spin_wait with the time parameter:

spin_wait(time="90m")
spin_wait(time="06:00")  # Handles next-day wraparound

Useful for periodic check-in loops (e.g., QM/dancing partner patterns).

Model selection and timeouts

# Route quick tasks to haiku (fast, cheap)
spin("Summarize this file", model="haiku")

# Complex work to opus
spin("Design the new architecture", model="opus")

# Auto-kill if it takes too long
spin("Should be quick", timeout=60)

Continue a session

# Get session ID from completed spool
result = unspool(spool_id)  # includes session_id

# Continue that conversation
new_id = respin(session_id, "Follow up question")

If the session has expired on Claude's end, respin automatically falls back to transcript injection to recreate context.

Cancel running work

spin_drop(spool_id)

List all spools

spools()

Search and filter

# Search prompts and results
spool_search("authentication")

# Filter by status and time
spool_results(status="error", since="1h")

# Regex search results
spool_grep("error|failed|exception")

# Get statistics
spool_stats()

# Export to file
spool_export("all", format="md")

Multi-Harness Support

Spindle supports multiple AI agent harnesses, allowing you to choose the best tool for each task.

Available Harnesses

Claude Code (default) - Anthropic's Claude models via claude CLI

• Superior code understanding and reasoning
• Best for complex refactoring, architecture decisions
• Slower startup (~3-4 minutes to first response)
• Use harness="claude-code" or omit harness parameter

Codex CLI - OpenAI's GPT-5 Codex models via codex CLI

• Extremely fast startup (~10 seconds to first response)
• Good for quick edits, simple tasks, prototyping
• Requires ChatGPT Plus/Pro/Enterprise
• Use harness="codex"

Basic Usage

# Claude Code (default) - best for complex work
spool_id = spin("Refactor the auth module to use dependency injection")

# Codex CLI - fast for simple tasks
spool_id = spin(
    prompt="Add error handling to this function",
    harness="codex",
    working_dir="/path/to/project"
)

# All harnesses use the same API
result = unspool(spool_id)  # Auto-detects harness
respin(session_id, "Follow up")  # Auto-detects harness

Choosing a Harness

Use Claude Code when:

• Task requires deep reasoning or architecture decisions
• Working on complex refactoring across multiple files
• Need thorough code review or analysis
• Time isn't critical (can wait 3-4 minutes)

Use Codex when:

• Need quick edits or simple implementations
• Prototyping or exploring ideas rapidly
• Running many parallel tasks (faster = more throughput)
• Time is critical (10 second startup vs 3-4 minutes)

Requirements

Claude Code:

• Claude CLI installed and authenticated
• Anthropic API key or Claude subscription

Codex CLI:

• Codex CLI installed (npm i -g @openai/codex)
• ChatGPT Plus/Pro/Enterprise subscription
• Codex CLI authenticated
• Linux kernel 5.13+ for sandbox support (automatically bypassed on older kernels)

See docs/MULTI_HARNESS_GUIDE.md and docs/CODEX_SETUP.md for detailed documentation.

API

Unified API (works with both harnesses)

Tool	Purpose
spin(prompt, permission?, shard?, system_prompt?, working_dir?, allowed_tools?, tags?, model?, timeout?, harness?)	Spawn agent (Claude Code or Codex), return spool_id
unspool(spool_id)	Get result (auto-detects harness, non-blocking)
respin(session_id, prompt)	Continue session (auto-detects harness)

spin() parameters:

• prompt (required): The task for the agent
• harness (optional): "claude-code" (default) or "codex"
• working_dir (optional for Claude, required for Codex): Project directory
• permission (optional): "readonly", "careful" (default), "full", "shard", "careful+shard"
• model (optional): Model to use ("sonnet", "opus", "haiku" for Claude; "gpt-5-codex" for Codex)
• timeout (optional): Auto-kill after N seconds
• tags (optional): Comma-separated tags for organization
• shard (optional): Create isolated git worktree (can also use permission="shard")
• system_prompt (optional): Custom system prompt for Claude Code
• allowed_tools (optional): Override permission profile with explicit tool list

Spool Management (works with both harnesses)

Tool	Purpose
spools()	List all spools
spin_wait(spool_ids?, mode?, timeout?, time?)	Block until spools complete, or wait for duration
spin_sleep(duration)	Sleep for a duration (90m, 2h, 30s, HH:MM)
spin_drop(spool_id)	Cancel by killing process
spool_search(query, field?)	Search prompts/results
spool_results(status?, since?, limit?)	Bulk fetch with filters
spool_grep(pattern)	Regex search results
spool_retry(spool_id)	Re-run with same params
spool_peek(spool_id, lines?)	See partial output while running
spool_dashboard()	Overview of running/complete/needs-attention
spool_stats()	Get summary statistics
spool_export(spool_ids, format?, output_path?)	Export to file
shard_status(spool_id)	Check shard worktree status
shard_merge(spool_id, keep_branch?)	Merge shard to master
shard_abandon(spool_id, keep_branch?)	Discard shard

Storage

Spools persist to ~/.spindle/spools/{spool_id}.json:

{
  "id": "abc12345",
  "status": "complete",
  "prompt": "...",
  "result": "...",
  "session_id": "...",
  "permission": "careful",
  "allowed_tools": "...",
  "tags": ["batch-1"],
  "shard": {
    "worktree_path": "/path/to/worktrees/abc12345-...",
    "branch_name": "shard-abc12345-...",
    "shard_id": "..."
  },
  "pid": 12345,
  "created_at": "2025-11-26T...",
  "completed_at": "2025-11-26T..."
}

CLI Commands

spindle install-service  # Install background service (Linux/macOS)
spindle start            # Start via systemd (or background if no service)
spindle reload           # Restart via systemd to pick up code changes
spindle status           # Check if running (hits /health endpoint)
spindle serve --http     # Run MCP server directly

Background Service

For persistent background operation:

# Install and enable the service (Linux or macOS)
spindle install-service

# Start it
spindle start

Linux: Writes a systemd user service to ~/.config/systemd/user/spindle.service

macOS: Writes a launchd plist to ~/Library/LaunchAgents/com.spindle.server.plist and loads it immediately

Use --force to overwrite an existing service file. Then spindle reload restarts the service to pick up code changes.

Windows

On Windows, run spindle manually:

spindle serve --http

Or use NSSM to create a Windows service.

WSL

In WSL2 with systemd enabled, spindle install-service works like native Linux. If systemd isn't enabled, you'll get instructions to enable it or run manually.

Hot Reload (MCP tool)

From within Claude Code, call spindle_reload() to restart the server and pick up code changes.

Configuration

Environment variables:

Variable	Default	Description
SPINDLE_MAX_CONCURRENT	15	Maximum concurrent spools

Storage location: ~/.spindle/spools/

How It Works

1. spin() spawns a detached claude CLI process with the given prompt
2. The process runs in background, writing output to temporary files
3. A monitor thread polls for completion
4. unspool() returns the result once complete (non-blocking check)
5. Spool metadata persists to JSON files, surviving server restarts

For shards:

1. A git worktree is created with a new branch
2. The agent runs inside that worktree
3. After completion, merge back with shard_merge() or discard with shard_abandon()

Limits

• Max 15 concurrent spools (configurable via SPINDLE_MAX_CONCURRENT)
• 24h auto-cleanup of old spools
• Orphaned spools (dead process) marked as error on restart

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT - see LICENSE.