Tutti — Multi-Agent Orchestration

Orchestrate a team of AI coding agents from a declarative tutti.toml config. Launch agents in isolated git worktrees, run verification workflows, track token usage, and manage context handoffs — all through a single CLI.

When to use this skill

Use when the user asks you to:

• Launch, monitor, or stop a team of AI coding agents
• Run or verify automated workflows across agents
• Dispatch prompts to agents with auto-start and output capture
• Land agent work back to the main branch or open PRs
• Check agent status, health, or capacity usage
• Generate or apply context handoff packets
• Coordinate multi-agent development workflows

Prerequisites

1. tt binary installed and on PATH (install from https://github.com/nutthouse/tutti)
2. tmux installed
3. python3 available
4. A tutti.toml config file in the workspace root

Always run preflight checks before starting a workflow:

python3 tutti_openclaw.py doctor_check

Actions

All actions go through the wrapper script. Every action returns a consistent JSON envelope:

{
  "ok": true,
  "action": "action_name",
  "command": ["tt", "..."],
  "exit_code": 0,
  "data": {},
  "stdout": "",
  "stderr": ""
}

Lifecycle

Workflows

Git Operations

Handoffs

Permissions

Workflow step types

Workflows in tutti.toml support these step types:

Prompt steps support inject_files — an array of workspace-relative file paths that are copied into the agent's worktree before the prompt is sent. This enables stateful context passing between agents (e.g., injecting a snapshot JSON produced by another agent).

Nested workflow steps enable composition: observe → dispatch → fix → verify → land as a chain of workflow invocations.

Execution pattern

Follow this sequence for orchestrating a workspace:

1. Preflight — doctor_check. Stop and report if non-zero.
2. Launch — launch_team or launch_agent .
3. Monitor — team_status and agent_output to observe progress.
4. Dispatch — send_prompt "do something" --auto-up --wait --output to dispatch work and capture results.
5. Workflow — list_workflows to discover, then run_workflow .
6. Verify — verify_team --strict for gate-style quality checks.
7. Land — land_agent to cherry-pick work, or land_agent --pr to open a PR.
8. Handoff — generate_handoff when context is high, apply_handoff to resume.
9. Stop — stop_team or stop_agent when done.

Failure handling

• Non-zero exit: Surface the action, command, and stderr from the JSON envelope. Do not retry blindly.
• Verify warnings (non-strict): Report as warning. Include data from read_verify_status.
• Missing state files: Treat as transient — retry up to 3 times with short delays. If still missing, the workspace may not have been launched.
• Auth failures: If stderr contains auth errors, stop and escalate to the user. Do not retry auth failures.
• Agent not running: Use --auto-up on send_prompt to automatically start agents on demand rather than failing.

Configuration override

If tt is not on PATH or you need a specific version:

python3 tutti_openclaw.py --tt-bin /path/to/tt doctor_check
# or via environment variable
TUTTI_BIN=/path/to/tt python3 tutti_openclaw.py doctor_check

Rules

• Always run doctor_check before any launch or workflow operation.
• Never retry auth failures — escalate to the user immediately.
• Prefer team_status (reads state files directly) over agent_output for status checks.
• Use --strict flag on verify_team and run_workflow when results gate further actions.
• Use --auto-up on send_prompt when the target agent may not be running.
• Use --output on send_prompt to capture the agent's response for programmatic verification.
• Use --json output from tt commands when you need structured data (the wrapper handles this automatically).
• Do not parse stdout text output — always use the data field from the JSON envelope.