| name | Recursive Agentic Task Orchestration (Fractals) |
| description | A powerful orchestration skill for breaking down high-level objectives into hierarchical task graphs and resolving them through isolated multi-agent execution in dedicated worktrees. |
| category | Orchestration |
| version | 1.0.0 |
Fractals orchestrator
Overview
The fractals integration provides a self-similar, LLM-steered recursion flow that maps high-level goals into task trees with atomic leaves. Each leaf runs in its own git worktree under a dedicated workspace directory so agents stay isolated.
Capabilities context
Use the orchestrate_fractal_task MCP tool when the user gives a large composite goal (for example a full-stack build or a broad refactor) and single-shot agent runs are unlikely to succeed.
Phases:
- PLAN: Tasks are classified as composite or atomic up to
max_depth (default 3).
- EXECUTE: For each leaf, a worktree is created and the chosen provider runs the task there.
Workspace layout (actual behavior)
The MCP entrypoint mcp_tools.orchestrate_fractal_task uses a workspace directory:
- Path:
tempfile.gettempdir() / "fractals_workspace" (for example /tmp/fractals_workspace on Unix).
- Git root: That directory is initialized as its own repository if needed.
- Leaf worktrees:
WorkspaceManager creates <workspace_path>/.worktrees/<task_id>/ per leaf (see workspace.py).
Do not merge these worktrees by hand unless you understand the fractal run; treat them as disposable sandboxes for that orchestration run.
Providers
provider="claude" (default): runs the Anthropic Claude CLI in each worktree (executor.py).provider="codomyrmex": uses the internal codomyrmex agent path in the same worktree.
Best practices
- Set
max_depth deliberately; excessive depth adds coordination overhead.
- Expect sequential leaf execution in the current MCP wrapper (concurrency may be added at async boundaries).
Related
