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AgentLoom

Build and run multi-agent applications from YAML.

Typed Workers, permissioned edits, resumable Runs, explicit Goal completion, and review-gated memory share one runtime truth.

AgentLoom treats a multi-agent system as an Application with an execution contract. YAML defines the Supervisor, typed Workers, models, tools, Skills, Hooks, permissions, and runtime policy. Run Applications through the CLI or Python API and inspect structured execution evidence.

Why AgentLoom

Workers become typed tools

A Supervisor explicitly selects Workers through worker_agents; each selected Worker becomes a callable tool named and described by that Worker. Simple Workers use an empty Tool argument object and text output; each Worker's task comes from its YAML. Complex Workers declare Draft 2020-12 input_schema and output_schema, which runtimes validate as executable contracts rather than prompt conventions.

Runs produce evidence, not terminal guesses

Every allocated Run receives an immutable run_id, manifest, and versioned lifecycle events, with bounded file logs when enabled plus audit records and artifacts. A logical task_id survives resume. The CLI JSON/JSONL and Python API read the same canonical state. Preflight rejection occurs before a Run or its storage is allocated.

Long-running work has an explicit owner

Goal Mode keeps one root Supervisor objective active across continuation segments and Worker delegation. Only that Supervisor can mark the Goal complete with evidence. Checkpointing preserves the Goal and completed work across interruptions and resume.

Memory has review boundaries

Self-Learning v6 stores searchable history and evidence-gated memory separately. Fact and experience candidates pass evidence gates and the configured scope-approval policy; promotion to Project scope is always initiated by a person.

Extensions do not silently gain authority

Skills are model-context packages loaded on demand. Hooks are separately and explicitly authorized runtime code. Built-in tool metadata is discoverable without importing implementations, while actual tool, file, Shell, and MCP access remains governed by Agent configuration and permissions.

Quick start

The source installer uses uv to create an isolated Python environment with locked dependencies and installs the agentloom and loom commands for this checkout:

git clone https://github.com/linora-u/AgentLoom.git
cd AgentLoom
./install

It currently supports macOS and Linux shells and requires Git and Bash. It installs missing uv through its official installer, then places the environment in this checkout's .venv and the launchers/install records in .agentloom. The project pins its managed Python version in .python-version; ./install uses that version even when another Python is available on PATH. No environment activation is needed. Set AGENTLOOM_INSTALL_DIR=/path/to/install for a custom installation with its environment in /path/to/install/venv. Open a new terminal and verify it:

agentloom --help
loom --help