MAS Framework — Hands-On Tutorials¶
Progressive tutorials for the MAS Framework — from single-agent basics to full lab experiments. They are one of several ways to work with MAS Lab; you can also reproduce paper labs or use the Web UI to design and inspect agents and experiments.
Start with Tutorial 0 — Docker or developer install, LLM credentials, and verification. Same setup for CLI and web UI.
| # | Tutorial | What you learn |
|---|---|---|
| 0 | Environment Setup | Install, PATH, model endpoint, API key wiring |
| 1 | Build an agent | Agent manifest, tools, skills, memory, CLI, traces |
| 2 | Orchestrate your MAS | MAS manifest, delegation, topology overlays |
| 3 | Run an experiment | Experiments, benchmarks, pipelines, MCEv1 evaluation |
After Tutorial 3, reproduce all paper results across 3 labs: Paper.
Start from Tutorial 0 for install and first commands — CLI and optional web UI use the same setup.
Quick start¶
See Tutorial 0 — Environment setup for Docker and developer install paths, LLM credentials, and verification steps.
Each tutorial ships demo/scenario.yaml — structured steps with commands and
expected output used by tests/tutorials/test_scenario_commands.py.
Replay all offline commands with logs (stdout/stderr per tutorial under /tmp):
python scripts/run_tutorial_scenarios.py
# Live LLM steps too:
TUTORIAL_ONLINE=1 python scripts/run_tutorial_scenarios.py
# Logs: /tmp/tutorial-00.log … /tmp/tutorial-03.log
Knowledge-graph normalization (mas-lab graph) is not part of this
open-source repository. OSS tutorials use telemetry, trajectory plots, and
benchmark pipelines on run logs directly.
What's next¶
After Tutorial 3:
- Labs — runnable experiment artifacts live in
labs/:design-space.lab(design patterns + topologies),lifecycle-control.lab,extensions.lab— see paper index - Custom evaluation — extend the Tutorial 3 pipeline with your own metrics and reports
- New benchmark scenarios — add datasets and overlays to compare routing and topology choices