These prompt-first tutorials use the small public Python palmerpenguins repository because it has real package code, tests, and bundled CSV data without requiring a private repository.
You don't need to memorize commands. Practice a repeatable conversation in your coding harness: ask a normal coding question, let the agent check Loom, and confirm that its answer reflects current shared memory. The prompts work in both Claude Code and Codex unless a tutorial calls out a harness-specific step.
Everything you build here runs on the same memory model behind Loom's benchmark scores on STALE, LongMemEval, and AMA-Bench.
[loom_demo], and multi-agent coordination in [kanban].The order is deliberate. First you establish a safe connection and learn how Loom organizes memory. Then you practice saving and retrieving project knowledge, letting relevant context resurface automatically, and turning repeated work into reusable capabilities. The final tutorials apply those skills to planning, maintenance, and multi-agent coordination.
Store names vary by account. Tutorial 02 shows how to list, load, describe, and unload your stores without replacing the default store for the session.
Every walkthrough uses the same three-part format:
Memory compounds when you close the loop. At the end of a working session, use this prompt:
Run this as a habit, not as a demand to create a fact every time.
Connect your coding harness, enable Loom's hooks, and verify that shared memory is ready.
Save useful project context and see it return in a fresh coding session.
Connect one agent to multiple stores while keeping each memory boundary clear.
Give Loom enough project context to return the right memory at the right time.
Learn how entities, relations, and built-in types turn notes into connected knowledge.
Capture standing instructions and verified discoveries so agents stop relearning them.
See how Loom surfaces relevant memory during work and carries unfinished work forward.
Create a reusable type when your team needs to remember domain-specific information.
Build a coding plan from current roadmap items and decisions instead of starting cold.
Turn repeated reasoning into a reusable rule that saves time and tokens.
Coordinate several agent sessions through a shared Loom Kanban board.
Keep shared memory accurate, preserve completed work, and record delivery evidence.
Run a repeatable store-health audit, apply bounded repairs, and verify the result without erasing history.