Loop vs graph engineering

August 3, 2026 · View on GitHub

A loop discovers what to do next. A graph pre-determines what happens next.

Both ship in this repo, doing the same job, so you can read them against each other: waku brief is a loop, waku gather is a graph. Everything below was measured on those two.

The ladder — each rung is something you took control of

The buzzwords arrived in an order, and the order isn't marketing. Each one is a different part of the system you stopped leaving to chance.

	you control	you stopped worrying about
prompt engineering	the words	phrasing
context engineering	what's in the window	what the model can see
skills	the procedure	how it does a known task
loop engineering	what happens between calls	tools, memory, retries
graph engineering	the shape of the calls	order and concurrency

Two honest caveats, so nobody catches you out:

These overlapped; they didn't replace each other. Context engineering and loops grew up together, and RAG sat in the middle of both.
Everyone still does all five. A graph doesn't retire your prompts.

The rung most people skip: skills

A skill is a procedure written in English that the model may follow. A graph is the same procedure written in code that it cannot not follow.

This repo has both. skills/weekly-brief/SKILL.md describes the morning routine step by step, and the loop reads it and mostly complies — but on a bad day it skips a step, reorders, or forgets a source. The graph version can't skip a node.

That gives you a rule for "should I build a graph?"

Write the skill first. Harden it into a graph only once it stops changing.

A skill is ten seconds to edit, no code, no deploy. A graph you have to rewrite. Pay that cost when the shape has settled, not before.

When each one wins

The loop wins when you can't draw the steps in advance.

"Work this PR" — read the diff, run the tests, discover it needs a rebase, decide what next. Step three depends on what step two found. Any graph you commit to is wrong by then. This is most of what an assistant does.

The graph wins when you already know the shape and parts of it can overlap.

"Brief me every morning" — GitHub, the web, the calendar, memory. You don't discover that list; you know it before you wake up. Letting a model work it out one tool call at a time buys nothing and costs a round trip each time.

The measurement

Same task, both engines, against this repo. Full output in the commit history.

The loop — the model decides what to call:

 5701ms  tool  github_read
 6078ms  tool  github_read
 6740ms  tool  list_events
 TOTAL: 15297ms

The graph — the shape is known, so the scans run together:

    0ms  START scan_github · scan_web · scan_calendar · scan_memory
 1889ms  done  scan_github   (1506ms)
 1890ms  done  scan_web      (1887ms)
 1890ms  done  scan_calendar    (2ms)
 1890ms  done  scan_memory      (1ms)
13170ms  done  synthesize   (11279ms)
 TOTAL: 13171ms

Read this carefully, because the headline number is the boring one

Gathering: 1.9s vs 6.7s — 3.5×. That's the real effect.

Totals: 13.2s vs 15.3s — 14%. Because an 11-second synthesis call dominates both runs.

A graph speeds up the part that can be parallel, and nothing else. If your workflow is one big model call wearing a hat, a graph buys you almost nothing.

The finding that isn't about speed at all

The loop made three tool calls. It never searched the web and never searched memory. It didn't fail — it decided it had enough and stopped.

The graph fetched four, because the shape says four.

A graph can't forget.

For something you run every morning, that reliability is worth more than the two seconds. This is the argument to lead with.

"Isn't this just deterministic workflows from 2023?"

Mostly yes, and you should say so. Airflow, Temporal and Step Functions have run conditional parallel DAGs for a decade. The shape is old.

What's actually new is narrow, and it's worth being precise about:

The nodes are non-deterministic. A node is a model call; the same input can produce different output.
A model can pick the edge. Routing is data-dependent in a way a DAG scheduler never had to handle.
So you need guardrails Airflow never needed. waku's engine caps visits per node and total steps, because a graph with a model in it can otherwise loop forever with perfect confidence.

Old shape, new contents.

The thing worth realising

Open waku's architecture diagram — gateway → memory → loop → tools → reply, same order every turn.

That's already a graph. It was just hardcoded in Python instead of nodes and edges.

So a graph engine doesn't give you the shape. You had it. What it gives you is the shape as data instead of code: inspectable, drawable from the code itself, partly parallel, and re-routable without rewriting a function.

That's the honest pitch. It's smaller than the hype and more useful.

How it's built here

waku/graph/engine.py             nodes, edges, waves — the whole mechanism
waku/graph/nodes.py              tool_node / llm_node / agent_node / key_router
waku/graph/workflows/triage.py   the per-message front door
waku/graph/workflows/gather.py   the morning routine
waku/ops/gather.py               where the pure workflow meets this machine

Three ideas carry the engine. State is one dict every node reads and merges into — parallel nodes must write disjoint keys, and a collision raises rather than silently losing a write. Routers are plain Python functions over that state: models write it, code reads it, and no model ever decides control flow directly. Guards are the loop's two-guardrail pattern generalised — per-node visit caps for bounded cycles, plus a global step cap.

The loop becomes one node. agent_node runs a whole run_loop turn. This is the part people miss: a graph doesn't replace your loop, it arranges calls to it. In triage, the full_agent node is the ordinary loop, unchanged.

Waves, not a scheduler. Nodes whose dependencies are all satisfied run together in a thread pool; the next wave waits for the slowest. That's a real trade — a fast branch idles at the barrier — taken for legibility. The dashboard prints the wait rather than hiding it.

The two workflows are two different jobs

	triage	gather
Trigger	every message, automatically	you — `make gather`
Frequency	every turn	once a morning
`WAKU_GRAPH_WORKFLOWS`	gated by it	ignores it

Worth stating plainly, because two charts on one page look like two options you choose between. They aren't.

Try it

make gather            # the graph — four sources at once
make brief             # the loop — same job, model decides
make dashboard         # localhost:7777 → Graph → Run gather

The Graph tab shows the topology and a live card row. The chart shows the shape; the cards show it happening, with each wave's bars sharing a left edge because the nodes shared a start.

Safety, because a morning routine that acts on your behalf is a different animal

gather proposes and never acts. Not by instruction — by construction:

no agent_node, no run_loop, no ToolRegistry anywhere in it
its one model call passes no tools parameter, so the model is never handed a schema it could use to send, merge or create anything
the only write is a markdown file in .waku/outbox/ for a human to read

A source-level test fails CI if any of that changes, because the day someone adds a tool-using node "to make the digest smarter", nothing else would notice.

A model that is never given tools cannot use them. That's a guarantee about capability, not about instructions — and it's the only kind worth relying on.