FE-666: Basic Monte Carlo Simulation by kube · Pull Request #8709 · hashintel/hash

kube · 2026-05-10T13:49:18Z

🌟 What is the purpose of this PR?

Reworks Petrinaut simulation internals and adds the first Monte Carlo experiment workflow. The branch separates Core/React/UI boundaries, moves frame access behind reader/binary-frame APIs, introduces seeded Monte Carlo runs with streaming token-count distributions, and exposes experiments in the Simulate tab plus CLI tooling.

flowchart LR
  UI[Simulate > Experiments] --> Context[ExperimentsContext]
  Context --> Runtime[Experiment runtime]
  Runtime <--> Worker[Monte Carlo worker protocol]
  Worker --> Simulator[Monte Carlo simulator]
  Simulator --> Metrics[Streaming token-count distributions]
  Metrics --> Context
  Context --> Timeline[uPlot experiment timeline]

🔗 Related links

Changeset — Petrinaut patch release note

🚫 Blocked by

None.

🔍 What does this change?

Refactors simulation Core boundaries, public APIs, frame readers, runtime modules, and worker-facing types.
Moves engine frames toward binary ArrayBuffer storage and keeps layout details behind SimulationFrameReader.
Makes simulation inputs JSON-serializable, including initial markings.
Simplifies deterministic simulation/playback lifecycle and reorganizes the Simulation Timeline modules.
Adds a seeded Monte Carlo simulator with per-run buffers, active-only token-count histograms, runtime orchestration, and worker tests.
Adds Simulate > Experiments UI for creating, running, cancelling, removing, and viewing Monte Carlo experiments.
Adds a Petrinaut Monte Carlo CLI and removes the temporary local stress-test file.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

modifies an npm-publishable library and I have added a changeset file(s)

📜 Does this require a change to the docs?

The changes in this PR:

require changes to docs which are made as part of this PR

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

I am unsure / need advice

⚠️ Known issues

The Monte Carlo metric storage is still a first implementation; windowed/binary metric persistence is left for follow-up work.

🐾 Next steps

Narrow the Simulation Engine around binary frame storage, metric storage, and stable worker protocols.
Improve Monte Carlo metric aggregation/storage before scaling the UI to very large experiments.

🛡 What tests cover this?

yarn workspace @hashintel/petrinaut lint:tsc
yarn workspace @hashintel/petrinaut lint:eslint
yarn workspace @hashintel/petrinaut test:unit src/core/simulation/monte-carlo/runtime/experiment.test.ts src/react/experiments/provider.test.tsx src/core/simulation/monte-carlo/monte-carlo-simulator.test.ts src/core/simulation/authoring/scenario/compile-scenario.test.ts --run

❓ How to test this?

Open Petrinaut and go to Simulate > Experiments.
Create an experiment with a scenario, parameters, max time, and run count.
Click Run and confirm the dialog shows progress, closes on success, and opens the new experiment.
Confirm the token-count distribution timeline streams p10/median/mean/p90 data per place.
Cancel/remove an experiment and confirm the provider state updates correctly.

vercel · 2026-05-10T13:49:23Z

The latest updates on your projects. Learn more about Vercel for GitHub.

Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview, Comment	May 16, 2026 3:12pm
petrinaut	Ready	Preview, Comment	May 16, 2026 3:12pm

1 Skipped Deployment

Project	Deployment	Actions	Updated (UTC)
hashdotdesign-tokens	Ignored	Preview	May 16, 2026 3:12pm

codecov · 2026-05-10T13:53:54Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 6.20%. Comparing base (7b67b01) to head (d3127af).
⚠️ Report is 4 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #8709       +/-   ##
==========================================
- Coverage   62.31%   6.20%   -56.12%     
==========================================
  Files        1354     506      -848     
  Lines      137313   14817   -122496     
  Branches     5793    2994     -2799     
==========================================
- Hits        85568     919    -84649     
+ Misses      50838   13865    -36973     
+ Partials      907      33      -874

Flag	Coverage Δ
apps.hash-ai-worker-py	`?`
apps.hash-ai-worker-ts	`1.41% <ø> (ø)`
apps.hash-api	`0.00% <ø> (ø)`
backend-integration-tests	`?`
blockprotocol.type-system	`?`
deer	`?`
error-stack	`?`
local.claude-hooks	`?`
local.harpc-client	`51.24% <ø> (ø)`
local.hash-backend-utils	`2.81% <ø> (ø)`
local.hash-graph-sdk	`9.63% <ø> (ø)`
local.hash-isomorphic-utils	`0.00% <ø> (ø)`
local.hash-subgraph	`?`
rust.antsi	`?`
rust.deer	`?`
rust.error-stack	`?`
rust.harpc-codec	`?`
rust.harpc-net	`?`
rust.harpc-tower	`?`
rust.harpc-types	`?`
rust.harpc-wire-protocol	`?`
rust.hash-codec	`?`
rust.hash-graph-api	`?`
rust.hash-graph-authorization	`?`
rust.hash-graph-postgres-store	`?`
rust.hash-graph-store	`?`
rust.hash-graph-temporal-versioning	`?`
rust.hash-graph-types	`?`
rust.hash-graph-validation	`?`
rust.hashql-ast	`?`
rust.hashql-compiletest	`?`
rust.hashql-core	`?`
rust.hashql-diagnostics	`?`
rust.hashql-eval	`?`
rust.hashql-hir	`?`
rust.hashql-mir	`?`
rust.hashql-syntax-jexpr	`?`
rust.sarif	`?`
sarif	`?`
tests.hash-backend-integration	`?`
unit-tests	`?`

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codspeed-hq · 2026-05-10T14:02:08Z

Merging this PR will not alter performance

✅ 80 untouched benchmarks

_{Comparing cf/fe-666-stochastic-simulation-experiments-v1 (a880232) with main (b922dd2)¹}

No successful run was found on main (8452cc0) during the generation of this report, so b922dd2 was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

github-actions · 2026-05-10T15:06:19Z

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$26.5 \mathrm{ms} \pm 228 \mathrm{μs}\left({\color{gray}1.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.52 \mathrm{ms} \pm 19.2 \mathrm{μs}\left({\color{gray}2.99 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.3 \mathrm{ms} \pm 75.2 \mathrm{μs}\left({\color{gray}-1.471 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.3 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}-0.211 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$15.1 \mathrm{ms} \pm 115 \mathrm{μs}\left({\color{gray}3.13 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.2 \mathrm{ms} \pm 139 \mathrm{μs}\left({\color{gray}-4.023 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.9 \mathrm{ms} \pm 161 \mathrm{μs}\left({\color{gray}1.67 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.87 \mathrm{ms} \pm 20.8 \mathrm{μs}\left({\color{gray}3.70 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.2 \mathrm{ms} \pm 87.9 \mathrm{μs}\left({\color{gray}-0.309 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.68 \mathrm{ms} \pm 22.4 \mathrm{μs}\left({\color{gray}-2.123 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.98 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{gray}-1.841 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.36 \mathrm{ms} \pm 23.8 \mathrm{μs}\left({\color{gray}0.307 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.05 \mathrm{ms} \pm 27.6 \mathrm{μs}\left({\color{gray}-0.597 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.53 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-0.118 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.10 \mathrm{ms} \pm 23.0 \mathrm{μs}\left({\color{gray}0.100 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.35 \mathrm{ms} \pm 26.2 \mathrm{μs}\left({\color{gray}-1.078 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.40 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}-2.555 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.03 \mathrm{ms} \pm 25.2 \mathrm{μs}\left({\color{gray}-0.717 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.63 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{gray}0.495 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.55 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}0.559 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.58 \mathrm{ms} \pm 11.2 \mathrm{μs}\left({\color{gray}-0.240 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.86 \mathrm{ms} \pm 12.9 \mathrm{μs}\left({\color{gray}0.053 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.61 \mathrm{ms} \pm 10.1 \mathrm{μs}\left({\color{gray}-2.382 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.81 \mathrm{ms} \pm 10.1 \mathrm{μs}\left({\color{gray}-1.171 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.01 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{gray}0.163 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.74 \mathrm{ms} \pm 15.2 \mathrm{μs}\left({\color{gray}-0.598 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.96 \mathrm{ms} \pm 11.1 \mathrm{μs}\left({\color{gray}0.719 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.36 \mathrm{ms} \pm 22.5 \mathrm{μs}\left({\color{gray}-1.467 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.94 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}-1.906 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.26 \mathrm{ms} \pm 16.5 \mathrm{μs}\left({\color{gray}-0.501 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.29 \mathrm{ms} \pm 18.1 \mathrm{μs}\left({\color{gray}-0.852 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.96 \mathrm{ms} \pm 11.8 \mathrm{μs}\left({\color{gray}-0.353 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.28 \mathrm{ms} \pm 17.6 \mathrm{μs}\left({\color{gray}-0.252 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$54.6 \mathrm{ms} \pm 349 \mathrm{μs}\left({\color{gray}0.491 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$46.1 \mathrm{ms} \pm 184 \mathrm{μs}\left({\color{gray}-0.314 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$51.5 \mathrm{ms} \pm 340 \mathrm{μs}\left({\color{gray}-0.440 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.6 \mathrm{ms} \pm 363 \mathrm{μs}\left({\color{gray}2.40 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$62.3 \mathrm{ms} \pm 313 \mathrm{μs}\left({\color{gray}-0.984 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$61.0 \mathrm{ms} \pm 334 \mathrm{μs}\left({\color{gray}-0.528 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$55.7 \mathrm{ms} \pm 311 \mathrm{μs}\left({\color{gray}-0.498 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$102 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{gray}-1.590 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$61.9 \mathrm{ms} \pm 4.26 \mathrm{ms}\left({\color{red}31.7 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$290 \mathrm{ms} \pm 763 \mathrm{μs}\left({\color{gray}-0.950 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$19.4 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}-1.094 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$20.2 \mathrm{ms} \pm 103 \mathrm{μs}\left({\color{gray}0.398 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.3 \mathrm{ms} \pm 116 \mathrm{μs}\left({\color{gray}0.679 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$19.2 \mathrm{ms} \pm 63.1 \mathrm{μs}\left({\color{gray}-0.156 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.8 \mathrm{ms} \pm 173 \mathrm{μs}\left({\color{gray}3.97 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$19.2 \mathrm{ms} \pm 93.1 \mathrm{μs}\left({\color{gray}-0.220 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.2 \mathrm{ms} \pm 96.8 \mathrm{μs}\left({\color{gray}-2.065 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$19.7 \mathrm{ms} \pm 115 \mathrm{μs}\left({\color{gray}0.994 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$20.0 \mathrm{ms} \pm 81.4 \mathrm{μs}\left({\color{gray}-1.055 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$26.8 \mathrm{ms} \pm 200 \mathrm{μs}\left({\color{gray}-0.515 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/block/v/1`	$$35.5 \mathrm{ms} \pm 213 \mathrm{μs}\left({\color{gray}-0.274 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$35.8 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}1.11 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$34.8 \mathrm{ms} \pm 263 \mathrm{μs}\left({\color{gray}-1.419 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$36.4 \mathrm{ms} \pm 368 \mathrm{μs}\left({\color{red}8.15 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$35.4 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{gray}1.96 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$34.9 \mathrm{ms} \pm 280 \mathrm{μs}\left({\color{gray}3.44 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$34.8 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}2.87 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$34.9 \mathrm{ms} \pm 338 \mathrm{μs}\left({\color{gray}0.195 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$34.6 \mathrm{ms} \pm 354 \mathrm{μs}\left({\color{gray}2.00 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.57 \mathrm{ms} \pm 56.3 \mathrm{μs}\left({\color{gray}1.63 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$93.5 \mathrm{ms} \pm 551 \mathrm{μs}\left({\color{gray}2.03 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$148 \mathrm{ms} \pm 598 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$100 \mathrm{ms} \pm 625 \mathrm{μs}\left({\color{gray}2.48 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$111 \mathrm{ms} \pm 512 \mathrm{μs}\left({\color{gray}1.31 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$118 \mathrm{ms} \pm 552 \mathrm{μs}\left({\color{gray}1.84 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$127 \mathrm{ms} \pm 537 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$103 \mathrm{ms} \pm 414 \mathrm{μs}\left({\color{gray}0.367 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$133 \mathrm{ms} \pm 448 \mathrm{μs}\left({\color{gray}1.33 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$111 \mathrm{ms} \pm 665 \mathrm{μs}\left({\color{gray}1.67 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 539 \mathrm{μs}\left({\color{gray}0.575 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$122 \mathrm{ms} \pm 679 \mathrm{μs}\left({\color{gray}0.956 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 443 \mathrm{μs}\left({\color{gray}0.350 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$189 \mathrm{ms} \pm 533 \mathrm{μs}\left({\color{red}21.5 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$169 \mathrm{ms} \pm 2.41 \mathrm{ms}\left({\color{red}13.0 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$40.3 \mathrm{ms} \pm 178 \mathrm{μs}\left({\color{gray}0.122 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$557 \mathrm{ms} \pm 927 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph

cursor · 2026-05-12T23:20:16Z

PR Summary

High Risk
High risk because it reshapes core simulation APIs (frame representation, initial marking format, playback backpressure typing) and adds new worker-driven Monte Carlo execution paths that could affect simulation correctness and downstream consumers.

Overview
Adds a first-cut Monte Carlo experiment capability to @hashintel/petrinaut/core, exposing new factories (createMonteCarloExperiment/createMonteCarloSimulator/createMonteCarloWorker) and metrics like createPlaceTokenCountDistributionMetric via the public core exports.

Refactors the simulation core API to hide engine storage behind SimulationFrameReader and JSON-serializable InitialMarking, and updates scenario compilation/tests accordingly (including support for overriding scenario parameters and stricter validation for colored-place token rows). Playback typing is tightened by introducing ComputePlayMode and restricting getPlayModeBackpressure to compute modes only.

Also includes small packaging/docs churn: adds a changeset, updates Panda CSS keyframes (spin), ignores analysis/ in libs/.gitignore, and removes the now-stale rfc/0001-core-react-ui-split markdown docs.

^{Reviewed by Cursor Bugbot for commit f7da09e. Bugbot is set up for automated code reviews on this repo. Configure here.}

augmentcode · 2026-05-12T23:20:19Z

This pull request is abnormally large and would use a significant amount of tokens to review. If you still wish to review it, comment "augment review" and we will review it.

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Comment thread libs/@hashintel/petrinaut/src/cli/app.tsx Outdated

Comment thread libs/@hashintel/petrinaut/src/core/simulation/monte-carlo/runtime/experiment.ts

graphite-app Bot assigned kube May 13, 2026

kube added 20 commits May 16, 2026 17:01

Add Monte Carlo simulator

ded34c5

Delete MonteCarloSimulator.md

59f4032

Add Monte Carlo experiments UI

94ddfb1

Add Petrinaut Monte Carlo CLI

4cd33d4

Remove local Monte Carlo stress test

0799de2

Add Petrinaut Monte Carlo changeset

11aaa6b

Remove Petrinaut CLI

8189f54

Update .gitignore to exclude analysis folder

d0209bb

Fix Petrinaut simulation pre-merge issues

0e56791

Remove Petrinaut analysis artifacts

d8d6d05

Update smooth-lizards-run.md

e77dd2a

Fix empty colored token row validation

a9e79aa

Adjust experiment drawer body spacing

a2f47a9

Normalize drawer body spacing

fbc6491

Avoid cloning Monte Carlo initial marking

803aa20

Resolve timeline icon and metric imports

03e939a

Remove unused drawer style imports

0edaa31

Clean up Monte Carlo transport on init failure

349e180

Make inline code editor tabs move focus

dc5c53a

Fix Monte Carlo simulation time drift

f7da09e

kube force-pushed the cf/fe-666-stochastic-simulation-experiments-v1 branch from de3c31d to f7da09e Compare May 16, 2026 15:01

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kube added this pull request to the merge queue May 16, 2026

Merged via the queue into main with commit 2c1c977 May 16, 2026
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kube deleted the cf/fe-666-stochastic-simulation-experiments-v1 branch May 16, 2026 15:40

hashdotai mentioned this pull request May 16, 2026

Version Packages #8723

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🌟 What is the purpose of this PR?

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codspeed-hq Bot commented May 10, 2026

Merging this PR will not alter performance

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github-actions Bot commented May 10, 2026

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

policy_resolution_medium

policy_resolution_none

policy_resolution_small

read_scaling_complete

read_scaling_linkless

representative_read_entity

representative_read_entity_type

representative_read_multiple_entities

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kube commented May 10, 2026 •

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