FE-1270: Extract Python bindings for the Petrinaut CLI into @local/petrinaut-python by kube · Pull Request #9228 · hashintel/hash

kube · 2026-08-16T18:05:11Z

🌟 What is the purpose of this PR?

The only way to use Petrinaut from Python was a copy-paste subprocess wrapper in the CLI's docs, while apps/petrinaut-opt carried its own hardened client that exposed just 2 of the CLI's 5 protocol methods. This PR extracts that client into a reusable package — libs/@local/petrinaut-python — so the AI lab can run simulations and optimization studies from Python directly, and the optimizer service consumes the same code instead of a private copy.

Stack #9226: FE-1410 → FE-1411 → FE-1408 → FE-1413 → this PR → (next: FE-1412).

🔗 Related links

FE-1270 (internal) — this PR

🔍 What does this change?

New package @local/petrinaut-python (internal-only, stdlib-only, POSIX-only)

PetrinautSession — one CLI process per session: from_model_file / from_model (stdin bootstrap), healthz(), metadata(), run(params), a generic request(method, params) escape hatch, context-manager lifecycle, and the hardened close(graceful=) semantics the optimizer relies on.
OptimizationSession(PetrinautSession) — opaque-manifest studies: describe_optimization(), objective(values), and evaluate(values) returning the full result including per-seed replicates.
The transport is petrinaut-opt's client moved, not rewritten: env scrubbing, bounded reads, process-group SIGTERM→SIGKILL shutdown are byte-for-byte (git tracks the move at 70/84% similarity). PetrinautRequestError aliases PetrinautRunError for the generic reading.

Monorepo wiring (the first cross-package Python dependency in the repo)

petrinaut-opt depends on it as an editable uv path dependency, mirrored as a workspace:* edge in package.json — uv deps are invisible to Turbo, and without the mirror, CI pruning deletes the lib and lib changes never trigger the service's tests.
codegen inputs now include uv.lock and the bindings' sources, so the OpenAPI drift gate can't serve a stale cache.
Docker: the python-deps stage keeps the repo layout so the path dep resolves, and the runner copies the bindings source (editable install). deploy.yml's path filter gains the new lib.

Retired

apps/petrinaut-opt/src/petrinaut_client.py (moved), PYTHON_INTEGRATION.md, examples/python_stdio.py. MODEL_EXAMPLES.md and the usage manual now use the bindings.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

modifies workspaces but not a publishable library (@local/petrinaut-python is private and internal-only)

📜 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
- Bindings README, MODEL_EXAMPLES.md, and the usage manual's Python section. In-app user docs unaffected.

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

affected the execution graph, and the turbo.json's have been updated to reflect this
- @apps/petrinaut-opt → @local/petrinaut-python → @hashintel/petrinaut-cli edges (the latter so the bindings' e2e test always runs against a fresh CLI bundle); widened codegen inputs.

⚠️ Known issues

The bindings are POSIX-only (os.killpg, select on descriptors) — documented, not guarded.
The Unix-socket transport and Windows support are deliberately out of scope.

🛡 What tests cover this?

Bindings (23 tests): the optimizer client suite moved and re-pointed; new coverage for from_model_file/from_model bootstrap behaviour, healthz/metadata/run, error-frame recovery, request() passthrough, manifest-path sessions, evaluate with replicates; an end-to-end test spawning the real built dist/cli.js (deterministic same-seed rerun included) — the first Python test in the repo to exercise the real CLI.
petrinaut-opt (75 tests): full suite plus the OpenAPI drift gate, now importing the package.

❓ How to test this?

turbo run test:unit --filter @local/petrinaut-python --filter @apps/petrinaut-opt

🤖 Generated with Claude Code

Adds libs/@local/petrinaut-python: a stdlib-only, POSIX-only package wrapping the CLI's JSON-lines protocol. PetrinautSession serves a model (from a file or over stdin) with healthz/metadata/run plus a generic request escape hatch; OptimizationSession adds describe/evaluate/ objective over an opaque manifest, with evaluate exposing per-seed replicates. The code is petrinaut-opt's hardened client, generalized: spawn scrubbing, bounded reads, process-group shutdown are unchanged. petrinaut-opt consumes the package as an editable uv path dependency, mirrored as a workspace:* edge so Turbo pruning and change detection see it; its codegen inputs, the Docker python-deps/runner stages, and the deploy path filter follow. The old src/petrinaut_client.py, the PYTHON_INTEGRATION.md walkthrough, and examples/python_stdio.py are retired; MODEL_EXAMPLES.md and the usage manual now use the bindings. A new end-to-end pytest drives the real built CLI bundle.

vercel · 2026-08-16T18:05:16Z

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Project	Deployment	Actions	Updated (UTC)
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codspeed-hq · 2026-08-16T18:14:10Z

Merging this PR will not alter performance

✅ 98 untouched benchmarks

_{Comparing cf/fe-1270-create-python-bindings-to-petrinaut-core (c1e9f74) with main (1f543d0)¹}

No successful run was found on cf/fe-1413-arch-docs-cover-petrinaut-cli-and-attach-a-usage-manual (a44e5b9) during the generation of this report, so main (1f543d0) was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

github-actions · 2026-08-16T19:57:14Z

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	$$26.7 \mathrm{ms} \pm 208 \mathrm{μs}\left({\color{gray}3.47 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.59 \mathrm{ms} \pm 39.3 \mathrm{μs}\left({\color{red}5.82 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$12.5 \mathrm{ms} \pm 92.1 \mathrm{μs}\left({\color{gray}1.80 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.8 \mathrm{ms} \pm 368 \mathrm{μs}\left({\color{gray}0.235 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.5 \mathrm{ms} \pm 103 \mathrm{μs}\left({\color{gray}1.06 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$24.5 \mathrm{ms} \pm 147 \mathrm{μs}\left({\color{gray}1.99 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$27.8 \mathrm{ms} \pm 221 \mathrm{μs}\left({\color{gray}2.31 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.95 \mathrm{ms} \pm 38.6 \mathrm{μs}\left({\color{red}5.13 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.3 \mathrm{ms} \pm 80.3 \mathrm{μs}\left({\color{gray}-2.594 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.81 \mathrm{ms} \pm 34.9 \mathrm{μs}\left({\color{gray}2.47 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.99 \mathrm{ms} \pm 16.7 \mathrm{μs}\left({\color{gray}-0.554 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$3.32 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{gray}-0.796 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.17 \mathrm{ms} \pm 33.3 \mathrm{μs}\left({\color{gray}1.91 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.53 \mathrm{ms} \pm 19.1 \mathrm{μs}\left({\color{gray}-0.401 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$4.07 \mathrm{ms} \pm 25.4 \mathrm{μs}\left({\color{gray}-0.297 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.39 \mathrm{ms} \pm 30.0 \mathrm{μs}\left({\color{gray}1.46 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.44 \mathrm{ms} \pm 19.8 \mathrm{μs}\left({\color{gray}-1.780 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.02 \mathrm{ms} \pm 19.4 \mathrm{μs}\left({\color{gray}-0.754 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.70 \mathrm{ms} \pm 19.6 \mathrm{μs}\left({\color{gray}2.67 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.48 \mathrm{ms} \pm 11.3 \mathrm{μs}\left({\color{gray}-0.521 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.64 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}-0.082 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.92 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{gray}-0.107 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.72 \mathrm{ms} \pm 13.6 \mathrm{μs}\left({\color{gray}0.417 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.89 \mathrm{ms} \pm 15.1 \mathrm{μs}\left({\color{gray}-1.461 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.28 \mathrm{ms} \pm 30.0 \mathrm{μs}\left({\color{red}8.73 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.80 \mathrm{ms} \pm 20.4 \mathrm{μs}\left({\color{gray}1.84 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$3.05 \mathrm{ms} \pm 18.1 \mathrm{μs}\left({\color{red}5.58 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.94 \mathrm{ms} \pm 51.4 \mathrm{μs}\left({\color{red}15.3 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.05 \mathrm{ms} \pm 17.3 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$3.28 \mathrm{ms} \pm 23.4 \mathrm{μs}\left({\color{gray}2.47 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$4.01 \mathrm{ms} \pm 43.5 \mathrm{μs}\left({\color{red}19.7 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.03 \mathrm{ms} \pm 17.8 \mathrm{μs}\left({\color{gray}1.80 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.28 \mathrm{ms} \pm 22.2 \mathrm{μs}\left({\color{gray}-0.609 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$41.6 \mathrm{ms} \pm 271 \mathrm{μs}\left({\color{gray}-1.961 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$32.7 \mathrm{ms} \pm 239 \mathrm{μs}\left({\color{gray}-2.507 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$36.5 \mathrm{ms} \pm 206 \mathrm{μs}\left({\color{gray}3.41 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$32.0 \mathrm{ms} \pm 211 \mathrm{μs}\left({\color{gray}-0.679 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$42.5 \mathrm{ms} \pm 235 \mathrm{μs}\left({\color{gray}2.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$49.1 \mathrm{ms} \pm 243 \mathrm{μs}\left({\color{gray}-0.635 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$40.2 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}0.473 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$84.7 \mathrm{ms} \pm 622 \mathrm{μs}\left({\color{lightgreen}-5.765 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$33.9 \mathrm{ms} \pm 197 \mathrm{μs}\left({\color{gray}0.286 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$299 \mathrm{ms} \pm 857 \mathrm{μs}\left({\color{red}18.2 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$10.9 \mathrm{ms} \pm 82.3 \mathrm{μs}\left({\color{gray}0.414 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$11.1 \mathrm{ms} \pm 80.0 \mathrm{μs}\left({\color{gray}-0.841 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$11.0 \mathrm{ms} \pm 83.5 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$11.0 \mathrm{ms} \pm 71.7 \mathrm{μs}\left({\color{gray}1.15 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$11.1 \mathrm{ms} \pm 75.5 \mathrm{μs}\left({\color{gray}1.56 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$10.9 \mathrm{ms} \pm 62.5 \mathrm{μs}\left({\color{gray}-1.680 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$11.1 \mathrm{ms} \pm 112 \mathrm{μs}\left({\color{gray}0.814 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$11.1 \mathrm{ms} \pm 73.1 \mathrm{μs}\left({\color{gray}0.475 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$10.9 \mathrm{ms} \pm 61.4 \mathrm{μs}\left({\color{lightgreen}-5.259 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$11.3 \mathrm{ms} \pm 71.6 \mathrm{μs}\left({\color{gray}-0.220 \mathrm{\%}}\right) $$	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`	$$11.3 \mathrm{ms} \pm 57.8 \mathrm{μs}\left({\color{gray}-1.208 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$11.4 \mathrm{ms} \pm 64.4 \mathrm{μs}\left({\color{gray}1.73 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$11.4 \mathrm{ms} \pm 80.2 \mathrm{μs}\left({\color{gray}-2.029 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$11.4 \mathrm{ms} \pm 65.9 \mathrm{μs}\left({\color{gray}-3.786 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$11.3 \mathrm{ms} \pm 54.9 \mathrm{μs}\left({\color{gray}1.01 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$11.3 \mathrm{ms} \pm 56.0 \mathrm{μs}\left({\color{gray}0.908 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$11.4 \mathrm{ms} \pm 80.8 \mathrm{μs}\left({\color{gray}-1.226 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$11.5 \mathrm{ms} \pm 99.1 \mathrm{μs}\left({\color{gray}2.15 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$11.3 \mathrm{ms} \pm 47.0 \mathrm{μs}\left({\color{gray}-0.728 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.37 \mathrm{ms} \pm 45.5 \mathrm{μs}\left({\color{gray}1.65 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$57.7 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{gray}3.73 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$109 \mathrm{ms} \pm 701 \mathrm{μs}\left({\color{gray}-1.234 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$66.0 \mathrm{ms} \pm 612 \mathrm{μs}\left({\color{red}5.13 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$75.3 \mathrm{ms} \pm 565 \mathrm{μs}\left({\color{gray}1.11 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$83.4 \mathrm{ms} \pm 567 \mathrm{μs}\left({\color{gray}0.729 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$88.7 \mathrm{ms} \pm 674 \mathrm{μs}\left({\color{gray}-3.038 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$43.4 \mathrm{ms} \pm 270 \mathrm{μs}\left({\color{gray}0.842 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$70.9 \mathrm{ms} \pm 386 \mathrm{μs}\left({\color{gray}-2.620 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$49.1 \mathrm{ms} \pm 242 \mathrm{μs}\left({\color{gray}0.491 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$59.3 \mathrm{ms} \pm 414 \mathrm{μs}\left({\color{gray}-0.981 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$60.3 \mathrm{ms} \pm 447 \mathrm{μs}\left({\color{gray}-3.157 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$60.7 \mathrm{ms} \pm 424 \mathrm{μs}\left({\color{gray}-4.070 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$124 \mathrm{ms} \pm 576 \mathrm{μs}\left({\color{gray}-0.084 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$136 \mathrm{ms} \pm 464 \mathrm{μs}\left({\color{gray}0.814 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$18.9 \mathrm{ms} \pm 133 \mathrm{μs}\left({\color{gray}0.107 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$573 \mathrm{ms} \pm 1.06 \mathrm{ms}\left({\color{gray}1.90 \mathrm{\%}}\right) $$	Flame Graph

kube self-assigned this Aug 16, 2026

kube changed the title ~~FE-1270: Extract Python bindings for the Petrinaut CLI~~ FE-1270: Extract Python bindings for the Petrinaut CLI into @local/petrinaut-python Aug 16, 2026

kube deployed to pull-request August 16, 2026 18:06 — with GitHub Actions Active

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FE-1270: Extract Python bindings for the Petrinaut CLI into @local/petrinaut-python - #9228

FE-1270: Extract Python bindings for the Petrinaut CLI into @local/petrinaut-python#9228
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@rust/hash-graph-benches – Integrations

policy_resolution_large

policy_resolution_medium

policy_resolution_none

policy_resolution_small

read_scaling_complete

read_scaling_linkless

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🌟 What is the purpose of this PR?

🔗 Related links

🔍 What does this change?

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

📜 Does this require a change to the docs?

🕸️ Does this require a change to the Turbo Graph?

⚠️ Known issues

🛡 What tests cover this?

❓ How to test this?

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Merging this PR will not alter performance

Footnotes

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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

scenarios

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