FE-1467: Lint and type-check the Python packages in CI by kube · Pull Request #9261 · hashintel/hash

kube · 2026-08-21T00:08:31Z

Note

Replaces #9260, which GitHub's stack automation marked as merged during a stack reorder. Same commits, same branch; review history lives on the old PR.

🌟 What is the purpose of this PR?

The two Python packages had no linter, no formatter, and no type checker; CI ran their pytest suites and nothing else. This PR configures ruff and basedpyright in both packages and runs them in CI.

There are no editor-specific files: every editor reads the configuration from pyproject.toml, and both tools are pinned as uv dev dependencies so an editor and CI resolve the same versions from uv.lock.

Sits on FE-1270 (#9228) in stack #9269; FE-1468 (#9262) is above.

🔗 Related links

FE-1467 (internal): this PR

🔍 What does this change?

Configuration, in each package's pyproject.toml:

ruff lints with E, F, I, UP, B, SIM, RUF, TRY, DTZ, PTH and formats with ruff format.
basedpyright runs at typeCheckingMode = "standard" over src. FE-1468 raises it to strict.
Three rules are ignored, each with its reason in the file: TRY003 (error messages here name the value that failed), TRY004 (a non-conforming protocol response raises ValueError, not TypeError), and E501 (ruff format owns line width; it cannot split long strings or the single-line @role annotation).

Four bugs the type checker found:

telemetry.py annotated its exporters as LogExporter, which the OTLP exporters do not implement; the runtime base is LogRecordExporter.
__enter__ returned PetrinautSession, so with OptimizationSession(...) as session lost the optimization methods. It now returns the calling type.
petrinaut_optimizer.py shadowed its callback parameter with a nested function of the same name.
OptimizationSession.__init__ passed Mapping | None where Mapping was required; the branches now narrow it.

One root change: !**/.venv joins the workspace globs in package.json. basedpyright bundles a Node binary, so its package.json inside each .venv was discovered as a yarn workspace and the two copies broke Turborepo entirely.

CI: lint:ruff and lint:types scripts in both packages, declared in the root turbo.json, detected by the per-package task check in lint.yml, which reports false for every non-Python package.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

modifies a workspace but not a publishable library.

📜 Does this require a change to the docs?

The changes in this PR:

do not require changes to docs. Tool configuration lives in pyproject.toml.

🕸️ 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: lint:ruff and lint:types are declared with no dependencies, since uv run syncs each package's environment from its own lockfile.

🛡 What tests cover this?

turbo run lint:ruff lint:types passes for both packages — exactly what the new CI steps run.
Both pytest suites pass, including the OpenAPI regenerate-and-diff check.

❓ How to test this?

turbo run lint:ruff lint:types --filter @local/petrinaut-python --filter @apps/petrinaut-opt
Open apps/petrinaut-opt/src/telemetry.py in an editor with the ruff and basedpyright language servers: diagnostics appear with no editor configuration in the repository.

🤖 Generated with Claude Code

vercel · 2026-08-21T00:08:37Z

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

Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview	Aug 21, 2026 12:17pm
hashdotdesign-tokens	Ready	Preview	Aug 21, 2026 12:17pm
petrinaut	Ready	Preview	Aug 21, 2026 12:17pm
petrinaut-docs	Ready	Preview	Aug 21, 2026 12:17pm

cursor · 2026-08-21T00:14:42Z

PR Summary

Medium Risk
Touches CI lint detection and a few production type/control-flow paths (session context manager, Optuna callback, telemetry exporter types). Behavior should be equivalent, but a misconfigured turbo/CI skip could miss Python lint until noticed.

Overview
Adds ruff and basedpyright to @local/petrinaut-python and @apps/petrinaut-opt, and runs them in the existing per-package lint workflow via new lint:ruff / lint:types turbo tasks.

Config lives in each package’s pyproject.toml (standard type-checking on src only). Yarn now ignores **/.venv so basedpyright’s bundled Node binary is not treated as a workspace.

Type checking also drove a few real fixes: LogRecordExporter in telemetry, PetrinautSession.__enter__ returning the subclass type, no more nested callback shadowing in the study worker, UTC-aware study names, and narrower OptimizationSession constructor branches.

^{Reviewed by Cursor Bugbot for commit 554f741. Bugbot is set up for automated code reviews on this repo. Configure here.}

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codspeed-hq · 2026-08-21T00:17:27Z

Merging this PR will not alter performance

✅ 98 untouched benchmarks

_{Comparing cf/fe-1467-python-lint-and-type-check (554f741) with cf/fe-1270-create-python-bindings-to-petrinaut-core (7f897d7)}

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 method; OptimizationSession adds describe/evaluate/objective over an opaque manifest, and evaluate returns per-seed replicates when the manifest asks for more than one seed. The code comes from petrinaut-opt's client: spawn scrubbing, bounded reads, and 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, and MODEL_EXAMPLES.md now uses the bindings. Two end-to-end pytests drive the real built CLI bundle, one per session type.

The method sat on a class already named OptimizationSession, while its siblings evaluate() and objective() carry no prefix. describe() maps 1:1 to the protocol's optimization.describe. The previous name remains as a class-level alias. Raised in review on #9228.

Encoding failures happen before anything is written: params or a bootstrap payload that will not serialize to JSON raise TypeError, and a payload over the line cap raises ValueError. Both previously raised PetrinautClientError, whose contract says the process or transport is no longer usable — a caller treating it as terminal would discard a healthy session. The request id is now consumed only after a successful encode, so a rejected request leaves the session exactly as it was. Raised by Cursor and in review on #9228.

ruff and basedpyright are configured in each package's pyproject.toml and pinned as uv dev dependencies, so an editor and CI resolve the same versions with no editor-specific files. New lint:ruff and lint:types tasks are wired into the per-package probe in lint.yml.

github-actions · 2026-08-21T13:31:14Z

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	$$26.1 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}-2.179 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.44 \mathrm{ms} \pm 22.8 \mathrm{μs}\left({\color{gray}-0.498 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$12.6 \mathrm{ms} \pm 123 \mathrm{μs}\left({\color{lightgreen}-5.194 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.2 \mathrm{ms} \pm 403 \mathrm{μs}\left({\color{gray}-2.325 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.5 \mathrm{ms} \pm 123 \mathrm{μs}\left({\color{lightgreen}-6.013 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$24.0 \mathrm{ms} \pm 218 \mathrm{μs}\left({\color{lightgreen}-6.254 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.8 \mathrm{ms} \pm 234 \mathrm{μs}\left({\color{gray}-1.837 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.75 \mathrm{ms} \pm 25.7 \mathrm{μs}\left({\color{gray}-1.278 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.4 \mathrm{ms} \pm 114 \mathrm{μs}\left({\color{lightgreen}-6.349 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.76 \mathrm{ms} \pm 26.2 \mathrm{μs}\left({\color{gray}2.17 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.04 \mathrm{ms} \pm 21.3 \mathrm{μs}\left({\color{gray}0.400 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$3.34 \mathrm{ms} \pm 16.3 \mathrm{μs}\left({\color{gray}-0.481 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.11 \mathrm{ms} \pm 33.0 \mathrm{μs}\left({\color{gray}-0.747 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.53 \mathrm{ms} \pm 18.5 \mathrm{μs}\left({\color{gray}-0.932 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$4.21 \mathrm{ms} \pm 42.9 \mathrm{μs}\left({\color{gray}1.95 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.36 \mathrm{ms} \pm 31.8 \mathrm{μs}\left({\color{gray}0.324 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.45 \mathrm{ms} \pm 17.1 \mathrm{μs}\left({\color{gray}-1.950 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.05 \mathrm{ms} \pm 26.3 \mathrm{μs}\left({\color{gray}0.695 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.61 \mathrm{ms} \pm 14.8 \mathrm{μs}\left({\color{gray}-3.683 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.50 \mathrm{ms} \pm 11.1 \mathrm{μs}\left({\color{gray}-1.245 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.59 \mathrm{ms} \pm 17.4 \mathrm{μs}\left({\color{gray}-4.184 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.87 \mathrm{ms} \pm 16.3 \mathrm{μs}\left({\color{gray}-3.649 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.66 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{lightgreen}-6.743 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.89 \mathrm{ms} \pm 20.5 \mathrm{μs}\left({\color{gray}-2.474 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.00 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-1.070 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.78 \mathrm{ms} \pm 18.5 \mathrm{μs}\left({\color{gray}-1.374 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$3.03 \mathrm{ms} \pm 25.8 \mathrm{μs}\left({\color{gray}3.66 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.45 \mathrm{ms} \pm 27.2 \mathrm{μs}\left({\color{gray}-0.114 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.04 \mathrm{ms} \pm 23.5 \mathrm{μs}\left({\color{gray}0.870 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$3.31 \mathrm{ms} \pm 22.6 \mathrm{μs}\left({\color{gray}-0.430 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.34 \mathrm{ms} \pm 22.9 \mathrm{μs}\left({\color{gray}-1.306 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.98 \mathrm{ms} \pm 20.2 \mathrm{μs}\left({\color{gray}0.462 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.28 \mathrm{ms} \pm 17.7 \mathrm{μs}\left({\color{gray}0.199 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$43.0 \mathrm{ms} \pm 307 \mathrm{μs}\left({\color{gray}-4.030 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$33.6 \mathrm{ms} \pm 163 \mathrm{μs}\left({\color{gray}-2.958 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$36.0 \mathrm{ms} \pm 236 \mathrm{μs}\left({\color{gray}-4.284 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$39.0 \mathrm{ms} \pm 1.06 \mathrm{ms}\left({\color{lightgreen}-11.139 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$42.9 \mathrm{ms} \pm 194 \mathrm{μs}\left({\color{gray}-1.085 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$50.1 \mathrm{ms} \pm 261 \mathrm{μs}\left({\color{gray}-1.336 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$40.5 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}-4.253 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$90.3 \mathrm{ms} \pm 582 \mathrm{μs}\left({\color{lightgreen}-5.386 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$34.1 \mathrm{ms} \pm 233 \mathrm{μs}\left({\color{lightgreen}-20.983 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$277 \mathrm{ms} \pm 925 \mathrm{μs}\left({\color{gray}-2.485 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$11.0 \mathrm{ms} \pm 85.2 \mathrm{μs}\left({\color{gray}0.283 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$11.1 \mathrm{ms} \pm 63.3 \mathrm{μs}\left({\color{gray}-1.305 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$11.2 \mathrm{ms} \pm 68.8 \mathrm{μs}\left({\color{gray}0.382 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$11.1 \mathrm{ms} \pm 69.6 \mathrm{μs}\left({\color{gray}-0.424 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$11.0 \mathrm{ms} \pm 73.2 \mathrm{μs}\left({\color{gray}-1.755 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$11.1 \mathrm{ms} \pm 91.1 \mathrm{μs}\left({\color{gray}-0.396 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$11.2 \mathrm{ms} \pm 59.5 \mathrm{μs}\left({\color{gray}-0.583 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$11.3 \mathrm{ms} \pm 97.6 \mathrm{μs}\left({\color{gray}1.96 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$11.3 \mathrm{ms} \pm 68.4 \mathrm{μs}\left({\color{lightgreen}-7.588 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$11.4 \mathrm{ms} \pm 75.0 \mathrm{μs}\left({\color{lightgreen}-5.744 \mathrm{\%}}\right) $$	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`	$$11.5 \mathrm{ms} \pm 58.6 \mathrm{μs}\left({\color{gray}-2.572 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$11.9 \mathrm{ms} \pm 91.3 \mathrm{μs}\left({\color{gray}1.53 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$12.0 \mathrm{ms} \pm 88.5 \mathrm{μs}\left({\color{gray}2.67 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$11.7 \mathrm{ms} \pm 72.9 \mathrm{μs}\left({\color{gray}-1.486 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$12.2 \mathrm{ms} \pm 172 \mathrm{μs}\left({\color{gray}4.81 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$11.8 \mathrm{ms} \pm 80.4 \mathrm{μs}\left({\color{lightgreen}-5.058 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$11.9 \mathrm{ms} \pm 81.6 \mathrm{μs}\left({\color{gray}0.807 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$11.6 \mathrm{ms} \pm 73.3 \mathrm{μs}\left({\color{gray}-2.688 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$11.5 \mathrm{ms} \pm 88.6 \mathrm{μs}\left({\color{gray}-4.380 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.48 \mathrm{ms} \pm 47.0 \mathrm{μs}\left({\color{gray}-0.946 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$56.6 \mathrm{ms} \pm 452 \mathrm{μs}\left({\color{lightgreen}-11.862 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$107 \mathrm{ms} \pm 604 \mathrm{μs}\left({\color{lightgreen}-8.244 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$65.9 \mathrm{ms} \pm 554 \mathrm{μs}\left({\color{lightgreen}-8.784 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$74.2 \mathrm{ms} \pm 549 \mathrm{μs}\left({\color{lightgreen}-8.819 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$82.7 \mathrm{ms} \pm 640 \mathrm{μs}\left({\color{lightgreen}-8.150 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$87.3 \mathrm{ms} \pm 792 \mathrm{μs}\left({\color{lightgreen}-8.176 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$43.6 \mathrm{ms} \pm 257 \mathrm{μs}\left({\color{lightgreen}-5.629 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$74.0 \mathrm{ms} \pm 593 \mathrm{μs}\left({\color{gray}-2.837 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$49.7 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{lightgreen}-5.490 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$60.9 \mathrm{ms} \pm 441 \mathrm{μs}\left({\color{gray}-1.457 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$62.1 \mathrm{ms} \pm 320 \mathrm{μs}\left({\color{lightgreen}-6.143 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$62.7 \mathrm{ms} \pm 469 \mathrm{μs}\left({\color{gray}-3.362 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$122 \mathrm{ms} \pm 650 \mathrm{μs}\left({\color{gray}-2.082 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$132 \mathrm{ms} \pm 558 \mathrm{μs}\left({\color{gray}-4.439 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$18.7 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{lightgreen}-5.585 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$551 \mathrm{ms} \pm 1.17 \mathrm{ms}\left({\color{gray}1.85 \mathrm{\%}}\right) $$	Flame Graph

github-actions Bot added area/infra Relates to version control, CI, CD or IaC (area) area/libs Relates to first-party libraries/crates/packages (area) type/eng > backend Owned by the @backend team area/tests New or updated tests area/apps labels Aug 21, 2026

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kube changed the title ~~cf/fe 1467 python lint and type check~~ FE-1467: Lint and type-check the Python packages in CI Aug 21, 2026

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FE-1468: Generate pydantic models from the CLI's protocol schemas #9262

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

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Base automatically changed from cf/fe-1270-create-python-bindings-to-petrinaut-core to main August 21, 2026 14:49

kube requested a review from a team as a code owner August 21, 2026 14:49

github-actions Bot added area/deps Relates to third-party dependencies (area) type/eng > frontend Owned by the @frontend team type/legal Owned by the @legal team labels Aug 21, 2026

Merged via the queue into main with commit bf42b43 Aug 21, 2026
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kube deleted the cf/fe-1467-python-lint-and-type-check branch August 21, 2026 14:52

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FE-1467: Lint and type-check the Python packages in CI - #9261

FE-1467: Lint and type-check the Python packages in CI#9261
kube merged 4 commits into
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cf/fe-1467-python-lint-and-type-check

kube commented Aug 21, 2026 •

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github-actions Bot commented Aug 21, 2026

@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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representative_read_multiple_entities

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kube commented Aug 21, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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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?

🛡 What tests cover this?

❓ How to test this?

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codspeed-hq Bot commented Aug 21, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Merging this PR will not alter performance

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github-actions Bot commented Aug 21, 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

scenarios

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Development

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3 participants

kube commented Aug 21, 2026 •

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