The Fix
pip install pydantic==2.11.0
Based on closed pydantic/pydantic issue #10285 · PR/commit linked
@@ -1,19 +1,20 @@
from __future__ import annotations
+import inspect
import os
-from collections import defaultdict
repro.py
from pydantic_core import CoreSchema
from pydantic import TypeAdapter
list_schema = TypeAdapter(list[int]).core_schema
#> {'type': 'list', 'items_schema': {'type': 'int'}}
def handler_func(s: CoreSchema, recurse: Recurse) -> CoreSchema: # Recurse is a complex recursive type, omitted here.
print(s)
return recurse(s, handler_func)
verify
Re-run the minimal reproduction on your broken version, then apply the fix and re-run.
fix.md
Option A — Upgrade to fixed release\npip install pydantic==2.11.0\nWhen NOT to use: This fix should not be used if the schema relies on the previous cleaning behavior.\n\n
Why This Fix Works in Production
- Trigger: Performance issues related to schema cleaning
- Mechanism: Performance issues arise from inefficient schema cleaning logic during model creation
- Why the fix works: Refactor and optimize schema cleaning logic to improve performance and memory usage. (first fixed release: 2.11.0).
Why This Breaks in Prod
- Shows up under Python 3.12 in real deployments (not just unit tests).
- Performance issues arise from inefficient schema cleaning logic during model creation
- Production symptom (often without a traceback): Performance issues related to schema cleaning
Proof / Evidence
- GitHub issue: #10285
- Fix PR: https://github.com/pydantic/pydantic/pull/11244
- First fixed release: 2.11.0
- Reproduced locally: No (not executed)
- Last verified: 2026-02-09
- Confidence: 0.85
- Did this fix it?: Yes (upstream fix exists)
- Own content ratio: 0.65
Discussion
High-signal excerpts from the issue thread (symptoms, repros, edge-cases).
“This issue gathers info about performance issues related to schema cleaning. This strongly relates to https://github.com/pydantic/pydantic/discussions/6748. Both performance and memory consumption are in scope. https://github.com/pydantic/p”
Failure Signature (Search String)
- Performance issues related to schema cleaning
- The `k8s_v2.py` contains 7242 Pydantic models, with an average of ~2.7 fields per model. There are two models with over 500 fields, referencing other Pydantic models:
Copy-friendly signature
signature.txt
Failure Signature
-----------------
Performance issues related to schema cleaning
The `k8s_v2.py` contains 7242 Pydantic models, with an average of ~2.7 fields per model. There are two models with over 500 fields, referencing other Pydantic models:
Error Message
Signature-only (no traceback captured)
error.txt
Error Message
-------------
Performance issues related to schema cleaning
The `k8s_v2.py` contains 7242 Pydantic models, with an average of ~2.7 fields per model. There are two models with over 500 fields, referencing other Pydantic models:
Minimal Reproduction
repro.py
from pydantic_core import CoreSchema
from pydantic import TypeAdapter
list_schema = TypeAdapter(list[int]).core_schema
#> {'type': 'list', 'items_schema': {'type': 'int'}}
def handler_func(s: CoreSchema, recurse: Recurse) -> CoreSchema: # Recurse is a complex recursive type, omitted here.
print(s)
return recurse(s, handler_func)
Environment
- Python: 3.12
What Broke
Users experience significant delays and increased memory usage when creating models with many fields.
Why It Broke
Performance issues arise from inefficient schema cleaning logic during model creation
Fix Options (Details)
Option A — Upgrade to fixed release Safe default (recommended)
pip install pydantic==2.11.0
When NOT to use: This fix should not be used if the schema relies on the previous cleaning behavior.
Use when you can deploy the upstream fix. It is usually lower-risk than long-lived workarounds.
Option D — Guard side-effects with OnceOnly Guardrail for side-effects
Mitigate duplicate external side-effects under retries/timeouts/agent loops by gating the operation before calling external systems.
- Place OnceOnly between your code/agent and real side-effects (Stripe, emails, CRM, APIs).
- Use a stable key per side-effect (e.g., customer_id + action + idempotency_key).
- Fail-safe: configure fail-open vs fail-closed based on blast radius and spend risk.
Show example snippet (optional)
onceonly.py
from onceonly import OnceOnly
import os
once = OnceOnly(api_key=os.environ["ONCEONLY_API_KEY"], fail_open=True)
# Stable idempotency key per real side-effect.
# Use a request id / job id / webhook delivery id / Stripe event id, etc.
event_id = "evt_..." # replace
key = f"stripe:webhook:{event_id}"
res = once.check_lock(key=key, ttl=3600)
if res.duplicate:
return {"status": "already_processed"}
# Safe to execute the side-effect exactly once.
handle_event(event_id)
When NOT to use: Do not use this to hide logic bugs or data corruption. Use it to block duplicate external side-effects and enforce tool permissions/spend caps.
Fix reference: https://github.com/pydantic/pydantic/pull/11244
First fixed release: 2.11.0
Last verified: 2026-02-09. Validate in your environment.
When NOT to Use This Fix
- This fix should not be used if the schema relies on the previous cleaning behavior.
- Do not use this to hide logic bugs or data corruption. Use it to block duplicate external side-effects and enforce tool permissions/spend caps.
Verify Fix
verify
Re-run the minimal reproduction on your broken version, then apply the fix and re-run.
Did This Fix Work in Your Case?
Quick signal helps us prioritize which fixes to verify and improve.
Prevention
- Add a CI check that diffs key outputs after upgrades (OpenAPI schema snapshots, JSON payload shapes, CLI output).
- Upgrade behind a canary and run integration tests against the canary before 100% rollout.
Version Compatibility Table
| Version | Status |
|---|---|
| 2.11.0 | Fixed |
Related Issues
No related fixes found.
Sources
We don’t republish the full GitHub discussion text. Use the links above for context.