Allow repository-backed flow agents

Flow declarations can now reference full repository-backed Agent config
without duplicating it inline. The repository response is merged the same
way as `Agent(from_repository=...)`, so values like `role`, `goal`,
`backstory`, `tools`, `planning_config`, deprecated `reasoning`, `memory`,
and other Agent fields can come from the repository. Local YAML fields
still override repository values.

Standalone agent action:

```yaml
do:
  call: agent
  with:
    from_repository: support_specialist
    input: "${state.question}"
```

Inline crew agent:

```yaml
do:
  call: crew
  with:
    name: inline_research
    agents:
      researcher:
        from_repository: researcher
    tasks:
      - description: Research {topic}
        expected_output: Findings about {topic}
        agent: researcher
```

.github/workflows/vulnerability-scan.yml

@@ -73,13 +73,11 @@ jobs:
             --ignore-vuln PYSEC-2025-216 \
             --ignore-vuln PYSEC-2025-217 \
             --ignore-vuln PYSEC-2025-218 \
-            --ignore-vuln PYSEC-2026-597 \
             --ignore-vuln GHSA-f4j7-r4q5-qw2c
         # Ignored CVEs:
         #   PYSEC-2024-277      - joblib 1.5.3: disputed; NumpyArrayWrapper only used with trusted caches
         #   PYSEC-2026-89       - markdown 3.10.2: DoS via malformed HTML; fix 3.8.1 — already past, advisory range is stale
         #   PYSEC-2026-97       - nltk 3.9.4: arbitrary file read in filestring(); no fix available
-        #   PYSEC-2026-597      - nltk 3.9.4 (CVE-2026-12243): path traversal via _UNSAFE_NO_PROTOCOL_RE bypass (incomplete fix of nltk#3504); 3.9.4 is the latest release, no fix available
         #   PYSEC-2025-148      - onnx 1.21.0: path traversal in save_external_data; no fix available
         #   PYSEC-2025-183      - pyjwt 2.12.1: disputed weak-encryption claim; key length is application-chosen
         #   PYSEC-2025-189..197 - torch 2.11.0: memory-corruption/DoS in functions only reachable via untrusted models; no fix available

.pre-commit-config.yaml

@@ -34,7 +34,6 @@ repos:
           --ignore-vuln PYSEC-2024-277
           --ignore-vuln PYSEC-2026-89
           --ignore-vuln PYSEC-2026-97
-          --ignore-vuln PYSEC-2026-597
           --ignore-vuln PYSEC-2025-148
           --ignore-vuln PYSEC-2025-183
           --ignore-vuln PYSEC-2025-189

docs/docs.json

@@ -34616,42 +34616,6 @@
     "suggestEdit": true
   },
   "redirects": [
-    {
-      "source": "/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/en/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/en/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/en/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/edge/en/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/en/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/pt-BR/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/pt-BR/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/edge/pt-BR/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/pt-BR/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/ko/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/ko/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/edge/ko/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/ko/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/ar/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/ar/enterprise/features/agent-control-plane/policies"
-    },
-    {
-      "source": "/edge/ar/enterprise/features/agent-control-plane/rules",
-      "destination": "/edge/ar/enterprise/features/agent-control-plane/policies"
-    },
     {
       "source": "/api-reference",
       "destination": "/v1.15.1/en/api-reference/introduction"
@@ -34776,4 +34740,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/edge/ar/enterprise/features/agent-control-plane/monitoring.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
   - [نظرة عامة](/ar/enterprise/features/agent-control-plane/overview)
   - **المراقبة** *(أنت هنا)*
-  - [السياسات](/edge/ar/enterprise/features/agent-control-plane/policies)
+  - [القواعد](/ar/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## نظرة عامة
@@ -58,7 +58,7 @@ mode: "wide"
 | **Last execution** | الوقت المنقضي منذ آخر تنفيذ. |
 | **Health Status Breakdown** | شريط مكدّس بنسب `Critical` / `Warning` / `Healthy` لعمليات التنفيذ في النافذة. |
 | **Executions with Errors** | إجمالي عمليات التنفيذ الفاشلة في النافذة. |
-| **PII detection applied** | `Yes` إذا كان هناك تكوين PII لكل deployment أو [سياسة PII](/edge/ar/enterprise/features/agent-control-plane/policies) مطابِقة نشطة. |
+| **PII detection applied** | `Yes` إذا كان هناك تكوين PII لكل deployment أو [قاعدة PII](/ar/enterprise/features/agent-control-plane/rules) مطابِقة نشطة. |
 | **Executions** | إجمالي عمليات التنفيذ في النافذة. |
 | **Last updated** | متى أُعيد نشر الـ deployment آخر مرة. |
 | **Crew Version** | إصدار `crewai` الذي يُبلِّغ عنه الـ deployment. يشير أيقونة المعلومات بجانب الإصدارات الأقل من `1.13` إلى صفوف لا يمكنها المساهمة بالمقاييس. |
@@ -96,8 +96,8 @@ mode: "wide"
   <Card title="Agent Control Plane — نظرة عامة" icon="book-open" href="/ar/enterprise/features/agent-control-plane/overview">
     ما هو ACP، المتطلبات، مستويات الخطط، و RBAC.
   </Card>
-  <Card title="Agent Control Plane — السياسات" icon="shield-check" href="/edge/ar/enterprise/features/agent-control-plane/policies">
-    طبّق سياسات PII Redaction على مستوى المؤسسة عبر العديد من الأتمتات.
+  <Card title="Agent Control Plane — القواعد" icon="shield-check" href="/ar/enterprise/features/agent-control-plane/rules">
+    طبّق قواعد PII Redaction على مستوى المؤسسة عبر العديد من الأتمتات.
   </Card>
   <Card title="Traces" icon="timeline" href="/ar/enterprise/features/traces">
     تعمّق في تنفيذ واحد لرؤية تفكير الوكيل واستدعاءات الأدوات واستخدام الرموز.

docs/edge/ar/enterprise/features/agent-control-plane/overview.mdx

@@ -10,17 +10,17 @@ icon: "book-open"
 
   - **نظرة عامة** *(أنت هنا)*
   - [المراقبة](/ar/enterprise/features/agent-control-plane/monitoring)
-  - [السياسات](/edge/ar/enterprise/features/agent-control-plane/policies)
+  - [القواعد](/ar/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## نظرة عامة
 
-**Agent Control Plane** (ACP) هو مركز العمليات لكل ما يعمل لديك على CrewAI AMP. إنها شاشة واحدة — مقسّمة إلى تبويبَي **Automations** و **Policies** — تمنح فريقك القدرة على:
+**Agent Control Plane** (ACP) هو مركز العمليات لكل ما يعمل لديك على CrewAI AMP. إنها شاشة واحدة — مقسّمة إلى تبويبَي **Automations** و **Rules** — تمنح فريقك القدرة على:
 
 - مراقبة **حالة (الصحة)** كل أتمتة حيّة (crew أو flow) بتفصيل `Critical` / `Warning` / `Healthy` وعدد عمليات التنفيذ.
 - تتبع **استهلاك LLM** — الرموز (tokens) والتكلفة — لكل أتمتة ولكل مزود ولكل نموذج، مع الفرق مقابل الفترة السابقة.
 - التعمّق في أي أتمتة منفردة أو مزود نماذج لرؤية المخططات الزمنية وتفصيل البيانات لكل مزود.
-- تطبيق **سياسات (Policies)** على مستوى المؤسسة (اليوم: PII Redaction) عبر العديد من الأتمتات دفعة واحدة بدلاً من تعديل كل deployment على حدة.
+- تطبيق **قواعد (Rules)** على مستوى المؤسسة (اليوم: PII Redaction) عبر العديد من الأتمتات دفعة واحدة بدلاً من تعديل كل deployment على حدة.
 
 <Frame>
   ![نظرة عامة على Agent Control Plane](/images/enterprise/acp-overview-automations-sankey.png)
@@ -33,7 +33,7 @@ icon: "book-open"
 يجيب التبويبان عن سؤالَين مختلفَين:
 
 - **Automations** — *"كيف يتصرف أسطولي الآن، وكم يكلّفني؟"* راجع [المراقبة](/ar/enterprise/features/agent-control-plane/monitoring).
-- **Policies** — *"كيف أفرض سياسة (مثل PII redaction) عبر العديد من عمليات النشر دون إعادة نشر كل واحدة؟"* راجع [السياسات](/edge/ar/enterprise/features/agent-control-plane/policies).
+- **Rules** — *"كيف أفرض سياسة (مثل PII redaction) عبر العديد من عمليات النشر دون إعادة نشر كل واحدة؟"* راجع [القواعد](/ar/enterprise/features/agent-control-plane/rules).
 
 ## المتطلبات
 
@@ -42,11 +42,11 @@ icon: "book-open"
 </Warning>
 
 <Warning>
-  يُشترط **خطة Enterprise أو Ultra** لإنشاء أو تعديل [السياسات](/edge/ar/enterprise/features/agent-control-plane/policies). يمكن للمؤسسات على الخطط الأدنى فتح تبويب Policies وعرض السياسات الموجودة، ولكن يُعرض المحرر للقراءة فقط مع شارة قفل "Enterprise" والتنبيه *"PII Redaction policies require an Enterprise plan."*. المراقبة (تبويب Automations) متاحة في جميع الخطط حيث يكون هذا الميزة مفعّلة.
+  يُشترط **خطة Enterprise أو Ultra** لإنشاء أو تعديل [القواعد](/ar/enterprise/features/agent-control-plane/rules). يمكن للمؤسسات على الخطط الأدنى فتح تبويب Rules وعرض القواعد الموجودة، ولكن يُعرض المحرر للقراءة فقط مع شارة قفل "Enterprise" والتنبيه *"PII Redaction rules require an Enterprise plan."*. المراقبة (تبويب Automations) متاحة في جميع الخطط حيث يكون هذا الميزة مفعّلة.
 </Warning>
 
 - يجب أن تكون ميزة **Agent Control Plane** مفعّلة لمؤسستك. إن لم ترها في الشريط الجانبي، اطلب من مالك الحساب تفعيلها.
-- داخل ACP، يحكم [RBAC](/ar/enterprise/features/rbac) الوصول: `read` للعرض في لوحة المعلومات والسياسات، و`manage` لإنشاء وتعديل وتشغيل/إيقاف وحذف السياسات.
+- داخل ACP، يحكم [RBAC](/ar/enterprise/features/rbac) الوصول: `read` للعرض في لوحة المعلومات والقواعد، و`manage` لإنشاء وتعديل وتشغيل/إيقاف وحذف القواعد.
 - يمكن ضبط نطاق جميع المخططات والجداول إلى **آخر 24 ساعة** أو **الأسبوع الماضي** أو **آخر 30 يوماً** عبر مُحدّد الوقت في أعلى اليمين. تقارن قيم الفرق (`↑ 8 vs yesterday`, `↓ $20.57 vs yesterday` وغيرها) النافذة المختارة بالنافذة السابقة بنفس الطول.
 
 ## ما يمكنك فعله هنا
@@ -55,7 +55,7 @@ icon: "book-open"
   <Card title="المراقبة" icon="gauge" href="/ar/enterprise/features/agent-control-plane/monitoring">
     راقب صحة الأسطول وإنفاق LLM عبر بطاقات المقاييس و sankey التفاعلي وجداول لكل أتمتة ولوحات جانبية للتعمق في أي أتمتة أو مزود.
   </Card>
-  <Card title="السياسات" icon="shield-check" href="/edge/ar/enterprise/features/agent-control-plane/policies">
+  <Card title="القواعد" icon="shield-check" href="/ar/enterprise/features/agent-control-plane/rules">
     طبّق سياسات PII Redaction على مستوى المؤسسة بنطاق محدد بالأدوات والوسوم. تسري التغييرات في التنفيذ التالي — دون الحاجة لإعادة نشر.
   </Card>
 </CardGroup>
@@ -67,10 +67,10 @@ icon: "book-open"
     تعمّق في تنفيذ واحد لرؤية تفكير الوكيل واستدعاءات الأدوات واستخدام الرموز.
   </Card>
   <Card title="RBAC" icon="users" href="/ar/enterprise/features/rbac">
-    أدِر من يمكنه قراءة Agent Control Plane ومن يمكنه تعديل السياسات.
+    أدِر من يمكنه قراءة Agent Control Plane ومن يمكنه تعديل القواعد.
   </Card>
   <Card title="PII Redaction للـ Traces" icon="lock" href="/ar/enterprise/features/pii-trace-redactions">
-    كتالوج الكيانات وضبط PII لكل deployment التي تستند إليها السياسات.
+    كتالوج الكيانات وضبط PII لكل deployment التي تستند إليها القواعد.
   </Card>
   <Card title="النشر إلى AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
     انشر crew على إصدار crewAI يدعم Agent Control Plane.
@@ -78,5 +78,5 @@ icon: "book-open"
 </CardGroup>
 
 <Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
-  تواصل مع فريق الدعم للمساعدة في تفسير المقاييس أو تصميم السياسات.
+  تواصل مع فريق الدعم للمساعدة في تفسير المقاييس أو تصميم القواعد.
 </Card>

docs/edge/ar/enterprise/features/agent-control-plane/rules.mdx

@@ -1,7 +1,7 @@
 ---
-title: "إعداد السياسات"
+title: "إعداد القواعد"
 description: "طبّق سياسات على مستوى المؤسسة عبر العديد من الأتمتات من مكان واحد."
-sidebarTitle: "السياسات"
+sidebarTitle: "القواعد"
 icon: "shield-check"
 mode: "wide"
 ---
@@ -11,50 +11,50 @@ mode: "wide"
 
   - [نظرة عامة](/ar/enterprise/features/agent-control-plane/overview)
   - [المراقبة](/ar/enterprise/features/agent-control-plane/monitoring)
-  - **السياسات** *(أنت هنا)*
+  - **القواعد** *(أنت هنا)*
 </Info>
 
 ## نظرة عامة
 
-تتيح لك السياسات تطبيق سياسات — اليوم: **PII Redaction** — عبر العديد من الأتمتات دفعة واحدة، بدلاً من ضبط كل deployment على حدة. افتح تبويب **Policies** في [Agent Control Plane](/ar/enterprise/features/agent-control-plane/overview) لإدارتها.
+تتيح لك القواعد تطبيق سياسات — اليوم: **PII Redaction** — عبر العديد من الأتمتات دفعة واحدة، بدلاً من ضبط كل deployment على حدة. افتح تبويب **Rules** في [Agent Control Plane](/ar/enterprise/features/agent-control-plane/overview) لإدارتها.
 
 <Frame>
-  ![قائمة السياسات](/images/enterprise/acp-policies-list.png)
+  ![قائمة القواعد](/images/enterprise/acp-rules-list.png)
 </Frame>
 
-تعرض كل بطاقة سياسة الاسم والوصف و**النطاق (scope)** الذي تنطبق عليه السياسة (الأدوات والوسوم المختارة) وعدد **الأتمتات المُفعَّلة** — عمليات النشر التي تطابق النطاق حالياً. يقوم المُفتاح على اليمين بتشغيل السياسة أو إيقافها دون حذفها.
+تعرض كل بطاقة قاعدة الاسم والوصف و**النطاق (scope)** الذي تنطبق عليه القاعدة (الأدوات والوسوم المختارة) وعدد **الأتمتات المُفعَّلة** — عمليات النشر التي تطابق النطاق حالياً. يقوم المُفتاح على اليمين بتشغيل القاعدة أو إيقافها دون حذفها.
 
 ## المتطلبات
 
 <Warning>
-  يُشترط **خطة Enterprise أو Ultra** لإنشاء أو تعديل سياسات PII Redaction. يمكن للمؤسسات على الخطط الأدنى فتح تبويب Policies وعرض السياسات الموجودة، ولكن يُعرض المحرر للقراءة فقط مع شارة قفل "Enterprise" والتنبيه *"PII Redaction policies require an Enterprise plan."* — تواصل مع مالك حسابك أو المبيعات للترقية.
+  يُشترط **خطة Enterprise أو Ultra** لإنشاء أو تعديل قواعد PII Redaction. يمكن للمؤسسات على الخطط الأدنى فتح تبويب Rules وعرض القواعد الموجودة، ولكن يُعرض المحرر للقراءة فقط مع شارة قفل "Enterprise" والتنبيه *"PII Redaction rules require an Enterprise plan."* — تواصل مع مالك حسابك أو المبيعات للترقية.
 </Warning>
 
 - يجب أن تكون ميزة **Agent Control Plane** مفعّلة لمؤسستك. راجع [نظرة عامة — المتطلبات](/ar/enterprise/features/agent-control-plane/overview#المتطلبات).
-- تحتاج إلى صلاحية `manage` ضمن [RBAC](/ar/enterprise/features/rbac) على Agent Control Plane لإنشاء وتعديل وتشغيل/إيقاف وحذف السياسات. صلاحية `read` كافية لعرضها.
-- تُسجَّل جميع تغييرات السياسات بإصدارات للتدقيق.
+- تحتاج إلى صلاحية `manage` ضمن [RBAC](/ar/enterprise/features/rbac) على Agent Control Plane لإنشاء وتعديل وتشغيل/إيقاف وحذف القواعد. صلاحية `read` كافية لعرضها.
+- تُسجَّل جميع تغييرات القواعد بإصدارات للتدقيق.
 
-## أنواع السياسات المتاحة
+## أنواع القواعد المتاحة
 
 | النوع | ما تفعله |
 |------|---------------|
 | **PII Redaction** | تطبّق PII redaction على عمليات التنفيذ لكل أتمتة مطابِقة، باستخدام نفس كتالوج الكيانات و recognizers المخصصة الموثَّقة في [PII Redaction للـ Traces](/ar/enterprise/features/pii-trace-redactions). |
 
-سيتم إضافة أنواع سياسات أخرى مع الوقت.
+سيتم إضافة أنواع قواعد أخرى مع الوقت.
 
-## إنشاء سياسة
+## إنشاء قاعدة
 
 <Frame>
-  <img src="/images/enterprise/acp-policies-new-side-panel.png" alt="لوحة تعديل سياسة جانبية بالشروط ونوع قناع PII" width="450" />
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="لوحة تعديل قاعدة جانبية بالشروط ونوع قناع PII" width="450" />
 </Frame>
 
 <Steps>
   <Step title="افتح المحرر">
-    انقر على **+ Create new** في أعلى يمين تبويب Policies، أو على **View Details** في بطاقة سياسة موجودة.
+    انقر على **+ Create new** في أعلى يمين تبويب Rules، أو على **View Details** في بطاقة قاعدة موجودة.
   </Step>
 
-  <Step title="سَمِّ السياسة وصِفها">
-    أعطِ السياسة اسماً واضحاً (مثل *Mask PII (CC)*) ووصفاً يشرح متى تنطبق. يظهر كلاهما على بطاقة السياسة وفي مودال Engaged Automations.
+  <Step title="سَمِّ القاعدة وصِفها">
+    أعطِ القاعدة اسماً واضحاً (مثل *Mask PII (CC)*) ووصفاً يشرح متى تنطبق. يظهر كلاهما على بطاقة القاعدة وفي مودال Engaged Automations.
   </Step>
 
   <Step title="اختر النوع">
@@ -62,12 +62,12 @@ mode: "wide"
   </Step>
 
   <Step title="حدّد الشروط">
-    تحدد الشروط الأتمتات التي تنخرط معها السياسة. كلاهما اختياري ويستخدم دلالات **مساواة المجموعات (set-equality)**:
+    تحدد الشروط الأتمتات التي تنخرط معها القاعدة. كلاهما اختياري ويستخدم دلالات **مساواة المجموعات (set-equality)**:
 
     - **Tools** — تنخرط فقط الأتمتات التي تتطابق مجموعة أدواتها **تطابقاً تامّاً** مع الأدوات المختارة. اختر من تطبيقات Studio و MCPs والأدوات مفتوحة المصدر وأدوات سجل Tool Repository.
     - **Automations** — تنخرط فقط الأتمتات التي تتطابق مجموعة وسومها **تطابقاً تامّاً** مع الوسوم المختارة.
 
-    ترك مُحدِّد فارغ يعني "بدون تصفية على هذا البعد". ترك كليهما فارغَين يعني أن السياسة تنطبق على **كل** أتمتة في المؤسسة.
+    ترك مُحدِّد فارغ يعني "بدون تصفية على هذا البعد". ترك كليهما فارغَين يعني أن القاعدة تنطبق على **كل** أتمتة في المؤسسة.
   </Step>
 
   <Step title="اضبط جدول PII Mask Type">
@@ -75,30 +75,30 @@ mode: "wide"
   </Step>
 
   <Step title="احفظ">
-    تنطبق السياسة على عمليات التنفيذ **المستقبلية** لكل أتمتة مُفعَّلة بمجرد الحفظ. لا حاجة لإعادة النشر.
+    تنطبق القاعدة على عمليات التنفيذ **المستقبلية** لكل أتمتة مُفعَّلة بمجرد الحفظ. لا حاجة لإعادة النشر.
   </Step>
 </Steps>
 
 ## الأتمتات المُفعَّلة
 
-انقر على **Engaged N automations** في أي بطاقة سياسة لرؤية أي عمليات النشر تطابقها السياسة حالياً بالضبط، إلى جانب آخر تنفيذ لكل منها.
+انقر على **Engaged N automations** في أي بطاقة قاعدة لرؤية أي عمليات النشر تطابقها القاعدة حالياً بالضبط، إلى جانب آخر تنفيذ لكل منها.
 
 <Frame>
-  ![مودال الأتمتات المُفعَّلة](/images/enterprise/acp-policies-engaged-modal.png)
+  ![مودال الأتمتات المُفعَّلة](/images/enterprise/acp-rules-engaged-modal.png)
 </Frame>
 
-هذه هي أسرع طريقة للتحقق من نطاق سياسة قبل تمكينها — على سبيل المثال، للتأكد من أن سياسة محدَّدة بنطاق وسم `production` لا تطابق عن طريق الخطأ deployment تجريبي.
+هذه هي أسرع طريقة للتحقق من نطاق قاعدة قبل تمكينها — على سبيل المثال، للتأكد من أن قاعدة محدَّدة بنطاق وسم `production` لا تطابق عن طريق الخطأ deployment تجريبي.
 
-## سياسات على مستوى المؤسسة مقابل إعدادات لكل deployment
+## قواعد على مستوى المؤسسة مقابل إعدادات لكل deployment
 
 يمكن ضبط PII Redaction في مكانين:
 
 - **لكل deployment** — ضمن **Settings → PII Protection** على كل deployment على حدة ([الدليل](/ar/enterprise/features/pii-trace-redactions))
-- **على مستوى المؤسسة** — كسياسة في هذه الصفحة
+- **على مستوى المؤسسة** — كقاعدة في هذه الصفحة
 
-عندما يتطابق نطاق سياسة مُفعَّلة على مستوى المؤسسة مع deployment، يُجاوز تكوين الكيانات الخاص بالسياسة **إعدادات PII المملوكة من قبل الـ deployment** لعمليات تنفيذ ذلك الـ deployment — تصبح السياسة المصدر الوحيد للحقيقة طالما هي مرتبطة. عطّل السياسة أو فُكَّ ارتباطها (أو غيِّر نطاقها بحيث لا تتطابق بعد الآن) ويعود الـ deployment إلى إعدادات PII Protection الخاصة به.
+عندما يتطابق نطاق قاعدة مُفعَّلة على مستوى المؤسسة مع deployment، يُجاوز تكوين الكيانات الخاص بالقاعدة **إعدادات PII المملوكة من قبل الـ deployment** لعمليات تنفيذ ذلك الـ deployment — تصبح القاعدة المصدر الوحيد للحقيقة طالما هي مرتبطة. عطّل القاعدة أو فُكَّ ارتباطها (أو غيِّر نطاقها بحيث لا تتطابق بعد الآن) ويعود الـ deployment إلى إعدادات PII Protection الخاصة به.
 
-فضّل السياسات على مستوى المؤسسة عندما تريد فرض سياسة متسقة عبر العديد من عمليات النشر؛ احتفظ بالضبط لكل deployment للاستثناءات الفردية.
+فضّل القواعد على مستوى المؤسسة عندما تريد فرض سياسة متسقة عبر العديد من عمليات النشر؛ احتفظ بالضبط لكل deployment للاستثناءات الفردية.
 
 ## ذو صلة
 
@@ -113,10 +113,10 @@ mode: "wide"
     كتالوج الكيانات، recognizers المخصصة، والضبط لكل deployment.
   </Card>
   <Card title="RBAC" icon="users" href="/ar/enterprise/features/rbac">
-    أدِر من يمكنه إنشاء أو تعديل السياسات.
+    أدِر من يمكنه إنشاء أو تعديل القواعد.
   </Card>
 </CardGroup>
 
 <Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
-  تواصل مع فريق الدعم للمساعدة في تصميم سياسات لمؤسستك.
+  تواصل مع فريق الدعم للمساعدة في تصميم قواعد لمؤسستك.
 </Card>

docs/edge/en/enterprise/features/agent-control-plane/monitoring.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
   - [Overview](/en/enterprise/features/agent-control-plane/overview)
   - **Monitoring** *(you are here)*
-  - [Policies](/edge/en/enterprise/features/agent-control-plane/policies)
+  - [Rules](/en/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## Overview
@@ -58,7 +58,7 @@ The **Automations** sub-tab is the per-deployment breakdown of fleet health. Eac
 | **Last execution** | Time since the most recent run. |
 | **Health Status Breakdown** | Stacked bar of `Critical` / `Warning` / `Healthy` percentages for executions in the window. |
 | **Executions with Errors** | Total failed executions in the window. |
-| **PII detection applied** | `Yes` if a per-deployment PII config or a matching [PII policy](/edge/en/enterprise/features/agent-control-plane/policies) is active. |
+| **PII detection applied** | `Yes` if a per-deployment PII config or a matching [PII rule](/en/enterprise/features/agent-control-plane/rules) is active. |
 | **Executions** | Total executions in the window. |
 | **Last updated** | When the deployment was last re-deployed. |
 | **Crew Version** | The `crewai` version reported by the deployment. An info icon next to versions below `1.13` flags rows that can't contribute metrics. |
@@ -96,8 +96,8 @@ Filter by **LLM provider** and sort by `Cost`, `Executions`, or `Last run`.
   <Card title="Agent Control Plane — Overview" icon="book-open" href="/en/enterprise/features/agent-control-plane/overview">
     What ACP is, requirements, plan tiers, and RBAC.
   </Card>
-  <Card title="Agent Control Plane — Policies" icon="shield-check" href="/edge/en/enterprise/features/agent-control-plane/policies">
-    Apply organization-wide PII Redaction policies across many automations.
+  <Card title="Agent Control Plane — Rules" icon="shield-check" href="/en/enterprise/features/agent-control-plane/rules">
+    Apply organization-wide PII Redaction rules across many automations.
   </Card>
   <Card title="Traces" icon="timeline" href="/en/enterprise/features/traces">
     Drill into a single execution to see agent reasoning, tool calls, and token usage.

docs/edge/en/enterprise/features/agent-control-plane/overview.mdx

@@ -10,17 +10,17 @@ icon: "book-open"
 
   - **Overview** *(you are here)*
   - [Monitoring](/en/enterprise/features/agent-control-plane/monitoring)
-  - [Policies](/edge/en/enterprise/features/agent-control-plane/policies)
+  - [Rules](/en/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## Overview
 
-The **Agent Control Plane** (ACP) is the operations hub for everything you have running on CrewAI AMP. It is a single screen — split into **Automations** and **Policies** tabs — that lets your team:
+The **Agent Control Plane** (ACP) is the operations hub for everything you have running on CrewAI AMP. It is a single screen — split into **Automations** and **Rules** tabs — that lets your team:
 
 - Monitor the **health** of every live automation (crew or flow), with `Critical` / `Warning` / `Healthy` breakdowns and execution counts.
 - Track **LLM consumption** — tokens and cost — per automation, per provider, and per model, with a delta vs the previous period.
 - Drill into any single automation or model provider for time-series charts and per-provider breakdowns.
-- Apply organization-wide **Policies** (today: PII Redaction and Cost Limit) across many automations at once instead of editing each deployment individually.
+- Apply organization-wide **Rules** (today: PII Redaction and Cost Limit) across many automations at once instead of editing each deployment individually.
 
 <Frame>
   ![Agent Control Plane overview](/images/enterprise/acp-overview-automations-sankey.png)
@@ -33,7 +33,7 @@ The **Agent Control Plane** (ACP) is the operations hub for everything you have
 The two tabs answer two different questions:
 
 - **Automations** — *"How is my fleet behaving right now, and what is it costing me?"* See [Monitoring](/en/enterprise/features/agent-control-plane/monitoring).
-- **Policies** — *"How do I enforce a policy (e.g. PII redaction or a spend budget) across many deployments without re-deploying each one?"* See [Policies](/edge/en/enterprise/features/agent-control-plane/policies).
+- **Rules** — *"How do I enforce a policy (e.g. PII redaction or a spend budget) across many deployments without re-deploying each one?"* See [Rules](/en/enterprise/features/agent-control-plane/rules).
 
 ## Requirements
 
@@ -42,11 +42,11 @@ The two tabs answer two different questions:
 </Warning>
 
 <Warning>
-  **Enterprise Plan or Ultra Plan** is required to create or edit **PII Redaction** [Policies](/edge/en/enterprise/features/agent-control-plane/policies). Lower-tier organizations can open the Policies tab and view existing policies, but the PII editor renders read-only with an "Enterprise" lock pill and the alert *"PII Redaction policies require an Enterprise plan."* **Cost Limit** policies and Monitoring (the Automations tab) are available on all plans where the feature is enabled.
+  **Enterprise Plan or Ultra Plan** is required to create or edit **PII Redaction** [Rules](/en/enterprise/features/agent-control-plane/rules). Lower-tier organizations can open the Rules tab and view existing rules, but the PII editor renders read-only with an "Enterprise" lock pill and the alert *"PII Redaction rules require an Enterprise plan."* **Cost Limit** rules and Monitoring (the Automations tab) are available on all plans where the feature is enabled.
 </Warning>
 
 - The **Agent Control Plane** feature must be enabled for your organization. If you don't see it in the sidebar, ask your account owner to request enablement.
-- Inside ACP, [RBAC](/en/enterprise/features/rbac) governs access: `read` to view the dashboard and policies, `manage` to create, edit, toggle, or delete policies.
+- Inside ACP, [RBAC](/en/enterprise/features/rbac) governs access: `read` to view the dashboard and rules, `manage` to create, edit, toggle, or delete rules.
 - All charts and tables can be scoped to the **Last 24 hours**, **Last Week**, or **Last 30 days** using the time selector at the top right. Deltas (`↑ 8 vs yesterday`, `↓ $20.57 vs yesterday`, etc.) compare the selected window against the previous one of the same length.
 
 ## What you can do here
@@ -55,7 +55,7 @@ The two tabs answer two different questions:
   <Card title="Monitoring" icon="gauge" href="/en/enterprise/features/agent-control-plane/monitoring">
     Watch fleet health and LLM spend with metric cards, an interactive sankey, per-automation tables, and drill-down side panels for any automation or provider.
   </Card>
-  <Card title="Policies" icon="shield-check" href="/edge/en/enterprise/features/agent-control-plane/policies">
+  <Card title="Rules" icon="shield-check" href="/en/enterprise/features/agent-control-plane/rules">
     Apply organization-wide PII Redaction and Cost Limit policies scoped by tools and tags. Changes take effect on the next execution — no re-deploy required.
   </Card>
 </CardGroup>
@@ -67,10 +67,10 @@ The two tabs answer two different questions:
     Drill into a single execution to see agent reasoning, tool calls, and token usage.
   </Card>
   <Card title="RBAC" icon="users" href="/en/enterprise/features/rbac">
-    Manage who can read the Agent Control Plane and who can edit policies.
+    Manage who can read the Agent Control Plane and who can edit rules.
   </Card>
   <Card title="PII Redaction for Traces" icon="lock" href="/en/enterprise/features/pii-trace-redactions">
-    Entity catalog and per-deployment PII configuration referenced by Policies.
+    Entity catalog and per-deployment PII configuration referenced by Rules.
   </Card>
   <Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
     Deploy a crew on a crewAI version that supports the Agent Control Plane.
@@ -78,5 +78,5 @@ The two tabs answer two different questions:
 </CardGroup>
 
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for help interpreting metrics or designing policies.
+  Contact our support team for help interpreting metrics or designing rules.
 </Card>

docs/edge/en/enterprise/features/agent-control-plane/rules.mdx

@@ -1,7 +1,7 @@
 ---
-title: "Set up the Policies"
+title: "Set up the Rules"
 description: "Apply organization-wide policies across many automations from a single place."
-sidebarTitle: "Policies"
+sidebarTitle: "Rules"
 icon: "shield-check"
 mode: "wide"
 ---
@@ -11,39 +11,39 @@ mode: "wide"
 
   - [Overview](/en/enterprise/features/agent-control-plane/overview)
   - [Monitoring](/en/enterprise/features/agent-control-plane/monitoring)
-  - **Policies** *(you are here)*
+  - **Rules** *(you are here)*
 </Info>
 
 ## Overview
 
-Policies let you enforce organization-wide controls — today **PII Redaction** and **Cost Limit** — across many automations at once, instead of configuring each deployment individually. Open the **Policies** tab in the [Agent Control Plane](/en/enterprise/features/agent-control-plane/overview) to manage them.
+Rules let you apply policies — today **PII Redaction** and **Cost Limit** — across many automations at once, instead of configuring each deployment individually. Open the **Rules** tab in the [Agent Control Plane](/en/enterprise/features/agent-control-plane/overview) to manage them.
 
 <Frame>
-  ![Policies list](/images/enterprise/acp-policies-list.png)
+  ![Rules list](/images/enterprise/acp-rules-list.png)
 </Frame>
 
-Each policy card shows the name, description, the **scope** the policy applies to (selected tools and tags), and a count of **engaged automations** — deployments that currently match the scope. The toggle on the right enables or disables the policy without deleting it.
+Each rule card shows the name, description, the **scope** the rule applies to (selected tools and tags), and a count of **engaged automations** — deployments that currently match the scope. The toggle on the right enables or disables the rule without deleting it.
 
 ## Requirements
 
 <Warning>
-  **Enterprise Plan or Ultra Plan** is required to create or edit **PII Redaction** policies. Lower-tier organizations can still open the Policies tab and view existing policies, but the PII editor renders read-only with an "Enterprise" lock pill and the alert *"PII Redaction policies require an Enterprise plan."* — contact your account owner or sales to upgrade. **Cost Limit** policies are **not** plan-gated and can be created on any plan where the Agent Control Plane is enabled.
+  **Enterprise Plan or Ultra Plan** is required to create or edit **PII Redaction** rules. Lower-tier organizations can still open the Rules tab and view existing rules, but the PII editor renders read-only with an "Enterprise" lock pill and the alert *"PII Redaction rules require an Enterprise plan."* — contact your account owner or sales to upgrade. **Cost Limit** rules are **not** plan-gated and can be created on any plan where the Agent Control Plane is enabled.
 </Warning>
 
 - The **Agent Control Plane** feature must be enabled for your organization. See [Overview — Requirements](/en/enterprise/features/agent-control-plane/overview#requirements).
-- The `manage` [RBAC permission](/en/enterprise/features/rbac) on Agent Control Plane is required to create, edit, toggle, or delete policies. The `read` permission is enough to view them.
-- All policy changes are versioned for auditing.
+- The `manage` [RBAC permission](/en/enterprise/features/rbac) on Agent Control Plane is required to create, edit, toggle, or delete rules. The `read` permission is enough to view them.
+- All rule changes are versioned for auditing.
 
-## Policy types
+## Rule types
 
-Every policy is one of the types below. Open the tab for the policy you want to enforce.
+Every rule is one of the types below. Open the tab for the policy you want to enforce.
 
 <Tabs>
 <Tab title="PII Redaction">
 Applies PII redaction to executions of every matching automation, using the same entity catalog and custom recognizers documented in [PII Redaction for Traces](/en/enterprise/features/pii-trace-redactions).
 
 <Warning>
-Creating or editing PII Redaction policies requires an **Enterprise** or **Ultra** plan. On lower tiers the PII editor renders read-only with an "Enterprise" lock pill.
+Creating or editing PII Redaction rules requires an **Enterprise** or **Ultra** plan. On lower tiers the PII editor renders read-only with an "Enterprise" lock pill.
 </Warning>
 
 **Configuration** — in the **PII Mask Type** table, check each entity type you want covered and choose how to handle it:
@@ -58,7 +58,7 @@ See [PII Redaction for Traces](/en/enterprise/features/pii-trace-redactions) for
 Emails the recipients you choose when a matching automation's LLM spend exceeds a budget threshold in the selected period. Available on **all plans** where the Agent Control Plane is enabled — it is not Enterprise-gated.
 
 <Warning>
-Cost Limit policies are **notify-only**. They never pause, throttle, or stop a run — they only send an email so a human can decide what to do. Adjust the budget or remove the policy if you no longer want the alert.
+Cost Limit rules are **notify-only**. They never pause, throttle, or stop a run — they only send an email so a human can decide what to do. Adjust the budget or remove the rule if you no longer want the alert.
 </Warning>
 
 **Configuration**
@@ -74,7 +74,7 @@ Cost Limit policies are **notify-only**. They never pause, throttle, or stop a r
 **How spend is measured and matched**
 
 - The threshold is evaluated **per automation**, not summed across the whole scope. Each engaged automation has its own running total for the period.
-- A policy can match many automations via its conditions (tools/tags), and a single automation can be covered by **multiple** Cost Limit policies at once. Each policy tracks its own budget and alert state independently — they don't merge.
+- A rule can match many automations via its conditions (tools/tags), and a single automation can be covered by **multiple** Cost Limit rules at once. Each rule tracks its own budget and alert state independently — they don't merge.
 - A background check compares each engaged automation's period-to-date spend against the threshold and sends the email when it's exceeded. Because the check runs periodically, expect a short delay between crossing the threshold and the email arriving.
 
 **The alert email**
@@ -83,76 +83,76 @@ When an automation goes over budget, recipients get an email summarizing the ove
 </Tab>
 </Tabs>
 
-More policy types will be added over time.
+More rule types will be added over time.
 
-## Creating a policy
+## Creating a rule
 
 <Tabs>
 <Tab title="PII Redaction">
 <Frame>
-  <img src="/images/enterprise/acp-policies-new-side-panel.png" alt="New Policy side panel configured for PII Redaction with the PII mask type table" width="450" />
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="New Rule side panel configured for PII Redaction with the PII mask type table" width="450" />
 </Frame>
 </Tab>
 <Tab title="Cost Limit">
 <Frame>
-  <img src="/images/enterprise/acp-policies-edit-cost-limit.png" alt="New Policy side panel configured for Cost Limit with budget period, threshold, and recipient emails" width="450" />
+  <img src="/images/enterprise/acp-rules-edit-cost-limit.png" alt="New Rule side panel configured for Cost Limit with budget period, threshold, and recipient emails" width="450" />
 </Frame>
 </Tab>
 </Tabs>
 
 <Steps>
   <Step title="Open the editor">
-    Click **+ Create new** at the top-right of the Policies tab, or **View Details** on an existing policy card.
+    Click **+ Create new** at the top-right of the Rules tab, or **View Details** on an existing rule card.
   </Step>
 
-  <Step title="Name and describe the policy">
-    Give the policy a clear name (e.g. *Mask PII (CC)* or *Monthly $100 budget*) and a description explaining when it applies. Both show up on the policy card and in the Engaged Automations modal.
+  <Step title="Name and describe the rule">
+    Give the rule a clear name (e.g. *Mask PII (CC)* or *Monthly $100 budget*) and a description explaining when it applies. Both show up on the rule card and in the Engaged Automations modal.
   </Step>
 
   <Step title="Pick the type">
-    Choose **PII Redaction** or **Cost Limit**. The type determines which configuration section appears below the conditions. The type is fixed once the policy is created — to switch, create a new policy.
+    Choose **PII Redaction** or **Cost Limit**. The type determines which configuration section appears below the conditions. The type is fixed once the rule is created — to switch, create a new rule.
   </Step>
 
   <Step title="Set the conditions">
-    Conditions decide which automations the policy engages with. Both are optional and use **set-equality** semantics:
+    Conditions decide which automations the rule engages with. Both are optional and use **set-equality** semantics:
 
     - **Tools** — only automations whose tool set **exactly matches** the selected tools will engage. Picks from Studio apps, MCPs, OSS tools, and Tool Repository registry tools.
     - **Automations** — only automations whose tag set **exactly matches** the selected tags will engage.
 
-    Leaving a picker empty means "no filter on this dimension". Leaving both empty means the policy applies to **every** automation in the organization.
+    Leaving a picker empty means "no filter on this dimension". Leaving both empty means the rule applies to **every** automation in the organization.
   </Step>
 
   <Step title="Configure the type-specific section">
-    The editor shows the configuration for the type you picked — the **PII Mask Type** table for PII Redaction, or the budget fields for Cost Limit. See [Policy types](#policy-types) for what each field does.
+    The editor shows the configuration for the type you picked — the **PII Mask Type** table for PII Redaction, or the budget fields for Cost Limit. See [Rule types](#rule-types) for what each field does.
   </Step>
 
   <Step title="Save">
-    The policy applies to **future** executions of every engaged automation as soon as you save. No re-deploy is needed.
+    The rule applies to **future** executions of every engaged automation as soon as you save. No re-deploy is needed.
   </Step>
 </Steps>
 
 ## Engaged automations
 
-Click **Engaged N automations** on any policy card to see exactly which deployments the policy is currently matching, along with each one's last execution.
+Click **Engaged N automations** on any rule card to see exactly which deployments the rule is currently matching, along with each one's last execution.
 
 <Frame>
-  ![Engaged automations modal](/images/enterprise/acp-policies-engaged-modal.png)
+  ![Engaged automations modal](/images/enterprise/acp-rules-engaged-modal.png)
 </Frame>
 
-This is the fastest way to sanity-check a policy's scope before enabling it — for example, to confirm that a policy scoped to the `production` tag isn't accidentally matching a staging deployment.
+This is the fastest way to sanity-check a rule's scope before enabling it — for example, to confirm that a rule scoped to the `production` tag isn't accidentally matching a staging deployment.
 
-## Org-wide policies vs per-deployment settings
+## Org-wide rules vs per-deployment settings
 
-Both PII Redaction and Cost Limit can be configured in two places: org-wide as a Policy on this page, or per-deployment under that deployment's **Settings**. When an enabled org-wide policy's scope matches a deployment, the policy takes precedence over the deployment-owned setting while it's attached.
+Both PII Redaction and Cost Limit can be configured in two places: org-wide as a Rule on this page, or per-deployment under that deployment's **Settings**. When an enabled org-wide rule's scope matches a deployment, the rule takes precedence over the deployment-owned setting while it's attached.
 
-| Policy | Per-deployment setting | What an attached org-wide policy does |
+| Policy | Per-deployment setting | What an attached org-wide rule does |
 |--------|------------------------|-------------------------------------|
-| **PII Redaction** | **Settings → PII Protection** ([guide](/en/enterprise/features/pii-trace-redactions)) | The policy's entity configuration **overrides** the deployment's PII settings for that deployment's executions. |
-| **Cost Limit** | **Settings → Cost Alerts** | The deployment's manual cost alert is **paused** and the attached cost policy(s) fire instead. The per-deployment form stays editable as a fallback. |
+| **PII Redaction** | **Settings → PII Protection** ([guide](/en/enterprise/features/pii-trace-redactions)) | The rule's entity configuration **overrides** the deployment's PII settings for that deployment's executions. |
+| **Cost Limit** | **Settings → Cost Alerts** | The deployment's manual cost alert is **paused** and the attached cost rule(s) fire instead. The per-deployment form stays editable as a fallback. |
 
-Disable or detach the policy (or change its scope so it no longer matches) and the deployment falls back to its own per-deployment settings.
+Disable or detach the rule (or change its scope so it no longer matches) and the deployment falls back to its own per-deployment settings.
 
-Prefer org-wide policies when you want to enforce a consistent policy across many deployments; reserve per-deployment configuration for one-off exceptions.
+Prefer org-wide rules when you want to enforce a consistent policy across many deployments; reserve per-deployment configuration for one-off exceptions.
 
 ## Related
 
@@ -167,10 +167,10 @@ Prefer org-wide policies when you want to enforce a consistent policy across man
     Entity catalog, custom recognizers, and per-deployment configuration.
   </Card>
   <Card title="RBAC" icon="users" href="/en/enterprise/features/rbac">
-    Manage who can create or edit policies.
+    Manage who can create or edit rules.
   </Card>
 </CardGroup>
 
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for help designing policies for your organization.
+  Contact our support team for help designing rules for your organization.
 </Card>

docs/edge/ko/enterprise/features/agent-control-plane/monitoring.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
   - [개요](/ko/enterprise/features/agent-control-plane/overview)
   - **모니터링** *(현재 페이지)*
-  - [정책](/edge/ko/enterprise/features/agent-control-plane/policies)
+  - [규칙](/ko/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## 개요
@@ -58,7 +58,7 @@ mode: "wide"
 | **Last execution** | 가장 최근 실행 이후 경과 시간. |
 | **Health Status Breakdown** | 윈도우 내 실행에 대한 `Critical` / `Warning` / `Healthy` 비율의 누적 막대. |
 | **Executions with Errors** | 윈도우 내 총 실패 실행 수. |
-| **PII detection applied** | deployment별 PII 설정 또는 일치하는 [PII 정책](/edge/ko/enterprise/features/agent-control-plane/policies)이 활성화된 경우 `Yes`. |
+| **PII detection applied** | deployment별 PII 설정 또는 일치하는 [PII 규칙](/ko/enterprise/features/agent-control-plane/rules)이 활성화된 경우 `Yes`. |
 | **Executions** | 윈도우 내 총 실행 수. |
 | **Last updated** | deployment의 마지막 재배포 시점. |
 | **Crew Version** | deployment가 보고한 `crewai` 버전. `1.13` 미만 버전 옆의 정보 아이콘은 메트릭에 기여할 수 없는 행을 표시합니다. |
@@ -96,8 +96,8 @@ mode: "wide"
   <Card title="Agent Control Plane — 개요" icon="book-open" href="/ko/enterprise/features/agent-control-plane/overview">
     ACP란 무엇이며, 요구사항, 플랜 등급, RBAC에 대해.
   </Card>
-  <Card title="Agent Control Plane — 정책" icon="shield-check" href="/edge/ko/enterprise/features/agent-control-plane/policies">
-    조직 단위 PII Redaction 정책을 여러 자동화에 적용합니다.
+  <Card title="Agent Control Plane — 규칙" icon="shield-check" href="/ko/enterprise/features/agent-control-plane/rules">
+    조직 단위 PII Redaction 규칙을 여러 자동화에 적용합니다.
   </Card>
   <Card title="Traces" icon="timeline" href="/ko/enterprise/features/traces">
     개별 실행을 드릴다운하여 에이전트의 추론, 도구 호출, 토큰 사용량을 확인합니다.

docs/edge/ko/enterprise/features/agent-control-plane/overview.mdx

@@ -10,17 +10,17 @@ icon: "book-open"
 
   - **개요** *(현재 페이지)*
   - [모니터링](/ko/enterprise/features/agent-control-plane/monitoring)
-  - [정책](/edge/ko/enterprise/features/agent-control-plane/policies)
+  - [규칙](/ko/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## 개요
 
-**Agent Control Plane**(ACP)은 CrewAI AMP에서 실행 중인 모든 워크로드를 위한 운영 허브입니다. **Automations**와 **Policies** 두 개의 탭으로 구성된 단일 화면에서 다음 작업을 할 수 있습니다:
+**Agent Control Plane**(ACP)은 CrewAI AMP에서 실행 중인 모든 워크로드를 위한 운영 허브입니다. **Automations**와 **Rules** 두 개의 탭으로 구성된 단일 화면에서 다음 작업을 할 수 있습니다:
 
 - 모든 라이브 자동화(crew 또는 flow)의 **상태(health)**를 `Critical` / `Warning` / `Healthy` 분포와 실행 횟수로 모니터링합니다.
 - 자동화별·공급자별·모델별 **LLM 소비**(토큰 및 비용)를 추적하고, 이전 기간 대비 변화량을 확인합니다.
 - 임의의 자동화 또는 모델 공급자를 드릴다운하여 시계열 차트와 공급자별 분해를 살펴봅니다.
-- 조직 전체에 **정책(Policies)**(현재: PII Redaction)을 적용하여 각 deployment를 개별 편집하지 않고 한 번에 여러 자동화에 정책을 강제합니다.
+- 조직 전체에 **규칙(Rules)**(현재: PII Redaction)을 적용하여 각 deployment를 개별 편집하지 않고 한 번에 여러 자동화에 정책을 강제합니다.
 
 <Frame>
   ![Agent Control Plane 개요](/images/enterprise/acp-overview-automations-sankey.png)
@@ -33,7 +33,7 @@ icon: "book-open"
 두 탭은 서로 다른 두 가지 질문에 답합니다:
 
 - **Automations** — *"지금 내 플릿은 어떻게 동작하고 있고, 얼마나 비용이 들고 있는가?"* [모니터링](/ko/enterprise/features/agent-control-plane/monitoring)을 참고하세요.
-- **Policies** — *"정책(예: PII redaction)을 매번 재배포하지 않고 여러 deployment에 어떻게 강제할 수 있는가?"* [정책](/edge/ko/enterprise/features/agent-control-plane/policies)을 참고하세요.
+- **Rules** — *"정책(예: PII redaction)을 매번 재배포하지 않고 여러 deployment에 어떻게 강제할 수 있는가?"* [규칙](/ko/enterprise/features/agent-control-plane/rules)을 참고하세요.
 
 ## 요구사항
 
@@ -42,11 +42,11 @@ icon: "book-open"
 </Warning>
 
 <Warning>
-  [정책](/edge/ko/enterprise/features/agent-control-plane/policies)을 생성하거나 편집하려면 **Enterprise 또는 Ultra 플랜**이 필요합니다. 하위 플랜의 조직도 Policies 탭을 열고 기존 정책을 볼 수 있지만, 편집기는 "Enterprise" 잠금 핀과 *"PII Redaction policies require an Enterprise plan."* 경고와 함께 읽기 전용으로 표시됩니다. 모니터링(Automations 탭)은 기능이 활성화된 모든 플랜에서 사용할 수 있습니다.
+  [규칙](/ko/enterprise/features/agent-control-plane/rules)을 생성하거나 편집하려면 **Enterprise 또는 Ultra 플랜**이 필요합니다. 하위 플랜의 조직도 Rules 탭을 열고 기존 규칙을 볼 수 있지만, 편집기는 "Enterprise" 잠금 핀과 *"PII Redaction rules require an Enterprise plan."* 경고와 함께 읽기 전용으로 표시됩니다. 모니터링(Automations 탭)은 기능이 활성화된 모든 플랜에서 사용할 수 있습니다.
 </Warning>
 
 - **Agent Control Plane** 기능이 조직에 대해 활성화되어 있어야 합니다. 사이드바에 보이지 않으면 계정 오너에게 활성화를 요청하세요.
-- ACP 내부에서는 [RBAC](/ko/enterprise/features/rbac)가 접근 권한을 관리합니다: 대시보드 및 정책을 보려면 `read`, 정책을 생성·편집·토글·삭제하려면 `manage` 권한이 필요합니다.
+- ACP 내부에서는 [RBAC](/ko/enterprise/features/rbac)가 접근 권한을 관리합니다: 대시보드 및 규칙을 보려면 `read`, 규칙을 생성·편집·토글·삭제하려면 `manage` 권한이 필요합니다.
 - 모든 차트와 테이블은 오른쪽 상단의 시간 선택기를 통해 **지난 24시간**, **지난 1주**, **지난 30일**로 범위를 조정할 수 있습니다. 변화량(`↑ 8 vs yesterday`, `↓ $20.57 vs yesterday` 등)은 선택한 윈도우를 같은 길이의 이전 윈도우와 비교합니다.
 
 ## 여기에서 할 수 있는 일
@@ -55,7 +55,7 @@ icon: "book-open"
   <Card title="모니터링" icon="gauge" href="/ko/enterprise/features/agent-control-plane/monitoring">
     메트릭 카드, 인터랙티브 sankey, 자동화별 테이블, 자동화 또는 공급자별 드릴다운 사이드 패널로 플릿 상태와 LLM 지출을 살펴봅니다.
   </Card>
-  <Card title="정책" icon="shield-check" href="/edge/ko/enterprise/features/agent-control-plane/policies">
+  <Card title="규칙" icon="shield-check" href="/ko/enterprise/features/agent-control-plane/rules">
     도구와 태그로 범위를 지정한 PII Redaction 정책을 조직 단위로 적용합니다. 변경 사항은 다음 실행부터 적용되며 재배포가 필요 없습니다.
   </Card>
 </CardGroup>
@@ -67,10 +67,10 @@ icon: "book-open"
     개별 실행을 드릴다운하여 에이전트의 추론, 도구 호출, 토큰 사용량을 확인합니다.
   </Card>
   <Card title="RBAC" icon="users" href="/ko/enterprise/features/rbac">
-    누가 Agent Control Plane을 읽을 수 있고 누가 정책을 편집할 수 있는지 관리합니다.
+    누가 Agent Control Plane을 읽을 수 있고 누가 규칙을 편집할 수 있는지 관리합니다.
   </Card>
   <Card title="Traces용 PII Redaction" icon="lock" href="/ko/enterprise/features/pii-trace-redactions">
-    정책이 참조하는 엔티티 카탈로그 및 deployment 단위 PII 설정.
+    규칙이 참조하는 엔티티 카탈로그 및 deployment 단위 PII 설정.
   </Card>
   <Card title="AMP에 배포" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
     Agent Control Plane을 지원하는 crewAI 버전으로 crew를 배포합니다.
@@ -78,5 +78,5 @@ icon: "book-open"
 </CardGroup>
 
 <Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
-  메트릭 해석 또는 정책 설계에 도움이 필요하시면 지원 팀에 문의하세요.
+  메트릭 해석 또는 규칙 설계에 도움이 필요하시면 지원 팀에 문의하세요.
 </Card>

docs/edge/ko/enterprise/features/agent-control-plane/rules.mdx

@@ -1,7 +1,7 @@
 ---
-title: "정책 설정하기"
+title: "규칙 설정하기"
 description: "한 곳에서 조직 단위 정책을 여러 자동화에 적용합니다."
-sidebarTitle: "정책"
+sidebarTitle: "규칙"
 icon: "shield-check"
 mode: "wide"
 ---
@@ -11,50 +11,50 @@ mode: "wide"
 
   - [개요](/ko/enterprise/features/agent-control-plane/overview)
   - [모니터링](/ko/enterprise/features/agent-control-plane/monitoring)
-  - **정책** *(현재 페이지)*
+  - **규칙** *(현재 페이지)*
 </Info>
 
 ## 개요
 
-정책(Policies)은 각 deployment를 개별 설정하는 대신, 정책 — 현재: **PII Redaction** — 을 한 번에 여러 자동화에 적용할 수 있게 해줍니다. 관리하려면 [Agent Control Plane](/ko/enterprise/features/agent-control-plane/overview)에서 **Policies** 탭을 엽니다.
+규칙(Rules)은 각 deployment를 개별 설정하는 대신, 정책 — 현재: **PII Redaction** — 을 한 번에 여러 자동화에 적용할 수 있게 해줍니다. 관리하려면 [Agent Control Plane](/ko/enterprise/features/agent-control-plane/overview)에서 **Rules** 탭을 엽니다.
 
 <Frame>
-  ![정책 목록](/images/enterprise/acp-policies-list.png)
+  ![규칙 목록](/images/enterprise/acp-rules-list.png)
 </Frame>
 
-각 정책 카드에는 이름, 설명, 정책이 적용되는 **범위(scope)**(선택된 도구와 태그), 그리고 현재 범위와 일치하는 deployment의 수인 **engaged automations**가 표시됩니다. 오른쪽 토글로 정책을 삭제하지 않고 활성/비활성할 수 있습니다.
+각 규칙 카드에는 이름, 설명, 규칙이 적용되는 **범위(scope)**(선택된 도구와 태그), 그리고 현재 범위와 일치하는 deployment의 수인 **engaged automations**가 표시됩니다. 오른쪽 토글로 규칙을 삭제하지 않고 활성/비활성할 수 있습니다.
 
 ## 요구사항
 
 <Warning>
-  PII Redaction 정책을 생성하거나 편집하려면 **Enterprise 또는 Ultra 플랜**이 필요합니다. 하위 플랜의 조직도 Policies 탭을 열고 기존 정책을 볼 수는 있지만, 편집기는 "Enterprise" 잠금 핀과 *"PII Redaction policies require an Enterprise plan."* 경고와 함께 읽기 전용으로 렌더링됩니다. 업그레이드하려면 계정 오너 또는 영업팀에 문의하세요.
+  PII Redaction 규칙을 생성하거나 편집하려면 **Enterprise 또는 Ultra 플랜**이 필요합니다. 하위 플랜의 조직도 Rules 탭을 열고 기존 규칙을 볼 수는 있지만, 편집기는 "Enterprise" 잠금 핀과 *"PII Redaction rules require an Enterprise plan."* 경고와 함께 읽기 전용으로 렌더링됩니다. 업그레이드하려면 계정 오너 또는 영업팀에 문의하세요.
 </Warning>
 
 - **Agent Control Plane** 기능이 조직에 대해 활성화되어 있어야 합니다. [개요 — 요구사항](/ko/enterprise/features/agent-control-plane/overview#요구사항)을 참고하세요.
-- 정책을 생성·편집·토글·삭제하려면 Agent Control Plane에 대한 [RBAC](/ko/enterprise/features/rbac)의 `manage` 권한이 필요합니다. 보려면 `read` 권한만으로 충분합니다.
-- 모든 정책 변경은 감사를 위해 버전 관리됩니다.
+- 규칙을 생성·편집·토글·삭제하려면 Agent Control Plane에 대한 [RBAC](/ko/enterprise/features/rbac)의 `manage` 권한이 필요합니다. 보려면 `read` 권한만으로 충분합니다.
+- 모든 규칙 변경은 감사를 위해 버전 관리됩니다.
 
-## 사용 가능한 정책 유형
+## 사용 가능한 규칙 유형
 
 | 유형 | 동작 |
 |------|---------------|
 | **PII Redaction** | 일치하는 모든 자동화의 실행에 PII redaction을 적용합니다. [Traces용 PII Redaction](/ko/enterprise/features/pii-trace-redactions)에 문서화된 동일한 엔티티 카탈로그와 커스텀 recognizer를 사용합니다. |
 
-향후 더 많은 정책 유형이 추가될 예정입니다.
+향후 더 많은 규칙 유형이 추가될 예정입니다.
 
-## 정책 만들기
+## 규칙 만들기
 
 <Frame>
-  <img src="/images/enterprise/acp-policies-new-side-panel.png" alt="조건 및 PII 마스크 유형이 있는 정책 편집 사이드 패널" width="450" />
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="조건 및 PII 마스크 유형이 있는 규칙 편집 사이드 패널" width="450" />
 </Frame>
 
 <Steps>
   <Step title="편집기 열기">
-    Policies 탭 오른쪽 상단의 **+ Create new**를 클릭하거나, 기존 정책 카드의 **View Details**를 클릭합니다.
+    Rules 탭 오른쪽 상단의 **+ Create new**를 클릭하거나, 기존 규칙 카드의 **View Details**를 클릭합니다.
   </Step>
 
-  <Step title="정책 이름과 설명 작성">
-    정책에 명확한 이름(예: *Mask PII (CC)*)과 적용 시점을 설명하는 description을 부여합니다. 둘 다 정책 카드와 Engaged Automations 모달에 표시됩니다.
+  <Step title="규칙 이름과 설명 작성">
+    규칙에 명확한 이름(예: *Mask PII (CC)*)과 적용 시점을 설명하는 description을 부여합니다. 둘 다 규칙 카드와 Engaged Automations 모달에 표시됩니다.
   </Step>
 
   <Step title="유형 선택">
@@ -62,12 +62,12 @@ mode: "wide"
   </Step>
 
   <Step title="조건 설정">
-    조건은 정책이 어떤 자동화에 engage 할지 결정합니다. 둘 다 선택 사항이며 **집합 동일성(set-equality)** 의미론을 사용합니다:
+    조건은 규칙이 어떤 자동화에 engage 할지 결정합니다. 둘 다 선택 사항이며 **집합 동일성(set-equality)** 의미론을 사용합니다:
 
     - **Tools** — 도구 집합이 선택된 도구와 **정확히 일치**하는 자동화만 engage 됩니다. Studio 앱, MCP, OSS 도구, Tool Repository registry 도구 중에서 선택합니다.
     - **Automations** — 태그 집합이 선택된 태그와 **정확히 일치**하는 자동화만 engage 됩니다.
 
-    피커를 비워두면 "이 차원에서 필터링하지 않음"을 의미합니다. 두 피커를 모두 비워두면 정책이 조직의 **모든** 자동화에 적용됩니다.
+    피커를 비워두면 "이 차원에서 필터링하지 않음"을 의미합니다. 두 피커를 모두 비워두면 규칙이 조직의 **모든** 자동화에 적용됩니다.
   </Step>
 
   <Step title="PII Mask Type 테이블 구성">
@@ -75,30 +75,30 @@ mode: "wide"
   </Step>
 
   <Step title="저장">
-    저장하는 즉시 engage 된 모든 자동화의 **향후** 실행에 정책이 적용됩니다. 재배포는 필요 없습니다.
+    저장하는 즉시 engage 된 모든 자동화의 **향후** 실행에 규칙이 적용됩니다. 재배포는 필요 없습니다.
   </Step>
 </Steps>
 
 ## Engaged automations
 
-정책 카드의 **Engaged N automations**를 클릭하면 현재 정책이 일치시키고 있는 deployment와 각 deployment의 마지막 실행을 정확히 확인할 수 있습니다.
+규칙 카드의 **Engaged N automations**를 클릭하면 현재 규칙이 일치시키고 있는 deployment와 각 deployment의 마지막 실행을 정확히 확인할 수 있습니다.
 
 <Frame>
-  ![Engaged automations 모달](/images/enterprise/acp-policies-engaged-modal.png)
+  ![Engaged automations 모달](/images/enterprise/acp-rules-engaged-modal.png)
 </Frame>
 
-정책을 활성화하기 전에 범위를 빠르게 점검하는 가장 좋은 방법입니다. 예를 들어, `production` 태그로 범위를 지정한 정책이 의도치 않게 staging deployment를 일치시키지 않는지 확인할 수 있습니다.
+규칙을 활성화하기 전에 범위를 빠르게 점검하는 가장 좋은 방법입니다. 예를 들어, `production` 태그로 범위를 지정한 규칙이 의도치 않게 staging deployment를 일치시키지 않는지 확인할 수 있습니다.
 
-## 조직 단위 정책 vs deployment 단위 설정
+## 조직 단위 규칙 vs deployment 단위 설정
 
 PII Redaction은 두 곳에서 설정할 수 있습니다:
 
 - **deployment 단위** — 각 deployment의 **Settings → PII Protection** ([가이드](/ko/enterprise/features/pii-trace-redactions))
-- **조직 단위** — 이 페이지의 정책
+- **조직 단위** — 이 페이지의 규칙
 
-활성화된 조직 단위 정책의 범위가 어떤 deployment와 일치하면, 정책의 엔티티 구성이 그 deployment의 실행에 대해 **deployment가 소유한 PII 설정을 덮어씁니다**. 정책이 연결된 동안에는 정책이 단일 진실 공급원이 됩니다. 정책을 비활성화하거나 분리하면(또는 범위를 변경하여 더 이상 일치하지 않게 만들면) deployment는 자체 PII Protection 설정으로 되돌아갑니다.
+활성화된 조직 단위 규칙의 범위가 어떤 deployment와 일치하면, 규칙의 엔티티 구성이 그 deployment의 실행에 대해 **deployment가 소유한 PII 설정을 덮어씁니다**. 규칙이 연결된 동안에는 규칙이 단일 진실 공급원이 됩니다. 규칙을 비활성화하거나 분리하면(또는 범위를 변경하여 더 이상 일치하지 않게 만들면) deployment는 자체 PII Protection 설정으로 되돌아갑니다.
 
-여러 deployment에 걸쳐 일관된 정책을 강제하고 싶을 때는 조직 단위 정책을 선호하고, 일회성 예외에 대해서는 deployment 단위 설정을 사용하세요.
+여러 deployment에 걸쳐 일관된 정책을 강제하고 싶을 때는 조직 단위 규칙을 선호하고, 일회성 예외에 대해서는 deployment 단위 설정을 사용하세요.
 
 ## 관련 문서
 
@@ -113,10 +113,10 @@ PII Redaction은 두 곳에서 설정할 수 있습니다:
     엔티티 카탈로그, 커스텀 recognizer, deployment 단위 구성.
   </Card>
   <Card title="RBAC" icon="users" href="/ko/enterprise/features/rbac">
-    누가 정책을 만들거나 편집할 수 있는지 관리합니다.
+    누가 규칙을 만들거나 편집할 수 있는지 관리합니다.
   </Card>
 </CardGroup>
 
 <Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
-  조직의 정책을 설계하는 데 도움이 필요하시면 지원 팀에 문의하세요.
+  조직의 규칙을 설계하는 데 도움이 필요하시면 지원 팀에 문의하세요.
 </Card>

docs/edge/pt-BR/enterprise/features/agent-control-plane/monitoring.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
   - [Visão Geral](/pt-BR/enterprise/features/agent-control-plane/overview)
   - **Monitoramento** *(você está aqui)*
-  - [Políticas](/edge/pt-BR/enterprise/features/agent-control-plane/policies)
+  - [Regras](/pt-BR/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## Visão Geral
@@ -58,7 +58,7 @@ A sub-aba **Automações** é o detalhamento por deployment da saúde da frota.
 | **Última execução** | Tempo decorrido desde a execução mais recente. |
 | **Health Status Breakdown** | Barra empilhada com percentuais de `Critical` / `Warning` / `Healthy` para as execuções na janela. |
 | **Executions with Errors** | Total de execuções com falha na janela. |
-| **PII detection applied** | `Yes` se houver configuração de PII por deployment ou uma [política de PII](/edge/pt-BR/enterprise/features/agent-control-plane/policies) correspondente ativa. |
+| **PII detection applied** | `Yes` se houver configuração de PII por deployment ou uma [regra de PII](/pt-BR/enterprise/features/agent-control-plane/rules) correspondente ativa. |
 | **Executions** | Total de execuções na janela. |
 | **Last updated** | Quando o deployment foi re-implantado pela última vez. |
 | **Crew Version** | A versão do `crewai` reportada pelo deployment. Um ícone de informação ao lado de versões abaixo de `1.13` indica linhas que não conseguem contribuir com métricas. |
@@ -96,8 +96,8 @@ Filtre por **LLM provider** e ordene por `Cost`, `Executions` ou `Last run`.
   <Card title="Agent Control Plane — Visão Geral" icon="book-open" href="/pt-BR/enterprise/features/agent-control-plane/overview">
     O que é o ACP, requisitos, planos suportados e RBAC.
   </Card>
-  <Card title="Agent Control Plane — Políticas" icon="shield-check" href="/edge/pt-BR/enterprise/features/agent-control-plane/policies">
-    Aplique políticas de PII Redaction em nível de organização em muitas automações.
+  <Card title="Agent Control Plane — Regras" icon="shield-check" href="/pt-BR/enterprise/features/agent-control-plane/rules">
+    Aplique regras de PII Redaction em nível de organização em muitas automações.
   </Card>
   <Card title="Traces" icon="timeline" href="/pt-BR/enterprise/features/traces">
     Aprofunde em uma única execução para ver o raciocínio do agente, chamadas de ferramentas e uso de tokens.

docs/edge/pt-BR/enterprise/features/agent-control-plane/overview.mdx

@@ -10,17 +10,17 @@ icon: "book-open"
 
   - **Visão Geral** *(você está aqui)*
   - [Monitoramento](/pt-BR/enterprise/features/agent-control-plane/monitoring)
-  - [Políticas](/edge/pt-BR/enterprise/features/agent-control-plane/policies)
+  - [Regras](/pt-BR/enterprise/features/agent-control-plane/rules)
 </Info>
 
 ## Visão Geral
 
-O **Agent Control Plane** (ACP) é o hub de operações para tudo que você tem rodando no CrewAI AMP. É uma tela única — dividida nas abas **Automações** e **Políticas** — que permite à sua equipe:
+O **Agent Control Plane** (ACP) é o hub de operações para tudo que você tem rodando no CrewAI AMP. É uma tela única — dividida nas abas **Automações** e **Regras** — que permite à sua equipe:
 
 - Monitorar a **saúde** de cada automação ao vivo (crew ou flow), com detalhamentos `Critical` / `Warning` / `Healthy` e contagem de execuções.
 - Acompanhar o **consumo de LLM** — tokens e custo — por automação, por provedor e por modelo, com a variação em relação ao período anterior.
 - Aprofundar em qualquer automação ou provedor de modelo para ver gráficos de série temporal e detalhamentos por provedor.
-- Aplicar **Políticas** em nível de organização (hoje: PII Redaction) em muitas automações de uma só vez, em vez de editar cada deployment individualmente.
+- Aplicar **Regras** em nível de organização (hoje: PII Redaction) em muitas automações de uma só vez, em vez de editar cada deployment individualmente.
 
 <Frame>
   ![Visão geral do Agent Control Plane](/images/enterprise/acp-overview-automations-sankey.png)
@@ -33,7 +33,7 @@ O **Agent Control Plane** (ACP) é o hub de operações para tudo que você tem
 As duas abas respondem a duas perguntas distintas:
 
 - **Automações** — *"Como minha frota está se comportando agora e quanto está me custando?"* Veja [Monitoramento](/pt-BR/enterprise/features/agent-control-plane/monitoring).
-- **Políticas** — *"Como aplico uma política (por exemplo, PII redaction) em muitos deployments sem precisar reimplantar cada um?"* Veja [Políticas](/edge/pt-BR/enterprise/features/agent-control-plane/policies).
+- **Regras** — *"Como aplico uma política (por exemplo, PII redaction) em muitos deployments sem precisar reimplantar cada um?"* Veja [Regras](/pt-BR/enterprise/features/agent-control-plane/rules).
 
 ## Requisitos
 
@@ -42,11 +42,11 @@ As duas abas respondem a duas perguntas distintas:
 </Warning>
 
 <Warning>
-  **Plano Enterprise ou Ultra** é necessário para criar ou editar [Políticas](/edge/pt-BR/enterprise/features/agent-control-plane/policies). Organizações em planos inferiores ainda podem abrir a aba Políticas e visualizar políticas existentes, mas o editor é renderizado em modo somente leitura, com um selo "Enterprise" de bloqueio e o alerta *"PII Redaction policies require an Enterprise plan."* O Monitoramento (a aba Automações) está disponível em todos os planos em que o recurso esteja habilitado.
+  **Plano Enterprise ou Ultra** é necessário para criar ou editar [Regras](/pt-BR/enterprise/features/agent-control-plane/rules). Organizações em planos inferiores ainda podem abrir a aba Regras e visualizar regras existentes, mas o editor é renderizado em modo somente leitura, com um selo "Enterprise" de bloqueio e o alerta *"PII Redaction rules require an Enterprise plan."* O Monitoramento (a aba Automações) está disponível em todos os planos em que o recurso esteja habilitado.
 </Warning>
 
 - O recurso **Agent Control Plane** precisa estar habilitado para sua organização. Se você não o vê na barra lateral, peça ao owner da conta para solicitar a habilitação.
-- Dentro do ACP, o [RBAC](/pt-BR/enterprise/features/rbac) controla o acesso: `read` para visualizar o dashboard e as políticas, `manage` para criar, editar, ligar/desligar ou excluir políticas.
+- Dentro do ACP, o [RBAC](/pt-BR/enterprise/features/rbac) controla o acesso: `read` para visualizar o dashboard e as regras, `manage` para criar, editar, ligar/desligar ou excluir regras.
 - Todos os gráficos e tabelas podem ser ajustados para **Últimas 24 horas**, **Última Semana** ou **Últimos 30 dias** usando o seletor de tempo no canto superior direito. As variações (`↑ 8 vs ontem`, `↓ $20.57 vs ontem`, etc.) comparam a janela selecionada com a janela anterior de mesma duração.
 
 ## O que você pode fazer aqui
@@ -55,7 +55,7 @@ As duas abas respondem a duas perguntas distintas:
   <Card title="Monitoramento" icon="gauge" href="/pt-BR/enterprise/features/agent-control-plane/monitoring">
     Acompanhe a saúde da frota e o gasto com LLM com cards de métricas, um sankey interativo, tabelas por automação e painéis laterais de detalhamento para qualquer automação ou provedor.
   </Card>
-  <Card title="Políticas" icon="shield-check" href="/edge/pt-BR/enterprise/features/agent-control-plane/policies">
+  <Card title="Regras" icon="shield-check" href="/pt-BR/enterprise/features/agent-control-plane/rules">
     Aplique políticas de PII Redaction em nível de organização, com escopo por ferramentas e tags. As mudanças entram em vigor na próxima execução — sem necessidade de re-implantação.
   </Card>
 </CardGroup>
@@ -67,10 +67,10 @@ As duas abas respondem a duas perguntas distintas:
     Aprofunde em uma única execução para ver o raciocínio do agente, chamadas de ferramentas e uso de tokens.
   </Card>
   <Card title="RBAC" icon="users" href="/pt-BR/enterprise/features/rbac">
-    Gerencie quem pode ler o Agent Control Plane e quem pode editar políticas.
+    Gerencie quem pode ler o Agent Control Plane e quem pode editar regras.
   </Card>
   <Card title="PII Redaction para Traces" icon="lock" href="/pt-BR/enterprise/features/pii-trace-redactions">
-    Catálogo de entidades e configuração de PII por deployment, referenciados pelas Políticas.
+    Catálogo de entidades e configuração de PII por deployment, referenciados pelas Regras.
   </Card>
   <Card title="Deploy no AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
     Implante uma crew em uma versão do crewAI que suporta o Agent Control Plane.
@@ -78,5 +78,5 @@ As duas abas respondem a duas perguntas distintas:
 </CardGroup>
 
 <Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
-  Entre em contato com nosso time de suporte para interpretar métricas ou desenhar políticas.
+  Entre em contato com nosso time de suporte para interpretar métricas ou desenhar regras.
 </Card>

docs/edge/pt-BR/enterprise/features/agent-control-plane/policies.mdx

@@ -1,122 +0,0 @@
----
-title: "Configure as Políticas"
-description: "Aplique políticas em nível de organização em muitas automações a partir de um único lugar."
-sidebarTitle: "Políticas"
-icon: "shield-check"
-mode: "wide"
----
-
-<Info>
-  **Navegação da Documentação do ACP (Beta)**
-
-  - [Visão Geral](/pt-BR/enterprise/features/agent-control-plane/overview)
-  - [Monitoramento](/pt-BR/enterprise/features/agent-control-plane/monitoring)
-  - **Políticas** *(você está aqui)*
-</Info>
-
-## Visão Geral
-
-As Políticas permitem aplicar políticas — hoje: **PII Redaction** — em muitas automações de uma só vez, em vez de configurar cada deployment individualmente. Abra a aba **Políticas** no [Agent Control Plane](/pt-BR/enterprise/features/agent-control-plane/overview) para gerenciá-las.
-
-<Frame>
-  ![Lista de políticas](/images/enterprise/acp-policies-list.png)
-</Frame>
-
-Cada card de política mostra o nome, a descrição, o **escopo** ao qual a política se aplica (ferramentas e tags selecionadas) e a contagem de **automações engajadas** — deployments que atualmente correspondem ao escopo. O toggle à direita ativa ou desativa a política sem excluí-la.
-
-## Requisitos
-
-<Warning>
-  **Plano Enterprise ou Ultra** é necessário para criar ou editar políticas de PII Redaction. Organizações em planos inferiores ainda podem abrir a aba Políticas e visualizar políticas existentes, mas o editor é renderizado em modo somente leitura, com um selo "Enterprise" de bloqueio e o alerta *"PII Redaction policies require an Enterprise plan."* — entre em contato com o owner da sua conta ou com vendas para fazer upgrade.
-</Warning>
-
-- O recurso **Agent Control Plane** precisa estar habilitado para sua organização. Veja [Visão Geral — Requisitos](/pt-BR/enterprise/features/agent-control-plane/overview#requisitos).
-- A permissão `manage` no [RBAC](/pt-BR/enterprise/features/rbac) sobre o Agent Control Plane é necessária para criar, editar, ligar/desligar ou excluir políticas. A permissão `read` é suficiente para visualizá-las.
-- Todas as mudanças de políticas são versionadas para auditoria.
-
-## Tipos de política disponíveis
-
-| Tipo | O que faz |
-|------|---------------|
-| **PII Redaction** | Aplica PII redaction às execuções de cada automação correspondente, usando o mesmo catálogo de entidades e recognizers customizados documentados em [PII Redaction para Traces](/pt-BR/enterprise/features/pii-trace-redactions). |
-
-Mais tipos de políticas serão adicionados ao longo do tempo.
-
-## Criando uma política
-
-<Frame>
-  <img src="/images/enterprise/acp-policies-new-side-panel.png" alt="Painel lateral de edição de política com condições e tipo de máscara de PII" width="450" />
-</Frame>
-
-<Steps>
-  <Step title="Abra o editor">
-    Clique em **+ Create new** no canto superior direito da aba Políticas, ou em **View Details** em um card de política existente.
-  </Step>
-
-  <Step title="Dê um nome e descreva a política">
-    Dê à política um nome claro (ex.: *Mask PII (CC)*) e uma descrição explicando quando ela se aplica. Ambos aparecem no card da política e no modal de Automações Engajadas.
-  </Step>
-
-  <Step title="Escolha o tipo">
-    Hoje só **PII Redaction** está disponível.
-  </Step>
-
-  <Step title="Defina as condições">
-    As condições decidem quais automações a política engaja. Ambas são opcionais e usam a semântica de **igualdade de conjuntos**:
-
-    - **Tools** — apenas automações cujo conjunto de ferramentas **corresponde exatamente** às ferramentas selecionadas serão engajadas. Selecione entre apps do Studio, MCPs, ferramentas OSS e ferramentas do Tool Repository.
-    - **Automations** — apenas automações cujo conjunto de tags **corresponde exatamente** às tags selecionadas serão engajadas.
-
-    Deixar um seletor vazio significa "sem filtro nesta dimensão". Deixar ambos vazios significa que a política se aplica a **todas** as automações da organização.
-  </Step>
-
-  <Step title="Configure a tabela PII Mask Type">
-    Marque cada tipo de entidade que deseja cobrir e escolha **Mask** (substitui pelo rótulo da entidade, ex.: `<CREDIT_CARD>`) ou **Redact** (remove o texto correspondente por completo). Veja [PII Redaction para Traces](/pt-BR/enterprise/features/pii-trace-redactions) para o catálogo completo de entidades e como adicionar recognizers customizados em nível de organização.
-  </Step>
-
-  <Step title="Salve">
-    A política se aplica a **futuras** execuções de cada automação engajada assim que você salva. Nenhuma re-implantação é necessária.
-  </Step>
-</Steps>
-
-## Automações engajadas
-
-Clique em **Engaged N automations** em qualquer card de política para ver exatamente quais deployments a política está correspondendo no momento, junto com a última execução de cada um.
-
-<Frame>
-  ![Modal de automações engajadas](/images/enterprise/acp-policies-engaged-modal.png)
-</Frame>
-
-Esta é a forma mais rápida de validar o escopo de uma política antes de habilitá-la — por exemplo, para confirmar que uma política com escopo na tag `production` não está acidentalmente correspondendo a um deployment de staging.
-
-## Políticas em nível de organização vs configurações por deployment
-
-A PII Redaction pode ser configurada em dois lugares:
-
-- **Por deployment** — em **Settings → PII Protection** em cada deployment individual ([guia](/pt-BR/enterprise/features/pii-trace-redactions))
-- **Em nível de organização** — como uma Política nesta página
-
-Quando o escopo de uma política habilitada em nível de organização corresponde a um deployment, a configuração de entidades da política **sobrescreve** as configurações de PII pertencentes ao deployment para as execuções daquele deployment — a política se torna a fonte única da verdade enquanto está vinculada. Desabilite ou desvincule a política (ou altere o escopo para que ela não corresponda mais) e o deployment volta às suas próprias configurações de PII Protection.
-
-Prefira políticas em nível de organização quando quiser impor uma política consistente em muitos deployments; reserve a configuração por deployment para exceções pontuais.
-
-## Relacionados
-
-<CardGroup cols={2}>
-  <Card title="Agent Control Plane — Visão Geral" icon="book-open" href="/pt-BR/enterprise/features/agent-control-plane/overview">
-    O que é o ACP, requisitos, planos suportados e RBAC.
-  </Card>
-  <Card title="Agent Control Plane — Monitoramento" icon="gauge" href="/pt-BR/enterprise/features/agent-control-plane/monitoring">
-    Acompanhe automações e consumo de LLM em toda a sua frota.
-  </Card>
-  <Card title="PII Redaction para Traces" icon="lock" href="/pt-BR/enterprise/features/pii-trace-redactions">
-    Catálogo de entidades, recognizers customizados e configuração por deployment.
-  </Card>
-  <Card title="RBAC" icon="users" href="/pt-BR/enterprise/features/rbac">
-    Gerencie quem pode criar ou editar políticas.
-  </Card>
-</CardGroup>
-
-<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
-  Entre em contato com nosso time de suporte para ajudar a desenhar políticas para a sua organização.
-</Card>

docs/edge/pt-BR/enterprise/features/agent-control-plane/rules.mdx

@@ -0,0 +1,122 @@
+---
+title: "Configure as Regras"
+description: "Aplique políticas em nível de organização em muitas automações a partir de um único lugar."
+sidebarTitle: "Regras"
+icon: "shield-check"
+mode: "wide"
+---
+
+<Info>
+  **Navegação da Documentação do ACP (Beta)**
+
+  - [Visão Geral](/pt-BR/enterprise/features/agent-control-plane/overview)
+  - [Monitoramento](/pt-BR/enterprise/features/agent-control-plane/monitoring)
+  - **Regras** *(você está aqui)*
+</Info>
+
+## Visão Geral
+
+As Regras permitem aplicar políticas — hoje: **PII Redaction** — em muitas automações de uma só vez, em vez de configurar cada deployment individualmente. Abra a aba **Regras** no [Agent Control Plane](/pt-BR/enterprise/features/agent-control-plane/overview) para gerenciá-las.
+
+<Frame>
+  ![Lista de regras](/images/enterprise/acp-rules-list.png)
+</Frame>
+
+Cada card de regra mostra o nome, a descrição, o **escopo** ao qual a regra se aplica (ferramentas e tags selecionadas) e a contagem de **automações engajadas** — deployments que atualmente correspondem ao escopo. O toggle à direita ativa ou desativa a regra sem excluí-la.
+
+## Requisitos
+
+<Warning>
+  **Plano Enterprise ou Ultra** é necessário para criar ou editar regras de PII Redaction. Organizações em planos inferiores ainda podem abrir a aba Regras e visualizar regras existentes, mas o editor é renderizado em modo somente leitura, com um selo "Enterprise" de bloqueio e o alerta *"PII Redaction rules require an Enterprise plan."* — entre em contato com o owner da sua conta ou com vendas para fazer upgrade.
+</Warning>
+
+- O recurso **Agent Control Plane** precisa estar habilitado para sua organização. Veja [Visão Geral — Requisitos](/pt-BR/enterprise/features/agent-control-plane/overview#requisitos).
+- A permissão `manage` no [RBAC](/pt-BR/enterprise/features/rbac) sobre o Agent Control Plane é necessária para criar, editar, ligar/desligar ou excluir regras. A permissão `read` é suficiente para visualizá-las.
+- Todas as mudanças de regras são versionadas para auditoria.
+
+## Tipos de regra disponíveis
+
+| Tipo | O que faz |
+|------|---------------|
+| **PII Redaction** | Aplica PII redaction às execuções de cada automação correspondente, usando o mesmo catálogo de entidades e recognizers customizados documentados em [PII Redaction para Traces](/pt-BR/enterprise/features/pii-trace-redactions). |
+
+Mais tipos de regras serão adicionados ao longo do tempo.
+
+## Criando uma regra
+
+<Frame>
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="Painel lateral de edição de regra com condições e tipo de máscara de PII" width="450" />
+</Frame>
+
+<Steps>
+  <Step title="Abra o editor">
+    Clique em **+ Create new** no canto superior direito da aba Regras, ou em **View Details** em um card de regra existente.
+  </Step>
+
+  <Step title="Dê um nome e descreva a regra">
+    Dê à regra um nome claro (ex.: *Mask PII (CC)*) e uma descrição explicando quando ela se aplica. Ambos aparecem no card da regra e no modal de Automações Engajadas.
+  </Step>
+
+  <Step title="Escolha o tipo">
+    Hoje só **PII Redaction** está disponível.
+  </Step>
+
+  <Step title="Defina as condições">
+    As condições decidem quais automações a regra engaja. Ambas são opcionais e usam a semântica de **igualdade de conjuntos**:
+
+    - **Tools** — apenas automações cujo conjunto de ferramentas **corresponde exatamente** às ferramentas selecionadas serão engajadas. Selecione entre apps do Studio, MCPs, ferramentas OSS e ferramentas do Tool Repository.
+    - **Automations** — apenas automações cujo conjunto de tags **corresponde exatamente** às tags selecionadas serão engajadas.
+
+    Deixar um seletor vazio significa "sem filtro nesta dimensão". Deixar ambos vazios significa que a regra se aplica a **todas** as automações da organização.
+  </Step>
+
+  <Step title="Configure a tabela PII Mask Type">
+    Marque cada tipo de entidade que deseja cobrir e escolha **Mask** (substitui pelo rótulo da entidade, ex.: `<CREDIT_CARD>`) ou **Redact** (remove o texto correspondente por completo). Veja [PII Redaction para Traces](/pt-BR/enterprise/features/pii-trace-redactions) para o catálogo completo de entidades e como adicionar recognizers customizados em nível de organização.
+  </Step>
+
+  <Step title="Salve">
+    A regra se aplica a **futuras** execuções de cada automação engajada assim que você salva. Nenhuma re-implantação é necessária.
+  </Step>
+</Steps>
+
+## Automações engajadas
+
+Clique em **Engaged N automations** em qualquer card de regra para ver exatamente quais deployments a regra está correspondendo no momento, junto com a última execução de cada um.
+
+<Frame>
+  ![Modal de automações engajadas](/images/enterprise/acp-rules-engaged-modal.png)
+</Frame>
+
+Esta é a forma mais rápida de validar o escopo de uma regra antes de habilitá-la — por exemplo, para confirmar que uma regra com escopo na tag `production` não está acidentalmente correspondendo a um deployment de staging.
+
+## Regras em nível de organização vs configurações por deployment
+
+A PII Redaction pode ser configurada em dois lugares:
+
+- **Por deployment** — em **Settings → PII Protection** em cada deployment individual ([guia](/pt-BR/enterprise/features/pii-trace-redactions))
+- **Em nível de organização** — como uma Regra nesta página
+
+Quando o escopo de uma regra habilitada em nível de organização corresponde a um deployment, a configuração de entidades da regra **sobrescreve** as configurações de PII pertencentes ao deployment para as execuções daquele deployment — a regra se torna a fonte única da verdade enquanto está vinculada. Desabilite ou desvincule a regra (ou altere o escopo para que ela não corresponda mais) e o deployment volta às suas próprias configurações de PII Protection.
+
+Prefira regras em nível de organização quando quiser impor uma política consistente em muitos deployments; reserve a configuração por deployment para exceções pontuais.
+
+## Relacionados
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — Visão Geral" icon="book-open" href="/pt-BR/enterprise/features/agent-control-plane/overview">
+    O que é o ACP, requisitos, planos suportados e RBAC.
+  </Card>
+  <Card title="Agent Control Plane — Monitoramento" icon="gauge" href="/pt-BR/enterprise/features/agent-control-plane/monitoring">
+    Acompanhe automações e consumo de LLM em toda a sua frota.
+  </Card>
+  <Card title="PII Redaction para Traces" icon="lock" href="/pt-BR/enterprise/features/pii-trace-redactions">
+    Catálogo de entidades, recognizers customizados e configuração por deployment.
+  </Card>
+  <Card title="RBAC" icon="users" href="/pt-BR/enterprise/features/rbac">
+    Gerencie quem pode criar ou editar regras.
+  </Card>
+</CardGroup>
+
+<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nosso time de suporte para ajudar a desenhar regras para a sua organização.
+</Card>

lib/cli/src/crewai_cli/cli.py

@@ -526,8 +526,8 @@ def run(
     inputs: str | None,
 ) -> None:
     """Run the Crew or Flow."""
-    # --inputs no longer requires --definition: with no override it resolves the
-    # configured [tool.crewai] flow, same as a bare `crewai run`.
+    if inputs is not None and definition is None:
+        raise click.UsageError("--inputs requires --definition")
     if trained_agents_file is not None and definition is not None:
         raise click.UsageError("--filename can only be used when running crews")
 

lib/cli/src/crewai_cli/create_json_crew.py

@@ -14,7 +14,6 @@ from rich.text import Text
 
 from crewai_cli.constants import ENV_VARS
 from crewai_cli.git import initialize_if_git_available
-from crewai_cli.model_catalog import get_provider_models
 from crewai_cli.tui_picker import pick_many, pick_one
 from crewai_cli.utils import (
     enable_prompt_line_editing,
@@ -43,50 +42,41 @@ _PROVIDERS: list[tuple[str, str]] = [
     ("watson", "IBM watsonx"),
 ]
 
-# Curated offline fallback / label source. The picker prefers models pulled
-# live from the vendor's own API via ``model_catalog.get_provider_models``;
-# this list is the hand-verified backstop used when no API key is available.
-# Keep entries to real, current model ids — last verified against each vendor's
-# official model docs on 2026-07-05.
 _PROVIDER_MODELS: dict[str, list[tuple[str, str]]] = {
     "openai": [
         ("gpt-5.5", "GPT-5.5"),
         ("gpt-5.5-pro", "GPT-5.5 Pro"),
         ("gpt-5.4", "GPT-5.4"),
-        ("gpt-5.4-mini", "GPT-5.4 Mini"),
-        ("gpt-5.2", "GPT-5.2"),
+        ("o4-mini", "o4-mini"),
         ("gpt-4.1", "GPT-4.1"),
+        ("gpt-4.1-mini", "GPT-4.1 Mini"),
     ],
     "anthropic": [
-        ("claude-fable-5", "Claude Fable 5"),
-        ("claude-opus-4-8", "Claude Opus 4.8"),
-        ("claude-sonnet-5", "Claude Sonnet 5"),
-        ("claude-opus-4-7", "Claude Opus 4.7"),
-        ("claude-haiku-4-5", "Claude Haiku 4.5"),
+        ("claude-opus-4-6", "Claude Opus 4.6"),
         ("claude-sonnet-4-6", "Claude Sonnet 4.6"),
+        ("claude-haiku-4-5-20251001", "Claude Haiku 4.5"),
+        ("claude-3-7-sonnet-20250219", "Claude 3.7 Sonnet"),
+        ("claude-3-5-sonnet-20241022", "Claude 3.5 Sonnet"),
     ],
     "gemini": [
-        ("gemini-3.5-flash", "Gemini 3.5 Flash"),
-        ("gemini-3.1-pro-preview", "Gemini 3.1 Pro (preview)"),
-        ("gemini-3-flash-preview", "Gemini 3 Flash (preview)"),
-        ("gemini-2.5-pro", "Gemini 2.5 Pro"),
-        ("gemini-2.5-flash", "Gemini 2.5 Flash"),
-        ("gemini-2.5-flash-lite", "Gemini 2.5 Flash Lite"),
+        ("gemini-3-pro-preview", "Gemini 3 Pro (preview)"),
+        ("gemini-2.5-pro-exp-03-25", "Gemini 2.5 Pro"),
+        ("gemini-2.5-flash-preview-04-17", "Gemini 2.5 Flash"),
+        ("gemini-2.0-flash-001", "Gemini 2.0 Flash"),
+        ("gemini-1.5-pro", "Gemini 1.5 Pro"),
     ],
     "groq": [
-        ("meta-llama/llama-4-maverick-17b-128e-instruct", "Llama 4 Maverick"),
-        ("meta-llama/llama-4-scout-17b-16e-instruct", "Llama 4 Scout"),
-        ("openai/gpt-oss-120b", "GPT-OSS 120B"),
-        ("qwen/qwen3-32b", "Qwen3 32B"),
-        ("moonshotai/kimi-k2-instruct-0905", "Kimi K2"),
         ("llama-3.3-70b-versatile", "Llama 3.3 70B"),
+        ("llama-3.1-70b-versatile", "Llama 3.1 70B"),
+        ("llama-3.1-8b-instant", "Llama 3.1 8B"),
+        ("deepseek-r1-distill-llama-70b", "DeepSeek R1 70B"),
+        ("mixtral-8x7b-32768", "Mixtral 8x7B"),
     ],
     "ollama": [
         ("llama3.3", "Llama 3.3"),
-        ("qwen3", "Qwen 3"),
+        ("llama3.1", "Llama 3.1"),
         ("deepseek-r1", "DeepSeek R1"),
-        ("gpt-oss", "GPT-OSS"),
-        ("gemma3", "Gemma 3"),
+        ("qwen2.5", "Qwen 2.5"),
         ("mistral", "Mistral"),
     ],
 }
@@ -768,9 +758,7 @@ def _select_model() -> str:
     provider_key, provider_name = _PROVIDERS[p_idx]
     click.secho(f"  → {provider_name}", fg="green")
 
-    # Prefer the latest models pulled live from the vendor / LiteLLM; the
-    # curated ``_PROVIDER_MODELS`` entry is the offline fallback and label source.
-    models = get_provider_models(provider_key, _PROVIDER_MODELS.get(provider_key, []))
+    models = _PROVIDER_MODELS.get(provider_key, [])
     if not models:
         custom = click.prompt(
             click.style(f"  Enter model name for {provider_key}/", fg="cyan"),

lib/cli/src/crewai_cli/input_prompt.py

@@ -1,84 +0,0 @@
-"""Shared interactive prompting for runtime inputs (flows and crews).
-
-``crewai run`` asks the user for values that were not provided up front — for a
-declarative flow (derived from its state schema) and for a declarative (JSON)
-crew (derived from the ``{placeholder}`` references in its agents and tasks).
-Both paths go through this module so the experience is identical: the same
-header, the same per-field prompt styling, and prompt chrome on stderr so
-stdout carries only the run's result.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable, Iterable
-import difflib
-import json
-import sys
-from typing import Any
-
-import click
-
-from crewai_cli.utils import enable_prompt_line_editing, is_dmn_mode_enabled
-
-
-def parse_inputs_json(inputs: str | None) -> dict[str, Any] | None:
-    """Parse a ``--inputs`` JSON object, exiting with a pointed error if invalid."""
-    if inputs is None:
-        return None
-
-    try:
-        parsed = json.loads(inputs)
-    except json.JSONDecodeError as exc:
-        click.echo(f"Invalid --inputs JSON: {exc}", err=True)
-        raise SystemExit(1) from exc
-
-    if not isinstance(parsed, dict):
-        click.echo("Invalid --inputs JSON: expected an object.", err=True)
-        raise SystemExit(1)
-
-    return parsed
-
-
-def closest_name(key: str, candidates: Iterable[str]) -> str | None:
-    """Nearest candidate name to a likely typo, if one is close enough."""
-    matches = difflib.get_close_matches(key, list(candidates), n=1, cutoff=0.7)
-    return matches[0] if matches else None
-
-
-def is_interactive() -> bool:
-    """Prompt only in an interactive terminal, never in non-interactive mode."""
-    return not is_dmn_mode_enabled() and sys.stdin.isatty()
-
-
-def prompt_for_inputs(
-    names: list[str],
-    *,
-    title: str,
-    subtitle: str,
-    describe: Callable[[str], str | None] | None = None,
-    coerce: Callable[[str, str], Any] | None = None,
-) -> dict[str, Any]:
-    """Prompt for each name and return ``{name: value}``.
-
-    ``describe(name)`` returns an optional hint shown dim above the prompt (used
-    by flows to surface a field's schema description). ``coerce(name, raw)``
-    converts the typed string to the stored value (used by flows to coerce to
-    the field's JSON-schema type); by default the raw string is kept as-is.
-
-    Prompt chrome is written to stderr so stdout carries only the run result.
-    """
-    enable_prompt_line_editing()
-    click.echo(err=True)
-    click.secho(f"  {title}", fg="cyan", bold=True, err=True)
-    click.secho(f"  {subtitle}", dim=True, err=True)
-
-    collected: dict[str, Any] = {}
-    for name in names:
-        if describe is not None and (hint := describe(name)):
-            click.secho(f"  {hint}", dim=True, err=True)
-        raw = click.prompt(
-            click.style(f"  {name}", fg="cyan"),
-            prompt_suffix=click.style(" > ", fg="bright_white"),
-        )
-        collected[name] = coerce(name, raw) if coerce is not None else raw
-    return collected

lib/cli/src/crewai_cli/model_catalog.py

@@ -1,657 +0,0 @@
-"""Dynamic model catalog for the crew-creation wizard.
-
-Resolves the models to offer for a given provider using a three-tier strategy:
-
-1. **Vendor API** - when the provider's API key is already present in the
-   environment, query the vendor's own model-listing endpoint. This is the only
-   source that reliably reflects the *latest* models (real release dates /
-   display names, straight from the vendor).
-2. **Curated hardcoded fallback** - the hand-verified list baked into the
-   wizard, used when no API key is available. Authoritative but frozen, so it is
-   refreshed periodically.
-3. **LiteLLM feed** - the community ``model_prices_and_context_window.json`` the
-   CLI already caches. Only used for providers with *no* curated list: the feed
-   lags real releases badly (it can miss a vendor's newest models entirely), so
-   it must never preempt the curated fallback.
-
-Every tier is best-effort: any network error, timeout, missing key, or empty
-result quietly falls through to the next tier, and the caller's hardcoded list
-is always the final backstop. The picker never blocks for long — network calls
-use a short timeout and successful results are cached.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable
-import contextlib
-import json
-import os
-from pathlib import Path
-import re
-import time
-from typing import Any
-
-import certifi
-import httpx
-
-from crewai_cli.constants import JSON_URL
-
-
-# ── Tunables ─────────────────────────────────────────────────────
-
-#: How many models to surface per provider.
-MAX_MODELS = 8
-
-#: Timeout (seconds) for any network call made while resolving models.
-_TIMEOUT = 6.0
-
-#: How long a resolved (dynamic) catalog stays fresh before we refetch.
-_CATALOG_TTL = 6 * 3600
-
-#: How long a fallback result is cached after a failed/empty fetch. Short, so a
-#: newly-added API key takes effect soon, but long enough to spare the picker a
-#: repeated timeout-prone network attempt on every call within one session.
-_NEGATIVE_TTL = 300
-
-#: How long the shared LiteLLM feed cache stays fresh.
-_LITELLM_TTL = 24 * 3600
-
-#: Env vars that may hold each provider's API key, in priority order. A
-#: provider with an empty tuple (e.g. local Ollama) needs no key. Gemini accepts
-#: either name, matching crewai's own Gemini provider.
-_PROVIDER_KEY_ENV: dict[str, tuple[str, ...]] = {
-    "openai": ("OPENAI_API_KEY",),
-    "anthropic": ("ANTHROPIC_API_KEY",),
-    "gemini": ("GEMINI_API_KEY", "GOOGLE_API_KEY"),
-    "groq": ("GROQ_API_KEY",),
-    "cerebras": ("CEREBRAS_API_KEY",),
-    "ollama": (),
-}
-
-
-def _provider_api_key(provider_key: str) -> str | None:
-    """First non-empty API key found among the provider's env vars."""
-    for env in _PROVIDER_KEY_ENV.get(provider_key, ()):
-        value = os.environ.get(env)
-        if value:
-            return value
-    return None
-
-
-# Substrings that mark a model id as *not* a chat/completion model. Used to
-# filter noisy OpenAI-compatible ``/models`` listings.
-_NON_CHAT_MARKERS = (
-    "embedding",
-    "embed",
-    "whisper",
-    "tts",
-    "audio",
-    "transcribe",
-    "realtime",
-    "dall-e",
-    "dalle",
-    "image",
-    "moderation",
-    "similarity",
-    "-edit",
-    "davinci-002",
-    "babbage-002",
-    "computer-use",
-    "guard",
-)
-
-_ACRONYMS = {
-    "gpt": "GPT",
-    "ai": "AI",
-    "nim": "NIM",
-    "llm": "LLM",
-    "hd": "HD",
-    "us": "US",
-    "eu": "EU",
-    "oss": "OSS",
-    "it": "IT",
-}
-
-# Tokens with non-title-case brand capitalization.
-_BRAND_TOKENS = {
-    "deepseek": "DeepSeek",
-    "chatgpt": "ChatGPT",
-    "qwq": "QwQ",
-}
-
-
-# ── Public API ───────────────────────────────────────────────────
-
-
-def get_provider_models(
-    provider_key: str, fallback: list[tuple[str, str]]
-) -> list[tuple[str, str]]:
-    """Return ``(model_id, label)`` pairs for ``provider_key``, newest first.
-
-    Tries the vendor API (if a key is in the environment) first, since it is the
-    only reliably-fresh source. When no key is available it returns the curated
-    ``fallback`` verbatim — the LiteLLM feed is consulted **only** for providers
-    with no curated list, because the feed lags real releases and would
-    otherwise surface a staler list than the hand-verified fallback. Never
-    raises: any failure degrades to the next tier.
-
-    Args:
-        provider_key: Short provider identifier, e.g. ``"anthropic"``.
-        fallback: Curated ``(model_id, label)`` pairs to use as the backstop and
-            to source friendly labels for known models.
-
-    Returns:
-        Up to :data:`MAX_MODELS` ``(model_id, label)`` pairs. Falls back to
-        ``fallback`` verbatim when no fresher list can be resolved.
-    """
-    cached = _read_catalog_cache(provider_key)
-    if cached is not None:
-        return cached
-
-    label_map = {model_id: label for model_id, label in fallback}
-
-    # A non-None vendor result is authoritative — even when empty (e.g. a
-    # reachable Ollama with no models installed): show that rather than
-    # hardcoded suggestions the crew can't actually run. The picker handles an
-    # empty list by prompting for manual entry.
-    vendor = _from_vendor(provider_key)
-    if vendor is not None:
-        result = _finalize(vendor, label_map)
-        if result:
-            _write_catalog_cache(provider_key, result, source="dynamic")
-        return result
-
-    # Vendor tier unavailable. The LiteLLM feed lags real releases, so only
-    # reach for it when we have no curated fallback — never override the fallback.
-    entries = _from_litellm(provider_key) if not fallback else None
-    result = _finalize(entries, label_map) if entries else []
-    if result:
-        _write_catalog_cache(provider_key, result, source="dynamic")
-        return result
-
-    # Nothing fresher than the curated list. Cache it briefly (negative cache)
-    # so a failed vendor/LiteLLM fetch isn't retried on every subsequent call.
-    # Skip Ollama: it's a local, fast-failing server, so re-probing is cheap and
-    # avoids serving suggestions after the server comes up within the TTL.
-    if fallback and provider_key != "ollama":
-        _write_catalog_cache(provider_key, fallback, source="fallback")
-    return fallback
-
-
-# ── Tier 1: vendor APIs ──────────────────────────────────────────
-
-
-def _from_vendor(provider_key: str) -> list[dict[str, Any]] | None:
-    """Fetch models from the vendor.
-
-    Returns the model list on a successful fetch — **including an empty list**,
-    which is meaningful (e.g. a reachable Ollama server with nothing installed).
-    Returns ``None`` only when the vendor tier is unavailable: no fetcher, no
-    API key, or the request failed.
-    """
-    fetcher = _VENDOR_FETCHERS.get(provider_key)
-    if fetcher is None:
-        return None
-
-    api_key = _provider_api_key(provider_key)
-    if _PROVIDER_KEY_ENV.get(provider_key) and not api_key:
-        # Provider needs a key and none is set — skip to the next tier.
-        return None
-
-    try:
-        return fetcher(api_key)
-    except Exception:
-        # Network error, auth failure, unexpected payload — degrade quietly.
-        return None
-
-
-def _fetch_openai(api_key: str | None) -> list[dict[str, Any]]:
-    return _fetch_openai_compatible("https://api.openai.com/v1", api_key)
-
-
-def _fetch_groq(api_key: str | None) -> list[dict[str, Any]]:
-    return _fetch_openai_compatible("https://api.groq.com/openai/v1", api_key)
-
-
-def _fetch_cerebras(api_key: str | None) -> list[dict[str, Any]]:
-    return _fetch_openai_compatible("https://api.cerebras.ai/v1", api_key)
-
-
-def _fetch_openai_compatible(
-    base_url: str, api_key: str | None
-) -> list[dict[str, Any]]:
-    """Parse an OpenAI-shaped ``GET /models`` response."""
-    data = _http_get_json(
-        f"{base_url}/models",
-        headers={"Authorization": f"Bearer {api_key}"},
-    )
-    entries: list[dict[str, Any]] = []
-    for item in data.get("data", []):
-        model_id = item.get("id")
-        if not model_id or not _is_chat_model(model_id) or _is_fine_tune(model_id):
-            continue
-        created = _as_float(item.get("created"))
-        entries.append(_entry(model_id, _humanize(model_id), created=created))
-    return entries
-
-
-def _fetch_anthropic(api_key: str | None) -> list[dict[str, Any]]:
-    data = _http_get_json(
-        "https://api.anthropic.com/v1/models",
-        headers={"x-api-key": api_key or "", "anthropic-version": "2023-06-01"},
-    )
-    entries: list[dict[str, Any]] = []
-    for item in data.get("data", []):
-        model_id = item.get("id")
-        if not model_id:
-            continue
-        label = item.get("display_name") or _humanize(model_id)
-        created = _parse_iso(item.get("created_at"))
-        entries.append(_entry(model_id, label, created=created))
-    return entries
-
-
-def _fetch_gemini(api_key: str | None) -> list[dict[str, Any]]:
-    entries: list[dict[str, Any]] = []
-    params: dict[str, Any] = {"key": api_key or "", "pageSize": 200}
-    # models.list is paginated and not guaranteed newest-first, so walk pages
-    # (bounded) to see the full set — _finalize does the sort + truncation.
-    for _ in range(10):
-        try:
-            data = _http_get_json(
-                "https://generativelanguage.googleapis.com/v1beta/models",
-                params=params,
-            )
-        except Exception:
-            # Later-page failure: keep the models already gathered. First-page
-            # failure (nothing gathered yet) is a real outage — re-raise so the
-            # caller falls back to the curated list rather than mistaking it for
-            # a successful empty result.
-            if entries:
-                break
-            raise
-        for item in data.get("models", []):
-            methods = item.get("supportedGenerationMethods") or []
-            if "generateContent" not in methods:
-                continue
-            name = (item.get("name") or "").removeprefix("models/")
-            if not name or not _is_chat_model(name) or "aqa" in name:
-                continue
-            label = item.get("displayName") or _humanize(name)
-            # Gemini has no timestamp; rank by the version in name/version.
-            version_hint = f"{name} {item.get('version') or ''}"
-            entries.append(_entry(name, label, version_hint=version_hint))
-        token = data.get("nextPageToken")
-        if not token:
-            break
-        params = {"key": api_key or "", "pageSize": 200, "pageToken": token}
-    return entries
-
-
-def _ollama_base() -> str:
-    """Resolve the Ollama server base URL from the environment.
-
-    Checks ``OLLAMA_API_BASE`` / ``API_BASE`` (what LiteLLM and the generated
-    crew use) first, then ``OLLAMA_HOST`` (the Ollama runtime convention), so a
-    user who only set ``OLLAMA_HOST`` sees models from the right server.
-    """
-    base = (
-        os.environ.get("OLLAMA_API_BASE")
-        or os.environ.get("API_BASE")
-        or os.environ.get("OLLAMA_HOST")
-        or "http://localhost:11434"
-    ).strip()
-    # OLLAMA_HOST is often scheme-less (e.g. "127.0.0.1:11434").
-    if "://" not in base:
-        base = f"http://{base}"
-    return base.rstrip("/")
-
-
-def _fetch_ollama(_api_key: str | None) -> list[dict[str, Any]]:
-    """List models installed on the local Ollama server (no API key)."""
-    data = _http_get_json(f"{_ollama_base()}/api/tags")
-    entries: list[dict[str, Any]] = []
-    for item in data.get("models", []):
-        model_id = item.get("model") or item.get("name")
-        if not model_id or not _is_chat_model(model_id) or _is_fine_tune(model_id):
-            # /api/tags lists everything installed, including embedding models.
-            continue
-        # Ollama returns an ISO 8601 modified_at we can rank by.
-        created = _parse_iso(item.get("modified_at"))
-        entries.append(_entry(model_id, _humanize(model_id), created=created))
-    return entries
-
-
-_VENDOR_FETCHERS: dict[str, Callable[[str | None], list[dict[str, Any]]]] = {
-    "openai": _fetch_openai,
-    "anthropic": _fetch_anthropic,
-    "gemini": _fetch_gemini,
-    "groq": _fetch_groq,
-    "cerebras": _fetch_cerebras,
-    "ollama": _fetch_ollama,
-}
-
-
-# ── Tier 2: LiteLLM feed ─────────────────────────────────────────
-
-# Process-level memo so a single CLI run attempts the LiteLLM download at most
-# once — repeated picker calls otherwise each incur a multi-second timeout when
-# the feed is stale/unreachable. Reset via _reset_litellm_memo() in tests.
-_UNSET: Any = object()
-_litellm_memo: Any = _UNSET
-
-
-def _reset_litellm_memo() -> None:
-    """Clear the process-level LiteLLM memo (test hook)."""
-    global _litellm_memo
-    _litellm_memo = _UNSET
-
-
-def _from_litellm(provider_key: str) -> list[dict[str, Any]] | None:
-    """Build chat-model entries for ``provider_key`` from the LiteLLM feed."""
-    data = _load_litellm_data()
-    # A corrupt feed (non-mapping JSON root) must not crash the picker.
-    if not isinstance(data, dict):
-        return None
-
-    entries: list[dict[str, Any]] = []
-    for model_name, props in data.items():
-        if not isinstance(props, dict):
-            continue
-        # `litellm_provider` can be present-but-null in the feed; coerce before
-        # string ops so a null value is skipped rather than raising.
-        if (props.get("litellm_provider") or "").strip().lower() != provider_key:
-            continue
-        if props.get("mode") != "chat":
-            continue
-        # LiteLLM keys are sometimes prefixed with the provider; the picker
-        # re-adds ``provider/`` itself, so strip a leading one to avoid dupes.
-        model_id = model_name
-        if model_id.startswith(f"{provider_key}/"):
-            model_id = model_id[len(provider_key) + 1 :]
-        if not model_id:
-            continue
-        entries.append(_entry(model_id, _humanize(model_id), version_hint=model_id))
-    return entries or None
-
-
-def _load_litellm_data() -> dict[str, Any] | None:
-    """Return the LiteLLM feed, memoized once per process (see _litellm_memo)."""
-    global _litellm_memo
-    if _litellm_memo is _UNSET:
-        _litellm_memo = _fetch_litellm_data()
-    memoized: dict[str, Any] | None = _litellm_memo
-    return memoized
-
-
-def _fetch_litellm_data() -> dict[str, Any] | None:
-    """Read the cached LiteLLM feed, fetching it once if the cache is cold."""
-    cache_file = _litellm_cache_file()
-    fresh = (
-        cache_file.exists()
-        and (time.time() - cache_file.stat().st_mtime) < _LITELLM_TTL
-    )
-    if fresh:
-        data = _read_json(cache_file)
-        # A corrupt/non-mapping fresh cache must not block a recoverable
-        # download — only short-circuit on a usable mapping.
-        if isinstance(data, dict) and data:
-            return data
-
-    try:
-        data = _http_get_json(JSON_URL)
-    except Exception:
-        # Fall back to a stale cache if we have one, else give up on this tier.
-        return _read_json(cache_file)
-
-    # Best-effort cache write; a failure (e.g. read-only home) is non-fatal
-    # since we already hold the freshly-fetched data.
-    with contextlib.suppress(OSError):
-        cache_file.parent.mkdir(parents=True, exist_ok=True)
-        cache_file.write_text(json.dumps(data), encoding="utf-8")
-    return data
-
-
-# ── Ranking + labelling ──────────────────────────────────────────
-
-
-def _finalize(
-    entries: list[dict[str, Any]], label_map: dict[str, str]
-) -> list[tuple[str, str]]:
-    """Sort newest-first, dedupe, relabel with curated names, and truncate."""
-    entries.sort(key=lambda e: e["sort"], reverse=True)
-    seen: set[str] = set()
-    out: list[tuple[str, str]] = []
-    for entry in entries:
-        model_id = entry["id"]
-        if model_id in seen:
-            continue
-        seen.add(model_id)
-        label = label_map.get(model_id) or entry["label"]
-        out.append((model_id, label))
-        if len(out) >= MAX_MODELS:
-            break
-    return out
-
-
-def _entry(
-    model_id: str,
-    label: str,
-    *,
-    created: float = 0.0,
-    version_hint: str | None = None,
-) -> dict[str, Any]:
-    """Build a rankable catalog entry.
-
-    ``sort`` is a comparable tuple ``(created, date_int, version_tuple)`` so a
-    real vendor timestamp wins, then a date embedded in the id, then the numeric
-    version. Types line up positionally, so entries compare cleanly.
-    """
-    date_int, version = _version_key(version_hint or model_id)
-    return {
-        "id": model_id,
-        "label": label,
-        "sort": (created, date_int, version),
-    }
-
-
-_DATE_RE = re.compile(r"(20\d{2})[-_]?(0[1-9]|1[0-2])[-_]?(0[1-9]|[12]\d|3[01])")
-_NUM_RE = re.compile(r"\d+")
-
-
-def _version_key(text: str) -> tuple[int, tuple[int, ...]]:
-    """Extract a ``(date_int, version_tuple)`` sort key from a model id.
-
-    A trailing/embedded ``YYYYMMDD`` (or ``YYYY-MM-DD``) becomes ``date_int``;
-    remaining numbers become the version tuple. ``claude-opus-4-6`` → version
-    ``(4, 6)``; ``claude-3-5-sonnet-20241022`` → date ``20241022`` version
-    ``(3, 5)``.
-    """
-    text = text or ""
-    date_int = 0
-    match = _DATE_RE.search(text)
-    if match:
-        date_int = int(match.group(1) + match.group(2) + match.group(3))
-        text = _DATE_RE.sub(" ", text)
-    version = tuple(int(n) for n in _NUM_RE.findall(text)[:4])
-    return date_int, version
-
-
-def _is_chat_model(model_id: str) -> bool:
-    """Heuristically reject embedding/audio/image/etc. models by their id."""
-    lowered = model_id.lower()
-    return not any(marker in lowered for marker in _NON_CHAT_MARKERS)
-
-
-def _is_fine_tune(model_id: str) -> bool:
-    """A user fine-tune or training checkpoint (``ft:...`` / ``...:ckpt-step-N``).
-
-    These are account-specific artifacts: they clutter the picker, crowd out the
-    foundation models (their recent ``created`` timestamps rank them first), and
-    humanize into unreadable labels. Excluded from the auto-list; a user who
-    wants one can still enter it via the picker's "Other" option.
-    """
-    lowered = model_id.lower()
-    return lowered.startswith("ft:") or ":ckpt" in lowered
-
-
-_SIZE_RE = re.compile(r"^\d+(?:\.\d+)?[bmk]$")  # 8b, 70b, 1.5b, 120m, 32k
-_OSERIES_RE = re.compile(r"^o\d+$")  # o1, o3, o4 — kept lowercase (OpenAI brand)
-
-
-def _humanize(model_id: str) -> str:
-    """Derive a readable label from a raw model id.
-
-    Best-effort only — vendor display names and the curated label map take
-    precedence. Drops embedded dates and applies light casing so raw ids read
-    cleanly: ``gpt-oss-120b`` → ``GPT OSS 120B``, ``qwen3-32b`` → ``Qwen3 32B``,
-    ``deepseek-r1:671b`` → ``DeepSeek R1 671B``, ``o3-mini`` → ``o3 Mini``.
-    """
-    base = model_id.split("/")[-1]
-    # Drop embedded release dates — they're noise in a label, and the picker
-    # already shows the full model id alongside it.
-    base = _DATE_RE.sub(" ", base)
-    words: list[str] = []
-    # Split on separators including ``:`` so Ollama tags (llama3.3:70b) read well.
-    for part in re.split(r"[-_\s:]+", base):
-        if not part:
-            continue
-        low = part.lower()
-        if low in _ACRONYMS:
-            words.append(_ACRONYMS[low])
-        elif low in _BRAND_TOKENS:
-            words.append(_BRAND_TOKENS[low])
-        elif _SIZE_RE.match(low):
-            words.append(low[:-1] + low[-1].upper())  # 70b -> 70B
-        elif _OSERIES_RE.match(low):
-            words.append(low)  # o3 stays lowercase
-        elif part[0].isalpha():
-            # Capitalize the leading letter, preserve the rest (so a fused
-            # family+version keeps its digits): qwen3 -> Qwen3, mini -> Mini.
-            words.append(part[0].upper() + part[1:])
-        else:
-            words.append(part)  # starts with a digit (4o, 4.1, 0905) — leave as-is
-    return " ".join(words) or base
-
-
-# ── HTTP + parsing helpers ───────────────────────────────────────
-
-
-def _http_get_json(
-    url: str,
-    *,
-    headers: dict[str, str] | None = None,
-    params: dict[str, Any] | None = None,
-) -> dict[str, Any]:
-    """GET ``url`` and return parsed JSON, with a short timeout and TLS verify."""
-    ssl_config = os.environ.get("SSL_CERT_FILE") or certifi.where()
-    response = httpx.get(
-        url,
-        headers=headers,
-        params=params,
-        timeout=_TIMEOUT,
-        verify=ssl_config,
-        follow_redirects=True,
-    )
-    response.raise_for_status()
-    result: dict[str, Any] = response.json()
-    return result
-
-
-def _parse_iso(value: Any) -> float:
-    """Parse an ISO 8601 timestamp to an epoch float; ``0.0`` on failure."""
-    if not value or not isinstance(value, str):
-        return 0.0
-    from datetime import datetime
-
-    try:
-        return datetime.fromisoformat(value.replace("Z", "+00:00")).timestamp()
-    except ValueError:
-        return 0.0
-
-
-def _as_float(value: Any) -> float:
-    try:
-        return float(value)
-    except (TypeError, ValueError):
-        return 0.0
-
-
-def _read_json(path: Path) -> dict[str, Any] | None:
-    try:
-        data: dict[str, Any] = json.loads(path.read_text(encoding="utf-8"))
-        return data
-    except (OSError, json.JSONDecodeError):
-        return None
-
-
-# ── Caching ──────────────────────────────────────────────────────
-
-
-def _cache_dir() -> Path:
-    return Path.home() / ".crewai"
-
-
-def _catalog_cache_file() -> Path:
-    return _cache_dir() / "model_catalog_cache.json"
-
-
-def _litellm_cache_file() -> Path:
-    # Shared with crewai_cli.provider so both flows warm the same cache.
-    return _cache_dir() / "provider_cache.json"
-
-
-def _cache_key(provider_key: str) -> str:
-    """Cache key for a provider's resolved model list.
-
-    Includes the inputs that change what a fetch would return, so a cached
-    entry is only reused when those inputs still match:
-
-    - Ollama lists models from a base URL that can change between runs.
-    - Whether the vendor's API key is present flips between a live fetch and
-      the negatively-cached fallback — so a key added after a no-key call is
-      not shadowed by the cached fallback.
-    """
-    if provider_key == "ollama":
-        return f"ollama@{_ollama_base()}"
-    suffix = "key" if _provider_api_key(provider_key) else "nokey"
-    return f"{provider_key}#{suffix}"
-
-
-def _read_catalog_cache(provider_key: str) -> list[tuple[str, str]] | None:
-    """Return a fresh cached catalog for ``provider_key``, or ``None``."""
-    payload = _read_json(_catalog_cache_file())
-    if not isinstance(payload, dict):
-        return None
-    entry = payload.get(_cache_key(provider_key))
-    if not isinstance(entry, dict):
-        return None
-    # Fallback (negative) entries expire fast; dynamic ones live the full TTL.
-    ttl = _NEGATIVE_TTL if entry.get("source") == "fallback" else _CATALOG_TTL
-    if (time.time() - _as_float(entry.get("ts"))) >= ttl:
-        return None
-    models = entry.get("models")
-    if not isinstance(models, list) or not models:
-        return None
-    try:
-        return [(str(m[0]), str(m[1])) for m in models]
-    except (IndexError, TypeError):
-        return None
-
-
-def _write_catalog_cache(
-    provider_key: str, models: list[tuple[str, str]], *, source: str
-) -> None:
-    cache_file = _catalog_cache_file()
-    payload = _read_json(cache_file)
-    if not isinstance(payload, dict):
-        payload = {}
-    payload[_cache_key(provider_key)] = {
-        "ts": time.time(),
-        "source": source,
-        "models": [[model_id, label] for model_id, label in models],
-    }
-    # Best-effort cache write; a failure (e.g. read-only home) is non-fatal.
-    with contextlib.suppress(OSError):
-        cache_file.parent.mkdir(parents=True, exist_ok=True)
-        cache_file.write_text(json.dumps(payload), encoding="utf-8")

lib/cli/src/crewai_cli/run_crew.py

@@ -12,14 +12,9 @@ import click
 from crewai_core.constants import CREWAI_TRAINED_AGENTS_FILE_ENV
 from packaging import version
 
-from crewai_cli.input_prompt import (
-    closest_name,
-    is_interactive,
-    parse_inputs_json,
-    prompt_for_inputs,
-)
 from crewai_cli.utils import (
     build_env_with_all_tool_credentials,
+    enable_prompt_line_editing,
     is_dmn_mode_enabled,
 )
 from crewai_cli.version import get_crewai_tools_dependency, get_crewai_version
@@ -36,7 +31,6 @@ _INPUT_PLACEHOLDER_RE = re.compile(r"(?<!{){([A-Za-z_][A-Za-z0-9_\-]*)}(?!})")
 _CREWAI_CLI_RUNNER_PACKAGE_DIR_ENV = "CREWAI_CLI_RUNNER_PACKAGE_DIR"
 _CREWAI_RUNNER_SOURCE_DIR_ENV = "CREWAI_RUNNER_SOURCE_DIR"
 _CREWAI_JSON_CREW_DEFINITION_ENV = "CREWAI_JSON_CREW_DEFINITION"
-_CREWAI_JSON_CREW_INPUTS_ENV = "CREWAI_JSON_CREW_INPUTS"
 _FULL_CREWAI_INSTALL_MESSAGE = f"""\
 CrewAI CLI is installed without the `crewai` package required to run crews.
 
@@ -85,8 +79,6 @@ kwargs = {
 }
 if crew_definition := os.getenv("CREWAI_JSON_CREW_DEFINITION"):
     kwargs["crew_path"] = crew_definition
-if crew_inputs := os.getenv("CREWAI_JSON_CREW_INPUTS"):
-    kwargs["inputs"] = crew_inputs
 
 try:
     module._run_json_crew(**kwargs)
@@ -146,8 +138,8 @@ def _extract_input_placeholders(text: str | None) -> set[str]:
     return set(_INPUT_PLACEHOLDER_RE.findall(text))
 
 
-def _referenced_input_names(crew: Any) -> set[str]:
-    """All ``{placeholder}`` names referenced by a crew's agents and tasks."""
+def _missing_input_names(crew: Any, inputs: dict[str, Any]) -> list[str]:
+    """Return input placeholders used by a crew but not provided as defaults."""
     placeholders: set[str] = set()
 
     for agent in getattr(crew, "agents", []) or []:
@@ -168,70 +160,32 @@ def _referenced_input_names(crew: Any) -> set[str]:
             _extract_input_placeholders(getattr(task, "output_file", None))
         )
 
-    return placeholders
+    return sorted(name for name in placeholders if name not in inputs)
 
 
-def _missing_input_names(crew: Any, inputs: dict[str, Any]) -> list[str]:
-    """Return input placeholders referenced by a crew but not provided as inputs."""
-    return sorted(name for name in _referenced_input_names(crew) if name not in inputs)
-
-
-def _resolve_crew_inputs(
-    crew: Any,
-    default_inputs: dict[str, Any],
-    provided: dict[str, Any] | None,
-    *,
-    interactive: bool,
+def _prompt_for_missing_inputs(
+    crew: Any, default_inputs: dict[str, Any]
 ) -> dict[str, Any]:
-    """Resolve kickoff inputs for a declarative crew.
-
-    Mirrors the declarative-flow experience (``_resolve_flow_inputs``): layers
-    ``--inputs`` over the crew's declared ``inputs`` defaults, warns on provided
-    keys that aren't referenced as ``{placeholder}``s, prompts for any
-    still-missing placeholders when interactive, and exits with a pointed
-    message when one is still missing.
-
-    Unlike flows — whose state schema is an authoritative contract, so unknown
-    keys are dropped — the crew placeholder scan is heuristic (it only covers
-    agent/task text fields). An unrecognized key is therefore warned about but
-    *kept*, never dropped: dropping could silently discard a value that a field
-    the scan doesn't cover actually relies on.
-    """
-    referenced = _referenced_input_names(crew)
+    """Ask for runtime values for placeholders that lack default inputs."""
     inputs = dict(default_inputs or {})
-
-    for key, value in (provided or {}).items():
-        if key not in referenced:
-            suggestion = closest_name(key, referenced)
-            hint = f" Did you mean '{suggestion}'?" if suggestion else ""
-            click.secho(
-                f"  Input '{key}' isn't referenced by any {{placeholder}} "
-                f"in the crew.{hint}",
-                fg="yellow",
-                err=True,
-            )
-        inputs[key] = value
-
     missing = _missing_input_names(crew, inputs)
-    if missing and interactive:
-        inputs.update(
-            prompt_for_inputs(
-                missing,
-                title="Crew inputs",
-                subtitle="This crew needs the following to run.",
-            )
-        )
-        missing = _missing_input_names(crew, inputs)
+    if not missing:
+        return inputs
 
-    if missing:
-        for name in missing:
-            click.secho(f"  Missing required input '{name}'", fg="red", err=True)
-        click.secho(
-            "  Provide them via --inputs or the `inputs` object in crew.json(c).",
-            dim=True,
-            err=True,
+    enable_prompt_line_editing()
+
+    click.echo()
+    click.secho("  Runtime inputs", fg="cyan", bold=True)
+    click.secho(
+        "  Values for {placeholder} references in your agents and tasks.",
+        dim=True,
+    )
+
+    for name in missing:
+        inputs[name] = click.prompt(
+            click.style(f"  {name}", fg="cyan"),
+            prompt_suffix=click.style(" > ", fg="bright_white"),
         )
-        raise SystemExit(1)
 
     return inputs
 
@@ -289,14 +243,29 @@ def _prepare_json_crew_for_tui(crew: Any) -> None:
             agent.llm.stream = True
 
 
-def _run_json_crew_without_tui(crew_path: Path, provided: dict[str, Any] | None) -> Any:
+def _runtime_inputs_without_prompt(
+    crew: Any, default_inputs: dict[str, Any]
+) -> dict[str, Any]:
+    """Return runtime inputs in non-interactive mode or exit on missing values."""
+    inputs = dict(default_inputs or {})
+    missing = _missing_input_names(crew, inputs)
+    if missing:
+        missing_list = ", ".join(missing)
+        click.echo(
+            "Missing runtime inputs for CREWAI_DMN mode: "
+            f"{missing_list}. Add them to the `inputs` object in crew.json(c).",
+            err=True,
+        )
+        raise SystemExit(1)
+    return inputs
+
+
+def _run_json_crew_without_tui(crew_path: Path) -> Any:
     """Run a JSON-defined crew with plain terminal output."""
     with _json_loading_status("Preparing crew..."):
         crew, default_inputs = _load_json_crew(crew_path)
 
-    runtime_inputs = _resolve_crew_inputs(
-        crew, default_inputs, provided, interactive=False
-    )
+    runtime_inputs = _runtime_inputs_without_prompt(crew, default_inputs)
     result = crew.kickoff(inputs=runtime_inputs)
     if result is not None:
         click.echo(str(result))
@@ -306,7 +275,6 @@ def _run_json_crew_without_tui(crew_path: Path, provided: dict[str, Any] | None)
 def _run_json_crew(
     trained_agents_file: str | None = None,
     crew_path: str | Path | None = None,
-    inputs: str | None = None,
 ) -> Any:
     """Load and run a JSON-defined crew."""
     from dotenv import load_dotenv
@@ -328,17 +296,13 @@ def _run_json_crew(
         )
     crew_path = Path(crew_path)
 
-    provided = parse_inputs_json(inputs)
-
     if is_dmn_mode_enabled():
-        return _run_json_crew_without_tui(crew_path, provided)
+        return _run_json_crew_without_tui(crew_path)
 
     crew_run_app_cls, crew, default_inputs, task_names, agent_names = (
         _load_json_crew_for_tui(crew_path)
     )
-    runtime_inputs = _resolve_crew_inputs(
-        crew, default_inputs, provided, interactive=is_interactive()
-    )
+    runtime_inputs = _prompt_for_missing_inputs(crew, default_inputs)
 
     app = crew_run_app_cls(
         crew_name=crew.name or "Crew",
@@ -447,18 +411,12 @@ def _json_crew_run_command(project_root: Path | None = None) -> list[str]:
 def _run_json_crew_in_project_env(
     trained_agents_file: str | None = None,
     crew_path: str | Path | None = None,
-    inputs: str | None = None,
 ) -> Any:
     """Run JSON crews from the project's uv-managed environment."""
-    # Validate --inputs up front so bad JSON fails before we spin up the uv env.
-    if inputs is not None:
-        parse_inputs_json(inputs)
-
     if not (Path.cwd() / "pyproject.toml").is_file():
         return _run_json_crew(
             trained_agents_file=trained_agents_file,
             crew_path=crew_path,
-            inputs=inputs,
         )
 
     _install_json_crew_dependencies_if_needed()
@@ -472,8 +430,6 @@ def _run_json_crew_in_project_env(
         env[CREWAI_TRAINED_AGENTS_FILE_ENV] = trained_agents_file
     if crew_path is not None:
         env[_CREWAI_JSON_CREW_DEFINITION_ENV] = str(crew_path)
-    if inputs is not None:
-        env[_CREWAI_JSON_CREW_INPUTS_ENV] = inputs
 
     try:
         subprocess.run(  # noqa: S603
@@ -613,11 +569,11 @@ def run_crew(
             ``CREWAI_TRAINED_AGENTS_FILE`` so agents load suggestions from this
             file instead of the default ``trained_agents_data.pkl``.
         definition: Optional path to a declarative Flow definition.
-        inputs: Optional JSON object of runtime inputs for a declarative flow
-            or declarative (JSON) crew. Layered over the definition's own
-            defaults; missing required values are prompted for interactively.
+        inputs: Optional JSON object passed to a declarative Flow.
     """
-    # --definition is a pure override: run that flow directly.
+    if inputs is not None and definition is None:
+        raise click.UsageError("--inputs requires --definition")
+
     if definition is not None:
         _run_explicit_declarative_flow(
             definition=definition,
@@ -628,13 +584,9 @@ def run_crew(
 
     pyproject_data = read_toml()
     if json_crew_definition := configured_project_json_crew(pyproject_data):
-        # Declarative (JSON) crews resolve inputs the same way flows do: --inputs
-        # layers over the crew's declared defaults, missing {placeholder}s are
-        # prompted for, and unknown keys are flagged. Forward the raw JSON.
         _run_json_crew_in_project_env(
             trained_agents_file=trained_agents_file,
             crew_path=json_crew_definition,
-            inputs=inputs,
         )
         return
 
@@ -642,29 +594,18 @@ def run_crew(
     project_type = get_crewai_project_type(pyproject_data)
 
     if project_type == "flow":
-        # No --definition: resolve the configured [tool.crewai] flow — the same
-        # resolution as a bare `crewai run` — and pass --inputs straight through.
         _run_flow_project(
             pyproject_data=pyproject_data,
             trained_agents_file=trained_agents_file,
-            inputs=inputs,
         )
         return
 
-    _reject_inputs_for_non_flow(inputs)
     _run_classic_crew_project(
         pyproject_data=pyproject_data,
         trained_agents_file=trained_agents_file,
     )
 
 
-def _reject_inputs_for_non_flow(inputs: str | None) -> None:
-    if inputs is not None:
-        raise click.UsageError(
-            "--inputs is only supported for declarative flows and crews"
-        )
-
-
 def _run_explicit_declarative_flow(
     definition: str, inputs: str | None, trained_agents_file: str | None
 ) -> None:
@@ -677,9 +618,7 @@ def _run_explicit_declarative_flow(
 
 
 def _run_flow_project(
-    pyproject_data: dict[str, Any],
-    trained_agents_file: str | None,
-    inputs: str | None = None,
+    pyproject_data: dict[str, Any], trained_agents_file: str | None
 ) -> None:
     if trained_agents_file is not None:
         raise click.UsageError("--filename can only be used when running crews")
@@ -690,16 +629,9 @@ def _run_flow_project(
     )
 
     if definition := configured_project_declarative_flow(pyproject_data):
-        run_declarative_flow_in_project_env(definition=definition, inputs=inputs)
+        run_declarative_flow_in_project_env(definition=definition)
         return
 
-    # No configured declarative flow definition to resolve inputs against.
-    if inputs is not None:
-        raise click.UsageError(
-            "--inputs requires a declarative flow definition "
-            "([tool.crewai].definition) or --definition"
-        )
-
     from crewai_cli.kickoff_flow import (
         _load_conversational_flow_from_kickoff_script,
         _run_conversational_flow_tui,

lib/cli/src/crewai_cli/run_declarative_flow.py

@@ -9,12 +9,6 @@ import click
 from crewai_core.project import ProjectDefinitionError, configured_project_definition
 from pydantic import ValidationError
 
-from crewai_cli.input_prompt import (
-    closest_name,
-    is_interactive,
-    parse_inputs_json,
-    prompt_for_inputs,
-)
 from crewai_cli.utils import build_env_with_all_tool_credentials
 
 
@@ -26,13 +20,10 @@ def run_declarative_flow_in_project_env(
         run_declarative_flow(definition=definition, inputs=inputs)
         return
 
-    # Re-run inside the project env (so the flow loads with the project's deps).
-    # The configured definition is re-resolved there; forward --inputs so the
-    # in-env run kicks off with the same values instead of losing them.
-    command = ["uv", "run", "crewai", "run"]
     if inputs is not None:
-        command += ["--inputs", inputs]
-    _execute_declarative_flow_command(command)
+        raise click.UsageError("--inputs is only supported with --definition")
+
+    _execute_declarative_flow_command(["uv", "run", "crewai", "run"])
 
 
 def plot_declarative_flow_in_project_env(definition: str | Path) -> None:
@@ -45,29 +36,12 @@ def plot_declarative_flow_in_project_env(definition: str | Path) -> None:
 
 
 def run_declarative_flow(definition: str | Path, inputs: str | None = None) -> None:
-    """Run a declarative flow from a definition path.
-
-    Inputs come from one place: the flow's own state schema. Any ``--inputs``
-    JSON is layered on top as an override, missing required fields are prompted
-    for interactively, and everything is validated against the schema before
-    kickoff — so a bare ``crewai run`` on a configured flow just works.
-    """
-    # Load the project's .env before kickoff, mirroring the JSON-crew path
-    # (run_crew._run_json_crew) so flow projects pick up API keys/config the
-    # same way regardless of where crewai is installed.
-    from dotenv import load_dotenv
-
-    env_file = Path.cwd() / ".env"
-    if env_file.exists():
-        load_dotenv(env_file, override=True)
-
-    provided = parse_inputs_json(inputs) or {}
-
-    flow = load_declarative_flow(definition)
-    resolved_inputs = _resolve_flow_inputs(flow, provided)
+    """Run a declarative flow from a definition path."""
+    parsed_inputs = _parse_inputs(inputs)
 
     try:
-        result = flow.kickoff(inputs=resolved_inputs or None)
+        flow = load_declarative_flow(definition)
+        result = flow.kickoff(inputs=parsed_inputs)
     except Exception as exc:
         click.echo(
             f"An error occurred while running the declarative flow: {exc}", err=True
@@ -77,152 +51,6 @@ def run_declarative_flow(definition: str | Path, inputs: str | None = None) -> N
     click.echo(_format_result(result))
 
 
-def _resolve_flow_inputs(flow: Any, provided: dict[str, Any]) -> dict[str, Any]:
-    """Resolve kickoff inputs from the flow's state schema.
-
-    Warns on unknown keys, prompts for missing required fields (unless
-    non-interactive), and validates types before kickoff. Exits with a pointed
-    message when a required input is still missing or an input is invalid.
-    """
-    schema = _flow_state_schema(flow)
-    if schema is None:
-        # dict / unschematized state — nothing to derive; pass inputs through.
-        return dict(provided)
-
-    properties = {
-        name: spec
-        for name, spec in (schema.get("properties") or {}).items()
-        if name != "id"
-    }
-    state_model = type(flow.state)
-    defaults = _flow_state_defaults(flow)
-
-    # ``id`` signals a persistence restore: kickoff hydrates the full state from
-    # storage, so required fields may come from the restored state rather than
-    # --inputs. We still filter the rest of the payload below, but skip the
-    # required-field prompt and pre-kickoff validation, which would otherwise
-    # fail on fields the resume will supply.
-    restoring = "id" in provided
-
-    # Unknown keys are almost always typos — warn and drop them (they'd fail
-    # structured-state validation at kickoff anyway). ``id`` is a reserved
-    # kickoff key rather than a state field, so forward it untouched.
-    collected: dict[str, Any] = {}
-    for key, value in provided.items():
-        if key == "id":
-            collected["id"] = value
-            continue
-        if key in properties:
-            collected[key] = value
-            continue
-        suggestion = closest_name(key, properties)
-        hint = f" Did you mean '{suggestion}'?" if suggestion else ""
-        click.secho(
-            f"  Ignoring unknown input '{key}' — not in the flow's state schema.{hint}",
-            fg="yellow",
-            err=True,
-        )
-
-    if restoring:
-        return collected
-
-    missing = _missing_required(state_model, {**defaults, **collected})
-    if missing and _is_interactive():
-        collected.update(
-            prompt_for_inputs(
-                missing,
-                title="Flow inputs",
-                subtitle="This flow needs the following to run.",
-                describe=lambda name: (properties.get(name) or {}).get("description"),
-                coerce=lambda name, raw: _coerce_input(raw, properties.get(name) or {}),
-            )
-        )
-        missing = _missing_required(state_model, {**defaults, **collected})
-
-    if missing:
-        for name in missing:
-            description = (properties.get(name) or {}).get("description")
-            suffix = f" — {description}" if description else ""
-            click.secho(
-                f"  Missing required input '{name}'{suffix}", fg="red", err=True
-            )
-        raise SystemExit(1)
-
-    _validate_flow_inputs(state_model, {**defaults, **collected})
-    return collected
-
-
-def _is_interactive() -> bool:
-    """Prompt only in an interactive terminal, never in non-interactive mode."""
-    return is_interactive()
-
-
-def _flow_state_schema(flow: Any) -> dict[str, Any] | None:
-    """Return the flow's state JSON schema, or ``None`` for dict/plain state."""
-    state = getattr(flow, "state", None)
-    if state is None or isinstance(state, dict):
-        return None
-    model_json_schema = getattr(type(state), "model_json_schema", None)
-    if not callable(model_json_schema):
-        return None
-    try:
-        schema = model_json_schema()
-    except Exception:
-        return None
-    return schema if isinstance(schema, dict) else None
-
-
-def _flow_state_defaults(flow: Any) -> dict[str, Any]:
-    """Declared state defaults (``state.default``) from the flow definition."""
-    state_definition = getattr(getattr(flow, "_definition", None), "state", None)
-    default = getattr(state_definition, "default", None)
-    return dict(default) if isinstance(default, dict) else {}
-
-
-def _missing_required(state_model: Any, values: dict[str, Any]) -> list[str]:
-    """Required state fields not satisfied by ``values`` (defaults + inputs)."""
-    try:
-        state_model.model_validate(values)
-    except ValidationError as exc:
-        return [
-            str(error["loc"][0])
-            for error in exc.errors()
-            if error.get("type") == "missing" and error.get("loc")
-        ]
-    return []
-
-
-def _validate_flow_inputs(state_model: Any, values: dict[str, Any]) -> None:
-    """Validate inputs against the state schema; exit with pointed type errors."""
-    try:
-        state_model.model_validate(values)
-    except ValidationError as exc:
-        for error in exc.errors():
-            location = ".".join(str(part) for part in error.get("loc", ()))
-            click.secho(
-                f"  Invalid input '{location}': {error.get('msg')}", fg="red", err=True
-            )
-        raise SystemExit(1) from exc
-
-
-def _coerce_input(raw: str, spec: dict[str, Any]) -> Any:
-    """Best-effort coerce a prompted string to the field's JSON-schema type."""
-    field_type = spec.get("type")
-    if field_type == "integer":
-        try:
-            return int(raw)
-        except ValueError:
-            return raw
-    if field_type == "number":
-        try:
-            return float(raw)
-        except ValueError:
-            return raw
-    if field_type == "boolean":
-        return raw.strip().lower() in {"1", "true", "yes", "y", "on"}
-    return raw
-
-
 def plot_declarative_flow(definition: str | Path) -> None:
     """Plot a declarative flow from a definition path."""
     try:
@@ -324,6 +152,23 @@ def _has_project_file(project_root: Path | None = None) -> bool:
     return (root / "pyproject.toml").is_file()
 
 
+def _parse_inputs(inputs: str | None) -> dict[str, Any] | None:
+    if inputs is None:
+        return None
+
+    try:
+        parsed = json.loads(inputs)
+    except json.JSONDecodeError as exc:
+        click.echo(f"Invalid --inputs JSON: {exc}", err=True)
+        raise SystemExit(1) from exc
+
+    if not isinstance(parsed, dict):
+        click.echo("Invalid --inputs JSON: expected an object.", err=True)
+        raise SystemExit(1)
+
+    return parsed
+
+
 def _format_result(result: Any) -> str:
     raw_result = getattr(result, "raw", result)
     if isinstance(raw_result, str):

lib/cli/src/crewai_cli/templates/declarative_flow/AGENTS.md

@@ -258,9 +258,10 @@ Fields:
 #### Crew Agent Definition (`methods.<name>.do[call=crew].with.agents.<name>`)
 
 Fields:
-- `role` (required): string. Crew agent role. Crew inputs are interpolated with `{name}` placeholders such as `{topic}`; this is not CEL. Example: `Research analyst`
-- `goal` (required): string. Crew agent goal. Crew inputs are interpolated with `{name}` placeholders such as `{topic}`; this is not CEL. Example: `Research {topic}`
-- `backstory` (required): string. Crew agent backstory. Crew inputs are interpolated with `{name}` placeholders such as `{topic}`; this is not CEL. Example: `Expert at concise technical research.`
+- `role` (optional): string | null; default `null`. Crew agent role. Crew inputs are interpolated with `{name}` placeholders such as `{topic}`; this is not CEL. Example: `Research analyst`
+- `goal` (optional): string | null; default `null`. Crew agent goal. Crew inputs are interpolated with `{name}` placeholders such as `{topic}`; this is not CEL. Example: `Research {topic}`
+- `backstory` (optional): string | null; default `null`. Crew agent backstory. Crew inputs are interpolated with `{name}` placeholders such as `{topic}`; this is not CEL. Example: `Expert at concise technical research.`
+- `from_repository` (optional): string | null; default `null`. Agent repository name to load. Repository values supply missing agent configuration; explicitly provided local fields override the repository values. Example: `researcher`
 - `settings` (optional): map of string to any. Additional agent settings passed to the loader. Example: `{"llm": "openai/gpt-4o-mini"}`
 - `llm` (optional): string or inline LLM config; default `null`. Language model that runs this crew agent. Use an object when setting LLM options such as `max_tokens`. Example: `{"max_tokens": 4096, "model": "openai/gpt-4o-mini"}`
 - `planning_config` (optional): object | null; default `null`. Agent planning configuration. Set `max_attempts` to limit planning refinement attempts before task execution. Example: `{"max_attempts": 3}`
@@ -292,15 +293,16 @@ Shape:
 - `call: agent`
 
 Fields:
-- `call` (required): must be `agent`. Action discriminator. Use agent to run an individual inline Agent definition outside of a crew. Example: `agent`
+- `call` (required): must be `agent`. Action discriminator. Use agent to run an individual inline or repository-backed Agent definition outside of a crew. Example: `agent`
 - `with` (required): any. Individual Agent definition to load and execute outside of a crew for this action. Put the agent input in `with.input`; agent actions do not support action-level `inputs`. Example: `{"backstory": "Precise and concise.", "goal": "Answer user questions", "input": "${state.question}", "role": "Analyst", "settings": {"llm": "openai/gpt-4o-mini"}}`
 
 #### Agent Definition (`methods.<name>.do[call=agent].with`)
 
 Fields:
-- `role` (required): string. Individual agent role used by a Flow agent action outside of a crew. Example: `Support specialist`
-- `goal` (required): string. Individual agent goal for the Flow agent action outside of a crew. Example: `Draft a concise customer reply`
-- `backstory` (required): string. Individual agent backstory used to shape behavior outside of a crew. Example: `Expert at resolving SaaS support questions.`
+- `role` (optional): string | null; default `null`. Individual agent role used by a Flow agent action outside of a crew. Example: `Support specialist`
+- `goal` (optional): string | null; default `null`. Individual agent goal for the Flow agent action outside of a crew. Example: `Draft a concise customer reply`
+- `backstory` (optional): string | null; default `null`. Individual agent backstory used to shape behavior outside of a crew. Example: `Expert at resolving SaaS support questions.`
+- `from_repository` (optional): string | null; default `null`. Agent repository name to load. Repository values supply missing agent configuration; explicitly provided local fields override the repository values. Example: `support_specialist`
 - `settings` (optional): map of string to any. Additional agent settings passed to the loader. Example: `{"llm": "openai/gpt-4o-mini"}`
 - `llm` (optional): string or inline LLM config; default `null`. Language model that runs this agent. Use an object when setting LLM options such as `max_tokens`. Example: `{"max_tokens": 4096, "model": "openai/gpt-4o-mini"}`
 - `planning_config` (optional): object | null; default `null`. Agent planning configuration. Set `max_attempts` to limit planning refinement attempts before task execution. Example: `{"max_attempts": 3}`

lib/cli/tests/test_cli.py

@@ -151,14 +151,12 @@ def test_run_with_definition_uses_project_runner(run_crew, runner):
 
 
 @mock.patch("crewai_cli.cli.run_crew")
-def test_run_inputs_without_definition_calls_run_crew(run_crew, runner):
-    # --inputs no longer requires --definition; the resolution happens in run_crew.
+def test_run_rejects_inputs_without_definition(run_crew, runner):
     result = runner.invoke(run, ["--inputs", '{"topic":"AI"}'])
 
-    assert result.exit_code == 0
-    run_crew.assert_called_once_with(
-        trained_agents_file=None, definition=None, inputs='{"topic":"AI"}'
-    )
+    assert result.exit_code == 2
+    assert "Error: --inputs requires --definition" in result.output
+    run_crew.assert_not_called()
 
 
 @mock.patch("crewai_cli.cli.run_crew")

lib/cli/tests/test_input_prompt.py

@@ -1,82 +0,0 @@
-"""Tests for the shared runtime-input prompting used by flows and crews."""
-
-from __future__ import annotations
-
-import pytest
-
-from crewai_cli.input_prompt import (
-    closest_name,
-    parse_inputs_json,
-    prompt_for_inputs,
-)
-
-
-def test_parse_inputs_json_returns_none_for_none():
-    assert parse_inputs_json(None) is None
-
-
-def test_parse_inputs_json_parses_object():
-    assert parse_inputs_json('{"topic": "AI"}') == {"topic": "AI"}
-
-
-def test_parse_inputs_json_rejects_invalid_json(capsys):
-    with pytest.raises(SystemExit) as exc_info:
-        parse_inputs_json("not json")
-
-    assert exc_info.value.code == 1
-    assert "Invalid --inputs JSON" in capsys.readouterr().err
-
-
-def test_parse_inputs_json_rejects_non_object(capsys):
-    with pytest.raises(SystemExit) as exc_info:
-        parse_inputs_json("[1, 2, 3]")
-
-    assert exc_info.value.code == 1
-    assert "expected an object" in capsys.readouterr().err
-
-
-def test_closest_name_suggests_near_miss():
-    assert closest_name("prospect_emai", ["prospect_email", "topic"]) == "prospect_email"
-
-
-def test_closest_name_returns_none_when_nothing_close():
-    assert closest_name("zzzzz", ["prospect_email", "topic"]) is None
-
-
-def test_prompt_for_inputs_uses_describe_and_coerce(monkeypatch, capsys):
-    seen: list[str] = []
-
-    def fake_prompt(text: str, **kwargs: object) -> str:
-        seen.append(text)
-        return "42"
-
-    monkeypatch.setattr("crewai_cli.input_prompt.click.prompt", fake_prompt)
-
-    result = prompt_for_inputs(
-        ["count"],
-        title="Flow inputs",
-        subtitle="This flow needs the following to run.",
-        describe=lambda name: f"How many {name}?",
-        coerce=lambda name, raw: int(raw),
-    )
-
-    captured = capsys.readouterr()
-    assert result == {"count": 42}
-    assert any("count" in text for text in seen)
-    # Header, subtitle, and description hint all render on stderr.
-    assert "Flow inputs" in captured.err
-    assert "How many count?" in captured.err
-
-
-def test_prompt_for_inputs_keeps_raw_string_without_coerce(monkeypatch):
-    monkeypatch.setattr(
-        "crewai_cli.input_prompt.click.prompt", lambda text, **kwargs: "AI"
-    )
-
-    result = prompt_for_inputs(
-        ["topic"],
-        title="Crew inputs",
-        subtitle="This crew needs the following to run.",
-    )
-
-    assert result == {"topic": "AI"}

lib/cli/tests/test_model_catalog.py

@@ -1,551 +0,0 @@
-"""Tests for the dynamic model catalog used by the crew-creation wizard."""
-
-from __future__ import annotations
-
-import json
-
-import pytest
-
-import crewai_cli.model_catalog as mc
-
-_ALL_KEY_ENVS = [
-    "OPENAI_API_KEY",
-    "ANTHROPIC_API_KEY",
-    "GEMINI_API_KEY",
-    "GOOGLE_API_KEY",
-    "GROQ_API_KEY",
-    "CEREBRAS_API_KEY",
-    "OLLAMA_API_BASE",
-    "API_BASE",
-    "OLLAMA_HOST",
-]
-
-FALLBACK_ANTHROPIC = [
-    ("claude-opus-4-6", "Claude Opus 4.6"),
-    ("claude-sonnet-4-6", "Claude Sonnet 4.6"),
-]
-
-
-@pytest.fixture(autouse=True)
-def isolated_env(monkeypatch, tmp_path):
-    """Point the cache at a temp dir and clear provider keys for every test."""
-    monkeypatch.setattr(mc, "_cache_dir", lambda: tmp_path)
-    mc._reset_litellm_memo()  # clear the process-level LiteLLM memo per test
-    for key in _ALL_KEY_ENVS:
-        monkeypatch.delenv(key, raising=False)
-
-
-# ── version / label helpers ──────────────────────────────────────
-
-
-def test_version_key_parses_embedded_date():
-    date_int, version = mc._version_key("claude-3-5-sonnet-20241022")
-    assert date_int == 20241022
-    assert version == (3, 5)
-
-
-def test_version_key_parses_dashed_date():
-    date_int, _ = mc._version_key("gpt-4o-2024-08-06")
-    assert date_int == 20240806
-
-
-def test_version_key_version_only():
-    date_int, version = mc._version_key("claude-opus-4-6")
-    assert date_int == 0
-    assert version == (4, 6)
-
-
-def test_version_key_ranks_newer_higher():
-    older = mc._version_key("claude-sonnet-4-5")
-    newer = mc._version_key("claude-sonnet-4-6")
-    assert newer > older
-
-
-def test_is_chat_model_rejects_non_chat():
-    assert mc._is_chat_model("gpt-4.1-mini")
-    assert not mc._is_chat_model("text-embedding-3-large")
-    assert not mc._is_chat_model("whisper-1")
-    assert not mc._is_chat_model("dall-e-3")
-
-
-def test_search_substring_not_treated_as_non_chat():
-    # 'search' must not drop legitimate completion models: a token like
-    # *-search-preview, or 'research' (which contains 'search' as a substring).
-    assert mc._is_chat_model("gpt-4o-search-preview")
-    assert mc._is_chat_model("o3-deep-research")
-    # genuine non-chat markers still filter
-    assert not mc._is_chat_model("text-embedding-3-large")
-
-
-def test_humanize():
-    assert mc._humanize("gpt-4.1-mini") == "GPT 4.1 Mini"
-    assert mc._humanize("anthropic/claude-opus-4-6") == "Claude Opus 4 6"
-    # size suffixes uppercased, acronyms/brands cased, o-series preserved, ':' split
-    assert mc._humanize("openai/gpt-oss-120b") == "GPT OSS 120B"
-    assert mc._humanize("qwen/qwen3-32b") == "Qwen3 32B"
-    assert mc._humanize("deepseek-r1-distill-llama-70b") == "DeepSeek R1 Distill Llama 70B"
-    assert mc._humanize("o3-mini") == "o3 Mini"
-    assert mc._humanize("chatgpt-4o-latest") == "ChatGPT 4o Latest"
-    assert mc._humanize("llama3.3:70b") == "Llama3.3 70B"
-    assert mc._humanize("gemma2-9b-it") == "Gemma2 9B IT"
-
-
-# ── vendor tier ──────────────────────────────────────────────────
-
-
-def test_vendor_anthropic_ranks_by_date_and_uses_display_name(monkeypatch):
-    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-test")
-    payload = {
-        "data": [
-            {
-                "id": "claude-3-5-sonnet-20240620",
-                "display_name": "Claude 3.5 Sonnet (old)",
-                "created_at": "2024-06-20T00:00:00Z",
-            },
-            {
-                "id": "claude-opus-4-6",
-                "display_name": "Claude Opus 4.6",
-                "created_at": "2026-02-01T00:00:00Z",
-            },
-            {
-                "id": "claude-haiku-4-5-20251001",
-                "display_name": "Claude Haiku 4.5",
-                "created_at": "2025-10-01T00:00:00Z",
-            },
-        ]
-    }
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: payload)
-
-    models = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-
-    # Newest first by created_at, display names preserved.
-    assert models[0] == ("claude-opus-4-6", "Claude Opus 4.6")
-    assert models[1] == ("claude-haiku-4-5-20251001", "Claude Haiku 4.5")
-    assert models[2] == ("claude-3-5-sonnet-20240620", "Claude 3.5 Sonnet (old)")
-
-
-def test_vendor_openai_filters_non_chat_models(monkeypatch):
-    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
-    payload = {
-        "data": [
-            {"id": "gpt-4.1", "created": 1_700_000_000},
-            {"id": "text-embedding-3-large", "created": 1_800_000_000},
-            {"id": "whisper-1", "created": 1_800_000_000},
-            {"id": "gpt-5.5", "created": 1_750_000_000},
-        ]
-    }
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: payload)
-
-    models = mc.get_provider_models("openai", [])
-    ids = [m for m, _ in models]
-
-    assert ids == ["gpt-5.5", "gpt-4.1"]  # embeddings/whisper dropped, newest first
-
-
-def test_vendor_gemini_requires_generate_content(monkeypatch):
-    monkeypatch.setenv("GEMINI_API_KEY", "key")
-    payload = {
-        "models": [
-            {
-                "name": "models/gemini-2.5-pro",
-                "displayName": "Gemini 2.5 Pro",
-                "supportedGenerationMethods": ["generateContent"],
-            },
-            {
-                "name": "models/text-embedding-004",
-                "displayName": "Embedding",
-                "supportedGenerationMethods": ["embedContent"],
-            },
-            {
-                "name": "models/gemini-1.5-pro",
-                "displayName": "Gemini 1.5 Pro",
-                "supportedGenerationMethods": ["generateContent"],
-            },
-        ]
-    }
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: payload)
-
-    models = mc.get_provider_models("gemini", [])
-    ids = [m for m, _ in models]
-
-    # "models/" prefix stripped, embedding excluded, newer version first.
-    assert ids == ["gemini-2.5-pro", "gemini-1.5-pro"]
-
-
-def test_openai_excludes_fine_tunes_and_checkpoints(monkeypatch):
-    # Fine-tunes/checkpoints have recent `created` timestamps and would otherwise
-    # crowd out (and rank above) the base models — they must be excluded so the
-    # picker shows clean foundation models.
-    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
-    payload = {
-        "data": [
-            {"id": "ft:gpt-4o-mini-2024-07-18:crewai::DyJG86uF", "created": 1_900_000_000},
-            {
-                "id": "ft:gpt-4o-mini-2024-07-18:crewai::DyJG7Q9N:ckpt-step-84",
-                "created": 1_900_000_001,
-            },
-            {"id": "gpt-5.5", "created": 1_800_000_000},
-            {"id": "gpt-4.1", "created": 1_700_000_000},
-        ]
-    }
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: payload)
-
-    ids = [m for m, _ in mc.get_provider_models("openai", [])]
-    assert ids == ["gpt-5.5", "gpt-4.1"]  # fine-tunes + checkpoints dropped
-
-
-def test_vendor_gemini_paginates(monkeypatch):
-    monkeypatch.setenv("GEMINI_API_KEY", "key")
-    pages = {
-        None: {
-            "models": [
-                {
-                    "name": "models/gemini-3.5-flash",
-                    "displayName": "Gemini 3.5 Flash",
-                    "supportedGenerationMethods": ["generateContent"],
-                }
-            ],
-            "nextPageToken": "p2",
-        },
-        "p2": {
-            "models": [
-                {
-                    "name": "models/gemini-2.5-pro",
-                    "displayName": "Gemini 2.5 Pro",
-                    "supportedGenerationMethods": ["generateContent"],
-                }
-            ]
-        },
-    }
-
-    def fetch(url, headers=None, params=None):
-        return pages[(params or {}).get("pageToken")]
-
-    monkeypatch.setattr(mc, "_http_get_json", fetch)
-
-    ids = sorted(m for m, _ in mc.get_provider_models("gemini", []))
-    # Both pages contributed (newest-first ranking is _finalize's job).
-    assert ids == ["gemini-2.5-pro", "gemini-3.5-flash"]
-
-
-def test_vendor_gemini_first_page_error_uses_fallback(monkeypatch):
-    # A total (first-page) Gemini failure with a key set must fall back to the
-    # curated list, not be mistaken for a successful empty result.
-    monkeypatch.setenv("GEMINI_API_KEY", "key")
-
-    def boom(*a, **k):
-        raise RuntimeError("gemini down")
-
-    monkeypatch.setattr(mc, "_http_get_json", boom)
-    models = mc.get_provider_models("gemini", [("gemini-x", "Gemini X")])
-    assert models == [("gemini-x", "Gemini X")]
-
-
-def test_vendor_gemini_keeps_partial_on_later_page_error(monkeypatch):
-    monkeypatch.setenv("GEMINI_API_KEY", "key")
-
-    def fetch(url, headers=None, params=None):
-        if (params or {}).get("pageToken"):
-            raise RuntimeError("page 2 down")
-        return {
-            "models": [
-                {
-                    "name": "models/gemini-3.5-flash",
-                    "displayName": "Gemini 3.5 Flash",
-                    "supportedGenerationMethods": ["generateContent"],
-                }
-            ],
-            "nextPageToken": "p2",
-        }
-
-    monkeypatch.setattr(mc, "_http_get_json", fetch)
-
-    # Page-1 models are kept; the later-page error doesn't force the fallback.
-    models = mc.get_provider_models("gemini", [("fallback-x", "Fallback X")])
-    assert [m for m, _ in models] == ["gemini-3.5-flash"]
-
-
-def test_ollama_empty_response_not_filled_with_fallback(monkeypatch):
-    # A reachable Ollama with nothing installed -> empty (manual entry), not the
-    # curated suggestions the crew can't actually run.
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: {"models": []})
-    assert mc.get_provider_models("ollama", [("llama3.3", "Llama 3.3")]) == []
-
-
-def test_ollama_unreachable_uses_fallback(monkeypatch):
-    # Server down (fetch raises) is different from empty -> fall back to suggestions.
-    def boom(*a, **k):
-        raise RuntimeError("connection refused")
-
-    monkeypatch.setattr(mc, "_http_get_json", boom)
-    models = mc.get_provider_models("ollama", [("llama3.3", "Llama 3.3")])
-    assert models == [("llama3.3", "Llama 3.3")]
-
-
-def test_ollama_excludes_embedding_models(monkeypatch):
-    # /api/tags lists everything installed, including embeddings — filter them.
-    monkeypatch.setattr(
-        mc,
-        "_http_get_json",
-        lambda *a, **k: {
-            "models": [
-                {"model": "llama3.3:70b"},
-                {"model": "nomic-embed-text"},
-                {"model": "mxbai-embed-large"},
-            ]
-        },
-    )
-    ids = [m for m, _ in mc.get_provider_models("ollama", [])]
-    assert ids == ["llama3.3:70b"]
-
-
-def test_ollama_base_honors_ollama_host(monkeypatch):
-    # OLLAMA_HOST (scheme-less runtime convention) is resolved with a scheme.
-    monkeypatch.setenv("OLLAMA_HOST", "10.0.0.5:11434")
-    assert mc._ollama_base() == "http://10.0.0.5:11434"
-
-
-def test_ollama_recovery_not_blocked_by_negative_cache(monkeypatch):
-    # Ollama down -> fallback, but not negatively cached; once the server is up
-    # the next call fetches live models rather than serving suggestions.
-    calls = {"n": 0}
-
-    def flaky(*a, **k):
-        calls["n"] += 1
-        if calls["n"] == 1:
-            raise RuntimeError("connection refused")
-        return {"models": [{"model": "llama-installed"}]}
-
-    monkeypatch.setattr(mc, "_http_get_json", flaky)
-    first = mc.get_provider_models("ollama", [("llama3.3", "Llama 3.3")])
-    assert first == [("llama3.3", "Llama 3.3")]  # down -> fallback (not cached)
-    second = mc.get_provider_models("ollama", [("llama3.3", "Llama 3.3")])
-    assert [m for m, _ in second] == ["llama-installed"]  # recovered live
-
-
-def test_gemini_honors_google_api_key(monkeypatch):
-    # GOOGLE_API_KEY (equivalent to GEMINI_API_KEY in crewai) enables the live tier.
-    monkeypatch.setenv("GOOGLE_API_KEY", "key")
-    monkeypatch.setattr(
-        mc,
-        "_http_get_json",
-        lambda *a, **k: {
-            "models": [
-                {
-                    "name": "models/gemini-3.5-flash",
-                    "displayName": "Gemini 3.5 Flash",
-                    "supportedGenerationMethods": ["generateContent"],
-                }
-            ]
-        },
-    )
-    models = mc.get_provider_models("gemini", [("gemini-x", "Gemini X")])
-    assert [m for m, _ in models] == ["gemini-3.5-flash"]  # live, not fallback
-
-
-def test_curated_label_overrides_raw_vendor_label(monkeypatch):
-    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
-    payload = {"data": [{"id": "gpt-5.5", "created": 1}]}
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: payload)
-
-    models = mc.get_provider_models("openai", [("gpt-5.5", "GPT-5.5 (curated)")])
-    assert models == [("gpt-5.5", "GPT-5.5 (curated)")]
-
-
-def test_truncates_to_max_models(monkeypatch):
-    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
-    payload = {
-        "data": [{"id": f"gpt-test-{i}", "created": i} for i in range(20)]
-    }
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: payload)
-
-    models = mc.get_provider_models("openai", [])
-    assert len(models) == mc.MAX_MODELS
-
-
-# ── litellm tier ─────────────────────────────────────────────────
-
-
-def test_litellm_tier_for_uncurated_provider(monkeypatch):
-    # A provider with no curated fallback ([]) -> the LiteLLM feed is consulted.
-    litellm_data = {
-        "claude-opus-4-6": {"litellm_provider": "anthropic", "mode": "chat"},
-        "claude-sonnet-4-5": {"litellm_provider": "anthropic", "mode": "chat"},
-        "voyage-embed": {"litellm_provider": "anthropic", "mode": "embedding"},
-        "gpt-4.1": {"litellm_provider": "openai", "mode": "chat"},
-    }
-    mc._litellm_cache_file().write_text(json.dumps(litellm_data), encoding="utf-8")
-
-    models = mc.get_provider_models("anthropic", [])  # empty == uncurated
-    ids = [m for m, _ in models]
-
-    # Only anthropic chat models, embedding + other providers excluded.
-    assert ids == ["claude-opus-4-6", "claude-sonnet-4-5"]
-
-
-def test_null_litellm_provider_does_not_crash(monkeypatch):
-    # A present-but-null litellm_provider must be skipped, not raise.
-    litellm_data = {
-        "weird-model": {"litellm_provider": None, "mode": "chat"},
-        "anthropic.claude-v2": {"litellm_provider": "bedrock", "mode": "chat"},
-    }
-    mc._litellm_cache_file().write_text(json.dumps(litellm_data), encoding="utf-8")
-
-    models = mc.get_provider_models("bedrock", [])
-    assert [m for m, _ in models] == ["anthropic.claude-v2"]
-
-
-def test_litellm_strips_provider_prefix(monkeypatch):
-    litellm_data = {
-        "gemini/gemini-1.5-pro": {"litellm_provider": "gemini", "mode": "chat"},
-    }
-    mc._litellm_cache_file().write_text(json.dumps(litellm_data), encoding="utf-8")
-
-    models = mc.get_provider_models("gemini", [])
-    assert models == [("gemini-1.5-pro", "Gemini 1.5 Pro")]
-
-
-# ── fallback + caching ───────────────────────────────────────────
-
-
-def test_falls_back_when_everything_fails(monkeypatch):
-    # No key, no litellm cache, network raises -> curated fallback verbatim.
-    def boom(*a, **k):
-        raise RuntimeError("network down")
-
-    monkeypatch.setattr(mc, "_http_get_json", boom)
-    models = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-    assert models == FALLBACK_ANTHROPIC
-
-
-def test_result_is_cached(monkeypatch):
-    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-test")
-    calls = {"n": 0}
-
-    def fetch(*a, **k):
-        calls["n"] += 1
-        return {"data": [{"id": "claude-opus-4-6", "created_at": "2026-01-01T00:00:00Z"}]}
-
-    monkeypatch.setattr(mc, "_http_get_json", fetch)
-
-    first = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-    # Second call must hit the cache and not touch the network again.
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: pytest.fail("refetched"))
-    second = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-
-    assert first == second
-    assert calls["n"] == 1
-
-
-def test_curated_fallback_preferred_over_litellm(monkeypatch):
-    # The feed lags real releases, so a non-empty curated fallback must win even
-    # when a fresh LiteLLM cache is present (regression: Anthropic's feed lacked
-    # Fable 5 / Opus 4.8 / Sonnet 5).
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: pytest.fail("no net"))
-    litellm_data = {
-        "claude-opus-4-6": {"litellm_provider": "anthropic", "mode": "chat"},
-    }
-    mc._litellm_cache_file().write_text(json.dumps(litellm_data), encoding="utf-8")
-
-    models = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-    assert models == FALLBACK_ANTHROPIC
-
-
-def test_added_key_bypasses_negative_cache(monkeypatch):
-    # A no-key call negatively-caches the fallback; adding a key afterwards must
-    # fetch live models rather than serve the cached fallback (distinct cache key).
-    first = mc.get_provider_models("openai", [("gpt-x", "GPT X")])
-    assert first == [("gpt-x", "GPT X")]  # no key -> fallback
-
-    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
-    monkeypatch.setattr(
-        mc, "_http_get_json", lambda *a, **k: {"data": [{"id": "gpt-5.5", "created": 1}]}
-    )
-    second = mc.get_provider_models("openai", [("gpt-x", "GPT X")])
-    assert [m for m, _ in second] == ["gpt-5.5"]  # live fetch, not cached fallback
-
-
-def test_invalid_litellm_cache_falls_through_to_download(monkeypatch):
-    # A corrupt-but-fresh cache must neither crash the picker nor block a
-    # recoverable download — it falls through and refetches.
-    mc._litellm_cache_file().write_text("[1, 2, 3]", encoding="utf-8")
-    monkeypatch.setattr(
-        mc,
-        "_http_get_json",
-        lambda *a, **k: {
-            "anthropic.claude-v2": {"litellm_provider": "bedrock", "mode": "chat"}
-        },
-    )
-    models = mc.get_provider_models("bedrock", [])
-    assert [m for m, _ in models] == ["anthropic.claude-v2"]  # recovered via download
-
-
-def test_litellm_fetch_attempted_once_per_process(monkeypatch):
-    # With no cache and a failing download, the feed is fetched at most once per
-    # process — repeated lookups (across providers) must not re-hit the network.
-    calls = {"n": 0}
-
-    def boom(*a, **k):
-        calls["n"] += 1
-        raise RuntimeError("offline")
-
-    monkeypatch.setattr(mc, "_http_get_json", boom)
-    mc.get_provider_models("bedrock", [])
-    mc.get_provider_models("azure", [])
-    assert calls["n"] == 1  # memoized after the first failed attempt
-
-
-def test_litellm_fills_uncurated_bedrock(monkeypatch):
-    # No vendor fetcher and no curated fallback -> LiteLLM feed fills the gap.
-    monkeypatch.setattr(mc, "_http_get_json", lambda *a, **k: pytest.fail("no net"))
-    litellm_data = {
-        "anthropic.claude-v2": {"litellm_provider": "bedrock", "mode": "chat"},
-    }
-    mc._litellm_cache_file().write_text(json.dumps(litellm_data), encoding="utf-8")
-
-    models = mc.get_provider_models("bedrock", [])
-    assert models == [("anthropic.claude-v2", "Anthropic.claude V2")]
-
-
-def test_failed_fetch_is_negatively_cached(monkeypatch):
-    # A failed vendor fetch must not be retried on every call — the fallback is
-    # cached briefly so the picker doesn't re-hit the timeout-prone endpoint.
-    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-test")
-    calls = {"n": 0}
-
-    def boom(*a, **k):
-        calls["n"] += 1
-        raise RuntimeError("down")
-
-    monkeypatch.setattr(mc, "_http_get_json", boom)
-    first = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-    second = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-
-    assert first == second == FALLBACK_ANTHROPIC
-    assert calls["n"] == 1  # second call served from the negative cache
-
-
-def test_bad_cache_json_does_not_crash(monkeypatch):
-    # A corrupt cache whose root is not a mapping must not raise (get_provider_models
-    # is documented to never raise).
-    mc._catalog_cache_file().write_text("[1, 2, 3]", encoding="utf-8")
-
-    models = mc.get_provider_models("anthropic", FALLBACK_ANTHROPIC)
-    assert models == FALLBACK_ANTHROPIC
-
-
-def test_ollama_cache_keyed_by_base(monkeypatch):
-    # Changing OLLAMA_API_BASE must not serve the previous host's cached models.
-    monkeypatch.setenv("OLLAMA_API_BASE", "http://host-a:11434")
-    monkeypatch.setattr(
-        mc, "_http_get_json", lambda *a, **k: {"models": [{"model": "llama-a"}]}
-    )
-    first = mc.get_provider_models("ollama", [])
-    assert [m for m, _ in first] == ["llama-a"]
-
-    monkeypatch.setenv("OLLAMA_API_BASE", "http://host-b:11434")
-    monkeypatch.setattr(
-        mc, "_http_get_json", lambda *a, **k: {"models": [{"model": "llama-b"}]}
-    )
-    second = mc.get_provider_models("ollama", [])
-    assert [m for m, _ in second] == ["llama-b"]  # not the host-a cache

lib/cli/tests/test_plus_api.py

@@ -1,6 +1,8 @@
 import os
 import unittest
-from unittest.mock import ANY, MagicMock, patch
+from unittest.mock import ANY, AsyncMock, MagicMock, patch
+
+import pytest
 
 from crewai_cli.plus_api import PlusAPI
 
@@ -341,23 +343,28 @@ class TestPlusAPI(unittest.TestCase):
         )
 
 
-@patch("crewai_core.plus_api.PlusAPI._make_request")
-def test_get_agent(mock_make_request):
+@pytest.mark.asyncio
+@patch("httpx.AsyncClient")
+async def test_get_agent(mock_async_client_class):
     api = PlusAPI("test_api_key")
     mock_response = MagicMock()
-    mock_make_request.return_value = mock_response
+    mock_client_instance = AsyncMock()
+    mock_client_instance.get.return_value = mock_response
+    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
 
-    response = api.get_agent("test_agent_handle")
+    response = await api.get_agent("test_agent_handle")
 
-    mock_make_request.assert_called_once_with(
-        "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
+    mock_client_instance.get.assert_called_once_with(
+        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
+        headers=api.headers,
     )
     assert response == mock_response
 
 
-@patch("crewai_core.plus_api.PlusAPI._make_request")
+@pytest.mark.asyncio
+@patch("httpx.AsyncClient")
 @patch("crewai_core.plus_api.Settings")
-def test_get_agent_with_org_uuid(mock_settings_class, mock_make_request):
+async def test_get_agent_with_org_uuid(mock_settings_class, mock_async_client_class):
     org_uuid = "test-org-uuid"
     mock_settings = MagicMock()
     mock_settings.org_uuid = org_uuid
@@ -367,12 +374,15 @@ def test_get_agent_with_org_uuid(mock_settings_class, mock_make_request):
     api = PlusAPI("test_api_key")
 
     mock_response = MagicMock()
-    mock_make_request.return_value = mock_response
+    mock_client_instance = AsyncMock()
+    mock_client_instance.get.return_value = mock_response
+    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
 
-    response = api.get_agent("test_agent_handle")
+    response = await api.get_agent("test_agent_handle")
 
-    mock_make_request.assert_called_once_with(
-        "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
+    mock_client_instance.get.assert_called_once_with(
+        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
+        headers=api.headers,
     )
     assert "X-Crewai-Organization-Id" in api.headers
     assert api.headers["X-Crewai-Organization-Id"] == org_uuid

lib/cli/tests/test_run_crew.py

@@ -9,7 +9,6 @@ import click
 import pytest
 from crewai_core.constants import CREWAI_TRAINED_AGENTS_FILE_ENV
 
-import crewai_cli.input_prompt as input_prompt_module
 import crewai_cli.run_crew as run_crew_module
 
 
@@ -45,12 +44,9 @@ def test_run_crew_forwards_trained_agents_file_to_json_crews(monkeypatch):
     )
     called: dict = {}
 
-    def fake_run_json_crew_in_project_env(
-        trained_agents_file=None, crew_path=None, inputs=None
-    ):
+    def fake_run_json_crew_in_project_env(trained_agents_file=None, crew_path=None):
         called["trained_agents_file"] = trained_agents_file
         called["crew_path"] = crew_path
-        called["inputs"] = inputs
 
     monkeypatch.setattr(
         run_crew_module,
@@ -63,7 +59,6 @@ def test_run_crew_forwards_trained_agents_file_to_json_crews(monkeypatch):
     assert called == {
         "trained_agents_file": "some.pkl",
         "crew_path": Path("crew.jsonc"),
-        "inputs": None,
     }
 
 
@@ -310,10 +305,9 @@ def test_json_run_without_pyproject_runs_in_process(monkeypatch, tmp_path: Path)
     monkeypatch.chdir(tmp_path)
     called: dict = {}
 
-    def fake_run_json_crew(trained_agents_file=None, crew_path=None, inputs=None):
+    def fake_run_json_crew(trained_agents_file=None, crew_path=None):
         called["trained_agents_file"] = trained_agents_file
         called["crew_path"] = crew_path
-        called["inputs"] = inputs
         return "result"
 
     monkeypatch.setattr(run_crew_module, "_run_json_crew", fake_run_json_crew)
@@ -324,11 +318,7 @@ def test_json_run_without_pyproject_runs_in_process(monkeypatch, tmp_path: Path)
         )
         == "result"
     )
-    assert called == {
-        "trained_agents_file": "trained.pkl",
-        "crew_path": None,
-        "inputs": None,
-    }
+    assert called == {"trained_agents_file": "trained.pkl", "crew_path": None}
 
 
 def test_json_project_env_run_failure_exits_nonzero(monkeypatch, tmp_path: Path):
@@ -545,9 +535,7 @@ def _patch_tui_run(monkeypatch, status: str):
         lambda _path: (FakeApp, crew, {}, [], []),
     )
     monkeypatch.setattr(
-        run_crew_module,
-        "_resolve_crew_inputs",
-        lambda _crew, default_inputs, _provided, *, interactive: default_inputs,
+        run_crew_module, "_prompt_for_missing_inputs", lambda _crew, inputs: inputs
     )
     monkeypatch.setattr(run_crew_module, "_print_post_tui_summary", lambda _app: None)
 
@@ -648,168 +636,7 @@ def test_run_json_crew_dmn_mode_exits_on_missing_inputs(
 
     captured = capsys.readouterr()
     assert exc_info.value.code == 1
-    assert "Missing required input 'topic'" in captured.err
-
-
-# ── Declarative-crew inputs: merge --inputs, warn, prompt (flow parity) ──
-
-
-def _crew_with_placeholders(*names: str) -> object:
-    """A minimal crew whose agent goal references each ``{name}`` placeholder."""
-    from types import SimpleNamespace
-
-    goal = " ".join(f"{{{name}}}" for name in names)
-    return SimpleNamespace(
-        agents=[SimpleNamespace(role="Researcher", goal=goal, backstory="")],
-        tasks=[],
-    )
-
-
-def test_resolve_crew_inputs_merges_inputs_over_defaults():
-    crew = _crew_with_placeholders("topic")
-
-    resolved = run_crew_module._resolve_crew_inputs(
-        crew, {"topic": "AI"}, {"topic": "ML"}, interactive=False
-    )
-
-    assert resolved == {"topic": "ML"}
-
-
-def test_resolve_crew_inputs_warns_and_keeps_unknown_input(capsys):
-    # The placeholder scan is heuristic, so an unreferenced key is flagged but
-    # kept (never silently dropped like a flow's schema would).
-    crew = _crew_with_placeholders("topic")
-
-    resolved = run_crew_module._resolve_crew_inputs(
-        crew, {}, {"topic": "AI", "topi": "typo"}, interactive=False
-    )
-
-    captured = capsys.readouterr()
-    assert resolved == {"topic": "AI", "topi": "typo"}
-    assert "isn't referenced by any {placeholder}" in captured.err
-    assert "Did you mean 'topic'?" in captured.err
-
-
-def test_resolve_crew_inputs_prompts_when_interactive(monkeypatch):
-    crew = _crew_with_placeholders("topic")
-    prompted: list[str] = []
-
-    def fake_prompt(text: str, **kwargs: object) -> str:
-        prompted.append(text)
-        return "AI"
-
-    monkeypatch.setattr(input_prompt_module.click, "prompt", fake_prompt)
-
-    resolved = run_crew_module._resolve_crew_inputs(
-        crew, {}, None, interactive=True
-    )
-
-    assert resolved == {"topic": "AI"}
-    assert any("topic" in text for text in prompted)
-
-
-def test_resolve_crew_inputs_errors_when_missing_non_interactive(capsys):
-    crew = _crew_with_placeholders("topic")
-
-    with pytest.raises(SystemExit) as exc_info:
-        run_crew_module._resolve_crew_inputs(crew, {}, None, interactive=False)
-
-    assert exc_info.value.code == 1
-    assert "Missing required input 'topic'" in capsys.readouterr().err
-
-
-def test_run_json_crew_accepts_inputs_argument(monkeypatch, tmp_path: Path, capsys):
-    monkeypatch.chdir(tmp_path)
-    monkeypatch.setenv("CREWAI_DMN", "True")
-    crew_path = tmp_path / "crew.jsonc"
-    crew_path.write_text("{}")
-    kickoff_calls: list[dict] = []
-
-    class FakeCrew:
-        name = "Demo"
-        agents = [_crew_with_placeholders("topic").agents[0]]
-        tasks: list = []
-
-        def kickoff(self, inputs):
-            kickoff_calls.append(inputs)
-            return "ok"
-
-    monkeypatch.setattr(
-        run_crew_module, "configured_project_json_crew", lambda: crew_path
-    )
-    monkeypatch.setattr(
-        run_crew_module, "_load_json_crew", lambda _path: (FakeCrew(), {})
-    )
-
-    assert run_crew_module._run_json_crew(inputs='{"topic":"AI"}') == "ok"
-    assert kickoff_calls == [{"topic": "AI"}]
-
-
-def test_run_json_crew_in_project_env_forwards_inputs(monkeypatch, tmp_path: Path):
-    monkeypatch.chdir(tmp_path)
-    (tmp_path / "pyproject.toml").write_text("[project]\nname = 'demo'\n")
-    monkeypatch.setattr(
-        run_crew_module, "_install_json_crew_dependencies_if_needed", lambda: None
-    )
-    monkeypatch.setattr(
-        run_crew_module, "build_env_with_all_tool_credentials", lambda: {}
-    )
-    captured_kwargs: list[dict] = []
-    monkeypatch.setattr(
-        run_crew_module.subprocess,
-        "run",
-        lambda command, **kwargs: captured_kwargs.append(kwargs),
-    )
-
-    run_crew_module._run_json_crew_in_project_env(
-        crew_path=tmp_path / "crew.jsonc", inputs='{"topic":"AI"}'
-    )
-
-    env = captured_kwargs[0]["env"]
-    assert env[run_crew_module._CREWAI_JSON_CREW_INPUTS_ENV] == '{"topic":"AI"}'
-
-
-def test_run_json_crew_in_project_env_rejects_invalid_inputs_json(
-    monkeypatch, tmp_path: Path, capsys
-):
-    monkeypatch.chdir(tmp_path)
-    (tmp_path / "pyproject.toml").write_text("[project]\nname = 'demo'\n")
-    monkeypatch.setattr(
-        run_crew_module.subprocess,
-        "run",
-        lambda *a, **k: pytest.fail("subprocess must not run on invalid --inputs"),
-    )
-
-    with pytest.raises(SystemExit) as exc_info:
-        run_crew_module._run_json_crew_in_project_env(
-            crew_path=tmp_path / "crew.jsonc", inputs="not json"
-        )
-
-    assert exc_info.value.code == 1
-    assert "Invalid --inputs JSON" in capsys.readouterr().err
-
-
-def test_run_crew_forwards_inputs_to_json_crew(monkeypatch):
-    monkeypatch.setattr(run_crew_module, "read_toml", lambda *a, **k: {})
-    monkeypatch.setattr(
-        run_crew_module,
-        "configured_project_json_crew",
-        lambda *a, **k: Path("crew.jsonc"),
-    )
-    called: dict = {}
-    monkeypatch.setattr(
-        run_crew_module,
-        "_run_json_crew_in_project_env",
-        lambda **kw: called.update(kw),
-    )
-
-    run_crew_module.run_crew(inputs='{"topic":"AI"}')
-
-    assert called == {
-        "trained_agents_file": None,
-        "crew_path": Path("crew.jsonc"),
-        "inputs": '{"topic":"AI"}',
-    }
+    assert "Missing runtime inputs for CREWAI_DMN mode: topic" in captured.err
 
 
 def test_configured_project_json_crew_defers_to_declared_flow_type(
@@ -855,51 +682,11 @@ def test_configured_project_json_crew_ignores_missing_pyproject(
     assert run_crew_module.configured_project_json_crew() is None
 
 
-def test_run_crew_inputs_rejected_for_classic_crew(monkeypatch):
-    # --inputs works for declarative flows and declarative (JSON) crews, but a
-    # classic crew takes its inputs from main.py, so it errors clearly.
-    monkeypatch.setattr(run_crew_module, "read_toml", lambda *a, **k: {})
-    monkeypatch.setattr(
-        run_crew_module, "configured_project_json_crew", lambda *a, **k: None
-    )
-    monkeypatch.setattr(
-        run_crew_module, "_warn_if_old_poetry_project", lambda *a, **k: None
-    )
-    monkeypatch.setattr(run_crew_module, "get_crewai_project_type", lambda *a, **k: "crew")
-
+def test_run_crew_rejects_inputs_without_definition():
     with pytest.raises(click.UsageError) as exc_info:
         run_crew_module.run_crew(inputs='{"topic":"AI"}')
 
-    assert (
-        "--inputs is only supported for declarative flows and crews"
-        in exc_info.value.message
-    )
-
-
-def test_run_crew_inputs_without_definition_resolves_configured_flow(monkeypatch):
-    # --inputs with no --definition resolves the configured [tool.crewai] flow,
-    # exactly like a bare `crewai run`, and forwards the inputs.
-    import crewai_cli.run_declarative_flow as rdf
-
-    calls: dict[str, object] = {}
-    monkeypatch.setattr(run_crew_module, "read_toml", lambda *a, **k: {})
-    monkeypatch.setattr(
-        run_crew_module, "configured_project_json_crew", lambda *a, **k: None
-    )
-    monkeypatch.setattr(
-        run_crew_module, "_warn_if_old_poetry_project", lambda *a, **k: None
-    )
-    monkeypatch.setattr(run_crew_module, "get_crewai_project_type", lambda *a, **k: "flow")
-    monkeypatch.setattr(
-        rdf, "configured_project_declarative_flow", lambda *a, **k: Path("flow.yaml")
-    )
-    monkeypatch.setattr(
-        rdf, "run_declarative_flow_in_project_env", lambda **kw: calls.update(kw)
-    )
-
-    run_crew_module.run_crew(inputs='{"topic":"AI"}')
-
-    assert calls == {"definition": Path("flow.yaml"), "inputs": '{"topic":"AI"}'}
+    assert "--inputs requires --definition" in exc_info.value.message
 
 
 def test_run_crew_rejects_filename_with_explicit_definition():

lib/cli/tests/test_run_declarative_flow.py

@@ -1,11 +1,9 @@
 from __future__ import annotations
 
-import os
 from pathlib import Path
 
 import pytest
 
-import crewai_cli.input_prompt as input_prompt_module
 import crewai_cli.run_declarative_flow as run_declarative_flow_module
 
 
@@ -148,255 +146,3 @@ def test_run_declarative_flow_in_process_inside_uv(
     )
 
     assert capsys.readouterr().out == "AI\n"
-
-
-def test_run_declarative_flow_in_project_env_forwards_inputs(
-    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
-) -> None:
-    subprocess_calls = []
-    monkeypatch.chdir(tmp_path)
-    monkeypatch.delenv("UV_RUN_RECURSION_DEPTH", raising=False)
-    (tmp_path / "pyproject.toml").write_text("[project]\nname = 'demo'\n")
-    monkeypatch.setattr(
-        run_declarative_flow_module,
-        "build_env_with_all_tool_credentials",
-        lambda: {},
-    )
-    monkeypatch.setattr(
-        run_declarative_flow_module.subprocess,
-        "run",
-        lambda command, **kwargs: subprocess_calls.append(command),
-    )
-
-    run_declarative_flow_module.run_declarative_flow_in_project_env(
-        "flow.yaml", '{"topic":"AI"}'
-    )
-
-    # --inputs is forwarded to the in-env run instead of being rejected.
-    assert subprocess_calls == [
-        ["uv", "run", "crewai", "run", "--inputs", '{"topic":"AI"}']
-    ]
-
-
-# ── Schema-driven inputs: prompt, validate, override ────────────────
-
-REQUIRED_FLOW_YAML = """\
-schema: crewai.flow/v1
-name: RequiredInputFlow
-config:
-  suppress_flow_events: true
-state:
-  type: json_schema
-  json_schema:
-    type: object
-    properties:
-      prospect_email:
-        type: string
-        description: Email address of the prospect to research
-    required: [prospect_email]
-methods:
-  begin:
-    start: true
-    do:
-      call: expression
-      expr: state.prospect_email
-"""
-
-DEFAULTS_FLOW_YAML = """\
-schema: crewai.flow/v1
-name: DefaultsFlow
-config:
-  suppress_flow_events: true
-state:
-  type: json_schema
-  json_schema:
-    type: object
-    properties:
-      topic: {type: string}
-      audience: {type: string}
-    required: [topic, audience]
-  default:
-    topic: AI
-methods:
-  begin:
-    start: true
-    do:
-      call: expression
-      expr: state.audience
-"""
-
-TYPED_FLOW_YAML = """\
-schema: crewai.flow/v1
-name: TypedFlow
-config:
-  suppress_flow_events: true
-state:
-  type: json_schema
-  json_schema:
-    type: object
-    properties:
-      count: {type: integer}
-    required: [count]
-methods:
-  begin:
-    start: true
-    do:
-      call: expression
-      expr: state.count
-"""
-
-
-def _write(tmp_path: Path, contents: str) -> Path:
-    path = tmp_path / "flow.yaml"
-    path.write_text(contents, encoding="utf-8")
-    return path
-
-
-def test_inputs_flag_satisfies_required_field(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str]
-) -> None:
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-
-    run_declarative_flow_module.run_declarative_flow(
-        str(path), '{"prospect_email":"a@b.com"}'
-    )
-
-    assert capsys.readouterr().out == "a@b.com\n"
-
-
-def test_missing_required_reports_pointed_error(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str], monkeypatch: pytest.MonkeyPatch
-) -> None:
-    monkeypatch.setattr(run_declarative_flow_module, "_is_interactive", lambda: False)
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-
-    with pytest.raises(SystemExit):
-        run_declarative_flow_module.run_declarative_flow(str(path))
-
-    assert (
-        "Missing required input 'prospect_email' — "
-        "Email address of the prospect to research" in capsys.readouterr().err
-    )
-
-
-def test_prompts_for_missing_required_when_interactive(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str], monkeypatch: pytest.MonkeyPatch
-) -> None:
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-    monkeypatch.setattr(run_declarative_flow_module, "_is_interactive", lambda: True)
-    prompted: list[str] = []
-
-    def fake_prompt(text: str, **kwargs: object) -> str:
-        prompted.append(text)
-        return "typed@example.com"
-
-    monkeypatch.setattr(input_prompt_module.click, "prompt", fake_prompt)
-
-    run_declarative_flow_module.run_declarative_flow(str(path))
-
-    assert capsys.readouterr().out == "typed@example.com\n"
-    assert any("prospect_email" in text for text in prompted)
-
-
-def test_defaults_satisfy_required_and_are_not_prompted(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str], monkeypatch: pytest.MonkeyPatch
-) -> None:
-    monkeypatch.setattr(run_declarative_flow_module, "_is_interactive", lambda: False)
-    path = _write(tmp_path, DEFAULTS_FLOW_YAML)
-
-    with pytest.raises(SystemExit):
-        run_declarative_flow_module.run_declarative_flow(str(path))
-
-    err = capsys.readouterr().err
-    # topic has a state default -> satisfied; only audience is missing.
-    assert "Missing required input 'audience'" in err
-    assert "'topic'" not in err
-
-
-def test_warns_on_unknown_input_with_suggestion(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str]
-) -> None:
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-
-    run_declarative_flow_module.run_declarative_flow(
-        str(path), '{"prospect_email":"a@b.com","prospect_emai":"typo"}'
-    )
-
-    captured = capsys.readouterr()
-    assert captured.out == "a@b.com\n"
-    assert "Ignoring unknown input 'prospect_emai'" in captured.err
-    assert "Did you mean 'prospect_email'?" in captured.err
-
-
-def test_validates_input_types_before_kickoff(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str]
-) -> None:
-    path = _write(tmp_path, TYPED_FLOW_YAML)
-
-    with pytest.raises(SystemExit):
-        run_declarative_flow_module.run_declarative_flow(str(path), '{"count":"nope"}')
-
-    assert "Invalid input 'count'" in capsys.readouterr().err
-
-
-def test_reserved_id_input_is_forwarded_not_dropped(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str]
-) -> None:
-    # `id` is a reserved kickoff key (persistence restore); it must pass through
-    # instead of being flagged as an unknown key and dropped.
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-
-    run_declarative_flow_module.run_declarative_flow(
-        str(path), '{"id":"run-123","prospect_email":"a@b.com"}'
-    )
-
-    captured = capsys.readouterr()
-    assert captured.out == "a@b.com\n"
-    assert "Ignoring unknown input 'id'" not in captured.err
-
-
-def test_run_declarative_flow_loads_project_env(
-    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
-) -> None:
-    # Flow projects must pick up the project's .env, like crew projects do,
-    # overriding any pre-existing value.
-    monkeypatch.chdir(tmp_path)
-    monkeypatch.setenv("DECL_FLOW_ENV_PROBE", "old")
-    (tmp_path / ".env").write_text("DECL_FLOW_ENV_PROBE=from_dotenv\n", encoding="utf-8")
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-
-    run_declarative_flow_module.run_declarative_flow(
-        str(path), '{"prospect_email":"a@b.com"}'
-    )
-
-    assert os.environ["DECL_FLOW_ENV_PROBE"] == "from_dotenv"
-
-
-def test_id_only_input_skips_required_validation(tmp_path: Path) -> None:
-    # Resume via `crewai run --inputs '{"id":"..."}'` must not be blocked by the
-    # required-field check: kickoff hydrates required state from persistence.
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-    flow = run_declarative_flow_module.load_declarative_flow(str(path))
-
-    resolved = run_declarative_flow_module._resolve_flow_inputs(flow, {"id": "run-123"})
-
-    assert resolved == {"id": "run-123"}
-
-
-def test_id_restore_still_drops_unknown_keys(
-    tmp_path: Path, capsys: pytest.CaptureFixture[str]
-) -> None:
-    # A persistence restore (`id` present) still filters typo keys so they don't
-    # reach kickoff and trip strict (extra="forbid") state models — it only
-    # skips the required-field prompt/validation, not the unknown-key warning.
-    path = _write(tmp_path, REQUIRED_FLOW_YAML)
-    flow = run_declarative_flow_module.load_declarative_flow(str(path))
-
-    resolved = run_declarative_flow_module._resolve_flow_inputs(
-        flow, {"id": "run-123", "prospect_emai": "typo"}
-    )
-
-    captured = capsys.readouterr()
-    assert resolved == {"id": "run-123"}  # id kept, typo dropped
-    assert "Ignoring unknown input 'prospect_emai'" in captured.err
-    assert "Ignoring unknown input 'id'" not in captured.err

lib/crewai-core/src/crewai_core/plus_api.py

@@ -232,8 +232,10 @@ class PlusAPI:
     def get_tool(self, handle: str) -> httpx.Response:
         return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
 
-    def get_agent(self, handle: str) -> httpx.Response:
-        return self._make_request("GET", f"{self.AGENTS_RESOURCE}/{handle}")
+    async def get_agent(self, handle: str) -> httpx.Response:
+        url = urljoin(self.base_url, f"{self.AGENTS_RESOURCE}/{handle}")
+        async with httpx.AsyncClient() as client:
+            return await client.get(url, headers=cast(dict[str, str], self.headers))
 
     def publish_tool(
         self,

lib/crewai-core/src/crewai_core/telemetry.py

@@ -264,12 +264,10 @@ class Telemetry:
 
     def flow_creation_span(self, flow_name: str) -> None:
         """Records the creation of a new flow."""
-        from crewai_core.version import get_crewai_version
 
         def _operation() -> None:
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Creation")
-            self._add_attribute(span, "crewai_version", get_crewai_version())
             self._add_attribute(span, "flow_name", flow_name)
             close_span(span)
 

lib/crewai-core/tests/test_smoke.py

@@ -233,31 +233,3 @@ def test_core_telemetry_records_feature_usage(
     tracer.start_span.assert_called_once_with("Feature Usage")
     span.set_attribute.assert_any_call("feature", "cli_usage:view_traces")
     span.end.assert_called_once()
-
-
-def test_core_telemetry_records_flow_creation_version(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    from crewai_core.telemetry import Telemetry
-
-    Telemetry._instance = None
-    monkeypatch.delenv("OTEL_SDK_DISABLED", raising=False)
-    monkeypatch.delenv("CREWAI_DISABLE_TELEMETRY", raising=False)
-    monkeypatch.delenv("CREWAI_DISABLE_TRACKING", raising=False)
-    monkeypatch.setattr("crewai_core.version.get_crewai_version", lambda: "1.0.0")
-
-    tracer = Mock()
-    span = Mock()
-    tracer.start_span.return_value = span
-    monkeypatch.setattr(
-        "crewai_core.telemetry.trace.get_tracer",
-        lambda _name: tracer,
-    )
-
-    telemetry = Telemetry()
-    telemetry.flow_creation_span("ResearchFlow")
-
-    tracer.start_span.assert_called_once_with("Flow Creation")
-    span.set_attribute.assert_any_call("crewai_version", "1.0.0")
-    span.set_attribute.assert_any_call("flow_name", "ResearchFlow")
-    span.end.assert_called_once()

lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/tavily_extractor_tool.py

@@ -87,11 +87,9 @@ class TavilyExtractorTool(BaseTool):
         """
         super().__init__(**kwargs)
         if TAVILY_AVAILABLE:
-            self.client = TavilyClient(
-                api_key=self.api_key, proxies=self.proxies, client_name="crewai"
-            )
+            self.client = TavilyClient(api_key=self.api_key, proxies=self.proxies)
             self.async_client = AsyncTavilyClient(
-                api_key=self.api_key, proxies=self.proxies, client_name="crewai"
+                api_key=self.api_key, proxies=self.proxies
             )
         else:
             try:

lib/crewai-tools/src/crewai_tools/tools/tavily_get_research_tool/tavily_get_research_tool.py

@@ -54,10 +54,8 @@ class TavilyGetResearchTool(BaseTool):
         super().__init__(**kwargs)
         if TAVILY_AVAILABLE:
             api_key = os.getenv("TAVILY_API_KEY")
-            self._client = TavilyClient(api_key=api_key, client_name="crewai")
-            self._async_client = AsyncTavilyClient(
-                api_key=api_key, client_name="crewai"
-            )
+            self._client = TavilyClient(api_key=api_key)
+            self._async_client = AsyncTavilyClient(api_key=api_key)
         else:
             try:
                 import subprocess

lib/crewai-tools/src/crewai_tools/tools/tavily_research_tool/tavily_research_tool.py

@@ -90,10 +90,8 @@ class TavilyResearchTool(BaseTool):
         super().__init__(**kwargs)
         if TAVILY_AVAILABLE:
             api_key = os.getenv("TAVILY_API_KEY")
-            self._client = TavilyClient(api_key=api_key, client_name="crewai")
-            self._async_client = AsyncTavilyClient(
-                api_key=api_key, client_name="crewai"
-            )
+            self._client = TavilyClient(api_key=api_key)
+            self._async_client = AsyncTavilyClient(api_key=api_key)
         else:
             try:
                 import subprocess

lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/tavily_search_tool.py

@@ -115,11 +115,9 @@ class TavilySearchTool(BaseTool):
     def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
         if TAVILY_AVAILABLE:
-            self.client = TavilyClient(
-                api_key=self.api_key, proxies=self.proxies, client_name="crewai"
-            )
+            self.client = TavilyClient(api_key=self.api_key, proxies=self.proxies)
             self.async_client = AsyncTavilyClient(
-                api_key=self.api_key, proxies=self.proxies, client_name="crewai"
+                api_key=self.api_key, proxies=self.proxies
             )
         else:
             try:

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -106,7 +106,6 @@ from crewai.utilities.planning_types import (
     TodoItem,
     TodoList,
 )
-from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
 from crewai.utilities.step_execution_context import StepExecutionContext, StepResult
 from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
@@ -119,6 +118,7 @@ if TYPE_CHECKING:
     from crewai.agents.tools_handler import ToolsHandler
     from crewai.llms.base_llm import BaseLLM
     from crewai.tools.tool_types import ToolResult
+    from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
 
 _RouteT = TypeVar("_RouteT", bound=str)
 
@@ -218,7 +218,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
     _instance_id: str = PrivateAttr(default_factory=lambda: str(uuid4())[:8])
     _step_executor: Any = PrivateAttr(default=None)
     _planner_observer: PlannerObserver | None = PrivateAttr(default=None)
-    _is_feedback_iteration: bool = PrivateAttr(default=False)
 
     @model_validator(mode="after")
     def _setup_executor(self) -> Self:
@@ -297,33 +296,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         """Set state messages."""
         self._state.messages = value
 
-    def _setup_messages(self, inputs: dict[str, Any]) -> None:
-        """Set up messages for the agent execution."""
-        provider = get_provider()
-        if provider.setup_messages(cast("ExecutorContext", self)):
-            return
-
-        from crewai.llms.cache import mark_cache_breakpoint
-
-        if isinstance(self.prompt, SystemPromptResult):
-            system_prompt = self._format_prompt(self.prompt["system"], inputs)
-            user_prompt = self._format_prompt(self.prompt["user"], inputs)
-            self.state.messages.append(
-                mark_cache_breakpoint(
-                    format_message_for_llm(system_prompt, role="system")
-                )
-            )
-            self.state.messages.append(
-                mark_cache_breakpoint(format_message_for_llm(user_prompt))
-            )
-        elif isinstance(self.prompt, StandardPromptResult):
-            user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
-            self.state.messages.append(
-                mark_cache_breakpoint(format_message_for_llm(user_prompt))
-            )
-
-        provider.post_setup_messages(cast("ExecutorContext", self))
-
     @property
     def ask_for_human_input(self) -> bool:
         """Compatibility property - returns state ask_for_human_input."""
@@ -342,8 +314,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         enabled on the agent, it generates a plan before execution begins.
         The plan is stored in state and todos are created from the steps.
         """
-        if self._is_feedback_iteration:
-            return
         if not getattr(self.agent, "planning_enabled", False):
             return
 
@@ -2791,7 +2761,27 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                     "AgentExecutor.llm or .prompt is unset; the executor was "
                     "not fully restored or initialized before execution."
                 )
-            self._setup_messages(inputs)
+            if "system" in self.prompt:
+                from crewai.llms.cache import mark_cache_breakpoint
+
+                prompt = cast("SystemPromptResult", self.prompt)
+                system_prompt = self._format_prompt(prompt["system"], inputs)
+                user_prompt = self._format_prompt(prompt["user"], inputs)
+                self.state.messages.append(
+                    mark_cache_breakpoint(
+                        format_message_for_llm(system_prompt, role="system")
+                    )
+                )
+                self.state.messages.append(
+                    mark_cache_breakpoint(format_message_for_llm(user_prompt))
+                )
+            else:
+                from crewai.llms.cache import mark_cache_breakpoint
+
+                user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
+                self.state.messages.append(
+                    mark_cache_breakpoint(format_message_for_llm(user_prompt))
+                )
 
             self._inject_files_from_inputs(inputs)
 
@@ -2877,7 +2867,27 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                     "AgentExecutor.llm or .prompt is unset; the executor was "
                     "not fully restored or initialized before execution."
                 )
-            self._setup_messages(inputs)
+            if "system" in self.prompt:
+                from crewai.llms.cache import mark_cache_breakpoint
+
+                prompt = cast("SystemPromptResult", self.prompt)
+                system_prompt = self._format_prompt(prompt["system"], inputs)
+                user_prompt = self._format_prompt(prompt["user"], inputs)
+                self.state.messages.append(
+                    mark_cache_breakpoint(
+                        format_message_for_llm(system_prompt, role="system")
+                    )
+                )
+                self.state.messages.append(
+                    mark_cache_breakpoint(format_message_for_llm(user_prompt))
+                )
+            else:
+                from crewai.llms.cache import mark_cache_breakpoint
+
+                user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
+                self.state.messages.append(
+                    mark_cache_breakpoint(format_message_for_llm(user_prompt))
+                )
 
             await self._ainject_files_from_inputs(inputs)
 
@@ -3159,13 +3169,8 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         Returns:
             Final answer after feedback.
         """
-        self.messages = self.state.messages
         provider = get_provider()
-        final_answer = provider.handle_feedback(
-            formatted_answer, cast("ExecutorContext", self)
-        )
-        self._complete_feedback(final_answer)
-        return final_answer
+        return provider.handle_feedback(formatted_answer, cast("ExecutorContext", self))
 
     async def _ahandle_human_feedback(
         self, formatted_answer: AgentFinish
@@ -3178,63 +3183,10 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         Returns:
             Final answer after feedback.
         """
-        self.messages = self.state.messages
         provider = get_provider()
-        final_answer = await provider.handle_feedback_async(
+        return await provider.handle_feedback_async(
             formatted_answer, cast("AsyncExecutorContext", self)
         )
-        self._complete_feedback(final_answer)
-        return final_answer
-
-    def _complete_feedback(self, final_answer: AgentFinish) -> None:
-        """Mark the final reviewed answer as the completed executor state."""
-        self.state.current_answer = final_answer
-        self.state.is_finished = True
-        self.state.ask_for_human_input = False
-        self._finalize_called = True
-
-    def _prepare_feedback_iteration(self) -> None:
-        """Reset flow completion state before rerunning with feedback."""
-        self._finalize_called = False
-        self._is_feedback_iteration = True
-        self.state.current_answer = None
-        self.state.is_finished = False
-        self.state.iterations = 0
-        self.state.use_native_tools = False
-        self.state.pending_tool_calls = []
-        self.state.plan = None
-        self.state.plan_ready = False
-        self.state.todos = TodoList()
-        self.state.replan_count = 0
-        self.state.last_replan_reason = None
-        self.state.observations = {}
-        self.state.execution_log = []
-
-    def _invoke_loop(self) -> AgentFinish:
-        """Re-run the executor flow using the existing feedback messages."""
-        self._prepare_feedback_iteration()
-        try:
-            self.kickoff()
-        finally:
-            self._is_feedback_iteration = False
-
-        if not isinstance(self.state.current_answer, AgentFinish):
-            raise RuntimeError("Agent execution ended without reaching a final answer.")
-
-        return self.state.current_answer
-
-    async def _ainvoke_loop(self) -> AgentFinish:
-        """Re-run the executor flow asynchronously using feedback messages."""
-        self._prepare_feedback_iteration()
-        try:
-            await self.kickoff_async()
-        finally:
-            self._is_feedback_iteration = False
-
-        if not isinstance(self.state.current_answer, AgentFinish):
-            raise RuntimeError("Agent execution ended without reaching a final answer.")
-
-        return self.state.current_answer
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -3244,12 +3196,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         """
         return bool(self.crew and self.crew._train)
 
-    def _format_feedback_message(self, feedback: str) -> LLMMessage:
-        """Format human feedback as an LLM message."""
-        return format_message_for_llm(
-            I18N_DEFAULT.slice("feedback_instructions").format(feedback=feedback)
-        )
-
 
 # Backward compatibility alias (deprecated)
 CrewAgentExecutorFlow = AgentExecutor

lib/crewai/src/crewai/flow/flow_definition.py

@@ -472,8 +472,8 @@ class FlowAgentActionDefinition(BaseModel):
 
     call: Literal["agent"] = Field(
         description=(
-            "Action discriminator. Use agent to run an individual inline Agent "
-            "definition outside of a crew."
+            "Action discriminator. Use agent to run an individual inline or "
+            "repository-backed Agent definition outside of a crew."
         ),
         examples=["agent"],
     )
@@ -481,7 +481,8 @@ class FlowAgentActionDefinition(BaseModel):
         alias="with",
         description=(
             "Individual Agent definition to load and execute outside of a crew "
-            "for this action."
+            "for this action. Set from_repository to load agent configuration "
+            "from the agent repository."
         ),
         examples=[
             {

lib/crewai/src/crewai/flow/runtime/_actions.py

@@ -138,12 +138,11 @@ class CrewAction:
 
         local_context = _pop_local_context(kwargs)
         if self.definition.from_declaration is not None:
-            crew, default_inputs = await asyncio.to_thread(
-                load_crew,
+            crew, default_inputs = load_crew(
                 _resolve_crew_declaration(
                     self.definition.from_declaration,
                     base_dir=self.flow._definition.source_dir,
-                ),
+                )
             )
             input_template = {**default_inputs, **(self.definition.inputs or {})}
         else:
@@ -156,9 +155,7 @@ class CrewAction:
                 **crew_definition.inputs,
                 **(self.definition.inputs or {}),
             }
-            crew, _ = await asyncio.to_thread(
-                load_crew_from_definition, crew_definition, source="crew action"
-            )
+            crew, _ = load_crew_from_definition(crew_definition, source="crew action")
 
         inputs = Expression.from_flow(
             cast(ExpressionData, input_template),
@@ -187,8 +184,7 @@ class AgentAction:
         if not isinstance(rendered_input, str):
             raise ValueError("agent input must render to a string")
 
-        agent, response_format = await asyncio.to_thread(
-            load_agent_from_definition,
+        agent, response_format = load_agent_from_definition(
             self.definition.with_,
             source="agent action",
         )

lib/crewai/src/crewai/project/crew_definition.py

@@ -62,7 +62,7 @@ class LLMDefinition(BaseModel):
 
 
 class CrewAgentDefinition(BaseModel):
-    """Inline agent definition used by a crew definition."""
+    """Agent definition used by a crew definition."""
 
     model_config = ConfigDict(extra="allow")
 
@@ -191,9 +191,27 @@ class CrewAgentDefinition(BaseModel):
             raise ValueError("agent.settings must be a mapping")
         return value or {}
 
+    @model_validator(mode="after")
+    def _validate_agent_source(self) -> CrewAgentDefinition:
+        if self.from_repository:
+            return self
+
+        missing = [
+            field
+            for field in ("role", "goal", "backstory")
+            if getattr(self, field) is None
+        ]
+        if missing:
+            missing_fields = ", ".join(f"'{field}'" for field in missing)
+            raise ValueError(
+                f"agent definition requires {missing_fields} unless "
+                "from_repository is set"
+            )
+        return self
+
 
 class AgentDefinition(CrewAgentDefinition):
-    """Inline individual agent definition used outside of a crew."""
+    """Individual agent definition used outside of a crew."""
 
     role: str | None = Field(
         default=None,
@@ -217,6 +235,15 @@ class AgentDefinition(CrewAgentDefinition):
         description="Optional built-in type or Python reference used to load the agent.",
         examples=["agent", {"python": "my_project.agents.SupportAgent"}],
     )
+    from_repository: str | None = Field(
+        default=None,
+        description=(
+            "Agent repository name to load. Repository values supply missing "
+            "agent configuration; explicitly provided local fields override the "
+            "repository values."
+        ),
+        examples=["support_specialist"],
+    )
     settings: dict[str, Any] = Field(
         default_factory=dict,
         description="Additional agent settings passed to the loader.",

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -949,7 +949,6 @@ class Telemetry:
         def _operation() -> None:
             tracer = trace.get_tracer("crewai.telemetry")
             span = tracer.start_span("Flow Creation")
-            self._add_attribute(span, "crewai_version", version("crewai"))
             self._add_attribute(span, "flow_name", flow_name)
             close_span(span)
 

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -1125,7 +1125,7 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
 
             client = PlusAPI(api_key=get_auth_token())
         _print_current_organization()
-        response = client.get_agent(from_repository)
+        response = asyncio.run(client.get_agent(from_repository))
         if response.status_code == 404:
             raise AgentRepositoryError(
                 f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization."
@@ -1158,8 +1158,6 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
                         raise AgentRepositoryError(
                             f"Tool {tool['name']} could not be loaded: {e}"
                         ) from e
-            elif key == "skills" and value == []:
-                continue
             else:
                 attributes[key] = value
     return attributes

lib/crewai/tests/agents/test_agent.py

@@ -3,14 +3,13 @@
 import os
 import threading
 from unittest import mock
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 import warnings
 
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
 from crewai.constants import DEFAULT_LLM_MODEL
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent
-from crewai.experimental.agent_executor import AgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -803,97 +802,6 @@ def test_agent_human_input():
         assert output.strip().lower() == "hello"
 
 
-def test_agent_default_executor_human_input():
-    from crewai.core.providers.human_input import SyncHumanInputProvider
-
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-    )
-    task = Task(
-        agent=agent,
-        description="Say the word: Hi",
-        expected_output="The word: Hi",
-        human_input=True,
-    )
-    answers = iter(
-        [
-            AgentFinish(output="Hi", thought="", text="Hi"),
-            AgentFinish(output="Hello", thought="", text="Hello"),
-        ]
-    )
-    feedback_responses = iter(["Don't say hi, say Hello instead!", ""])
-
-    def kickoff_side_effect(executor, *_args, **_kwargs):
-        executor.state.current_answer = next(answers)
-        executor.state.is_finished = True
-
-    with (
-        patch.object(
-            SyncHumanInputProvider,
-            "_prompt_input",
-            side_effect=lambda *_args, **_kwargs: next(feedback_responses),
-        ) as mock_prompt_input,
-        patch.object(
-            AgentExecutor, "kickoff", autospec=True, side_effect=kickoff_side_effect
-        ) as mock_kickoff,
-    ):
-        output = agent.execute_task(task)
-
-    assert output == "Hello"
-    assert mock_prompt_input.call_count == 2
-    assert mock_kickoff.call_count == 2
-
-
-@pytest.mark.asyncio
-async def test_agent_default_executor_async_human_input():
-    from crewai.core.providers.human_input import SyncHumanInputProvider
-
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-    )
-    task = Task(
-        agent=agent,
-        description="Say the word: Hi",
-        expected_output="The word: Hi",
-        human_input=True,
-    )
-    answers = iter(
-        [
-            AgentFinish(output="Hi", thought="", text="Hi"),
-            AgentFinish(output="Hello", thought="", text="Hello"),
-        ]
-    )
-    feedback_responses = iter(["Don't say hi, say Hello instead!", ""])
-
-    async def kickoff_side_effect(executor, *_args, **_kwargs):
-        executor.state.current_answer = next(answers)
-        executor.state.is_finished = True
-
-    with (
-        patch.object(
-            SyncHumanInputProvider,
-            "_prompt_input_async",
-            new_callable=AsyncMock,
-            side_effect=lambda *_args, **_kwargs: next(feedback_responses),
-        ) as mock_prompt_input,
-        patch.object(
-            AgentExecutor,
-            "kickoff_async",
-            autospec=True,
-            side_effect=kickoff_side_effect,
-        ) as mock_kickoff,
-    ):
-        output = await agent.aexecute_task(task)
-
-    assert output == "Hello"
-    assert mock_prompt_input.await_count == 2
-    assert mock_kickoff.await_count == 2
-
-
 def test_interpolate_inputs():
     agent = Agent(
         role="{topic} specialist",
@@ -2335,27 +2243,6 @@ def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth
     assert isinstance(agent.tools[0], SerperDevTool)
 
 
-@patch("crewai.plus_api.PlusAPI.get_agent")
-def test_agent_from_repository_ignores_empty_skills(
-    mock_get_agent, mock_get_auth_token
-):
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 200
-    mock_get_response.json.return_value = {
-        "role": "test role",
-        "goal": "test goal",
-        "backstory": "test backstory",
-        "tools": [],
-        "skills": [],
-    }
-    mock_get_agent.return_value = mock_get_response
-
-    agent = Agent(from_repository="test_agent")
-
-    assert agent.role == "test role"
-    assert agent.skills is None
-
-
 @patch("crewai.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository_with_invalid_tools(mock_get_agent, mock_get_auth_token):
     mock_get_response = MagicMock()

lib/crewai/tests/agents/test_agent_executor.py

@@ -18,7 +18,6 @@ import pytest
 from pydantic import BaseModel
 
 from crewai.agents.tools_handler import ToolsHandler as _ToolsHandler
-from crewai.core.providers.human_input import SyncHumanInputProvider
 from crewai.agents.step_executor import StepExecutor
 
 
@@ -28,13 +27,6 @@ def _build_executor(**kwargs: Any) -> AgentExecutor:
     Uses model_construct to skip Pydantic validators so plain Mock()
     objects are accepted for typed fields like llm, agent, crew, task.
     """
-    prompt = kwargs.get("prompt")
-    if isinstance(prompt, dict):
-        if "system" in prompt:
-            kwargs["prompt"] = SystemPromptResult(**prompt)
-        else:
-            kwargs["prompt"] = StandardPromptResult(**prompt)
-
     executor = AgentExecutor.model_construct(**kwargs)
     executor._state = AgentExecutorState()
     executor._methods = {}
@@ -58,7 +50,6 @@ def _build_executor(**kwargs: Any) -> AgentExecutor:
     executor._last_context_error = None
     executor._step_executor = None
     executor._planner_observer = None
-    executor._is_feedback_iteration = False
     return executor
 from crewai.agents.planner_observer import PlannerObserver
 from crewai.experimental.agent_executor import (
@@ -77,8 +68,7 @@ from crewai.events.types.tool_usage_events import (
 )
 from crewai.tools.tool_types import ToolResult
 from crewai.utilities.step_execution_context import StepExecutionContext
-from crewai.utilities.planning_types import TodoItem, TodoList
-from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
+from crewai.utilities.planning_types import TodoItem
 from crewai.utilities.file_store import clear_files, clear_task_files, store_files
 from crewai_files import TextFile
 
@@ -129,189 +119,6 @@ class TestAgentExecutor:
     class StructuredResult(BaseModel):
         value: str
 
-    def test_setup_messages_calls_human_input_provider_hooks(self):
-        """Message setup should preserve the HumanInputProvider hook contract."""
-        executor = _build_executor(
-            prompt=StandardPromptResult(prompt="Original task: {input}"),
-        )
-        provider = Mock()
-        provider.setup_messages.return_value = False
-
-        def post_setup(context: AgentExecutor) -> None:
-            context.messages.append(
-                {"role": "system", "content": "provider post setup"}
-            )
-
-        provider.post_setup_messages.side_effect = post_setup
-
-        with patch(
-            "crewai.experimental.agent_executor.get_provider", return_value=provider
-        ):
-            executor._setup_messages(
-                {"input": "draft this", "tool_names": "", "tools": ""}
-            )
-
-        provider.setup_messages.assert_called_once_with(executor)
-        provider.post_setup_messages.assert_called_once_with(executor)
-        assert executor.state.messages[0]["role"] == "user"
-        assert executor.state.messages[0]["content"] == "Original task: draft this"
-        assert executor.state.messages[1] == {
-            "role": "system",
-            "content": "provider post setup",
-        }
-
-    def test_setup_messages_can_be_owned_by_human_input_provider(self):
-        """Providers can skip standard prompt setup by returning True."""
-        executor = _build_executor(
-            prompt=StandardPromptResult(prompt="Original task: {input}"),
-        )
-        provider = Mock()
-
-        def setup(context: AgentExecutor) -> bool:
-            context.messages.append({"role": "user", "content": "provider message"})
-            return True
-
-        provider.setup_messages.side_effect = setup
-
-        with patch(
-            "crewai.experimental.agent_executor.get_provider", return_value=provider
-        ):
-            executor._setup_messages(
-                {"input": "draft this", "tool_names": "", "tools": ""}
-            )
-
-        provider.setup_messages.assert_called_once_with(executor)
-        provider.post_setup_messages.assert_not_called()
-        assert executor.state.messages == [
-            {"role": "user", "content": "provider message"}
-        ]
-
-    def test_human_feedback_reruns_flow_with_state_messages(self):
-        """Human feedback should use AgentExecutor state messages."""
-        executor = _build_executor(agent=SimpleNamespace(verbose=False), crew=None)
-        executor.state.messages = [{"role": "user", "content": "original task"}]
-        executor.state.current_answer = AgentFinish(
-            thought="", output="draft", text="draft"
-        )
-        executor.state.is_finished = True
-        executor._finalize_called = True
-        executor.ask_for_human_input = True
-        executor.state.iterations = executor.max_iter
-        executor.state.plan = "completed plan"
-        executor.state.plan_ready = True
-        executor.state.todos = TodoList(
-            items=[TodoItem(step_number=1, description="Done", status="completed")]
-        )
-
-        improved_answer = AgentFinish(thought="", output="improved", text="improved")
-        feedback_responses = iter(["make it friendlier", ""])
-
-        def finish_feedback_iteration(*_args: Any, **_kwargs: Any) -> None:
-            assert executor._is_feedback_iteration is True
-            assert executor.state.iterations == 0
-            assert executor.state.plan is None
-            assert executor.state.todos.items == []
-            executor.state.current_answer = improved_answer
-            executor.state.is_finished = True
-
-        with (
-            patch.object(
-                SyncHumanInputProvider,
-                "_prompt_input",
-                side_effect=lambda *_args, **_kwargs: next(feedback_responses),
-            ) as mock_prompt_input,
-            patch.object(
-                AgentExecutor, "kickoff", side_effect=finish_feedback_iteration
-            ) as mock_kickoff,
-        ):
-            result = executor._handle_human_feedback(
-                AgentFinish(thought="", output="draft", text="draft")
-            )
-
-        assert result is improved_answer
-        assert mock_prompt_input.call_count == 2
-        mock_kickoff.assert_called_once()
-        assert executor.messages is executor.state.messages
-        assert "make it friendlier" in executor.state.messages[-1]["content"]
-        assert executor.ask_for_human_input is False
-        assert executor.state.current_answer is improved_answer
-        assert executor.state.is_finished is True
-        assert executor._finalize_called is True
-        assert executor._is_feedback_iteration is False
-
-    @pytest.mark.asyncio
-    async def test_async_human_feedback_reruns_flow_with_state_messages(self):
-        """Async human feedback should use AgentExecutor state messages."""
-        executor = _build_executor(agent=SimpleNamespace(verbose=False), crew=None)
-        executor.state.messages = [{"role": "user", "content": "original task"}]
-        executor.state.current_answer = AgentFinish(
-            thought="", output="draft", text="draft"
-        )
-        executor.state.is_finished = True
-        executor._finalize_called = True
-        executor.ask_for_human_input = True
-        executor.state.iterations = executor.max_iter
-        executor.state.plan = "completed plan"
-        executor.state.plan_ready = True
-        executor.state.todos = TodoList(
-            items=[TodoItem(step_number=1, description="Done", status="completed")]
-        )
-
-        improved_answer = AgentFinish(thought="", output="improved", text="improved")
-        feedback_responses = iter(["make it friendlier", ""])
-
-        async def finish_feedback_iteration(*_args: Any, **_kwargs: Any) -> None:
-            assert executor._is_feedback_iteration is True
-            assert executor.state.iterations == 0
-            assert executor.state.plan is None
-            assert executor.state.todos.items == []
-            executor.state.current_answer = improved_answer
-            executor.state.is_finished = True
-
-        with (
-            patch.object(
-                SyncHumanInputProvider,
-                "_prompt_input_async",
-                new_callable=AsyncMock,
-                side_effect=lambda *_args, **_kwargs: next(feedback_responses),
-            ) as mock_prompt_input,
-            patch.object(
-                AgentExecutor,
-                "kickoff_async",
-                new_callable=AsyncMock,
-                side_effect=finish_feedback_iteration,
-            ) as mock_kickoff,
-        ):
-            result = await executor._ahandle_human_feedback(
-                AgentFinish(thought="", output="draft", text="draft")
-            )
-
-        assert result is improved_answer
-        assert mock_prompt_input.await_count == 2
-        mock_kickoff.assert_awaited_once()
-        assert executor.messages is executor.state.messages
-        assert "make it friendlier" in executor.state.messages[-1]["content"]
-        assert executor.ask_for_human_input is False
-        assert executor.state.current_answer is improved_answer
-        assert executor.state.is_finished is True
-        assert executor._finalize_called is True
-        assert executor._is_feedback_iteration is False
-
-    def test_feedback_iteration_skips_plan_generation(self):
-        """Feedback reruns should reason over feedback without regenerating a plan."""
-        executor = _build_executor(
-            agent=SimpleNamespace(planning_enabled=True, verbose=False),
-            task=SimpleNamespace(),
-        )
-        executor._is_feedback_iteration = True
-
-        with patch("crewai.utilities.reasoning_handler.AgentReasoning") as reasoning:
-            executor.generate_plan()
-
-        reasoning.assert_not_called()
-        assert executor.state.plan is None
-        assert executor.state.todos.items == []
-
     def test_inject_files_from_crew_task_store(self):
         """Crew-level input_files should attach to the LLM user message."""
         crew_id = uuid4()

lib/crewai/tests/cli/test_plus_api.py

@@ -1,6 +1,8 @@
 import os
 import unittest
-from unittest.mock import ANY, MagicMock, patch
+from unittest.mock import ANY, AsyncMock, MagicMock, patch
+
+import pytest
 
 from crewai.plus_api import PlusAPI
 
@@ -394,23 +396,28 @@ class TestPlusAPI(unittest.TestCase):
         )
 
 
-@patch("crewai_core.plus_api.PlusAPI._make_request")
-def test_get_agent(mock_make_request):
+@pytest.mark.asyncio
+@patch("httpx.AsyncClient")
+async def test_get_agent(mock_async_client_class):
     api = PlusAPI("test_api_key")
     mock_response = MagicMock()
-    mock_make_request.return_value = mock_response
+    mock_client_instance = AsyncMock()
+    mock_client_instance.get.return_value = mock_response
+    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
 
-    response = api.get_agent("test_agent_handle")
+    response = await api.get_agent("test_agent_handle")
 
-    mock_make_request.assert_called_once_with(
-        "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
+    mock_client_instance.get.assert_called_once_with(
+        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
+        headers=api.headers,
     )
     assert response == mock_response
 
 
-@patch("crewai_core.plus_api.PlusAPI._make_request")
+@pytest.mark.asyncio
+@patch("httpx.AsyncClient")
 @patch("crewai_core.plus_api.Settings")
-def test_get_agent_with_org_uuid(mock_settings_class, mock_make_request):
+async def test_get_agent_with_org_uuid(mock_settings_class, mock_async_client_class):
     org_uuid = "test-org-uuid"
     mock_settings = MagicMock()
     mock_settings.org_uuid = org_uuid
@@ -420,12 +427,15 @@ def test_get_agent_with_org_uuid(mock_settings_class, mock_make_request):
     api = PlusAPI("test_api_key")
 
     mock_response = MagicMock()
-    mock_make_request.return_value = mock_response
+    mock_client_instance = AsyncMock()
+    mock_client_instance.get.return_value = mock_response
+    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
 
-    response = api.get_agent("test_agent_handle")
+    response = await api.get_agent("test_agent_handle")
 
-    mock_make_request.assert_called_once_with(
-        "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
+    mock_client_instance.get.assert_called_once_with(
+        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
+        headers=api.headers,
     )
     assert "X-Crewai-Organization-Id" in api.headers
     assert api.headers["X-Crewai-Organization-Id"] == org_uuid

lib/crewai/tests/cli/test_run_crew.py

@@ -14,7 +14,7 @@ from crewai_cli.run_crew import (
     _execute_uv_script,
     _load_json_crew_for_tui,
     _missing_input_names,
-    _resolve_crew_inputs,
+    _prompt_for_missing_inputs,
 )
 
 
@@ -105,7 +105,7 @@ def test_missing_input_names_scans_agent_and_task_placeholders() -> None:
     ]
 
 
-def test_resolve_crew_inputs_merges_runtime_values(monkeypatch) -> None:
+def test_prompt_for_missing_inputs_merges_runtime_values(monkeypatch) -> None:
     crew = SimpleNamespace(
         agents=[SimpleNamespace(role="Researcher", goal="Cover {topic}", backstory="")],
         tasks=[
@@ -123,10 +123,9 @@ def test_resolve_crew_inputs_merges_runtime_values(monkeypatch) -> None:
             return values["audience"]
         raise AssertionError(f"Unexpected prompt: {label}")
 
-    # Prompting now lives in the shared crewai_cli.input_prompt helper.
-    monkeypatch.setattr("crewai_cli.input_prompt.click.prompt", prompt)
+    monkeypatch.setattr("crewai_cli.run_crew.click.prompt", prompt)
 
-    assert _resolve_crew_inputs(crew, {"topic": "AI"}, None, interactive=True) == {
+    assert _prompt_for_missing_inputs(crew, {"topic": "AI"}) == {
         "topic": "AI",
         "audience": "developers",
     }

lib/crewai/tests/project/test_json_loader.py

@@ -355,22 +355,16 @@ class TestLoadAgent:
         with pytest.raises(Exception):
             load_agent(agent_file)
 
-    @pytest.mark.parametrize("field", ["role", "goal", "backstory"])
-    def test_load_agent_rejects_null_required_fields(
-        self, tmp_path: Path, field: str
-    ):
+    def test_load_agent_rejects_null_required_fields(self, tmp_path: Path):
         agent_def = {
-            "role": "Researcher",
+            "role": None,
             "goal": "Find information",
             "backstory": "Expert researcher.",
         }
-        agent_def[field] = None
         agent_file = tmp_path / "agent.json"
         agent_file.write_text(json.dumps(agent_def))
 
-        with pytest.raises(
-            JSONProjectValidationError, match=f"missing required field '{field}'"
-        ):
+        with pytest.raises(JSONProjectValidationError, match="missing required field 'role'"):
             load_agent(agent_file)
 
     def test_load_agent_file_not_found(self):

lib/crewai/tests/telemetry/test_telemetry.py

@@ -96,32 +96,6 @@ def test_flow_execution_span_records_crewai_version():
     span.set_attribute.assert_any_call("flow_name", "ResearchFlow")
 
 
-def test_flow_creation_span_records_crewai_version():
-    tracer = Mock()
-    span = Mock()
-    tracer.start_span.return_value = span
-
-    with (
-        patch.dict(
-            os.environ,
-            {
-                "CREWAI_DISABLE_TELEMETRY": "false",
-                "CREWAI_DISABLE_TRACKING": "false",
-                "OTEL_SDK_DISABLED": "false",
-            },
-        ),
-        patch("crewai.telemetry.telemetry.TracerProvider"),
-        patch("crewai.telemetry.telemetry.trace.get_tracer", return_value=tracer),
-        patch("crewai.telemetry.telemetry.version", return_value="9.9.9"),
-    ):
-        telemetry = Telemetry()
-        telemetry.flow_creation_span("ResearchFlow")
-
-    tracer.start_span.assert_called_once_with("Flow Creation")
-    span.set_attribute.assert_any_call("crewai_version", "9.9.9")
-    span.set_attribute.assert_any_call("flow_name", "ResearchFlow")
-
-
 @patch("crewai.telemetry.telemetry.logger.error")
 @patch(
     "opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter.export",

lib/crewai/tests/test_crew.py

@@ -2908,6 +2908,12 @@ def test_manager_agent_with_tools_raises_exception(researcher, writer):
         crew.kickoff()
 
 
+@pytest.mark.xfail(
+    strict=True,
+    reason="crew.train() relies on CrewAgentExecutor._format_feedback_message; "
+    "AgentExecutor (the new default) does not implement training feedback yet. "
+    "Remove this xfail once training is migrated to AgentExecutor.",
+)
 @pytest.mark.vcr()
 def test_crew_train_success(researcher, writer, monkeypatch):
     task = Task(

lib/crewai/tests/test_flow_definition.py

@@ -88,7 +88,7 @@ def test_flow_definition_json_schema_carries_reference_descriptions():
     agent_properties = defs["FlowAgentActionDefinition"]["properties"]
     assert "Individual Agent definition" in agent_properties["with"]["description"]
     assert "outside of a crew" in agent_properties["with"]["description"]
-    assert "individual inline Agent" in agent_properties["call"]["description"]
+    assert "repository-backed Agent" in agent_properties["call"]["description"]
 
     expression_rule = FLOW_TEMPLATE_EXPRESSION_RULES[0]
     code_properties = defs["FlowCodeActionDefinition"]["properties"]

lib/crewai/tests/test_flow_from_definition.py

@@ -1166,35 +1166,28 @@ methods:
 def test_agent_action_runs_repository_yaml_definition(
     monkeypatch: pytest.MonkeyPatch,
 ):
+    import crewai.agent.core as agent_core
     from crewai import Agent
-    from crewai.plus_api import PlusAPI
 
-    fetched_agents: list[str] = []
-
-    class FakeResponse:
-        status_code = 200
-        text = ""
-
-        def json(self) -> dict[str, Any]:
-            return {
-                "role": "Repository specialist",
-                "goal": "Answer support questions",
-                "backstory": "Loaded from the agent repository.",
-                "max_iter": 3,
-                "tools": [],
-            }
-
-    def fake_get_agent(self: PlusAPI, handle: str) -> FakeResponse:
-        fetched_agents.append(handle)
-        return FakeResponse()
+    def fake_load_agent_from_repository(from_repository: str) -> dict[str, Any]:
+        assert from_repository == "support_specialist"
+        return {
+            "role": "Repository specialist",
+            "goal": "Answer support questions",
+            "backstory": "Loaded from the agent repository.",
+            "max_iter": 3,
+        }
 
     async def fake_kickoff_async(
         self: Agent, messages: str, **_kwargs: Any
     ) -> dict[str, Any]:
         return {"agent": self.role, "input": messages, "max_iter": self.max_iter}
 
-    monkeypatch.setattr("crewai.auth.token.get_auth_token", lambda: "test-token")
-    monkeypatch.setattr(PlusAPI, "get_agent", fake_get_agent)
+    monkeypatch.setattr(
+        agent_core,
+        "load_agent_from_repository",
+        fake_load_agent_from_repository,
+    )
     monkeypatch.setattr(Agent, "kickoff_async", fake_kickoff_async)
 
     yaml_str = """
@@ -1217,83 +1210,6 @@ methods:
         "input": "What is CrewAI?",
         "max_iter": 3,
     }
-    assert fetched_agents == ["support_specialist"]
-
-
-def test_agent_action_repository_fetch_does_not_block_event_loop(
-    monkeypatch: pytest.MonkeyPatch,
-):
-    from crewai import Agent
-    from crewai.plus_api import PlusAPI
-
-    loop_marker_ran = threading.Event()
-    fetch_started = threading.Event()
-    release_fetch = threading.Event()
-    fetch_saw_loop_marker = False
-
-    class FakeResponse:
-        status_code = 200
-        text = ""
-
-        def json(self) -> dict[str, Any]:
-            return {
-                "role": "Repository specialist",
-                "goal": "Answer support questions",
-                "backstory": "Loaded from the agent repository.",
-                "tools": [],
-            }
-
-    def fake_get_agent(self: PlusAPI, handle: str) -> FakeResponse:
-        nonlocal fetch_saw_loop_marker
-        fetch_started.set()
-        release_fetch.wait(timeout=1)
-        fetch_saw_loop_marker = loop_marker_ran.is_set()
-        return FakeResponse()
-
-    async def fake_kickoff_async(
-        self: Agent, messages: str, **_kwargs: Any
-    ) -> str:
-        return f"{self.role}:{messages}"
-
-    monkeypatch.setattr("crewai.auth.token.get_auth_token", lambda: "test-token")
-    monkeypatch.setattr(PlusAPI, "get_agent", fake_get_agent)
-    monkeypatch.setattr(Agent, "kickoff_async", fake_kickoff_async)
-
-    yaml_str = """
-schema: crewai.flow/v1
-name: AgentFlow
-methods:
-  answer:
-    do:
-      call: agent
-      with:
-        from_repository: support_specialist
-        input: "${state.question}"
-    start: true
-"""
-
-    flow = Flow.from_declaration(contents=yaml_str)
-
-    async def run_flow() -> str:
-        async def mark_loop_progress() -> None:
-            while not fetch_started.is_set():
-                await asyncio.sleep(0)
-            loop_marker_ran.set()
-            release_fetch.set()
-
-        marker_task = asyncio.create_task(mark_loop_progress())
-        kickoff_task = asyncio.create_task(
-            flow.kickoff_async(inputs={"question": "What is CrewAI?"})
-        )
-        try:
-            result = await asyncio.wait_for(kickoff_task, timeout=2)
-            await asyncio.wait_for(marker_task, timeout=2)
-            return result
-        finally:
-            release_fetch.set()
-
-    assert asyncio.run(run_flow()) == "Repository specialist:What is CrewAI?"
-    assert fetch_saw_loop_marker
 
 
 def test_agent_action_renders_text_custom_expression_input(
@@ -1519,167 +1435,6 @@ methods:
     }
 
 
-def test_crew_action_runs_repository_agent_yaml_definition(
-    monkeypatch: pytest.MonkeyPatch,
-):
-    from crewai import Crew
-    from crewai.plus_api import PlusAPI
-
-    fetched_agents: list[str] = []
-
-    class FakeResponse:
-        status_code = 200
-        text = ""
-
-        def json(self) -> dict[str, Any]:
-            return {
-                "role": "Repository researcher",
-                "goal": "Research {topic}",
-                "backstory": "Loaded from the agent repository.",
-                "max_iter": 5,
-                "tools": [],
-            }
-
-    def fake_get_agent(self: PlusAPI, handle: str) -> FakeResponse:
-        fetched_agents.append(handle)
-        return FakeResponse()
-
-    async def fake_kickoff_async(
-        self: Crew, inputs: dict[str, Any] | None = None, **_kwargs: Any
-    ) -> dict[str, Any]:
-        return {
-            "crew": self.name,
-            "agents": [
-                {"role": agent.role, "max_iter": agent.max_iter}
-                for agent in self.agents
-            ],
-            "tasks": [task.description for task in self.tasks],
-            "inputs": inputs,
-        }
-
-    monkeypatch.setattr("crewai.auth.token.get_auth_token", lambda: "test-token")
-    monkeypatch.setattr(PlusAPI, "get_agent", fake_get_agent)
-    monkeypatch.setattr(Crew, "kickoff_async", fake_kickoff_async)
-
-    yaml_str = """
-schema: crewai.flow/v1
-name: CrewFlow
-methods:
-  research:
-    do:
-      call: crew
-      with:
-        name: inline_research
-        agents:
-          researcher:
-            from_repository: researcher
-        tasks:
-          - name: research_task
-            description: Research {topic}
-            expected_output: Findings about {topic}
-            agent: researcher
-      inputs:
-        topic: "${state.topic}"
-    start: true
-"""
-
-    flow = Flow.from_declaration(contents=yaml_str)
-
-    assert flow.kickoff(inputs={"topic": "AI"}) == {
-        "crew": "inline_research",
-        "agents": [{"role": "Repository researcher", "max_iter": 5}],
-        "tasks": ["Research {topic}"],
-        "inputs": {"topic": "AI"},
-    }
-    assert fetched_agents == ["researcher"]
-
-
-def test_crew_action_repository_fetch_does_not_block_event_loop(
-    monkeypatch: pytest.MonkeyPatch,
-):
-    from crewai import Crew
-    from crewai.plus_api import PlusAPI
-
-    loop_marker_ran = threading.Event()
-    fetch_started = threading.Event()
-    release_fetch = threading.Event()
-    fetch_saw_loop_marker = False
-
-    class FakeResponse:
-        status_code = 200
-        text = ""
-
-        def json(self) -> dict[str, Any]:
-            return {
-                "role": "Repository researcher",
-                "goal": "Research {topic}",
-                "backstory": "Loaded from the agent repository.",
-                "tools": [],
-            }
-
-    def fake_get_agent(self: PlusAPI, handle: str) -> FakeResponse:
-        nonlocal fetch_saw_loop_marker
-        fetch_started.set()
-        release_fetch.wait(timeout=1)
-        fetch_saw_loop_marker = loop_marker_ran.is_set()
-        return FakeResponse()
-
-    async def fake_kickoff_async(
-        self: Crew, inputs: dict[str, Any] | None = None, **_kwargs: Any
-    ) -> dict[str, Any]:
-        return {"agents": [agent.role for agent in self.agents], "inputs": inputs}
-
-    monkeypatch.setattr("crewai.auth.token.get_auth_token", lambda: "test-token")
-    monkeypatch.setattr(PlusAPI, "get_agent", fake_get_agent)
-    monkeypatch.setattr(Crew, "kickoff_async", fake_kickoff_async)
-
-    yaml_str = """
-schema: crewai.flow/v1
-name: CrewFlow
-methods:
-  research:
-    do:
-      call: crew
-      with:
-        agents:
-          researcher:
-            from_repository: researcher
-        tasks:
-          - description: Research {topic}
-            expected_output: Findings about {topic}
-            agent: researcher
-      inputs:
-        topic: "${state.topic}"
-    start: true
-"""
-
-    flow = Flow.from_declaration(contents=yaml_str)
-
-    async def run_flow() -> dict[str, Any]:
-        async def mark_loop_progress() -> None:
-            while not fetch_started.is_set():
-                await asyncio.sleep(0)
-            loop_marker_ran.set()
-            release_fetch.set()
-
-        marker_task = asyncio.create_task(mark_loop_progress())
-        kickoff_task = asyncio.create_task(
-            flow.kickoff_async(inputs={"topic": "AI"})
-        )
-        try:
-            result = await asyncio.wait_for(kickoff_task, timeout=2)
-            await asyncio.wait_for(marker_task, timeout=2)
-            return result
-        finally:
-            release_fetch.set()
-
-    assert asyncio.run(run_flow()) == {
-        "agents": ["Repository researcher"],
-        "inputs": {"topic": "AI"},
-    }
-    assert fetch_saw_loop_marker
-
-
 def test_crew_action_interpolates_runtime_strings_and_lists(
     monkeypatch: pytest.MonkeyPatch,
 ):
@@ -1732,6 +1487,73 @@ methods:
     }
 
 
+def test_crew_action_runs_repository_agent_yaml_definition(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    import crewai.agent.core as agent_core
+    from crewai import Crew
+
+    def fake_load_agent_from_repository(from_repository: str) -> dict[str, Any]:
+        assert from_repository == "researcher"
+        return {
+            "role": "Repository researcher",
+            "goal": "Research {topic}",
+            "backstory": "Loaded from the agent repository.",
+            "max_iter": 5,
+        }
+
+    async def fake_kickoff_async(
+        self: Crew, inputs: dict[str, Any] | None = None, **_kwargs: Any
+    ) -> dict[str, Any]:
+        return {
+            "crew": self.name,
+            "agents": [
+                {"role": agent.role, "max_iter": agent.max_iter}
+                for agent in self.agents
+            ],
+            "tasks": [task.description for task in self.tasks],
+            "inputs": inputs,
+        }
+
+    monkeypatch.setattr(
+        agent_core,
+        "load_agent_from_repository",
+        fake_load_agent_from_repository,
+    )
+    monkeypatch.setattr(Crew, "kickoff_async", fake_kickoff_async)
+
+    yaml_str = """
+schema: crewai.flow/v1
+name: CrewFlow
+methods:
+  research:
+    do:
+      call: crew
+      with:
+        name: inline_research
+        agents:
+          researcher:
+            from_repository: researcher
+        tasks:
+          - name: research_task
+            description: Research {topic}
+            expected_output: Findings about {topic}
+            agent: researcher
+      inputs:
+        topic: "${state.topic}"
+    start: true
+"""
+
+    flow = Flow.from_declaration(contents=yaml_str)
+
+    assert flow.kickoff(inputs={"topic": "AI"}) == {
+        "crew": "inline_research",
+        "agents": [{"role": "Repository researcher", "max_iter": 5}],
+        "tasks": ["Research {topic}"],
+        "inputs": {"topic": "AI"},
+    }
+
+
 def test_crew_action_runs_crew_from_declaration(
     monkeypatch: pytest.MonkeyPatch, tmp_path: Path
 ):
@@ -2024,45 +1846,36 @@ def test_crew_action_json_schema_describes_inline_crew_definitions():
 
 
 def test_crew_action_rejects_incomplete_inline_agent_definition():
-    from crewai.project.crew_loader import load_crew_from_definition
-    from crewai.project.json_loader import JSONProjectValidationError
-
-    definition = FlowDefinition.from_declaration(contents=
-        {
-            "schema": "crewai.flow/v1",
-            "name": "CrewFlow",
-            "methods": {
-                "research": {
-                    "start": True,
-                    "do": {
-                        "call": "crew",
-                        "with": {
-                            "agents": {
-                                "researcher": {
-                                    "role": "Researcher",
-                                    "backstory": "Knows things.",
-                                }
+    with pytest.raises(ValidationError, match="goal"):
+        FlowDefinition.from_declaration(contents=
+            {
+                "schema": "crewai.flow/v1",
+                "name": "CrewFlow",
+                "methods": {
+                    "research": {
+                        "start": True,
+                        "do": {
+                            "call": "crew",
+                            "with": {
+                                "agents": {
+                                    "researcher": {
+                                        "role": "Researcher",
+                                        "backstory": "Knows things.",
+                                    }
+                                },
+                                "tasks": [
+                                    {
+                                        "description": "Research",
+                                        "expected_output": "Findings",
+                                        "agent": "researcher",
+                                    }
+                                ],
                             },
-                            "tasks": [
-                                {
-                                    "description": "Research",
-                                    "expected_output": "Findings",
-                                    "agent": "researcher",
-                                }
-                            ],
                         },
-                    },
-                }
-            },
-        }
-    )
-    crew_definition = definition.methods["research"].do.with_
-    assert crew_definition.agents["researcher"].goal is None
-
-    with pytest.raises(
-        JSONProjectValidationError, match="missing required field 'goal'"
-    ):
-        load_crew_from_definition(crew_definition, source="crew action")
+                    }
+                },
+            }
+        )
 
 
 def test_crew_action_rejects_python_ref_field():

lib/crewai/tests/test_flow_human_input_integration.py

@@ -138,27 +138,4 @@ class TestFlowHumanInputIntegration:
                 for call in call_args
                 if call[0]
             )
-            assert training_panel_found
-
-    @patch("builtins.input", return_value="please make it warmer")
-    def test_non_empty_input_prints_processing_feedback(self, mock_input):
-        """Non-empty input should be displayed as feedback to process."""
-        provider = SyncHumanInputProvider()
-        crew = MagicMock()
-        crew._train = False
-
-        formatter = event_listener.formatter
-
-        with (
-            patch.object(formatter, "pause_live_updates"),
-            patch.object(formatter, "resume_live_updates"),
-            patch.object(formatter.console, "print") as mock_console_print,
-        ):
-            result = provider._prompt_input(crew)
-
-        assert result == "please make it warmer"
-        mock_input.assert_called_once()
-        printed_text = "\n".join(
-            str(call.args[0]) for call in mock_console_print.call_args_list
-        )
-        assert "Processing your feedback" in printed_text
+            assert training_panel_found

uv.lock

@@ -13,7 +13,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-07-04T15:35:51.457693Z"
+exclude-newer = "2026-06-28T20:06:34.114646Z"
 exclude-newer-span = "P3D"
 
 [options.exclude-newer-package]
@@ -5463,7 +5463,7 @@ wheels = [
 
 [[package]]
 name = "onnx"
-version = "1.22.0"
+version = "1.21.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "ml-dtypes" },
@@ -5472,26 +5472,30 @@ dependencies = [
     { name = "protobuf" },
     { name = "typing-extensions" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/c5/93/942d2a0f6a70538eea042ce0445c8aefd46559ad153469986f29a743c01c/onnx-1.21.0.tar.gz", hash = "sha256:4d8b67d0aaec5864c87633188b91cc520877477ec0254eda122bef8be43cd764", size = 12074608, upload-time = "2026-03-27T21:33:36.118Z" }
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