Split flow DSL monolith into focused decorator modules

The Flow DSL lived in one 1033-line `dsl.py` that mixed every decorator
(`@start`/`@listen`/`@router`), the `human_feedback` decorator,
condition combinators, and FlowDefinition extraction helpers in a single
file.

Split it into a `dsl/` package where each decorator gets its own module
(`start.py` 68 lines, `listen.py` 55, `router.py` 164,
`human_feedback.py` 98) and the shared extraction/condition helpers stay
in `utils.py`. The public API is re-exported from `dsl/__init__.py`, so
import paths are unchanged.

This is simpler because each decorator is now read and changed in
isolation instead of scanning a 1000-line file to find one of them, and
router-specific annotation parsing no longer sits next to unrelated
start/listen logic.

.github/workflows/vulnerability-scan.yml

@@ -71,7 +71,8 @@ jobs:
             --ignore-vuln PYSEC-2025-215 \
             --ignore-vuln PYSEC-2025-216 \
             --ignore-vuln PYSEC-2025-217 \
-            --ignore-vuln PYSEC-2025-218
+            --ignore-vuln PYSEC-2025-218 \
+            --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
@@ -81,6 +82,9 @@ jobs:
         #   PYSEC-2025-189..197 - torch 2.11.0: memory-corruption/DoS in functions only reachable via untrusted models; no fix available
         #   PYSEC-2025-210, PYSEC-2026-139 - torch 2.11.0: profiler/deserialization issues; no fix available
         #   PYSEC-2025-211..218 - transformers 5.5.4: deserialization/code injection via malicious model checkpoints; no fix available
+        #   GHSA-f4j7-r4q5-qw2c - chromadb 1.1.1 (CVE-2026-45829): pre-auth RCE via /api/v2/tenants/{tenant}/databases/{db}/collections when trust_remote_code=true.
+        #                         Advisory: vulnerable >=1.0.0,<=1.5.9, firstPatchedVersion=none. We only use chromadb.PersistentClient (lib/crewai/src/crewai/rag/chromadb/factory.py)
+        #                         and chromadb.utils.embedding_functions; the chromadb HTTP server is never started, so the vulnerable route is not exposed.
         continue-on-error: true
 
       - name: Display results

.pre-commit-config.yaml

@@ -28,7 +28,34 @@ repos:
     hooks:
       - id: pip-audit
         name: pip-audit
-        entry: bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable --ignore-vuln CVE-2026-3219' --
+        # Keep this ignore list in sync with .github/workflows/vulnerability-scan.yml.
+        entry: >-
+          bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable
+          --ignore-vuln PYSEC-2024-277
+          --ignore-vuln PYSEC-2026-89
+          --ignore-vuln PYSEC-2026-97
+          --ignore-vuln PYSEC-2025-148
+          --ignore-vuln PYSEC-2025-183
+          --ignore-vuln PYSEC-2025-189
+          --ignore-vuln PYSEC-2025-190
+          --ignore-vuln PYSEC-2025-191
+          --ignore-vuln PYSEC-2025-192
+          --ignore-vuln PYSEC-2025-193
+          --ignore-vuln PYSEC-2025-194
+          --ignore-vuln PYSEC-2025-195
+          --ignore-vuln PYSEC-2025-196
+          --ignore-vuln PYSEC-2025-197
+          --ignore-vuln PYSEC-2025-210
+          --ignore-vuln PYSEC-2026-139
+          --ignore-vuln PYSEC-2025-211
+          --ignore-vuln PYSEC-2025-212
+          --ignore-vuln PYSEC-2025-213
+          --ignore-vuln PYSEC-2025-214
+          --ignore-vuln PYSEC-2025-215
+          --ignore-vuln PYSEC-2025-216
+          --ignore-vuln PYSEC-2025-217
+          --ignore-vuln PYSEC-2025-218
+          --ignore-vuln GHSA-f4j7-r4q5-qw2c' --
         language: system
         pass_filenames: false
         stages: [pre-push, manual]

docs/ar/changelog.mdx

@@ -4,6 +4,76 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="3 يونيو 2026">
+  ## v1.14.7a1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة دعم ملفات الوكلاء المدربين
+  - إضافة مزود LLM الأصلي لـ Snowflake Cortex
+  - إضافة دليل تكامل Databricks
+  - إضافة دليل تكامل Snowflake
+
+  ### إصلاحات الأخطاء
+  - إصلاح CLI عن طريق استعادة `[project.scripts]` في حزمة crewai لتثبيت أداة UV
+  - حل مشكلات موثوقية إدخال الملفات
+  - إصلاح تاريخ نتائج الأدوات غير المكتملة في Snowflake Claude
+  - التعامل مع استدعاءات الأدوات الممثلة كسلاسل لـ Snowflake Claude
+  - إعادة تفعيل مستمعي `or_` متعدد المصادر عبر دورات مدفوعة بالموجه
+
+  ### الأداء
+  - تحسين سرعة استيراد crewai عن طريق تحميل استيرادات docling بشكل كسول
+
+  ### إعادة هيكلة
+  - تقسيم `flow.py` إلى DSL، تعريف، وتشغيل
+
+  ## المساهمون
+
+  @Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
+
+</Update>
+
+<Update label="28 مايو 2026">
+  ## v1.14.6
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.6)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - تحسين StdioTransport لمنع تسرب متغيرات البيئة
+  - تعزيز تكوين التخطيط ومعالجة الملاحظات
+  - إعلان env_vars على DatabricksQueryTool
+  - إضافة وثائق خطة التحكم في الوكيل
+
+  ### إصلاحات الأخطاء
+  - إصلاح تسرب المخرجات المنظمة في حلقات استدعاء الأدوات
+  - حذف ردود الاستدعاء غير القابلة للعودة وحالة المحول في نقطة التحقق
+  - تسلسل الحقول من النوع [BaseModel] كـ JSON schema في نقطة التحقق
+  - تجنب مهمة orphan task_started عند استعادة نطاق الاستئناف
+  - السماح لـ AgentExecutor بالاستعادة من نقطة التحقق
+  - تصحيح خطأ الكتابة من mongodb إلى pymongo في package_dependencies
+
+  ### الوثائق
+  - إضافة كتلة تنقل وثائق ACP (بيتا) إلى صفحات خطة التحكم في الوكيل
+  - إزالة المراجع إلى العمليات التوافقية من صفحة العمليات
+  - إعادة هيكلة صفحة نقاط التحقق
+  - توثيق خطوة تثبيت حزمة الإدارة لمرة واحدة
+  - نقل Secrets Manager / Workload Identity من replicated-config
+  - إزالة تعبيرات `{" "}` JSX التي تكسر عرض `<Steps>`
+
+  ### إعادة الهيكلة
+  - نقل مستودع المهارات إلى experimental + CREWAI_EXPERIMENTAL gate
+
+  ## المساهمون
+
+  @akaKuruma, @alex-clawd, @github-actions[bot], @greysonlalonde, @heitorado, @iris-clawd, @lorenzejay, @lucasgomide, @mattatcha, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="27 مايو 2026">
   ## v1.14.6a2
 

docs/ar/concepts/llms.mdx

@@ -107,7 +107,7 @@ mode: "wide"
 </Tabs>
 
 <Info>
-  يوفر CrewAI تكاملات SDK أصلية لـ OpenAI و Anthropic و Google (Gemini API) و Azure و AWS Bedrock -- لا حاجة لتثبيت إضافي بخلاف الملحقات الخاصة بالمزود (مثل `uv add "crewai[openai]"`).
+  يوفر CrewAI تكاملات SDK أصلية لـ OpenAI و Anthropic و Google (Gemini API) و Azure و AWS Bedrock و Snowflake Cortex -- لا حاجة لتثبيت إضافي بخلاف الملحقات الخاصة بالمزود (مثل `uv add "crewai[openai]"`).
 
   جميع المزودين الآخرين مدعومون بواسطة **LiteLLM**. إذا كنت تخطط لاستخدام أي منهم، أضفه كتبعية لمشروعك:
   ```bash
@@ -291,6 +291,55 @@ mode: "wide"
     ```
   </Accordion>
 
+  <Accordion title="Snowflake Cortex">
+    يوفر CrewAI تكاملًا أصليًا مع Snowflake Cortex REST API عبر endpoint Chat Completions المتوافق مع OpenAI. تستخدم نماذج `snowflake/...` هذا المسار بدون fallback إلى LiteLLM. يدعم Snowflake Cortex في CrewAI حاليًا Chat Completions فقط، لذلك استخدم وضع `api` الافتراضي ولا تضبط `api="responses"`.
+
+    ```toml Code
+    # Required
+    SNOWFLAKE_PAT=<your-programmatic-access-token>
+    SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
+
+    # Alternative account configuration
+    SNOWFLAKE_ACCOUNT=<account-identifier>
+    ```
+
+    **الاستخدام الأساسي:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/openai-gpt-4.1",
+        temperature=0.7,
+        max_completion_tokens=1024,
+    )
+    ```
+
+    **نماذج Claude على Cortex:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/claude-sonnet-4-5",
+        max_completion_tokens=1024,
+        stream=True,
+    )
+    ```
+
+    **متغيرات البيئة المدعومة:**
+    - `SNOWFLAKE_PAT` أو `SNOWFLAKE_TOKEN` أو `SNOWFLAKE_JWT`: الرمز المستخدم كاعتماد Bearer
+    - `SNOWFLAKE_ACCOUNT_URL`: عنوان URL الكامل لحساب Snowflake
+    - `SNOWFLAKE_ACCOUNT` أو `SNOWFLAKE_ACCOUNT_ID` أو `SNOWFLAKE_ACCOUNT_IDENTIFIER`: معرف الحساب المستخدم لبناء URL
+
+    تستخدم طلبات Snowflake REST الدور الافتراضي للمستخدم. تأكد من أن هذا الدور لديه `SNOWFLAKE.CORTEX_USER` أو `SNOWFLAKE.CORTEX_REST_API_USER`. لا يتطلب endpoint Cortex REST Chat Completions معاملات database أو schema أو warehouse أو role صريح.
+
+    **الميزات:**
+    - اختيار provider أصلي باستخدام `model="snowflake/<model-name>"`
+    - Chat Completions مع streaming وبدونه فقط؛ `api="responses"` غير مدعوم
+    - تتبع استخدام الرموز
+    - استدعاء الدوال لنماذج OpenAI و Claude المستضافة في Snowflake
+    - إزالة assistant prefill النهائي غير الصالح تلقائيًا لنماذج Claude في Snowflake
+  </Accordion>
+
   <Accordion title="Anthropic">
     يوفر CrewAI تكاملًا أصليًا مع Anthropic من خلال Anthropic Python SDK.
 

docs/ar/concepts/processes.mdx

@@ -16,7 +16,6 @@ mode: "wide"
 
 - **تسلسلي**: ينفذ المهام بالتتابع، مما يضمن إكمال المهام بتقدم منظم.
 - **هرمي**: ينظم المهام في تسلسل إداري هرمي، حيث يتم تفويض المهام وتنفيذها بناءً على سلسلة أوامر منظمة. يجب تحديد نموذج لغة المدير (`manager_llm`) أو وكيل مدير مخصص (`manager_agent`) في الطاقم لتفعيل العملية الهرمية، مما يسهّل إنشاء وإدارة المهام من قبل المدير.
-- **العملية التوافقية (مخطط لها)**: تهدف إلى اتخاذ القرارات بشكل تعاوني بين الوكلاء حول تنفيذ المهام، وتقدم هذه العملية نهجًا ديمقراطيًا لإدارة المهام داخل CrewAI. وهي مخطط لها للتطوير المستقبلي وغير مطبقة حاليًا في قاعدة الكود.
 
 ## دور العمليات في العمل الجماعي
 تُمكّن العمليات الوكلاء الأفراد من العمل كوحدة متماسكة، مما يبسّط جهودهم لتحقيق أهداف مشتركة بكفاءة وتناسق.
@@ -59,9 +58,9 @@ crew = Crew(
 
 ## فئة Process: نظرة عامة مفصلة
 
-تم تنفيذ فئة `Process` كتعداد (`Enum`)، مما يضمن أمان الأنواع ويقيّد قيم العملية على الأنواع المحددة (`sequential`، `hierarchical`). العملية التوافقية مخطط لإدراجها مستقبلاً، مما يؤكد التزامنا بالتطوير والابتكار المستمر.
+تم تنفيذ فئة `Process` كتعداد (`Enum`)، مما يضمن أمان الأنواع ويقيّد قيم العملية على الأنواع المحددة (`sequential`، `hierarchical`).
 
 ## الخلاصة
 
 التعاون المنظم الذي تسهّله العمليات داخل CrewAI ضروري لتمكين العمل الجماعي المنهجي بين الوكلاء.
-تم تحديث هذه الوثائق لتعكس أحدث الميزات والتحسينات والتكامل المخطط للعملية التوافقية، مما يضمن وصول المستخدمين إلى أحدث المعلومات وأكثرها شمولاً.
+تم تحديث هذه الوثائق لتعكس أحدث الميزات والتحسينات، مما يضمن وصول المستخدمين إلى أحدث المعلومات وأكثرها شمولاً.

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

@@ -6,6 +6,14 @@ icon: "gauge"
 mode: "wide"
 ---
 
+<Info>
+  **تنقل وثائق ACP (إصدار تجريبي)**
+
+  - [نظرة عامة](/ar/enterprise/features/agent-control-plane/overview)
+  - **المراقبة** *(أنت هنا)*
+  - [القواعد](/ar/enterprise/features/agent-control-plane/rules)
+</Info>
+
 ## نظرة عامة
 
 تبويب **Automations** هو عرض العمليات للقراءة فقط في [Agent Control Plane](/ar/enterprise/features/agent-control-plane/overview). يجمع بين بطاقتَي مقاييس و sankey تفاعلي وجدولين فرعيين — **Automations** و **Consumption** — يمكنك البحث والتصفية والفرز فيهما.

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

@@ -5,6 +5,14 @@ sidebarTitle: نظرة عامة
 icon: "book-open"
 ---
 
+<Info>
+  **تنقل وثائق ACP (إصدار تجريبي)**
+
+  - **نظرة عامة** *(أنت هنا)*
+  - [المراقبة](/ar/enterprise/features/agent-control-plane/monitoring)
+  - [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 **Rules** — تمنح فريقك القدرة على:

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

@@ -6,6 +6,14 @@ icon: "shield-check"
 mode: "wide"
 ---
 
+<Info>
+  **تنقل وثائق ACP (إصدار تجريبي)**
+
+  - [نظرة عامة](/ar/enterprise/features/agent-control-plane/overview)
+  - [المراقبة](/ar/enterprise/features/agent-control-plane/monitoring)
+  - **القواعد** *(أنت هنا)*
+</Info>
+
 ## نظرة عامة
 
 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.

docs/ar/enterprise/guides/deploy-to-amp.mdx

@@ -164,6 +164,12 @@ crewai deploy remove <deployment_id>
       ![اختيار المستودع](/images/enterprise/select-repo.png)
     </Frame>
 
+    <Tip>
+      إذا كان Crew أو Flow داخل مجلد فرعي في monorepo، فوسّع **Advanced**
+      وعيّن دليل عمل قبل النشر. راجع
+      [النشر من Monorepo](/ar/enterprise/guides/monorepo-deployments).
+    </Tip>
+
   </Step>
 
   <Step title="تعيين متغيرات البيئة">

docs/ar/enterprise/guides/monorepo-deployments.mdx

@@ -0,0 +1,220 @@
+---
+title: "النشر من Monorepo"
+description: "انشر Crew أو Flow من مجلد فرعي داخل مستودع أكبر"
+icon: "folder-tree"
+mode: "wide"
+---
+
+<Note>
+  استخدم دليل عمل عندما يكون Crew أو Flow داخل مستودع أكبر. يتحقق CrewAI AMP
+  من الأتمتة ويبنيها ويشغلها من ذلك المجلد الفرعي بدلاً من جذر المستودع.
+</Note>
+
+## متى تستخدم ذلك
+
+يكون النشر من monorepo مفيداً عندما يحتوي مستودع واحد على عدة أتمتات أو حزم
+مشتركة أو كود تطبيقات آخر:
+
+```text
+company-ai/
+|-- uv.lock
+|-- packages/
+|   `-- shared_tools/
+`-- crews/
+    |-- support_agent/
+    |   |-- pyproject.toml
+    |   `-- src/
+    |       `-- support_agent/
+    |           |-- main.py
+    |           `-- crew.py
+    `-- research_flow/
+        |-- pyproject.toml
+        `-- src/
+            `-- research_flow/
+                `-- main.py
+```
+
+لنشر `support_agent`، اضبط دليل العمل على:
+
+```text
+crews/support_agent
+```
+
+لا يزال AMP يجلب المستودع كاملاً أو يرفعه، لكنه يتعامل مع المجلد المحدد كجذر
+مشروع الأتمتة.
+
+## ما الذي يتحكم به دليل العمل
+
+عند تعيين دليل عمل، يستخدم AMP ذلك المجلد من أجل:
+
+- التحقق من المشروع، بما في ذلك `pyproject.toml` و`src/` ونقطة دخول Crew أو Flow
+- تثبيت الاعتماديات باستخدام `uv`
+- دليل العمل للعملية قيد التشغيل
+- متغير البيئة `CREW_ROOT_DIR`
+
+ترك الحقل فارغاً يحافظ على السلوك الحالي ويستخدم جذر المستودع.
+
+## المصادر المدعومة
+
+يمكنك تعيين دليل عمل عند إنشاء نشر من:
+
+- مستودع GitHub متصل
+- مستودع Git مكوّن في AMP
+- رفع ملف ZIP
+
+<Info>
+  اضبط أدلة العمل من واجهة AMP على الويب. لا يطلب تدفق CLI
+  `crewai deploy create` هذا الحقل.
+</Info>
+
+يمكنك أيضاً إضافة دليل العمل أو تغييره في نشر موجود من صفحة **Settings** الخاصة
+بالنشر. يسري التغيير في النشر التالي.
+
+<Warning>
+  لا يمكن استخدام أدلة العمل وauto-deploy معاً. إذا كان للنشر دليل عمل، يتم
+  تعطيل auto-deploy لذلك النشر. أوقف auto-deploy قبل تعيين دليل عمل.
+</Warning>
+
+## إعداد نشر جديد
+
+<Steps>
+  <Step title="افتح Deploy from Code">
+    في CrewAI AMP، أنشئ نشراً جديداً واختر المصدر: GitHub أو Git Repository أو
+    رفع ZIP.
+  </Step>
+
+  <Step title="اختر المستودع أو الفرع أو ملف ZIP">
+    اختر المستودع والفرع اللذين يحتويان على monorepo، أو ارفع ملف ZIP يحتوي
+    جذره على محتويات monorepo.
+  </Step>
+
+  <Step title="افتح الإعدادات المتقدمة">
+    وسّع قسم **Advanced** في نموذج النشر.
+  </Step>
+
+  <Step title="أدخل دليل العمل">
+    أدخل المسار من جذر المستودع إلى مشروع Crew أو Flow:
+
+    ```text
+    crews/support_agent
+    ```
+
+    لا تضف شرطة مائلة في البداية.
+  </Step>
+
+  <Step title="انشر">
+    أضف أي متغيرات بيئة مطلوبة، ثم ابدأ النشر.
+  </Step>
+</Steps>
+
+## إعداد نشر موجود
+
+<Steps>
+  <Step title="افتح إعدادات النشر">
+    انتقل إلى الأتمتة في AMP وافتح **Settings**.
+  </Step>
+
+  <Step title="أوقف auto-deploy إذا لزم الأمر">
+    إذا كان auto-deploy مفعلاً، أوقفه أولاً. لا يكون حقل دليل العمل متاحاً
+    أثناء تشغيل auto-deploy.
+  </Step>
+
+  <Step title="عيّن دليل العمل">
+    في **Basic settings**، أدخل مسار المجلد الفرعي، مثل:
+
+    ```text
+    crews/support_agent
+    ```
+  </Step>
+
+  <Step title="أعد النشر">
+    احفظ الإعداد وأعد نشر الأتمتة. سيتم استخدام دليل العمل الجديد في النشر
+    التالي.
+  </Step>
+</Steps>
+
+## قواعد المسار
+
+يجب أن يكون دليل العمل مساراً نسبياً داخل جذر المستودع أو ZIP.
+
+| القاعدة | المثال |
+|---------|--------|
+| استخدم مساراً نسبياً | `crews/support_agent` |
+| لا تبدأ بـ `/` | `/crews/support_agent` غير صالح |
+| لا تستخدم مقاطع المسار `.` أو `..` | `crews/../support_agent` غير صالح |
+| استخدم الأحرف والأرقام والشرطات والشرطات السفلية والنقاط والشرطات المائلة فقط | `crews/support agent` غير صالح |
+| اجعل المسار 255 حرفاً أو أقل | يتم رفض المسارات الأطول |
+
+يزيل AMP المسافات البيضاء في البداية والنهاية، ويضغط الشرطات المائلة المتكررة،
+ويزيل الشرطة المائلة النهائية. تستخدم القيمة الفارغة جذر المستودع.
+
+## ملفات القفل وUV Workspaces
+
+يجب أن يحتوي المجلد المحدد على `pyproject.toml` ودليل `src/` الخاصين بالأتمتة.
+يمكن أن يوجد ملف `uv.lock` أو `poetry.lock` إما في المجلد المحدد أو في جذر
+المستودع.
+
+يدعم هذا التخطيطين الشائعين في monorepo:
+
+<Tabs>
+  <Tab title="ملف قفل المشروع">
+    ```text
+    company-ai/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            |-- uv.lock
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+
+  <Tab title="ملف قفل workspace">
+    ```text
+    company-ai/
+    |-- uv.lock
+    |-- packages/
+    |   `-- shared_tools/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+</Tabs>
+
+<Tip>
+  إذا كانت الأتمتة تستورد حزماً مشتركة من مكان آخر في monorepo، فصرّح بهذه
+  الحزم في `pyproject.toml` باستخدام إعدادات UV workspace أو path أو source.
+  يشغل AMP الأتمتة من المجلد المحدد، لذلك يجب تثبيت الكود المشترك كاعتمادية
+  بدلاً من الاعتماد على وجود جذر المستودع في Python path.
+</Tip>
+
+## استكشاف الأخطاء وإصلاحها
+
+### لم يتم العثور على دليل العمل
+
+تحقق من أن المسار نسبي إلى جذر المستودع أو ZIP. بالنسبة لرفع ZIP، يجب أن
+تتضمن محتويات ZIP مسار دليل العمل تماماً كما أدخلته.
+
+### pyproject.toml مفقود
+
+يجب أن يشير دليل العمل إلى مجلد مشروع Crew أو Flow، وليس فقط إلى مجلد أب
+يحتوي على عدة مشاريع.
+
+### uv.lock أو poetry.lock مفقود
+
+اعمل commit لملف قفل إما في مجلد المشروع المحدد أو في جذر المستودع. بالنسبة
+إلى UV workspaces، يتم دعم إبقاء `uv.lock` في جذر workspace.
+
+### Auto-Deploy غير متاح
+
+يتم تعطيل auto-deploy أثناء تعيين دليل عمل. استخدم إعادة النشر اليدوية أو شغّل
+إعادة النشر من CI/CD باستخدام AMP API.
+
+<Card title="النشر على AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
+  تابع دليل النشر بعد اختيار دليل عمل monorepo.
+</Card>

docs/ar/enterprise/integrations/databricks.mdx

@@ -0,0 +1,123 @@
+---
+title: تكامل Databricks
+description: "اربط وكلاء CrewAI بـ Databricks Genie وSQL وUnity Catalog Functions وVector Search عبر خوادم MCP المُدارة من Databricks."
+icon: "layer-group"
+mode: "wide"
+---
+
+## نظرة عامة
+
+اربط وكلاء CrewAI مباشرةً بمساحة عمل Databricks الخاصة بك عبر [خوادم MCP المُدارة من Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp). يتيح تكامل Databricks لوكلائك طرح أسئلة بلغة طبيعية باستخدام **Genie**، وتنفيذ **SQL** خاضع للحوكمة، واستدعاء **Unity Catalog Functions**، واسترجاع المستندات باستخدام **Vector Search** — كل ذلك دون كتابة أو استضافة أي كود موصِّل، مع تطبيق أذونات Unity Catalog في كل استدعاء.
+
+في الخلفية، يُعدّ تكامل Databricks غلافًا مُدارًا حول دعم [خوادم MCP المخصصة](/ar/enterprise/guides/custom-mcp-server) في CrewAI. تكشف Databricks عن كل قدرة كنقطة نهاية [Model Context Protocol](https://modelcontextprotocol.io/) خاصة بها، ويتصل بها CrewAI بأمان نيابةً عنك. ولأن كل خادم يُضاف بشكل منفصل، يمكنك تفعيل القدرات التي تحتاجها فرقك (crews) بالضبط.
+
+## القدرات الرئيسية
+
+<CardGroup cols={2}>
+  <Card title="Genie" icon="comments">
+    اطرح أسئلة بلغة طبيعية واحصل على إجابات مستندة إلى بياناتك باستخدام [Genie](https://docs.databricks.com/aws/en/genie/)، الذي يستعلم من Genie Spaces وUnity Catalog ويوفّر روابط تعود إلى واجهة Databricks.
+  </Card>
+  <Card title="Databricks SQL" icon="database">
+    نفّذ SQL خاضعًا للحوكمة على مستودعات Databricks لديك للاستعلام عن البيانات وتحويلها وإنشاء خطوط أنابيب البيانات مباشرةً من وكلائك.
+  </Card>
+  <Card title="Unity Catalog Functions" icon="function">
+    استدعِ [دوال Unity Catalog](https://docs.databricks.com/aws/en/udf/unity-catalog) لتنفيذ SQL مُعرّف مسبقًا ومنطق أعمال مخصّص كأدوات قابلة لإعادة الاستخدام وخاضعة للحوكمة.
+  </Card>
+  <Card title="Vector Search" icon="magnifying-glass">
+    استرجع المستندات ذات الصلة لسير عمل RAG والمعرفة من فهارس [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) باستخدام التشابه الدلالي.
+  </Card>
+</CardGroup>
+
+تعمل جميع الخوادم خلف Unity AI Gateway وتطبّق ضوابط الوصول في Unity Catalog، بحيث لا يرى وكلاؤك سوى البيانات والأدوات المصرَّح لهم باستخدامها.
+
+## المتطلبات المسبقة
+
+قبل استخدام تكامل Databricks، تأكّد من توفّر ما يلي:
+
+- حساب [CrewAI AMP](https://app.crewai.com) باشتراك نشط
+- مساحة عمل Databricks تحتوي على القدرات التي تريد كشفها (Genie Spaces، مستودعات SQL، دوال Unity Catalog، أو فهارس Vector Search)
+- [امتيازات Unity Catalog](https://docs.databricks.com/aws/en/data-governance/unity-catalog) المناسبة على الكائنات الأساسية
+- اسم مضيف مساحة عمل Databricks الخاص بك (مثال: `your-workspace.cloud.databricks.com`)
+
+## خوادم MCP المُدارة من Databricks
+
+تنشر Databricks خادم MCP مُدارًا منفصلًا لكل قدرة. يكشف CrewAI عنها كاتصالات فردية، يُهيَّأ كل منها باستخدام مضيف مساحة العمل ومعرّفات Unity Catalog ذات الصلة. تتبع نقاط النهاية الأنماط التالية:
+
+| الخادم | الوظيفة | نمط عنوان MCP |
+|--------|---------|---------------|
+| **Genie** | أسئلة وأجوبة بلغة طبيعية على Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
+| **Databricks SQL** | تنفيذ SQL على مستودعاتك | `https://<workspace-hostname>/api/2.0/mcp/sql` |
+| **Unity Catalog Functions** | تشغيل دوال UC المسجّلة | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
+| **Vector Search** | الاستعلام من فهرس Vector Search | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
+
+<Note>
+لا حاجة لإنشاء عناوين URL هذه يدويًا — يُنشئ CrewAI كل نقطة نهاية من مضيف مساحة العمل والمعرّفات (Genie Space ID، أو catalog/schema) التي تقدّمها عند تهيئة الاتصال. للاطّلاع على المواصفات الكاملة وأحدث تفاصيل نقاط النهاية، راجع [وثائق MCP المُدارة من Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp).
+</Note>
+
+## ربط Databricks في CrewAI AMP
+
+<Frame>
+    <img src="/images/enterprise/databricks-configure.png" alt="تهيئة خادم MCP مُدار من Databricks في CrewAI AMP" />
+</Frame>
+
+تظهر كل قدرة من قدرات Databricks — **Databricks Genie** و**Databricks SQL** و**Databricks Unity Catalog Functions** و**Databricks Vector Search** — كخادم MCP خاص بها ضمن مجموعة Databricks في صفحة **Tools & Integrations**. هيّئ ما تحتاجه:
+
+<Steps>
+    <Step title="افتح Tools & Integrations">
+        انتقل إلى **Tools & Integrations** في الشريط الجانبي الأيسر في CrewAI AMP وحدِّد مجموعة **Databricks** في قائمة Connections. سترى خوادم Genie وSQL وUnity Catalog Functions وVector Search مُدرجة أسفلها.
+    </Step>
+
+    <Step title="هيّئ خادمًا">
+        انقر على **Configure** بجوار القدرة التي تريد تفعيلها وقدّم تفاصيل الاتصال الخاصة بها:
+
+        - **Workspace Host** — اسم مضيف مساحة عمل Databricks الخاص بك (مثال: `my-workspace.cloud.databricks.com`).
+        - **Genie** — **Genie Space ID** المراد الاستعلام عنه.
+        - **Unity Catalog Functions** — الـ **catalog** والـ **schema** اللذان يحتويان على دوالك.
+        - **Vector Search** — الـ **catalog** والـ **schema** اللذان يحتويان على الفهرس.
+        - **Databricks SQL** — لا توجد معرّفات إضافية؛ تُنفَّذ الاستعلامات على مستودعات SQL في مساحة عملك.
+    </Step>
+
+    <Step title="اختر طريقة المصادقة">
+        اختر كيف يصادق CrewAI على Databricks. يُوصى باستخدام **OAuth**.
+
+        - **Use OAuth** — اتصل بأمان باستخدام OAuth 2.0. يصادق كل مستخدم على حدة، وتُصدر Databricks رموزًا (tokens) محدّدة النطاق للقدرة (`genie` أو `sql` أو `unity-catalog` أو `vector-search`). يتولّى CrewAI تدفّق التفويض ويُجدّد الرموز تلقائيًا.
+        - **Use personal access token** — صادِق باستخدام [رمز وصول شخصي من Databricks](https://docs.databricks.com/aws/en/dev-tools/auth/pat). استخدم هوية بأقل الامتيازات للحدّ من التعرّض.
+    </Step>
+
+    <Step title="صادِق">
+        أكمل المصادقة. بمجرد الاتصال، تصبح أدوات الخادم متاحة لفرقك. كرّر العملية لأي قدرات Databricks أخرى تريد تفعيلها.
+    </Step>
+</Steps>
+
+<Tip>
+لأن كل قدرة هي اتصال منفصل، يمكنك المزج والمطابقة — على سبيل المثال، فعّل Genie وVector Search لفريق بحث، مع حجز SQL وUnity Catalog Functions لفريق هندسة البيانات. تتيح لك إعدادات الرؤية (Visibility) التحكّم في أعضاء الفريق الذين يمكنهم استخدام كل منها.
+</Tip>
+
+## استخدام أدوات Databricks في فرقك
+
+بمجرد الاتصال، تظهر الأدوات التي يكشفها كل خادم MCP جنبًا إلى جنب مع الاتصالات المدمجة في صفحة **Tools & Integrations**. يمكنك:
+
+- **إسناد الأدوات إلى الوكلاء** في فرقك تمامًا مثل أي أداة أخرى في CrewAI.
+- **إدارة الرؤية** للتحكّم في أعضاء الفريق الذين يمكنهم استخدام كل اتصال.
+- **تعديل أو إزالة** أي اتصال في أي وقت من قائمة Connections.
+
+يمكن لوكلائك الآن طلب إجابات مستندة من Genie، وتنفيذ SQL على مستودعاتك، واستدعاء دوال Unity Catalog، والبحث في فهارس Vector Search — مع تدفّق النتائج تلقائيًا إلى استدلالهم.
+
+<Warning>
+تطبّق Databricks الحوكمة عبر Unity Catalog وUnity AI Gateway: لا يمكن للمستخدم اكتشاف الأدوات واستدعاؤها إلا تلك المصرَّح بها لهوية مساحة عمله. إذا فشل استدعاء أداة، فتأكّد من أن المستخدم المتصل (أو هوية الرمز) يمتلك امتيازات Unity Catalog المطلوبة على Genie Space أو المستودع أو الدالة أو الفهرس. تُنفَّذ بعض استعلامات Genie وSQL بشكل غير متزامن وقد تستغرق لحظة لإرجاع النتائج.
+</Warning>
+
+## مزيد من المعلومات
+
+<CardGroup cols={2}>
+  <Card title="خوادم MCP المُدارة من Databricks" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
+    وثائق Databricks الرسمية لخوادم MCP المُدارة Genie وSQL وUnity Catalog Functions وVector Search.
+  </Card>
+  <Card title="خوادم MCP المخصصة في CrewAI" icon="plug" href="/ar/enterprise/guides/custom-mcp-server">
+    تعرّف على كيفية اتصال CrewAI بأي خادم MCP، وهو الأساس الذي يُبنى عليه تكامل Databricks.
+  </Card>
+</CardGroup>
+
+<Card title="بحاجة إلى مساعدة؟" icon="headset" href="mailto:support@crewai.com">
+  تواصل مع فريق الدعم للحصول على المساعدة في تهيئة تكامل Databricks أو في حل المشكلات.
+</Card>

docs/ar/enterprise/integrations/snowflake.mdx

@@ -0,0 +1,134 @@
+---
+title: تكامل Snowflake
+description: "ربط وكلاء CrewAI بـ Snowflake Cortex Analyst و Cortex Search وتنفيذ SQL من خلال خادم MCP المُدار من Snowflake."
+icon: "snowflake"
+mode: "wide"
+---
+
+## نظرة عامة
+
+اربط وكلاء CrewAI مباشرة ببيانات Snowflake الخاصة بك من خلال [خادم MCP المُدار من Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp). يتيح تكامل Snowflake لوكلائك الاستعلام عن البيانات المنظمة باستخدام **Cortex Analyst**، والبحث في البيانات غير المنظمة باستخدام **Cortex Search**، وتنفيذ SQL مُدار على مستودعات البيانات الخاصة بك — كل ذلك دون كتابة أو استضافة أي كود للموصّل.
+
+داخلياً، تكامل Snowflake هو غلاف مُدار حول دعم [Custom MCP Server](/ar/enterprise/guides/custom-mcp-server) في CrewAI. يكشف Snowflake عن قدرات Cortex AI الخاصة به من خلال نقطة نهاية [Model Context Protocol](https://modelcontextprotocol.io/)، ويتصل CrewAI بها بشكل آمن نيابةً عنك. أي أداة تكشفها على جانب Snowflake — Cortex Analyst أو Cortex Search أو تنفيذ SQL أو Cortex Agents أو أدواتك المخصصة — تصبح متاحة لطواقمك.
+
+## القدرات الرئيسية
+
+<CardGroup cols={3}>
+  <Card title="Cortex Analyst" icon="chart-bar">
+    اطرح أسئلة بلغة طبيعية ودع [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) يولّد وينفذ SQL على بياناتك **المنظمة** باستخدام نماذج دلالية غنية.
+  </Card>
+  <Card title="Cortex Search" icon="magnifying-glass">
+    استرجع البيانات **غير المنظمة** ذات الصلة لسير عمل RAG والمعرفة باستخدام [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)، خدمة البحث المُدارة بالكامل من Snowflake.
+  </Card>
+  <Card title="تنفيذ SQL" icon="database">
+    نفّذ استعلامات SQL مُدارة مباشرة على مستودعات Snowflake الخاصة بك، مع وضع القراءة فقط القابل للتكوين، والمهلات الزمنية، واختيار المستودع.
+  </Card>
+</CardGroup>
+
+نظراً لأن التكامل يكشف عن أي أدوات ينشرها خادم MCP الخاص بك، يمكنك أيضاً كشف **Cortex Agents** و**الأدوات المخصصة** (الدوال المعرّفة من المستخدم والإجراءات المخزّنة) لوكلاء CrewAI.
+
+## المتطلبات الأساسية
+
+قبل استخدام تكامل Snowflake، تأكد من توفر ما يلي:
+
+- حساب [CrewAI AMP](https://app.crewai.com) مع اشتراك فعّال
+- حساب Snowflake مع إمكانية الوصول إلى ميزات Cortex AI
+- [خادم MCP مُدار من Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp) مُكوّن بالأدوات التي تريد كشفها
+- صلاحيات Snowflake المناسبة (USAGE/SELECT) على خادم MCP والكائنات الأساسية
+
+## إعداد خادم Snowflake MCP
+
+يعمل خادم MCP المُدار من Snowflake داخل حساب Snowflake الخاص بك ويحدد الأدوات المتاحة للعملاء الخارجيين مثل CrewAI. أنشئ واحداً باستخدام أمر [`CREATE MCP SERVER`](https://docs.snowflake.com/en/sql-reference/sql/create-mcp-server)، مع سرد خدمات Cortex Search وعروض Cortex Analyst الدلالية وأدوات SQL التي تريد كشفها.
+
+```sql
+CREATE MCP SERVER my_mcp_server
+  FROM SPECIFICATION $$
+    tools:
+      - name: "sales_analyst"
+        type: "CORTEX_ANALYST"
+        identifier: "MY_DATABASE.MY_SCHEMA.sales_semantic_view"
+        description: "Answer questions about sales metrics"
+      - name: "docs_search"
+        type: "CORTEX_SEARCH_SERVICE_QUERY"
+        identifier: "MY_DATABASE.MY_SCHEMA.support_docs_search"
+        description: "Search internal support documentation"
+      - name: "run_sql"
+        type: "SQL_EXECUTION"
+        description: "Execute read-only SQL queries"
+  $$;
+```
+
+<Note>
+تتبع نقطة نهاية MCP التنسيق `https://<account_URL>/api/v2/databases/{database}/schemas/{schema}/mcp-servers/{name}`. يبني CrewAI هذا العنوان تلقائياً من **عنوان URL للحساب** و**قاعدة البيانات** و**المخطط** و**اسم خادم MCP** الذي تقدمه عند تكوين التكامل.
+</Note>
+
+للمواصفات الكاملة — بما في ذلك Cortex Agents والأدوات المخصصة وحدود حجم الاستجابة وخيارات الحوكمة — راجع [وثائق خادم MCP المُدار من Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp).
+
+## ربط Snowflake في CrewAI AMP
+
+<Frame>
+    <img src="/images/enterprise/snowflake-configure.png" alt="تكوين تكامل Snowflake في CrewAI AMP" />
+</Frame>
+
+<Steps>
+    <Step title="فتح الأدوات والتكاملات">
+        انتقل إلى **الأدوات والتكاملات** في الشريط الجانبي الأيسر لـ CrewAI AMP، وابحث عن **Snowflake** في قائمة التطبيقات، وافتح لوحة التكوين الخاصة به.
+    </Step>
+
+    <Step title="تقديم تفاصيل الاتصال">
+        املأ حقول الاتصال التي يستخدمها CrewAI للوصول إلى خادم Snowflake MCP الخاص بك:
+
+        | الحقل | مطلوب | الوصف |
+        |-------|-------|-------|
+        | **الاسم** | نعم | اسم وصفي لهذا الاتصال (القيمة الافتراضية `Snowflake`). |
+        | **الوصف** | لا | ملخص اختياري لما يوفره هذا الاتصال. |
+        | **عنوان URL للحساب** | نعم | عنوان URL لحساب Snowflake الخاص بك، مثل `xy12345.us-east-1.snowflakecomputing.com`. |
+        | **قاعدة البيانات** | نعم | قاعدة البيانات التي تحتوي على خادم MCP الخاص بك (مثل `MY_DATABASE`). |
+        | **المخطط** | نعم | المخطط الذي يحتوي على خادم MCP الخاص بك (مثل `MY_SCHEMA`). |
+        | **اسم خادم MCP** | نعم | اسم كائن خادم MCP الذي أنشأته في Snowflake (مثل `MY_MCP_SERVER`). |
+    </Step>
+
+    <Step title="اختيار طريقة المصادقة">
+        اختر كيفية مصادقة CrewAI مع Snowflake. يُوصى باستخدام **OAuth**.
+
+        - **استخدام OAuth** — اتصل بشكل آمن باستخدام OAuth 2.0 للمصادقة القائمة على الرموز دون مشاركة بيانات الاعتماد الخاصة بك. يتعامل CrewAI مع تدفق التفويض الكامل ويجدد الرموز تلقائياً. انسخ **عنوان URI لإعادة التوجيه** المعروض في النموذج (`https://oauth.crewai.com/oauth/add`) وسجّله كعنوان URI لإعادة التوجيه المعتمد في [تكامل أمان OAuth](https://docs.snowflake.com/en/user-guide/oauth-custom) في Snowflake.
+        - **استخدام رمز وصول شخصي** — المصادقة باستخدام [رمز وصول برمجي](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens) مُنشأ من إعدادات حساب Snowflake الخاص بك. قم بتعيين دور بأقل صلاحيات للرمز للحد من التعرض.
+    </Step>
+
+    <Step title="المصادقة">
+        انقر على **المصادقة**. بالنسبة لـ OAuth، ستتم إعادة توجيهك إلى Snowflake لتفويض الوصول. بمجرد المصادقة، يظهر خادم Snowflake في قائمة الاتصالات وتصبح أدواته متاحة لطواقمك.
+    </Step>
+</Steps>
+
+<Tip>
+مع OAuth، يتم مصادقة كل مستخدم بشكل فردي وتُنفّذ الاستعلامات بدور `DEFAULT_ROLE` الخاص به في Snowflake. تأكد من أن المستخدمين المتصلين لديهم دور ومستودع افتراضي محدد (`ALTER USER <username> SET DEFAULT_ROLE = '<role>' DEFAULT_WAREHOUSE = '<warehouse>'`) حتى تتوفر موارد الحوسبة لأدوات Cortex Analyst و SQL.
+</Tip>
+
+## استخدام أدوات Snowflake في طواقمك
+
+بمجرد الاتصال، تظهر الأدوات التي يكشفها خادم MCP الخاص بك إلى جانب الاتصالات المدمجة في صفحة **الأدوات والتكاملات**. يمكنك:
+
+- **تعيين الأدوات للوكلاء** في طواقمك تماماً مثل أي أداة CrewAI أخرى.
+- **إدارة الرؤية** للتحكم في أعضاء الفريق الذين يمكنهم استخدام الاتصال.
+- **تعديل أو إزالة** الاتصال في أي وقت من قائمة الاتصالات.
+
+يمكن لوكلائك الآن سؤال Cortex Analyst عن المقاييس، وتشغيل Cortex Search على مستنداتك، وتنفيذ SQL — مع تدفق النتائج تلقائياً إلى استدلالهم.
+
+<Warning>
+يفرض Snowflake الحوكمة على خادم MCP: يحدد التحكم في الوصول القائم على الأدوار الأدوات التي يمكن للمستخدم اكتشافها واستدعاؤها، وتنطبق حدود على حجم الاستجابة وعدد الأدوات (بحد أقصى 50 لكل خادم) وعمق التكرار. إذا فشل استدعاء أداة، تأكد من أن دور المستخدم المتصل لديه الصلاحيات المطلوبة على خادم MCP والكائنات الأساسية.
+</Warning>
+
+## معرفة المزيد
+
+<CardGroup cols={2}>
+  <Card title="خادم MCP المُدار من Snowflake" icon="snowflake" href="https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp">
+    الوثائق الرسمية من Snowflake لإنشاء وإدارة خادم MCP.
+  </Card>
+  <Card title="خوادم Custom MCP في CrewAI" icon="plug" href="/ar/enterprise/guides/custom-mcp-server">
+    تعرّف على كيفية اتصال CrewAI بأي خادم MCP، الأساس الذي يبني عليه تكامل Snowflake.
+  </Card>
+</CardGroup>
+
+<Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
+  تواصل مع فريق الدعم للحصول على المساعدة في تكامل Snowflake أو استكشاف الأخطاء وإصلاحها.
+</Card>

docs/ar/guides/flows/conversational-flows.mdx

@@ -0,0 +1,451 @@
+---
+title: تدفقات المحادثة
+description: أنشئ تطبيقات دردشة متعددة الجولات مع kickoff لكل جولة وسجل الرسائل وتوجيه النية والتتبع وجسور WebSocket.
+icon: comments
+mode: "wide"
+---
+
+## نظرة عامة
+
+تعامل التطبيقات المحادثية مع كل سطر من المستخدم كـ **تشغيل flow جديد** بنفس **معرّف الجلسة**. توفر CrewAI مساعدات لسجل الرسائل وتصنيف النية الاختياري وتأجيل التتبع وجسور الواجهة — دون API منفصل `chat()` على `Flow`.
+
+| المفهوم | التنفيذ |
+|---------|---------|
+| معرّف الجلسة | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
+| سطر المستخدم | `kickoff(user_message=...)` يُضاف إلى `state.messages` قبل تشغيل الرسم |
+| اكتمال الجولة | `FlowFinished` لهذا **التشغيل** فقط؛ تستمر المحادثة في `kickoff` التالي |
+| تتبع الجلسة | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
+
+## نقطة دخول واحدة: `kickoff`
+
+استخدم **`flow.kickoff(user_message=..., session_id=...)`** لكل رسالة مستخدم (REST أو WebSocket أو CLI). لا تنشئ غلاف `chat()` مخصصاً على `Flow`.
+
+| API | الاستخدام |
+|-----|-----------|
+| `kickoff(user_message=..., session_id=...)` | كل رسالة مستخدم |
+| `kickoff_async(...)` | نفس المعاملات؛ دخول async أصلي |
+| `ask()` | مطالبة حاجزة **داخل** خطوة واحدة |
+| `@human_feedback` | الموافقة/الرفض على **مخرجات خطوة** — وليس السطر التالي |
+| `ChatSession.handle_turn(...)` | طبقة نقل فوق `kickoff` |
+
+## بداية سريعة
+
+```python
+from uuid import uuid4
+
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    Flow,
+    listen,
+    or_,
+    persist,
+    router,
+    start,
+)
+from crewai.flow.persistence import SQLiteFlowPersistence
+
+
+class SupportFlow(Flow[ChatState]):
+    conversational_config = ConversationalConfig(
+        default_intents=["order", "help", "goodbye"],
+        intent_llm="gpt-4o-mini",
+        defer_trace_finalization=True,
+    )
+
+    @start()
+    def bootstrap(self):
+        if not self.state.session_ready:
+            self.state.session_ready = True
+        return "ready"
+
+    @router(bootstrap)
+    def route(self):
+        return self.state.last_intent or "help"
+
+    @listen("order")
+    def handle_order(self):
+        reply = "طلبك في الطريق."
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("help")
+    def handle_help(self):
+        reply = "كيف يمكنني المساعدة؟"
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("goodbye")
+    def handle_goodbye(self):
+        reply = "وداعاً!"
+        self.append_message("assistant", reply)
+        return reply
+
+    @persist(SQLiteFlowPersistence("support.db"))
+    @listen(or_(handle_order, handle_help, handle_goodbye))
+    def finalize(self):
+        return self.state.model_dump()
+
+
+session_id = str(uuid4())
+flow = SupportFlow()
+
+flow.kickoff(user_message="أين طلبي؟", session_id=session_id)
+flow.kickoff(user_message="وماذا عن الإرجاع؟", session_id=session_id)
+flow.finalize_session_traces()
+```
+
+## دورة حياة الجولة
+
+كل `kickoff` مع `user_message` يشغّل:
+
+1. **`_configure_conversational_kickoff`** — دمج `session_id` / `user_message` في `inputs` وتطبيق `ConversationalConfig`.
+2. **استعادة الحالة** — عند وجود `inputs["id"]` و`@persist`.
+3. **`FlowStarted`** — في أول جولة للجلسة المؤجلة فقط.
+4. **`prepare_conversational_turn`** — إضافة رسالة المستخدم و`last_user_message` وتصنيف اختياري.
+5. **تنفيذ الرسم** — `@start` → `@router` → معالجات `@listen`.
+6. **نهاية التشغيل** — يُتخطى `flow_finished` والتتبع لكل جولة عند التأجيل؛ `Agent.kickoff()` / crews لا تغلق دفعة الأب.
+
+استدعِ **`append_message("assistant", reply)`** في المعالجات. سطر المستخدم محفوظ عند kickoff — لا تُضفه مرة أخرى.
+
+## `ConversationalConfig` (افتراضيات على مستوى الصنف)
+
+عيّن على صنف `Flow` كـ `conversational_config: ClassVar[ConversationalConfig | None]`.
+
+| الحقل | الافتراضي | الغرض |
+|-------|-----------|--------|
+| `default_intents` | `None` | تسميات outcome للتصنيف التلقائي قبل kickoff |
+| `intent_llm` | `None` | نموذج التصنيف (مطلوب عند وجود intents) |
+| `interactive_prompt` | `"You: "` | مطالبة `kickoff(interactive=True)` |
+| `interactive_timeout` | `None` | مهلة لكل سطر في الوضع التفاعلي |
+| `exit_commands` | `exit`, `quit` | كلمات إنهاء الوضع التفاعلي |
+| `defer_trace_finalization` | `True` | إبقاء دفعة trace واحدة مفتوحة بين الجولات |
+
+يمكن التجاوز لكل kickoff عبر `intents=` و`intent_llm=`.
+
+## `ChatState` (شكل الحالة الموصى به للحفظ)
+
+```python
+from crewai.flow import ChatState
+
+
+class MyChatState(ChatState):
+    # موروث: id, messages, last_user_message, last_intent, session_ready
+    research_turn_count: int = 0
+    custom_flag: bool = False
+```
+
+| الحقل | الدور |
+|-------|------|
+| `id` | UUID الجلسة (مثل `session_id` / `inputs["id"]`) |
+| `messages` | قائمة `{role, content}` لسجل LLM |
+| `last_user_message` | آخر سطر مستخدم في هذه الجولة |
+| `last_intent` | تسمية المسار بعد التصنيف (إن وُجد) |
+| `session_ready` | علم bootstrap لمرة واحدة |
+
+`ConversationalInputs` هو `TypedDict` لـ `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
+
+## API المحادثة على `Flow`
+
+### معاملات `kickoff` / `kickoff_async`
+
+| المعامل | الغرض |
+|---------|--------|
+| `user_message` | نص هذه الجولة (أو `{"role": "user", "content": "..."}`) |
+| `session_id` | UUID المحادثة → `inputs["id"]` / `state.id` |
+| `intents` | تسميات outcome لـ `classify_intent` قبل kickoff |
+| `intent_llm` | LLM للتصنيف (مطلوب مع `intents`) |
+| `interactive` | حلقة CLI عبر `ask()` (للعروض المحلية فقط) |
+| `interactive_prompt` | مطالبة الوضع التفاعلي |
+| `interactive_timeout` | مهلة `ask()` لكل سطر |
+| `exit_commands` | كلمات إنهاء الوضع التفاعلي |
+| `inputs` | حقول حالة إضافية |
+| `restore_from_state_id` | استنساخ من flow محفوظ آخر |
+
+### سمات المثيل
+
+| السمة | الغرض |
+|-------|--------|
+| `conversational_config` | افتراضيات `ConversationalConfig` على مستوى الصنف |
+| `defer_trace_finalization` | علم المثيل؛ يُضبط تلقائياً من config عند kickoff |
+| `suppress_flow_events` | يخفي لوحات console؛ **التتبع يُسجّل** |
+| `stream` | بث؛ مع `ChatSession.handle_turn(..., stream=True)` |
+
+### طرق وخصائص
+
+| الاسم | الوصف |
+|------|--------|
+| `append_message(role, content, **extra)` | إضافة إلى `state.messages` |
+| `conversation_messages` | سجل للقراءة فقط لاستدعاءات LLM |
+| `classify_intent(text, outcomes, *, llm, context=None)` | تعيين outcome |
+| `receive_user_message(text, *, outcomes=None, llm=None)` | إضافة رسالة مستخدم؛ `last_intent` اختياري |
+| `finalize_session_traces()` | إصدار `flow_finished` المؤجل وإنهاء دفعة trace |
+| `_should_defer_trace_finalization()` | هل يُؤجل إنهاء trace لكل جولة |
+| `input_history` | سجل تدقيق مطالبات وردود `ask()` |
+
+### مساعدات الوحدة (`crewai.flow.conversation`)
+
+| الدالة | الوصف |
+|--------|--------|
+| `normalize_kickoff_inputs(...)` | دمج kwargs المحادثة في `inputs` |
+| `get_conversation_messages(flow)` | قراءة الرسائل من الحالة أو المخزن |
+| `append_message(flow, ...)` | مثل طريقة المثيل |
+| `prepare_conversational_turn(flow, ...)` | تهيئة الجولة (عادةً kickoff يستدعيها) |
+| `receive_user_message(flow, ...)` | مثل طريقة المثيل |
+| `set_state_field(flow, name, value)` | تعيين حقل dict أو Pydantic |
+| `get_conversational_config(flow)` | قراءة `conversational_config` |
+| `input_history_to_messages(entries)` | تحويل `input_history` لصيغة رسائل LLM |
+
+## أنماط توجيه النية
+
+### أ. تصنيف مسبق عبر `ConversationalConfig` (الأبسط)
+
+عيّن `default_intents` و`intent_llm`. كل kickoff يصنّف قبل `@router`؛ اقرأ `self.state.last_intent` في `route()`.
+
+### ب. تصنيف داخل `@router` (مطالبات أغنى)
+
+عيّن `default_intents=None` ليضيف kickoff الرسالة فقط. في `route()` استدعِ `classify_intent`:
+
+```python
+@router(bootstrap)
+def route(self):
+    intent = self.classify_intent(
+        self._routing_prompt(self.state.last_user_message),
+        ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
+    )
+    self.state.last_intent = intent
+    return intent
+```
+
+للبحث على الويب أو أدوات متعددة الخطوات استخدم **`@listen("RESEARCH")`** مع `Agent.kickoff()` وأدوات — وليس `LLM.call()` فقط.
+
+## عندما ينتهي الـ flow ويستمر المستخدم
+
+`FlowFinished` يعني أن **تنفيذ الرسم هذا** اكتمل. تستمر المحادثة بـ `kickoff` آخر ونفس `session_id`. `@persist` يستعيد `messages` والأعلام والسياق.
+
+**نمط الحفظ:** يُفضّل `@persist` على **خطوة نهائية واحدة** (مثل `finalize`) وليس على صنف `Flow` بالكامل. الحفظ على مستوى الصنف بعد كل method قد يفقد تحديثات المعالجات في نفس الجولة.
+
+لا تستخدم `@human_feedback` لأسطر المتابعة في الدردشة إلا عند الحاجة لموافقة بشرية على مخرجات خطوة محددة.
+
+## `Flow` المحادثاتي (تجريبي)
+
+<Warning>
+  **ميزة تجريبية.** سطح `Flow` المحادثاتي (`conversational = True`،
+  `handle_turn`، `ConversationConfig`، `RouterConfig`،
+  `ConversationState`، الرسم البياني المدمج والمساعدات) يقع تحت
+  `crewai.experimental` وقد يتغير شكله قبل التخرج. ثبّت إصدار CrewAI إذا
+  كنت تعتمد على سلوك محدد، وراقب changelog للتحديثات الكاسرة. الملاحظات
+  والمشاكل مرحب بها.
+</Warning>
+
+فعّل الرسم المحادثاتي بتعيين `conversational = True` على صنف فرعي من `Flow`. عندئذٍ يُظهر `Flow` الأساسي رسم `@start` / `@router` / `converse_turn` / `end_conversation` مدمجاً، ويدير `state.messages`، ويُشغّل LLM التوجيه، ويبقي دفعة trace مفتوحة عبر الجولات. أنت تكتب **المسارات المخصصة** فقط؛ والإطار يتولى الباقي.
+
+استخدمه عندما تريد دردشة متعددة الجولات مع موجّه قائم على LLM ومعالجات لكل مسار دون توصيل دورة الحياة يدوياً. استخدم `Flow[ChatState]` (النمط الأدنى مستوى في الأعلى) عندما تحتاج تحكماً كاملاً.
+
+### مثال سريع
+
+```python
+from crewai import LLM, Flow
+from crewai.flow import listen
+from crewai.experimental.conversational import (
+    ConversationConfig,
+    ConversationState,
+    RouterConfig,
+)
+
+
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # المسارات + الأوصاف تُكتشف تلقائياً من معالجات @listen
+)
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    @listen("INTERNET_SEARCH")
+    def handle_internet_search(self) -> str:
+        """Fresh web research, current news, real-time lookups."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+    @listen("CREWAI_DOCS")
+    def handle_crewai_docs(self) -> str:
+        """Look up the CrewAI documentation for framework/API questions."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+
+flow = SupportFlow()
+try:
+    flow.handle_turn("ماذا يمكنك أن تفعل؟")                   # يوجَّه إلى converse (مدمج)
+    flow.handle_turn("ابحث في الويب عن أخبار الذكاء الاصطناعي.")  # يوجَّه إلى INTERNET_SEARCH
+    flow.handle_turn("لخص النتيجة الأولى.")                    # يعود إلى converse
+finally:
+    flow.finalize_session_traces()
+```
+
+### `ConversationConfig`
+
+مزخرف صنف يُلحق افتراضيات الدردشة على مستوى الصنف.
+
+| الحقل | الافتراضي | الغرض |
+|-------|-----------|-------|
+| `system_prompt` | `slices.conversational_system_prompt` من i18n | رسالة system يستخدمها `converse_turn` المدمج. مرر `""` للتعطيل التام. |
+| `llm` | `None` | LLM المحادثة (يستخدمه `converse_turn` وكاحتياطي للموجّه). |
+| `router` | `None` | `RouterConfig` للتوجيه عبر LLM. بدونه، يسقط الـ flow دائماً إلى `converse`. |
+| `answer_from_history_prompt` | افتراضي الإطار | رسالة system للمسار الاختياري `answer_from_history`. |
+| `answer_from_history_llm` | `None` | يُفعّل الاختصار `answer_from_history` عند تعيينه. |
+| `intent_llm` | `None` | LLM لمسار التصنيف المسبق القديم `intents=`/`default_intents`. |
+| `default_intents` | `None` | تسميات النتائج للتصنيف المسبق القديم. |
+| `visible_agent_outputs` | `None` | `"all"` أو قائمة بأسماء الـ agents الذين تُرفع مخرجاتهم من `append_agent_result()` إلى رسائل عامة. |
+| `defer_trace_finalization` | `True` | يبقي دفعة trace واحدة مفتوحة عبر استدعاءات `handle_turn()`. |
+
+### `RouterConfig` وفهرس المسارات المُولَّد تلقائياً
+
+```python
+RouterConfig(
+    prompt="تأطير اختياري للنطاق (سياسة، صوت، شخصية).",
+    response_format=MyRoute,        # اختياري؛ يُولَّد تلقائياً عند الإغفال
+    llm=ROUTER_LLM,                  # يسقط إلى ConversationConfig.llm
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # اختياري؛ يُستنتج من المستمعين
+    route_descriptions={
+        "INTERNET_SEARCH": "تجاوز الـ docstring لهذا المسار فقط.",
+    },
+    default_intent="converse",       # يُستخدم عند فشل LLM أو غيابه
+    fallback_intent="converse",      # يُستخدم عندما يعيد LLM مساراً غير صالح
+    intent_field="intent",
+)
+```
+
+تُبنى رسالة الموجّه إلى LLM تلقائياً. لكل مسار يختار الإطار وصفاً بهذا الترتيب من الأولوية:
+
+1. `RouterConfig.route_descriptions[label]` — تجاوز صريح.
+2. `Flow.builtin_route_descriptions[label]` — نص جاهز من الإطار لـ `converse` و`end` و`answer_from_history` (مصاغ لـ LLM التوجيه).
+3. أول سطر غير فارغ من docstring معالج `@listen(label)`.
+4. فارغ (المسار يظهر في الفهرس بلا وصف).
+
+عملياً، **إضافة مسار جديد = `@listen("X")` + docstring من سطر واحد**:
+
+```python
+@listen("INTERNET_SEARCH")
+def handle_internet_search(self) -> str:
+    """Fresh web research, current news, real-time lookups."""
+    ...
+```
+
+…وسيرى LLM التوجيه:
+
+```
+Routes:
+- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
+- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
+- converse: Ordinary chat, follow-ups, summaries, clarifications…
+- end: User signals the conversation is finished (goodbye, exit, done).
+```
+
+`RouterConfig.prompt` مخصص لـ **تأطير النطاق** (شخصية المساعد، قواعد العمل، النبرة). فهرس المسارات يُبنى تلقائياً — لا تُدرج المسارات في `prompt`؛ سيختل التزامن لحظة إضافة معالج جديد.
+
+### المسارات المدمجة
+
+| المسار | المعالج | الغرض |
+|--------|---------|-------|
+| `converse` | `converse_turn` | معالج الدردشة الافتراضي. يستدعي `ConversationConfig.llm` بـ system prompt + التاريخ القانوني للرسائل. |
+| `end` | `end_conversation` | يضبط `state.ended = True` ويُصدر رد إنهاء. |
+| `answer_from_history` | `answer_from_history_turn` | اختياري. يُوجَّه إليه عندما يكون `ConversationConfig.answer_from_history_llm` مُعيَّناً ويمكن الإجابة على الرسالة من التاريخ فقط. |
+
+يمكنك تجاوز أي من هذه بتعريف معالج بنفس الاسم في الصنف الفرعي.
+
+### دلالات `handle_turn()`
+
+`flow.handle_turn(message)` يُشغّل جولة واحدة:
+
+1. يعيد ضبط تعقّب التنفيذ لكل جولة (`_completed_methods`, `_method_outputs`) ليُعاد تشغيل الرسم — بدون ذلك، استدعاءات `kickoff` المتكررة على نفس النسخة ستُحدث دائرة قصر من الجولة الثانية لأن `Flow.kickoff_async` يعتبر `inputs={"id": ...}` استعادة من نقطة تفتيش.
+2. يُلحق رسالة المستخدم بـ `state.messages` ويضبط `current_user_message` / `last_user_message`. يُحافَظ على `last_intent` **من الجولة السابقة** كي يستخدمها LLM التوجيه كإشارة.
+3. يُشغّل `conversation_start` → `route_conversation` → معالج `@listen` المختار.
+4. يخزّن الموجّه قراره في `state.last_intent` (يكون مرئياً لسياق التوجيه في الجولة التالية).
+5. إذا أعاد معالجك سلسلة نصية ولم يستدعِ `append_assistant_message`، فإن `handle_turn` يُلحقها نيابةً عنك.
+
+يمكنك أيضاً استدعاء `flow.kickoff(user_message=..., session_id=...)` مباشرةً — نفس منطق الإعادة والتشغيل يعمل. `handle_turn` هو الغلاف المريح.
+
+### سلوك موجّه مخصص
+
+لتشغيل آثار جانبية (إعداد ناقل أحداث، قياس عن بُعد) في كل قرار توجيه، تجاوز `route_turn`:
+
+```python
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    def route_turn(self, context: dict[str, Any]) -> str | None:
+        self.event_bus = MyBus(self)
+        return super().route_turn(context)
+```
+
+لتجاوز موجّه LLM واختيار مسار برمجياً، أعد سلسلة نصية من `route_turn`؛ إعادة `None` تسقط إلى `_route_with_config(...)`.
+
+### `append_assistant_message` و`append_agent_result`
+
+داخل معالج `@listen(label)`، اختر:
+
+- `self.append_assistant_message(text)` — يضيف جولة مساعد مرئية للمستخدم إلى `state.messages`. سيراها `converse_turn` في الجولة التالية.
+- `self.append_agent_result(agent_name, result, visibility="private")` — يسجّل حدثاً منظماً في `state.events` وموضوعاً في `state.agent_threads[agent_name]`. الرؤية العامة تستدعي `append_assistant_message` أيضاً. استخدم النتائج الخاصة للعمل الجانبي الذي يجب ألا يلوث التاريخ القانوني.
+
+يمكن لـ `ConversationConfig.visible_agent_outputs` رفع النتائج الخاصة لـ agents محددين إلى عامة عالمياً (`"all"` أو قائمة بالأسماء).
+
+## التتبع عبر الجولات
+
+مع `defer_trace_finalization=True` (افتراضي في `ConversationalConfig`):
+
+- **دفعة trace واحدة** لجلسة الدردشة.
+- **`flow_started`** في الجولة الأولى فقط؛ **`flow_finished`** مرة في `finalize_session_traces()`.
+- **`kickoff` لكل جولة** لا يطبع "Trace batch finalized".
+- **العمل المتداخل** (`Agent.kickoff()`, crews, Exa) يُلحق بدفعة **الأب**؛ flow داخلي من `AgentExecutor` لا يغلق دفعة الجلسة مبكراً.
+
+```python
+try:
+    while True:
+        line = input("You: ").strip()
+        if not line:
+            break
+        flow.kickoff(user_message=line, session_id=session_id)
+finally:
+    flow.finalize_session_traces()
+```
+
+`ChatSession.close()` يستدعي `finalize_session_traces()` عند التأجيل.
+
+`suppress_flow_events=True` يخفي لوحات Rich فقط؛ أحداث trace والـ methods تُصدر.
+
+### دورة حياة trace لـ `Flow` المحادثاتي
+
+يستخدم [`Flow` المحادثاتي](#flow-المحادثاتي-تجريبي) التجريبي نفس دورة حياة tracing: `defer_trace_finalization` افتراضياً `True`، فيبقي كل `handle_turn()` أثر الجلسة مفتوحاً. أنهِ دوماً عند نهاية الجلسة — لُف حلقتك بـ `try/finally` واستدعِ `flow.finalize_session_traces()` عند الخروج. بدون ذلك، تبقى الدفعة مفتوحة وقد لا تُصدَّر آخر محادثة أبداً.
+
+## البث
+
+اضبط `stream = True` على صنف `Flow`. عندئذٍ يُصدر `kickoff(...)` أحداث `assistant_delta` (وما يرتبط بها) عبر ناقل الأحداث القياسي.
+
+## الاستيراد
+
+```python
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    ConversationalInputs,
+    Flow,
+    listen,
+    persist,
+    router,
+    start,
+)
+```
+
+## مراجع
+
+- [إتقان إدارة حالة Flow](/ar/guides/flows/mastering-flow-state)
+- [أنشئ أول Flow](/ar/guides/flows/first-flow)
+- Demo: `lib/crewai/runner_conversational_flow_simple.py` — REPL بسيط مع `RESEARCH` ووكيل Exa

docs/ar/guides/flows/first-flow.mdx

@@ -272,6 +272,7 @@ crewai flow plot
 3. استكشف دوال `and_` و`or_` لتنفيذ متوازٍ أكثر تعقيدًا
 4. اربط Flow بواجهات API خارجية وقواعد بيانات وواجهات مستخدم
 5. ادمج عدة Crews متخصصة في Flow واحد
+6. أنشئ تطبيقات دردشة متعددة الجولات مع [تدفقات المحادثة](/ar/guides/flows/conversational-flows) (`kickoff` لكل رسالة، `ChatSession`، تأجيل التتبع)
 
 <Check>
 تهانينا! لقد بنيت بنجاح أول CrewAI Flow يجمع بين الكود العادي واستدعاءات LLM المباشرة ومعالجة Crew لإنشاء دليل شامل. هذه المهارات الأساسية تمكّنك من إنشاء تطبيقات AI متطورة بشكل متزايد.

docs/ar/guides/flows/mastering-flow-state.mdx

@@ -20,6 +20,8 @@ mode: "wide"
 5. **توسيع تطبيقاتك** - دعم سير العمل المعقدة بتنظيم بيانات مناسب
 6. **تمكين التطبيقات الحوارية** - تخزين والوصول إلى سجل المحادثات للتفاعلات الواعية بالسياق
 
+للدردشة متعددة الجولات (`kickoff` لكل سطر مستخدم، `ChatState`، توجيه النية، تأجيل التتبع، و`ChatSession`)، راجع [تدفقات المحادثة](/ar/guides/flows/conversational-flows).
+
 ## أساسيات إدارة الحالة
 
 ### نهجان لإدارة الحالة

docs/en/changelog.mdx

@@ -4,6 +4,76 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Jun 03, 2026">
+  ## v1.14.7a1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
+
+  ## What's Changed
+
+  ### Features
+  - Add crew trained agents file support
+  - Add native Snowflake Cortex LLM provider
+  - Add Databricks integration guide
+  - Add Snowflake integration guide
+
+  ### Bug Fixes
+  - Fix CLI by restoring `[project.scripts]` in crewai package for UV tool install
+  - Resolve file input reliability issues
+  - Fix incomplete tool result histories in Snowflake Claude
+  - Handle stringified tool calls for Snowflake Claude
+  - Re-arm multi-source `or_` listeners across router-driven cycles
+
+  ### Performance
+  - Improve crewai import speed by lazy-loading docling imports
+
+  ### Refactoring
+  - Split `flow.py` into DSL, definition, and runtime
+
+  ## Contributors
+
+  @Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
+
+</Update>
+
+<Update label="May 28, 2026">
+  ## v1.14.6
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.6)
+
+  ## What's Changed
+
+  ### Features
+  - Enhance StdioTransport to prevent environment variable leakage
+  - Enhance planning configuration and observation handling
+  - Declare env_vars on DatabricksQueryTool
+  - Add Agent Control Plane docs
+
+  ### Bug Fixes
+  - Fix structured output leaks in tool-calling loops
+  - Drop unroundtrippable callbacks and adapter state in checkpoint
+  - Serialize type[BaseModel] fields as JSON schema in checkpoint
+  - Avoid orphan task_started on resume scope restore
+  - Allow AgentExecutor to restore from checkpoint
+  - Correct mongodb typo to pymongo in package_dependencies
+
+  ### Documentation
+  - Add ACP (Beta) docs navigation block to Agent Control Plane pages
+  - Remove consensual process references from processes page
+  - Restructure checkpointing page
+  - Document one-time admin package install step
+  - Migrate Secrets Manager / Workload Identity from replicated-config
+  - Remove `{" "}` JSX expressions breaking `<Steps>` render
+
+  ### Refactoring
+  - Move Skills Repository to experimental + CREWAI_EXPERIMENTAL gate
+
+  ## Contributors
+
+  @akaKuruma, @alex-clawd, @github-actions[bot], @greysonlalonde, @heitorado, @iris-clawd, @lorenzejay, @lucasgomide, @mattatcha, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="May 27, 2026">
   ## v1.14.6a2
 

docs/en/concepts/llms.mdx

@@ -107,7 +107,7 @@ There are different places in CrewAI code where you can specify the model to use
 </Tabs>
 
 <Info>
-  CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, and AWS Bedrock — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`).
+  CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock, and Snowflake Cortex — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`).
 
   All other providers are powered by **LiteLLM**. If you plan to use any of them, add it as a dependency to your project:
   ```bash
@@ -291,6 +291,55 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
+  <Accordion title="Snowflake Cortex">
+    CrewAI provides native integration with the Snowflake Cortex REST API through its OpenAI-compatible Chat Completions endpoint. This avoids LiteLLM fallback for `snowflake/...` models. Snowflake Cortex currently supports Chat Completions only in CrewAI, so use the default `api` mode and do not set `api="responses"`.
+
+    ```toml Code
+    # Required
+    SNOWFLAKE_PAT=<your-programmatic-access-token>
+    SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
+
+    # Alternative account configuration
+    SNOWFLAKE_ACCOUNT=<account-identifier>
+    ```
+
+    **Basic Usage:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/openai-gpt-4.1",
+        temperature=0.7,
+        max_completion_tokens=1024,
+    )
+    ```
+
+    **Claude Models on Cortex:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/claude-sonnet-4-5",
+        max_completion_tokens=1024,
+        stream=True,
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `SNOWFLAKE_PAT`, `SNOWFLAKE_TOKEN`, or `SNOWFLAKE_JWT`: token used as the Bearer credential
+    - `SNOWFLAKE_ACCOUNT_URL`: full Snowflake account URL
+    - `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_ACCOUNT_ID`, or `SNOWFLAKE_ACCOUNT_IDENTIFIER`: account identifier used to build the account URL
+
+    Snowflake REST requests use the user's default Snowflake role. Make sure that role has `SNOWFLAKE.CORTEX_USER` or `SNOWFLAKE.CORTEX_REST_API_USER`. Database, schema, warehouse, and explicit role parameters are not required by the Cortex REST Chat Completions endpoint.
+
+    **Features:**
+    - Native provider selection with `model="snowflake/<model-name>"`
+    - Streaming and non-streaming Chat Completions only; `api="responses"` is not supported
+    - Token usage tracking
+    - Function calling for Snowflake-hosted OpenAI and Claude models
+    - Automatic removal of invalid trailing assistant prefill for Snowflake Claude models
+  </Accordion>
+
   <Accordion title="Anthropic">
     CrewAI provides native integration with Anthropic through the Anthropic Python SDK.
 
@@ -903,6 +952,61 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
+  <Accordion title="NVIDIA Nemotron">
+    NVIDIA Nemotron models are designed for demanding agentic workloads, including complex reasoning, long-context analysis, tool use, multilingual tasks, and high-stakes RAG.
+
+    The `NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` model is a frontier-scale open-weight model from NVIDIA with 550B total parameters and 55B active parameters. It uses a LatentMoE architecture that combines Mamba-2, MoE, Attention, and Multi-Token Prediction (MTP), and supports context lengths up to 1M tokens.
+
+    <Info>
+      `NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` is a very large model. NVIDIA lists minimum serving requirements of 4x GB200, 4x B200, 4x GB300, 4x B300, or 8x H100 GPUs. For most CrewAI users, the recommended path is to use NVIDIA NIM or another OpenAI-compatible hosted endpoint rather than running it locally.
+    </Info>
+
+    **Hosted NVIDIA NIM usage:**
+    ```toml Code
+    NVIDIA_API_KEY=<your-api-key>
+    ```
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="nvidia_nim/nvidia/nvidia-nemotron-3-ultra-550b-a55b",
+        temperature=0.2,
+        max_tokens=4096,
+    )
+    ```
+
+    **Self-hosted OpenAI-compatible endpoint:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="openai/nvidia-nemotron-3-ultra-550b-a55b-nvfp4",
+        base_url="https://your-nemotron-endpoint.example.com/v1",
+        api_key="your-api-key",
+        temperature=0.2,
+        max_tokens=4096,
+    )
+    ```
+
+    **Model details:**
+
+    | Model | Context Window | Best For |
+    |-------|----------------|----------|
+    | `nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` | Up to 1M tokens | Frontier reasoning, complex agentic workflows, long-context analysis, tool use, multilingual reasoning, and high-stakes RAG |
+
+    **Supported languages:** English, French, Spanish, Italian, German, Japanese, Korean, Hindi, Brazilian Portuguese, and Chinese.
+
+    **Reasoning mode:** Nemotron 3 Ultra supports configurable reasoning via its chat template using `enable_thinking=True` or `enable_thinking=False`. If you are using a hosted endpoint, check your provider's documentation for how that flag is exposed.
+
+    For model details, license, and deployment guidance, see the [NVIDIA Nemotron 3 Ultra model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4).
+
+    **Note:** Hosted NVIDIA NIM usage uses LiteLLM. Add it as a dependency to your project:
+    ```bash
+    uv add 'crewai[litellm]'
+    ```
+  </Accordion>
+
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
 
     NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).

docs/en/concepts/processes.mdx

@@ -16,7 +16,6 @@ mode: "wide"
 
 - **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
 - **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. A manager language model (`manager_llm`) or a custom manager agent (`manager_agent`) must be specified in the crew to enable the hierarchical process, facilitating the creation and management of tasks by the manager.
-- **Consensual Process (Planned)**: Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.
 
 ## The Role of Processes in Teamwork
 Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
@@ -59,9 +58,9 @@ Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent
 
 ## Process Class: Detailed Overview
 
-The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
+The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`).
 
 ## Conclusion
 
 The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. 
-This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.
+This documentation has been updated to reflect the latest features and enhancements, ensuring users have access to the most current and comprehensive information.

docs/en/concepts/training.mdx

@@ -187,7 +187,7 @@ flowchart TD
 - **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
 - **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
 - Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
-- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
+- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, pass that path with `Crew(trained_agents_file="my_custom_trained.pkl")`, set `CREWAI_TRAINED_AGENTS_FILE`, or use `crewai run -f my_custom_trained.pkl`.
 - You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
 
 It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.

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

@@ -6,6 +6,14 @@ icon: "gauge"
 mode: "wide"
 ---
 
+<Info>
+  **ACP (Beta) Docs Navigation**
+
+  - [Overview](/en/enterprise/features/agent-control-plane/overview)
+  - **Monitoring** *(you are here)*
+  - [Rules](/en/enterprise/features/agent-control-plane/rules)
+</Info>
+
 ## Overview
 
 The **Automations** tab is the read-only operations view of the [Agent Control Plane](/en/enterprise/features/agent-control-plane/overview). It combines two metric cards, an interactive sankey, and two sub-tables — **Automations** and **Consumption** — that you can search, filter, and sort.

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

@@ -5,6 +5,14 @@ sidebarTitle: Overview
 icon: "book-open"
 ---
 
+<Info>
+  **ACP (Beta) Docs Navigation**
+
+  - **Overview** *(you are here)*
+  - [Monitoring](/en/enterprise/features/agent-control-plane/monitoring)
+  - [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 **Rules** tabs — that lets your team:

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

@@ -6,6 +6,14 @@ icon: "shield-check"
 mode: "wide"
 ---
 
+<Info>
+  **ACP (Beta) Docs Navigation**
+
+  - [Overview](/en/enterprise/features/agent-control-plane/overview)
+  - [Monitoring](/en/enterprise/features/agent-control-plane/monitoring)
+  - **Rules** *(you are here)*
+</Info>
+
 ## Overview
 
 Rules let you apply policies — today: **PII Redaction** — 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.

docs/en/enterprise/guides/deploy-to-amp.mdx

@@ -164,6 +164,12 @@ You need to push your crew to a GitHub repository. If you haven't created a crew
       ![Select Repository](/images/enterprise/select-repo.png)
     </Frame>
 
+    <Tip>
+      If your Crew or Flow is inside a monorepo subfolder, expand **Advanced**
+      and set a working directory before deploying. See
+      [Monorepo Deployments](/en/enterprise/guides/monorepo-deployments).
+    </Tip>
+
   </Step>
 
   <Step title="Set Environment Variables">

docs/en/enterprise/guides/monorepo-deployments.mdx

@@ -0,0 +1,225 @@
+---
+title: "Monorepo Deployments"
+description: "Deploy a Crew or Flow from a subfolder in a larger repository"
+icon: "folder-tree"
+mode: "wide"
+---
+
+<Note>
+  Use a working directory when your Crew or Flow lives inside a larger
+  repository. CrewAI AMP validates, builds, tests, and runs the automation from
+  that subfolder instead of the repository root.
+</Note>
+
+## When to Use This
+
+Monorepo deployments are useful when one repository contains multiple
+automations, shared packages, or other application code:
+
+```text
+company-ai/
+|-- uv.lock
+|-- packages/
+|   `-- shared_tools/
+`-- crews/
+    |-- support_agent/
+    |   |-- pyproject.toml
+    |   `-- src/
+    |       `-- support_agent/
+    |           |-- main.py
+    |           `-- crew.py
+    `-- research_flow/
+        |-- pyproject.toml
+        `-- src/
+            `-- research_flow/
+                `-- main.py
+```
+
+To deploy `support_agent`, set the working directory to:
+
+```text
+crews/support_agent
+```
+
+AMP still pulls or uploads the whole repository, but it treats the selected
+folder as the automation project root.
+
+## What the Working Directory Controls
+
+When a working directory is set, AMP uses that folder for:
+
+- Project validation, including `pyproject.toml`, `src/`, and the Crew or Flow entry point
+- Dependency installation with `uv`
+- The running process working directory
+- The `CREW_ROOT_DIR` environment variable
+
+Leaving the field empty keeps the existing behavior and uses the repository
+root.
+
+## Supported Sources
+
+You can set a working directory when creating a deployment from:
+
+- A connected GitHub repository
+- A Git repository configured in AMP
+- A ZIP upload
+
+<Info>
+  Configure working directories in the AMP web interface. The
+  `crewai deploy create` CLI flow does not prompt for this field.
+</Info>
+
+You can also add or change the working directory on an existing deployment from
+the deployment's **Settings** page. The change takes effect on the next deploy.
+
+<Warning>
+  Working directories and auto-deploy cannot be used together. If a deployment
+  has a working directory, auto-deploy is disabled for that deployment. Turn
+  auto-deploy off before setting a working directory.
+</Warning>
+
+## Configure a New Deployment
+
+<Steps>
+  <Step title="Open Deploy from Code">
+    In CrewAI AMP, create a new deployment and choose your source: GitHub, Git
+    Repository, or ZIP upload.
+  </Step>
+
+  <Step title="Select the repository, branch, or ZIP file">
+    Choose the repository and branch that contain your monorepo, or upload a ZIP
+    file whose root contains the monorepo contents.
+  </Step>
+
+  <Step title="Open Advanced settings">
+    Expand the **Advanced** section in the deploy form.
+  </Step>
+
+  <Step title="Enter the working directory">
+    Enter the path from the repository root to the Crew or Flow project:
+
+    ```text
+    crews/support_agent
+    ```
+
+    Do not include a leading slash.
+  </Step>
+
+  <Step title="Deploy">
+    Add any required environment variables, then start the deployment.
+  </Step>
+</Steps>
+
+## Configure an Existing Deployment
+
+<Steps>
+  <Step title="Open the deployment settings">
+    Go to your automation in AMP and open **Settings**.
+  </Step>
+
+  <Step title="Turn off auto-deploy if needed">
+    If auto-deploy is enabled, disable it first. The working directory field is
+    unavailable while auto-deploy is on.
+  </Step>
+
+  <Step title="Set the working directory">
+    In **Basic settings**, enter the subfolder path, such as:
+
+    ```text
+    crews/support_agent
+    ```
+  </Step>
+
+  <Step title="Redeploy">
+    Save the setting and redeploy the automation. The new working directory is
+    used on the next deploy.
+  </Step>
+</Steps>
+
+## Path Rules
+
+The working directory must be a relative path inside the repository or ZIP root.
+
+| Rule | Example |
+|------|---------|
+| Use a relative path | `crews/support_agent` |
+| Do not start with `/` | `/crews/support_agent` is invalid |
+| Do not use `.` or `..` path segments | `crews/../support_agent` is invalid |
+| Use only letters, numbers, dashes, underscores, dots, and forward slashes | `crews/support agent` is invalid |
+| Keep the path at 255 characters or fewer | Longer paths are rejected |
+
+AMP trims leading and trailing whitespace, collapses repeated slashes, and
+removes trailing slashes. A blank value uses the repository root.
+
+## Lock Files and UV Workspaces
+
+The selected folder must contain the automation's `pyproject.toml` and `src/`
+directory. A `uv.lock` or `poetry.lock` file can live either in the selected
+folder or at the repository root.
+
+This supports both common monorepo layouts:
+
+<Tabs>
+  <Tab title="Project lock file">
+    ```text
+    company-ai/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            |-- uv.lock
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+
+  <Tab title="Workspace lock file">
+    ```text
+    company-ai/
+    |-- uv.lock
+    |-- packages/
+    |   `-- shared_tools/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+</Tabs>
+
+<Tip>
+  If your automation imports shared packages from elsewhere in the monorepo,
+  declare those packages in `pyproject.toml` using UV workspace, path, or source
+  configuration. AMP runs the automation from the selected folder, so shared
+  code should be installed as a dependency instead of relying on the repository
+  root being on the Python path.
+</Tip>
+
+## Troubleshooting
+
+### Working Directory Not Found
+
+Check that the path is relative to the repository or ZIP root. For ZIP uploads,
+the ZIP contents must include the working directory path exactly as entered.
+
+### Missing pyproject.toml
+
+The working directory should point to the Crew or Flow project folder, not just
+to a parent folder that contains several projects.
+
+### Missing uv.lock or poetry.lock
+
+Commit a lock file either in the selected project folder or in the repository
+root. For UV workspaces, keeping `uv.lock` at the workspace root is supported.
+
+### Auto-Deploy Is Unavailable
+
+Auto-deploy is disabled while a working directory is set. Use manual redeploys
+or trigger redeployments from CI/CD with the AMP API instead.
+
+<Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
+  Continue with the deployment guide after choosing your monorepo working
+  directory.
+</Card>

docs/en/enterprise/integrations/databricks.mdx

@@ -0,0 +1,123 @@
+---
+title: Databricks Integration
+description: "Connect CrewAI agents to Databricks Genie, SQL, Unity Catalog Functions, and Vector Search through Databricks managed MCP servers."
+icon: "layer-group"
+mode: "wide"
+---
+
+## Overview
+
+Connect your CrewAI agents directly to your Databricks workspace through [Databricks managed MCP servers](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp). The Databricks integration lets your agents ask natural-language questions with **Genie**, run governed **SQL**, call **Unity Catalog Functions**, and retrieve documents with **Vector Search** — all without writing or hosting any connector code, and with Unity Catalog permissions enforced on every call.
+
+Under the hood, the Databricks integration is a managed wrapper around CrewAI's [Custom MCP Server](/en/enterprise/guides/custom-mcp-server) support. Databricks exposes each capability as its own [Model Context Protocol](https://modelcontextprotocol.io/) endpoint, and CrewAI connects to them securely on your behalf. Because each server is added separately, you can enable exactly the capabilities your crews need.
+
+## Key Capabilities
+
+<CardGroup cols={2}>
+  <Card title="Genie" icon="comments">
+    Ask questions in plain language and get grounded answers from your data with [Genie](https://docs.databricks.com/aws/en/genie/), which queries Genie Spaces and Unity Catalog and links back to the Databricks UI.
+  </Card>
+  <Card title="Databricks SQL" icon="database">
+    Run governed SQL against your Databricks warehouses to query, transform, and author data pipelines directly from your agents.
+  </Card>
+  <Card title="Unity Catalog Functions" icon="function">
+    Invoke [Unity Catalog functions](https://docs.databricks.com/aws/en/udf/unity-catalog) to run predefined SQL and custom business logic as governed, reusable tools.
+  </Card>
+  <Card title="Vector Search" icon="magnifying-glass">
+    Retrieve relevant documents for RAG and knowledge workflows from [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) indexes using semantic similarity.
+  </Card>
+</CardGroup>
+
+Every server runs behind the Unity AI Gateway and enforces Unity Catalog access controls, so your agents only ever see the data and tools they're permitted to use.
+
+## Prerequisites
+
+Before using the Databricks integration, ensure you have:
+
+- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
+- A Databricks workspace with the capabilities you want to expose (Genie Spaces, SQL warehouses, Unity Catalog functions, or Vector Search indexes)
+- Appropriate [Unity Catalog privileges](https://docs.databricks.com/aws/en/data-governance/unity-catalog) on the underlying objects
+- Your Databricks workspace hostname (e.g. `your-workspace.cloud.databricks.com`)
+
+## Databricks Managed MCP Servers
+
+Databricks publishes a separate managed MCP server for each capability. CrewAI exposes these as individual connections, each configured with your workspace host and the relevant Unity Catalog identifiers. The endpoints follow these patterns:
+
+| Server | What it does | MCP URL pattern |
+|--------|--------------|-----------------|
+| **Genie** | Natural-language Q&A over a Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
+| **Databricks SQL** | Execute SQL against your warehouses | `https://<workspace-hostname>/api/2.0/mcp/sql` |
+| **Unity Catalog Functions** | Run registered UC functions | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
+| **Vector Search** | Query a Vector Search index | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
+
+<Note>
+You don't construct these URLs by hand — CrewAI builds each endpoint from the workspace host and identifiers (Genie Space ID, or catalog/schema) you provide when configuring the connection. For the full specification and the latest endpoint details, see the [Databricks managed MCP documentation](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp).
+</Note>
+
+## Connecting Databricks in CrewAI AMP
+
+<Frame>
+    <img src="/images/enterprise/databricks-configure.png" alt="Configure a Databricks managed MCP server in CrewAI AMP" />
+</Frame>
+
+Each Databricks capability — **Databricks Genie**, **Databricks SQL**, **Databricks Unity Catalog Functions**, and **Databricks Vector Search** — appears as its own MCP server under the Databricks group on the **Tools & Integrations** page. Configure the ones you need:
+
+<Steps>
+    <Step title="Open Tools & Integrations">
+        Navigate to **Tools & Integrations** in the left sidebar of CrewAI AMP and locate the **Databricks** group in the Connections list. You'll see the Genie, SQL, Unity Catalog Functions, and Vector Search servers listed beneath it.
+    </Step>
+
+    <Step title="Configure a server">
+        Click **Configure** next to the capability you want to enable and provide its connection details:
+
+        - **Workspace Host** — your Databricks workspace hostname (e.g. `my-workspace.cloud.databricks.com`).
+        - **Genie** — the **Genie Space ID** to query.
+        - **Unity Catalog Functions** — the **catalog** and **schema** that contain your functions.
+        - **Vector Search** — the **catalog** and **schema** that contain your index.
+        - **Databricks SQL** — no additional identifiers; queries run against your workspace's SQL warehouses.
+    </Step>
+
+    <Step title="Choose an authentication method">
+        Select how CrewAI authenticates to Databricks. **OAuth** is recommended.
+
+        - **Use OAuth** — Connect securely using OAuth 2.0. Each user authenticates individually, and Databricks issues tokens scoped to the capability (`genie`, `sql`, `unity-catalog`, or `vector-search`). CrewAI handles the authorization flow and refreshes tokens automatically.
+        - **Use personal access token** — Authenticate with a [Databricks personal access token](https://docs.databricks.com/aws/en/dev-tools/auth/pat). Use a least-privileged identity to limit exposure.
+    </Step>
+
+    <Step title="Authenticate">
+        Complete authentication. Once connected, the server's tools become available to your crews. Repeat for any other Databricks capabilities you want to enable.
+    </Step>
+</Steps>
+
+<Tip>
+Because each capability is a separate connection, you can mix and match — for example, enable Genie and Vector Search for a research crew while reserving SQL and Unity Catalog Functions for a data-engineering crew. Visibility settings let you control which team members can use each one.
+</Tip>
+
+## Using Databricks Tools in Your Crews
+
+Once connected, the tools each MCP server exposes appear alongside built-in connections on the **Tools & Integrations** page. You can:
+
+- **Assign tools to agents** in your crews just like any other CrewAI tool.
+- **Manage visibility** to control which team members can use each connection.
+- **Edit or remove** any connection at any time from the Connections list.
+
+Your agents can now ask Genie for grounded answers, run SQL against your warehouses, call Unity Catalog functions, and search Vector Search indexes — with results flowing back into their reasoning automatically.
+
+<Warning>
+Databricks enforces governance through Unity Catalog and the Unity AI Gateway: a user can only discover and invoke tools their workspace identity is permitted to use. If a tool call fails, confirm the connecting user (or token identity) has the required Unity Catalog privileges on the Genie Space, warehouse, function, or index. Some Genie and SQL queries run asynchronously and may take a moment to return results.
+</Warning>
+
+## Learn More
+
+<CardGroup cols={2}>
+  <Card title="Databricks Managed MCP Servers" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
+    Official Databricks documentation for the managed Genie, SQL, Unity Catalog Functions, and Vector Search MCP servers.
+  </Card>
+  <Card title="Custom MCP Servers in CrewAI" icon="plug" href="/en/enterprise/guides/custom-mcp-server">
+    Learn how CrewAI connects to any MCP server, the foundation the Databricks integration builds on.
+  </Card>
+</CardGroup>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with the Databricks integration or troubleshooting.
+</Card>

docs/en/enterprise/integrations/snowflake.mdx

@@ -0,0 +1,134 @@
+---
+title: Snowflake Integration
+description: "Connect CrewAI agents to Snowflake Cortex Analyst, Cortex Search, and SQL execution through the Snowflake-managed MCP server."
+icon: "snowflake"
+mode: "wide"
+---
+
+## Overview
+
+Connect your CrewAI agents directly to your Snowflake data through the [Snowflake-managed MCP server](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp). The Snowflake integration lets your agents query structured data with **Cortex Analyst**, search unstructured data with **Cortex Search**, and run governed SQL against your warehouses — all without writing or hosting any connector code.
+
+Under the hood, the Snowflake integration is a managed wrapper around CrewAI's [Custom MCP Server](/en/enterprise/guides/custom-mcp-server) support. Snowflake exposes its Cortex AI capabilities through a [Model Context Protocol](https://modelcontextprotocol.io/) endpoint, and CrewAI connects to it securely on your behalf. Any tool you expose on the Snowflake side — Cortex Analyst, Cortex Search, SQL execution, Cortex Agents, or your own custom tools — becomes available to your crews.
+
+## Key Capabilities
+
+<CardGroup cols={3}>
+  <Card title="Cortex Analyst" icon="chart-bar">
+    Ask questions in natural language and let [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) generate and run SQL against your **structured** data using rich semantic models.
+  </Card>
+  <Card title="Cortex Search" icon="magnifying-glass">
+    Retrieve relevant **unstructured** data for RAG and knowledge workflows with [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview), Snowflake's fully managed search service.
+  </Card>
+  <Card title="SQL Execution" icon="database">
+    Run governed SQL queries directly against your Snowflake warehouses, with configurable read-only mode, timeouts, and warehouse selection.
+  </Card>
+</CardGroup>
+
+Because the integration surfaces whatever tools your MCP server publishes, you can also expose **Cortex Agents** and **custom tools** (user-defined functions and stored procedures) to your CrewAI agents.
+
+## Prerequisites
+
+Before using the Snowflake integration, ensure you have:
+
+- A [CrewAI AMP](https://app.crewai.com) account with an active subscription
+- A Snowflake account with access to Cortex AI features
+- A [Snowflake-managed MCP server](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp) configured with the tools you want to expose
+- Appropriate Snowflake privileges (USAGE/SELECT) on the MCP server and its underlying objects
+
+## Setting Up the Snowflake MCP Server
+
+The Snowflake-managed MCP server runs inside your Snowflake account and defines which tools are available to external clients like CrewAI. Create one with the [`CREATE MCP SERVER`](https://docs.snowflake.com/en/sql-reference/sql/create-mcp-server) command, listing the Cortex Search services, Cortex Analyst semantic views, and SQL tools you want to expose.
+
+```sql
+CREATE MCP SERVER my_mcp_server
+  FROM SPECIFICATION $$
+    tools:
+      - name: "sales_analyst"
+        type: "CORTEX_ANALYST"
+        identifier: "MY_DATABASE.MY_SCHEMA.sales_semantic_view"
+        description: "Answer questions about sales metrics"
+      - name: "docs_search"
+        type: "CORTEX_SEARCH_SERVICE_QUERY"
+        identifier: "MY_DATABASE.MY_SCHEMA.support_docs_search"
+        description: "Search internal support documentation"
+      - name: "run_sql"
+        type: "SQL_EXECUTION"
+        description: "Execute read-only SQL queries"
+  $$;
+```
+
+<Note>
+The MCP endpoint follows the format `https://<account_URL>/api/v2/databases/{database}/schemas/{schema}/mcp-servers/{name}`. CrewAI builds this URL automatically from the **Account URL**, **Database**, **Schema**, and **MCP Server Name** you provide when configuring the integration.
+</Note>
+
+For the complete specification — including Cortex Agents, custom tools, response-size limits, and governance options — see the [Snowflake-managed MCP server documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp).
+
+## Connecting Snowflake in CrewAI AMP
+
+<Frame>
+    <img src="/images/enterprise/snowflake-configure.png" alt="Configure Snowflake integration in CrewAI AMP" />
+</Frame>
+
+<Steps>
+    <Step title="Open Tools & Integrations">
+        Navigate to **Tools & Integrations** in the left sidebar of CrewAI AMP, find **Snowflake** in the list of applications, and open its configuration panel.
+    </Step>
+
+    <Step title="Provide connection details">
+        Fill in the connection fields that CrewAI uses to reach your Snowflake MCP server:
+
+        | Field | Required | Description |
+        |-------|----------|-------------|
+        | **Name** | Yes | A descriptive name for this connection (defaults to `Snowflake`). |
+        | **Description** | No | An optional summary of what this connection provides. |
+        | **Account URL** | Yes | Your Snowflake account URL, e.g. `xy12345.us-east-1.snowflakecomputing.com`. |
+        | **Database** | Yes | The database that contains your MCP server (e.g. `MY_DATABASE`). |
+        | **Schema** | Yes | The schema that contains your MCP server (e.g. `MY_SCHEMA`). |
+        | **MCP Server Name** | Yes | The name of the MCP server object you created in Snowflake (e.g. `MY_MCP_SERVER`). |
+    </Step>
+
+    <Step title="Choose an authentication method">
+        Select how CrewAI authenticates to Snowflake. **OAuth** is recommended.
+
+        - **Use OAuth** — Connect securely using OAuth 2.0 for token-based authentication without sharing your credentials. CrewAI handles the full authorization flow and refreshes tokens automatically. Copy the **Redirect URI** shown in the form (`https://oauth.crewai.com/oauth/add`) and register it as an authorized redirect URI in your Snowflake [OAuth security integration](https://docs.snowflake.com/en/user-guide/oauth-custom).
+        - **Use personal access token** — Authenticate using a [programmatic access token](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens) generated from your Snowflake account settings. Assign a least-privileged role to the token to limit exposure.
+    </Step>
+
+    <Step title="Authenticate">
+        Click **Authenticate**. For OAuth, you'll be redirected to Snowflake to authorize access. Once authenticated, the Snowflake server appears in your Connections and its tools become available to your crews.
+    </Step>
+</Steps>
+
+<Tip>
+With OAuth, each user authenticates individually and queries run with their Snowflake `DEFAULT_ROLE`. Make sure connecting users have a default role and warehouse set (`ALTER USER <username> SET DEFAULT_ROLE = '<role>' DEFAULT_WAREHOUSE = '<warehouse>'`) so Cortex Analyst and SQL tools have compute to run on.
+</Tip>
+
+## Using Snowflake Tools in Your Crews
+
+Once connected, the tools your MCP server exposes appear alongside built-in connections on the **Tools & Integrations** page. You can:
+
+- **Assign tools to agents** in your crews just like any other CrewAI tool.
+- **Manage visibility** to control which team members can use the connection.
+- **Edit or remove** the connection at any time from the Connections list.
+
+Your agents can now ask Cortex Analyst for metrics, run Cortex Search over your documents, and execute SQL — with results flowing back into their reasoning automatically.
+
+<Warning>
+Snowflake enforces governance on the MCP server: role-based access control determines which tools a user can discover and invoke, and limits apply to response size, tool count (max 50 per server), and recursion depth. If a tool call fails, confirm the connecting user's role has the required privileges on the MCP server and its underlying objects.
+</Warning>
+
+## Learn More
+
+<CardGroup cols={2}>
+  <Card title="Snowflake-managed MCP Server" icon="snowflake" href="https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp">
+    Official Snowflake documentation for creating and governing the MCP server.
+  </Card>
+  <Card title="Custom MCP Servers in CrewAI" icon="plug" href="/en/enterprise/guides/custom-mcp-server">
+    Learn how CrewAI connects to any MCP server, the foundation the Snowflake integration builds on.
+  </Card>
+</CardGroup>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with the Snowflake integration or troubleshooting.
+</Card>

docs/en/guides/flows/conversational-flows.mdx

@@ -0,0 +1,454 @@
+---
+title: Conversational Flows
+description: Build multi-turn chat apps with kickoff per turn, message history, intent routing, tracing, and WebSocket bridges.
+icon: comments
+mode: "wide"
+---
+
+## Overview
+
+Conversational apps treat each user line as a **new flow run** with the **same session id**. CrewAI adds helpers for message history, optional intent classification, deferred tracing, and UI bridges — without a separate `chat()` API on `Flow`.
+
+| Concept | Implementation |
+|---------|----------------|
+| Session id | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
+| User line | `kickoff(user_message=...)` appends to `state.messages` before the graph runs |
+| Turn complete | `FlowFinished` for **this run** only; chat continues on the next `kickoff` |
+| Full-session trace | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
+
+## One entry point: `kickoff`
+
+Use **`flow.kickoff(user_message=..., session_id=...)`** for every user message (REST, WebSocket, CLI). Do not add a custom `chat()` wrapper on `Flow`.
+
+| API | Use for |
+|-----|---------|
+| `kickoff(user_message=..., session_id=...)` | Each user message |
+| `kickoff_async(...)` | Same parameters; native async entry |
+| `ask()` | Blocking prompt **inside** one step (wizard, clarification) |
+| `@human_feedback` | Approve/reject **a step output** — not the next chat line |
+| `ChatSession.handle_turn(...)` | Transport layer over `kickoff` (SSE / WebSocket) |
+
+## Quick start
+
+```python
+from uuid import uuid4
+
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    Flow,
+    listen,
+    or_,
+    persist,
+    router,
+    start,
+)
+from crewai.flow.persistence import SQLiteFlowPersistence
+
+
+class SupportFlow(Flow[ChatState]):
+    conversational_config = ConversationalConfig(
+        default_intents=["order", "help", "goodbye"],
+        intent_llm="gpt-4o-mini",
+        defer_trace_finalization=True,
+    )
+
+    @start()
+    def bootstrap(self):
+        if not self.state.session_ready:
+            self.state.session_ready = True
+        return "ready"
+
+    @router(bootstrap)
+    def route(self):
+        # last_intent set in prepare_conversational_turn when default_intents is set
+        return self.state.last_intent or "help"
+
+    @listen("order")
+    def handle_order(self):
+        reply = "Your order is on the way."
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("help")
+    def handle_help(self):
+        reply = "How can I help?"
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("goodbye")
+    def handle_goodbye(self):
+        reply = "Goodbye!"
+        self.append_message("assistant", reply)
+        return reply
+
+    @persist(SQLiteFlowPersistence("support.db"))
+    @listen(or_(handle_order, handle_help, handle_goodbye))
+    def finalize(self):
+        return self.state.model_dump()
+
+
+session_id = str(uuid4())
+flow = SupportFlow()
+
+flow.kickoff(user_message="Where is my order?", session_id=session_id)
+flow.kickoff(user_message="What about returns?", session_id=session_id)
+flow.finalize_session_traces()  # one trace link for the whole chat
+```
+
+## Turn lifecycle
+
+Each `kickoff` with `user_message` runs this pipeline:
+
+1. **`_configure_conversational_kickoff`** — merges `session_id` / `user_message` into `inputs`, applies `ConversationalConfig`, enables deferred tracing when configured.
+2. **State restore** — if `inputs["id"]` exists and `@persist` is configured, loads the latest snapshot.
+3. **`FlowStarted`** — emitted on the first deferred session turn only.
+4. **`prepare_conversational_turn`** — appends the user message to `state.messages`, sets `last_user_message`, clears `last_intent`, optionally classifies when `intents` / `default_intents` + `intent_llm` are set.
+5. **Graph execution** — `@start` → `@router` → `@listen` handlers.
+6. **End of run** — per-turn `flow_finished` and trace finalization are **skipped** when deferral is enabled; nested `Agent.kickoff()` / crews do not close the parent batch either.
+
+Handlers should call **`append_message("assistant", reply)`** so the next turn’s `conversation_messages` includes assistant text. The user line is already stored at kickoff — do not append it again in handlers.
+
+## `ConversationalConfig` (class-level defaults)
+
+Set on your `Flow` subclass as `conversational_config: ClassVar[ConversationalConfig | None]`.
+
+| Field | Default | Purpose |
+|-------|---------|---------|
+| `default_intents` | `None` | Outcome labels for automatic pre-kickoff classification |
+| `intent_llm` | `None` | Model for classification (required when intents are used) |
+| `interactive_prompt` | `"You: "` | Prompt for `kickoff(interactive=True)` |
+| `interactive_timeout` | `None` | Per-line timeout in interactive mode |
+| `exit_commands` | `exit`, `quit` | Words that end interactive mode |
+| `defer_trace_finalization` | `True` | Keep one trace batch open across turns |
+
+Override per kickoff with `intents=` and `intent_llm=` keyword arguments.
+
+## `ChatState` (recommended persisted shape)
+
+```python
+from crewai.flow import ChatState
+
+
+class MyChatState(ChatState):
+    # Inherited: id, messages, last_user_message, last_intent, session_ready
+    research_turn_count: int = 0
+    custom_flag: bool = False
+```
+
+| Field | Role |
+|-------|------|
+| `id` | Session UUID (same as `session_id` / `inputs["id"]`) |
+| `messages` | `list` of `{role, content}` for LLM history |
+| `last_user_message` | Latest user line for this turn |
+| `last_intent` | Route label after classification (if used) |
+| `session_ready` | One-time bootstrap flag (permissions, caches, etc.) |
+
+`ConversationalInputs` is a `TypedDict` for conventional `kickoff(inputs={...})` keys: `id`, `user_message`, `last_intent`.
+
+## `Flow` conversational API
+
+### `kickoff` / `kickoff_async` parameters
+
+| Parameter | Purpose |
+|-----------|---------|
+| `user_message` | This turn’s text (or `{"role": "user", "content": "..."}`) |
+| `session_id` | Conversation UUID → `inputs["id"]` / `state.id` |
+| `intents` | Outcome labels for pre-kickoff `classify_intent` |
+| `intent_llm` | LLM for classification (required with `intents`) |
+| `interactive` | CLI loop via `ask()` (local demos only) |
+| `interactive_prompt` | Override prompt in interactive mode |
+| `interactive_timeout` | Per-line `ask()` timeout |
+| `exit_commands` | Words that end interactive mode |
+| `inputs` | Additional state fields (merged with conversational keys) |
+| `restore_from_state_id` | Fork hydration from another persisted flow |
+
+### Instance attributes
+
+| Attribute | Purpose |
+|-----------|---------|
+| `conversational_config` | Class-level `ConversationalConfig` defaults |
+| `defer_trace_finalization` | Instance flag; set automatically from config on kickoff |
+| `suppress_flow_events` | Hides console flow panels; **tracing still records** method/flow events |
+| `stream` | Enable streaming; use with `ChatSession.handle_turn(..., stream=True)` |
+
+### Methods and properties
+
+| Name | Description |
+|------|-------------|
+| `append_message(role, content, **extra)` | Append to `state.messages` (roles: `user`, `assistant`, `system`, `tool`) |
+| `conversation_messages` | Read-only history for LLM calls |
+| `classify_intent(text, outcomes, *, llm, context=None)` | Map text to one outcome (same collapse logic as `@human_feedback`) |
+| `receive_user_message(text, *, outcomes=None, llm=None)` | Append user message; optionally set `last_intent` |
+| `finalize_session_traces()` | Emit deferred `flow_finished` and finalize the session trace batch |
+| `_should_defer_trace_finalization()` | Whether this flow defers per-turn trace finalization |
+| `input_history` | Audit trail of `ask()` prompts and responses |
+
+### Module helpers (`crewai.flow.conversation`)
+
+Importable for tests or custom orchestration:
+
+| Function | Description |
+|----------|-------------|
+| `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | Merge conversational kwargs into `inputs` |
+| `get_conversation_messages(flow)` | Read messages from state or internal buffer |
+| `append_message(flow, role, content, **extra)` | Same as instance method |
+| `prepare_conversational_turn(flow, user_message=..., intents=..., intent_llm=..., config=...)` | Turn hydration (usually called by kickoff) |
+| `receive_user_message(flow, text, ...)` | Same as instance method |
+| `set_state_field(flow, name, value)` | Set a field on dict or Pydantic state |
+| `get_conversational_config(flow)` | Read class `conversational_config` |
+| `input_history_to_messages(entries)` | Convert `input_history` to LLM message format |
+
+## Intent routing patterns
+
+### A. Pre-classify via `ConversationalConfig` (simplest)
+
+Set `default_intents` and `intent_llm`. Each kickoff runs classification before your `@router`; read `self.state.last_intent` in `route()`.
+
+### B. Classify inside `@router` (richer prompts)
+
+Set `default_intents=None` so kickoff only appends the user message. In `route()`, call `classify_intent` with a custom prompt or descriptions:
+
+```python
+@router(bootstrap)
+def route(self):
+    intent = self.classify_intent(
+        self._routing_prompt(self.state.last_user_message),
+        ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
+    )
+    self.state.last_intent = intent
+    return intent
+```
+
+Use **`@listen("RESEARCH")`** (or similar) for steps that run `Agent.kickoff()` with tools — not bare `LLM.call()` — when you need web research or multi-step tool use.
+
+## When the flow finishes but the user keeps chatting
+
+`FlowFinished` means **this graph run** completed. The conversation continues with another `kickoff` and the same `session_id`. `@persist` restores `messages`, flags, and context.
+
+**Persist pattern:** prefer `@persist` on a **single terminal step** (for example `finalize`) rather than on the whole `Flow` class. Class-level persist saves after every method; `load_state` uses the latest row, which may be a mid-run snapshot (for example right after `bootstrap`) and miss handler updates from the same turn.
+
+Do **not** use `@human_feedback` for follow-up chat lines unless a human must approve a specific step output before it is shown.
+
+## Conversational `Flow` (experimental)
+
+<Warning>
+  **This is an experimental feature.** The conversational `Flow` surface
+  (`conversational = True`, `handle_turn`, `ConversationConfig`,
+  `RouterConfig`, `ConversationState`, the built-in graph + helpers) lives
+  under `crewai.experimental` and may change shape before it graduates.
+  Pin your CrewAI version if you depend on specific behavior, and watch the
+  changelog for breaking updates. Open issues / feedback welcome.
+</Warning>
+
+Opt into the conversational chat graph by setting `conversational = True` on a `Flow` subclass. The base `Flow` then ships a built-in `@start` / `@router` / `converse_turn` / `end_conversation` graph, manages `state.messages`, drives the router LLM, and keeps the trace batch open across turns. You write the **custom routes**; the framework owns the rest.
+
+Use this when you want a multi-turn chat with an LLM-driven router and per-route handlers without wiring the lifecycle yourself. Use `Flow[ChatState]` (the lower-level pattern above) when you need full control.
+
+### Quick example
+
+```python
+from crewai import LLM, Flow
+from crewai.flow import listen
+from crewai.experimental.conversational import (
+    ConversationConfig,
+    ConversationState,
+    RouterConfig,
+)
+
+
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # routes + descriptions auto-discovered from @listen handlers
+)
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    @listen("INTERNET_SEARCH")
+    def handle_internet_search(self) -> str:
+        """Fresh web research, current news, real-time lookups."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+    @listen("CREWAI_DOCS")
+    def handle_crewai_docs(self) -> str:
+        """Look up the CrewAI documentation for framework/API questions."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+
+flow = SupportFlow()
+try:
+    flow.handle_turn("What can you do?")              # routes to converse (built-in)
+    flow.handle_turn("Search the web for AI news.")   # routes to INTERNET_SEARCH
+    flow.handle_turn("Summarize the first result.")   # routes back to converse
+finally:
+    flow.finalize_session_traces()
+```
+
+### `ConversationConfig`
+
+Class decorator that attaches per-class chat defaults.
+
+| Field | Default | Purpose |
+|-------|---------|---------|
+| `system_prompt` | `slices.conversational_system_prompt` from i18n | System message used by the built-in `converse_turn`. Pass `""` to opt out entirely. |
+| `llm` | `None` | Conversation LLM (used by `converse_turn` and as router fallback). |
+| `router` | `None` | `RouterConfig` for LLM-driven routing. Without it, the flow always falls through to `converse`. |
+| `answer_from_history_prompt` | Framework default | System message for the optional `answer_from_history` route. |
+| `answer_from_history_llm` | `None` | Enables the `answer_from_history` short-circuit when set. |
+| `intent_llm` | `None` | LLM for legacy `intents=`/`default_intents` pre-classification. |
+| `default_intents` | `None` | Outcome labels for legacy pre-classification. |
+| `visible_agent_outputs` | `None` | `"all"`, or a list of agent names whose `append_agent_result()` calls should be promoted to public assistant messages. |
+| `defer_trace_finalization` | `True` | Keep one trace batch open across `handle_turn()` calls. |
+
+### `RouterConfig` and the auto-built route catalog
+
+```python
+RouterConfig(
+    prompt="Optional domain framing (policy, voice, persona).",
+    response_format=MyRoute,        # optional; auto-generated otherwise
+    llm=ROUTER_LLM,                  # falls back to ConversationConfig.llm
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # optional; inferred from listeners
+    route_descriptions={
+        "INTERNET_SEARCH": "Override the docstring for this one route.",
+    },
+    default_intent="converse",       # used when LLM call fails or no LLM available
+    fallback_intent="converse",      # used when LLM returns an invalid route
+    intent_field="intent",
+)
+```
+
+The router prompt that gets sent to the LLM is built automatically. For each route the framework picks a description with this precedence:
+
+1. `RouterConfig.route_descriptions[label]` — explicit override.
+2. `Flow.builtin_route_descriptions[label]` — framework-canned text for `converse`, `end`, `answer_from_history` (phrased for the router LLM).
+3. First non-empty line of the `@listen(label)` handler's docstring.
+4. Empty (the route is listed without a description).
+
+So in practice, **adding a new route is `@listen("X")` + a one-line docstring**:
+
+```python
+@listen("INTERNET_SEARCH")
+def handle_internet_search(self) -> str:
+    """Fresh web research, current news, real-time lookups."""
+    ...
+```
+
+…and the router LLM sees:
+
+```
+Routes:
+- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
+- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
+- converse: Ordinary chat, follow-ups, summaries, clarifications…
+- end: User signals the conversation is finished (goodbye, exit, done).
+```
+
+`RouterConfig.prompt` is for **domain framing** (assistant persona, business rules, voice). The route catalog is auto-built — don't list routes in `prompt`; they'll drift the moment you add a handler.
+
+### Built-in routes
+
+| Route | Handler | Purpose |
+|-------|---------|---------|
+| `converse` | `converse_turn` | Default chat handler. Calls `ConversationConfig.llm` with the system prompt + canonical message history. |
+| `end` | `end_conversation` | Sets `state.ended = True` and emits a terminator reply. |
+| `answer_from_history` | `answer_from_history_turn` | Optional. Routes here when `ConversationConfig.answer_from_history_llm` is set and the message can be answered from existing history. |
+
+You can override any of these by defining a same-named handler in your subclass.
+
+### `handle_turn()` semantics
+
+`flow.handle_turn(message)` runs one turn:
+
+1. Resets per-execution tracking (`_completed_methods`, `_method_outputs`) so the graph re-runs — without this, repeated `kickoff` calls on the same flow instance would short-circuit on turn 2+ because `Flow.kickoff_async` treats `inputs={"id": ...}` as a checkpoint restore.
+2. Appends the user message to `state.messages`, sets `current_user_message` / `last_user_message`. `last_intent` is **preserved from the prior turn** so the router LLM can use it as a signal.
+3. Runs `conversation_start` → `route_conversation` → the chosen `@listen` handler.
+4. The router stores its decision in `state.last_intent` (visible to the next turn's router context).
+5. If your handler returned a string and didn't already call `append_assistant_message`, `handle_turn` appends it for you.
+
+You can also call `flow.kickoff(user_message=..., session_id=...)` directly — the same reset/run logic fires. `handle_turn` is the ergonomic wrapper.
+
+### Custom router behavior
+
+To run side effects (event bus setup, telemetry) on every routing decision, override `route_turn`:
+
+```python
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    def route_turn(self, context: dict[str, Any]) -> str | None:
+        self.event_bus = MyBus(self)
+        return super().route_turn(context)
+```
+
+To bypass the LLM router entirely and pick a route programmatically, return a string from `route_turn`; returning `None` falls back to `_route_with_config(...)`.
+
+### `append_assistant_message` and `append_agent_result`
+
+Inside a `@listen(label)` handler, choose:
+
+- `self.append_assistant_message(text)` — adds a user-visible assistant turn to `state.messages`. The next turn's `converse_turn` sees it.
+- `self.append_agent_result(agent_name, result, visibility="private")` — records a structured event in `state.events` and a thread in `state.agent_threads[agent_name]`. Public visibility also calls `append_assistant_message` for you. Use private results for scratch work that shouldn't pollute the canonical history.
+
+`ConversationConfig.visible_agent_outputs` can promote specific agents' private results to public globally (`"all"`, or a list of agent names).
+
+## Tracing across turns
+
+With `defer_trace_finalization=True` (default in `ConversationalConfig`):
+
+- **One trace batch** for the whole chat session.
+- **`flow_started`** on the first turn only; **`flow_finished`** once in `finalize_session_traces()`.
+- **Per-turn** `kickoff` does not print “Trace batch finalized”.
+- **Nested work** (`Agent.kickoff()`, crews, Exa tools) appends to the **parent** batch; inner `AgentExecutor` flows do not close the session batch early.
+
+```python
+try:
+    while True:
+        line = input("You: ").strip()
+        if not line:
+            break
+        flow.kickoff(user_message=line, session_id=session_id)
+finally:
+    flow.finalize_session_traces()
+```
+
+`ChatSession.close()` calls `finalize_session_traces()` when deferral is enabled.
+
+`suppress_flow_events=True` only hides Rich console panels; trace and method events still emit for observability.
+
+### Conversational `Flow` trace lifecycle
+
+The experimental [conversational `Flow`](#conversational-flow-experimental) uses the same tracing lifecycle: `defer_trace_finalization` defaults to `True`, so each `handle_turn()` keeps the session trace open. Always finalize at the end of the session — wrap your REPL/loop in `try/finally` and call `flow.finalize_session_traces()` on exit. Without it, the trace batch stays open and the final conversation may never export.
+
+## Streaming
+
+Set `stream = True` on the `Flow` class. `kickoff(...)` will then emit `assistant_delta` (and related) events through the standard event bus.
+
+## Imports
+
+```python
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    ConversationalInputs,
+    Flow,
+    listen,
+    persist,
+    router,
+    start,
+)
+```
+
+## See also
+
+- [Mastering Flow State Management](/en/guides/flows/mastering-flow-state) — persistence, Pydantic state, `@persist`
+- [Build Your First Flow](/en/guides/flows/first-flow) — flow basics
+- Demo: `lib/crewai/runner_conversational_flow_simple.py` — minimal REPL with `RESEARCH` + Exa agent

docs/en/guides/flows/first-flow.mdx

@@ -617,6 +617,7 @@ Now that you've built your first flow, you can:
 3. Explore the `and_` and `or_` functions for more complex parallel execution
 4. Connect your flow to external APIs, databases, or user interfaces
 5. Combine multiple specialized crews in a single flow
+6. Build multi-turn chat apps with [Conversational Flows](/en/guides/flows/conversational-flows) (`kickoff` per message, `ChatSession`, deferred tracing)
 
 <Check>
 Congratulations! You've successfully built your first CrewAI Flow that combines regular code, direct LLM calls, and crew-based processing to create a comprehensive guide. These foundational skills enable you to create increasingly sophisticated AI applications that can tackle complex, multi-stage problems through a combination of procedural control and collaborative intelligence.

docs/en/guides/flows/mastering-flow-state.mdx

@@ -22,6 +22,8 @@ Effective state management enables you to:
 5. **Scale your applications** - Support complex workflows with proper data organization
 6. **Enable conversational applications** - Store and access conversation history for context-aware AI interactions
 
+For multi-turn chat (`kickoff` per user line, `ChatState`, intent routing, deferred tracing, and `ChatSession`), see [Conversational Flows](/en/guides/flows/conversational-flows).
+
 Let's explore how to leverage these capabilities effectively.
 
 ## State Management Fundamentals

docs/ko/changelog.mdx

@@ -4,6 +4,76 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 6월 3일">
+  ## v1.14.7a1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
+
+  ## 변경 사항
+
+  ### 기능
+  - 승무원 교육을 받은 에이전트 파일 지원 추가
+  - 네이티브 Snowflake Cortex LLM 공급자 추가
+  - Databricks 통합 가이드 추가
+  - Snowflake 통합 가이드 추가
+
+  ### 버그 수정
+  - UV 도구 설치를 위한 crewai 패키지에서 `[project.scripts]` 복원하여 CLI 수정
+  - 파일 입력 신뢰성 문제 해결
+  - Snowflake Claude에서 불완전한 도구 결과 기록 수정
+  - Snowflake Claude를 위한 문자열화된 도구 호출 처리
+  - 라우터 주도 사이클 전반에 걸쳐 다중 소스 `or_` 리스너 재장착
+
+  ### 성능
+  - docling 가져오기를 지연 로딩하여 crewai 가져오기 속도 개선
+
+  ### 리팩토링
+  - `flow.py`를 DSL, 정의 및 런타임으로 분할
+
+  ## 기여자
+
+  @Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
+
+</Update>
+
+<Update label="2026년 5월 28일">
+  ## v1.14.6
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.6)
+
+  ## 변경 사항
+
+  ### 기능
+  - 환경 변수 유출을 방지하기 위해 StdioTransport 강화
+  - 계획 구성 및 관찰 처리 개선
+  - DatabricksQueryTool에서 env_vars 선언
+  - 에이전트 제어 평면 문서 추가
+
+  ### 버그 수정
+  - 도구 호출 루프에서 구조화된 출력 유출 수정
+  - 체크포인트에서 원형으로 돌아갈 수 없는 콜백 및 어댑터 상태 제거
+  - 체크포인트에서 type[BaseModel] 필드를 JSON 스키마로 직렬화
+  - 복원 범위 복원 시 고아 task_started 방지
+  - AgentExecutor가 체크포인트에서 복원할 수 있도록 허용
+  - package_dependencies에서 mongodb 오타를 pymongo로 수정
+
+  ### 문서
+  - 에이전트 제어 평면 페이지에 ACP (Beta) 문서 탐색 블록 추가
+  - 프로세스 페이지에서 합의 프로세스 참조 제거
+  - 체크포인트 페이지 구조 재편성
+  - 일회성 관리자 패키지 설치 단계 문서화
+  - Secrets Manager / Workload Identity를 replicated-config에서 마이그레이션
+  - `<Steps>` 렌더링을 방해하는 `{" "}` JSX 표현 제거
+
+  ### 리팩토링
+  - Skills Repository를 실험적 + CREWAI_EXPERIMENTAL 게이트로 이동
+
+  ## 기여자
+
+  @akaKuruma, @alex-clawd, @github-actions[bot], @greysonlalonde, @heitorado, @iris-clawd, @lorenzejay, @lucasgomide, @mattatcha, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="2026년 5월 27일">
   ## v1.14.6a2
 

docs/ko/concepts/llms.mdx

@@ -106,7 +106,7 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치
 </Tabs>
 
 <Info>
-  CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다.
+  CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock, Snowflake Cortex에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다.
 
   그 외 모든 제공자는 **LiteLLM**을 통해 지원됩니다. 이를 사용하려면 프로젝트에 의존성으로 추가하세요:
   ```bash
@@ -230,6 +230,55 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     ```
   </Accordion>
 
+  <Accordion title="Snowflake Cortex">
+    CrewAI는 OpenAI 호환 Chat Completions 엔드포인트를 통해 Snowflake Cortex REST API와 네이티브로 통합됩니다. `snowflake/...` 모델은 LiteLLM fallback 없이 사용됩니다. CrewAI에서 Snowflake Cortex는 현재 Chat Completions만 지원하므로 기본 `api` 모드를 사용하고 `api="responses"`를 설정하지 마세요.
+
+    ```toml Code
+    # Required
+    SNOWFLAKE_PAT=<your-programmatic-access-token>
+    SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
+
+    # Alternative account configuration
+    SNOWFLAKE_ACCOUNT=<account-identifier>
+    ```
+
+    **기본 사용법:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/openai-gpt-4.1",
+        temperature=0.7,
+        max_completion_tokens=1024,
+    )
+    ```
+
+    **Cortex의 Claude 모델:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/claude-sonnet-4-5",
+        max_completion_tokens=1024,
+        stream=True,
+    )
+    ```
+
+    **지원 환경 변수:**
+    - `SNOWFLAKE_PAT`, `SNOWFLAKE_TOKEN`, 또는 `SNOWFLAKE_JWT`: Bearer 자격 증명으로 사용할 토큰
+    - `SNOWFLAKE_ACCOUNT_URL`: 전체 Snowflake 계정 URL
+    - `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_ACCOUNT_ID`, 또는 `SNOWFLAKE_ACCOUNT_IDENTIFIER`: 계정 URL을 만들 계정 식별자
+
+    Snowflake REST 요청은 사용자의 기본 Snowflake role을 사용합니다. 해당 role에 `SNOWFLAKE.CORTEX_USER` 또는 `SNOWFLAKE.CORTEX_REST_API_USER`가 있는지 확인하세요. Cortex REST Chat Completions 엔드포인트에는 database, schema, warehouse, 명시적 role 파라미터가 필요하지 않습니다.
+
+    **기능:**
+    - `model="snowflake/<model-name>"`을 통한 네이티브 provider 선택
+    - Streaming 및 non-streaming Chat Completions만 지원; `api="responses"`는 지원되지 않음
+    - 토큰 사용량 추적
+    - Snowflake 호스팅 OpenAI 및 Claude 모델의 함수 호출
+    - Snowflake Claude 모델에서 유효하지 않은 마지막 assistant prefill 자동 제거
+  </Accordion>
+
   <Accordion title="Anthropic">
     ```toml Code
     # Required

docs/ko/concepts/processes.mdx

@@ -16,7 +16,6 @@ mode: "wide"
 
 - **순차적(Sequential)**: 작업을 순차적으로 실행하여 작업이 질서 있게 진행되도록 보장합니다.
 - **계층적(Hierarchical)**: 작업을 관리 계층 구조로 조직하며, 작업은 체계적인 명령 체계를 기반으로 위임 및 실행됩니다. 계층적 프로세스를 활성화하려면 매니저 언어 모델(`manager_llm`) 또는 커스텀 매니저 에이전트(`manager_agent`)를 crew에서 지정해야 하며, 이를 통해 매니저가 작업을 생성하고 관리할 수 있도록 지원합니다.
-- **합의 프로세스(Consensual Process, 계획됨)**: 에이전트들 간에 작업 실행에 대한 협력적 의사결정을 목표로 하며, 이 프로세스 유형은 CrewAI 내에서 작업 관리를 민주적으로 접근하도록 도입됩니다. 앞으로 개발될 예정이며, 현재 코드베이스에는 구현되어 있지 않습니다.
 
 ## 팀워크에서 프로세스의 역할
 프로세스는 개별 에이전트가 통합된 단위로 작동할 수 있도록 하여, 공통된 목표를 효율적이고 일관성 있게 달성하도록 노력하는 과정을 간소화합니다.
@@ -59,9 +58,9 @@ crew = Crew(
 
 ## Process 클래스: 상세 개요
 
-`Process` 클래스는 열거형(`Enum`)으로 구현되어 타입 안전성을 보장하며, 프로세스 값을 정의된 타입(`sequential`, `hierarchical`)으로 제한합니다. 합의 기반(consensual) 프로세스는 향후 추가될 예정이며, 이는 지속적인 개발과 혁신에 대한 우리의 의지를 강조합니다.
+`Process` 클래스는 열거형(`Enum`)으로 구현되어 타입 안전성을 보장하며, 프로세스 값을 정의된 타입(`sequential`, `hierarchical`)으로 제한합니다.
 
 ## 결론
 
 CrewAI 내의 프로세스를 통해 촉진되는 구조화된 협업은 에이전트 간 체계적인 팀워크를 가능하게 하는 데 매우 중요합니다.  
-이 문서는 최신 기능, 향상 사항, 그리고 예정된 Consensual Process 통합을 반영하도록 업데이트되었으며, 사용자가 가장 최신이고 포괄적인 정보를 이용할 수 있도록 보장합니다.
+이 문서는 최신 기능과 향상 사항을 반영하도록 업데이트되었으며, 사용자가 가장 최신이고 포괄적인 정보를 이용할 수 있도록 보장합니다.

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

@@ -6,6 +6,14 @@ icon: "gauge"
 mode: "wide"
 ---
 
+<Info>
+  **ACP (베타) 문서 내비게이션**
+
+  - [개요](/ko/enterprise/features/agent-control-plane/overview)
+  - **모니터링** *(현재 페이지)*
+  - [규칙](/ko/enterprise/features/agent-control-plane/rules)
+</Info>
+
 ## 개요
 
 **Automations** 탭은 [Agent Control Plane](/ko/enterprise/features/agent-control-plane/overview)의 읽기 전용 운영 뷰입니다. 두 개의 메트릭 카드, 인터랙티브 sankey, 그리고 **Automations**와 **Consumption** 두 개의 서브 테이블을 결합해 검색·필터·정렬을 지원합니다.

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

@@ -5,6 +5,14 @@ sidebarTitle: 개요
 icon: "book-open"
 ---
 
+<Info>
+  **ACP (베타) 문서 내비게이션**
+
+  - **개요** *(현재 페이지)*
+  - [모니터링](/ko/enterprise/features/agent-control-plane/monitoring)
+  - [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 **Rules** 두 개의 탭으로 구성된 단일 화면에서 다음 작업을 할 수 있습니다:

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

@@ -6,6 +6,14 @@ icon: "shield-check"
 mode: "wide"
 ---
 
+<Info>
+  **ACP (베타) 문서 내비게이션**
+
+  - [개요](/ko/enterprise/features/agent-control-plane/overview)
+  - [모니터링](/ko/enterprise/features/agent-control-plane/monitoring)
+  - **규칙** *(현재 페이지)*
+</Info>
+
 ## 개요
 
 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.

docs/ko/enterprise/guides/deploy-to-amp.mdx

@@ -163,6 +163,12 @@ Crew를 GitHub 저장소에 푸시해야 합니다. 아직 Crew를 만들지 않
       ![Select Repository](/images/enterprise/select-repo.png)
     </Frame>
 
+    <Tip>
+      Crew 또는 Flow가 모노레포 하위 폴더 안에 있다면 배포 전에
+      **Advanced**를 펼치고 작업 디렉터리를 설정하세요.
+      [모노레포 배포](/ko/enterprise/guides/monorepo-deployments)를 참조하세요.
+    </Tip>
+
   </Step>
 
   <Step title="환경 변수 설정하기">
@@ -440,4 +446,4 @@ type = "flow"
 
 <Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
   배포 문제 또는 AMP 플랫폼에 대한 문의 사항이 있으시면 지원팀에 연락해 주세요.
-</Card>
+</Card>

docs/ko/enterprise/guides/monorepo-deployments.mdx

@@ -0,0 +1,222 @@
+---
+title: "모노레포 배포"
+description: "더 큰 저장소의 하위 폴더에서 Crew 또는 Flow 배포하기"
+icon: "folder-tree"
+mode: "wide"
+---
+
+<Note>
+  Crew 또는 Flow가 더 큰 저장소 안에 있을 때 작업 디렉터리를 사용하세요.
+  CrewAI AMP는 저장소 루트 대신 해당 하위 폴더에서 자동화를 검증, 빌드,
+  실행합니다.
+</Note>
+
+## 사용 시점
+
+모노레포 배포는 하나의 저장소에 여러 자동화, 공유 패키지 또는 다른 애플리케이션
+코드가 함께 있을 때 유용합니다:
+
+```text
+company-ai/
+|-- uv.lock
+|-- packages/
+|   `-- shared_tools/
+`-- crews/
+    |-- support_agent/
+    |   |-- pyproject.toml
+    |   `-- src/
+    |       `-- support_agent/
+    |           |-- main.py
+    |           `-- crew.py
+    `-- research_flow/
+        |-- pyproject.toml
+        `-- src/
+            `-- research_flow/
+                `-- main.py
+```
+
+`support_agent`를 배포하려면 작업 디렉터리를 다음과 같이 설정합니다:
+
+```text
+crews/support_agent
+```
+
+AMP는 여전히 전체 저장소를 가져오거나 업로드하지만, 선택한 폴더를 자동화
+프로젝트 루트로 처리합니다.
+
+## 작업 디렉터리가 제어하는 항목
+
+작업 디렉터리가 설정되면 AMP는 해당 폴더를 다음 용도로 사용합니다:
+
+- `pyproject.toml`, `src/`, Crew 또는 Flow 진입점을 포함한 프로젝트 검증
+- `uv`를 사용한 종속성 설치
+- 실행 중인 프로세스의 작업 디렉터리
+- `CREW_ROOT_DIR` 환경 변수
+
+필드를 비워 두면 기존 동작이 유지되며 저장소 루트를 사용합니다.
+
+## 지원되는 소스
+
+다음 소스에서 배포를 만들 때 작업 디렉터리를 설정할 수 있습니다:
+
+- 연결된 GitHub 저장소
+- AMP에 구성된 Git 저장소
+- ZIP 업로드
+
+<Info>
+  작업 디렉터리는 AMP 웹 인터페이스에서 구성하세요.
+  `crewai deploy create` CLI 흐름은 이 필드를 묻지 않습니다.
+</Info>
+
+기존 배포의 **Settings** 페이지에서도 작업 디렉터리를 추가하거나 변경할 수
+있습니다. 변경 사항은 다음 배포부터 적용됩니다.
+
+<Warning>
+  작업 디렉터리와 auto-deploy는 함께 사용할 수 없습니다. 배포에 작업
+  디렉터리가 설정되어 있으면 해당 배포의 auto-deploy가 비활성화됩니다.
+  작업 디렉터리를 설정하기 전에 auto-deploy를 끄세요.
+</Warning>
+
+## 새 배포 구성
+
+<Steps>
+  <Step title="Deploy from Code 열기">
+    CrewAI AMP에서 새 배포를 만들고 소스를 선택합니다: GitHub, Git
+    Repository 또는 ZIP 업로드.
+  </Step>
+
+  <Step title="저장소, 브랜치 또는 ZIP 파일 선택">
+    모노레포가 들어 있는 저장소와 브랜치를 선택하거나, 루트에 모노레포 내용이
+    포함된 ZIP 파일을 업로드합니다.
+  </Step>
+
+  <Step title="고급 설정 열기">
+    배포 양식에서 **Advanced** 섹션을 펼칩니다.
+  </Step>
+
+  <Step title="작업 디렉터리 입력">
+    저장소 루트에서 Crew 또는 Flow 프로젝트까지의 경로를 입력합니다:
+
+    ```text
+    crews/support_agent
+    ```
+
+    앞에 슬래시를 붙이지 마세요.
+  </Step>
+
+  <Step title="배포">
+    필요한 환경 변수를 추가한 다음 배포를 시작합니다.
+  </Step>
+</Steps>
+
+## 기존 배포 구성
+
+<Steps>
+  <Step title="배포 설정 열기">
+    AMP에서 자동화로 이동한 뒤 **Settings**를 엽니다.
+  </Step>
+
+  <Step title="필요한 경우 auto-deploy 끄기">
+    auto-deploy가 활성화되어 있으면 먼저 끄세요. auto-deploy가 켜져 있는
+    동안에는 작업 디렉터리 필드를 사용할 수 없습니다.
+  </Step>
+
+  <Step title="작업 디렉터리 설정">
+    **Basic settings**에서 다음과 같은 하위 폴더 경로를 입력합니다:
+
+    ```text
+    crews/support_agent
+    ```
+  </Step>
+
+  <Step title="다시 배포">
+    설정을 저장하고 자동화를 다시 배포합니다. 새 작업 디렉터리는 다음 배포부터
+    사용됩니다.
+  </Step>
+</Steps>
+
+## 경로 규칙
+
+작업 디렉터리는 저장소 또는 ZIP 루트 안의 상대 경로여야 합니다.
+
+| 규칙 | 예시 |
+|------|------|
+| 상대 경로를 사용합니다 | `crews/support_agent` |
+| `/`로 시작하지 않습니다 | `/crews/support_agent`는 유효하지 않습니다 |
+| `.` 또는 `..` 경로 세그먼트를 사용하지 않습니다 | `crews/../support_agent`는 유효하지 않습니다 |
+| 문자, 숫자, 하이픈, 밑줄, 점, 슬래시만 사용합니다 | `crews/support agent`는 유효하지 않습니다 |
+| 경로는 255자 이하로 유지합니다 | 더 긴 경로는 거부됩니다 |
+
+AMP는 앞뒤 공백을 제거하고, 반복된 슬래시를 하나로 줄이며, 끝의 슬래시를
+제거합니다. 빈 값은 저장소 루트를 사용합니다.
+
+## Lock 파일과 UV 워크스페이스
+
+선택한 폴더에는 자동화의 `pyproject.toml`과 `src/` 디렉터리가 있어야
+합니다. `uv.lock` 또는 `poetry.lock` 파일은 선택한 폴더나 저장소 루트에
+둘 수 있습니다.
+
+이 방식은 일반적인 두 가지 모노레포 레이아웃을 모두 지원합니다:
+
+<Tabs>
+  <Tab title="프로젝트 lock 파일">
+    ```text
+    company-ai/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            |-- uv.lock
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+
+  <Tab title="워크스페이스 lock 파일">
+    ```text
+    company-ai/
+    |-- uv.lock
+    |-- packages/
+    |   `-- shared_tools/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+</Tabs>
+
+<Tip>
+  자동화가 모노레포의 다른 위치에 있는 공유 패키지를 가져온다면, UV
+  workspace, path 또는 source 설정을 사용해 해당 패키지를 `pyproject.toml`에
+  선언하세요. AMP는 선택한 폴더에서 자동화를 실행하므로, 저장소 루트가
+  Python path에 있다고 가정하기보다 공유 코드를 종속성으로 설치해야 합니다.
+</Tip>
+
+## 문제 해결
+
+### 작업 디렉터리를 찾을 수 없음
+
+경로가 저장소 또는 ZIP 루트를 기준으로 한 상대 경로인지 확인하세요. ZIP
+업로드의 경우 ZIP 내용에 입력한 작업 디렉터리 경로가 정확히 포함되어야 합니다.
+
+### pyproject.toml 누락
+
+작업 디렉터리는 여러 프로젝트를 담은 상위 폴더가 아니라 Crew 또는 Flow 프로젝트
+폴더를 가리켜야 합니다.
+
+### uv.lock 또는 poetry.lock 누락
+
+선택한 프로젝트 폴더 또는 저장소 루트에 lock 파일을 커밋하세요. UV
+워크스페이스의 경우 `uv.lock`을 워크스페이스 루트에 두는 방식이 지원됩니다.
+
+### Auto-Deploy를 사용할 수 없음
+
+작업 디렉터리가 설정되어 있으면 auto-deploy가 비활성화됩니다. 수동 재배포를
+사용하거나 AMP API로 CI/CD에서 재배포를 트리거하세요.
+
+<Card title="AMP에 배포하기" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
+  모노레포 작업 디렉터리를 선택한 뒤 배포 가이드를 계속 진행하세요.
+</Card>

docs/ko/enterprise/integrations/databricks.mdx

@@ -0,0 +1,123 @@
+---
+title: Databricks 연동
+description: "Databricks 관리형 MCP 서버를 통해 CrewAI 에이전트를 Databricks Genie, SQL, Unity Catalog Functions, Vector Search에 연결하세요."
+icon: "layer-group"
+mode: "wide"
+---
+
+## 개요
+
+[Databricks 관리형 MCP 서버](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp)를 통해 CrewAI 에이전트를 Databricks 워크스페이스에 직접 연결하세요. Databricks 연동을 사용하면 에이전트가 **Genie**로 자연어 질문을 하고, 거버넌스가 적용된 **SQL**을 실행하며, **Unity Catalog Functions**를 호출하고, **Vector Search**로 문서를 검색할 수 있습니다. 커넥터 코드를 작성하거나 호스팅할 필요가 없으며, 모든 호출에 Unity Catalog 권한이 적용됩니다.
+
+내부적으로 Databricks 연동은 CrewAI의 [커스텀 MCP 서버](/ko/enterprise/guides/custom-mcp-server) 지원을 감싼 관리형 래퍼입니다. Databricks는 각 기능을 개별 [Model Context Protocol](https://modelcontextprotocol.io/) 엔드포인트로 노출하며, CrewAI가 사용자를 대신해 안전하게 연결합니다. 각 서버를 개별적으로 추가하므로 크루에 필요한 기능만 정확히 활성화할 수 있습니다.
+
+## 주요 기능
+
+<CardGroup cols={2}>
+  <Card title="Genie" icon="comments">
+    [Genie](https://docs.databricks.com/aws/en/genie/)로 자연어 질문을 하고 데이터 기반의 근거 있는 답변을 받으세요. Genie는 Genie Spaces와 Unity Catalog를 조회하고 Databricks UI로 연결되는 링크를 제공합니다.
+  </Card>
+  <Card title="Databricks SQL" icon="database">
+    에이전트에서 직접 Databricks 웨어하우스에 거버넌스가 적용된 SQL을 실행하여 데이터를 조회, 변환하고 데이터 파이프라인을 작성하세요.
+  </Card>
+  <Card title="Unity Catalog Functions" icon="function">
+    [Unity Catalog 함수](https://docs.databricks.com/aws/en/udf/unity-catalog)를 호출하여 사전 정의된 SQL과 맞춤형 비즈니스 로직을 거버넌스가 적용된 재사용 가능한 도구로 실행하세요.
+  </Card>
+  <Card title="Vector Search" icon="magnifying-glass">
+    [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) 인덱스에서 의미 유사도를 사용해 RAG 및 지식 워크플로우에 필요한 관련 문서를 검색하세요.
+  </Card>
+</CardGroup>
+
+모든 서버는 Unity AI Gateway 뒤에서 실행되며 Unity Catalog 접근 제어를 적용하므로, 에이전트는 허용된 데이터와 도구만 볼 수 있습니다.
+
+## 사전 준비사항
+
+Databricks 연동을 사용하기 전에 다음을 준비해야 합니다:
+
+- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정
+- 노출하려는 기능이 있는 Databricks 워크스페이스(Genie Spaces, SQL 웨어하우스, Unity Catalog 함수 또는 Vector Search 인덱스)
+- 기본 객체에 대한 적절한 [Unity Catalog 권한](https://docs.databricks.com/aws/en/data-governance/unity-catalog)
+- Databricks 워크스페이스 호스트명(예: `your-workspace.cloud.databricks.com`)
+
+## Databricks 관리형 MCP 서버
+
+Databricks는 각 기능마다 별도의 관리형 MCP 서버를 게시합니다. CrewAI는 이를 개별 연결로 노출하며, 각 연결은 워크스페이스 호스트와 관련 Unity Catalog 식별자로 구성됩니다. 엔드포인트는 다음 패턴을 따릅니다:
+
+| 서버 | 기능 | MCP URL 패턴 |
+|------|------|--------------|
+| **Genie** | Genie Space에 대한 자연어 Q&A | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
+| **Databricks SQL** | 웨어하우스에 SQL 실행 | `https://<workspace-hostname>/api/2.0/mcp/sql` |
+| **Unity Catalog Functions** | 등록된 UC 함수 실행 | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
+| **Vector Search** | Vector Search 인덱스 조회 | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
+
+<Note>
+이러한 URL을 직접 만들 필요는 없습니다. CrewAI는 연결을 구성할 때 입력한 워크스페이스 호스트와 식별자(Genie Space ID 또는 catalog/schema)로 각 엔드포인트를 생성합니다. 전체 사양과 최신 엔드포인트 세부 정보는 [Databricks 관리형 MCP 문서](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp)를 참고하세요.
+</Note>
+
+## CrewAI AMP에서 Databricks 연결하기
+
+<Frame>
+    <img src="/images/enterprise/databricks-configure.png" alt="CrewAI AMP에서 Databricks 관리형 MCP 서버 구성" />
+</Frame>
+
+각 Databricks 기능(**Databricks Genie**, **Databricks SQL**, **Databricks Unity Catalog Functions**, **Databricks Vector Search**)은 **Tools & Integrations** 페이지의 Databricks 그룹 아래에 별도의 MCP 서버로 표시됩니다. 필요한 것을 구성하세요:
+
+<Steps>
+    <Step title="Tools & Integrations 열기">
+        CrewAI AMP 왼쪽 사이드바에서 **Tools & Integrations**로 이동하여 Connections 목록에서 **Databricks** 그룹을 찾습니다. 그 아래에 Genie, SQL, Unity Catalog Functions, Vector Search 서버가 나열됩니다.
+    </Step>
+
+    <Step title="서버 구성하기">
+        활성화하려는 기능 옆의 **Configure**를 클릭하고 연결 세부 정보를 입력합니다:
+
+        - **Workspace Host** — Databricks 워크스페이스 호스트명(예: `my-workspace.cloud.databricks.com`).
+        - **Genie** — 조회할 **Genie Space ID**.
+        - **Unity Catalog Functions** — 함수가 포함된 **catalog**와 **schema**.
+        - **Vector Search** — 인덱스가 포함된 **catalog**와 **schema**.
+        - **Databricks SQL** — 추가 식별자가 필요 없으며, 쿼리는 워크스페이스의 SQL 웨어하우스에서 실행됩니다.
+    </Step>
+
+    <Step title="인증 방법 선택하기">
+        CrewAI가 Databricks에 인증하는 방법을 선택합니다. **OAuth**를 권장합니다.
+
+        - **Use OAuth** — OAuth 2.0으로 안전하게 연결합니다. 각 사용자가 개별적으로 인증하며, Databricks는 해당 기능(`genie`, `sql`, `unity-catalog` 또는 `vector-search`)으로 범위가 지정된 토큰을 발급합니다. CrewAI가 인증 흐름을 처리하고 토큰을 자동으로 갱신합니다.
+        - **Use personal access token** — [Databricks 개인 액세스 토큰](https://docs.databricks.com/aws/en/dev-tools/auth/pat)으로 인증합니다. 노출을 제한하려면 최소 권한 ID를 사용하세요.
+    </Step>
+
+    <Step title="인증하기">
+        인증을 완료합니다. 연결되면 해당 서버의 도구를 크루에서 사용할 수 있습니다. 활성화하려는 다른 Databricks 기능에 대해서도 반복합니다.
+    </Step>
+</Steps>
+
+<Tip>
+각 기능이 별도의 연결이므로 자유롭게 조합할 수 있습니다. 예를 들어 리서치 크루에는 Genie와 Vector Search를 활성화하고, 데이터 엔지니어링 크루에는 SQL과 Unity Catalog Functions를 사용하도록 할 수 있습니다. 가시성(Visibility) 설정으로 각 기능을 사용할 수 있는 팀원을 제어할 수 있습니다.
+</Tip>
+
+## 크루에서 Databricks 도구 사용하기
+
+연결되면 각 MCP 서버가 노출하는 도구가 **Tools & Integrations** 페이지의 기본 제공 연결과 함께 표시됩니다. 다음을 할 수 있습니다:
+
+- 다른 CrewAI 도구와 마찬가지로 크루의 에이전트에 **도구 할당**.
+- 각 연결을 사용할 수 있는 팀원을 제어하는 **가시성 관리**.
+- Connections 목록에서 언제든지 연결 **편집 또는 제거**.
+
+이제 에이전트는 Genie에 근거 있는 답변을 요청하고, 웨어하우스에 SQL을 실행하며, Unity Catalog 함수를 호출하고, Vector Search 인덱스를 검색할 수 있으며, 그 결과가 자동으로 추론에 반영됩니다.
+
+<Warning>
+Databricks는 Unity Catalog와 Unity AI Gateway를 통해 거버넌스를 적용합니다. 사용자는 워크스페이스 ID에 허용된 도구만 검색하고 호출할 수 있습니다. 도구 호출이 실패하면 연결하는 사용자(또는 토큰 ID)가 Genie Space, 웨어하우스, 함수 또는 인덱스에 필요한 Unity Catalog 권한을 가지고 있는지 확인하세요. 일부 Genie 및 SQL 쿼리는 비동기로 실행되어 결과를 반환하는 데 시간이 걸릴 수 있습니다.
+</Warning>
+
+## 더 알아보기
+
+<CardGroup cols={2}>
+  <Card title="Databricks 관리형 MCP 서버" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
+    관리형 Genie, SQL, Unity Catalog Functions, Vector Search MCP 서버에 대한 공식 Databricks 문서입니다.
+  </Card>
+  <Card title="CrewAI의 커스텀 MCP 서버" icon="plug" href="/ko/enterprise/guides/custom-mcp-server">
+    Databricks 연동의 기반이 되는, CrewAI가 모든 MCP 서버에 연결하는 방법을 알아보세요.
+  </Card>
+</CardGroup>
+
+<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
+  Databricks 연동 구성 또는 문제 해결에 대한 지원이 필요하면 지원팀에 문의하세요.
+</Card>

docs/ko/enterprise/integrations/snowflake.mdx

@@ -0,0 +1,134 @@
+---
+title: Snowflake 연동
+description: "Snowflake 관리형 MCP 서버를 통해 CrewAI 에이전트를 Snowflake Cortex Analyst, Cortex Search 및 SQL 실행에 연결합니다."
+icon: "snowflake"
+mode: "wide"
+---
+
+## 개요
+
+[Snowflake 관리형 MCP 서버](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp)를 통해 CrewAI 에이전트를 Snowflake 데이터에 직접 연결하세요. Snowflake 연동을 사용하면 에이전트가 **Cortex Analyst**로 구조화된 데이터를 쿼리하고, **Cortex Search**로 비구조화된 데이터를 검색하며, 커넥터 코드를 작성하거나 호스팅할 필요 없이 웨어하우스에 대해 관리되는 SQL을 실행할 수 있습니다.
+
+내부적으로 Snowflake 연동은 CrewAI의 [Custom MCP Server](/ko/enterprise/guides/custom-mcp-server) 지원을 기반으로 하는 관리형 래퍼입니다. Snowflake는 [Model Context Protocol](https://modelcontextprotocol.io/) 엔드포인트를 통해 Cortex AI 기능을 노출하며, CrewAI가 이를 안전하게 연결합니다. Snowflake 측에서 노출하는 모든 도구 — Cortex Analyst, Cortex Search, SQL 실행, Cortex Agents 또는 사용자 정의 도구 — 가 크루에서 사용할 수 있게 됩니다.
+
+## 주요 기능
+
+<CardGroup cols={3}>
+  <Card title="Cortex Analyst" icon="chart-bar">
+    자연어로 질문하고 [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)가 풍부한 시맨틱 모델을 사용하여 **구조화된** 데이터에 대해 SQL을 생성하고 실행하도록 합니다.
+  </Card>
+  <Card title="Cortex Search" icon="magnifying-glass">
+    Snowflake의 완전 관리형 검색 서비스인 [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)를 사용하여 RAG 및 지식 워크플로우를 위한 관련 **비구조화된** 데이터를 검색합니다.
+  </Card>
+  <Card title="SQL 실행" icon="database">
+    구성 가능한 읽기 전용 모드, 타임아웃 및 웨어하우스 선택을 통해 Snowflake 웨어하우스에 대해 관리되는 SQL 쿼리를 직접 실행합니다.
+  </Card>
+</CardGroup>
+
+연동이 MCP 서버가 게시하는 도구를 노출하므로, **Cortex Agents** 및 **사용자 정의 도구**(사용자 정의 함수 및 저장 프로시저)도 CrewAI 에이전트에 노출할 수 있습니다.
+
+## 사전 준비 사항
+
+Snowflake 연동을 사용하기 전에 다음을 확인하십시오:
+
+- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정
+- Cortex AI 기능에 액세스할 수 있는 Snowflake 계정
+- 노출하려는 도구가 구성된 [Snowflake 관리형 MCP 서버](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp)
+- MCP 서버 및 기본 객체에 대한 적절한 Snowflake 권한(USAGE/SELECT)
+
+## Snowflake MCP 서버 설정
+
+Snowflake 관리형 MCP 서버는 Snowflake 계정 내에서 실행되며 CrewAI와 같은 외부 클라이언트에서 사용할 수 있는 도구를 정의합니다. [`CREATE MCP SERVER`](https://docs.snowflake.com/en/sql-reference/sql/create-mcp-server) 명령을 사용하여 노출하려는 Cortex Search 서비스, Cortex Analyst 시맨틱 뷰 및 SQL 도구를 나열하여 생성합니다.
+
+```sql
+CREATE MCP SERVER my_mcp_server
+  FROM SPECIFICATION $$
+    tools:
+      - name: "sales_analyst"
+        type: "CORTEX_ANALYST"
+        identifier: "MY_DATABASE.MY_SCHEMA.sales_semantic_view"
+        description: "Answer questions about sales metrics"
+      - name: "docs_search"
+        type: "CORTEX_SEARCH_SERVICE_QUERY"
+        identifier: "MY_DATABASE.MY_SCHEMA.support_docs_search"
+        description: "Search internal support documentation"
+      - name: "run_sql"
+        type: "SQL_EXECUTION"
+        description: "Execute read-only SQL queries"
+  $$;
+```
+
+<Note>
+MCP 엔드포인트는 `https://<account_URL>/api/v2/databases/{database}/schemas/{schema}/mcp-servers/{name}` 형식을 따릅니다. CrewAI는 연동 구성 시 제공하는 **계정 URL**, **데이터베이스**, **스키마** 및 **MCP 서버 이름**을 사용하여 이 URL을 자동으로 구성합니다.
+</Note>
+
+Cortex Agents, 사용자 정의 도구, 응답 크기 제한 및 거버넌스 옵션을 포함한 전체 사양은 [Snowflake 관리형 MCP 서버 문서](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp)를 참조하세요.
+
+## CrewAI AMP에서 Snowflake 연결
+
+<Frame>
+    <img src="/images/enterprise/snowflake-configure.png" alt="CrewAI AMP에서 Snowflake 연동 구성" />
+</Frame>
+
+<Steps>
+    <Step title="도구 및 연동 열기">
+        CrewAI AMP 왼쪽 사이드바에서 **도구 및 연동**으로 이동하고, 애플리케이션 목록에서 **Snowflake**를 찾아 구성 패널을 엽니다.
+    </Step>
+
+    <Step title="연결 세부 정보 제공">
+        CrewAI가 Snowflake MCP 서버에 연결하는 데 사용하는 연결 필드를 채웁니다:
+
+        | 필드 | 필수 | 설명 |
+        |------|------|------|
+        | **이름** | 예 | 이 연결의 설명적 이름(기본값: `Snowflake`). |
+        | **설명** | 아니오 | 이 연결이 제공하는 내용에 대한 선택적 요약. |
+        | **계정 URL** | 예 | Snowflake 계정 URL, 예: `xy12345.us-east-1.snowflakecomputing.com`. |
+        | **데이터베이스** | 예 | MCP 서버가 포함된 데이터베이스(예: `MY_DATABASE`). |
+        | **스키마** | 예 | MCP 서버가 포함된 스키마(예: `MY_SCHEMA`). |
+        | **MCP 서버 이름** | 예 | Snowflake에서 생성한 MCP 서버 객체의 이름(예: `MY_MCP_SERVER`). |
+    </Step>
+
+    <Step title="인증 방법 선택">
+        CrewAI가 Snowflake에 인증하는 방법을 선택합니다. **OAuth**가 권장됩니다.
+
+        - **OAuth 사용** — 자격 증명을 공유하지 않고 토큰 기반 인증을 위해 OAuth 2.0을 사용하여 안전하게 연결합니다. CrewAI가 전체 인증 흐름을 처리하고 자동으로 토큰을 갱신합니다. 양식에 표시된 **리디렉트 URI**(`https://oauth.crewai.com/oauth/add`)를 복사하여 Snowflake [OAuth 보안 연동](https://docs.snowflake.com/en/user-guide/oauth-custom)에 인증된 리디렉트 URI로 등록하세요.
+        - **개인 액세스 토큰 사용** — Snowflake 계정 설정에서 생성한 [프로그래밍 방식 액세스 토큰](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens)을 사용하여 인증합니다. 노출을 제한하기 위해 토큰에 최소 권한 역할을 할당하세요.
+    </Step>
+
+    <Step title="인증">
+        **인증**을 클릭합니다. OAuth의 경우 Snowflake로 리디렉션되어 액세스를 승인합니다. 인증되면 Snowflake 서버가 연결 목록에 나타나고 해당 도구를 크루에서 사용할 수 있게 됩니다.
+    </Step>
+</Steps>
+
+<Tip>
+OAuth를 사용하면 각 사용자가 개별적으로 인증하며 쿼리는 해당 Snowflake `DEFAULT_ROLE`로 실행됩니다. 연결하는 사용자에게 기본 역할과 웨어하우스가 설정되어 있는지 확인하세요(`ALTER USER <username> SET DEFAULT_ROLE = '<role>' DEFAULT_WAREHOUSE = '<warehouse>'`). 그래야 Cortex Analyst 및 SQL 도구에 실행할 컴퓨팅이 있습니다.
+</Tip>
+
+## 크루에서 Snowflake 도구 사용
+
+연결되면 MCP 서버가 노출하는 도구가 **도구 및 연동** 페이지에서 기본 연결과 함께 표시됩니다. 다음을 수행할 수 있습니다:
+
+- 다른 CrewAI 도구처럼 크루의 **에이전트에 도구를 할당**합니다.
+- **가시성을 관리**하여 어떤 팀원이 연결을 사용할 수 있는지 제어합니다.
+- 연결 목록에서 언제든지 연결을 **편집하거나 제거**합니다.
+
+이제 에이전트가 Cortex Analyst에 메트릭을 요청하고, 문서에 대해 Cortex Search를 실행하고, SQL을 실행할 수 있으며 — 결과가 자동으로 추론에 반영됩니다.
+
+<Warning>
+Snowflake는 MCP 서버에 거버넌스를 적용합니다: 역할 기반 액세스 제어가 사용자가 발견하고 호출할 수 있는 도구를 결정하며, 응답 크기, 도구 수(서버당 최대 50개) 및 재귀 깊이에 제한이 적용됩니다. 도구 호출이 실패하면 연결하는 사용자의 역할에 MCP 서버 및 기본 객체에 대한 필수 권한이 있는지 확인하세요.
+</Warning>
+
+## 자세히 알아보기
+
+<CardGroup cols={2}>
+  <Card title="Snowflake 관리형 MCP 서버" icon="snowflake" href="https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp">
+    MCP 서버를 생성하고 관리하기 위한 공식 Snowflake 문서.
+  </Card>
+  <Card title="CrewAI의 Custom MCP 서버" icon="plug" href="/ko/enterprise/guides/custom-mcp-server">
+    CrewAI가 모든 MCP 서버에 연결하는 방법을 알아보세요. Snowflake 연동이 기반으로 하는 기초입니다.
+  </Card>
+</CardGroup>
+
+<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
+  Snowflake 연동 또는 문제 해결에 대해 지원팀에 문의하세요.
+</Card>

docs/ko/guides/flows/conversational-flows.mdx

@@ -0,0 +1,453 @@
+---
+title: 대화형 Flow
+description: 턴마다 kickoff, 메시지 기록, 의도 라우팅, 트레이싱, WebSocket 브리지로 멀티턴 채팅 앱을 만듭니다.
+icon: comments
+mode: "wide"
+---
+
+## 개요
+
+대화형 앱은 각 사용자 입력을 **동일한 세션 id**로 **새 flow 실행**으로 처리합니다. CrewAI는 메시지 기록, 선택적 의도 분류, 지연 트레이싱, UI 브리지를 제공하며, `Flow`에 별도 `chat()` API는 없습니다.
+
+| 개념 | 구현 |
+|------|------|
+| 세션 id | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
+| 사용자 입력 | `kickoff(user_message=...)`가 그래프 실행 전 `state.messages`에 추가 |
+| 턴 완료 | `FlowFinished`는 **이번 실행**만 의미; 다음 `kickoff`로 대화 계속 |
+| 세션 전체 트레이스 | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
+
+## 단일 진입점: `kickoff`
+
+모든 사용자 메시지에 **`flow.kickoff(user_message=..., session_id=...)`**를 사용하세요 (REST, WebSocket, CLI). `Flow`에 커스텀 `chat()` 래퍼를 만들지 마세요.
+
+| API | 용도 |
+|-----|------|
+| `kickoff(user_message=..., session_id=...)` | 각 사용자 메시지 |
+| `kickoff_async(...)` | 동일 파라미터; 네이티브 async 진입 |
+| `ask()` | 한 스텝 **내부** 블로킹 프롬프트 (마법사, 확인) |
+| `@human_feedback` | **스텝 출력** 승인/거부 — 다음 채팅 줄이 아님 |
+| `ChatSession.handle_turn(...)` | `kickoff` 위의 전송 계층 (SSE / WebSocket) |
+
+## 빠른 시작
+
+```python
+from uuid import uuid4
+
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    Flow,
+    listen,
+    or_,
+    persist,
+    router,
+    start,
+)
+from crewai.flow.persistence import SQLiteFlowPersistence
+
+
+class SupportFlow(Flow[ChatState]):
+    conversational_config = ConversationalConfig(
+        default_intents=["order", "help", "goodbye"],
+        intent_llm="gpt-4o-mini",
+        defer_trace_finalization=True,
+    )
+
+    @start()
+    def bootstrap(self):
+        if not self.state.session_ready:
+            self.state.session_ready = True
+        return "ready"
+
+    @router(bootstrap)
+    def route(self):
+        # default_intents 설정 시 prepare_conversational_turn에서 last_intent 설정
+        return self.state.last_intent or "help"
+
+    @listen("order")
+    def handle_order(self):
+        reply = "주문이 배송 중입니다."
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("help")
+    def handle_help(self):
+        reply = "무엇을 도와드릴까요?"
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("goodbye")
+    def handle_goodbye(self):
+        reply = "안녕히 가세요!"
+        self.append_message("assistant", reply)
+        return reply
+
+    @persist(SQLiteFlowPersistence("support.db"))
+    @listen(or_(handle_order, handle_help, handle_goodbye))
+    def finalize(self):
+        return self.state.model_dump()
+
+
+session_id = str(uuid4())
+flow = SupportFlow()
+
+flow.kickoff(user_message="주문 어디까지 왔나요?", session_id=session_id)
+flow.kickoff(user_message="반품은 어떻게 하나요?", session_id=session_id)
+flow.finalize_session_traces()  # 전체 대화에 대한 단일 trace 링크
+```
+
+## 턴 생명주기
+
+`user_message`가 있는 각 `kickoff`는 다음 파이프라인을 실행합니다:
+
+1. **`_configure_conversational_kickoff`** — `session_id` / `user_message`를 `inputs`에 병합, `ConversationalConfig` 적용, 설정 시 지연 트레이싱 활성화.
+2. **상태 복원** — `inputs["id"]`가 있고 `@persist`가 설정되면 최신 스냅샷 로드.
+3. **`FlowStarted`** — 지연 세션의 첫 턴에서만 발생.
+4. **`prepare_conversational_turn`** — 사용자 메시지를 `state.messages`에 추가, `last_user_message` 설정, `last_intent` 초기화, `intents` / `default_intents` + `intent_llm` 설정 시 분류.
+5. **그래프 실행** — `@start` → `@router` → `@listen` 핸들러.
+6. **실행 종료** — 지연 활성화 시 턴별 `flow_finished` 및 trace 종료 **건너뜀**; 중첩 `Agent.kickoff()` / crew도 부모 batch를 닫지 않음.
+
+핸들러는 **`append_message("assistant", reply)`**를 호출해 다음 턴의 `conversation_messages`에 어시스턴트 응답이 포함되게 하세요. 사용자 입력은 kickoff 시 이미 저장됩니다 — 핸들러에서 다시 추가하지 마세요.
+
+## `ConversationalConfig` (클래스 수준 기본값)
+
+`Flow` 서브클래스에 `conversational_config: ClassVar[ConversationalConfig | None]`로 설정합니다.
+
+| 필드 | 기본값 | 목적 |
+|------|--------|------|
+| `default_intents` | `None` | kickoff 전 자동 분류용 outcome 라벨 |
+| `intent_llm` | `None` | 분류용 모델 (intent 사용 시 필수) |
+| `interactive_prompt` | `"You: "` | `kickoff(interactive=True)` 프롬프트 |
+| `interactive_timeout` | `None` | 대화형 모드 줄 단위 타임아웃 |
+| `exit_commands` | `exit`, `quit` | 대화형 모드 종료 단어 |
+| `defer_trace_finalization` | `True` | 턴 간 하나의 trace batch 유지 |
+
+`intents=` 및 `intent_llm=` 키워드로 kickoff마다 재정의할 수 있습니다.
+
+## `ChatState` (권장 persist 형태)
+
+```python
+from crewai.flow import ChatState
+
+
+class MyChatState(ChatState):
+    # 상속: id, messages, last_user_message, last_intent, session_ready
+    research_turn_count: int = 0
+    custom_flag: bool = False
+```
+
+| 필드 | 역할 |
+|------|------|
+| `id` | 세션 UUID (`session_id` / `inputs["id"]`와 동일) |
+| `messages` | LLM 기록용 `{role, content}` 리스트 |
+| `last_user_message` | 이번 턴의 최신 사용자 입력 |
+| `last_intent` | 분류 후 라우트 라벨 (사용 시) |
+| `session_ready` | 일회성 bootstrap 플래그 |
+
+`ConversationalInputs`는 `kickoff(inputs={...})`용 `TypedDict`: `id`, `user_message`, `last_intent`.
+
+## `Flow` 대화 API
+
+### `kickoff` / `kickoff_async` 파라미터
+
+| 파라미터 | 목적 |
+|----------|------|
+| `user_message` | 이번 턴 텍스트 (또는 `{"role": "user", "content": "..."}`) |
+| `session_id` | 대화 UUID → `inputs["id"]` / `state.id` |
+| `intents` | kickoff 전 `classify_intent`용 outcome 라벨 |
+| `intent_llm` | 분류 LLM (`intents`와 함께 필수) |
+| `interactive` | `ask()` CLI 루프 (로컬 데모 전용) |
+| `interactive_prompt` | 대화형 모드 프롬프트 |
+| `interactive_timeout` | 줄 단위 `ask()` 타임아웃 |
+| `exit_commands` | 대화형 모드 종료 단어 |
+| `inputs` | 추가 상태 필드 |
+| `restore_from_state_id` | 다른 persist flow에서 fork 복원 |
+
+### 인스턴스 속성
+
+| 속성 | 목적 |
+|------|------|
+| `conversational_config` | 클래스 수준 `ConversationalConfig` |
+| `defer_trace_finalization` | 인스턴스 플래그; kickoff 시 config에서 자동 설정 |
+| `suppress_flow_events` | 콘솔 flow 패널 숨김; **트레이싱은 계속 기록** |
+| `stream` | 스트리밍; `ChatSession.handle_turn(..., stream=True)`와 함께 |
+
+### 메서드 및 프로퍼티
+
+| 이름 | 설명 |
+|------|------|
+| `append_message(role, content, **extra)` | `state.messages`에 추가 |
+| `conversation_messages` | LLM 호출용 읽기 전용 기록 |
+| `classify_intent(text, outcomes, *, llm, context=None)` | outcome 매핑 (`@human_feedback`와 동일 collapse) |
+| `receive_user_message(text, *, outcomes=None, llm=None)` | 사용자 메시지 추가; 선택적 `last_intent` |
+| `finalize_session_traces()` | 지연 `flow_finished` 발생 및 세션 trace batch 종료 |
+| `_should_defer_trace_finalization()` | 턴별 trace 종료 지연 여부 |
+| `input_history` | `ask()` 프롬프트/응답 감사 기록 |
+
+### 모듈 헬퍼 (`crewai.flow.conversation`)
+
+테스트 또는 커스텀 오케스트레이션용:
+
+| 함수 | 설명 |
+|------|------|
+| `normalize_kickoff_inputs(...)` | 대화 kwargs를 `inputs`에 병합 |
+| `get_conversation_messages(flow)` | 상태 또는 내부 버퍼에서 메시지 읽기 |
+| `append_message(flow, ...)` | 인스턴스 메서드와 동일 |
+| `prepare_conversational_turn(flow, ...)` | 턴 수화 (보통 kickoff가 호출) |
+| `receive_user_message(flow, ...)` | 인스턴스 메서드와 동일 |
+| `set_state_field(flow, name, value)` | dict 또는 Pydantic 상태 필드 설정 |
+| `get_conversational_config(flow)` | 클래스 `conversational_config` 읽기 |
+| `input_history_to_messages(entries)` | `input_history`를 LLM 메시지 형식으로 |
+
+## 의도 라우팅 패턴
+
+### A. `ConversationalConfig`로 사전 분류 (가장 단순)
+
+`default_intents`와 `intent_llm` 설정. 각 kickoff가 `@router` 전에 분류; `route()`에서 `self.state.last_intent` 읽기.
+
+### B. `@router` 내부에서 분류 (풍부한 프롬프트)
+
+`default_intents=None`으로 kickoff는 메시지만 추가. `route()`에서 커스텀 프롬프트로 `classify_intent` 호출:
+
+```python
+@router(bootstrap)
+def route(self):
+    intent = self.classify_intent(
+        self._routing_prompt(self.state.last_user_message),
+        ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
+    )
+    self.state.last_intent = intent
+    return intent
+```
+
+웹 리서치나 다단계 tool이 필요하면 **`@listen("RESEARCH")`** 등에서 `Agent.kickoff()`와 tool 사용 — 단순 `LLM.call()` 대신.
+
+## flow가 끝났지만 사용자는 계속 대화할 때
+
+`FlowFinished`는 **이번 그래프 실행**이 완료됨을 의미합니다. 같은 `session_id`로 또 다른 `kickoff`로 대화가 이어집니다. `@persist`가 `messages`, 플래그, 컨텍스트를 복원합니다.
+
+**Persist 패턴:** 전체 `Flow` 클래스보다 **단일 종료 스텝**(예: `finalize`)에 `@persist`를 두는 것이 좋습니다. 클래스 수준 persist는 매 메서드 후 저장하며, `load_state`는 최신 행을 사용해 같은 턴의 핸들러 업데이트를 놓칠 수 있습니다.
+
+후속 채팅 줄에 `@human_feedback`를 쓰지 마세요. 특정 스텝 출력을 사람이 승인해야 할 때만 사용하세요.
+
+## 대화형 `Flow` (실험적)
+
+<Warning>
+  **실험적 기능입니다.** 대화형 `Flow`의 API 표면(`conversational = True`,
+  `handle_turn`, `ConversationConfig`, `RouterConfig`, `ConversationState`,
+  내장 그래프와 헬퍼)은 `crewai.experimental` 하위에 있으며 정식 출시
+  전까지 변경될 수 있습니다. 특정 동작에 의존한다면 CrewAI 버전을 고정하고
+  변경 사항이 있는지 changelog를 확인하세요. 피드백과 이슈 환영합니다.
+</Warning>
+
+`Flow` 서브클래스에 `conversational = True`를 지정하면 대화형 챗 그래프가 활성화됩니다. 베이스 `Flow`가 `@start` / `@router` / `converse_turn` / `end_conversation` 그래프를 노출하고, `state.messages`를 관리하며, router LLM을 구동하고, 턴 간 trace 배치를 열린 상태로 유지합니다. 여러분은 **커스텀 라우트**만 작성하면 되고, 나머지는 프레임워크가 담당합니다.
+
+LLM 기반 라우터와 라우트별 핸들러로 멀티턴 챗을 만들고 싶지만 라이프사이클을 직접 배선하고 싶지 않을 때 사용하세요. 완전한 제어가 필요하면 위의 `Flow[ChatState]`로 내려가세요.
+
+### 빠른 예제
+
+```python
+from crewai import LLM, Flow
+from crewai.flow import listen
+from crewai.experimental.conversational import (
+    ConversationConfig,
+    ConversationState,
+    RouterConfig,
+)
+
+
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # 라우트 + 설명은 @listen 핸들러에서 자동 발견
+)
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    @listen("INTERNET_SEARCH")
+    def handle_internet_search(self) -> str:
+        """Fresh web research, current news, real-time lookups."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+    @listen("CREWAI_DOCS")
+    def handle_crewai_docs(self) -> str:
+        """Look up the CrewAI documentation for framework/API questions."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+
+flow = SupportFlow()
+try:
+    flow.handle_turn("뭘 할 수 있어?")                # converse(빌트인)로 라우팅
+    flow.handle_turn("AI 뉴스를 웹에서 찾아줘.")        # INTERNET_SEARCH로 라우팅
+    flow.handle_turn("첫 번째 결과를 요약해줘.")        # 다시 converse로 라우팅
+finally:
+    flow.finalize_session_traces()
+```
+
+### `ConversationConfig`
+
+클래스 단위의 챗 기본값을 부착하는 클래스 데코레이터입니다.
+
+| 필드 | 기본값 | 목적 |
+|------|--------|------|
+| `system_prompt` | i18n `slices.conversational_system_prompt` | 빌트인 `converse_turn`이 사용하는 system 메시지. 빈 문자열(`""`)을 전달하면 system 메시지를 끕니다. |
+| `llm` | `None` | 대화용 LLM (빌트인 `converse_turn`이 사용하고 router 폴백도 됨). |
+| `router` | `None` | LLM 기반 라우팅을 위한 `RouterConfig`. 없으면 항상 `converse`로 떨어집니다. |
+| `answer_from_history_prompt` | 프레임워크 기본값 | 선택적인 `answer_from_history` 라우트용 system 메시지. |
+| `answer_from_history_llm` | `None` | 설정되면 `answer_from_history` 단축 경로가 활성화됩니다. |
+| `intent_llm` | `None` | 레거시 `intents=`/`default_intents` 사전 분류용 LLM. |
+| `default_intents` | `None` | 레거시 사전 분류용 outcome 레이블. |
+| `visible_agent_outputs` | `None` | `"all"` 또는 `append_agent_result()` 결과를 사용자에게 공개로 승격할 에이전트 이름 목록. |
+| `defer_trace_finalization` | `True` | `handle_turn()` 호출들 사이에서 하나의 trace 배치를 열어 둡니다. |
+
+### `RouterConfig`와 자동 생성되는 라우트 카탈로그
+
+```python
+RouterConfig(
+    prompt="선택적인 도메인 프레이밍 (정책, 톤, 페르소나).",
+    response_format=MyRoute,        # 선택; 없으면 자동 생성
+    llm=ROUTER_LLM,                  # ConversationConfig.llm으로 폴백
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # 선택; 리스너에서 추론
+    route_descriptions={
+        "INTERNET_SEARCH": "이 라우트만 docstring 대신 사용할 설명.",
+    },
+    default_intent="converse",       # LLM 호출 실패 또는 LLM 없음일 때 사용
+    fallback_intent="converse",      # LLM이 잘못된 라우트를 반환할 때 사용
+    intent_field="intent",
+)
+```
+
+router에 전달되는 프롬프트는 자동으로 만들어집니다. 각 라우트의 설명은 다음 우선순위로 결정됩니다:
+
+1. `RouterConfig.route_descriptions[label]` — 명시적 오버라이드.
+2. `Flow.builtin_route_descriptions[label]` — `converse`, `end`, `answer_from_history`용 프레임워크 캐닝 텍스트 (router LLM용으로 다듬어진 문구).
+3. `@listen(label)` 핸들러 docstring의 첫 줄(비어있지 않은 줄).
+4. 빈 문자열 (라우트만 카탈로그에 등장하고 설명은 없음).
+
+실제 사용에서 **새 라우트를 추가하는 방법은 `@listen("X")` + 한 줄짜리 docstring**입니다:
+
+```python
+@listen("INTERNET_SEARCH")
+def handle_internet_search(self) -> str:
+    """Fresh web research, current news, real-time lookups."""
+    ...
+```
+
+…그러면 router LLM은 다음을 봅니다:
+
+```
+Routes:
+- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
+- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
+- converse: Ordinary chat, follow-ups, summaries, clarifications…
+- end: User signals the conversation is finished (goodbye, exit, done).
+```
+
+`RouterConfig.prompt`는 **도메인 프레이밍** (어시스턴트 페르소나, 비즈니스 규칙, 톤)을 위한 자리입니다. 라우트 카탈로그는 자동 생성되니 `prompt` 안에 라우트 목록을 넣지 마세요. 핸들러를 추가하는 순간 동기화가 깨집니다.
+
+### 빌트인 라우트
+
+| 라우트 | 핸들러 | 목적 |
+|--------|--------|------|
+| `converse` | `converse_turn` | 기본 챗 핸들러. system prompt + 정식 메시지 히스토리와 함께 `ConversationConfig.llm`을 호출합니다. |
+| `end` | `end_conversation` | `state.ended = True`로 설정하고 종료 응답을 보냅니다. |
+| `answer_from_history` | `answer_from_history_turn` | 선택적. `ConversationConfig.answer_from_history_llm`이 설정되어 있고 메시지를 히스토리만으로 답할 수 있을 때 라우팅됩니다. |
+
+서브클래스에 같은 이름의 핸들러를 정의하면 어떤 것이든 오버라이드할 수 있습니다.
+
+### `handle_turn()` 시맨틱
+
+`flow.handle_turn(message)`는 한 턴을 실행합니다:
+
+1. 그래프가 다시 실행되도록 턴 단위 실행 추적(`_completed_methods`, `_method_outputs`)을 초기화합니다 — 이게 없으면 동일 인스턴스에서 반복 `kickoff` 호출 시 `Flow.kickoff_async`가 `inputs={"id": ...}`를 체크포인트 복원으로 간주해 2번째 턴부터 단락 회로가 발생합니다.
+2. 사용자 메시지를 `state.messages`에 추가하고 `current_user_message` / `last_user_message`를 설정합니다. `last_intent`는 **이전 턴 값이 유지**되어 router LLM이 신호로 활용할 수 있습니다.
+3. `conversation_start` → `route_conversation` → 선택된 `@listen` 핸들러 순으로 실행됩니다.
+4. router는 결정을 `state.last_intent`에 저장합니다 (다음 턴의 router 컨텍스트에서 보입니다).
+5. 핸들러가 문자열을 반환했지만 `append_assistant_message`를 직접 호출하지 않았다면, `handle_turn`이 대신 추가해 줍니다.
+
+`flow.kickoff(user_message=..., session_id=...)`를 직접 호출해도 동일한 reset/run 로직이 동작합니다. `handle_turn`은 그 위에 얹은 편의 래퍼입니다.
+
+### 커스텀 router 동작
+
+매 라우팅 결정마다 사이드 이펙트(이벤트 버스 셋업, 텔레메트리)를 실행하려면 `route_turn`을 오버라이드하세요:
+
+```python
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    def route_turn(self, context: dict[str, Any]) -> str | None:
+        self.event_bus = MyBus(self)
+        return super().route_turn(context)
+```
+
+LLM router를 우회해 프로그램적으로 라우트를 선택하려면 `route_turn`에서 문자열을 반환하세요. `None`을 반환하면 `_route_with_config(...)`로 떨어집니다.
+
+### `append_assistant_message`와 `append_agent_result`
+
+`@listen(label)` 핸들러 안에서 두 가지 중 선택하세요:
+
+- `self.append_assistant_message(text)` — 사용자에게 보이는 어시스턴트 턴을 `state.messages`에 추가합니다. 다음 턴의 `converse_turn`이 이 내용을 보게 됩니다.
+- `self.append_agent_result(agent_name, result, visibility="private")` — 구조화된 이벤트를 `state.events`에, 스레드를 `state.agent_threads[agent_name]`에 기록합니다. public 가시성은 자동으로 `append_assistant_message`도 호출합니다. 정식 히스토리를 더럽히지 말아야 할 임시 작업에는 private을 쓰세요.
+
+`ConversationConfig.visible_agent_outputs`로 특정 에이전트의 private 결과를 전역적으로 public으로 승격할 수 있습니다 (`"all"` 또는 이름 리스트).
+
+## 턴 간 트레이싱
+
+`defer_trace_finalization=True` (`ConversationalConfig` 기본값):
+
+- 채팅 세션 전체에 **하나의 trace batch**.
+- 첫 턴에만 **`flow_started`**; `finalize_session_traces()`에서 **`flow_finished`** 한 번.
+- 턴별 `kickoff`는 “Trace batch finalized”를 출력하지 않음.
+- **중첩 작업** (`Agent.kickoff()`, crew, Exa tool)은 **부모** batch에 추가; 내부 `AgentExecutor` flow가 세션 batch를 조기 종료하지 않음.
+
+```python
+try:
+    while True:
+        line = input("You: ").strip()
+        if not line:
+            break
+        flow.kickoff(user_message=line, session_id=session_id)
+finally:
+    flow.finalize_session_traces()
+```
+
+지연 활성화 시 `ChatSession.close()`가 `finalize_session_traces()`를 호출합니다.
+
+`suppress_flow_events=True`는 Rich 콘솔 패널만 숨깁니다. trace 및 method 이벤트는 계속 발생합니다.
+
+### 대화형 `Flow` trace 수명 주기
+
+실험적 [대화형 `Flow`](#대화형-flow-실험적)는 동일한 tracing 수명 주기를 따릅니다. `defer_trace_finalization` 기본값이 `True`이므로 각 `handle_turn()`이 세션 trace를 열어 둡니다. 세션 끝에서 항상 finalize하세요 — REPL/루프를 `try/finally`로 감싸고 종료 시 `flow.finalize_session_traces()`를 호출하세요. 호출하지 않으면 batch가 열린 채 남아 마지막 대화가 export되지 않을 수 있습니다.
+
+## 스트리밍
+
+`Flow` 클래스에 `stream = True`. `kickoff(...)`가 표준 이벤트 버스를 통해 `assistant_delta` 등 이벤트를 발생시킵니다.
+
+## import
+
+```python
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    ConversationalInputs,
+    Flow,
+    listen,
+    persist,
+    router,
+    start,
+)
+```
+
+## 참고
+
+- [Flow 상태 관리 마스터하기](/ko/guides/flows/mastering-flow-state)
+- [첫 Flow 만들기](/ko/guides/flows/first-flow)
+- 데모: `lib/crewai/runner_conversational_flow_simple.py`

docs/ko/guides/flows/first-flow.mdx

@@ -607,6 +607,7 @@ result = ContentCrew().crew().kickoff(inputs={
 3. 더 복잡한 병렬 실행을 위해 `and_` 및 `or_` 함수를 탐색해 보세요.
 4. flow를 외부 API, 데이터베이스 또는 사용자 인터페이스에 연결해 보세요.
 5. 여러 전문화된 crew를 하나의 flow에서 결합해 보세요.
+6. [대화형 Flow](/ko/guides/flows/conversational-flows)로 멀티턴 채팅 앱 구축 (`kickoff` per message, `ChatSession`, 지연 트레이싱)
 
 <Check>
 축하합니다! 정규 코드, 직접적인 LLM 호출, crew 기반 처리를 결합하여 포괄적인 가이드를 생성하는 첫 번째 CrewAI Flow를 성공적으로 구축하셨습니다. 이러한 기초적인 역량을 바탕으로 절차적 제어와 협업적 인텔리전스를 결합하여 복잡하고 다단계의 문제를 해결할 수 있는 점점 더 정교한 AI 애플리케이션을 만들 수 있습니다.

docs/ko/guides/flows/mastering-flow-state.mdx

@@ -22,6 +22,8 @@ State 관리는 모든 고급 AI 워크플로우의 중추입니다. CrewAI Flow
 5. **애플리케이션 확장** - 적절한 데이터 조직을 통해 복잡한 워크플로를 지원할 수 있습니다.
 6. **대화형 애플리케이션 활성화** - 컨텍스트 기반 AI 상호작용을 위해 대화 내역을 저장하고 접근할 수 있습니다.
 
+멀티턴 채팅(`kickoff` per user line, `ChatState`, 의도 라우팅, 지연 트레이싱, `ChatSession`)은 [대화형 Flow](/ko/guides/flows/conversational-flows)를 참고하세요.
+
 이러한 기능을 효과적으로 활용하는 방법을 살펴보겠습니다.
 
 ## 상태 관리 기본 사항

docs/pt-BR/changelog.mdx

@@ -4,6 +4,76 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="03 jun 2026">
+  ## v1.14.7a1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
+
+  ## O Que Mudou
+
+  ### Funcionalidades
+  - Adicionar suporte a arquivos de agentes treinados da equipe
+  - Adicionar provedor nativo Snowflake Cortex LLM
+  - Adicionar guia de integração com Databricks
+  - Adicionar guia de integração com Snowflake
+
+  ### Correções de Bugs
+  - Corrigir CLI restaurando `[project.scripts]` no pacote crewai para instalação da ferramenta UV
+  - Resolver problemas de confiabilidade na entrada de arquivos
+  - Corrigir históricos de resultados de ferramentas incompletos no Snowflake Claude
+  - Lidar com chamadas de ferramentas em formato de string para Snowflake Claude
+  - Re-armar ouvintes `or_` de múltiplas fontes em ciclos controlados por roteadores
+
+  ### Desempenho
+  - Melhorar a velocidade de importação do crewai através do carregamento preguiçoso de importações do docling
+
+  ### Refatoração
+  - Dividir `flow.py` em DSL, definição e tempo de execução
+
+  ## Contributors
+
+  @Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
+
+</Update>
+
+<Update label="28 mai 2026">
+  ## v1.14.6
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.6)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Aprimorar StdioTransport para evitar vazamento de variáveis de ambiente
+  - Aprimorar a configuração de planejamento e o manuseio de observações
+  - Declarar env_vars no DatabricksQueryTool
+  - Adicionar documentação do Agente Control Plane
+
+  ### Correções de Bugs
+  - Corrigir vazamentos de saída estruturada em loops de chamada de ferramenta
+  - Remover callbacks e estado de adaptador que não podem ser retornados em checkpoint
+  - Serializar campos type[BaseModel] como esquema JSON em checkpoint
+  - Evitar tarefa órfã task_started na restauração de escopo de retomar
+  - Permitir que AgentExecutor restaure a partir de checkpoint
+  - Corrigir erro de digitação de mongodb para pymongo em package_dependencies
+
+  ### Documentação
+  - Adicionar bloco de navegação de documentação ACP (Beta) às páginas do Agente Control Plane
+  - Remover referências a processos consensuais da página de processos
+  - Reestruturar a página de checkpointing
+  - Documentar passo de instalação do pacote administrativo único
+  - Migrar Secrets Manager / Workload Identity de replicated-config
+  - Remover expressões JSX `{" "}` que quebram a renderização de `<Steps>`
+
+  ### Refatoração
+  - Mover Skills Repository para experimental + CREWAI_EXPERIMENTAL gate
+
+  ## Contribuidores
+
+  @akaKuruma, @alex-clawd, @github-actions[bot], @greysonlalonde, @heitorado, @iris-clawd, @lorenzejay, @lucasgomide, @mattatcha, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="27 mai 2026">
   ## v1.14.6a2
 

docs/pt-BR/concepts/llms.mdx

@@ -106,7 +106,7 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
 </Tabs>
 
 <Info>
-  O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure e AWS Bedrock — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`).
+  O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock e Snowflake Cortex — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`).
 
   Todos os outros provedores são alimentados pelo **LiteLLM**. Se você planeja usar algum deles, adicione-o como dependência ao seu projeto:
   ```bash
@@ -230,6 +230,55 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     ```
   </Accordion>
 
+  <Accordion title="Snowflake Cortex">
+    O CrewAI oferece integração nativa com a API REST do Snowflake Cortex pelo endpoint Chat Completions compatível com OpenAI. Isso evita fallback para LiteLLM em modelos `snowflake/...`. Atualmente, o Snowflake Cortex no CrewAI oferece suporte apenas a Chat Completions, então use o modo `api` padrão e não defina `api="responses"`.
+
+    ```toml Code
+    # Obrigatório
+    SNOWFLAKE_PAT=<your-programmatic-access-token>
+    SNOWFLAKE_ACCOUNT_URL=https://<account-identifier>.snowflakecomputing.com
+
+    # Configuração alternativa da conta
+    SNOWFLAKE_ACCOUNT=<account-identifier>
+    ```
+
+    **Uso básico:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/openai-gpt-4.1",
+        temperature=0.7,
+        max_completion_tokens=1024,
+    )
+    ```
+
+    **Modelos Claude no Cortex:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="snowflake/claude-sonnet-4-5",
+        max_completion_tokens=1024,
+        stream=True,
+    )
+    ```
+
+    **Variáveis de ambiente suportadas:**
+    - `SNOWFLAKE_PAT`, `SNOWFLAKE_TOKEN` ou `SNOWFLAKE_JWT`: token usado como credencial Bearer
+    - `SNOWFLAKE_ACCOUNT_URL`: URL completa da conta Snowflake
+    - `SNOWFLAKE_ACCOUNT`, `SNOWFLAKE_ACCOUNT_ID` ou `SNOWFLAKE_ACCOUNT_IDENTIFIER`: identificador da conta usado para montar a URL
+
+    As requisições REST do Snowflake usam a role padrão do usuário. Garanta que essa role tenha `SNOWFLAKE.CORTEX_USER` ou `SNOWFLAKE.CORTEX_REST_API_USER`. Parâmetros de banco de dados, schema, warehouse e role explícita não são exigidos pelo endpoint Cortex REST Chat Completions.
+
+    **Recursos:**
+    - Seleção nativa com `model="snowflake/<model-name>"`
+    - Chat Completions com e sem streaming apenas; `api="responses"` não é compatível
+    - Rastreamento de uso de tokens
+    - Chamadas de função para modelos OpenAI e Claude hospedados no Snowflake
+    - Remoção automática de prefill final de assistant inválido para modelos Claude no Snowflake
+  </Accordion>
+
   <Accordion title="Anthropic">
     ```toml Code
     # Obrigatório

docs/pt-BR/concepts/processes.mdx

@@ -16,7 +16,6 @@ mode: "wide"
 
 - **Sequencial**: Executa tarefas de forma sequencial, garantindo que as tarefas sejam concluídas em uma progressão ordenada.
 - **Hierárquico**: Organiza tarefas em uma hierarquia gerencial, onde as tarefas são delegadas e executadas com base numa cadeia de comando estruturada. Um modelo de linguagem de gerente (`manager_llm`) ou um agente gerente personalizado (`manager_agent`) deve ser especificado na crew para habilitar o processo hierárquico, facilitando a criação e o gerenciamento de tarefas pelo gerente.
-- **Processo Consensual (Planejado)**: Visando a tomada de decisão colaborativa entre agentes para execução de tarefas, esse tipo de processo introduz uma abordagem democrática ao gerenciamento de tarefas dentro do CrewAI. Está planejado para desenvolvimento futuro e ainda não está implementado no código-fonte.
 
 ## O Papel dos Processos no Trabalho em Equipe
 Os processos permitem que agentes individuais atuem como uma unidade coesa, otimizando seus esforços para atingir objetivos comuns com eficiência e coerência.
@@ -59,9 +58,9 @@ Emulando uma hierarquia corporativa, o CrewAI permite especificar um agente gere
 
 ## Classe Process: Visão Detalhada
 
-A classe `Process` é implementada como uma enumeração (`Enum`), garantindo segurança de tipo e restringindo os valores de processos aos tipos definidos (`sequential`, `hierarchical`). O processo consensual está planejado para inclusão futura, reforçando nosso compromisso com o desenvolvimento contínuo e a inovação.
+A classe `Process` é implementada como uma enumeração (`Enum`), garantindo segurança de tipo e restringindo os valores de processos aos tipos definidos (`sequential`, `hierarchical`).
 
 ## Conclusão
 
 A colaboração estruturada possibilitada pelos processos dentro do CrewAI é fundamental para permitir o trabalho em equipe sistemático entre agentes.
-Esta documentação foi atualizada para refletir os mais recentes recursos, melhorias e a planejada integração do Processo Consensual, garantindo que os usuários tenham acesso às informações mais atuais e abrangentes.
+Esta documentação foi atualizada para refletir os mais recentes recursos e melhorias, garantindo que os usuários tenham acesso às informações mais atuais e abrangentes.

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

@@ -6,6 +6,14 @@ icon: "gauge"
 mode: "wide"
 ---
 
+<Info>
+  **Navegação da Documentação do ACP (Beta)**
+
+  - [Visão Geral](/pt-BR/enterprise/features/agent-control-plane/overview)
+  - **Monitoramento** *(você está aqui)*
+  - [Regras](/pt-BR/enterprise/features/agent-control-plane/rules)
+</Info>
+
 ## Visão Geral
 
 A aba **Automações** é a visão de operações somente leitura do [Agent Control Plane](/pt-BR/enterprise/features/agent-control-plane/overview). Ela combina dois cards de métricas, um sankey interativo e duas sub-tabelas — **Automações** e **Consumo** — nas quais você pode buscar, filtrar e ordenar.

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

@@ -5,6 +5,14 @@ sidebarTitle: Visão Geral
 icon: "book-open"
 ---
 
+<Info>
+  **Navegação da Documentação do ACP (Beta)**
+
+  - **Visão Geral** *(você está aqui)*
+  - [Monitoramento](/pt-BR/enterprise/features/agent-control-plane/monitoring)
+  - [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 **Regras** — que permite à sua equipe:

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

@@ -6,6 +6,14 @@ 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.

docs/pt-BR/enterprise/guides/deploy-to-amp.mdx

@@ -163,6 +163,12 @@ Você precisa enviar seu crew para um repositório do GitHub. Caso ainda não te
       ![Selecionar Repositório](/images/enterprise/select-repo.png)
     </Frame>
 
+    <Tip>
+      Se seu Crew ou Flow estiver dentro de uma subpasta de monorepo, expanda
+      **Advanced** e defina um diretório de trabalho antes de implantar. Consulte
+      [Implantações em Monorepo](/pt-BR/enterprise/guides/monorepo-deployments).
+    </Tip>
+
   </Step>
 
   <Step title="Definir as Variáveis de Ambiente">
@@ -441,4 +447,4 @@ type = "flow"
 <Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
   Entre em contato com nossa equipe de suporte para ajuda com questões de
   implantação ou dúvidas sobre a plataforma AMP.
-</Card>
+</Card>

docs/pt-BR/enterprise/guides/monorepo-deployments.mdx

@@ -0,0 +1,230 @@
+---
+title: "Implantações em Monorepo"
+description: "Implante um Crew ou Flow a partir de uma subpasta em um repositório maior"
+icon: "folder-tree"
+mode: "wide"
+---
+
+<Note>
+  Use um diretório de trabalho quando seu Crew ou Flow estiver dentro de um
+  repositório maior. O CrewAI AMP valida, faz o build e executa a automação a
+  partir dessa subpasta em vez da raiz do repositório.
+</Note>
+
+## Quando Usar
+
+Implantações em monorepo são úteis quando um repositório contém múltiplas
+automações, pacotes compartilhados ou outro código de aplicação:
+
+```text
+company-ai/
+|-- uv.lock
+|-- packages/
+|   `-- shared_tools/
+`-- crews/
+    |-- support_agent/
+    |   |-- pyproject.toml
+    |   `-- src/
+    |       `-- support_agent/
+    |           |-- main.py
+    |           `-- crew.py
+    `-- research_flow/
+        |-- pyproject.toml
+        `-- src/
+            `-- research_flow/
+                `-- main.py
+```
+
+Para implantar `support_agent`, defina o diretório de trabalho como:
+
+```text
+crews/support_agent
+```
+
+O AMP ainda baixa ou recebe o repositório inteiro, mas trata a pasta
+selecionada como a raiz do projeto da automação.
+
+## O Que o Diretório de Trabalho Controla
+
+Quando um diretório de trabalho é definido, o AMP usa essa pasta para:
+
+- Validação do projeto, incluindo `pyproject.toml`, `src/` e o ponto de entrada do Crew ou Flow
+- Instalação de dependências com `uv`
+- O diretório de trabalho do processo em execução
+- A variável de ambiente `CREW_ROOT_DIR`
+
+Deixar o campo vazio mantém o comportamento existente e usa a raiz do
+repositório.
+
+## Fontes Suportadas
+
+Você pode definir um diretório de trabalho ao criar uma implantação a partir de:
+
+- Um repositório GitHub conectado
+- Um repositório Git configurado no AMP
+- Um upload de ZIP
+
+<Info>
+  Configure diretórios de trabalho na interface web do AMP. O fluxo
+  `crewai deploy create` da CLI não solicita esse campo.
+</Info>
+
+Você também pode adicionar ou alterar o diretório de trabalho de uma implantação
+existente pela página **Settings** da implantação. A alteração passa a valer no
+próximo deploy.
+
+<Warning>
+  Diretórios de trabalho e auto-deploy não podem ser usados juntos. Se uma
+  implantação tiver um diretório de trabalho, o auto-deploy fica desabilitado
+  para ela. Desative o auto-deploy antes de definir um diretório de trabalho.
+</Warning>
+
+## Configurar uma Nova Implantação
+
+<Steps>
+  <Step title="Abra Deploy from Code">
+    No CrewAI AMP, crie uma nova implantação e escolha sua fonte: GitHub, Git
+    Repository ou upload de ZIP.
+  </Step>
+
+  <Step title="Selecione o repositório, branch ou arquivo ZIP">
+    Escolha o repositório e a branch que contêm seu monorepo, ou envie um ZIP
+    cuja raiz contenha os arquivos do monorepo.
+  </Step>
+
+  <Step title="Abra as configurações avançadas">
+    Expanda a seção **Advanced** no formulário de deploy.
+  </Step>
+
+  <Step title="Informe o diretório de trabalho">
+    Informe o caminho da raiz do repositório até o projeto Crew ou Flow:
+
+    ```text
+    crews/support_agent
+    ```
+
+    Não inclua uma barra inicial.
+  </Step>
+
+  <Step title="Implante">
+    Adicione as variáveis de ambiente necessárias e inicie a implantação.
+  </Step>
+</Steps>
+
+## Configurar uma Implantação Existente
+
+<Steps>
+  <Step title="Abra as configurações da implantação">
+    Acesse sua automação no AMP e abra **Settings**.
+  </Step>
+
+  <Step title="Desative o auto-deploy, se necessário">
+    Se o auto-deploy estiver habilitado, desative-o primeiro. O campo de
+    diretório de trabalho fica indisponível enquanto o auto-deploy está ativo.
+  </Step>
+
+  <Step title="Defina o diretório de trabalho">
+    Em **Basic settings**, informe o caminho da subpasta, como:
+
+    ```text
+    crews/support_agent
+    ```
+  </Step>
+
+  <Step title="Reimplante">
+    Salve a configuração e reimplante a automação. O novo diretório de trabalho
+    será usado no próximo deploy.
+  </Step>
+</Steps>
+
+## Regras de Caminho
+
+O diretório de trabalho deve ser um caminho relativo dentro da raiz do
+repositório ou do ZIP.
+
+| Regra | Exemplo |
+|-------|---------|
+| Use um caminho relativo | `crews/support_agent` |
+| Não comece com `/` | `/crews/support_agent` é inválido |
+| Não use segmentos de caminho `.` ou `..` | `crews/../support_agent` é inválido |
+| Use apenas letras, números, hifens, underscores, pontos e barras | `crews/support agent` é inválido |
+| Mantenha o caminho com 255 caracteres ou menos | Caminhos maiores são rejeitados |
+
+O AMP remove espaços em branco no início e no fim, reduz barras repetidas e
+remove barras finais. Um valor em branco usa a raiz do repositório.
+
+## Arquivos Lock e Workspaces UV
+
+A pasta selecionada deve conter o `pyproject.toml` e o diretório `src/` da
+automação. Um arquivo `uv.lock` ou `poetry.lock` pode ficar na pasta selecionada
+ou na raiz do repositório.
+
+Isso oferece suporte aos dois layouts comuns de monorepo:
+
+<Tabs>
+  <Tab title="Arquivo lock do projeto">
+    ```text
+    company-ai/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            |-- uv.lock
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+
+  <Tab title="Arquivo lock do workspace">
+    ```text
+    company-ai/
+    |-- uv.lock
+    |-- packages/
+    |   `-- shared_tools/
+    `-- crews/
+        `-- support_agent/
+            |-- pyproject.toml
+            `-- src/
+                `-- support_agent/
+                    `-- main.py
+    ```
+  </Tab>
+</Tabs>
+
+<Tip>
+  Se sua automação importar pacotes compartilhados de outro lugar do monorepo,
+  declare esses pacotes no `pyproject.toml` usando configuração de workspace,
+  caminho ou source do UV. O AMP executa a automação a partir da pasta
+  selecionada, então o código compartilhado deve ser instalado como dependência
+  em vez de depender da raiz do repositório no Python path.
+</Tip>
+
+## Solução de Problemas
+
+### Diretório de Trabalho Não Encontrado
+
+Verifique se o caminho é relativo à raiz do repositório ou do ZIP. Para uploads
+de ZIP, o conteúdo do ZIP deve incluir exatamente o caminho informado como
+diretório de trabalho.
+
+### pyproject.toml Ausente
+
+O diretório de trabalho deve apontar para a pasta do projeto Crew ou Flow, não
+apenas para uma pasta pai que contém vários projetos.
+
+### uv.lock ou poetry.lock Ausente
+
+Faça commit de um arquivo lock na pasta do projeto selecionada ou na raiz do
+repositório. Para workspaces UV, manter `uv.lock` na raiz do workspace é
+suportado.
+
+### Auto-Deploy Indisponível
+
+O auto-deploy fica desabilitado enquanto um diretório de trabalho está definido.
+Use reimplantações manuais ou acione reimplantações a partir de CI/CD com a API
+do AMP.
+
+<Card title="Deploy para AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
+  Continue com o guia de implantação depois de escolher o diretório de trabalho
+  do monorepo.
+</Card>

docs/pt-BR/enterprise/integrations/databricks.mdx

@@ -0,0 +1,123 @@
+---
+title: Integração com Databricks
+description: "Conecte agentes CrewAI ao Databricks Genie, SQL, Unity Catalog Functions e Vector Search por meio dos servidores MCP gerenciados do Databricks."
+icon: "layer-group"
+mode: "wide"
+---
+
+## Visão geral
+
+Conecte seus agentes CrewAI diretamente ao seu workspace do Databricks por meio dos [servidores MCP gerenciados do Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp). A integração com o Databricks permite que seus agentes façam perguntas em linguagem natural com o **Genie**, executem **SQL** governado, chamem **Unity Catalog Functions** e recuperem documentos com o **Vector Search** — tudo sem escrever ou hospedar qualquer código de conector, e com as permissões do Unity Catalog aplicadas em cada chamada.
+
+Nos bastidores, a integração com o Databricks é um wrapper gerenciado sobre o suporte a [Servidores MCP personalizados](/pt-BR/enterprise/guides/custom-mcp-server) do CrewAI. O Databricks expõe cada recurso como seu próprio endpoint do [Model Context Protocol](https://modelcontextprotocol.io/), e o CrewAI se conecta a eles com segurança em seu nome. Como cada servidor é adicionado separadamente, você pode habilitar exatamente os recursos de que suas crews precisam.
+
+## Principais recursos
+
+<CardGroup cols={2}>
+  <Card title="Genie" icon="comments">
+    Faça perguntas em linguagem natural e obtenha respostas fundamentadas em seus dados com o [Genie](https://docs.databricks.com/aws/en/genie/), que consulta Genie Spaces e o Unity Catalog e fornece links de volta para a interface do Databricks.
+  </Card>
+  <Card title="Databricks SQL" icon="database">
+    Execute SQL governado nos seus warehouses do Databricks para consultar, transformar e criar pipelines de dados diretamente a partir dos seus agentes.
+  </Card>
+  <Card title="Unity Catalog Functions" icon="function">
+    Invoque [funções do Unity Catalog](https://docs.databricks.com/aws/en/udf/unity-catalog) para executar SQL predefinido e lógica de negócio personalizada como ferramentas governadas e reutilizáveis.
+  </Card>
+  <Card title="Vector Search" icon="magnifying-glass">
+    Recupere documentos relevantes para fluxos de RAG e de conhecimento a partir de índices do [Mosaic AI Vector Search](https://docs.databricks.com/aws/en/generative-ai/vector-search) usando similaridade semântica.
+  </Card>
+</CardGroup>
+
+Todos os servidores são executados por trás do Unity AI Gateway e aplicam os controles de acesso do Unity Catalog, de modo que seus agentes só enxergam os dados e as ferramentas que têm permissão para usar.
+
+## Pré-requisitos
+
+Antes de usar a integração com o Databricks, certifique-se de ter:
+
+- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa
+- Um workspace do Databricks com os recursos que você deseja expor (Genie Spaces, warehouses SQL, funções do Unity Catalog ou índices do Vector Search)
+- [Privilégios apropriados do Unity Catalog](https://docs.databricks.com/aws/en/data-governance/unity-catalog) nos objetos subjacentes
+- O hostname do seu workspace do Databricks (ex.: `your-workspace.cloud.databricks.com`)
+
+## Servidores MCP gerenciados do Databricks
+
+O Databricks publica um servidor MCP gerenciado separado para cada recurso. O CrewAI os expõe como conexões individuais, cada uma configurada com o host do seu workspace e os identificadores relevantes do Unity Catalog. Os endpoints seguem estes padrões:
+
+| Servidor | O que faz | Padrão de URL MCP |
+|----------|-----------|-------------------|
+| **Genie** | Perguntas e respostas em linguagem natural sobre um Genie Space | `https://<workspace-hostname>/api/2.0/mcp/genie/{genie_space_id}` |
+| **Databricks SQL** | Executa SQL nos seus warehouses | `https://<workspace-hostname>/api/2.0/mcp/sql` |
+| **Unity Catalog Functions** | Executa funções UC registradas | `https://<workspace-hostname>/api/2.0/mcp/functions/{catalog}/{schema}` |
+| **Vector Search** | Consulta um índice do Vector Search | `https://<workspace-hostname>/api/2.0/mcp/vector-search/{catalog}/{schema}` |
+
+<Note>
+Você não precisa construir essas URLs manualmente — o CrewAI cria cada endpoint a partir do host do workspace e dos identificadores (Genie Space ID, ou catalog/schema) que você fornece ao configurar a conexão. Para a especificação completa e os detalhes mais recentes dos endpoints, consulte a [documentação de MCP gerenciado do Databricks](https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp).
+</Note>
+
+## Conectando o Databricks no CrewAI AMP
+
+<Frame>
+    <img src="/images/enterprise/databricks-configure.png" alt="Configurar um servidor MCP gerenciado do Databricks no CrewAI AMP" />
+</Frame>
+
+Cada recurso do Databricks — **Databricks Genie**, **Databricks SQL**, **Databricks Unity Catalog Functions** e **Databricks Vector Search** — aparece como seu próprio servidor MCP no grupo Databricks da página **Tools & Integrations**. Configure os que você precisar:
+
+<Steps>
+    <Step title="Abra Tools & Integrations">
+        Navegue até **Tools & Integrations** na barra lateral esquerda do CrewAI AMP e localize o grupo **Databricks** na lista de Connections. Você verá os servidores Genie, SQL, Unity Catalog Functions e Vector Search listados abaixo dele.
+    </Step>
+
+    <Step title="Configure um servidor">
+        Clique em **Configure** ao lado do recurso que deseja habilitar e forneça os detalhes da conexão:
+
+        - **Workspace Host** — o hostname do seu workspace do Databricks (ex.: `my-workspace.cloud.databricks.com`).
+        - **Genie** — o **Genie Space ID** a ser consultado.
+        - **Unity Catalog Functions** — o **catalog** e o **schema** que contêm suas funções.
+        - **Vector Search** — o **catalog** e o **schema** que contêm seu índice.
+        - **Databricks SQL** — sem identificadores adicionais; as consultas são executadas nos warehouses SQL do seu workspace.
+    </Step>
+
+    <Step title="Escolha um método de autenticação">
+        Selecione como o CrewAI se autentica no Databricks. **OAuth** é recomendado.
+
+        - **Use OAuth** — Conecte-se com segurança usando OAuth 2.0. Cada usuário se autentica individualmente, e o Databricks emite tokens com escopo para o recurso (`genie`, `sql`, `unity-catalog` ou `vector-search`). O CrewAI gerencia o fluxo de autorização e renova os tokens automaticamente.
+        - **Use personal access token** — Autentique-se com um [token de acesso pessoal do Databricks](https://docs.databricks.com/aws/en/dev-tools/auth/pat). Use uma identidade com privilégios mínimos para limitar a exposição.
+    </Step>
+
+    <Step title="Autentique">
+        Conclua a autenticação. Uma vez conectado, as ferramentas do servidor ficam disponíveis para suas crews. Repita para qualquer outro recurso do Databricks que você queira habilitar.
+    </Step>
+</Steps>
+
+<Tip>
+Como cada recurso é uma conexão separada, você pode combiná-los livremente — por exemplo, habilitar Genie e Vector Search para uma crew de pesquisa e reservar SQL e Unity Catalog Functions para uma crew de engenharia de dados. As configurações de visibilidade permitem controlar quais membros da equipe podem usar cada um.
+</Tip>
+
+## Usando as ferramentas do Databricks nas suas crews
+
+Uma vez conectado, as ferramentas que cada servidor MCP expõe aparecem junto às conexões integradas na página **Tools & Integrations**. Você pode:
+
+- **Atribuir ferramentas aos agentes** nas suas crews, como qualquer outra ferramenta do CrewAI.
+- **Gerenciar a visibilidade** para controlar quais membros da equipe podem usar cada conexão.
+- **Editar ou remover** qualquer conexão a qualquer momento na lista de Connections.
+
+Seus agentes agora podem pedir respostas fundamentadas ao Genie, executar SQL nos seus warehouses, chamar funções do Unity Catalog e pesquisar índices do Vector Search — com os resultados retornando automaticamente ao raciocínio deles.
+
+<Warning>
+O Databricks aplica governança por meio do Unity Catalog e do Unity AI Gateway: um usuário só pode descobrir e invocar ferramentas que a identidade do seu workspace tem permissão para usar. Se uma chamada de ferramenta falhar, confirme se o usuário (ou a identidade do token) que está conectando tem os privilégios necessários do Unity Catalog no Genie Space, warehouse, função ou índice. Algumas consultas do Genie e do SQL são executadas de forma assíncrona e podem levar um momento para retornar resultados.
+</Warning>
+
+## Saiba mais
+
+<CardGroup cols={2}>
+  <Card title="Servidores MCP gerenciados do Databricks" icon="layer-group" href="https://docs.databricks.com/aws/en/generative-ai/mcp/managed-mcp">
+    Documentação oficial do Databricks para os servidores MCP gerenciados Genie, SQL, Unity Catalog Functions e Vector Search.
+  </Card>
+  <Card title="Servidores MCP personalizados no CrewAI" icon="plug" href="/pt-BR/enterprise/guides/custom-mcp-server">
+    Saiba como o CrewAI se conecta a qualquer servidor MCP, a base sobre a qual a integração com o Databricks é construída.
+  </Card>
+</CardGroup>
+
+<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nossa equipe de suporte para obter ajuda com a configuração da integração com o Databricks ou com a solução de problemas.
+</Card>

docs/pt-BR/enterprise/integrations/snowflake.mdx

@@ -0,0 +1,134 @@
+---
+title: Integração com Snowflake
+description: "Conecte agentes CrewAI ao Snowflake Cortex Analyst, Cortex Search e execução SQL através do servidor MCP gerenciado pelo Snowflake."
+icon: "snowflake"
+mode: "wide"
+---
+
+## Visão Geral
+
+Conecte seus agentes CrewAI diretamente aos seus dados no Snowflake através do [servidor MCP gerenciado pelo Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp). A integração com o Snowflake permite que seus agentes consultem dados estruturados com **Cortex Analyst**, pesquisem dados não estruturados com **Cortex Search** e executem SQL governado nos seus warehouses — tudo sem escrever ou hospedar nenhum código de conector.
+
+Internamente, a integração com o Snowflake é um wrapper gerenciado em torno do suporte a [Custom MCP Server](/pt-BR/enterprise/guides/custom-mcp-server) do CrewAI. O Snowflake expõe suas capacidades de Cortex AI através de um endpoint [Model Context Protocol](https://modelcontextprotocol.io/), e o CrewAI se conecta a ele de forma segura em seu nome. Qualquer ferramenta que você exponha no lado do Snowflake — Cortex Analyst, Cortex Search, execução SQL, Cortex Agents ou suas próprias ferramentas personalizadas — fica disponível para suas crews.
+
+## Capacidades Principais
+
+<CardGroup cols={3}>
+  <Card title="Cortex Analyst" icon="chart-bar">
+    Faça perguntas em linguagem natural e deixe o [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) gerar e executar SQL nos seus dados **estruturados** usando modelos semânticos ricos.
+  </Card>
+  <Card title="Cortex Search" icon="magnifying-glass">
+    Recupere dados **não estruturados** relevantes para fluxos de trabalho de RAG e conhecimento com o [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview), o serviço de busca totalmente gerenciado do Snowflake.
+  </Card>
+  <Card title="Execução SQL" icon="database">
+    Execute consultas SQL governadas diretamente nos seus warehouses Snowflake, com modo somente leitura configurável, timeouts e seleção de warehouse.
+  </Card>
+</CardGroup>
+
+Como a integração expõe quaisquer ferramentas que seu servidor MCP publica, você também pode expor **Cortex Agents** e **ferramentas personalizadas** (funções definidas pelo usuário e stored procedures) para seus agentes CrewAI.
+
+## Pré-requisitos
+
+Antes de usar a integração com o Snowflake, certifique-se de que você tenha:
+
+- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa
+- Uma conta Snowflake com acesso aos recursos de Cortex AI
+- Um [servidor MCP gerenciado pelo Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp) configurado com as ferramentas que você deseja expor
+- Privilégios Snowflake apropriados (USAGE/SELECT) no servidor MCP e seus objetos subjacentes
+
+## Configurando o Servidor Snowflake MCP
+
+O servidor MCP gerenciado pelo Snowflake é executado dentro da sua conta Snowflake e define quais ferramentas estão disponíveis para clientes externos como o CrewAI. Crie um com o comando [`CREATE MCP SERVER`](https://docs.snowflake.com/en/sql-reference/sql/create-mcp-server), listando os serviços Cortex Search, visualizações semânticas do Cortex Analyst e ferramentas SQL que você deseja expor.
+
+```sql
+CREATE MCP SERVER my_mcp_server
+  FROM SPECIFICATION $$
+    tools:
+      - name: "sales_analyst"
+        type: "CORTEX_ANALYST"
+        identifier: "MY_DATABASE.MY_SCHEMA.sales_semantic_view"
+        description: "Answer questions about sales metrics"
+      - name: "docs_search"
+        type: "CORTEX_SEARCH_SERVICE_QUERY"
+        identifier: "MY_DATABASE.MY_SCHEMA.support_docs_search"
+        description: "Search internal support documentation"
+      - name: "run_sql"
+        type: "SQL_EXECUTION"
+        description: "Execute read-only SQL queries"
+  $$;
+```
+
+<Note>
+O endpoint MCP segue o formato `https://<account_URL>/api/v2/databases/{database}/schemas/{schema}/mcp-servers/{name}`. O CrewAI constrói esta URL automaticamente a partir do **URL da Conta**, **Banco de Dados**, **Schema** e **Nome do Servidor MCP** que você fornece ao configurar a integração.
+</Note>
+
+Para a especificação completa — incluindo Cortex Agents, ferramentas personalizadas, limites de tamanho de resposta e opções de governança — consulte a [documentação do servidor MCP gerenciado pelo Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp).
+
+## Conectando o Snowflake no CrewAI AMP
+
+<Frame>
+    <img src="/images/enterprise/snowflake-configure.png" alt="Configurar integração Snowflake no CrewAI AMP" />
+</Frame>
+
+<Steps>
+    <Step title="Abrir Ferramentas e Integrações">
+        Navegue até **Ferramentas e Integrações** na barra lateral esquerda do CrewAI AMP, encontre **Snowflake** na lista de aplicações e abra seu painel de configuração.
+    </Step>
+
+    <Step title="Fornecer detalhes da conexão">
+        Preencha os campos de conexão que o CrewAI usa para acessar seu servidor Snowflake MCP:
+
+        | Campo | Obrigatório | Descrição |
+        |-------|-------------|-----------|
+        | **Nome** | Sim | Um nome descritivo para esta conexão (padrão: `Snowflake`). |
+        | **Descrição** | Não | Um resumo opcional do que esta conexão fornece. |
+        | **URL da Conta** | Sim | A URL da sua conta Snowflake, ex.: `xy12345.us-east-1.snowflakecomputing.com`. |
+        | **Banco de Dados** | Sim | O banco de dados que contém seu servidor MCP (ex.: `MY_DATABASE`). |
+        | **Schema** | Sim | O schema que contém seu servidor MCP (ex.: `MY_SCHEMA`). |
+        | **Nome do Servidor MCP** | Sim | O nome do objeto de servidor MCP que você criou no Snowflake (ex.: `MY_MCP_SERVER`). |
+    </Step>
+
+    <Step title="Escolher um método de autenticação">
+        Selecione como o CrewAI se autentica no Snowflake. **OAuth** é recomendado.
+
+        - **Usar OAuth** — Conecte-se de forma segura usando OAuth 2.0 para autenticação baseada em tokens sem compartilhar suas credenciais. O CrewAI gerencia todo o fluxo de autorização e renova os tokens automaticamente. Copie o **URI de Redirecionamento** mostrado no formulário (`https://oauth.crewai.com/oauth/add`) e registre-o como um URI de redirecionamento autorizado na sua [integração de segurança OAuth](https://docs.snowflake.com/en/user-guide/oauth-custom) do Snowflake.
+        - **Usar token de acesso pessoal** — Autentique usando um [token de acesso programático](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens) gerado nas configurações da sua conta Snowflake. Atribua uma role com privilégios mínimos ao token para limitar a exposição.
+    </Step>
+
+    <Step title="Autenticar">
+        Clique em **Autenticar**. Para OAuth, você será redirecionado ao Snowflake para autorizar o acesso. Após autenticado, o servidor Snowflake aparece na sua lista de Conexões e suas ferramentas ficam disponíveis para suas crews.
+    </Step>
+</Steps>
+
+<Tip>
+Com OAuth, cada usuário se autentica individualmente e as consultas são executadas com seu `DEFAULT_ROLE` do Snowflake. Certifique-se de que os usuários que se conectam tenham uma role e warehouse padrão definidos (`ALTER USER <username> SET DEFAULT_ROLE = '<role>' DEFAULT_WAREHOUSE = '<warehouse>'`) para que as ferramentas Cortex Analyst e SQL tenham capacidade de computação para execução.
+</Tip>
+
+## Usando Ferramentas Snowflake nas Suas Crews
+
+Uma vez conectado, as ferramentas que seu servidor MCP expõe aparecem junto com as conexões integradas na página **Ferramentas e Integrações**. Você pode:
+
+- **Atribuir ferramentas a agentes** nas suas crews como qualquer outra ferramenta CrewAI.
+- **Gerenciar visibilidade** para controlar quais membros do time podem usar a conexão.
+- **Editar ou remover** a conexão a qualquer momento na lista de Conexões.
+
+Seus agentes agora podem solicitar métricas ao Cortex Analyst, executar Cortex Search nos seus documentos e executar SQL — com os resultados fluindo automaticamente para o raciocínio deles.
+
+<Warning>
+O Snowflake impõe governança no servidor MCP: o controle de acesso baseado em roles determina quais ferramentas um usuário pode descobrir e invocar, e limites se aplicam ao tamanho da resposta, contagem de ferramentas (máximo de 50 por servidor) e profundidade de recursão. Se uma chamada de ferramenta falhar, confirme que a role do usuário conectado possui os privilégios necessários no servidor MCP e seus objetos subjacentes.
+</Warning>
+
+## Saiba Mais
+
+<CardGroup cols={2}>
+  <Card title="Servidor MCP Gerenciado pelo Snowflake" icon="snowflake" href="https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp">
+    Documentação oficial do Snowflake para criar e governar o servidor MCP.
+  </Card>
+  <Card title="Servidores Custom MCP no CrewAI" icon="plug" href="/pt-BR/enterprise/guides/custom-mcp-server">
+    Saiba como o CrewAI se conecta a qualquer servidor MCP, a base sobre a qual a integração Snowflake é construída.
+  </Card>
+</CardGroup>
+
+<Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nossa equipe de suporte para obter ajuda com a integração Snowflake ou solução de problemas.
+</Card>

docs/pt-BR/guides/flows/conversational-flows.mdx

@@ -0,0 +1,454 @@
+---
+title: Flows Conversacionais
+description: Crie apps de chat multi-turno com kickoff por turno, histórico de mensagens, roteamento de intenção, tracing e pontes WebSocket.
+icon: comments
+mode: "wide"
+---
+
+## Visão geral
+
+Apps conversacionais tratam cada linha do usuário como uma **nova execução do flow** com o **mesmo id de sessão**. A CrewAI oferece helpers para histórico de mensagens, classificação opcional de intenção, tracing adiado e pontes para UI — sem uma API `chat()` separada em `Flow`.
+
+| Conceito | Implementação |
+|---------|----------------|
+| Id de sessão | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
+| Linha do usuário | `kickoff(user_message=...)` acrescenta em `state.messages` antes do grafo rodar |
+| Fim do turno | `FlowFinished` só para **esta execução**; o chat segue no próximo `kickoff` |
+| Trace da sessão | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
+
+## Um ponto de entrada: `kickoff`
+
+Use **`flow.kickoff(user_message=..., session_id=...)`** para cada mensagem (REST, WebSocket, CLI). Não crie um wrapper `chat()` customizado em `Flow`.
+
+| API | Uso |
+|-----|-----|
+| `kickoff(user_message=..., session_id=...)` | Cada mensagem do usuário |
+| `kickoff_async(...)` | Mesmos parâmetros; entrada async nativa |
+| `ask()` | Prompt bloqueante **dentro** de um passo (wizard, esclarecimento) |
+| `@human_feedback` | Aprovar/rejeitar **saída de um passo** — não a próxima linha do chat |
+| `ChatSession.handle_turn(...)` | Camada de transporte sobre `kickoff` (SSE / WebSocket) |
+
+## Início rápido
+
+```python
+from uuid import uuid4
+
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    Flow,
+    listen,
+    or_,
+    persist,
+    router,
+    start,
+)
+from crewai.flow.persistence import SQLiteFlowPersistence
+
+
+class SupportFlow(Flow[ChatState]):
+    conversational_config = ConversationalConfig(
+        default_intents=["order", "help", "goodbye"],
+        intent_llm="gpt-4o-mini",
+        defer_trace_finalization=True,
+    )
+
+    @start()
+    def bootstrap(self):
+        if not self.state.session_ready:
+            self.state.session_ready = True
+        return "ready"
+
+    @router(bootstrap)
+    def route(self):
+        # last_intent definido em prepare_conversational_turn quando default_intents está setado
+        return self.state.last_intent or "help"
+
+    @listen("order")
+    def handle_order(self):
+        reply = "Seu pedido está a caminho."
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("help")
+    def handle_help(self):
+        reply = "Como posso ajudar?"
+        self.append_message("assistant", reply)
+        return reply
+
+    @listen("goodbye")
+    def handle_goodbye(self):
+        reply = "Até logo!"
+        self.append_message("assistant", reply)
+        return reply
+
+    @persist(SQLiteFlowPersistence("support.db"))
+    @listen(or_(handle_order, handle_help, handle_goodbye))
+    def finalize(self):
+        return self.state.model_dump()
+
+
+session_id = str(uuid4())
+flow = SupportFlow()
+
+flow.kickoff(user_message="Onde está meu pedido?", session_id=session_id)
+flow.kickoff(user_message="E as devoluções?", session_id=session_id)
+flow.finalize_session_traces()  # um link de trace para o chat inteiro
+```
+
+## Ciclo de vida do turno
+
+Cada `kickoff` com `user_message` executa este pipeline:
+
+1. **`_configure_conversational_kickoff`** — mescla `session_id` / `user_message` em `inputs`, aplica `ConversationalConfig`, habilita tracing adiado quando configurado.
+2. **Restauração de estado** — se `inputs["id"]` existe e `@persist` está configurado, carrega o snapshot mais recente.
+3. **`FlowStarted`** — emitido apenas no primeiro turno da sessão adiada.
+4. **`prepare_conversational_turn`** — acrescenta a mensagem do usuário em `state.messages`, define `last_user_message`, limpa `last_intent`, classifica opcionalmente quando `intents` / `default_intents` + `intent_llm` estão definidos.
+5. **Execução do grafo** — `@start` → `@router` → handlers `@listen`.
+6. **Fim da execução** — `flow_finished` por turno e finalização de trace são **ignorados** com adiamento; `Agent.kickoff()` / crews aninhados também não fecham o batch pai.
+
+Os handlers devem chamar **`append_message("assistant", reply)`** para que o próximo turno inclua a resposta do assistente. A linha do usuário já é salva no kickoff — não acrescente de novo nos handlers.
+
+## `ConversationalConfig` (padrões em nível de classe)
+
+Defina na subclasse de `Flow` como `conversational_config: ClassVar[ConversationalConfig | None]`.
+
+| Campo | Padrão | Propósito |
+|-------|---------|-----------|
+| `default_intents` | `None` | Rótulos de outcome para classificação automática antes do kickoff |
+| `intent_llm` | `None` | Modelo para classificação (obrigatório quando há intents) |
+| `interactive_prompt` | `"You: "` | Prompt para `kickoff(interactive=True)` |
+| `interactive_timeout` | `None` | Timeout por linha no modo interativo |
+| `exit_commands` | `exit`, `quit` | Palavras que encerram o modo interativo |
+| `defer_trace_finalization` | `True` | Manter um batch de trace aberto entre turnos |
+
+Sobrescreva por kickoff com `intents=` e `intent_llm=`.
+
+## `ChatState` (formato persistido recomendado)
+
+```python
+from crewai.flow import ChatState
+
+
+class MyChatState(ChatState):
+    # Herdados: id, messages, last_user_message, last_intent, session_ready
+    research_turn_count: int = 0
+    custom_flag: bool = False
+```
+
+| Campo | Função |
+|-------|--------|
+| `id` | UUID da sessão (igual a `session_id` / `inputs["id"]`) |
+| `messages` | `list` de `{role, content}` para histórico de LLM |
+| `last_user_message` | Última linha do usuário neste turno |
+| `last_intent` | Rótulo de rota após classificação (se usado) |
+| `session_ready` | Flag de bootstrap único (permissões, caches, etc.) |
+
+`ConversationalInputs` é um `TypedDict` para `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
+
+## API conversacional em `Flow`
+
+### Parâmetros de `kickoff` / `kickoff_async`
+
+| Parâmetro | Propósito |
+|-----------|-----------|
+| `user_message` | Texto deste turno (ou `{"role": "user", "content": "..."}`) |
+| `session_id` | UUID da conversa → `inputs["id"]` / `state.id` |
+| `intents` | Rótulos de outcome para `classify_intent` antes do kickoff |
+| `intent_llm` | LLM para classificação (obrigatório com `intents`) |
+| `interactive` | Loop CLI via `ask()` (só demos locais) |
+| `interactive_prompt` | Prompt no modo interativo |
+| `interactive_timeout` | Timeout de `ask()` por linha |
+| `exit_commands` | Palavras que encerram o modo interativo |
+| `inputs` | Campos extras de estado (mesclados com chaves conversacionais) |
+| `restore_from_state_id` | Hidratação fork de outro flow persistido |
+
+### Atributos de instância
+
+| Atributo | Propósito |
+|-----------|-----------|
+| `conversational_config` | Padrões `ConversationalConfig` em nível de classe |
+| `defer_trace_finalization` | Flag de instância; definida automaticamente a partir do config no kickoff |
+| `suppress_flow_events` | Oculta painéis Rich no console; **tracing ainda registra** eventos |
+| `stream` | Habilita streaming; use com `ChatSession.handle_turn(..., stream=True)` |
+
+### Métodos e propriedades
+
+| Nome | Descrição |
+|------|-------------|
+| `append_message(role, content, **extra)` | Acrescenta em `state.messages` (roles: `user`, `assistant`, `system`, `tool`) |
+| `conversation_messages` | Histórico somente leitura para chamadas LLM |
+| `classify_intent(text, outcomes, *, llm, context=None)` | Mapeia texto a um outcome (mesma lógica de `@human_feedback`) |
+| `receive_user_message(text, *, outcomes=None, llm=None)` | Acrescenta mensagem do usuário; opcionalmente define `last_intent` |
+| `finalize_session_traces()` | Emite `flow_finished` adiado e finaliza o batch de trace da sessão |
+| `_should_defer_trace_finalization()` | Se este flow adia finalização de trace por turno |
+| `input_history` | Trilha de auditoria de prompts e respostas de `ask()` |
+
+### Helpers do módulo (`crewai.flow.conversation`)
+
+Importáveis para testes ou orquestração customizada:
+
+| Função | Descrição |
+|----------|-------------|
+| `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | Mescla kwargs conversacionais em `inputs` |
+| `get_conversation_messages(flow)` | Lê mensagens do estado ou buffer interno |
+| `append_message(flow, role, content, **extra)` | Igual ao método de instância |
+| `prepare_conversational_turn(flow, ...)` | Hidratação do turno (geralmente chamado pelo kickoff) |
+| `receive_user_message(flow, text, ...)` | Igual ao método de instância |
+| `set_state_field(flow, name, value)` | Define campo em estado dict ou Pydantic |
+| `get_conversational_config(flow)` | Lê `conversational_config` da classe |
+| `input_history_to_messages(entries)` | Converte `input_history` para formato de mensagens LLM |
+
+## Padrões de roteamento de intenção
+
+### A. Pré-classificar via `ConversationalConfig` (mais simples)
+
+Defina `default_intents` e `intent_llm`. Cada kickoff classifica antes do `@router`; leia `self.state.last_intent` em `route()`.
+
+### B. Classificar dentro do `@router` (prompts mais ricos)
+
+Defina `default_intents=None` para o kickoff só acrescentar a mensagem. Em `route()`, chame `classify_intent` com prompt ou descrições customizadas:
+
+```python
+@router(bootstrap)
+def route(self):
+    intent = self.classify_intent(
+        self._routing_prompt(self.state.last_user_message),
+        ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
+    )
+    self.state.last_intent = intent
+    return intent
+```
+
+Use **`@listen("RESEARCH")`** (ou similar) para passos com `Agent.kickoff()` e ferramentas — não `LLM.call()` puro — quando precisar de pesquisa web ou uso multi-etapa de tools.
+
+## Quando o flow termina mas o usuário continua conversando
+
+`FlowFinished` significa que **esta execução do grafo** terminou. A conversa segue com outro `kickoff` e o mesmo `session_id`. `@persist` restaura `messages`, flags e contexto.
+
+**Padrão de persistência:** prefira `@persist` em um **único passo terminal** (por exemplo `finalize`) em vez de na classe `Flow` inteira. Persist em nível de classe salva após cada método; `load_state` usa a linha mais recente, que pode ser snapshot no meio da execução e perder atualizações dos handlers no mesmo turno.
+
+Não use `@human_feedback` para linhas de chat de follow-up, a menos que um humano precise aprovar uma saída específica antes de exibi-la.
+
+## `Flow` conversacional (experimental)
+
+<Warning>
+  **Funcionalidade experimental.** A superfície do `Flow` conversacional
+  (`conversational = True`, `handle_turn`, `ConversationConfig`,
+  `RouterConfig`, `ConversationState`, o grafo embutido + helpers) vive em
+  `crewai.experimental` e pode mudar de formato antes de graduar. Fixe a
+  versão do CrewAI se depende de comportamento específico e acompanhe o
+  changelog para mudanças quebradoras. Feedback / issues bem-vindos.
+</Warning>
+
+Habilite o grafo conversacional definindo `conversational = True` em uma subclasse de `Flow`. O `Flow` base passa a expor um grafo embutido `@start` / `@router` / `converse_turn` / `end_conversation`, gerencia `state.messages`, dirige o LLM de roteamento e mantém o batch de trace aberto entre os turnos. Você escreve as **rotas customizadas**; o framework cuida do resto.
+
+Use isto quando quiser um chat multi-turno com router LLM e handlers por rota sem cablar o ciclo de vida na mão. Use `Flow[ChatState]` (o padrão de mais baixo nível acima) quando precisar de controle total.
+
+### Exemplo rápido
+
+```python
+from crewai import LLM, Flow
+from crewai.flow import listen
+from crewai.experimental.conversational import (
+    ConversationConfig,
+    ConversationState,
+    RouterConfig,
+)
+
+
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # rotas + descrições auto-descobertas pelos handlers @listen
+)
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    @listen("INTERNET_SEARCH")
+    def handle_internet_search(self) -> str:
+        """Fresh web research, current news, real-time lookups."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+    @listen("CREWAI_DOCS")
+    def handle_crewai_docs(self) -> str:
+        """Look up the CrewAI documentation for framework/API questions."""
+        ...
+        self.append_assistant_message(reply)
+        return reply
+
+
+flow = SupportFlow()
+try:
+    flow.handle_turn("O que você pode fazer?")               # roteia para converse (built-in)
+    flow.handle_turn("Pesquise na web por notícias de IA.")  # roteia para INTERNET_SEARCH
+    flow.handle_turn("Resuma o primeiro resultado.")         # volta para converse
+finally:
+    flow.finalize_session_traces()
+```
+
+### `ConversationConfig`
+
+Decorador de classe que anexa os defaults de chat por classe.
+
+| Campo | Padrão | Propósito |
+|-------|--------|-----------|
+| `system_prompt` | `slices.conversational_system_prompt` (i18n) | System message usado pelo `converse_turn` embutido. Passe `""` para desativar totalmente. |
+| `llm` | `None` | LLM de conversa (usado pelo `converse_turn` e como fallback do router). |
+| `router` | `None` | `RouterConfig` para roteamento por LLM. Sem ele, o flow sempre cai em `converse`. |
+| `answer_from_history_prompt` | padrão do framework | System message para a rota opcional `answer_from_history`. |
+| `answer_from_history_llm` | `None` | Habilita o atalho `answer_from_history` quando definido. |
+| `intent_llm` | `None` | LLM para o caminho legado `intents=`/`default_intents`. |
+| `default_intents` | `None` | Labels de outcome para pré-classificação legada. |
+| `visible_agent_outputs` | `None` | `"all"` ou lista de nomes de agentes cujos `append_agent_result()` devem virar mensagens públicas. |
+| `defer_trace_finalization` | `True` | Mantém um único batch de trace aberto entre chamadas de `handle_turn()`. |
+
+### `RouterConfig` e o catálogo de rotas auto-gerado
+
+```python
+RouterConfig(
+    prompt="Enquadramento de domínio opcional (política, voz, persona).",
+    response_format=MyRoute,        # opcional; auto-gerado caso contrário
+    llm=ROUTER_LLM,                  # usa ConversationConfig.llm como fallback
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # opcional; inferido dos listeners
+    route_descriptions={
+        "INTERNET_SEARCH": "Sobrescreve a docstring só desta rota.",
+    },
+    default_intent="converse",       # usado quando a chamada ao LLM falha ou não há LLM
+    fallback_intent="converse",      # usado quando o LLM retorna rota inválida
+    intent_field="intent",
+)
+```
+
+O prompt do router é montado automaticamente. Para cada rota o framework escolhe a descrição nesta precedência:
+
+1. `RouterConfig.route_descriptions[label]` — override explícito.
+2. `Flow.builtin_route_descriptions[label]` — texto canônico do framework para `converse`, `end`, `answer_from_history` (otimizado para o LLM de routing).
+3. Primeira linha não vazia da docstring do handler `@listen(label)`.
+4. Vazio (a rota aparece no catálogo sem descrição).
+
+Na prática, **adicionar uma rota é `@listen("X")` + uma docstring de uma linha**:
+
+```python
+@listen("INTERNET_SEARCH")
+def handle_internet_search(self) -> str:
+    """Fresh web research, current news, real-time lookups."""
+    ...
+```
+
+…e o LLM de routing vê:
+
+```
+Routes:
+- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
+- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
+- converse: Ordinary chat, follow-ups, summaries, clarifications…
+- end: User signals the conversation is finished (goodbye, exit, done).
+```
+
+`RouterConfig.prompt` é para **enquadramento de domínio** (persona do assistente, regras de negócio, voz). O catálogo de rotas é auto-gerado — não liste rotas em `prompt`; elas vão sair de sincronia assim que você adicionar um handler.
+
+### Rotas embutidas
+
+| Rota | Handler | Propósito |
+|------|---------|-----------|
+| `converse` | `converse_turn` | Handler de chat padrão. Chama `ConversationConfig.llm` com o system prompt + histórico canônico. |
+| `end` | `end_conversation` | Define `state.ended = True` e emite uma resposta de encerramento. |
+| `answer_from_history` | `answer_from_history_turn` | Opcional. Cai aqui quando `ConversationConfig.answer_from_history_llm` está definido e a mensagem pode ser respondida só pelo histórico. |
+
+Você pode sobrescrever qualquer uma definindo um handler com o mesmo nome na subclasse.
+
+### Semântica de `handle_turn()`
+
+`flow.handle_turn(message)` roda um turno:
+
+1. Reseta o tracking por execução (`_completed_methods`, `_method_outputs`) para o grafo re-rodar — sem isso, chamadas repetidas de `kickoff` na mesma instância dariam curto-circuito no turno 2+ porque `Flow.kickoff_async` trata `inputs={"id": ...}` como restauração de checkpoint.
+2. Anexa a mensagem do usuário em `state.messages`, define `current_user_message` / `last_user_message`. `last_intent` é **preservado do turno anterior** para que o LLM de routing possa usá-lo como sinal.
+3. Roda `conversation_start` → `route_conversation` → o handler `@listen` escolhido.
+4. O router grava sua decisão em `state.last_intent` (visível para o contexto de routing do próximo turno).
+5. Se seu handler retornou uma string e ainda não chamou `append_assistant_message`, `handle_turn` anexa para você.
+
+Você também pode chamar `flow.kickoff(user_message=..., session_id=...)` diretamente — a mesma lógica de reset/run é acionada. `handle_turn` é o wrapper ergonômico.
+
+### Comportamento customizado do router
+
+Para rodar efeitos colaterais (setup de event bus, telemetria) em toda decisão de routing, sobrescreva `route_turn`:
+
+```python
+class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
+    def route_turn(self, context: dict[str, Any]) -> str | None:
+        self.event_bus = MyBus(self)
+        return super().route_turn(context)
+```
+
+Para ignorar o router LLM e escolher uma rota programaticamente, retorne uma string de `route_turn`; retornar `None` cai no `_route_with_config(...)`.
+
+### `append_assistant_message` e `append_agent_result`
+
+Dentro de um handler `@listen(label)`, escolha:
+
+- `self.append_assistant_message(text)` — adiciona um turno de assistente visível ao usuário em `state.messages`. O `converse_turn` do próximo turno vai vê-lo.
+- `self.append_agent_result(agent_name, result, visibility="private")` — registra um evento estruturado em `state.events` e uma thread em `state.agent_threads[agent_name]`. Visibilidade pública também chama `append_assistant_message` automaticamente. Use resultados privados para trabalho de bastidor que não deve poluir o histórico canônico.
+
+`ConversationConfig.visible_agent_outputs` pode promover globalmente os resultados privados de agentes específicos para públicos (`"all"` ou lista de nomes).
+
+## Tracing entre turnos
+
+Com `defer_trace_finalization=True` (padrão em `ConversationalConfig`):
+
+- **Um batch de trace** para toda a sessão de chat.
+- **`flow_started`** só no primeiro turno; **`flow_finished`** uma vez em `finalize_session_traces()`.
+- **`kickoff` por turno** não exibe “Trace batch finalized”.
+- **Trabalho aninhado** (`Agent.kickoff()`, crews, tools Exa) acrescenta ao batch **pai**; flows internos de `AgentExecutor` não fecham o batch da sessão cedo.
+
+```python
+try:
+    while True:
+        line = input("You: ").strip()
+        if not line:
+            break
+        flow.kickoff(user_message=line, session_id=session_id)
+finally:
+    flow.finalize_session_traces()
+```
+
+`ChatSession.close()` chama `finalize_session_traces()` quando o adiamento está habilitado.
+
+`suppress_flow_events=True` só oculta painéis do console; eventos de trace e método ainda são emitidos.
+
+### Ciclo de vida de trace do `Flow` conversacional
+
+O [`Flow` conversacional](#flow-conversacional-experimental) experimental usa o mesmo ciclo de vida de tracing: `defer_trace_finalization` é `True` por padrão, então cada `handle_turn()` mantém o trace da sessão aberto. Sempre finalize ao fim da sessão — envolva seu loop em `try/finally` e chame `flow.finalize_session_traces()` na saída. Sem isso, o batch fica aberto e a última conversa pode nunca ser exportada.
+
+## Streaming
+
+Defina `stream = True` na classe `Flow`. `kickoff(...)` então emitirá `assistant_delta` (e eventos relacionados) pelo event bus padrão.
+
+## Imports
+
+```python
+from crewai.flow import (
+    ChatState,
+    ConversationalConfig,
+    ConversationalInputs,
+    Flow,
+    listen,
+    persist,
+    router,
+    start,
+)
+```
+
+## Veja também
+
+- [Dominando o Gerenciamento de Estado em Flows](/pt-BR/guides/flows/mastering-flow-state) — persistência, estado Pydantic, `@persist`
+- [Construa Seu Primeiro Flow](/pt-BR/guides/flows/first-flow) — fundamentos de flow
+- Demo: `lib/crewai/runner_conversational_flow_simple.py` — REPL mínimo com `RESEARCH` + agente Exa

docs/pt-BR/guides/flows/first-flow.mdx

@@ -614,6 +614,7 @@ Agora que você construiu seu primeiro flow, pode:
 3. Explorar as funções `and_` e `or_` para execuções paralelas e mais complexas
 4. Conectar seu flow a APIs externas, bancos de dados ou interfaces de usuário
 5. Combinar múltiplos crews especializados em um único flow
+6. Criar apps de chat multi-turn com [Flows conversacionais](/pt-BR/guides/flows/conversational-flows) (`kickoff` por mensagem, `ChatSession`, tracing adiado)
 
 <Check>
 Parabéns! Você construiu seu primeiro CrewAI Flow que combina código regular, chamadas diretas a LLM e processamento baseado em crews para criar um guia abrangente. Essas habilidades fundamentais permitem criar aplicações de IA cada vez mais sofisticadas, capazes de resolver problemas complexos de múltiplas etapas por meio de controle procedural e inteligência colaborativa.

docs/pt-BR/guides/flows/mastering-flow-state.mdx

@@ -22,6 +22,8 @@ Um gerenciamento de estado efetivo possibilita que você:
 5. **Escalone suas aplicações** – Ofereça suporte a workflows complexos com organização apropriada dos dados
 6. **Habilite aplicações conversacionais** – Armazene e acesse o histórico da conversa para interações de IA com contexto
 
+Para chat multi-turn (`kickoff` por linha do usuário, `ChatState`, roteamento por intenção, tracing adiado e `ChatSession`), veja [Flows conversacionais](/pt-BR/guides/flows/conversational-flows).
+
 Vamos explorar como aproveitar essas capacidades de forma eficiente.
 
 ## Fundamentos do Gerenciamento de Estado

lib/cli/pyproject.toml

@@ -8,14 +8,14 @@ authors = [
 ]
 requires-python = ">=3.10, <3.14"
 dependencies = [
-    "crewai-core==1.14.6a2",
-    "click~=8.1.7",
+    "crewai-core==1.14.7a1",
+    "click>=8.1.7,<9",
     "pydantic>=2.11.9,<2.13",
     "pydantic-settings~=2.10.1",
     "appdirs~=1.4.4",
     "cryptography>=42.0",
     "httpx~=0.28.1",
-    "pyjwt>=2.9.0,<3",
+    "pyjwt>=2.13.0,<3",
     "rich>=13.7.1",
     "tomli~=2.0.2",
     "tomli-w~=1.1.0",

lib/cli/src/crewai_cli/__init__.py

@@ -1 +1 @@
-__version__ = "1.14.6a2"
+__version__ = "1.14.7a1"

lib/cli/src/crewai_cli/cli.py

@@ -17,6 +17,7 @@ from crewai_cli.crew_chat import run_chat
 from crewai_cli.deploy.main import DeployCommand
 from crewai_cli.enterprise.main import EnterpriseConfigureCommand
 from crewai_cli.evaluate_crew import evaluate_crew
+from crewai_cli.experimental.skills.main import SkillCommand
 from crewai_cli.install_crew import install_crew
 from crewai_cli.kickoff_flow import kickoff_flow
 from crewai_cli.organization.main import OrganizationCommand
@@ -26,7 +27,6 @@ from crewai_cli.replay_from_task import replay_task_command
 from crewai_cli.reset_memories_command import reset_memories_command
 from crewai_cli.run_crew import run_crew
 from crewai_cli.settings.main import SettingsCommand
-from crewai_cli.skills.main import SkillCommand
 from crewai_cli.task_outputs import load_task_outputs
 from crewai_cli.tools.main import ToolCommand
 from crewai_cli.train_crew import train_crew
@@ -544,8 +544,19 @@ def tool_publish(is_public: bool, force: bool) -> None:
 
 
 @crewai.group()
+def experimental() -> None:
+    """Experimental, unstable commands. Subject to change without notice."""
+    import os
+
+    if os.environ.get("CREWAI_EXPERIMENTAL") != "1":
+        raise click.UsageError(
+            "Experimental commands are gated. Set CREWAI_EXPERIMENTAL=1 to enable."
+        )
+
+
+@experimental.group(name="skill")
 def skill() -> None:
-    """Skill Repository related commands."""
+    """Skill Repository related commands (experimental)."""
 
 
 @skill.command(name="create")

lib/cli/src/crewai_cli/experimental/skills/main.py

@@ -23,9 +23,10 @@ console = Console()
 _SKILL_MD_TEMPLATE = """\
 ---
 name: {name}
-version: 0.1.0
 description: |
   A short description of what this skill does.
+metadata:
+  version: 0.1.0
 ---
 
 ## Instructions
@@ -147,7 +148,7 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
             )
         else:
             try:
-                from crewai.skills.cache import SkillCacheManager
+                from crewai.experimental.skills.cache import SkillCacheManager
 
                 cache = SkillCacheManager()
                 cache.store(org, name, version, archive_bytes)
@@ -191,7 +192,10 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
             raise SystemExit(1) from exc
 
         name = frontmatter.get("name")
-        version = frontmatter.get("version")
+        raw_metadata = frontmatter.get("metadata")
+        version = (
+            raw_metadata.get("version") if isinstance(raw_metadata, dict) else None
+        )
         description = frontmatter.get("description")
 
         if not name:
@@ -362,10 +366,13 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
             return result
 
     def _read_version(self, skill_md: Path) -> str | None:
-        """Read the version field from a SKILL.md file, or None."""
+        """Read the version from a SKILL.md file's metadata, or None."""
         try:
             fm = self._parse_frontmatter(skill_md.read_text())
-            return fm.get("version")
+            raw_metadata = fm.get("metadata")
+            if isinstance(raw_metadata, dict):
+                return raw_metadata.get("version")
+            return None
         except Exception:
             return None
 

lib/cli/src/crewai_cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.5a2"
+    "crewai[tools]==1.14.7a1"
 ]
 
 [project.scripts]

lib/cli/src/crewai_cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.5a2"
+    "crewai[tools]==1.14.7a1"
 ]
 
 [project.scripts]

lib/cli/src/crewai_cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.5a2"
+    "crewai[tools]==1.14.7a1"
 ]
 
 [tool.crewai]

lib/cli/tests/experimental/skills/test_main.py

@@ -36,7 +36,7 @@ def skill_command():
             TokenManager().save_tokens(
                 "test-token", (datetime.now() + timedelta(seconds=36000)).timestamp()
             )
-            from crewai_cli.skills.main import SkillCommand
+            from crewai_cli.experimental.skills.main import SkillCommand
             cmd = SkillCommand()
             yield cmd
 
@@ -142,7 +142,7 @@ class TestSkillPublish:
                 mock_resp.status_code = 200
                 mock_resp.json.return_value = {}
                 mock_client.publish_skill.return_value = mock_resp
-                with patch("crewai_cli.skills.main.Settings") as mock_settings_cls:
+                with patch("crewai_cli.experimental.skills.main.Settings") as mock_settings_cls:
                     mock_settings_cls.return_value.org_name = None
                     mock_settings_cls.return_value.enterprise_base_url = None
                     with pytest.raises(SystemExit):
@@ -151,14 +151,14 @@ class TestSkillPublish:
     def test_publish_calls_api(self, skill_command):
         with in_temp_dir():
             Path("SKILL.md").write_text(
-                "---\nname: my-skill\nversion: 1.0.0\ndescription: A test skill.\n---\nInstructions."
+                "---\nname: my-skill\ndescription: A test skill.\nmetadata:\n  version: 1.0.0\n---\nInstructions."
             )
             mock_resp = MagicMock()
             mock_resp.is_success = True
             mock_resp.status_code = 200
             mock_resp.json.return_value = {}
             skill_command.plus_api_client.publish_skill = MagicMock(return_value=mock_resp)
-            with patch("crewai_cli.skills.main.Settings") as mock_settings_cls:
+            with patch("crewai_cli.experimental.skills.main.Settings") as mock_settings_cls:
                 mock_settings_cls.return_value.org_name = "acme"
                 mock_settings_cls.return_value.enterprise_base_url = None
 

lib/cli/tests/test_click_compatibility.py

@@ -0,0 +1,131 @@
+"""Tests for click dependency compatibility.
+
+Regression tests for https://github.com/crewAIInc/crewAI/issues/6002
+The click dependency was previously pinned to ~=8.1.7 (i.e. >=8.1.7,<8.2.0)
+which prevented users from upgrading to click 8.2+ as required by their
+security policies. The constraint has been widened to >=8.1.7,<9 to allow
+newer click 8.x releases while still guarding against a future major version
+break.
+"""
+
+from importlib.metadata import requires
+from pathlib import Path
+
+import click
+import pytest
+from click.testing import CliRunner
+from packaging.requirements import Requirement
+
+
+# ---------------------------------------------------------------------------
+# Verify the runtime click version satisfies the declared constraint
+# ---------------------------------------------------------------------------
+
+def _get_click_requirement_from_pyproject(package_dir: str) -> Requirement:
+    """Parse the click requirement directly from a pyproject.toml file."""
+    import tomli
+
+    pyproject_path = Path(__file__).resolve().parents[3] / package_dir / "pyproject.toml"
+    with open(pyproject_path, "rb") as f:
+        data = tomli.load(f)
+    deps = data["project"]["dependencies"]
+    for dep in deps:
+        req = Requirement(dep)
+        if req.name == "click":
+            return req
+    raise ValueError(f"click not found in {pyproject_path}")
+
+
+@pytest.mark.parametrize(
+    "package_dir",
+    [
+        "lib/crewai",
+        "lib/cli",
+        "lib/devtools",
+    ],
+)
+def test_click_constraint_allows_8_3_3(package_dir: str):
+    """The declared click constraint must accept click 8.3.3 (issue #6002)."""
+    req = _get_click_requirement_from_pyproject(package_dir)
+    # packaging's Requirement.specifier supports `__contains__` for version checks
+    assert "8.3.3" in req.specifier, (
+        f"{package_dir}: click constraint {req.specifier} does not allow 8.3.3"
+    )
+
+
+@pytest.mark.parametrize(
+    "package_dir",
+    [
+        "lib/crewai",
+        "lib/cli",
+        "lib/devtools",
+    ],
+)
+def test_click_constraint_allows_8_1_7(package_dir: str):
+    """The declared click constraint must still accept the original minimum (8.1.7)."""
+    req = _get_click_requirement_from_pyproject(package_dir)
+    assert "8.1.7" in req.specifier, (
+        f"{package_dir}: click constraint {req.specifier} does not allow 8.1.7"
+    )
+
+
+@pytest.mark.parametrize(
+    "package_dir",
+    [
+        "lib/crewai",
+        "lib/cli",
+        "lib/devtools",
+    ],
+)
+def test_click_constraint_rejects_next_major(package_dir: str):
+    """The declared click constraint must reject click 9.0.0."""
+    req = _get_click_requirement_from_pyproject(package_dir)
+    assert "9.0.0" not in req.specifier, (
+        f"{package_dir}: click constraint {req.specifier} should not allow 9.0.0"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Verify the installed click version works with the CLI
+# ---------------------------------------------------------------------------
+
+def test_click_version_is_compatible():
+    """The installed click version must be within the 8.x range."""
+    major = int(click.__version__.split(".")[0])
+    assert major == 8, f"Expected click 8.x, got {click.__version__}"
+
+
+def test_cli_runner_works_with_installed_click():
+    """Smoke-test: CliRunner from the installed click can invoke a trivial command."""
+
+    @click.command()
+    @click.option("--name", default="world")
+    def hello(name: str) -> None:
+        click.echo(f"Hello {name}!")
+
+    runner = CliRunner()
+    result = runner.invoke(hello, ["--name", "crewai"])
+    assert result.exit_code == 0
+    assert "Hello crewai!" in result.output
+
+
+def test_cli_group_works_with_installed_click():
+    """Smoke-test: click.group, click.option, click.argument all work."""
+
+    @click.group()
+    def grp() -> None:
+        pass
+
+    @grp.command()
+    @click.argument("task")
+    @click.option("--verbose", is_flag=True)
+    def run(task: str, verbose: bool) -> None:
+        if verbose:
+            click.echo(f"Running {task} (verbose)")
+        else:
+            click.echo(f"Running {task}")
+
+    runner = CliRunner()
+    result = runner.invoke(grp, ["run", "test-task", "--verbose"])
+    assert result.exit_code == 0
+    assert "Running test-task (verbose)" in result.output

lib/crewai-core/pyproject.toml

@@ -13,7 +13,7 @@ dependencies = [
     "httpx~=0.28.1",
     "packaging>=23.0",
     "portalocker~=2.7.0",
-    "pyjwt>=2.9.0,<3",
+    "pyjwt>=2.13.0,<3",
     "pydantic>=2.11.9,<2.13",
     "rich>=13.7.1",
     "opentelemetry-api~=1.34.0",

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

@@ -1 +1 @@
-__version__ = "1.14.6a2"
+__version__ = "1.14.7a1"

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.14.6a2"
+__version__ = "1.14.7a1"

lib/crewai-files/src/crewai_files/formatting/api.py

@@ -11,7 +11,10 @@ from crewai_files.formatting.anthropic import AnthropicFormatter
 from crewai_files.formatting.bedrock import BedrockFormatter
 from crewai_files.formatting.gemini import GeminiFormatter
 from crewai_files.formatting.openai import OpenAIFormatter, OpenAIResponsesFormatter
-from crewai_files.processing.constraints import get_constraints_for_provider
+from crewai_files.processing.constraints import (
+    get_constraints_for_provider,
+    uses_openai_responses_api,
+)
 from crewai_files.processing.processor import FileProcessor
 from crewai_files.resolution.resolver import FileResolver, FileResolverConfig
 from crewai_files.uploaders.factory import ProviderType
@@ -120,9 +123,11 @@ def format_multimodal_content(
     if not files:
         return content_blocks
 
-    constraints_key: str = provider_type
-    if api == "responses" and "openai" in provider_type.lower():
-        constraints_key = "openai_responses"
+    constraints_key = (
+        "openai_responses"
+        if uses_openai_responses_api(provider_type, api)
+        else provider_type
+    )
 
     processor = FileProcessor(constraints=constraints_key)
     processed_files = processor.process_files(files)
@@ -184,9 +189,11 @@ async def aformat_multimodal_content(
     if not files:
         return content_blocks
 
-    constraints_key: str = provider_type
-    if api == "responses" and "openai" in provider_type.lower():
-        constraints_key = "openai_responses"
+    constraints_key = (
+        "openai_responses"
+        if uses_openai_responses_api(provider_type, api)
+        else provider_type
+    )
 
     processor = FileProcessor(constraints=constraints_key)
     processed_files = await processor.aprocess_files(files)

lib/crewai-files/src/crewai_files/processing/constraints.py

@@ -346,6 +346,20 @@ def get_constraints_for_provider(
     return None
 
 
+def uses_openai_responses_api(provider: str, api: str | None = None) -> bool:
+    """Return whether provider/API should use OpenAI Responses file support."""
+    if api != "responses":
+        return False
+
+    provider_lower = provider.lower()
+    return (
+        "openai" in provider_lower
+        or provider_lower == "gpt"
+        or provider_lower.startswith("gpt-")
+        or "/gpt-" in provider_lower
+    )
+
+
 def get_supported_content_types(provider: str, api: str | None = None) -> list[str]:
     """Get supported MIME type prefixes for a provider.
 
@@ -356,9 +370,9 @@ def get_supported_content_types(provider: str, api: str | None = None) -> list[s
     Returns:
         List of supported MIME type prefixes (e.g., ["image/", "application/pdf"]).
     """
-    lookup_key = provider
-    if api == "responses" and "openai" in provider.lower():
-        lookup_key = "openai_responses"
+    lookup_key = (
+        "openai_responses" if uses_openai_responses_api(provider, api) else provider
+    )
 
     constraints = get_constraints_for_provider(lookup_key)
     if not constraints:

lib/crewai-files/tests/processing/test_constraints.py

@@ -11,6 +11,7 @@ from crewai_files.processing.constraints import (
     ProviderConstraints,
     VideoConstraints,
     get_constraints_for_provider,
+    get_supported_content_types,
 )
 import pytest
 
@@ -70,6 +71,13 @@ class TestPDFConstraints:
         assert constraints.max_size_bytes == 1000
         assert constraints.max_pages is None
 
+    @pytest.mark.parametrize("provider", ["openai", "gpt", "gpt-4o-mini"])
+    def test_openai_responses_supports_pdf_for_gpt_aliases(self, provider):
+        """OpenAI Responses PDF support applies to concrete GPT model names."""
+        supported_types = get_supported_content_types(provider, api="responses")
+
+        assert "application/pdf" in supported_types
+
 
 class TestAudioConstraints:
     """Tests for AudioConstraints dataclass."""

lib/crewai-tools/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     "pytube~=15.0.0",
     "requests>=2.33.0,<3",
-    "crewai==1.14.6a2",
+    "crewai==1.14.7a1",
     "tiktoken>=0.8.0,<0.13",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

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

@@ -330,4 +330,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.14.6a2"
+__version__ = "1.14.7a1"

lib/crewai/pyproject.toml

@@ -8,8 +8,8 @@ authors = [
 ]
 requires-python = ">=3.10, <3.14"
 dependencies = [
-    "crewai-core==1.14.6a2",
-    "crewai-cli==1.14.6a2",
+    "crewai-core==1.14.7a1",
+    "crewai-cli==1.14.7a1",
     # Core Dependencies
     "pydantic>=2.11.9,<2.13",
     "openai>=2.30.0,<3",
@@ -27,9 +27,9 @@ dependencies = [
     "openpyxl~=3.1.5",
     # Authentication and Security
     "python-dotenv>=1.2.2,<2",
-    "pyjwt>=2.9.0,<3",
+    "pyjwt>=2.13.0,<3",
     # Configuration and Utils
-    "click~=8.1.7",
+    "click>=8.1.7,<9",
     "appdirs~=1.4.4",
     "jsonref~=1.1.0",
     "json-repair~=0.25.2",
@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.14.6a2",
+    "crewai-tools==1.14.7a1",
 ]
 embeddings = [
     "tiktoken>=0.8.0,<0.13"
@@ -138,6 +138,9 @@ torchvision = [
 crewai-files = { workspace = true }
 
 
+[project.scripts]
+crewai = "crewai_cli.cli:crewai"
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

lib/crewai/src/crewai/__init__.py

@@ -48,7 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.14.6a2"
+__version__ = "1.14.7a1"
 
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "Memory": ("crewai.memory.unified_memory", "Memory"),

lib/crewai/src/crewai/agent/core.py

@@ -472,7 +472,7 @@ class Agent(BaseAgent):
 
         for item in items:
             if isinstance(item, str):
-                from crewai.skills.registry import (
+                from crewai.experimental.skills.registry import (
                     is_registry_ref,
                     parse_registry_ref,
                     resolve_registry_ref,
@@ -1219,9 +1219,17 @@ class Agent(BaseAgent):
 
     def _use_trained_data(self, task_prompt: str) -> str:
         """Use trained data for the agent task prompt to improve output."""
-        trained_file = os.getenv(
-            CREWAI_TRAINED_AGENTS_FILE_ENV, TRAINED_AGENTS_DATA_FILE
+        crew_trained_agents_file = (
+            getattr(self.crew, "trained_agents_file", None)
+            if self.crew and not isinstance(self.crew, str)
+            else None
         )
+        trained_file = (
+            os.fspath(crew_trained_agents_file)
+            if crew_trained_agents_file
+            else os.getenv(CREWAI_TRAINED_AGENTS_FILE_ENV, TRAINED_AGENTS_DATA_FILE)
+        )
+
         if data := CrewTrainingHandler(trained_file).load():
             if trained_data_output := data.get(self.role):
                 task_prompt += (

lib/crewai/src/crewai/crew.py

@@ -179,6 +179,7 @@ class Crew(FlowTrackable, BaseModel):
         max_rpm: Maximum number of requests per minute for the crew execution to
             be respected.
         prompt_file: Path to the prompt json file to be used for the crew.
+        trained_agents_file: Path to trained agent suggestions loaded during inference.
         id: A unique identifier for the crew instance.
         task_callback: Callback to be executed after each task for every agents
             execution.
@@ -303,6 +304,13 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
+    trained_agents_file: str | Path | None = Field(
+        default=None,
+        description=(
+            "Path to a trained-agents pickle produced by train(). "
+            "When set, agents load suggestions from this file during inference."
+        ),
+    )
     output_log_file: bool | str | None = Field(
         default=None,
         description="Path to the log file to be saved",

lib/crewai/src/crewai/events/event_listener.py

@@ -306,20 +306,24 @@ class EventListener(BaseEventListener):
             self._telemetry.flow_execution_span(
                 event.flow_name, list(source._methods.keys())
             )
-            self.formatter.handle_flow_created(event.flow_name, str(source.flow_id))
-            self.formatter.handle_flow_started(event.flow_name, str(source.flow_id))
+            if not getattr(source, "suppress_flow_events", False):
+                self.formatter.handle_flow_created(event.flow_name, str(source.flow_id))
+                self.formatter.handle_flow_started(event.flow_name, str(source.flow_id))
 
         @crewai_event_bus.on(FlowFinishedEvent)
         def on_flow_finished(source: Any, event: FlowFinishedEvent) -> None:
-            self.formatter.handle_flow_status(
-                event.flow_name,
-                source.flow_id,
-            )
+            if not getattr(source, "suppress_flow_events", False):
+                self.formatter.handle_flow_status(
+                    event.flow_name,
+                    source.flow_id,
+                )
 
         @crewai_event_bus.on(MethodExecutionStartedEvent)
         def on_method_execution_started(
-            _: Any, event: MethodExecutionStartedEvent
+            source: Any, event: MethodExecutionStartedEvent
         ) -> None:
+            if getattr(source, "suppress_flow_events", False):
+                return
             self.formatter.handle_method_status(
                 event.method_name,
                 "running",
@@ -327,8 +331,10 @@ class EventListener(BaseEventListener):
 
         @crewai_event_bus.on(MethodExecutionFinishedEvent)
         def on_method_execution_finished(
-            _: Any, event: MethodExecutionFinishedEvent
+            source: Any, event: MethodExecutionFinishedEvent
         ) -> None:
+            if getattr(source, "suppress_flow_events", False):
+                return
             self.formatter.handle_method_status(
                 event.method_name,
                 "completed",

lib/crewai/src/crewai/events/listeners/tracing/first_time_trace_handler.py

@@ -222,6 +222,8 @@ To enable tracing later, do any one of these:
             return
         self.batch_manager.batch_owner_type = None
         self.batch_manager.batch_owner_id = None
+        self.batch_manager.defer_session_finalization = False
+        self.batch_manager._batch_finalized = False
         self.batch_manager.current_batch = None
         self.batch_manager.event_buffer.clear()
         self.batch_manager.trace_batch_id = None

lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py

@@ -62,6 +62,7 @@ class TraceBatchManager:
         self._pending_events_lock = Lock()
         self._pending_events_cv = Condition(self._pending_events_lock)
         self._pending_events_count = 0
+        self._finalize_lock = Lock()
 
         self.is_current_batch_ephemeral = False
         self.trace_batch_id: str | None = None
@@ -70,6 +71,8 @@ class TraceBatchManager:
         self.execution_start_times: dict[str, datetime] = {}
         self.batch_owner_type: str | None = None
         self.batch_owner_id: str | None = None
+        self.defer_session_finalization: bool = False
+        self._batch_finalized: bool = False
         self.backend_initialized: bool = False
         self.ephemeral_trace_url: str | None = None
         try:
@@ -101,6 +104,7 @@ class TraceBatchManager:
                 user_context=user_context, execution_metadata=execution_metadata
             )
             self.is_current_batch_ephemeral = use_ephemeral
+            self._batch_finalized = False
 
             self.record_start_time("execution")
 
@@ -312,6 +316,9 @@ class TraceBatchManager:
     def finalize_batch(self) -> TraceBatch | None:
         """Finalize batch and return it for sending"""
 
+        if self._batch_finalized:
+            return None
+
         if not self.current_batch or not is_tracing_enabled_in_context():
             return None
 
@@ -340,16 +347,15 @@ class TraceBatchManager:
         self.current_batch.events = sorted_events
         events_sent_count = len(sorted_events)
         if sorted_events:
-            original_buffer = self.event_buffer
             self.event_buffer = sorted_events
             events_sent_to_backend_status = self._send_events_to_backend()
-            self.event_buffer = original_buffer
             if events_sent_to_backend_status == 500 and self.trace_batch_id:
                 self._mark_batch_as_failed(
                     self.trace_batch_id, "Error sending events to backend"
                 )
                 return None
-        self._finalize_backend_batch(events_sent_count)
+        if not self._finalize_backend_batch(events_sent_count):
+            return None
 
         finalized_batch = self.current_batch
 
@@ -360,80 +366,87 @@ class TraceBatchManager:
         self.event_buffer.clear()
         self.trace_batch_id = None
         self.is_current_batch_ephemeral = False
+        self._batch_finalized = True
 
         self._cleanup_batch_data()
 
         return finalized_batch
 
-    def _finalize_backend_batch(self, events_count: int = 0) -> None:
+    def _finalize_backend_batch(self, events_count: int = 0) -> bool:
         """Send batch finalization to backend
 
         Args:
             events_count: Number of events that were successfully sent
         """
-        if not self.plus_api or not self.trace_batch_id:
-            return
+        with self._finalize_lock:
+            batch_id = self.trace_batch_id
+            is_ephemeral = self.is_current_batch_ephemeral
+            if self._batch_finalized or not self.plus_api or not batch_id:
+                return True
 
-        try:
-            payload: TraceFinalizePayload = {
-                "status": "completed",
-                "duration_ms": self.calculate_duration("execution"),
-                "final_event_count": events_count,
-            }
+            try:
+                payload: TraceFinalizePayload = {
+                    "status": "completed",
+                    "duration_ms": self.calculate_duration("execution"),
+                    "final_event_count": events_count,
+                }
 
-            response = (
-                self.plus_api.finalize_ephemeral_trace_batch(
-                    self.trace_batch_id, payload
-                )
-                if self.is_current_batch_ephemeral
-                else self.plus_api.finalize_trace_batch(self.trace_batch_id, payload)
-            )
-
-            if response.status_code == 200:
-                access_code = response.json().get("access_code", None)
-                console = Console()
-                settings = Settings()
-                base_url = settings.enterprise_base_url or DEFAULT_CREWAI_ENTERPRISE_URL
-                return_link = (
-                    f"{base_url}/crewai_plus/trace_batches/{self.trace_batch_id}"
-                    if not self.is_current_batch_ephemeral and access_code is None
-                    else f"{base_url}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
+                response = (
+                    self.plus_api.finalize_ephemeral_trace_batch(batch_id, payload)
+                    if is_ephemeral
+                    else self.plus_api.finalize_trace_batch(batch_id, payload)
                 )
 
-                if self.is_current_batch_ephemeral:
-                    self.ephemeral_trace_url = return_link
+                if response.status_code == 200:
+                    self._batch_finalized = True
+                    access_code = response.json().get("access_code", None)
+                    console = Console()
+                    settings = Settings()
+                    base_url = (
+                        settings.enterprise_base_url or DEFAULT_CREWAI_ENTERPRISE_URL
+                    )
+                    return_link = (
+                        f"{base_url}/crewai_plus/trace_batches/{batch_id}"
+                        if not is_ephemeral and access_code is None
+                        else f"{base_url}/crewai_plus/ephemeral_trace_batches/{batch_id}?access_code={access_code}"
+                    )
 
-                message_parts = [
-                    f"✅ Trace batch finalized with session ID: {self.trace_batch_id}",
-                    "",
-                    f"🔗 View here: {return_link}",
-                ]
+                    if is_ephemeral:
+                        self.ephemeral_trace_url = return_link
 
-                if access_code:
-                    message_parts.append(f"🔑 Access Code: {access_code}")
+                    message_parts = [
+                        f"✅ Trace batch finalized with session ID: {batch_id}",
+                        "",
+                        f"🔗 View here: {return_link}",
+                    ]
 
-                panel = Panel(
-                    "\n".join(message_parts),
-                    title="Trace Batch Finalization",
-                    border_style="green",
-                )
-                if not should_auto_collect_first_time_traces():
-                    console.print(panel)
+                    if access_code:
+                        message_parts.append(f"🔑 Access Code: {access_code}")
+
+                    panel = Panel(
+                        "\n".join(message_parts),
+                        title="Trace Batch Finalization",
+                        border_style="green",
+                    )
+                    if not should_auto_collect_first_time_traces():
+                        console.print(panel)
+                    return True
 
-            else:
                 logger.error(
                     f"❌ Failed to finalize trace batch: {response.status_code} - {response.text}"
                 )
-                self._mark_batch_as_failed(self.trace_batch_id, response.text)
+                self._mark_batch_as_failed(batch_id, response.text)
+                return False
 
-        except Exception as e:
-            logger.error(f"❌ Error finalizing trace batch: {e}")
-            try:
-                self._mark_batch_as_failed(self.trace_batch_id, str(e))
-            except Exception:
-                logger.debug(
-                    "Could not mark trace batch as failed (network unavailable)"
-                )
+            except Exception as e:
+                logger.error(f"❌ Error finalizing trace batch: {e}")
+                try:
+                    self._mark_batch_as_failed(batch_id, str(e))
+                except Exception:
+                    logger.debug(
+                        "Could not mark trace batch as failed (network unavailable)"
+                    )
+                return False
 
     def _cleanup_batch_data(self) -> None:
         """Clean up batch data after successful finalization to free memory"""

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -1,5 +1,6 @@
 """Trace collection listener for orchestrating trace collection."""
 
+from datetime import datetime, timezone
 import os
 from typing import Any, ClassVar
 import uuid
@@ -230,11 +231,14 @@ class TraceCollectionListener(BaseEventListener):
 
         @event_bus.on(FlowStartedEvent)
         def on_flow_started(source: Any, event: FlowStartedEvent) -> None:
-            # Always call _initialize_flow_batch to claim ownership.
-            # If batch was already initialized by a concurrent action event
-            # (race condition), initialize_batch() returns early but
-            # batch_owner_type is still correctly set to "flow".
-            self._initialize_flow_batch(source, event)
+            # Only the first execution to open the session batch owns it. A flow
+            # that starts while a batch already exists is nested -- inside a crew
+            # (e.g. an agent's Flow-based executor), a conversational Flow, or a
+            # parent flow -- and must NOT re-claim ownership. Re-claiming would
+            # mark batch_owner_type="flow" and cause the nested flow to finalize
+            # the parent's batch prematurely when it completes.
+            if not self.batch_manager.is_batch_initialized():
+                self._initialize_flow_batch(source, event)
             self._handle_trace_event("flow_started", source, event)
 
         @event_bus.on(MethodExecutionStartedEvent)
@@ -264,18 +268,20 @@ class TraceCollectionListener(BaseEventListener):
 
         @event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
-            if self.batch_manager.batch_owner_type != "flow":
-                # Always call _initialize_crew_batch to claim ownership.
-                # If batch was already initialized by a concurrent action event
-                # (e.g. LLM/tool before crew_kickoff_started), initialize_batch()
-                # returns early but batch_owner_type is still correctly set to "crew".
-                # Skip only when a parent flow already owns the batch.
+            # Nested crew inside Flow.kickoff: never claim an existing flow session batch.
+            if not self._nested_in_flow_execution() and (
+                not self.batch_manager.is_batch_initialized()
+            ):
                 self._initialize_crew_batch(source, event)
             self._handle_trace_event("crew_kickoff_started", source, event)
 
         @event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source: Any, event: CrewKickoffCompletedEvent) -> None:
             self._handle_trace_event("crew_kickoff_completed", source, event)
+            if self.batch_manager.defer_session_finalization:
+                return
+            if self._nested_in_flow_execution():
+                return
             if self.batch_manager.batch_owner_type == "crew":
                 if self.first_time_handler.is_first_time:
                     self.first_time_handler.mark_events_collected()
@@ -286,10 +292,14 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(CrewKickoffFailedEvent)
         def on_crew_failed(source: Any, event: CrewKickoffFailedEvent) -> None:
             self._handle_trace_event("crew_kickoff_failed", source, event)
+            if self.batch_manager.defer_session_finalization:
+                return
+            if self._nested_in_flow_execution():
+                return
             if self.first_time_handler.is_first_time:
                 self.first_time_handler.mark_events_collected()
                 self.first_time_handler.handle_execution_completion()
-            else:
+            elif self.batch_manager.batch_owner_type == "crew":
                 self.batch_manager.finalize_batch()
 
         @event_bus.on(TaskStartedEvent)
@@ -707,8 +717,32 @@ class TraceCollectionListener(BaseEventListener):
         @on_signal
         def handle_signal(source: Any, event: SignalEvent) -> None:
             """Flush trace batch on system signals to prevent data loss."""
-            if self.batch_manager.is_batch_initialized():
-                self.batch_manager.finalize_batch()
+            if not self.batch_manager.is_batch_initialized():
+                return
+            # Multi-turn flows defer batch finalization to finalize_session_traces().
+            if self.batch_manager.defer_session_finalization:
+                return
+            self.batch_manager.finalize_batch()
+
+    @staticmethod
+    def _is_inside_active_flow_context() -> bool:
+        """True when ``kickoff_async`` has set ``current_flow_id`` (nested crew)."""
+        from crewai.flow.flow_context import current_flow_id
+
+        return current_flow_id.get() is not None
+
+    def _flow_owns_trace_batch(self) -> bool:
+        """True when an in-flight conversational flow already owns the trace batch."""
+        if self.batch_manager.batch_owner_type == "flow":
+            return True
+        batch = self.batch_manager.current_batch
+        if batch is not None:
+            return batch.execution_metadata.get("execution_type") == "flow"
+        return False
+
+    def _nested_in_flow_execution(self) -> bool:
+        """True when a crew runs inside a flow session (context or batch ownership)."""
+        return self._is_inside_active_flow_context() or self._flow_owns_trace_batch()
 
     def _initialize_crew_batch(self, source: Any, event: BaseEvent) -> None:
         """Initialize trace batch.
@@ -729,6 +763,33 @@ class TraceCollectionListener(BaseEventListener):
 
         self._initialize_batch(user_context, execution_metadata)
 
+    def _try_initialize_flow_batch_from_context(self, event: Any) -> bool:
+        """Claim a flow trace batch when an action event fires inside kickoff.
+
+        When ``suppress_flow_events=True``, console panels are hidden but
+        ``FlowStartedEvent`` and method lifecycle events still emit; if no
+        batch exists yet, LLM/tool events must not fall back to implicit crew
+        batches.
+        """
+        from crewai.flow.flow_context import current_flow_id, current_flow_name
+
+        flow_id = current_flow_id.get()
+        if flow_id is None:
+            return False
+
+        started_at = getattr(event, "timestamp", None) or datetime.now(timezone.utc)
+        user_context = self._get_user_context()
+        execution_metadata = {
+            "flow_name": current_flow_name.get() or "Unknown Flow",
+            "execution_start": started_at,
+            "crewai_version": get_crewai_version(),
+            "execution_type": "flow",
+        }
+        self.batch_manager.batch_owner_type = "flow"
+        self.batch_manager.batch_owner_id = flow_id
+        self._initialize_batch(user_context, execution_metadata)
+        return True
+
     def _initialize_flow_batch(self, source: Any, event: BaseEvent) -> None:
         """Initialize trace batch for Flow execution.
 
@@ -793,12 +854,19 @@ class TraceCollectionListener(BaseEventListener):
             event: Event object.
         """
         if not self.batch_manager.is_batch_initialized():
-            user_context = self._get_user_context()
-            execution_metadata = {
-                "crew_name": getattr(source, "name", "Unknown Crew"),
-                "crewai_version": get_crewai_version(),
-            }
-            self._initialize_batch(user_context, execution_metadata)
+            if self._try_initialize_flow_batch_from_context(event):
+                pass
+            elif not self._nested_in_flow_execution():
+                user_context = self._get_user_context()
+                execution_metadata = {
+                    "crew_name": getattr(source, "name", "Unknown Crew"),
+                    "crewai_version": get_crewai_version(),
+                }
+                self.batch_manager.batch_owner_type = "crew"
+                self.batch_manager.batch_owner_id = getattr(
+                    source, "id", str(uuid.uuid4())
+                )
+                self._initialize_batch(user_context, execution_metadata)
 
         self.batch_manager.begin_event_processing()
         try:

lib/crewai/src/crewai/events/types/skill_events.py

@@ -60,20 +60,3 @@ class SkillLoadFailedEvent(SkillEvent):
 
     type: Literal["skill_load_failed"] = "skill_load_failed"
     error: str
-
-
-class SkillDownloadStartedEvent(SkillEvent):
-    """Event emitted when a registry skill download begins."""
-
-    type: Literal["skill_download_started"] = "skill_download_started"
-    registry_ref: str
-    version: str | None = None
-
-
-class SkillDownloadCompletedEvent(SkillEvent):
-    """Event emitted when a registry skill download completes."""
-
-    type: Literal["skill_download_completed"] = "skill_download_completed"
-    registry_ref: str
-    version: str | None = None
-    cache_path: Path | None = None

lib/crewai/src/crewai/experimental/__init__.py

@@ -1,31 +1,32 @@
-from crewai.experimental.agent_executor import AgentExecutor, CrewAgentExecutorFlow
-from crewai.experimental.evaluation import (
-    AgentEvaluationResult,
-    AgentEvaluator,
-    BaseEvaluator,
-    EvaluationScore,
-    EvaluationTraceCallback,
-    ExperimentResult,
-    ExperimentResults,
-    ExperimentRunner,
-    GoalAlignmentEvaluator,
-    MetricCategory,
-    ParameterExtractionEvaluator,
-    ReasoningEfficiencyEvaluator,
-    SemanticQualityEvaluator,
-    ToolInvocationEvaluator,
-    ToolSelectionEvaluator,
-    create_default_evaluator,
-    create_evaluation_callbacks,
+"""Experimental CrewAI surface — APIs here may change without major-version bumps."""
+
+from __future__ import annotations
+
+from typing import Any
+
+# ``crewai.experimental.conversational`` is pure data shapes — no Flow or Task
+# imports — so it's safe to eager-import. Everything else is resolved lazily
+# below; otherwise the chain
+#     crewai → Flow → experimental.conversational → experimental.__init__
+#                  → experimental.agent_executor / experimental.evaluation
+#                  → Flow / Task (mid-load)
+# would deadlock with "partially initialized module" ImportErrors.
+from crewai.experimental.conversational import (
+    AgentMessage,
+    ConversationConfig,
+    ConversationEvent,
+    ConversationMessage,
+    ConversationState,
+    RouterConfig,
 )
 
 
-__all__ = [
+_LAZY_FROM_AGENT_EXECUTOR = {"AgentExecutor", "CrewAgentExecutorFlow"}
+
+_LAZY_FROM_EVALUATION = {
     "AgentEvaluationResult",
     "AgentEvaluator",
-    "AgentExecutor",
     "BaseEvaluator",
-    "CrewAgentExecutorFlow",  # Deprecated alias for AgentExecutor
     "EvaluationScore",
     "EvaluationTraceCallback",
     "ExperimentResult",
@@ -40,4 +41,62 @@ __all__ = [
     "ToolSelectionEvaluator",
     "create_default_evaluator",
     "create_evaluation_callbacks",
+}
+
+
+def __getattr__(name: str) -> Any:
+    """Lazily resolve symbols whose modules import ``Flow`` or ``Task``.
+
+    Eager re-exports would deadlock when ``Flow`` itself is the consumer that
+    triggered ``crewai.experimental.__init__`` (``Flow`` imports types from
+    :mod:`crewai.experimental.conversational`). Callers like
+    ``from crewai.experimental import AgentExecutor`` still work — the
+    real import just runs lazily, after the original loader finishes.
+    """
+    if name in _LAZY_FROM_AGENT_EXECUTOR:
+        from crewai.experimental.agent_executor import (
+            AgentExecutor,
+            CrewAgentExecutorFlow,
+        )
+
+        globals()["AgentExecutor"] = AgentExecutor
+        globals()["CrewAgentExecutorFlow"] = CrewAgentExecutorFlow
+        return globals()[name]
+
+    if name in _LAZY_FROM_EVALUATION:
+        from crewai.experimental import evaluation as _evaluation_mod
+
+        for attr in _LAZY_FROM_EVALUATION:
+            globals()[attr] = getattr(_evaluation_mod, attr)
+        return globals()[name]
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = [
+    "AgentEvaluationResult",
+    "AgentEvaluator",
+    "AgentExecutor",
+    "AgentMessage",
+    "BaseEvaluator",
+    "ConversationConfig",
+    "ConversationEvent",
+    "ConversationMessage",
+    "ConversationState",
+    "CrewAgentExecutorFlow",  # Deprecated alias for AgentExecutor
+    "EvaluationScore",
+    "EvaluationTraceCallback",
+    "ExperimentResult",
+    "ExperimentResults",
+    "ExperimentRunner",
+    "GoalAlignmentEvaluator",
+    "MetricCategory",
+    "ParameterExtractionEvaluator",
+    "ReasoningEfficiencyEvaluator",
+    "RouterConfig",
+    "SemanticQualityEvaluator",
+    "ToolInvocationEvaluator",
+    "ToolSelectionEvaluator",
+    "create_default_evaluator",
+    "create_evaluation_callbacks",
 ]

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

@@ -93,6 +93,7 @@ from crewai.utilities.agent_utils import (
     track_delegation_if_needed,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
+from crewai.utilities.file_store import aget_all_files, get_all_files
 from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.planning_types import (
     PlanStep,
@@ -2771,7 +2772,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                     mark_cache_breakpoint(format_message_for_llm(user_prompt))
                 )
 
-            self._inject_files_from_inputs(inputs)
+            await self._ainject_files_from_inputs(inputs)
 
             self.state.ask_for_human_input = bool(
                 inputs.get("ask_for_human_input", False)
@@ -2982,12 +2983,42 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         training_handler.save(training_data)
 
     def _inject_files_from_inputs(self, inputs: dict[str, Any]) -> None:
-        """Inject files from inputs into the last user message.
+        """Inject files into the last user message.
 
         Args:
             inputs: Input dictionary that may contain a 'files' key.
         """
-        files = inputs.get("files")
+        files: dict[str, Any] = {}
+
+        if self.crew and self.task:
+            stored_files = get_all_files(self.crew.id, self.task.id)
+            if stored_files:
+                files.update(stored_files)
+
+        if inputs.get("files"):
+            files.update(inputs["files"])
+
+        if not files:
+            return
+
+        for i in range(len(self.state.messages) - 1, -1, -1):
+            msg = self.state.messages[i]
+            if msg.get("role") == "user":
+                msg["files"] = files
+                break
+
+    async def _ainject_files_from_inputs(self, inputs: dict[str, Any]) -> None:
+        """Async inject files into the last user message."""
+        files: dict[str, Any] = {}
+
+        if self.crew and self.task:
+            stored_files = await aget_all_files(self.crew.id, self.task.id)
+            if stored_files:
+                files.update(stored_files)
+
+        if inputs.get("files"):
+            files.update(inputs["files"])
+
         if not files:
             return
 

lib/crewai/src/crewai/experimental/conversational.py

@@ -0,0 +1,184 @@
+"""Conversational types and helpers shared by ``Flow`` (experimental).
+
+The conversational chat surface (``Flow`` with ``conversational = True``) is
+EXPERIMENTAL. APIs in this module and the conversational methods on ``Flow``
+may change without a major-version bump until the feature graduates.
+
+This module hosts the **data shapes** — ``ConversationConfig``,
+``RouterConfig``, ``ConversationState`` and its message types — plus the
+``_conversational_only`` decorator used to gate built-in conversational
+methods on the base ``Flow`` class. The methods themselves live on ``Flow``
+directly.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+from typing import Any, Literal, TypeVar, cast
+from uuid import uuid4
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from crewai.utilities.types import LLMMessage
+
+
+ConversationMessageRole = Literal["user", "assistant", "system", "tool"]
+ConversationEventVisibility = Literal["private", "public"]
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+def _conversational_only(func: F) -> F:
+    """Mark a method as part of the conversational built-in graph.
+
+    Methods carrying this marker only register on a ``Flow`` subclass when
+    ``conversational = True``. Subclasses that don't opt in see them as
+    inert attributes — they don't fire and don't pollute the listener graph.
+    """
+    func.__conversational_only__ = True  # type: ignore[attr-defined]
+    return func
+
+
+@dataclass
+class RouterConfig:
+    """LLM router configuration for the experimental conversational ``Flow``.
+
+    .. warning::
+
+       **EXPERIMENTAL.** Part of the conversational ``Flow`` surface. Fields
+       and defaults may change before the feature graduates from
+       ``crewai.experimental``. Pin your CrewAI version if you depend on
+       a specific shape.
+
+    ``route_descriptions`` overrides the per-route descriptions used to build
+    the router LLM's "available routes" catalog. Routes without an entry fall
+    back to the handler's docstring first line (or, for built-in routes, the
+    framework's canned description). ``prompt`` is reserved for domain
+    policy/voice, not the route catalog — that's auto-built.
+    """
+
+    prompt: str | None = None
+    response_format: type[BaseModel] | None = None
+    llm: Any | None = None
+    routes: Sequence[str] | None = None
+    route_descriptions: dict[str, str] | None = None
+    default_intent: str | None = "converse"
+    fallback_intent: str | None = "converse"
+    intent_field: str = "intent"
+
+
+@dataclass
+class ConversationConfig:
+    """Class-level configuration for the experimental conversational ``Flow``.
+
+    .. warning::
+
+       **EXPERIMENTAL.** Part of the conversational ``Flow`` surface. Fields
+       and defaults may change before the feature graduates from
+       ``crewai.experimental``. Pin your CrewAI version if you depend on
+       a specific shape.
+
+    ``system_prompt`` defaults to the ``slices.conversational_system_prompt``
+    translation when left as ``None``. Pass an empty string to opt out of any
+    system prompt for ``converse_turn``. ``answer_from_history_prompt`` falls
+    back to ``slices.conversational_answer_from_history_prompt`` when ``None``.
+    """
+
+    system_prompt: str | None = None
+    llm: Any | None = None
+    router: RouterConfig | None = None
+    answer_from_history_prompt: str | None = None
+    default_intents: Sequence[str] | None = None
+    intent_llm: Any | None = None
+    answer_from_history_llm: Any | None = None
+    visible_agent_outputs: Sequence[str] | Literal["all"] | None = None
+    defer_trace_finalization: bool = True
+
+    def __call__(self, flow_cls: type[Any]) -> type[Any]:
+        """Use this config as a class decorator."""
+        flow_cls.conversational_config = self
+        return flow_cls
+
+
+class ConversationMessage(BaseModel):
+    """Canonical user-facing message shared across conversational turns."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    role: ConversationMessageRole
+    content: str | list[dict[str, Any]] | None
+    name: str | None = None
+    tool_call_id: str | None = None
+    tool_calls: list[dict[str, Any]] | None = None
+    files: dict[str, Any] | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class AgentMessage(BaseModel):
+    """Private per-agent message or scratch result."""
+
+    role: ConversationMessageRole | str = "assistant"
+    content: Any
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class ConversationEvent(BaseModel):
+    """Structured trace/event that is separate from user-visible messages."""
+
+    type: str
+    payload: dict[str, Any] = Field(default_factory=dict)
+    agent_name: str | None = None
+    visibility: ConversationEventVisibility = "private"
+
+
+class ConversationState(BaseModel):
+    """Structured state for the experimental conversational ``Flow``.
+
+    .. warning::
+
+       **EXPERIMENTAL.** Field shape and defaults may change before the
+       conversational ``Flow`` graduates from ``crewai.experimental``.
+
+    ``messages`` is the canonical user-facing history. Agent/tool scratch work
+    belongs in ``events`` or ``agent_threads`` unless explicitly made public.
+    """
+
+    id: str = Field(default_factory=lambda: str(uuid4()))
+    messages: list[ConversationMessage] = Field(default_factory=list)
+    current_user_message: str | None = None
+    last_user_message: str | None = None
+    last_intent: str | None = None
+    ended: bool = False
+    events: list[ConversationEvent] = Field(default_factory=list)
+    agent_threads: dict[str, list[AgentMessage]] = Field(default_factory=dict)
+    session_ready: bool = False
+
+
+def message_to_llm_dict(message: Any) -> LLMMessage:
+    """Coerce a stored ``ConversationMessage`` (or dict) into an ``LLMMessage``."""
+    if isinstance(message, BaseModel):
+        data = message.model_dump(exclude_none=True)
+    elif isinstance(message, dict):
+        data = dict(message)
+    else:
+        data = {"role": "user", "content": str(message)}
+
+    return cast(
+        LLMMessage,
+        {key: value for key, value in data.items() if key != "metadata"},
+    )
+
+
+__all__ = [
+    "AgentMessage",
+    "ConversationConfig",
+    "ConversationEvent",
+    "ConversationEventVisibility",
+    "ConversationMessage",
+    "ConversationMessageRole",
+    "ConversationState",
+    "RouterConfig",
+    "_conversational_only",
+    "message_to_llm_dict",
+]

lib/crewai/src/crewai/experimental/conversational_mixin.py

@@ -0,0 +1,814 @@
+"""Conversational graph + helpers as a mixin for ``Flow`` (experimental).
+
+The experimental conversational chat surface lives here as a mixin so that
+``crewai.flow.runtime`` stays focused on the execution engine. ``Flow``
+inherits from ``_ConversationalMixin``; the methods only register on
+subclasses that opt in via ``conversational = True`` (enforced by the
+``_conversational_only`` marker + ``FlowMeta`` gating in
+``crewai.flow.runtime``).
+
+Import surface:
+    - :class:`_ConversationalMixin` — internal; ``Flow`` mixes it in. Users
+      don't import it directly.
+    - The data types this mixin uses live in
+      :mod:`crewai.experimental.conversational`.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from enum import Enum
+import json
+import logging
+from typing import TYPE_CHECKING, Any, ClassVar, Literal, cast
+
+from pydantic import BaseModel, Field, create_model
+
+from crewai.experimental.conversational import (
+    AgentMessage,
+    ConversationConfig,
+    ConversationEvent,
+    ConversationMessage,
+    ConversationState,
+    RouterConfig,
+    _conversational_only,
+    message_to_llm_dict,
+)
+from crewai.flow.conversation import (
+    append_message as _append_conversation_message,
+    get_conversation_messages,
+    receive_user_message as _receive_user_message,
+)
+from crewai.flow.dsl import listen, router, start
+from crewai.utilities.types import LLMMessage
+
+
+if TYPE_CHECKING:
+    from crewai.flow.runtime import Flow
+    from crewai.llms.base_llm import BaseLLM
+
+
+logger = logging.getLogger(__name__)
+
+
+class _ConversationalMixin:
+    """Built-in conversational graph for ``Flow`` (gated on ``conversational``).
+
+    Mixed into ``Flow`` so its execution engine (``runtime.py``) stays focused
+    on running graphs. The methods here only register on subclasses that set
+    ``conversational = True``; non-chat flows see them as inert attributes.
+    """
+
+    # The metaclass + state attributes referenced below live on ``Flow`` —
+    # this mixin is never instantiated standalone. These type-only
+    # declarations exist so static analyzers don't flag attribute access.
+    # Class-level slots use ``ClassVar`` to match Flow's actual declarations
+    # (otherwise mypy flags "Cannot override instance variable with class
+    # variable" when Flow declares them as ``ClassVar``).
+    if TYPE_CHECKING:
+        conversational: ClassVar[bool]
+        conversational_config: ClassVar[ConversationConfig | None]
+        builtin_routes: ClassVar[tuple[str, ...]]
+        internal_routes: ClassVar[tuple[str, ...]]
+        builtin_route_descriptions: ClassVar[dict[str, str]]
+        # Registry ClassVars populated by ``FlowMeta`` at class creation.
+        _listeners: ClassVar[dict[Any, Any]]
+
+        # Instance attrs from ``Flow``.
+        state: Any
+        name: str | None
+        _completed_methods: set[Any]
+        _method_outputs: list[Any]
+        _pending_and_listeners: dict[Any, Any]
+        _method_call_counts: dict[Any, int]
+        _is_execution_resuming: bool
+        _pending_user_message: str | dict[str, Any] | None
+        _pending_intents: Sequence[str] | None
+        _pending_intent_llm: str | BaseLLM | None
+
+        def _clear_or_listeners(self) -> None:
+            pass
+
+        def _collapse_to_outcome(
+            self,
+            feedback: str,
+            outcomes: tuple[str, ...],
+            llm: str | BaseLLM | Any,
+        ) -> str:
+            pass
+
+        def _copy_and_serialize_state(self) -> dict[str, Any]:
+            pass
+
+        def kickoff(self, *args: Any, **kwargs: Any) -> Any:
+            pass
+
+    @start()
+    @_conversational_only
+    def conversation_start(self) -> str | None:
+        """Internal Flow entrypoint that hands the user message to the router.
+
+        In conversational mode, ``Flow.kickoff_async`` runs all ``@start``
+        methods sequentially and this one is registered last, so any user
+        ``@start`` methods (e.g. permission loading) have already finished
+        before the returned value triggers ``route_conversation``.
+        """
+        state = cast(ConversationState, self.state)
+        return state.current_user_message
+
+    @router(conversation_start)
+    @_conversational_only
+    def route_conversation(self) -> str:
+        """Route the current turn to a listener label."""
+        state = cast(ConversationState, self.state)
+        context = self.build_router_context()
+        configured_route = self.route_turn(context)
+        if configured_route:
+            state.last_intent = configured_route
+            return configured_route
+
+        if state.last_intent:
+            return state.last_intent
+
+        if self.can_answer_from_history(context):
+            state.last_intent = "answer_from_history"
+            return "answer_from_history"
+
+        state.last_intent = "converse"
+        return "converse"
+
+    @listen("converse")
+    @_conversational_only
+    def converse_turn(self) -> str:
+        """Built-in chat handler over canonical conversation history."""
+        llm = self._default_conversation_llm()
+        if llm is None:
+            content = "I can continue the conversation once an LLM is configured."
+            self.append_assistant_message(content)
+            return content
+
+        messages: list[LLMMessage] = []
+        system_prompt = self._resolve_system_prompt()
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.extend(self.conversation_messages)
+
+        response = self._coerce_llm(llm).call(messages=messages)
+        content = self._stringify_result(response)
+        self.append_assistant_message(content)
+        return content
+
+    @listen("end")
+    @_conversational_only
+    def end_conversation(self) -> str:
+        """Built-in conversation terminator."""
+        cast(ConversationState, self.state).ended = True
+        content = "Conversation ended."
+        self.append_assistant_message(content)
+        return content
+
+    @listen("answer_from_history")
+    @_conversational_only
+    def answer_from_history_turn(self) -> str | None:
+        """Answer directly from canonical conversation history when configured."""
+        config = self._conversation_config
+        if config is None:
+            return None
+        llm = config.answer_from_history_llm
+        if llm is None:
+            return None
+
+        llm_instance = self._coerce_llm(llm)
+        messages: list[LLMMessage] = [
+            {
+                "role": "system",
+                "content": self._resolve_answer_from_history_prompt(),
+            },
+            *self.build_agent_context("answer_from_history"),
+        ]
+        response = llm_instance.call(messages=messages)
+        content = self._stringify_result(response)
+        self.append_assistant_message(content)
+        return content
+
+    def handle_turn(
+        self,
+        message: str,
+        *,
+        session_id: str | None = None,
+        intents: Sequence[str] | None = None,
+        intent_llm: str | BaseLLM | None = None,
+        **kickoff_kwargs: Any,
+    ) -> Any:
+        """Append a user message, run one conversational turn, and return output.
+
+        .. warning::
+
+           **EXPERIMENTAL.** This is the public entry point for the
+           conversational ``Flow``. Signature and semantics may change before
+           the feature graduates from ``crewai.experimental``.
+
+        Available only when ``conversational = True`` is set on the subclass.
+        Stashes the message + session_id as pending turn state, runs kickoff
+        (which restores from persist and then applies the pending turn), and
+        promotes the result to an assistant message when the handler didn't.
+        """
+        state = cast(ConversationState, self.state)
+        sid = session_id or state.id
+
+        # Stash the pending turn so ``_apply_pending_conversational_turn``
+        # picks it up AFTER persist restore.
+        self._pending_user_message = message
+        self._pending_intents = list(intents) if intents else None
+        self._pending_intent_llm = intent_llm
+
+        # Each turn is a fresh execution; clear graph tracking so the second
+        # turn re-runs instead of being treated as a checkpoint restore.
+        if "from_checkpoint" not in kickoff_kwargs:
+            self._reset_turn_execution_state()
+
+        assistant_count = self._assistant_message_count()
+        try:
+            result = self.kickoff(inputs={"id": sid}, **kickoff_kwargs)
+        finally:
+            self._pending_user_message = None
+            self._pending_intents = None
+            self._pending_intent_llm = None
+
+        if (
+            result is not None
+            and self._assistant_message_count() == assistant_count
+            and self._is_public_turn_result(result)
+        ):
+            self.append_assistant_message(self._stringify_result(result))
+        return result
+
+    def build_router_context(self) -> dict[str, Any]:
+        """Build context used by the routing policy for the current turn."""
+        state = cast(ConversationState, self.state)
+        return {
+            "system_prompt": self._resolve_system_prompt(),
+            "current_user_message": state.current_user_message,
+            "message_history": self.conversation_messages,
+            "events": [event.model_dump() for event in state.events],
+            "last_intent": state.last_intent,
+        }
+
+    def build_agent_context(self, agent_name: str) -> list[LLMMessage]:
+        """Build canonical message context for an agent or direct LLM call."""
+        state = cast(ConversationState, self.state)
+        messages = list(self.conversation_messages)
+        thread = state.agent_threads.get(agent_name, [])
+        messages.extend(
+            cast(
+                LLMMessage,
+                {
+                    "role": msg.role,
+                    "content": self._stringify_result(msg.content),
+                },
+            )
+            for msg in thread
+        )
+        return messages
+
+    def route_turn(self, context: dict[str, Any]) -> str | None:
+        """Route the current turn via the LLM router.
+
+        When ``ConversationConfig.router`` is omitted, the router is
+        auto-enabled with default settings as long as the flow declares
+        custom ``@listen`` handlers (anything beyond the built-in
+        ``converse`` / ``end`` routes). ``@ConversationConfig(llm=ROUTER_LLM)``
+        is enough to dispatch to your custom handlers — no explicit
+        ``RouterConfig()`` needed.
+
+        Pass an explicit ``RouterConfig`` only to override the routing prompt,
+        supply per-route descriptions, or change the default/fallback intent.
+        Override this method to bypass the LLM router entirely (e.g.,
+        permission gates before the LLM decision).
+        """
+        config = self._conversation_config
+        if config is None:
+            return None
+
+        router_config = config.router
+        if router_config is None:
+            if config.default_intents:
+                return None
+            custom_routes = self._effective_routes(None) - set(self.builtin_routes)
+            if not custom_routes:
+                return None
+            router_config = RouterConfig()
+
+        return self._route_with_config(router_config, context)
+
+    def can_answer_from_history(self, context: dict[str, Any]) -> bool:
+        """Return whether this turn can be answered from message history."""
+        config = self._conversation_config
+        if config is None or config.answer_from_history_llm is None:
+            return False
+        if len(self.conversation_messages) < 2:
+            return False
+
+        feedback = (
+            f"{self._resolve_answer_from_history_prompt()}\n\n"
+            f"Current user message: {context.get('current_user_message')}\n\n"
+            f"Message history:\n{self._format_messages(self.conversation_messages)}"
+        )
+        outcome = self._collapse_to_outcome(
+            feedback,
+            ("answer_from_history", "route_to_flow"),
+            config.answer_from_history_llm,
+        )
+        return outcome == "answer_from_history"
+
+    def append_agent_result(
+        self,
+        agent_name: str,
+        result: Any,
+        *,
+        visibility: Literal["private", "public"] = "private",
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Record an agent result, optionally making it visible to the user."""
+        content = self._stringify_result(result)
+        event_visibility = self._resolve_visibility(agent_name, visibility)
+        event = ConversationEvent(
+            type="agent_result",
+            agent_name=agent_name,
+            visibility=event_visibility,
+            payload={"content": content, **(metadata or {})},
+        )
+        state = cast(ConversationState, self.state)
+        state.events.append(event)
+        state.agent_threads.setdefault(agent_name, []).append(
+            AgentMessage(content=content, metadata=metadata or {})
+        )
+        if event_visibility == "public":
+            self.append_assistant_message(content)
+
+    def append_assistant_message(
+        self,
+        content: str,
+        *,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Append a final user-visible assistant message."""
+        cast(ConversationState, self.state).messages.append(
+            ConversationMessage(
+                role="assistant",
+                content=content,
+                metadata=metadata or {},
+            )
+        )
+
+    def append_message(
+        self,
+        role: Literal["user", "assistant", "system", "tool"],
+        content: str,
+        **extra: Any,
+    ) -> None:
+        """Append a message to conversation history (legacy ChatState path)."""
+        _append_conversation_message(cast("Flow[Any]", self), role, content, **extra)
+
+    @property
+    def conversation_messages(self) -> list[LLMMessage]:
+        """Message history from state, coerced to LLM-shaped dicts."""
+        return [
+            message_to_llm_dict(message)
+            for message in get_conversation_messages(cast("Flow[Any]", self))
+        ]
+
+    def receive_user_message(
+        self,
+        text: str,
+        *,
+        outcomes: Sequence[str] | None = None,
+        llm: str | BaseLLM | None = None,
+    ) -> str:
+        """Append a user message and optionally classify intent.
+
+        Conversational flows push a ``ConversationMessage`` onto
+        ``state.messages`` and preserve ``last_intent`` across turns.
+        Non-conversational flows fall through to the legacy helper.
+        """
+        if self.conversational:
+            state = cast(ConversationState, self.state)
+            state.messages.append(ConversationMessage(role="user", content=text))
+            state.current_user_message = text
+            state.last_user_message = text
+            if outcomes and llm is not None:
+                intent = self.classify_intent(
+                    text,
+                    outcomes,
+                    llm=llm,
+                    context=self.conversation_messages,
+                )
+                state.last_intent = intent
+                return intent
+            return text
+
+        return _receive_user_message(
+            cast("Flow[Any]", self), text, outcomes=outcomes, llm=llm
+        )
+
+    def classify_intent(
+        self,
+        text: str,
+        outcomes: Sequence[str],
+        *,
+        llm: str | BaseLLM,
+        context: Sequence[Mapping[str, Any]] | None = None,
+    ) -> str:
+        """Map user text to one of the given outcomes using an LLM."""
+        if context:
+            context_blob = "\n".join(
+                f"{m.get('role', 'user')}: {m.get('content', '')}" for m in context
+            )
+            feedback = f"{context_blob}\n\nLatest user message: {text}"
+        else:
+            feedback = text
+        return self._collapse_to_outcome(feedback, tuple(outcomes), llm)
+
+    @property
+    def _conversation_config(self) -> ConversationConfig | None:
+        return getattr(type(self), "conversational_config", None)
+
+    def _should_defer_trace_finalization(self) -> bool:
+        """Whether per-turn ``FlowFinished`` + ``finalize_batch`` should be skipped.
+
+        True when either:
+          - ``flow.defer_trace_finalization`` is set on the instance, OR
+          - the class-level ``ConversationConfig.defer_trace_finalization``
+            on a conversational subclass is True.
+
+        Either source enables the deferred-session pattern. The caller
+        eventually invokes ``finalize_session_traces()`` to close the batch.
+        """
+        if getattr(self, "defer_trace_finalization", False):
+            return True
+        config = self._conversation_config
+        return bool(config and config.defer_trace_finalization)
+
+    def _reset_turn_execution_state(self) -> None:
+        """Clear per-execution tracking so the next turn re-runs the graph."""
+        self._completed_methods.clear()
+        self._method_outputs.clear()
+        self._pending_and_listeners.clear()
+        self._method_call_counts.clear()
+        self._clear_or_listeners()
+        self._is_execution_resuming = False
+
+    def _apply_pending_conversational_turn(self) -> None:
+        """Drain the stashed user message + classify if intents configured.
+
+        Called from ``Flow.kickoff_async`` AFTER persist state restore so
+        the appended message survives ``self.persistence.load_state(...)``.
+        """
+        if self._pending_user_message is None:
+            return
+
+        text = self._coerce_user_message_text(self._pending_user_message)
+        if not text.strip():
+            return
+
+        cfg = self._conversation_config
+        outcomes = self._pending_intents
+        if outcomes is None and cfg is not None:
+            outcomes = cfg.default_intents
+        llm = self._pending_intent_llm
+        if llm is None and cfg is not None:
+            llm = cfg.intent_llm
+
+        if outcomes:
+            if llm is None:
+                raise ValueError("intent_llm is required when intents are provided")
+            self.receive_user_message(text, outcomes=outcomes, llm=llm)
+        else:
+            self.receive_user_message(text)
+
+    def _resolve_system_prompt(self) -> str | None:
+        """Return the effective conversational system prompt."""
+        from crewai.utilities.i18n import I18N_DEFAULT
+
+        config = self._conversation_config
+        if config is None or config.system_prompt is None:
+            return I18N_DEFAULT.slice("conversational_system_prompt")
+        return config.system_prompt or None
+
+    def _resolve_answer_from_history_prompt(self) -> str:
+        """Return the effective ``answer_from_history`` prompt."""
+        from crewai.utilities.i18n import I18N_DEFAULT
+
+        config = self._conversation_config
+        if config is None or not config.answer_from_history_prompt:
+            return I18N_DEFAULT.slice("conversational_answer_from_history_prompt")
+        return config.answer_from_history_prompt
+
+    def _route_with_config(
+        self,
+        router_config: RouterConfig,
+        context: dict[str, Any],
+    ) -> str | None:
+        router_llm = self._default_router_llm(router_config)
+        if router_llm is None:
+            return router_config.default_intent
+
+        try:
+            llm = self._coerce_llm(router_llm)
+            response = self._call_router_llm(
+                llm,
+                messages=self._build_router_messages(router_config, context),
+                response_format=self._router_response_format(router_config),
+            )
+            intent = self._extract_router_intent(response, router_config.intent_field)
+        except Exception:
+            return router_config.fallback_intent or router_config.default_intent
+
+        if intent is None:
+            return router_config.fallback_intent or router_config.default_intent
+
+        valid_labels = self._effective_routes(router_config)
+        if valid_labels and intent not in valid_labels:
+            return router_config.fallback_intent or router_config.default_intent
+
+        return intent
+
+    def _default_router_llm(self, router_config: RouterConfig) -> Any | None:
+        config = self._conversation_config
+        return (
+            router_config.llm
+            or (config.intent_llm if config else None)
+            or (config.llm if config else None)
+        )
+
+    def _router_response_format(
+        self,
+        router_config: RouterConfig,
+    ) -> type[BaseModel]:
+        if router_config.response_format is not None:
+            return router_config.response_format
+
+        routes = sorted(self._effective_routes(router_config))
+        field_definitions: dict[str, Any] = {
+            router_config.intent_field: (
+                str,
+                Field(description=f"One of: {', '.join(routes)}"),
+            )
+        }
+        return cast(
+            type[BaseModel],
+            create_model("ConversationRoute", **field_definitions),
+        )
+
+    def _call_router_llm(
+        self,
+        llm: Any,
+        *,
+        messages: list[LLMMessage],
+        response_format: type[BaseModel],
+    ) -> Any:
+        try:
+            return llm.call(messages=messages, response_format=response_format)
+        except TypeError as exc:
+            if "response_format" not in str(exc):
+                raise
+            return llm.call(messages=messages, response_model=response_format)
+
+    def _build_router_messages(
+        self,
+        router_config: RouterConfig,
+        context: dict[str, Any],
+    ) -> list[LLMMessage]:
+        catalog = self._build_route_catalog(router_config)
+        context = {**context, "available_routes": sorted(catalog.keys())}
+        domain_prompt = f"{router_config.prompt}\n\n" if router_config.prompt else ""
+        routes_section = "Routes:\n" + "\n".join(
+            f"- {label}: {description}" if description else f"- {label}"
+            for label, description in sorted(catalog.items())
+        )
+        routing_prompt = (
+            domain_prompt
+            + routes_section
+            + "\n\nChoose exactly one route from the list above. Prefer "
+            "'converse' for follow-ups, summaries, and clarifications about "
+            "prior turns — even if they touch on a topic the user previously "
+            "invoked a custom route for. Use a custom route only when the user "
+            "is making a fresh request for that tool or workflow."
+        )
+        return [
+            {"role": "system", "content": routing_prompt},
+            {"role": "user", "content": json.dumps(context, default=str)},
+        ]
+
+    def _build_route_catalog(
+        self,
+        router_config: RouterConfig | None,
+    ) -> dict[str, str]:
+        label_to_method: dict[str, str] = {}
+        for listener_name, condition in self._listeners.items():
+            if isinstance(condition, tuple):
+                _, trigger_labels = condition
+                for trigger_label in trigger_labels:
+                    label_to_method.setdefault(str(trigger_label), str(listener_name))
+
+        routes = self._effective_routes(router_config)
+        overrides = (
+            router_config.route_descriptions
+            if router_config and router_config.route_descriptions
+            else {}
+        )
+
+        catalog: dict[str, str] = {}
+        for route_label in routes:
+            if route_label in overrides:
+                catalog[route_label] = overrides[route_label]
+                continue
+            if route_label in self.builtin_route_descriptions:
+                catalog[route_label] = self.builtin_route_descriptions[route_label]
+                continue
+            handler_name = label_to_method.get(route_label)
+            description = ""
+            if handler_name:
+                method = getattr(type(self), handler_name, None)
+                doc = getattr(method, "__doc__", None)
+                if doc:
+                    description = doc.strip().split("\n", 1)[0].strip()
+            catalog[route_label] = description
+
+        return catalog
+
+    def _extract_router_intent(self, response: Any, intent_field: str) -> str | None:
+        if isinstance(response, BaseModel):
+            value = getattr(response, intent_field, None)
+        elif isinstance(response, dict):
+            value = response.get(intent_field)
+        elif isinstance(response, str):
+            try:
+                parsed = json.loads(response)
+            except json.JSONDecodeError:
+                value = response.strip()
+            else:
+                value = parsed.get(intent_field)
+        else:
+            value = getattr(response, intent_field, None)
+
+        if value is None:
+            return None
+        if isinstance(value, Enum):
+            return str(value.value)
+        return str(value)
+
+    def _valid_route_labels(self) -> set[str]:
+        labels: set[str] = set()
+        for condition in self._listeners.values():
+            if isinstance(condition, tuple):
+                _, methods = condition
+                labels.update(str(method) for method in methods)
+        return labels
+
+    def _effective_routes(self, router_config: RouterConfig | None = None) -> set[str]:
+        custom_routes = set(router_config.routes or ()) if router_config else set()
+        if not custom_routes:
+            custom_routes = (
+                self._valid_route_labels()
+                - set(self.builtin_routes)
+                - set(self.internal_routes)
+            )
+        return custom_routes | set(self.builtin_routes)
+
+    def _default_conversation_llm(self) -> Any | None:
+        config = self._conversation_config
+        if config is None:
+            return None
+        if config.llm is not None:
+            return config.llm
+        if config.answer_from_history_llm is not None:
+            return config.answer_from_history_llm
+        if config.router is not None:
+            return config.router.llm
+        return config.intent_llm
+
+    def _resolve_visibility(
+        self,
+        agent_name: str,
+        visibility: Literal["private", "public"],
+    ) -> Literal["private", "public"]:
+        if visibility == "public":
+            return "public"
+        config = self._conversation_config
+        visible = config.visible_agent_outputs if config else None
+        if visible == "all" or (visible is not None and agent_name in visible):
+            return "public"
+        return "private"
+
+    def _assistant_message_count(self) -> int:
+        state = cast(ConversationState, self.state)
+        return sum(1 for message in state.messages if message.role == "assistant")
+
+    def _is_public_turn_result(self, result: Any) -> bool:
+        if not isinstance(result, str):
+            return False
+        if result in {
+            "conversation",
+            "converse",
+            "end",
+            "answer_from_history",
+            "route_to_flow",
+        }:
+            return False
+        return result != cast(ConversationState, self.state).last_intent
+
+    @staticmethod
+    def _coerce_user_message_text(user_message: str | dict[str, Any] | Any) -> str:
+        if isinstance(user_message, str):
+            return user_message
+        if isinstance(user_message, dict) and user_message.get("content") is not None:
+            return str(user_message["content"])
+        return str(user_message)
+
+    @staticmethod
+    def _stringify_result(result: Any) -> str:
+        if hasattr(result, "raw"):
+            return str(result.raw)
+        if isinstance(result, BaseModel):
+            return result.model_dump_json()
+        return str(result)
+
+    @staticmethod
+    def _format_messages(messages: Sequence[Mapping[str, Any]]) -> str:
+        return "\n".join(
+            f"{message.get('role', 'user')}: {message.get('content', '')}"
+            for message in messages
+        )
+
+    @staticmethod
+    def _coerce_llm(llm: str | BaseLLM | Any) -> Any:
+        from crewai.llm import LLM
+        from crewai.llms.base_llm import BaseLLM as BaseLLMClass
+
+        if isinstance(llm, str):
+            return LLM(model=llm)
+        if isinstance(llm, BaseLLMClass) or callable(getattr(llm, "call", None)):
+            return llm
+        raise ValueError(f"Invalid llm type: {type(llm)}. Expected str or BaseLLM.")
+
+    def finalize_session_traces(self) -> None:
+        """Emit a final ``FlowFinishedEvent`` and finalize the trace batch.
+
+        Pairs with ``flow.defer_trace_finalization = True`` (or
+        ``ConversationConfig(defer_trace_finalization=True)``): per-turn
+        ``handle_turn()`` skips the close, then a single call here at
+        session end emits one ``FlowFinishedEvent`` + ``finalize_batch()``
+        so the whole conversation lands as one trace.
+
+        Safe to call when not deferring — it's a no-op if the trace batch
+        was already finalized per-turn or never started.
+        """
+        from crewai.events.event_bus import crewai_event_bus
+        from crewai.events.event_context import restore_event_scope
+        from crewai.events.listeners.tracing.trace_listener import (
+            TraceCollectionListener,
+        )
+        from crewai.events.types.flow_events import FlowFinishedEvent
+
+        # Only emit the session-end event when a deferred flow_started is
+        # actually pending. ``_deferred_flow_started_event_id`` is set only by
+        # deferred kickoffs; when finalization was not deferred, each per-turn
+        # kickoff already emitted its own flow_finished, so emitting here would
+        # duplicate the session-end event and confuse tracing. Restoring the
+        # stashed scope also pairs this flow_finished with its opener instead
+        # of warning about an empty scope stack.
+        started_id = getattr(self, "_deferred_flow_started_event_id", None)
+        if started_id:
+            last_output = self._method_outputs[-1] if self._method_outputs else None
+            restore_event_scope(((started_id, "flow_started"),))
+            try:
+                crewai_event_bus.emit(
+                    self,
+                    FlowFinishedEvent(
+                        type="flow_finished",
+                        flow_name=self.name or self.__class__.__name__,
+                        result=last_output,
+                        state=self._copy_and_serialize_state(),
+                    ),
+                )
+            except Exception:
+                logger.warning(
+                    "FlowFinishedEvent emission failed during finalize_session_traces",
+                    exc_info=True,
+                )
+            finally:
+                restore_event_scope(())
+                object.__setattr__(self, "_deferred_flow_started_event_id", None)
+
+        trace_listener = TraceCollectionListener()
+        batch_manager = trace_listener.batch_manager
+        if batch_manager.batch_owner_type == "flow":
+            if trace_listener.first_time_handler.is_first_time:
+                trace_listener.first_time_handler.mark_events_collected()
+                trace_listener.first_time_handler.handle_execution_completion()
+            else:
+                batch_manager.finalize_batch()
+
+
+__all__ = ["_ConversationalMixin"]

lib/crewai/src/crewai/experimental/skills/__init__.py

@@ -0,0 +1,23 @@
+"""Experimental Skills Repository — registry refs, global cache, downloads.
+
+This package contains the registry-backed pieces of the skills feature
+(`@org/name` refs, `~/.crewai/skills/` cache, download events). The stable
+filesystem-based skill loader still lives in `crewai.skills`.
+"""
+
+from crewai.experimental.skills.cache import SkillCacheManager
+from crewai.experimental.skills.registry import (
+    SkillNotCachedError,
+    is_registry_ref,
+    parse_registry_ref,
+    resolve_registry_ref,
+)
+
+
+__all__ = [
+    "SkillCacheManager",
+    "SkillNotCachedError",
+    "is_registry_ref",
+    "parse_registry_ref",
+    "resolve_registry_ref",
+]

lib/crewai/src/crewai/experimental/skills/_flag.py

@@ -0,0 +1,24 @@
+"""Experimental feature gate for the Skills Repository."""
+
+from __future__ import annotations
+
+import os
+
+
+ENV_VAR = "CREWAI_EXPERIMENTAL"
+
+
+class ExperimentalFeatureDisabledError(RuntimeError):
+    """Raised when an experimental feature is used without the flag set."""
+
+
+def is_enabled() -> bool:
+    return os.environ.get(ENV_VAR) == "1"
+
+
+def require_experimental_skills() -> None:
+    if not is_enabled():
+        raise ExperimentalFeatureDisabledError(
+            "The Skills Repository (registry refs, cache, downloads) is "
+            f"experimental. Set {ENV_VAR}=1 to enable it."
+        )

lib/crewai/src/crewai/experimental/skills/events.py

@@ -0,0 +1,30 @@
+"""Download lifecycle events for registry-backed skills.
+
+These events are emitted only by the experimental Skills Repository
+(`@org/name` resolution + global cache). Local-file skill events still
+live in `crewai.events.types.skill_events`.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Literal
+
+from crewai.events.types.skill_events import SkillEvent
+
+
+class SkillDownloadStartedEvent(SkillEvent):
+    """Event emitted when a registry skill download begins."""
+
+    type: Literal["skill_download_started"] = "skill_download_started"
+    registry_ref: str
+    version: str | None = None
+
+
+class SkillDownloadCompletedEvent(SkillEvent):
+    """Event emitted when a registry skill download completes."""
+
+    type: Literal["skill_download_completed"] = "skill_download_completed"
+    registry_ref: str
+    version: str | None = None
+    cache_path: Path | None = None

lib/crewai/src/crewai/experimental/skills/registry.py

@@ -11,7 +11,7 @@ from pathlib import Path
 import sys
 from typing import Any
 
-from crewai.skills.cache import SkillCacheManager
+from crewai.experimental.skills.cache import SkillCacheManager
 
 
 _logger = logging.getLogger(__name__)
@@ -100,9 +100,11 @@ def resolve_registry_ref(
     Raises:
         SkillNotCachedError: When not cached and running in non-interactive mode.
     """
+    from crewai.experimental.skills._flag import require_experimental_skills
     from crewai.skills.loader import activate_skill
     from crewai.skills.parser import load_skill_metadata
 
+    require_experimental_skills()
     org, name = parse_registry_ref(ref)
 
     local_path = Path.cwd() / "skills" / name
@@ -152,7 +154,7 @@ def download_skill(
 
     try:
         from crewai.events.event_bus import crewai_event_bus
-        from crewai.events.types.skill_events import (
+        from crewai.experimental.skills.events import (
             SkillDownloadCompletedEvent,
             SkillDownloadStartedEvent,
         )

lib/crewai/src/crewai/flow/__init__.py

@@ -4,10 +4,14 @@ from crewai.flow.async_feedback import (
     HumanFeedbackProvider,
     PendingFeedbackContext,
 )
+from crewai.flow.conversation import (
+    ChatState,
+    ConversationalConfig,
+    ConversationalInputs,
+)
+from crewai.flow.dsl import HumanFeedbackResult, human_feedback
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 from crewai.flow.flow_config import flow_config
-from crewai.flow.flow_serializer import flow_structure
-from crewai.flow.human_feedback import HumanFeedbackResult, human_feedback
 from crewai.flow.input_provider import InputProvider, InputResponse
 from crewai.flow.persistence import persist
 from crewai.flow.visualization import (
@@ -18,7 +22,10 @@ from crewai.flow.visualization import (
 
 
 __all__ = [
+    "ChatState",
     "ConsoleProvider",
+    "ConversationalConfig",
+    "ConversationalInputs",
     "Flow",
     "FlowStructure",
     "HumanFeedbackPending",
@@ -30,7 +37,6 @@ __all__ = [
     "and_",
     "build_flow_structure",
     "flow_config",
-    "flow_structure",
     "human_feedback",
     "listen",
     "or_",

lib/crewai/src/crewai/flow/conversation.py

@@ -0,0 +1,246 @@
+"""Conversational turn helpers for CrewAI Flows.
+
+Provides message history utilities, kickoff input normalization, and optional
+class-level defaults via ``ConversationalConfig``. Session identity is ``state.id``
+(``inputs["id"]`` / ``kickoff(session_id=...)``), not a separate Flow field.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Literal, TypedDict, cast
+from uuid import uuid4
+
+from pydantic import BaseModel, Field
+
+from crewai.utilities.types import LLMMessage
+
+
+if TYPE_CHECKING:
+    from crewai.flow.flow import Flow
+    from crewai.llms.base_llm import BaseLLM
+
+
+_EXIT_COMMANDS_DEFAULT: tuple[str, ...] = ("exit", "quit")
+
+
+class ConversationalInputs(TypedDict, total=False):
+    """Conventional ``kickoff(inputs=...)`` keys for chat turns."""
+
+    id: str
+    user_message: str | dict[str, Any]
+    last_intent: str
+
+
+@dataclass
+class ConversationalConfig:
+    """Optional class-level defaults for conversational flows.
+
+    Override per kickoff via ``user_message``, ``session_id``, ``intents``, etc.
+    """
+
+    default_intents: Sequence[str] | None = None
+    intent_llm: str | None = None
+    interactive_prompt: str = "You: "
+    interactive_timeout: float | None = None
+    exit_commands: Sequence[str] = field(default_factory=lambda: _EXIT_COMMANDS_DEFAULT)
+    defer_trace_finalization: bool = True
+
+
+class ChatState(BaseModel):
+    """Recommended persisted state shape for multi-turn flows."""
+
+    id: str = Field(default_factory=lambda: str(uuid4()))
+    messages: list[LLMMessage] = Field(default_factory=list)
+    last_user_message: str | None = None
+    last_intent: str | None = None
+    session_ready: bool = False
+
+
+def _coerce_user_message_text(user_message: str | dict[str, Any] | Any) -> str:
+    if isinstance(user_message, str):
+        return user_message
+    if isinstance(user_message, dict):
+        content = user_message.get("content")
+        if content is not None:
+            return str(content)
+    return str(user_message)
+
+
+def normalize_kickoff_inputs(
+    inputs: dict[str, Any] | None,
+    *,
+    user_message: str | dict[str, Any] | None = None,
+    session_id: str | None = None,
+) -> dict[str, Any] | None:
+    """Merge conversational kickoff kwargs into the inputs dict.
+
+    Returns ``None`` when the caller passed no inputs and no conversational
+    kwargs — so ``FlowStartedEvent.inputs`` stays ``None`` for stateless flows
+    instead of being materialized as an empty dict.
+    """
+    if inputs is None and user_message is None and session_id is None:
+        return None
+
+    merged: dict[str, Any] = dict(inputs or {})
+
+    if session_id is not None:
+        merged["id"] = session_id
+
+    if user_message is not None:
+        merged["user_message"] = user_message
+
+    return merged
+
+
+def get_conversation_messages(flow: Flow[Any]) -> list[LLMMessage]:
+    """Read message history from flow state or the internal fallback buffer."""
+    buffer: list[LLMMessage] = getattr(flow, "_conversation_messages", [])
+    state = getattr(flow, "_state", None)
+    if state is None:
+        return list(buffer)
+
+    if isinstance(state, dict):
+        messages = state.get("messages")
+        if isinstance(messages, list):
+            return cast(list[LLMMessage], messages)
+    elif isinstance(state, BaseModel) and hasattr(state, "messages"):
+        messages = getattr(state, "messages", None)
+        if isinstance(messages, list):
+            return cast(list[LLMMessage], messages)
+
+    return list(buffer)
+
+
+def append_message(
+    flow: Flow[Any],
+    role: Literal["user", "assistant", "system", "tool"],
+    content: str,
+    **extra: Any,
+) -> None:
+    """Append a message to ``state.messages`` or the flow fallback buffer."""
+    message: LLMMessage = {"role": role, "content": content}
+    for key, value in extra.items():
+        if key in ("tool_call_id", "name", "tool_calls", "files"):
+            message[key] = value  # type: ignore[literal-required]
+
+    state = getattr(flow, "_state", None)
+    if state is not None:
+        if isinstance(state, dict):
+            messages = state.get("messages")
+            if isinstance(messages, list):
+                messages.append(message)
+                return
+        elif isinstance(state, BaseModel) and hasattr(state, "messages"):
+            messages = getattr(state, "messages", None)
+            if messages is None:
+                object.__setattr__(state, "messages", [])
+                messages = state.messages
+            if isinstance(messages, list):
+                messages.append(message)
+                return
+
+    if not hasattr(flow, "_conversation_messages"):
+        object.__setattr__(flow, "_conversation_messages", [])
+    flow._conversation_messages.append(message)
+
+
+def set_state_field(flow: Flow[Any], name: str, value: Any) -> None:
+    """Set a field on structured or dict flow state when present."""
+    state = getattr(flow, "_state", None)
+    if state is None:
+        return
+    if isinstance(state, dict):
+        state[name] = value
+    elif isinstance(state, BaseModel) and hasattr(state, name):
+        object.__setattr__(state, name, value)
+
+
+def receive_user_message(
+    flow: Flow[Any],
+    text: str,
+    *,
+    outcomes: Sequence[str] | None = None,
+    llm: str | BaseLLM | None = None,
+) -> str:
+    """Record a user turn: append message and optionally classify intent."""
+    append_message(flow, "user", text)
+    set_state_field(flow, "last_user_message", text)
+
+    if outcomes and llm is not None:
+        intent = flow.classify_intent(
+            text,
+            outcomes,
+            llm=llm,
+            context=get_conversation_messages(flow),
+        )
+        set_state_field(flow, "last_intent", intent)
+        return intent
+
+    return text
+
+
+def prepare_conversational_turn(
+    flow: Flow[Any],
+    *,
+    user_message: str | dict[str, Any] | None = None,
+    intents: Sequence[str] | None = None,
+    intent_llm: str | BaseLLM | None = None,
+    config: ConversationalConfig | None = None,
+) -> None:
+    """Hydrate conversation state after inputs are merged into flow state."""
+    if user_message is None:
+        state = getattr(flow, "_state", None)
+        if isinstance(state, dict) and "user_message" in state:
+            user_message = state["user_message"]
+        elif isinstance(state, BaseModel) and hasattr(state, "user_message"):
+            user_message = getattr(state, "user_message", None)
+
+    if user_message is None:
+        return
+
+    text = _coerce_user_message_text(user_message)
+    if not text.strip():
+        return
+
+    # Fresh classification each turn (do not reuse prior turn's route label).
+    set_state_field(flow, "last_intent", None)
+
+    resolved_intents = intents
+    if resolved_intents is None and config is not None:
+        resolved_intents = config.default_intents
+
+    resolved_llm = intent_llm
+    if resolved_llm is None and config is not None:
+        resolved_llm = config.intent_llm
+
+    if resolved_intents:
+        if resolved_llm is None:
+            raise ValueError("intent_llm is required when intents are provided")
+        receive_user_message(
+            flow,
+            text,
+            outcomes=resolved_intents,
+            llm=resolved_llm,
+        )
+    else:
+        receive_user_message(flow, text)
+
+
+def input_history_to_messages(entries: Sequence[Any]) -> list[LLMMessage]:
+    """Convert ``Flow.input_history`` entries to LLM message format."""
+    messages: list[LLMMessage] = []
+    for entry in entries:
+        prompt = entry.get("message") if isinstance(entry, dict) else None
+        response = entry.get("response") if isinstance(entry, dict) else None
+        if prompt:
+            messages.append({"role": "assistant", "content": str(prompt)})
+        if response:
+            messages.append({"role": "user", "content": str(response)})
+    return messages
+
+
+def get_conversational_config(flow: Flow[Any]) -> ConversationalConfig | None:
+    """Return class-level ``conversational_config`` if defined."""
+    return getattr(type(flow), "conversational_config", None)

lib/crewai/src/crewai/flow/dsl/__init__.py

@@ -0,0 +1,39 @@
+"""Flow DSL: the Python authoring layer for Flows.
+
+Provides the ``@start`` / ``@listen`` / ``@router`` decorators and the
+``or_`` / ``and_`` condition combinators used to write Flow classes in
+Python. The DSL is one way to produce a Flow Structure: this package
+extracts a :class:`~crewai.flow.flow_definition.FlowDefinition` from a
+Python Flow class. Execution is handled by ``runtime``.
+"""
+
+from crewai.flow.dsl.human_feedback import (
+    HumanFeedbackResult,
+    human_feedback,
+)
+from crewai.flow.dsl.listen import listen
+from crewai.flow.dsl.router import router
+from crewai.flow.dsl.start import start
+from crewai.flow.dsl.utils import (
+    _extract_all_methods as _extract_all_methods,
+    _extract_all_methods_recursive as _extract_all_methods_recursive,
+    _normalize_condition as _normalize_condition,
+    and_,
+    build_flow_definition as build_flow_definition,
+    extract_flow_definition as extract_flow_definition,
+    is_flow_condition_dict as is_flow_condition_dict,
+    is_flow_method as is_flow_method,
+    is_simple_flow_condition as is_simple_flow_condition,
+    or_,
+)
+
+
+__all__ = [
+    "HumanFeedbackResult",
+    "and_",
+    "human_feedback",
+    "listen",
+    "or_",
+    "router",
+    "start",
+]