fix: narrow Bedrock _init_clients except to credential errors only

The bare `except Exception` would silently swallow `TypeError`,
`AttributeError`, and other real bugs in `_build_sync_client` with
only a debug log. The intent is to defer on missing AWS credentials,
which boto3 surfaces as `BotoCoreError` / `ClientError` (and `ValueError`
for some validation paths). Catch only those; let everything else
propagate so genuine failures stay loud.

.github/workflows/linter.yml

@@ -6,7 +6,24 @@ permissions:
   contents: read
 
 jobs:
-  lint:
+  changes:
+    name: Detect changes
+    runs-on: ubuntu-latest
+    outputs:
+      code: ${{ steps.filter.outputs.code }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            code:
+              - '!docs/**'
+              - '!**/*.md'
+
+  lint-run:
+    needs: changes
+    if: needs.changes.outputs.code == 'true'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
@@ -48,3 +65,23 @@ jobs:
             ~/.local/share/uv
             .venv
           key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  lint:
+    name: lint
+    runs-on: ubuntu-latest
+    needs: [changes, lint-run]
+    if: always()
+    steps:
+      - name: Check results
+        run: |
+          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
+            echo "Docs-only change, skipping lint"
+            exit 0
+          fi
+          if [ "${{ needs.lint-run.result }}" == "success" ]; then
+            echo "Lint passed"
+          else
+            echo "Lint failed"
+            exit 1
+          fi

.github/workflows/tests.yml

@@ -6,8 +6,25 @@ permissions:
   contents: read
 
 jobs:
-  tests:
+  changes:
+    name: Detect changes
+    runs-on: ubuntu-latest
+    outputs:
+      code: ${{ steps.filter.outputs.code }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            code:
+              - '!docs/**'
+              - '!**/*.md'
+
+  tests-matrix:
     name: tests (${{ matrix.python-version }})
+    needs: changes
+    if: needs.changes.outputs.code == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
@@ -98,3 +115,23 @@ jobs:
             ~/.local/share/uv
             .venv
           key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  tests:
+    name: tests
+    runs-on: ubuntu-latest
+    needs: [changes, tests-matrix]
+    if: always()
+    steps:
+      - name: Check results
+        run: |
+          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
+            echo "Docs-only change, skipping tests"
+            exit 0
+          fi
+          if [ "${{ needs.tests-matrix.result }}" == "success" ]; then
+            echo "All tests passed"
+          else
+            echo "Tests failed"
+            exit 1
+          fi

.github/workflows/type-checker.yml

@@ -6,8 +6,25 @@ permissions:
   contents: read
 
 jobs:
+  changes:
+    name: Detect changes
+    runs-on: ubuntu-latest
+    outputs:
+      code: ${{ steps.filter.outputs.code }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            code:
+              - '!docs/**'
+              - '!**/*.md'
+
   type-checker-matrix:
     name: type-checker (${{ matrix.python-version }})
+    needs: changes
+    if: needs.changes.outputs.code == 'true'
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
@@ -57,14 +74,18 @@ jobs:
   type-checker:
     name: type-checker
     runs-on: ubuntu-latest
-    needs: type-checker-matrix
+    needs: [changes, type-checker-matrix]
     if: always()
     steps:
-      - name: Check matrix results
+      - name: Check results
         run: |
-          if [ "${{ needs.type-checker-matrix.result }}" == "success" ] || [ "${{ needs.type-checker-matrix.result }}" == "skipped" ]; then
-            echo "✅ All type checks passed"
+          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
+            echo "Docs-only change, skipping type checks"
+            exit 0
+          fi
+          if [ "${{ needs.type-checker-matrix.result }}" == "success" ]; then
+            echo "All type checks passed"
           else
-            echo "❌ Type checks failed"
+            echo "Type checks failed"
             exit 1
           fi

.pre-commit-config.yaml

@@ -24,6 +24,14 @@ repos:
     rev: 0.11.3
     hooks:
       - id: uv-lock
+  - repo: local
+    hooks:
+      - id: pip-audit
+        name: pip-audit
+        entry: bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable --ignore-vuln CVE-2025-69872 --ignore-vuln CVE-2026-25645 --ignore-vuln CVE-2026-27448 --ignore-vuln CVE-2026-27459 --ignore-vuln PYSEC-2023-235' --
+        language: system
+        pass_filenames: false
+        stages: [pre-push, manual]
   - repo: https://github.com/commitizen-tools/commitizen
     rev: v4.10.1
     hooks:

docs/ar/changelog.mdx

@@ -4,6 +4,123 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="10 أبريل 2026">
+  ## v1.14.2a2
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة واجهة مستخدم نصية لنقطة التحقق مع عرض شجري، ودعم التفرع، ومدخلات/مخرجات قابلة للتعديل
+  - إثراء تتبع رموز LLM مع رموز الاستدلال ورموز إنشاء التخزين المؤقت
+  - إضافة معلمة `from_checkpoint` إلى طرق الانطلاق
+  - تضمين `crewai_version` في نقاط التحقق مع إطار عمل الهجرة
+  - إضافة تفرع نقاط التحقق مع تتبع السلالة
+
+  ### إصلاحات الأخطاء
+  - إصلاح توجيه الوضع الصارم إلى مزودي Anthropic وBedrock
+  - تعزيز NL2SQLTool مع وضع القراءة فقط الافتراضي، والتحقق من الاستعلامات، والاستعلامات المعلمة
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.2a1
+
+  ## المساهمون
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
+
+</Update>
+
+<Update label="9 أبريل 2026">
+  ## v1.14.2a1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## ما الذي تغير
+
+  ### إصلاحات الأخطاء
+  - إصلاح إصدار حدث flow_finished بعد استئناف HITL
+  - إصلاح إصدار التشفير إلى 46.0.7 لمعالجة CVE-2026-39892
+
+  ### إعادة هيكلة
+  - إعادة هيكلة لاستخدام I18N_DEFAULT المشترك
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.1
+
+  ## المساهمون
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="9 أبريل 2026">
+  ## v1.14.1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة متصفح TUI لنقاط التفتيش غير المتزامنة
+  - إضافة دالة aclose()/close() ومدير سياق غير متزامن لمخرجات البث
+
+  ### إصلاحات الأخطاء
+  - إصلاح التعبير النمطي لزيادة إصدار pyproject.toml
+  - تنظيف أسماء الأدوات في مرشحات زخرفة الخطاف
+  - إصلاح تسجيل معالجات نقاط التفتيش عند إنشاء CheckpointConfig
+  - رفع إصدار transformers إلى 5.5.0 لحل CVE-2026-1839
+  - إزالة غلاف FilteredStream لـ stdout/stderr
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.1rc1
+
+  ### إعادة الهيكلة
+  - استبدال القائمة المحظورة الثابتة باستبعاد حقل BaseTool الديناميكي في توليد المواصفات
+  - استبدال التعبير النمطي بـ tomlkit في واجهة سطر أوامر أدوات التطوير
+  - استخدام كائن PRINTER المشترك
+  - جعل BaseProvider نموذجاً أساسياً مع مميز نوع المزود
+
+  ## المساهمون
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="9 أبريل 2026">
+  ## v1.14.1rc1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة متصفح TUI لنقطة التحقق غير المتزامنة
+  - إضافة aclose()/close() ومدير سياق غير متزامن لمخرجات البث
+
+  ### إصلاحات الأخطاء
+  - إصلاح زيادة إصدارات pyproject.toml باستخدام التعبيرات العادية
+  - تنظيف أسماء الأدوات في مرشحات ديكور المكونات
+  - زيادة إصدار transformers إلى 5.5.0 لحل CVE-2026-1839
+  - تسجيل معالجات نقطة التحقق عند إنشاء CheckpointConfig
+
+  ### إعادة الهيكلة
+  - استبدال القائمة المحظورة الثابتة باستبعاد حقل BaseTool الديناميكي في توليد المواصفات
+  - استبدال التعبيرات العادية بـ tomlkit في واجهة سطر الأوامر devtools
+  - استخدام كائن PRINTER المشترك
+  - جعل BaseProvider نموذجًا أساسيًا مع مميز نوع المزود
+  - إزالة غلاف stdout/stderr لـ FilteredStream
+  - إزالة flow/config.py غير المستخدمة
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.0
+
+  ## المساهمون
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
 <Update label="7 أبريل 2026">
   ## v1.14.0
 

docs/ar/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ asyncio.run(interactive_research())
 - **تجربة المستخدم**: تقليل زمن الاستجابة المتصور بعرض نتائج تدريجية
 - **لوحات المعلومات الحية**: بناء واجهات مراقبة تعرض حالة تنفيذ الطاقم
 
+## الإلغاء وتنظيف الموارد
+
+يدعم `CrewStreamingOutput` الإلغاء السلس بحيث يتوقف العمل الجاري فوراً عند انقطاع اتصال المستهلك.
+
+### مدير السياق غير المتزامن
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### الإلغاء الصريح
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # غير متزامن
+    # streaming.close()       # المكافئ المتزامن
+```
+
+بعد الإلغاء، يكون كل من `streaming.is_cancelled` و `streaming.is_completed` بقيمة `True`. كل من `aclose()` و `close()` متساويان القوة.
+
 ## ملاحظات مهمة
 
 - يفعّل البث تلقائياً بث LLM لجميع الوكلاء في الطاقم

docs/ar/learn/streaming-flow-execution.mdx

@@ -420,6 +420,34 @@ except Exception as e:
         print("Streaming completed but flow encountered an error")
 ```
 
+## الإلغاء وتنظيف الموارد
+
+يدعم `FlowStreamingOutput` الإلغاء السلس بحيث يتوقف العمل الجاري فوراً عند انقطاع اتصال المستهلك.
+
+### مدير السياق غير المتزامن
+
+```python Code
+streaming = await flow.kickoff_async()
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### الإلغاء الصريح
+
+```python Code
+streaming = await flow.kickoff_async()
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # غير متزامن
+    # streaming.close()       # المكافئ المتزامن
+```
+
+بعد الإلغاء، يكون كل من `streaming.is_cancelled` و `streaming.is_completed` بقيمة `True`. كل من `aclose()` و `close()` متساويان القوة.
+
 ## ملاحظات مهمة
 
 - يفعّل البث تلقائياً بث LLM لأي أطقم مستخدمة داخل التدفق

docs/ar/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: ثبّت crewaiinc/skills من السجل الرسمي على skills.sh—Flows وCrews ووكلاء مرتبطون بالوثائق لـ Claude Code وCursor وCodex وغيرها.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**امنح وكيل البرمجة سياق CrewAI في أمر واحد.**
+
+تُنشر **Skills** الخاصة بـ CrewAI على **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**—السجل الرسمي لـ `crewaiinc/skills`، بما في ذلك كل مهارة (مثل **design-agent** و**getting-started** و**design-task** و**ask-docs**) وإحصاءات التثبيت والتدقيقات. تعلّم وكلاء البرمجة—مثل Claude Code وCursor وCodex—هيكلة Flows وضبط Crews واستخدام الأدوات واتباع أنماط CrewAI. نفّذ الأمر أدناه (أو الصقه في الوكيل).
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+يضيف ذلك حزمة المهارات إلى سير عمل الوكيل لتطبيق اتفاقيات CrewAI دون إعادة شرح الإطار في كل جلسة. المصدر والقضايا على [GitHub](https://github.com/crewAIInc/skills).
+
+## ما يحصل عليه الوكيل
+
+- **Flows** — تطبيقات ذات حالة وخطوات وkickoffs للـ crew على نمط CrewAI
+- **Crews والوكلاء** — أنماط YAML أولاً، أدوار، مهام، وتفويض
+- **الأدوات والتكاملات** — ربط الوكلاء بالبحث وواجهات API وأدوات CrewAI الشائعة
+- **هيكل المشروع** — مواءمة مع قوالب CLI واتفاقيات المستودع
+- **أنماط محدثة** — تتبع المهارات وثائق CrewAI والممارسات الموصى بها
+
+## تعرّف أكثر على هذا الموقع
+
+<CardGroup cols={2}>
+  <Card title="أدوات البرمجة و AGENTS.md" icon="terminal" href="/ar/guides/coding-tools/agents-md">
+    استخدام `AGENTS.md` وسير عمل وكلاء البرمجة مع CrewAI.
+  </Card>
+  <Card title="البداية السريعة" icon="rocket" href="/ar/quickstart">
+    ابنِ أول Flow وcrew من البداية للنهاية.
+  </Card>
+  <Card title="التثبيت" icon="download" href="/ar/installation">
+    ثبّت CrewAI CLI وحزمة Python.
+  </Card>
+  <Card title="سجل Skills (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    القائمة الرسمية لـ `crewaiinc/skills`—المهارات والتثبيتات والتدقيقات.
+  </Card>
+  <Card title="المصدر على GitHub" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    مصدر الحزمة والتحديثات والقضايا.
+  </Card>
+</CardGroup>
+
+### فيديو: CrewAI مع مهارات وكلاء البرمجة
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/ar/tools/database-data/nl2sqltool.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
 يتيح ذلك سير عمل متعددة مثل أن يقوم وكيل بالوصول إلى قاعدة البيانات واسترجاع المعلومات بناءً على الهدف ثم استخدام تلك المعلومات لتوليد استجابة أو تقرير أو أي مخرجات أخرى. بالإضافة إلى ذلك، يوفر القدرة للوكيل على تحديث قاعدة البيانات بناءً على هدفه.
 
-**تنبيه**: تأكد من أن الوكيل لديه وصول إلى نسخة قراءة فقط أو أنه من المقبول أن يقوم الوكيل بتنفيذ استعلامات إدراج/تحديث على قاعدة البيانات.
+**تنبيه**: الأداة للقراءة فقط بشكل افتراضي (SELECT/SHOW/DESCRIBE/EXPLAIN فقط). تتطلب عمليات الكتابة تمرير `allow_dml=True` أو ضبط متغير البيئة `CREWAI_NL2SQL_ALLOW_DML=true`. عند تفعيل الكتابة، تأكد من أن الوكيل يستخدم مستخدم قاعدة بيانات محدود الصلاحيات أو نسخة قراءة كلما أمكن.
 
 ## نموذج الأمان
 
@@ -36,6 +36,74 @@ mode: "wide"
 - أضف خطافات `before_tool_call` لفرض أنماط الاستعلام المسموح بها
 - فعّل تسجيل الاستعلامات والتنبيهات للعبارات التدميرية
 
+## وضع القراءة فقط وتهيئة DML
+
+تعمل `NL2SQLTool` في **وضع القراءة فقط بشكل افتراضي**. لا يُسمح إلا بأنواع العبارات التالية دون تهيئة إضافية:
+
+- `SELECT`
+- `SHOW`
+- `DESCRIBE`
+- `EXPLAIN`
+
+أي محاولة لتنفيذ عملية كتابة (`INSERT`، `UPDATE`، `DELETE`، `DROP`، `CREATE`، `ALTER`، `TRUNCATE`، إلخ) ستُسبب خطأً ما لم يتم تفعيل DML صراحةً.
+
+كما تُحظر الاستعلامات متعددة العبارات التي تحتوي على فاصلة منقوطة (مثل `SELECT 1; DROP TABLE users`) في وضع القراءة فقط لمنع هجمات الحقن.
+
+### تفعيل عمليات الكتابة
+
+يمكنك تفعيل DML (لغة معالجة البيانات) بطريقتين:
+
+**الخيار الأول — معامل المُنشئ:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+**الخيار الثاني — متغير البيئة:**
+
+```bash
+CREWAI_NL2SQL_ALLOW_DML=true
+```
+
+```python
+from crewai_tools import NL2SQLTool
+
+# DML مفعّل عبر متغير البيئة
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+### أمثلة الاستخدام
+
+**القراءة فقط (الافتراضي) — آمن للتحليلات والتقارير:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# يُسمح فقط بـ SELECT/SHOW/DESCRIBE/EXPLAIN
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+**مع تفعيل DML — مطلوب لأعباء عمل الكتابة:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# يُسمح بـ INSERT وUPDATE وDELETE وDROP وغيرها
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+<Warning>
+يمنح تفعيل DML للوكيل القدرة على تعديل البيانات أو حذفها. لا تفعّله إلا عندما يتطلب حالة الاستخدام صراحةً وصولاً للكتابة، وتأكد من أن بيانات اعتماد قاعدة البيانات محدودة بالحد الأدنى من الصلاحيات المطلوبة.
+</Warning>
+
 ## المتطلبات
 
 - SqlAlchemy

docs/en/changelog.mdx

@@ -4,6 +4,123 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Apr 10, 2026">
+  ## v1.14.2a2
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
+
+  ## What's Changed
+
+  ### Features
+  - Add checkpoint TUI with tree view, fork support, and editable inputs/outputs
+  - Enrich LLM token tracking with reasoning tokens and cache creation tokens
+  - Add `from_checkpoint` parameter to kickoff methods
+  - Embed `crewai_version` in checkpoints with migration framework
+  - Add checkpoint forking with lineage tracking
+
+  ### Bug Fixes
+  - Fix strict mode forwarding to Anthropic and Bedrock providers
+  - Harden NL2SQLTool with read-only default, query validation, and parameterized queries
+
+  ### Documentation
+  - Update changelog and version for v1.14.2a1
+
+  ## Contributors
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
+
+</Update>
+
+<Update label="Apr 09, 2026">
+  ## v1.14.2a1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## What's Changed
+
+  ### Bug Fixes
+  - Fix emission of flow_finished event after HITL resume
+  - Fix cryptography version to 46.0.7 to address CVE-2026-39892
+
+  ### Refactoring
+  - Refactor to use shared I18N_DEFAULT singleton
+
+  ### Documentation
+  - Update changelog and version for v1.14.1
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="Apr 09, 2026">
+  ## v1.14.1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## What's Changed
+
+  ### Features
+  - Add async checkpoint TUI browser
+  - Add aclose()/close() and async context manager to streaming outputs
+
+  ### Bug Fixes
+  - Fix regex for template pyproject.toml version bumps
+  - Sanitize tool names in hook decorator filters
+  - Fix checkpoint handlers registration when CheckpointConfig is created
+  - Bump transformers to 5.5.0 to resolve CVE-2026-1839
+  - Remove FilteredStream stdout/stderr wrapper
+
+  ### Documentation
+  - Update changelog and version for v1.14.1rc1
+
+  ### Refactoring
+  - Replace hardcoded denylist with dynamic BaseTool field exclusion in spec gen
+  - Replace regex with tomlkit in devtools CLI
+  - Use shared PRINTER singleton
+  - Make BaseProvider a BaseModel with provider_type discriminator
+
+  ## Contributors
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="Apr 09, 2026">
+  ## v1.14.1rc1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## What's Changed
+
+  ### Features
+  - Add async checkpoint TUI browser
+  - Add aclose()/close() and async context manager to streaming outputs
+
+  ### Bug Fixes
+  - Fix template pyproject.toml version bumps using regex
+  - Sanitize tool names in hook decorator filters
+  - Bump transformers to 5.5.0 to resolve CVE-2026-1839
+  - Register checkpoint handlers when CheckpointConfig is created
+
+  ### Refactoring
+  - Replace hardcoded denylist with dynamic BaseTool field exclusion in spec gen
+  - Replace regex with tomlkit in devtools CLI
+  - Use shared PRINTER singleton
+  - Make BaseProvider a BaseModel with provider_type discriminator
+  - Remove FilteredStream stdout/stderr wrapper
+  - Remove unused flow/config.py
+
+  ### Documentation
+  - Update changelog and version for v1.14.0
+
+  ## Contributors
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
 <Update label="Apr 07, 2026">
   ## v1.14.0
 

docs/en/concepts/checkpointing.mdx

@@ -54,6 +54,7 @@ crew = Crew(
 | `on_events` | `list[str]` | `["task_completed"]` | Event types that trigger a checkpoint |
 | `provider` | `BaseProvider` | `JsonProvider()` | Storage backend |
 | `max_checkpoints` | `int \| None` | `None` | Max checkpoints to keep. Oldest are pruned after each write. Pruning is handled by the provider. |
+| `restore_from` | `Path \| str \| None` | `None` | Path to a checkpoint to restore from. Used when passing config via a kickoff method's `from_checkpoint` parameter. |
 
 ### Inheritance and Opt-Out
 
@@ -79,13 +80,42 @@ crew = Crew(
 
 ## Resuming from a Checkpoint
 
+Pass a `CheckpointConfig` with `restore_from` to any kickoff method. The crew restores from that checkpoint, skips completed tasks, and resumes.
+
 ```python
-# Restore and resume
-crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
-result = crew.kickoff()  # picks up from last completed task
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(agents=[...], tasks=[...])
+result = crew.kickoff(
+    from_checkpoint=CheckpointConfig(
+        restore_from="./my_checkpoints/20260407T120000_abc123.json",
+    ),
+)
 ```
 
-The restored crew skips already-completed tasks and resumes from the first incomplete one.
+Remaining `CheckpointConfig` fields apply to the new run, so checkpointing continues after the restore.
+
+You can also use the classmethod directly:
+
+```python
+config = CheckpointConfig(restore_from="./my_checkpoints/20260407T120000_abc123.json")
+crew = Crew.from_checkpoint(config)
+result = crew.kickoff()
+```
+
+## Forking from a Checkpoint
+
+`fork()` restores a checkpoint and starts a new execution branch. Useful for exploring alternative paths from the same point.
+
+```python
+from crewai import Crew, CheckpointConfig
+
+config = CheckpointConfig(restore_from="./my_checkpoints/20260407T120000_abc123.json")
+crew = Crew.fork(config, branch="experiment-a")
+result = crew.kickoff(inputs={"strategy": "aggressive"})
+```
+
+Each fork gets a unique lineage ID so checkpoints from different branches don't collide. The `branch` label is optional and auto-generated if omitted.
 
 ## Works on Crew, Flow, and Agent
 
@@ -125,7 +155,8 @@ flow = MyFlow(
 result = flow.kickoff()
 
 # Resume
-flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
+config = CheckpointConfig(restore_from="./flow_cp/20260407T120000_abc123.json")
+flow = MyFlow.from_checkpoint(config)
 result = flow.kickoff()
 ```
 
@@ -231,3 +262,44 @@ async def on_llm_done_async(source, event, state):
 The `state` argument is the `RuntimeState` passed automatically by the event bus when your handler accepts 3 parameters. You can register handlers on any event type listed in the [Event Listeners](/en/concepts/event-listener) documentation.
 
 Checkpointing is best-effort: if a checkpoint write fails, the error is logged but execution continues uninterrupted.
+
+## CLI
+
+The `crewai checkpoint` command gives you a TUI for browsing, inspecting, resuming, and forking checkpoints. It auto-detects whether your checkpoints are JSON files or a SQLite database.
+
+```bash
+# Launch the TUI — auto-detects .checkpoints/ or .checkpoints.db
+crewai checkpoint
+
+# Point at a specific location
+crewai checkpoint --location ./my_checkpoints
+crewai checkpoint --location ./.checkpoints.db
+```
+
+<Frame>
+  <img src="/images/checkpointing.png" alt="Checkpoint TUI" />
+</Frame>
+
+The left panel is a tree view. Checkpoints are grouped by branch, and forks nest under the checkpoint they diverged from. Select a checkpoint to see its metadata, entity state, and task progress in the detail panel. Hit **Resume** to pick up where it left off, or **Fork** to start a new branch from that point.
+
+### Editing inputs and task outputs
+
+When a checkpoint is selected, the detail panel shows:
+
+- **Inputs** — if the original kickoff had inputs (e.g. `{topic}`), they appear as editable fields pre-filled with the original values. Change them before resuming or forking.
+- **Task outputs** — completed tasks show their output in editable text areas. Edit a task's output to change the context that downstream tasks receive. When you modify a task output and hit Fork, all subsequent tasks are invalidated and re-run with the new context.
+
+This is useful for "what if" exploration — fork from a checkpoint, tweak a task's result, and see how it changes downstream behavior.
+
+### Subcommands
+
+```bash
+# List all checkpoints
+crewai checkpoint list ./my_checkpoints
+
+# Inspect a specific checkpoint
+crewai checkpoint info ./my_checkpoints/20260407T120000_abc123.json
+
+# Inspect latest in a SQLite database
+crewai checkpoint info ./.checkpoints.db
+```

docs/en/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ Streaming is particularly valuable for:
 - **User Experience**: Reduce perceived latency by showing incremental results
 - **Live Dashboards**: Build monitoring interfaces that display crew execution status
 
+## Cancellation and Resource Cleanup
+
+`CrewStreamingOutput` supports graceful cancellation so that in-flight work stops promptly when the consumer disconnects.
+
+### Async Context Manager
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### Explicit Cancellation
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # async
+    # streaming.close()       # sync equivalent
+```
+
+After cancellation, `streaming.is_cancelled` and `streaming.is_completed` are both `True`. Both `aclose()` and `close()` are idempotent.
+
 ## Important Notes
 
 - Streaming automatically enables LLM streaming for all agents in the crew

docs/en/learn/streaming-flow-execution.mdx

@@ -420,6 +420,34 @@ except Exception as e:
         print("Streaming completed but flow encountered an error")
 ```
 
+## Cancellation and Resource Cleanup
+
+`FlowStreamingOutput` supports graceful cancellation so that in-flight work stops promptly when the consumer disconnects.
+
+### Async Context Manager
+
+```python Code
+streaming = await flow.kickoff_async()
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### Explicit Cancellation
+
+```python Code
+streaming = await flow.kickoff_async()
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # async
+    # streaming.close()       # sync equivalent
+```
+
+After cancellation, `streaming.is_cancelled` and `streaming.is_completed` are both `True`. Both `aclose()` and `close()` are idempotent.
+
 ## Important Notes
 
 - Streaming automatically enables LLM streaming for any crews used within the flow

docs/en/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: Install crewaiinc/skills from the official registry at skills.sh—Flows, Crews, and docs-aware agents for Claude Code, Cursor, Codex, and more.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**Give your AI coding agent CrewAI context in one command.**
+
+CrewAI **Skills** are published on **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**—the official registry for `crewaiinc/skills`, including individual skills (for example **design-agent**, **getting-started**, **design-task**, and **ask-docs**), install stats, and audits. They teach coding agents—like Claude Code, Cursor, and Codex—how to scaffold Flows, configure Crews, use tools, and follow CrewAI patterns. Run the install below (or paste it into your agent).
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+That pulls the official skill pack into your agent workflow so it can apply CrewAI conventions without you re-explaining the framework each session. Source code and issues live on [GitHub](https://github.com/crewAIInc/skills).
+
+## What your agent gets
+
+- **Flows** — structure stateful apps, steps, and crew kickoffs the CrewAI way
+- **Crews & agents** — YAML-first patterns, roles, tasks, and delegation
+- **Tools & integrations** — hook agents to search, APIs, and common CrewAI tools
+- **Project layout** — align with CLI scaffolds and repo conventions
+- **Up-to-date patterns** — skills track current CrewAI docs and recommended practices
+
+## Learn more on this site
+
+<CardGroup cols={2}>
+  <Card title="Coding tools & AGENTS.md" icon="terminal" href="/en/guides/coding-tools/agents-md">
+    How to use `AGENTS.md` and coding-agent workflows with CrewAI.
+  </Card>
+  <Card title="Quickstart" icon="rocket" href="/en/quickstart">
+    Build your first Flow and crew end-to-end.
+  </Card>
+  <Card title="Installation" icon="download" href="/en/installation">
+    Install the CrewAI CLI and Python package.
+  </Card>
+  <Card title="Skills registry (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    Official listing for `crewaiinc/skills`—skills, installs, and audits.
+  </Card>
+  <Card title="GitHub source" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    Source, updates, and issues for the skill pack.
+  </Card>
+</CardGroup>
+
+### Video: CrewAI with coding agent skills
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/en/tools/database-data/nl2sqltool.mdx

@@ -13,7 +13,7 @@ This tool is used to convert natural language to SQL queries. When passed to the
 This enables multiple workflows like having an Agent to access the database fetch information based on the goal and then use the information to generate a response, report or any other output. 
 Along with that provides the ability for the Agent to update the database based on its goal.
 
-**Attention**: Make sure that the Agent has access to a Read-Replica or that is okay for the Agent to run insert/update queries on the database.
+**Attention**: By default the tool is read-only (SELECT/SHOW/DESCRIBE/EXPLAIN only). Write operations require `allow_dml=True` or the `CREWAI_NL2SQL_ALLOW_DML=true` environment variable. When write access is enabled, make sure the Agent uses a scoped database user or a read replica where possible.
 
 ## Security Model
 
@@ -38,6 +38,74 @@ Use all of the following in production:
 - Add `before_tool_call` hooks to enforce allowed query patterns
 - Enable query logging and alerting for destructive statements
 
+## Read-Only Mode & DML Configuration
+
+`NL2SQLTool` operates in **read-only mode by default**. Only the following statement types are permitted without additional configuration:
+
+- `SELECT`
+- `SHOW`
+- `DESCRIBE`
+- `EXPLAIN`
+
+Any attempt to execute a write operation (`INSERT`, `UPDATE`, `DELETE`, `DROP`, `CREATE`, `ALTER`, `TRUNCATE`, etc.) will raise an error unless DML is explicitly enabled.
+
+Multi-statement queries containing semicolons (e.g. `SELECT 1; DROP TABLE users`) are also blocked in read-only mode to prevent injection attacks.
+
+### Enabling Write Operations
+
+You can enable DML (Data Manipulation Language) in two ways:
+
+**Option 1 — constructor parameter:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+**Option 2 — environment variable:**
+
+```bash
+CREWAI_NL2SQL_ALLOW_DML=true
+```
+
+```python
+from crewai_tools import NL2SQLTool
+
+# DML enabled via environment variable
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+### Usage Examples
+
+**Read-only (default) — safe for analytics and reporting:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# Only SELECT/SHOW/DESCRIBE/EXPLAIN are permitted
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+**DML enabled — required for write workloads:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# INSERT, UPDATE, DELETE, DROP, etc. are permitted
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+<Warning>
+Enabling DML gives the agent the ability to modify or destroy data. Only enable this when your use case explicitly requires write access, and ensure the database credentials are scoped to the minimum required privileges.
+</Warning>
+
 ## Requirements
 
 - SqlAlchemy

docs/ko/changelog.mdx

@@ -4,6 +4,123 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 4월 10일">
+  ## v1.14.2a2
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
+
+  ## 변경 사항
+
+  ### 기능
+  - 트리 뷰, 포크 지원 및 편집 가능한 입력/출력을 갖춘 체크포인트 TUI 추가
+  - 추론 토큰 및 캐시 생성 토큰으로 LLM 토큰 추적 강화
+  - 킥오프 메서드에 `from_checkpoint` 매개변수 추가
+  - 마이그레이션 프레임워크와 함께 체크포인트에 `crewai_version` 포함
+  - 계보 추적이 가능한 체크포인트 포킹 추가
+
+  ### 버그 수정
+  - Anthropic 및 Bedrock 공급자로의 엄격 모드 포워딩 수정
+  - 읽기 전용 기본값, 쿼리 검증 및 매개변수화된 쿼리로 NL2SQLTool 강화
+
+  ### 문서
+  - v1.14.2a1에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
+
+</Update>
+
+<Update label="2026년 4월 9일">
+  ## v1.14.2a1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## 변경 사항
+
+  ### 버그 수정
+  - HITL 재개 후 flow_finished 이벤트 방출 수정
+  - CVE-2026-39892 문제를 해결하기 위해 암호화 버전을 46.0.7로 수정
+
+  ### 리팩토링
+  - 공유 I18N_DEFAULT 싱글톤을 사용하도록 리팩토링
+
+  ### 문서
+  - v1.14.1에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="2026년 4월 9일">
+  ## v1.14.1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## 변경 사항
+
+  ### 기능
+  - 비동기 체크포인트 TUI 브라우저 추가
+  - 스트리밍 출력에 aclose()/close() 및 비동기 컨텍스트 관리자 추가
+
+  ### 버그 수정
+  - 템플릿 pyproject.toml 버전 증가를 위한 정규 표현식 수정
+  - 훅 데코레이터 필터에서 도구 이름 정리
+  - CheckpointConfig 생성 시 체크포인트 핸들러 등록 수정
+  - CVE-2026-1839 해결을 위해 transformers를 5.5.0으로 업데이트
+  - FilteredStream stdout/stderr 래퍼 제거
+
+  ### 문서
+  - v1.14.1rc1에 대한 변경 로그 및 버전 업데이트
+
+  ### 리팩토링
+  - 하드코딩된 거부 목록을 동적 BaseTool 필드 제외로 교체
+  - devtools CLI에서 정규 표현식을 tomlkit으로 교체
+  - 공유 PRINTER 싱글톤 사용
+  - BaseProvider를 provider_type 식별자가 있는 BaseModel로 변경
+
+  ## 기여자
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="2026년 4월 9일">
+  ## v1.14.1rc1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## 변경 사항
+
+  ### 기능
+  - 비동기 체크포인트 TUI 브라우저 추가
+  - 스트리밍 출력에 aclose()/close() 및 비동기 컨텍스트 관리자 추가
+
+  ### 버그 수정
+  - 정규 표현식을 사용하여 템플릿 pyproject.toml 버전 증가 수정
+  - 후크 데코레이터 필터에서 도구 이름 정리
+  - CVE-2026-1839 해결을 위해 transformers를 5.5.0으로 업데이트
+  - CheckpointConfig가 생성될 때 체크포인트 핸들러 등록
+
+  ### 리팩토링
+  - 하드코딩된 거부 목록을 동적 BaseTool 필드 제외로 교체
+  - devtools CLI에서 정규 표현식을 tomlkit으로 교체
+  - 공유 PRINTER 싱글톤 사용
+  - BaseProvider를 provider_type 구분자가 있는 BaseModel로 변경
+  - FilteredStream stdout/stderr 래퍼 제거
+  - 사용되지 않는 flow/config.py 제거
+
+  ### 문서
+  - v1.14.0에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
 <Update label="2026년 4월 7일">
   ## v1.14.0
 

docs/ko/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ asyncio.run(interactive_research())
 - **사용자 경험**: 점진적인 결과를 표시하여 체감 지연 시간 감소
 - **라이브 대시보드**: crew 실행 상태를 표시하는 모니터링 인터페이스 구축
 
+## 취소 및 리소스 정리
+
+`CrewStreamingOutput`은 소비자가 연결을 끊을 때 진행 중인 작업을 즉시 중단하는 정상적인 취소를 지원합니다.
+
+### 비동기 컨텍스트 매니저
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### 명시적 취소
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # 비동기
+    # streaming.close()       # 동기 버전
+```
+
+취소 후 `streaming.is_cancelled`와 `streaming.is_completed`는 모두 `True`입니다. `aclose()`와 `close()` 모두 멱등성을 가집니다.
+
 ## 중요 사항
 
 - 스트리밍은 crew의 모든 에이전트에 대해 자동으로 LLM 스트리밍을 활성화합니다

docs/ko/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: skills.sh의 공식 레지스트리에서 crewaiinc/skills를 설치하세요. Claude Code, Cursor, Codex 등을 위한 Flow, Crew, 문서 연동 스킬.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**한 번의 명령으로 코딩 에이전트에 CrewAI 컨텍스트를 제공하세요.**
+
+CrewAI **Skills**는 **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**에 게시됩니다. `crewaiinc/skills`의 공식 레지스트리로, 개별 스킬(예: **design-agent**, **getting-started**, **design-task**, **ask-docs**), 설치 수, 감사 정보를 확인할 수 있습니다. Claude Code, Cursor, Codex 같은 코딩 에이전트에게 Flow 구성, Crew 설정, 도구 사용, CrewAI 패턴을 가르칩니다. 아래를 실행하거나 에이전트에 붙여 넣으세요.
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+에이전트 워크플로에 스킬 팩이 추가되어 세션마다 프레임워크를 다시 설명하지 않아도 CrewAI 관례를 적용할 수 있습니다. 소스와 이슈는 [GitHub](https://github.com/crewAIInc/skills)에서 관리합니다.
+
+## 에이전트가 얻는 것
+
+- **Flows** — CrewAI 방식의 상태ful 앱, 단계, crew kickoff
+- **Crew & 에이전트** — YAML 우선 패턴, 역할, 작업, 위임
+- **도구 & 통합** — 검색, API, 일반적인 CrewAI 도구 연결
+- **프로젝트 구조** — CLI 스캐폴드 및 저장소 관례와 정렬
+- **최신 패턴** — 스킬이 현재 CrewAI 문서 및 권장 사항을 반영
+
+## 이 사이트에서 더 알아보기
+
+<CardGroup cols={2}>
+  <Card title="코딩 도구 & AGENTS.md" icon="terminal" href="/ko/guides/coding-tools/agents-md">
+    CrewAI와 `AGENTS.md`, 코딩 에이전트 워크플로 사용법.
+  </Card>
+  <Card title="빠른 시작" icon="rocket" href="/ko/quickstart">
+    첫 Flow와 crew를 처음부터 끝까지 구축합니다.
+  </Card>
+  <Card title="설치" icon="download" href="/ko/installation">
+    CrewAI CLI와 Python 패키지를 설치합니다.
+  </Card>
+  <Card title="Skills 레지스트리 (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    `crewaiinc/skills` 공식 목록—스킬, 설치 수, 감사.
+  </Card>
+  <Card title="GitHub 소스" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    스킬 팩 소스, 업데이트, 이슈.
+  </Card>
+</CardGroup>
+
+### 영상: 코딩 에이전트 스킬과 CrewAI
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/ko/tools/database-data/nl2sqltool.mdx

@@ -11,7 +11,75 @@ mode: "wide"
 
 이를 통해 에이전트가 데이터베이스에 접근하여 목표에 따라 정보를 가져오고, 해당 정보를 사용해 응답, 보고서 또는 기타 출력물을 생성하는 다양한 워크플로우가 가능해집니다. 또한 에이전트가 자신의 목표에 맞춰 데이터베이스를 업데이트할 수 있는 기능도 제공합니다.
 
-**주의**: 에이전트가 Read-Replica에 접근할 수 있거나, 에이전트가 데이터베이스에 insert/update 쿼리를 실행해도 괜찮은지 반드시 확인하십시오.
+**주의**: 도구는 기본적으로 읽기 전용(SELECT/SHOW/DESCRIBE/EXPLAIN만 허용)으로 동작합니다. 쓰기 작업을 수행하려면 `allow_dml=True` 매개변수 또는 `CREWAI_NL2SQL_ALLOW_DML=true` 환경 변수가 필요합니다. 쓰기 접근이 활성화된 경우, 가능하면 권한이 제한된 데이터베이스 사용자나 읽기 복제본을 사용하십시오.
+
+## 읽기 전용 모드 및 DML 구성
+
+`NL2SQLTool`은 기본적으로 **읽기 전용 모드**로 동작합니다. 추가 구성 없이 허용되는 구문 유형은 다음과 같습니다:
+
+- `SELECT`
+- `SHOW`
+- `DESCRIBE`
+- `EXPLAIN`
+
+DML을 명시적으로 활성화하지 않으면 쓰기 작업(`INSERT`, `UPDATE`, `DELETE`, `DROP`, `CREATE`, `ALTER`, `TRUNCATE` 등)을 실행하려고 할 때 오류가 발생합니다.
+
+읽기 전용 모드에서는 세미콜론이 포함된 다중 구문 쿼리(예: `SELECT 1; DROP TABLE users`)도 인젝션 공격을 방지하기 위해 차단됩니다.
+
+### 쓰기 작업 활성화
+
+DML(데이터 조작 언어)을 활성화하는 방법은 두 가지입니다:
+
+**옵션 1 — 생성자 매개변수:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+**옵션 2 — 환경 변수:**
+
+```bash
+CREWAI_NL2SQL_ALLOW_DML=true
+```
+
+```python
+from crewai_tools import NL2SQLTool
+
+# 환경 변수를 통해 DML 활성화
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+### 사용 예시
+
+**읽기 전용(기본값) — 분석 및 보고 워크로드에 안전:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# SELECT/SHOW/DESCRIBE/EXPLAIN만 허용
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+**DML 활성화 — 쓰기 워크로드에 필요:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# INSERT, UPDATE, DELETE, DROP 등이 허용됨
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+<Warning>
+DML을 활성화하면 에이전트가 데이터를 수정하거나 삭제할 수 있습니다. 사용 사례에서 명시적으로 쓰기 접근이 필요한 경우에만 활성화하고, 데이터베이스 자격 증명이 최소 필요 권한으로 제한되어 있는지 확인하십시오.
+</Warning>
 
 ## 요구 사항
 

docs/pt-BR/changelog.mdx

@@ -4,6 +4,123 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="10 abr 2026">
+  ## v1.14.2a2
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar TUI de ponto de verificação com visualização em árvore, suporte a bifurcações e entradas/saídas editáveis
+  - Enriquecer o rastreamento de tokens LLM com tokens de raciocínio e tokens de criação de cache
+  - Adicionar parâmetro `from_checkpoint` aos métodos de inicialização
+  - Incorporar `crewai_version` em pontos de verificação com o framework de migração
+  - Adicionar bifurcação de ponto de verificação com rastreamento de linhagem
+
+  ### Correções de Bugs
+  - Corrigir o encaminhamento em modo estrito para os provedores Anthropic e Bedrock
+  - Fortalecer NL2SQLTool com padrão somente leitura, validação de consultas e consultas parametrizadas
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.2a1
+
+  ## Contributors
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
+
+</Update>
+
+<Update label="09 abr 2026">
+  ## v1.14.2a1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## O que Mudou
+
+  ### Correções de Bugs
+  - Corrigir a emissão do evento flow_finished após a retomada do HITL
+  - Corrigir a versão da criptografia para 46.0.7 para resolver o CVE-2026-39892
+
+  ### Refatoração
+  - Refatorar para usar o singleton I18N_DEFAULT compartilhado
+
+  ### Documentação
+  - Atualizar o changelog e a versão para v1.14.1
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="09 abr 2026">
+  ## v1.14.1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar navegador TUI de ponto de verificação assíncrono
+  - Adicionar aclose()/close() e gerenciador de contexto assíncrono para saídas de streaming
+
+  ### Correções de Bugs
+  - Corrigir regex para aumentos de versão do template pyproject.toml
+  - Sanitizar nomes de ferramentas nos filtros do decorador de hook
+  - Corrigir registro de manipuladores de ponto de verificação quando CheckpointConfig é criado
+  - Atualizar transformers para 5.5.0 para resolver CVE-2026-1839
+  - Remover wrapper stdout/stderr de FilteredStream
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.1rc1
+
+  ### Refatoração
+  - Substituir lista de negação codificada por exclusão dinâmica de campo BaseTool na geração de especificações
+  - Substituir regex por tomlkit na CLI do devtools
+  - Usar singleton PRINTER compartilhado
+  - Fazer BaseProvider um BaseModel com discriminador provider_type
+
+  ## Contribuidores
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="09 abr 2026">
+  ## v1.14.1rc1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar navegador TUI de ponto de verificação assíncrono
+  - Adicionar aclose()/close() e gerenciador de contexto assíncrono para saídas de streaming
+
+  ### Correções de Bugs
+  - Corrigir aumentos de versão do template pyproject.toml usando regex
+  - Sanitizar nomes de ferramentas nos filtros do decorador de hook
+  - Atualizar transformers para 5.5.0 para resolver CVE-2026-1839
+  - Registrar manipuladores de ponto de verificação quando CheckpointConfig é criado
+
+  ### Refatoração
+  - Substituir lista de negação codificada por exclusão dinâmica de campo BaseTool na geração de especificações
+  - Substituir regex por tomlkit na CLI do devtools
+  - Usar singleton PRINTER compartilhado
+  - Tornar BaseProvider um BaseModel com discriminador de tipo de provedor
+  - Remover wrapper stdout/stderr de FilteredStream
+  - Remover flow/config.py não utilizado
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.0
+
+  ## Contribuidores
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
 <Update label="07 abr 2026">
   ## v1.14.0
 

docs/pt-BR/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ O streaming é particularmente valioso para:
 - **Experiência do Usuário**: Reduzir latência percebida mostrando resultados incrementais
 - **Dashboards ao Vivo**: Construir interfaces de monitoramento que exibem status de execução da crew
 
+## Cancelamento e Limpeza de Recursos
+
+`CrewStreamingOutput` suporta cancelamento gracioso para que o trabalho em andamento pare imediatamente quando o consumidor desconecta.
+
+### Gerenciador de Contexto Assíncrono
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### Cancelamento Explícito
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # assíncrono
+    # streaming.close()       # equivalente síncrono
+```
+
+Após o cancelamento, `streaming.is_cancelled` e `streaming.is_completed` são ambos `True`. Tanto `aclose()` quanto `close()` são idempotentes.
+
 ## Notas Importantes
 
 - O streaming ativa automaticamente o streaming do LLM para todos os agentes na crew

docs/pt-BR/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: Instale crewaiinc/skills pelo registro oficial em skills.sh—Flows, Crews e agentes alinhados à documentação para Claude Code, Cursor, Codex e outros.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**Dê ao seu agente de código o contexto do CrewAI em um comando.**
+
+As **Skills** do CrewAI são publicadas em **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**—o registro oficial de `crewaiinc/skills`, com cada skill (por exemplo **design-agent**, **getting-started**, **design-task** e **ask-docs**), estatísticas de instalação e auditorias. Ensinam agentes de código—como Claude Code, Cursor e Codex—a estruturar Flows, configurar Crews, usar ferramentas e seguir os padrões do CrewAI. Execute o comando abaixo (ou cole no seu agente).
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+Isso adiciona o pacote de skills ao fluxo do seu agente para aplicar convenções do CrewAI sem precisar reexplicar o framework a cada sessão. Código-fonte e issues ficam no [GitHub](https://github.com/crewAIInc/skills).
+
+## O que seu agente ganha
+
+- **Flows** — apps com estado, passos e kickoffs de crew no estilo CrewAI
+- **Crews e agentes** — padrões YAML-first, papéis, tarefas e delegação
+- **Ferramentas e integrações** — conectar agentes a busca, APIs e ferramentas comuns
+- **Layout de projeto** — alinhar com scaffolds da CLI e convenções do repositório
+- **Padrões atualizados** — skills acompanham a documentação e as práticas recomendadas
+
+## Saiba mais neste site
+
+<CardGroup cols={2}>
+  <Card title="Ferramentas de codificação e AGENTS.md" icon="terminal" href="/pt-BR/guides/coding-tools/agents-md">
+    Como usar `AGENTS.md` e fluxos de agente de código com o CrewAI.
+  </Card>
+  <Card title="Início rápido" icon="rocket" href="/pt-BR/quickstart">
+    Construa seu primeiro Flow e crew ponta a ponta.
+  </Card>
+  <Card title="Instalação" icon="download" href="/pt-BR/installation">
+    Instale a CLI e o pacote Python do CrewAI.
+  </Card>
+  <Card title="Registro de skills (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    Listagem oficial de `crewaiinc/skills`—skills, instalações e auditorias.
+  </Card>
+  <Card title="Código no GitHub" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    Fonte, atualizações e issues do pacote de skills.
+  </Card>
+</CardGroup>
+
+### Vídeo: CrewAI com coding agent skills
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/pt-BR/tools/database-data/nl2sqltool.mdx

@@ -11,7 +11,75 @@ Esta ferramenta é utilizada para converter linguagem natural em consultas SQL.
 
 Isso possibilita múltiplos fluxos de trabalho, como por exemplo ter um Agente acessando o banco de dados para buscar informações com base em um objetivo e, então, usar essas informações para gerar uma resposta, relatório ou qualquer outro tipo de saída. Além disso, permite que o Agente atualize o banco de dados de acordo com seu objetivo.
 
-**Atenção**: Certifique-se de que o Agente tenha acesso a um Read-Replica ou que seja permitido que o Agente execute consultas de inserção/atualização no banco de dados.
+**Atenção**: Por padrão, a ferramenta opera em modo somente leitura (apenas SELECT/SHOW/DESCRIBE/EXPLAIN). Operações de escrita exigem `allow_dml=True` ou a variável de ambiente `CREWAI_NL2SQL_ALLOW_DML=true`. Quando o acesso de escrita estiver habilitado, certifique-se de que o Agente use um usuário de banco de dados com privilégios mínimos ou um Read-Replica sempre que possível.
+
+## Modo Somente Leitura e Configuração de DML
+
+O `NL2SQLTool` opera em **modo somente leitura por padrão**. Apenas os seguintes tipos de instrução são permitidos sem configuração adicional:
+
+- `SELECT`
+- `SHOW`
+- `DESCRIBE`
+- `EXPLAIN`
+
+Qualquer tentativa de executar uma operação de escrita (`INSERT`, `UPDATE`, `DELETE`, `DROP`, `CREATE`, `ALTER`, `TRUNCATE`, etc.) resultará em erro, a menos que o DML seja habilitado explicitamente.
+
+Consultas com múltiplas instruções contendo ponto e vírgula (ex.: `SELECT 1; DROP TABLE users`) também são bloqueadas no modo somente leitura para prevenir ataques de injeção.
+
+### Habilitando Operações de Escrita
+
+Você pode habilitar DML (Linguagem de Manipulação de Dados) de duas formas:
+
+**Opção 1 — parâmetro do construtor:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+**Opção 2 — variável de ambiente:**
+
+```bash
+CREWAI_NL2SQL_ALLOW_DML=true
+```
+
+```python
+from crewai_tools import NL2SQLTool
+
+# DML habilitado via variável de ambiente
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+### Exemplos de Uso
+
+**Somente leitura (padrão) — seguro para análise e relatórios:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# Apenas SELECT/SHOW/DESCRIBE/EXPLAIN são permitidos
+nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")
+```
+
+**Com DML habilitado — necessário para workloads de escrita:**
+
+```python
+from crewai_tools import NL2SQLTool
+
+# INSERT, UPDATE, DELETE, DROP, etc. são permitidos
+nl2sql = NL2SQLTool(
+    db_uri="postgresql://example@localhost:5432/test_db",
+    allow_dml=True,
+)
+```
+
+<Warning>
+Habilitar DML concede ao agente a capacidade de modificar ou destruir dados. Ative apenas quando o seu caso de uso exigir explicitamente acesso de escrita e certifique-se de que as credenciais do banco de dados estejam limitadas aos privilégios mínimos necessários.
+</Warning>
 
 ## Requisitos
 

lib/crewai-files/pyproject.toml

@@ -9,7 +9,7 @@ authors = [
 requires-python = ">=3.10, <3.14"
 dependencies = [
     "Pillow~=12.1.1",
-    "pypdf~=6.9.1",
+    "pypdf~=6.10.0",
     "python-magic>=0.4.27",
     "aiocache~=0.12.3",
     "aiofiles~=24.1.0",

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.14.0"
+__version__ = "1.14.2a2"

lib/crewai-tools/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
-    "crewai==1.14.0",
+    "crewai==1.14.2a2",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

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

@@ -305,4 +305,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.14.0"
+__version__ = "1.14.2a2"

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

@@ -154,21 +154,19 @@ class ToolSpecExtractor:
 
         return default_value
 
+    # Dynamically computed from BaseTool so that any future fields or
+    # computed_fields added to BaseTool are automatically excluded from
+    # the generated spec — no hardcoded denylist to maintain.
+    # ``package_dependencies`` is not a BaseTool field but is extracted
+    # into its own top-level key, so it's also excluded from init_params.
+    _BASE_TOOL_FIELDS: set[str] = (
+        set(BaseTool.model_fields)
+        | set(BaseTool.model_computed_fields)
+        | {"package_dependencies"}
+    )
+
     @staticmethod
     def _extract_init_params(tool_class: type[BaseTool]) -> dict[str, Any]:
-        ignored_init_params = [
-            "name",
-            "description",
-            "env_vars",
-            "args_schema",
-            "description_updated",
-            "cache_function",
-            "result_as_answer",
-            "max_usage_count",
-            "current_usage_count",
-            "package_dependencies",
-        ]
-
         json_schema = tool_class.model_json_schema(
             schema_generator=SchemaGenerator, mode="serialization"
         )
@@ -176,8 +174,14 @@ class ToolSpecExtractor:
         json_schema["properties"] = {
             key: value
             for key, value in json_schema["properties"].items()
-            if key not in ignored_init_params
+            if key not in ToolSpecExtractor._BASE_TOOL_FIELDS
         }
+        if "required" in json_schema:
+            json_schema["required"] = [
+                key
+                for key in json_schema["required"]
+                if key not in ToolSpecExtractor._BASE_TOOL_FIELDS
+            ]
         return json_schema
 
     def save_to_json(self, output_path: str) -> None:

lib/crewai-tools/src/crewai_tools/tools/nl2sql/nl2sql_tool.py

@@ -1,7 +1,17 @@
+from collections.abc import Iterator
+import logging
+import os
+import re
 from typing import Any
 
+
+try:
+    from typing import Self
+except ImportError:
+    from typing_extensions import Self
+
 from crewai.tools import BaseTool
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 
 try:
@@ -12,6 +22,186 @@ try:
 except ImportError:
     SQLALCHEMY_AVAILABLE = False
 
+logger = logging.getLogger(__name__)
+
+# Commands allowed in read-only mode
+# NOTE: WITH is intentionally excluded — writable CTEs start with WITH, so the
+# CTE body must be inspected separately (see _validate_statement).
+_READ_ONLY_COMMANDS = {"SELECT", "SHOW", "DESCRIBE", "DESC", "EXPLAIN"}
+
+# Commands that mutate state and are blocked by default
+_WRITE_COMMANDS = {
+    "INSERT",
+    "UPDATE",
+    "DELETE",
+    "DROP",
+    "ALTER",
+    "CREATE",
+    "TRUNCATE",
+    "GRANT",
+    "REVOKE",
+    "EXEC",
+    "EXECUTE",
+    "CALL",
+    "MERGE",
+    "REPLACE",
+    "UPSERT",
+    "LOAD",
+    "COPY",
+    "VACUUM",
+    "ANALYZE",
+    "ANALYSE",
+    "REINDEX",
+    "CLUSTER",
+    "REFRESH",
+    "COMMENT",
+    "SET",
+    "RESET",
+}
+
+
+# Subset of write commands that can realistically appear *inside* a CTE body.
+# Narrower than _WRITE_COMMANDS to avoid false positives on identifiers like
+# ``comment``, ``set``, or ``reset`` which are common column/table names.
+_CTE_WRITE_INDICATORS = {
+    "INSERT",
+    "UPDATE",
+    "DELETE",
+    "DROP",
+    "ALTER",
+    "CREATE",
+    "TRUNCATE",
+    "MERGE",
+}
+
+
+_AS_PAREN_RE = re.compile(r"\bAS\s*\(", re.IGNORECASE)
+
+
+def _iter_as_paren_matches(stmt: str) -> Iterator[re.Match[str]]:
+    """Yield regex matches for ``AS\\s*(`` outside of string literals."""
+    # Build a set of character positions that are inside string literals.
+    in_string: set[int] = set()
+    i = 0
+    while i < len(stmt):
+        if stmt[i] == "'":
+            start = i
+            end = _skip_string_literal(stmt, i)
+            in_string.update(range(start, end))
+            i = end
+        else:
+            i += 1
+
+    for m in _AS_PAREN_RE.finditer(stmt):
+        if m.start() not in in_string:
+            yield m
+
+
+def _detect_writable_cte(stmt: str) -> str | None:
+    """Return the first write command inside a CTE body, or None.
+
+    Instead of tokenizing the whole statement (which falsely matches column
+    names like ``comment``), this walks through parenthesized CTE bodies and
+    checks only the *first keyword after* an opening ``AS (`` for a write
+    command.  Uses a regex to handle any whitespace (spaces, tabs, newlines)
+    between ``AS`` and ``(``.  Skips matches inside string literals.
+    """
+    for m in _iter_as_paren_matches(stmt):
+        body = stmt[m.end() :].lstrip()
+        first_word = body.split()[0].upper().strip("()") if body.split() else ""
+        if first_word in _CTE_WRITE_INDICATORS:
+            return first_word
+    return None
+
+
+def _skip_string_literal(stmt: str, pos: int) -> int:
+    """Skip past a string literal starting at pos (single-quoted).
+
+    Handles escaped quotes ('') inside the literal.
+    Returns the index after the closing quote.
+    """
+    quote_char = stmt[pos]
+    i = pos + 1
+    while i < len(stmt):
+        if stmt[i] == quote_char:
+            # Check for escaped quote ('')
+            if i + 1 < len(stmt) and stmt[i + 1] == quote_char:
+                i += 2
+                continue
+            return i + 1
+        i += 1
+    return i  # Unterminated literal — return end
+
+
+def _find_matching_close_paren(stmt: str, start: int) -> int:
+    """Find the matching close paren, skipping string literals."""
+    depth = 1
+    i = start
+    while i < len(stmt) and depth > 0:
+        ch = stmt[i]
+        if ch == "'":
+            i = _skip_string_literal(stmt, i)
+            continue
+        if ch == "(":
+            depth += 1
+        elif ch == ")":
+            depth -= 1
+        i += 1
+    return i
+
+
+def _extract_main_query_after_cte(stmt: str) -> str | None:
+    """Extract the main (outer) query that follows all CTE definitions.
+
+    For ``WITH cte AS (SELECT 1) DELETE FROM users``, returns ``DELETE FROM users``.
+    Returns None if no main query is found after the last CTE body.
+    Handles parentheses inside string literals (e.g., ``SELECT '(' FROM t``).
+    """
+    last_cte_end = 0
+    for m in _iter_as_paren_matches(stmt):
+        last_cte_end = _find_matching_close_paren(stmt, m.end())
+
+    if last_cte_end > 0:
+        remainder = stmt[last_cte_end:].strip().lstrip(",").strip()
+        if remainder:
+            return remainder
+    return None
+
+
+def _resolve_explain_command(stmt: str) -> str | None:
+    """Resolve the underlying command from an EXPLAIN [ANALYZE] [VERBOSE] statement.
+
+    Returns the real command (e.g., 'DELETE') if ANALYZE is present, else None.
+    Handles both space-separated and parenthesized syntax.
+    """
+    rest = stmt.strip()[len("EXPLAIN") :].strip()
+    if not rest:
+        return None
+
+    analyze_found = False
+    explain_opts = {"ANALYZE", "ANALYSE", "VERBOSE"}
+
+    if rest.startswith("("):
+        close = rest.find(")")
+        if close != -1:
+            options_str = rest[1:close].upper()
+            analyze_found = any(
+                opt.strip() in ("ANALYZE", "ANALYSE") for opt in options_str.split(",")
+            )
+            rest = rest[close + 1 :].strip()
+    else:
+        while rest:
+            first_opt = rest.split()[0].upper().rstrip(";") if rest.split() else ""
+            if first_opt in ("ANALYZE", "ANALYSE"):
+                analyze_found = True
+            if first_opt not in explain_opts:
+                break
+            rest = rest[len(first_opt) :].strip()
+
+    if analyze_found and rest:
+        return rest.split()[0].upper().rstrip(";")
+    return None
+
 
 class NL2SQLToolInput(BaseModel):
     sql_query: str = Field(
@@ -21,20 +211,70 @@ class NL2SQLToolInput(BaseModel):
 
 
 class NL2SQLTool(BaseTool):
+    """Tool that converts natural language to SQL and executes it against a database.
+
+    By default the tool operates in **read-only mode**: only SELECT, SHOW,
+    DESCRIBE, EXPLAIN, and read-only CTEs (WITH … SELECT) are permitted.  Write
+    operations (INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, TRUNCATE, …) are
+    blocked unless ``allow_dml=True`` is set explicitly or the environment
+    variable ``CREWAI_NL2SQL_ALLOW_DML=true`` is present.
+
+    Writable CTEs (``WITH d AS (DELETE …) SELECT …``) and
+    ``EXPLAIN ANALYZE <write-stmt>`` are treated as write operations and are
+    blocked in read-only mode.
+
+    The ``_fetch_all_available_columns`` helper uses parameterised queries so
+    that table names coming from the database catalogue cannot be used as an
+    injection vector.
+    """
+
     name: str = "NL2SQLTool"
-    description: str = "Converts natural language to SQL queries and executes them."
+    description: str = (
+        "Converts natural language to SQL queries and executes them against a "
+        "database. Read-only by default — only SELECT/SHOW/DESCRIBE/EXPLAIN "
+        "queries (and read-only CTEs) are allowed unless configured with "
+        "allow_dml=True."
+    )
     db_uri: str = Field(
         title="Database URI",
         description="The URI of the database to connect to.",
     )
+    allow_dml: bool = Field(
+        default=False,
+        title="Allow DML",
+        description=(
+            "When False (default) only read statements are permitted. "
+            "Set to True to allow INSERT/UPDATE/DELETE/DROP and other "
+            "write operations."
+        ),
+    )
     tables: list[dict[str, Any]] = Field(default_factory=list)
     columns: dict[str, list[dict[str, Any]] | str] = Field(default_factory=dict)
     args_schema: type[BaseModel] = NL2SQLToolInput
 
+    @model_validator(mode="after")
+    def _apply_env_override(self) -> Self:
+        """Allow CREWAI_NL2SQL_ALLOW_DML=true to override allow_dml at runtime."""
+        if os.environ.get("CREWAI_NL2SQL_ALLOW_DML", "").strip().lower() == "true":
+            if not self.allow_dml:
+                logger.warning(
+                    "NL2SQLTool: CREWAI_NL2SQL_ALLOW_DML env var is set — "
+                    "DML/DDL operations are enabled. Ensure this is intentional."
+                )
+            self.allow_dml = True
+        return self
+
     def model_post_init(self, __context: Any) -> None:
         if not SQLALCHEMY_AVAILABLE:
             raise ImportError(
-                "sqlalchemy is not installed. Please install it with `pip install crewai-tools[sqlalchemy]`"
+                "sqlalchemy is not installed. Please install it with "
+                "`pip install crewai-tools[sqlalchemy]`"
+            )
+
+        if self.allow_dml:
+            logger.warning(
+                "NL2SQLTool: allow_dml=True — write operations (INSERT/UPDATE/"
+                "DELETE/DROP/…) are permitted. Use with caution."
             )
 
         data: dict[str, list[dict[str, Any]] | str] = {}
@@ -50,42 +290,216 @@ class NL2SQLTool(BaseTool):
         self.tables = tables
         self.columns = data
 
+    # ------------------------------------------------------------------
+    # Query validation
+    # ------------------------------------------------------------------
+
+    def _validate_query(self, sql_query: str) -> None:
+        """Raise ValueError if *sql_query* is not permitted under the current config.
+
+        Splits the query on semicolons and validates each statement
+        independently.  When ``allow_dml=False`` (the default), multi-statement
+        queries are rejected outright to prevent ``SELECT 1; DROP TABLE users``
+        style bypasses.  When ``allow_dml=True`` every statement is checked and
+        a warning is emitted for write operations.
+        """
+        statements = [s.strip() for s in sql_query.split(";") if s.strip()]
+
+        if not statements:
+            raise ValueError("NL2SQLTool received an empty SQL query.")
+
+        if not self.allow_dml and len(statements) > 1:
+            raise ValueError(
+                "NL2SQLTool blocked a multi-statement query in read-only mode. "
+                "Semicolons are not permitted when allow_dml=False."
+            )
+
+        for stmt in statements:
+            self._validate_statement(stmt)
+
+    def _validate_statement(self, stmt: str) -> None:
+        """Validate a single SQL statement (no semicolons)."""
+        command = self._extract_command(stmt)
+
+        # EXPLAIN ANALYZE / EXPLAIN ANALYSE actually *executes* the underlying
+        # query.  Resolve the real command so write operations are caught.
+        # Handles both space-separated ("EXPLAIN ANALYZE DELETE …") and
+        # parenthesized ("EXPLAIN (ANALYZE) DELETE …", "EXPLAIN (ANALYZE, VERBOSE) DELETE …").
+        # EXPLAIN ANALYZE actually executes the underlying query — resolve the
+        # real command so write operations are caught.
+        if command == "EXPLAIN":
+            resolved = _resolve_explain_command(stmt)
+            if resolved:
+                command = resolved
+
+        # WITH starts a CTE.  Read-only CTEs are fine; writable CTEs
+        # (e.g. WITH d AS (DELETE …) SELECT …) must be blocked in read-only mode.
+        if command == "WITH":
+            # Check for write commands inside CTE bodies.
+            write_found = _detect_writable_cte(stmt)
+            if write_found:
+                found = write_found
+                if not self.allow_dml:
+                    raise ValueError(
+                        f"NL2SQLTool is configured in read-only mode and blocked a "
+                        f"writable CTE containing a '{found}' statement. To allow "
+                        f"write operations set allow_dml=True or "
+                        f"CREWAI_NL2SQL_ALLOW_DML=true."
+                    )
+                logger.warning(
+                    "NL2SQLTool: executing writable CTE with '%s' because allow_dml=True.",
+                    found,
+                )
+                return
+
+            # Check the main query after the CTE definitions.
+            main_query = _extract_main_query_after_cte(stmt)
+            if main_query:
+                main_cmd = main_query.split()[0].upper().rstrip(";")
+                if main_cmd in _WRITE_COMMANDS:
+                    if not self.allow_dml:
+                        raise ValueError(
+                            f"NL2SQLTool is configured in read-only mode and blocked a "
+                            f"'{main_cmd}' statement after a CTE. To allow write "
+                            f"operations set allow_dml=True or "
+                            f"CREWAI_NL2SQL_ALLOW_DML=true."
+                        )
+                    logger.warning(
+                        "NL2SQLTool: executing '%s' after CTE because allow_dml=True.",
+                        main_cmd,
+                    )
+                elif main_cmd not in _READ_ONLY_COMMANDS:
+                    if not self.allow_dml:
+                        raise ValueError(
+                            f"NL2SQLTool blocked an unrecognised SQL command '{main_cmd}' "
+                            f"after a CTE. Only {sorted(_READ_ONLY_COMMANDS)} are allowed "
+                            f"in read-only mode."
+                        )
+            return
+
+        if command in _WRITE_COMMANDS:
+            if not self.allow_dml:
+                raise ValueError(
+                    f"NL2SQLTool is configured in read-only mode and blocked a "
+                    f"'{command}' statement. To allow write operations set "
+                    f"allow_dml=True or CREWAI_NL2SQL_ALLOW_DML=true."
+                )
+            logger.warning(
+                "NL2SQLTool: executing write statement '%s' because allow_dml=True.",
+                command,
+            )
+        elif command not in _READ_ONLY_COMMANDS:
+            # Unknown command — block by default unless DML is explicitly enabled
+            if not self.allow_dml:
+                raise ValueError(
+                    f"NL2SQLTool blocked an unrecognised SQL command '{command}'. "
+                    f"Only {sorted(_READ_ONLY_COMMANDS)} are allowed in read-only "
+                    f"mode."
+                )
+
+    @staticmethod
+    def _extract_command(sql_query: str) -> str:
+        """Return the uppercased first keyword of *sql_query*."""
+        stripped = sql_query.strip().lstrip("(")
+        first_token = stripped.split()[0] if stripped.split() else ""
+        return first_token.upper().rstrip(";")
+
+    # ------------------------------------------------------------------
+    # Schema introspection helpers
+    # ------------------------------------------------------------------
+
     def _fetch_available_tables(self) -> list[dict[str, Any]] | str:
         return self.execute_sql(
-            "SELECT table_name FROM information_schema.tables WHERE table_schema = 'public';"
+            "SELECT table_name FROM information_schema.tables "
+            "WHERE table_schema = 'public';"
         )
 
     def _fetch_all_available_columns(
         self, table_name: str
     ) -> list[dict[str, Any]] | str:
+        """Fetch columns for *table_name* using a parameterised query.
+
+        The table name is bound via SQLAlchemy's ``:param`` syntax to prevent
+        SQL injection from catalogue values.
+        """
         return self.execute_sql(
-            f"SELECT column_name, data_type FROM information_schema.columns WHERE table_name = '{table_name}';"  # noqa: S608
+            "SELECT column_name, data_type FROM information_schema.columns "
+            "WHERE table_name = :table_name",
+            params={"table_name": table_name},
         )
 
+    # ------------------------------------------------------------------
+    # Core execution
+    # ------------------------------------------------------------------
+
     def _run(self, sql_query: str) -> list[dict[str, Any]] | str:
         try:
+            self._validate_query(sql_query)
             data = self.execute_sql(sql_query)
+        except ValueError:
+            raise
         except Exception as exc:
             data = (
                 f"Based on these tables {self.tables} and columns {self.columns}, "
-                "you can create SQL queries to retrieve data from the database."
-                f"Get the original request {sql_query} and the error {exc} and create the correct SQL query."
+                "you can create SQL queries to retrieve data from the database. "
+                f"Get the original request {sql_query} and the error {exc} and "
+                "create the correct SQL query."
             )
 
         return data
 
-    def execute_sql(self, sql_query: str) -> list[dict[str, Any]] | str:
+    def execute_sql(
+        self,
+        sql_query: str,
+        params: dict[str, Any] | None = None,
+    ) -> list[dict[str, Any]] | str:
+        """Execute *sql_query* and return the results as a list of dicts.
+
+        Parameters
+        ----------
+        sql_query:
+            The SQL statement to run.
+        params:
+            Optional mapping of bind parameters (e.g. ``{"table_name": "users"}``).
+        """
         if not SQLALCHEMY_AVAILABLE:
             raise ImportError(
-                "sqlalchemy is not installed. Please install it with `pip install crewai-tools[sqlalchemy]`"
+                "sqlalchemy is not installed. Please install it with "
+                "`pip install crewai-tools[sqlalchemy]`"
             )
 
+        # Check ALL statements so that e.g. "SELECT 1; DROP TABLE t" triggers a
+        # commit when allow_dml=True, regardless of statement order.
+        _stmts = [s.strip() for s in sql_query.split(";") if s.strip()]
+
+        def _is_write_stmt(s: str) -> bool:
+            cmd = self._extract_command(s)
+            if cmd in _WRITE_COMMANDS:
+                return True
+            if cmd == "EXPLAIN":
+                # Resolve the underlying command for EXPLAIN ANALYZE
+                resolved = _resolve_explain_command(s)
+                if resolved and resolved in _WRITE_COMMANDS:
+                    return True
+            if cmd == "WITH":
+                if _detect_writable_cte(s):
+                    return True
+                main_q = _extract_main_query_after_cte(s)
+                if main_q:
+                    return main_q.split()[0].upper().rstrip(";") in _WRITE_COMMANDS
+            return False
+
+        is_write = any(_is_write_stmt(s) for s in _stmts)
+
         engine = create_engine(self.db_uri)
         Session = sessionmaker(bind=engine)  # noqa: N806
         session = Session()
         try:
-            result = session.execute(text(sql_query))
-            session.commit()
+            result = session.execute(text(sql_query), params or {})
+
+            # Only commit when the operation actually mutates state
+            if self.allow_dml and is_write:
+                session.commit()
 
             if result.returns_rows:  # type: ignore[attr-defined]
                 columns = result.keys()

lib/crewai-tools/tests/test_generate_tool_specs.py

@@ -45,6 +45,26 @@ class MockTool(BaseTool):
     )
 
 
+# --- Intermediate base class (like RagTool, BraveSearchToolBase) ---
+class MockIntermediateBase(BaseTool):
+    """Simulates an intermediate tool base class (e.g. RagTool, BraveSearchToolBase)."""
+
+    name: str = "Intermediate Base"
+    description: str = "An intermediate tool base"
+    shared_config: str = Field("default_config", description="Config from intermediate base")
+
+    def _run(self, query: str) -> str:
+        return query
+
+
+class MockDerivedTool(MockIntermediateBase):
+    """A tool inheriting from an intermediate base, like CodeDocsSearchTool(RagTool)."""
+
+    name: str = "Derived Tool"
+    description: str = "A tool that inherits from intermediate base"
+    derived_param: str = Field("derived_default", description="Param specific to derived tool")
+
+
 @pytest.fixture
 def extractor():
     ext = ToolSpecExtractor()
@@ -169,6 +189,87 @@ def test_extract_package_dependencies(mock_tool_extractor):
     ]
 
 
+def test_base_tool_fields_excluded_from_init_params(mock_tool_extractor):
+    """BaseTool internal fields (including computed_field like tool_type) must
+    never appear in init_params_schema. Studio reads this schema to render
+    the tool config UI — internal fields confuse users."""
+    init_schema = mock_tool_extractor["init_params_schema"]
+    props = set(init_schema.get("properties", {}).keys())
+    required = set(init_schema.get("required", []))
+
+    # These are all BaseTool's own fields — none should leak
+    base_fields = {"name", "description", "env_vars", "args_schema",
+                   "description_updated", "cache_function", "result_as_answer",
+                   "max_usage_count", "current_usage_count", "tool_type",
+                   "package_dependencies"}
+
+    leaked_props = base_fields & props
+    assert not leaked_props, (
+        f"BaseTool fields leaked into init_params_schema properties: {leaked_props}"
+    )
+    leaked_required = base_fields & required
+    assert not leaked_required, (
+        f"BaseTool fields leaked into init_params_schema required: {leaked_required}"
+    )
+
+
+def test_intermediate_base_fields_preserved_for_derived_tool(extractor):
+    """When a tool inherits from an intermediate base (e.g. RagTool),
+    the intermediate's fields should be included — only BaseTool's own
+    fields are excluded."""
+    with (
+        mock.patch(
+            "crewai_tools.generate_tool_specs.dir",
+            return_value=["MockDerivedTool"],
+        ),
+        mock.patch(
+            "crewai_tools.generate_tool_specs.getattr",
+            return_value=MockDerivedTool,
+        ),
+    ):
+        extractor.extract_all_tools()
+        assert len(extractor.tools_spec) == 1
+        tool_info = extractor.tools_spec[0]
+
+    props = set(tool_info["init_params_schema"].get("properties", {}).keys())
+
+    # Intermediate base's field should be preserved
+    assert "shared_config" in props, (
+        "Intermediate base class fields should be preserved in init_params_schema"
+    )
+    # Derived tool's own field should be preserved
+    assert "derived_param" in props, (
+        "Derived tool's own fields should be preserved in init_params_schema"
+    )
+    # BaseTool internals should still be excluded
+    assert "tool_type" not in props
+    assert "cache_function" not in props
+    assert "result_as_answer" not in props
+
+
+def test_future_base_tool_field_auto_excluded(extractor):
+    """If a new field is added to BaseTool in the future, it should be
+    automatically excluded from spec generation without needing to update
+    the ignored list. This test verifies the allowlist approach works
+    by checking that ONLY non-BaseTool fields appear."""
+    with (
+        mock.patch("crewai_tools.generate_tool_specs.dir", return_value=["MockTool"]),
+        mock.patch("crewai_tools.generate_tool_specs.getattr", return_value=MockTool),
+    ):
+        extractor.extract_all_tools()
+        tool_info = extractor.tools_spec[0]
+
+    props = set(tool_info["init_params_schema"].get("properties", {}).keys())
+    base_all = set(BaseTool.model_fields) | set(BaseTool.model_computed_fields)
+
+    leaked = base_all & props
+    assert not leaked, (
+        f"BaseTool fields should be auto-excluded but found: {leaked}. "
+        "The spec generator should dynamically compute BaseTool's fields "
+        "instead of using a hardcoded denylist."
+    )
+
+
 def test_save_to_json(extractor, tmp_path):
     extractor.tools_spec = [
         {

lib/crewai-tools/tests/tools/test_nl2sql_security.py

@@ -0,0 +1,671 @@
+"""Security tests for NL2SQLTool.
+
+Uses an in-memory SQLite database so no external service is needed.
+SQLite does not have information_schema, so we patch the schema-introspection
+helpers to avoid bootstrap failures and focus purely on the security logic.
+"""
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+# Skip the entire module if SQLAlchemy is not installed
+pytest.importorskip("sqlalchemy")
+
+from sqlalchemy import create_engine, text  # noqa: E402
+
+from crewai_tools.tools.nl2sql.nl2sql_tool import NL2SQLTool  # noqa: E402
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+SQLITE_URI = "sqlite://"  # in-memory
+
+
+def _make_tool(allow_dml: bool = False, **kwargs) -> NL2SQLTool:
+    """Return a NL2SQLTool wired to an in-memory SQLite DB.
+
+    Schema-introspection is patched out so we can create the tool without a
+    real PostgreSQL information_schema.
+    """
+    with (
+        patch.object(NL2SQLTool, "_fetch_available_tables", return_value=[]),
+        patch.object(NL2SQLTool, "_fetch_all_available_columns", return_value=[]),
+    ):
+        return NL2SQLTool(db_uri=SQLITE_URI, allow_dml=allow_dml, **kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Read-only enforcement (allow_dml=False)
+# ---------------------------------------------------------------------------
+
+
+class TestReadOnlyMode:
+    def test_select_allowed_by_default(self):
+        tool = _make_tool()
+        # SQLite supports SELECT without information_schema
+        result = tool.execute_sql("SELECT 1 AS val")
+        assert result == [{"val": 1}]
+
+    @pytest.mark.parametrize(
+        "stmt",
+        [
+            "INSERT INTO t VALUES (1)",
+            "UPDATE t SET col = 1",
+            "DELETE FROM t",
+            "DROP TABLE t",
+            "ALTER TABLE t ADD col TEXT",
+            "CREATE TABLE t (id INTEGER)",
+            "TRUNCATE TABLE t",
+            "GRANT SELECT ON t TO user1",
+            "REVOKE SELECT ON t FROM user1",
+            "EXEC sp_something",
+            "EXECUTE sp_something",
+            "CALL proc()",
+        ],
+    )
+    def test_write_statements_blocked_by_default(self, stmt: str):
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(stmt)
+
+    def test_explain_allowed(self):
+        tool = _make_tool()
+        # Should not raise
+        tool._validate_query("EXPLAIN SELECT 1")
+
+    def test_read_only_cte_allowed(self):
+        tool = _make_tool()
+        tool._validate_query("WITH cte AS (SELECT 1) SELECT * FROM cte")
+
+    def test_show_allowed(self):
+        tool = _make_tool()
+        tool._validate_query("SHOW TABLES")
+
+    def test_describe_allowed(self):
+        tool = _make_tool()
+        tool._validate_query("DESCRIBE users")
+
+
+# ---------------------------------------------------------------------------
+# DML enabled (allow_dml=True)
+# ---------------------------------------------------------------------------
+
+
+class TestDMLEnabled:
+    def test_insert_allowed_when_dml_enabled(self):
+        tool = _make_tool(allow_dml=True)
+        # Should not raise
+        tool._validate_query("INSERT INTO t VALUES (1)")
+
+    def test_delete_allowed_when_dml_enabled(self):
+        tool = _make_tool(allow_dml=True)
+        tool._validate_query("DELETE FROM t WHERE id = 1")
+
+    def test_drop_allowed_when_dml_enabled(self):
+        tool = _make_tool(allow_dml=True)
+        tool._validate_query("DROP TABLE t")
+
+    def test_dml_actually_persists(self):
+        """End-to-end: INSERT commits when allow_dml=True."""
+        # Use a file-based SQLite so we can verify persistence across sessions
+        import tempfile, os
+        with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+            db_path = f.name
+        uri = f"sqlite:///{db_path}"
+        try:
+            tool = _make_tool(allow_dml=True)
+            tool.db_uri = uri
+
+            engine = create_engine(uri)
+            with engine.connect() as conn:
+                conn.execute(text("CREATE TABLE items (id INTEGER PRIMARY KEY)"))
+                conn.commit()
+
+            tool.execute_sql("INSERT INTO items VALUES (42)")
+
+            with engine.connect() as conn:
+                rows = conn.execute(text("SELECT id FROM items")).fetchall()
+            assert (42,) in rows
+        finally:
+            os.unlink(db_path)
+
+
+# ---------------------------------------------------------------------------
+# Parameterised query — SQL injection prevention
+# ---------------------------------------------------------------------------
+
+
+class TestParameterisedQueries:
+    def test_table_name_is_parameterised(self):
+        """_fetch_all_available_columns must not interpolate table_name into SQL."""
+        tool = _make_tool()
+        captured_calls = []
+
+        def recording_execute_sql(self_inner, sql_query, params=None):
+            captured_calls.append((sql_query, params))
+            return []
+
+        with patch.object(NL2SQLTool, "execute_sql", recording_execute_sql):
+            tool._fetch_all_available_columns("users'; DROP TABLE users; --")
+
+        assert len(captured_calls) == 1
+        sql, params = captured_calls[0]
+        # The raw SQL must NOT contain the injected string
+        assert "DROP" not in sql
+        # The table name must be passed as a parameter
+        assert params is not None
+        assert params.get("table_name") == "users'; DROP TABLE users; --"
+        # The SQL template must use the :param syntax
+        assert ":table_name" in sql
+
+    def test_injection_string_not_in_sql_template(self):
+        """The f-string vulnerability is gone — table name never lands in the SQL."""
+        tool = _make_tool()
+        injection = "'; DROP TABLE users; --"
+        captured = {}
+
+        def spy(self_inner, sql_query, params=None):
+            captured["sql"] = sql_query
+            captured["params"] = params
+            return []
+
+        with patch.object(NL2SQLTool, "execute_sql", spy):
+            tool._fetch_all_available_columns(injection)
+
+        assert injection not in captured["sql"]
+        assert captured["params"]["table_name"] == injection
+
+
+# ---------------------------------------------------------------------------
+# session.commit() not called for read-only queries
+# ---------------------------------------------------------------------------
+
+
+class TestNoCommitForReadOnly:
+    def test_select_does_not_commit(self):
+        tool = _make_tool(allow_dml=False)
+
+        mock_session = MagicMock()
+        mock_result = MagicMock()
+        mock_result.returns_rows = True
+        mock_result.keys.return_value = ["val"]
+        mock_result.fetchall.return_value = [(1,)]
+        mock_session.execute.return_value = mock_result
+
+        mock_session_cls = MagicMock(return_value=mock_session)
+
+        with (
+            patch("crewai_tools.tools.nl2sql.nl2sql_tool.create_engine"),
+            patch(
+                "crewai_tools.tools.nl2sql.nl2sql_tool.sessionmaker",
+                return_value=mock_session_cls,
+            ),
+        ):
+            tool.execute_sql("SELECT 1")
+
+        mock_session.commit.assert_not_called()
+
+    def test_write_with_dml_enabled_does_commit(self):
+        tool = _make_tool(allow_dml=True)
+
+        mock_session = MagicMock()
+        mock_result = MagicMock()
+        mock_result.returns_rows = False
+        mock_session.execute.return_value = mock_result
+
+        mock_session_cls = MagicMock(return_value=mock_session)
+
+        with (
+            patch("crewai_tools.tools.nl2sql.nl2sql_tool.create_engine"),
+            patch(
+                "crewai_tools.tools.nl2sql.nl2sql_tool.sessionmaker",
+                return_value=mock_session_cls,
+            ),
+        ):
+            tool.execute_sql("INSERT INTO t VALUES (1)")
+
+        mock_session.commit.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Environment-variable escape hatch
+# ---------------------------------------------------------------------------
+
+
+class TestEnvVarEscapeHatch:
+    def test_env_var_enables_dml(self):
+        with patch.dict(os.environ, {"CREWAI_NL2SQL_ALLOW_DML": "true"}):
+            tool = _make_tool(allow_dml=False)
+        assert tool.allow_dml is True
+
+    def test_env_var_case_insensitive(self):
+        with patch.dict(os.environ, {"CREWAI_NL2SQL_ALLOW_DML": "TRUE"}):
+            tool = _make_tool(allow_dml=False)
+        assert tool.allow_dml is True
+
+    def test_env_var_absent_keeps_default(self):
+        env = {k: v for k, v in os.environ.items() if k != "CREWAI_NL2SQL_ALLOW_DML"}
+        with patch.dict(os.environ, env, clear=True):
+            tool = _make_tool(allow_dml=False)
+        assert tool.allow_dml is False
+
+    def test_env_var_false_does_not_enable_dml(self):
+        with patch.dict(os.environ, {"CREWAI_NL2SQL_ALLOW_DML": "false"}):
+            tool = _make_tool(allow_dml=False)
+        assert tool.allow_dml is False
+
+    def test_dml_write_blocked_without_env_var(self):
+        env = {k: v for k, v in os.environ.items() if k != "CREWAI_NL2SQL_ALLOW_DML"}
+        with patch.dict(os.environ, env, clear=True):
+            tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("DROP TABLE sensitive_data")
+
+
+# ---------------------------------------------------------------------------
+# _run() propagates ValueError from _validate_query
+# ---------------------------------------------------------------------------
+
+
+class TestRunValidation:
+    def test_run_raises_on_blocked_query(self):
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._run("DELETE FROM users")
+
+    def test_run_returns_results_for_select(self):
+        tool = _make_tool(allow_dml=False)
+        result = tool._run("SELECT 1 AS n")
+        assert result == [{"n": 1}]
+
+
+# ---------------------------------------------------------------------------
+# Multi-statement / semicolon injection prevention
+# ---------------------------------------------------------------------------
+
+
+class TestSemicolonInjection:
+    def test_multi_statement_blocked_in_read_only_mode(self):
+        """SELECT 1; DROP TABLE users must be rejected when allow_dml=False."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="multi-statement"):
+            tool._validate_query("SELECT 1; DROP TABLE users")
+
+    def test_multi_statement_blocked_even_with_only_selects(self):
+        """Two SELECT statements are still rejected in read-only mode."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="multi-statement"):
+            tool._validate_query("SELECT 1; SELECT 2")
+
+    def test_trailing_semicolon_allowed_single_statement(self):
+        """A single statement with a trailing semicolon should pass."""
+        tool = _make_tool(allow_dml=False)
+        # Should not raise — the part after the semicolon is empty
+        tool._validate_query("SELECT 1;")
+
+    def test_multi_statement_allowed_when_dml_enabled(self):
+        """Multiple statements are permitted when allow_dml=True."""
+        tool = _make_tool(allow_dml=True)
+        # Should not raise
+        tool._validate_query("SELECT 1; INSERT INTO t VALUES (1)")
+
+    def test_multi_statement_write_still_blocked_individually(self):
+        """Even with allow_dml=False, a single write statement is blocked."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("DROP TABLE users")
+
+
+# ---------------------------------------------------------------------------
+# Writable CTEs (WITH … DELETE/INSERT/UPDATE)
+# ---------------------------------------------------------------------------
+
+
+class TestWritableCTE:
+    def test_writable_cte_delete_blocked_in_read_only(self):
+        """WITH d AS (DELETE FROM users RETURNING *) SELECT * FROM d — blocked."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH deleted AS (DELETE FROM users RETURNING *) SELECT * FROM deleted"
+            )
+
+    def test_writable_cte_insert_blocked_in_read_only(self):
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH ins AS (INSERT INTO t VALUES (1) RETURNING id) SELECT * FROM ins"
+            )
+
+    def test_writable_cte_update_blocked_in_read_only(self):
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH upd AS (UPDATE t SET x=1 RETURNING id) SELECT * FROM upd"
+            )
+
+    def test_writable_cte_allowed_when_dml_enabled(self):
+        tool = _make_tool(allow_dml=True)
+        # Should not raise
+        tool._validate_query(
+            "WITH deleted AS (DELETE FROM users RETURNING *) SELECT * FROM deleted"
+        )
+
+    def test_plain_read_only_cte_still_allowed(self):
+        tool = _make_tool(allow_dml=False)
+        # No write commands in the CTE body — must pass
+        tool._validate_query("WITH cte AS (SELECT id FROM users) SELECT * FROM cte")
+
+    def test_cte_with_comment_column_not_false_positive(self):
+        """Column named 'comment' should NOT trigger writable CTE detection."""
+        tool = _make_tool(allow_dml=False)
+        # 'comment' is a column name, not a SQL command
+        tool._validate_query(
+            "WITH cte AS (SELECT comment FROM posts) SELECT * FROM cte"
+        )
+
+    def test_cte_with_set_column_not_false_positive(self):
+        """Column named 'set' should NOT trigger writable CTE detection."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query(
+            "WITH cte AS (SELECT set, reset FROM config) SELECT * FROM cte"
+        )
+
+
+# ---------------------------------------------------------------------------
+# EXPLAIN ANALYZE executes the underlying query
+# ---------------------------------------------------------------------------
+
+
+    def test_cte_with_write_main_query_blocked(self):
+        """WITH cte AS (SELECT 1) DELETE FROM users — main query must be caught."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH cte AS (SELECT 1) DELETE FROM users"
+            )
+
+    def test_cte_with_write_main_query_allowed_with_dml(self):
+        """Main query write after CTE should pass when allow_dml=True."""
+        tool = _make_tool(allow_dml=True)
+        tool._validate_query(
+            "WITH cte AS (SELECT id FROM users) INSERT INTO archive SELECT * FROM cte"
+        )
+
+    def test_cte_with_newline_before_paren_blocked(self):
+        """AS followed by newline then ( should still detect writable CTE."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH cte AS\n(DELETE FROM users RETURNING *) SELECT * FROM cte"
+            )
+
+    def test_cte_with_tab_before_paren_blocked(self):
+        """AS followed by tab then ( should still detect writable CTE."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH cte AS\t(DELETE FROM users RETURNING *) SELECT * FROM cte"
+            )
+
+
+class TestExplainAnalyze:
+    def test_explain_analyze_delete_blocked_in_read_only(self):
+        """EXPLAIN ANALYZE DELETE actually runs the delete — block it."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("EXPLAIN ANALYZE DELETE FROM users")
+
+    def test_explain_analyse_delete_blocked_in_read_only(self):
+        """British spelling ANALYSE is also caught."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("EXPLAIN ANALYSE DELETE FROM users")
+
+    def test_explain_analyze_drop_blocked_in_read_only(self):
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("EXPLAIN ANALYZE DROP TABLE users")
+
+    def test_explain_analyze_select_allowed_in_read_only(self):
+        """EXPLAIN ANALYZE on a SELECT is safe — must be permitted."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query("EXPLAIN ANALYZE SELECT * FROM users")
+
+    def test_explain_without_analyze_allowed(self):
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query("EXPLAIN SELECT * FROM users")
+
+    def test_explain_analyze_delete_allowed_when_dml_enabled(self):
+        tool = _make_tool(allow_dml=True)
+        tool._validate_query("EXPLAIN ANALYZE DELETE FROM users")
+
+    def test_explain_paren_analyze_delete_blocked_in_read_only(self):
+        """EXPLAIN (ANALYZE) DELETE actually runs the delete — block it."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("EXPLAIN (ANALYZE) DELETE FROM users")
+
+    def test_explain_paren_analyze_verbose_delete_blocked_in_read_only(self):
+        """EXPLAIN (ANALYZE, VERBOSE) DELETE actually runs the delete — block it."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("EXPLAIN (ANALYZE, VERBOSE) DELETE FROM users")
+
+    def test_explain_paren_verbose_select_allowed_in_read_only(self):
+        """EXPLAIN (VERBOSE) SELECT is safe — no ANALYZE means no execution."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query("EXPLAIN (VERBOSE) SELECT * FROM users")
+
+
+# ---------------------------------------------------------------------------
+# Multi-statement commit covers ALL statements (not just the first)
+# ---------------------------------------------------------------------------
+
+
+class TestMultiStatementCommit:
+    def test_select_then_insert_triggers_commit(self):
+        """SELECT 1; INSERT … — commit must happen because INSERT is a write."""
+        tool = _make_tool(allow_dml=True)
+
+        mock_session = MagicMock()
+        mock_result = MagicMock()
+        mock_result.returns_rows = False
+        mock_session.execute.return_value = mock_result
+        mock_session_cls = MagicMock(return_value=mock_session)
+
+        with (
+            patch("crewai_tools.tools.nl2sql.nl2sql_tool.create_engine"),
+            patch(
+                "crewai_tools.tools.nl2sql.nl2sql_tool.sessionmaker",
+                return_value=mock_session_cls,
+            ),
+        ):
+            tool.execute_sql("SELECT 1; INSERT INTO t VALUES (1)")
+
+        mock_session.commit.assert_called_once()
+
+    def test_select_only_multi_statement_does_not_commit(self):
+        """Two SELECTs must not trigger a commit even when allow_dml=True."""
+        tool = _make_tool(allow_dml=True)
+
+        mock_session = MagicMock()
+        mock_result = MagicMock()
+        mock_result.returns_rows = True
+        mock_result.keys.return_value = ["v"]
+        mock_result.fetchall.return_value = [(1,)]
+        mock_session.execute.return_value = mock_result
+        mock_session_cls = MagicMock(return_value=mock_session)
+
+        with (
+            patch("crewai_tools.tools.nl2sql.nl2sql_tool.create_engine"),
+            patch(
+                "crewai_tools.tools.nl2sql.nl2sql_tool.sessionmaker",
+                return_value=mock_session_cls,
+            ),
+        ):
+            tool.execute_sql("SELECT 1; SELECT 2")
+
+    def test_writable_cte_triggers_commit(self):
+        """WITH d AS (DELETE ...) must trigger commit when allow_dml=True."""
+        tool = _make_tool(allow_dml=True)
+
+        mock_session = MagicMock()
+        mock_result = MagicMock()
+        mock_result.returns_rows = True
+        mock_result.keys.return_value = ["id"]
+        mock_result.fetchall.return_value = [(1,)]
+        mock_session.execute.return_value = mock_result
+        mock_session_cls = MagicMock(return_value=mock_session)
+
+        with (
+            patch("crewai_tools.tools.nl2sql.nl2sql_tool.create_engine"),
+            patch(
+                "crewai_tools.tools.nl2sql.nl2sql_tool.sessionmaker",
+                return_value=mock_session_cls,
+            ),
+        ):
+            tool.execute_sql(
+                "WITH d AS (DELETE FROM users RETURNING *) SELECT * FROM d"
+            )
+            mock_session.commit.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Extended _WRITE_COMMANDS coverage
+# ---------------------------------------------------------------------------
+
+
+class TestExtendedWriteCommands:
+    @pytest.mark.parametrize(
+        "stmt",
+        [
+            "UPSERT INTO t VALUES (1)",
+            "LOAD DATA INFILE 'f.csv' INTO TABLE t",
+            "COPY t FROM '/tmp/f.csv'",
+            "VACUUM ANALYZE t",
+            "ANALYZE t",
+            "ANALYSE t",
+            "REINDEX TABLE t",
+            "CLUSTER t USING idx",
+            "REFRESH MATERIALIZED VIEW v",
+            "COMMENT ON TABLE t IS 'desc'",
+            "SET search_path = myschema",
+            "RESET search_path",
+        ],
+    )
+    def test_extended_write_commands_blocked_by_default(self, stmt: str):
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(stmt)
+
+
+# ---------------------------------------------------------------------------
+# EXPLAIN ANALYZE VERBOSE handling
+# ---------------------------------------------------------------------------
+
+
+class TestExplainAnalyzeVerbose:
+    def test_explain_analyze_verbose_select_allowed(self):
+        """EXPLAIN ANALYZE VERBOSE SELECT should be allowed (read-only)."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query("EXPLAIN ANALYZE VERBOSE SELECT * FROM users")
+
+    def test_explain_analyze_verbose_delete_blocked(self):
+        """EXPLAIN ANALYZE VERBOSE DELETE should be blocked."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query("EXPLAIN ANALYZE VERBOSE DELETE FROM users")
+
+    def test_explain_verbose_select_allowed(self):
+        """EXPLAIN VERBOSE SELECT (no ANALYZE) should be allowed."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query("EXPLAIN VERBOSE SELECT * FROM users")
+
+
+# ---------------------------------------------------------------------------
+# CTE with string literal parens
+# ---------------------------------------------------------------------------
+
+
+class TestCTEStringLiteralParens:
+    def test_cte_string_paren_does_not_bypass(self):
+        """Parens inside string literals should not confuse the paren walker."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH cte AS (SELECT '(' FROM t) DELETE FROM users"
+            )
+
+    def test_cte_string_paren_read_only_allowed(self):
+        """Read-only CTE with string literal parens should be allowed."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query(
+            "WITH cte AS (SELECT '(' FROM t) SELECT * FROM cte"
+        )
+
+
+# ---------------------------------------------------------------------------
+# EXPLAIN ANALYZE commit logic
+# ---------------------------------------------------------------------------
+
+
+class TestExplainAnalyzeCommit:
+    def test_explain_analyze_delete_triggers_commit(self):
+        """EXPLAIN ANALYZE DELETE should trigger commit when allow_dml=True."""
+        tool = _make_tool(allow_dml=True)
+
+        mock_session = MagicMock()
+        mock_result = MagicMock()
+        mock_result.returns_rows = True
+        mock_result.keys.return_value = ["QUERY PLAN"]
+        mock_result.fetchall.return_value = [("Delete on users",)]
+        mock_session.execute.return_value = mock_result
+        mock_session_cls = MagicMock(return_value=mock_session)
+
+        with (
+            patch("crewai_tools.tools.nl2sql.nl2sql_tool.create_engine"),
+            patch(
+                "crewai_tools.tools.nl2sql.nl2sql_tool.sessionmaker",
+                return_value=mock_session_cls,
+            ),
+        ):
+            tool.execute_sql("EXPLAIN ANALYZE DELETE FROM users")
+            mock_session.commit.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# AS( inside string literals must not confuse CTE detection
+# ---------------------------------------------------------------------------
+
+
+class TestCTEStringLiteralAS:
+    def test_as_paren_inside_string_does_not_bypass(self):
+        """'AS (' inside a string literal must not be treated as a CTE body."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="read-only mode"):
+            tool._validate_query(
+                "WITH cte AS (SELECT 'AS (' FROM t) DELETE FROM users"
+            )
+
+    def test_as_paren_inside_string_read_only_ok(self):
+        """Read-only CTE with 'AS (' in a string should be allowed."""
+        tool = _make_tool(allow_dml=False)
+        tool._validate_query(
+            "WITH cte AS (SELECT 'AS (' FROM t) SELECT * FROM cte"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Unknown command after CTE should be blocked
+# ---------------------------------------------------------------------------
+
+
+class TestCTEUnknownCommand:
+    def test_unknown_command_after_cte_blocked(self):
+        """WITH cte AS (SELECT 1) FOOBAR should be blocked as unknown."""
+        tool = _make_tool(allow_dml=False)
+        with pytest.raises(ValueError, match="unrecognised"):
+            tool._validate_query("WITH cte AS (SELECT 1) FOOBAR")

lib/crewai/pyproject.toml

@@ -40,7 +40,7 @@ dependencies = [
     "pydantic-settings~=2.10.1",
     "httpx~=0.28.1",
     "mcp~=1.26.0",
-    "uv~=0.9.13",
+    "uv~=0.11.6",
     "aiosqlite~=0.21.0",
     "pyyaml~=6.0",
     "aiofiles~=24.1.0",
@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.14.0",
+    "crewai-tools==1.14.2a2",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -68,14 +68,14 @@ openpyxl = [
 ]
 mem0 = ["mem0ai~=0.1.94"]
 docling = [
-    "docling~=2.75.0",
+    "docling~=2.84.0",
 ]
 qdrant = [
     "qdrant-client[fastembed]~=1.14.3",
 ]
 aws = [
-    "boto3~=1.40.38",
-    "aiobotocore~=2.25.2",
+    "boto3~=1.42.79",
+    "aiobotocore~=3.4.0",
 ]
 watson = [
     "ibm-watsonx-ai~=1.3.39",
@@ -87,7 +87,7 @@ litellm = [
     "litellm~=1.83.0",
 ]
 bedrock = [
-    "boto3~=1.40.45",
+    "boto3~=1.42.79",
 ]
 google-genai = [
     "google-genai~=1.65.0",

lib/crewai/src/crewai/__init__.py

@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.14.0"
+__version__ = "1.14.2a2"
 _telemetry_submitted = False
 
 
@@ -81,7 +81,6 @@ _track_install_async()
 
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "Memory": ("crewai.memory.unified_memory", "Memory"),
-    "MemoryPromptConfig": ("crewai.memory.types", "MemoryPromptConfig"),
 }
 
 
@@ -234,7 +233,6 @@ __all__ = [
     "Knowledge",
     "LLMGuardrail",
     "Memory",
-    "MemoryPromptConfig",
     "PlanningConfig",
     "Process",
     "RuntimeState",

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

@@ -98,6 +98,7 @@ from crewai.utilities.converter import Converter, ConverterError
 from crewai.utilities.env import get_env_context
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.prompts import Prompts, StandardPromptResult, SystemPromptResult
 from crewai.utilities.pydantic_schema_utils import generate_model_description
@@ -499,8 +500,8 @@ class Agent(BaseAgent):
             self.tools_handler.last_used_tool = None
 
         task_prompt = task.prompt()
-        task_prompt = build_task_prompt_with_schema(task, task_prompt, self.i18n)
-        task_prompt = format_task_with_context(task_prompt, context, self.i18n)
+        task_prompt = build_task_prompt_with_schema(task, task_prompt)
+        task_prompt = format_task_with_context(task_prompt, context)
         return self._retrieve_memory_context(task, task_prompt)
 
     def _finalize_task_prompt(
@@ -562,7 +563,7 @@ class Agent(BaseAgent):
                         m.format() for m in matches
                     )
             if memory.strip() != "":
-                task_prompt += self.i18n.slice("memory").format(memory=memory)
+                task_prompt += I18N_DEFAULT.slice("memory").format(memory=memory)
 
             crewai_event_bus.emit(
                 self,
@@ -968,14 +969,13 @@ class Agent(BaseAgent):
             agent=self,
             has_tools=len(raw_tools) > 0,
             use_native_tool_calling=use_native_tool_calling,
-            i18n=self.i18n,
             use_system_prompt=self.use_system_prompt,
             system_template=self.system_template,
             prompt_template=self.prompt_template,
             response_template=self.response_template,
         ).task_execution()
 
-        stop_words = [self.i18n.slice("observation")]
+        stop_words = [I18N_DEFAULT.slice("observation")]
         if self.response_template:
             stop_words.append(
                 self.response_template.split("{{ .Response }}")[1].strip()
@@ -1017,7 +1017,6 @@ class Agent(BaseAgent):
             self.agent_executor = self.executor_class(
                 llm=self.llm,
                 task=task,
-                i18n=self.i18n,
                 agent=self,
                 crew=self.crew,
                 tools=parsed_tools,
@@ -1262,10 +1261,10 @@ class Agent(BaseAgent):
                 from_agent=self,
             ),
         )
-        query = self.i18n.slice("knowledge_search_query").format(
+        query = I18N_DEFAULT.slice("knowledge_search_query").format(
             task_prompt=task_prompt
         )
-        rewriter_prompt = self.i18n.slice("knowledge_search_query_system_prompt")
+        rewriter_prompt = I18N_DEFAULT.slice("knowledge_search_query_system_prompt")
         if not isinstance(self.llm, BaseLLM):
             self._logger.log(
                 "warning",
@@ -1384,7 +1383,6 @@ class Agent(BaseAgent):
             request_within_rpm_limit=rpm_limit_fn,
             callbacks=[TokenCalcHandler(self._token_process)],
             response_model=response_format,
-            i18n=self.i18n,
         )
 
         all_files: dict[str, Any] = {}
@@ -1420,7 +1418,7 @@ class Agent(BaseAgent):
                         m.format() for m in matches
                     )
                 if memory_block:
-                    formatted_messages += "\n\n" + self.i18n.slice("memory").format(
+                    formatted_messages += "\n\n" + I18N_DEFAULT.slice("memory").format(
                         memory=memory_block
                     )
                 crewai_event_bus.emit(
@@ -1624,7 +1622,7 @@ class Agent(BaseAgent):
             try:
                 model_schema = generate_model_description(response_format)
                 schema = json.dumps(model_schema, indent=2)
-                instructions = self.i18n.slice("formatted_task_instructions").format(
+                instructions = I18N_DEFAULT.slice("formatted_task_instructions").format(
                     output_format=schema
                 )
 

lib/crewai/src/crewai/agent/utils.py

@@ -24,7 +24,6 @@ if TYPE_CHECKING:
     from crewai.agent.core import Agent
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
-    from crewai.utilities.i18n import I18N
 
 
 def handle_reasoning(agent: Agent, task: Task) -> None:
@@ -59,46 +58,50 @@ def handle_reasoning(agent: Agent, task: Task) -> None:
         agent._logger.log("error", f"Error during planning: {e!s}")
 
 
-def build_task_prompt_with_schema(task: Task, task_prompt: str, i18n: I18N) -> str:
+def build_task_prompt_with_schema(task: Task, task_prompt: str) -> str:
     """Build task prompt with JSON/Pydantic schema instructions if applicable.
 
     Args:
         task: The task being executed.
         task_prompt: The initial task prompt.
-        i18n: Internationalization instance.
 
     Returns:
         The task prompt potentially augmented with schema instructions.
     """
+    from crewai.utilities.i18n import I18N_DEFAULT
+
     if (task.output_json or task.output_pydantic) and not task.response_model:
         if task.output_json:
             schema_dict = generate_model_description(task.output_json)
             schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
-            task_prompt += "\n" + i18n.slice("formatted_task_instructions").format(
-                output_format=schema
-            )
+            task_prompt += "\n" + I18N_DEFAULT.slice(
+                "formatted_task_instructions"
+            ).format(output_format=schema)
         elif task.output_pydantic:
             schema_dict = generate_model_description(task.output_pydantic)
             schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
-            task_prompt += "\n" + i18n.slice("formatted_task_instructions").format(
-                output_format=schema
-            )
+            task_prompt += "\n" + I18N_DEFAULT.slice(
+                "formatted_task_instructions"
+            ).format(output_format=schema)
     return task_prompt
 
 
-def format_task_with_context(task_prompt: str, context: str | None, i18n: I18N) -> str:
+def format_task_with_context(task_prompt: str, context: str | None) -> str:
     """Format task prompt with context if provided.
 
     Args:
         task_prompt: The task prompt.
         context: Optional context string.
-        i18n: Internationalization instance.
 
     Returns:
         The task prompt formatted with context if provided.
     """
+    from crewai.utilities.i18n import I18N_DEFAULT
+
     if context:
-        return i18n.slice("task_with_context").format(task=task_prompt, context=context)
+        return I18N_DEFAULT.slice("task_with_context").format(
+            task=task_prompt, context=context
+        )
     return task_prompt
 
 

lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py

@@ -33,6 +33,7 @@ from crewai.tools.base_tool import BaseTool
 from crewai.types.callback import SerializableCallable
 from crewai.utilities import Logger
 from crewai.utilities.converter import Converter
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.import_utils import require
 
 
@@ -186,7 +187,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
             task_prompt = task.prompt() if hasattr(task, "prompt") else str(task)
 
             if context:
-                task_prompt = self.i18n.slice("task_with_context").format(
+                task_prompt = I18N_DEFAULT.slice("task_with_context").format(
                     task=task_prompt, context=context
                 )
 

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -32,6 +32,7 @@ from crewai.events.types.agent_events import (
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Logger
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.import_utils import require
 
 
@@ -133,7 +134,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         try:
             task_prompt: str = task.prompt()
             if context:
-                task_prompt = self.i18n.slice("task_with_context").format(
+                task_prompt = I18N_DEFAULT.slice("task_with_context").format(
                     task=task_prompt, context=context
                 )
             crewai_event_bus.emit(

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py

@@ -8,7 +8,7 @@ import json
 from typing import Any
 
 from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
-from crewai.utilities.i18n import get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 
 
 class OpenAIConverterAdapter(BaseConverterAdapter):
@@ -59,10 +59,8 @@ class OpenAIConverterAdapter(BaseConverterAdapter):
         if not self._output_format:
             return base_prompt
 
-        output_schema: str = (
-            get_i18n()
-            .slice("formatted_task_instructions")
-            .format(output_format=json.dumps(self._schema, indent=2))
+        output_schema: str = I18N_DEFAULT.slice("formatted_task_instructions").format(
+            output_format=json.dumps(self._schema, indent=2)
         )
 
         return f"{base_prompt}\n\n{output_schema}"

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -43,7 +43,6 @@ from crewai.state.checkpoint_config import CheckpointConfig, _coerce_checkpoint
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.types.callback import SerializableCallable
 from crewai.utilities.config import process_config
-from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.logger import Logger
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.string_utils import interpolate_only
@@ -52,7 +51,6 @@ from crewai.utilities.string_utils import interpolate_only
 if TYPE_CHECKING:
     from crewai.context import ExecutionContext
     from crewai.crew import Crew
-    from crewai.state.provider.core import BaseProvider
 
 
 def _validate_crew_ref(value: Any) -> Any:
@@ -179,7 +177,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         agent_executor: An instance of the CrewAgentExecutor class.
         llm (Any): Language model that will run the agent.
         crew (Any): Crew to which the agent belongs.
-        i18n (I18N): Internationalization settings.
+
         cache_handler ([CacheHandler]): An instance of the CacheHandler class.
         tools_handler ([ToolsHandler]): An instance of the ToolsHandler class.
         max_tokens: Maximum number of tokens for the agent to generate in a response.
@@ -269,9 +267,6 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             _serialize_crew_ref, return_type=str | None, when_used="always"
         ),
     ] = Field(default=None, description="Crew to which the agent belongs.")
-    i18n: I18N = Field(
-        default_factory=get_i18n, description="Internationalization settings."
-    )
     cache_handler: CacheHandler | None = Field(
         default=None, description="An instance of the CacheHandler class."
     )
@@ -342,19 +337,16 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     execution_context: ExecutionContext | None = Field(default=None)
 
     @classmethod
-    def from_checkpoint(
-        cls, path: str, *, provider: BaseProvider | None = None
-    ) -> Self:
-        """Restore an Agent from a checkpoint file."""
+    def from_checkpoint(cls, config: CheckpointConfig) -> Self:
+        """Restore an Agent from a checkpoint.
+
+        Args:
+            config: Checkpoint configuration with ``restore_from`` set.
+        """
         from crewai.context import apply_execution_context
-        from crewai.state.provider.json_provider import JsonProvider
         from crewai.state.runtime import RuntimeState
 
-        state = RuntimeState.from_checkpoint(
-            path,
-            provider=provider or JsonProvider(),
-            context={"from_checkpoint": True},
-        )
+        state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
         for entity in state.root:
             if isinstance(entity, cls):
                 if entity.execution_context is not None:
@@ -363,7 +355,9 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
                     entity.agent_executor.agent = entity
                     entity.agent_executor._resuming = True
                 return entity
-        raise ValueError(f"No {cls.__name__} found in checkpoint: {path}")
+        raise ValueError(
+            f"No {cls.__name__} found in checkpoint: {config.restore_from}"
+        )
 
     @model_validator(mode="before")
     @classmethod

lib/crewai/src/crewai/agents/agent_builder/base_agent_executor.py

@@ -14,7 +14,6 @@ if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.crew import Crew
     from crewai.task import Task
-    from crewai.utilities.i18n import I18N
 
 
 class BaseAgentExecutor(BaseModel):
@@ -28,7 +27,6 @@ class BaseAgentExecutor(BaseModel):
     max_iter: int = Field(default=25)
     messages: list[LLMMessage] = Field(default_factory=list)
     _resuming: bool = PrivateAttr(default=False)
-    _i18n: I18N | None = PrivateAttr(default=None)
 
     def _save_to_memory(self, output: AgentFinish) -> None:
         """Save task result to unified memory (memory or crew._memory)."""

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -67,7 +67,7 @@ from crewai.utilities.agent_utils import (
 )
 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, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -135,9 +135,8 @@ class CrewAgentExecutor(BaseAgentExecutor):
 
     model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
 
-    def __init__(self, i18n: I18N | None = None, **kwargs: Any) -> None:
+    def __init__(self, **kwargs: Any) -> None:
         super().__init__(**kwargs)
-        self._i18n = i18n or get_i18n()
         if not self.before_llm_call_hooks:
             self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         if not self.after_llm_call_hooks:
@@ -328,7 +327,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     formatted_answer = handle_max_iterations_exceeded(
                         formatted_answer,
                         printer=PRINTER,
-                        i18n=self._i18n,
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
@@ -401,7 +399,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                         agent_action=formatted_answer,
                         fingerprint_context=fingerprint_context,
                         tools=self.tools,
-                        i18n=self._i18n,
                         agent_key=self.agent.key if self.agent else None,
                         agent_role=self.agent.role if self.agent else None,
                         tools_handler=self.tools_handler,
@@ -438,7 +435,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
-                        i18n=self._i18n,
                         verbose=self.agent.verbose,
                     )
                     continue
@@ -484,7 +480,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     formatted_answer = handle_max_iterations_exceeded(
                         None,
                         printer=PRINTER,
-                        i18n=self._i18n,
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
@@ -575,7 +570,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
-                        i18n=self._i18n,
                         verbose=self.agent.verbose,
                     )
                     continue
@@ -771,7 +765,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     if tool_finish:
                         return tool_finish
 
-                reasoning_prompt = self._i18n.slice("post_tool_reasoning")
+                reasoning_prompt = I18N_DEFAULT.slice("post_tool_reasoning")
                 reasoning_message: LLMMessage = {
                     "role": "user",
                     "content": reasoning_prompt,
@@ -795,7 +789,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
         if tool_finish:
             return tool_finish
 
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
+        reasoning_prompt = I18N_DEFAULT.slice("post_tool_reasoning")
         reasoning_message = {
             "role": "user",
             "content": reasoning_prompt,
@@ -1170,7 +1164,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     formatted_answer = handle_max_iterations_exceeded(
                         formatted_answer,
                         printer=PRINTER,
-                        i18n=self._i18n,
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
@@ -1242,7 +1235,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                         agent_action=formatted_answer,
                         fingerprint_context=fingerprint_context,
                         tools=self.tools,
-                        i18n=self._i18n,
                         agent_key=self.agent.key if self.agent else None,
                         agent_role=self.agent.role if self.agent else None,
                         tools_handler=self.tools_handler,
@@ -1278,7 +1270,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
-                        i18n=self._i18n,
                         verbose=self.agent.verbose,
                     )
                     continue
@@ -1318,7 +1309,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     formatted_answer = handle_max_iterations_exceeded(
                         None,
                         printer=PRINTER,
-                        i18n=self._i18n,
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
@@ -1408,7 +1398,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                         messages=self.messages,
                         llm=cast("BaseLLM", self.llm),
                         callbacks=self.callbacks,
-                        i18n=self._i18n,
                         verbose=self.agent.verbose,
                     )
                     continue
@@ -1467,7 +1456,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
             Updated action or final answer.
         """
         # Special case for add_image_tool
-        add_image_tool = self._i18n.tools("add_image")
+        add_image_tool = I18N_DEFAULT.tools("add_image")
         if (
             isinstance(add_image_tool, dict)
             and formatted_answer.tool.casefold().strip()
@@ -1673,5 +1662,5 @@ class CrewAgentExecutor(BaseAgentExecutor):
             Formatted message dict.
         """
         return format_message_for_llm(
-            self._i18n.slice("feedback_instructions").format(feedback=feedback)
+            I18N_DEFAULT.slice("feedback_instructions").format(feedback=feedback)
         )

lib/crewai/src/crewai/agents/parser.py

@@ -19,10 +19,7 @@ from crewai.agents.constants import (
     MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
     UNABLE_TO_REPAIR_JSON_RESULTS,
 )
-from crewai.utilities.i18n import get_i18n
-
-
-_I18N = get_i18n()
+from crewai.utilities.i18n import I18N_DEFAULT as _I18N
 
 
 @dataclass

lib/crewai/src/crewai/agents/planner_observer.py

@@ -23,7 +23,7 @@ from crewai.events.types.observation_events import (
     StepObservationStartedEvent,
 )
 from crewai.utilities.agent_utils import extract_task_section
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.planning_types import StepObservation, TodoItem
 from crewai.utilities.types import LLMMessage
@@ -64,7 +64,6 @@ class PlannerObserver:
         self.task = task
         self.kickoff_input = kickoff_input
         self.llm = self._resolve_llm()
-        self._i18n: I18N = get_i18n()
 
     def _resolve_llm(self) -> Any:
         """Resolve which LLM to use for observation/planning.
@@ -246,7 +245,7 @@ class PlannerObserver:
             task_desc = extract_task_section(self.kickoff_input)
             task_goal = "Complete the task successfully"
 
-        system_prompt = self._i18n.retrieve("planning", "observation_system_prompt")
+        system_prompt = I18N_DEFAULT.retrieve("planning", "observation_system_prompt")
 
         # Build context of what's been done
         completed_summary = ""
@@ -273,7 +272,9 @@ class PlannerObserver:
                 remaining_lines
             )
 
-        user_prompt = self._i18n.retrieve("planning", "observation_user_prompt").format(
+        user_prompt = I18N_DEFAULT.retrieve(
+            "planning", "observation_user_prompt"
+        ).format(
             task_description=task_desc,
             task_goal=task_goal,
             completed_summary=completed_summary,

lib/crewai/src/crewai/agents/step_executor.py

@@ -38,7 +38,7 @@ from crewai.utilities.agent_utils import (
     process_llm_response,
     setup_native_tools,
 )
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.planning_types import TodoItem
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.step_execution_context import StepExecutionContext, StepResult
@@ -81,7 +81,7 @@ class StepExecutor:
         function_calling_llm: Optional separate LLM for function calling.
         request_within_rpm_limit: Optional RPM limit function.
         callbacks: Optional list of callbacks.
-        i18n: Optional i18n instance.
+
     """
 
     def __init__(
@@ -96,7 +96,6 @@ class StepExecutor:
         function_calling_llm: BaseLLM | None = None,
         request_within_rpm_limit: Callable[[], bool] | None = None,
         callbacks: list[Any] | None = None,
-        i18n: I18N | None = None,
     ) -> None:
         self.llm = llm
         self.tools = tools
@@ -108,7 +107,6 @@ class StepExecutor:
         self.function_calling_llm = function_calling_llm
         self.request_within_rpm_limit = request_within_rpm_limit
         self.callbacks = callbacks or []
-        self._i18n: I18N = i18n or get_i18n()
 
         # Native tool support — set up once
         self._use_native_tools = check_native_tool_support(
@@ -221,14 +219,14 @@ class StepExecutor:
         tools_section = ""
         if self.tools and not self._use_native_tools:
             tool_names = ", ".join(sanitize_tool_name(t.name) for t in self.tools)
-            tools_section = self._i18n.retrieve(
+            tools_section = I18N_DEFAULT.retrieve(
                 "planning", "step_executor_tools_section"
             ).format(tool_names=tool_names)
         elif self.tools:
             tool_names = ", ".join(sanitize_tool_name(t.name) for t in self.tools)
             tools_section = f"\n\nAvailable tools: {tool_names}"
 
-        return self._i18n.retrieve("planning", "step_executor_system_prompt").format(
+        return I18N_DEFAULT.retrieve("planning", "step_executor_system_prompt").format(
             role=role,
             backstory=backstory,
             goal=goal,
@@ -247,7 +245,7 @@ class StepExecutor:
             task_section = extract_task_section(context.task_description)
             if task_section:
                 parts.append(
-                    self._i18n.retrieve(
+                    I18N_DEFAULT.retrieve(
                         "planning", "step_executor_task_context"
                     ).format(
                         task_context=task_section,
@@ -255,14 +253,16 @@ class StepExecutor:
                 )
 
         parts.append(
-            self._i18n.retrieve("planning", "step_executor_user_prompt").format(
+            I18N_DEFAULT.retrieve("planning", "step_executor_user_prompt").format(
                 step_description=todo.description,
             )
         )
 
         if todo.tool_to_use:
             parts.append(
-                self._i18n.retrieve("planning", "step_executor_suggested_tool").format(
+                I18N_DEFAULT.retrieve(
+                    "planning", "step_executor_suggested_tool"
+                ).format(
                     tool_to_use=todo.tool_to_use,
                 )
             )
@@ -270,16 +270,16 @@ class StepExecutor:
         # Include dependency results (final results only, no traces)
         if context.dependency_results:
             parts.append(
-                self._i18n.retrieve("planning", "step_executor_context_header")
+                I18N_DEFAULT.retrieve("planning", "step_executor_context_header")
             )
             for step_num, result in sorted(context.dependency_results.items()):
                 parts.append(
-                    self._i18n.retrieve(
+                    I18N_DEFAULT.retrieve(
                         "planning", "step_executor_context_entry"
                     ).format(step_number=step_num, result=result)
                 )
 
-        parts.append(self._i18n.retrieve("planning", "step_executor_complete_step"))
+        parts.append(I18N_DEFAULT.retrieve("planning", "step_executor_complete_step"))
 
         return "\n".join(parts)
 
@@ -375,7 +375,6 @@ class StepExecutor:
                 agent_action=formatted,
                 fingerprint_context=fingerprint_context,
                 tools=self.tools,
-                i18n=self._i18n,
                 agent_key=self.agent.key if self.agent else None,
                 agent_role=self.agent.role if self.agent else None,
                 tools_handler=self.tools_handler,

lib/crewai/src/crewai/cli/checkpoint_cli.py

@@ -6,12 +6,16 @@ from datetime import datetime
 import glob
 import json
 import os
+import re
 import sqlite3
 from typing import Any
 
 import click
 
 
+_PLACEHOLDER_RE = re.compile(r"\{([A-Za-z_][A-Za-z0-9_\-]*)}")
+
+
 _SQLITE_MAGIC = b"SQLite format 3\x00"
 
 _SELECT_ALL = """
@@ -34,6 +38,25 @@ LIMIT 1
 """
 
 
+_DEFAULT_DIR = "./.checkpoints"
+_DEFAULT_DB = "./.checkpoints.db"
+
+
+def _detect_location(location: str) -> str:
+    """Resolve the default checkpoint location.
+
+    When the caller passes the default directory path, check whether a
+    SQLite database exists at the conventional ``.db`` path and prefer it.
+    """
+    if (
+        location == _DEFAULT_DIR
+        and not os.path.exists(_DEFAULT_DIR)
+        and os.path.exists(_DEFAULT_DB)
+    ):
+        return _DEFAULT_DB
+    return location
+
+
 def _is_sqlite(path: str) -> bool:
     """Check if a file is a SQLite database by reading its magic bytes."""
     if not os.path.isfile(path):
@@ -52,13 +75,7 @@ def _parse_checkpoint_json(raw: str, source: str) -> dict[str, Any]:
     nodes = data.get("event_record", {}).get("nodes", {})
     event_count = len(nodes)
 
-    trigger_event = None
-    if nodes:
-        last_node = max(
-            nodes.values(),
-            key=lambda n: n.get("event", {}).get("emission_sequence") or 0,
-        )
-        trigger_event = last_node.get("event", {}).get("type")
+    trigger_event = data.get("trigger")
 
     parsed_entities: list[dict[str, Any]] = []
     for entity in entities:
@@ -76,16 +93,47 @@ def _parse_checkpoint_json(raw: str, source: str) -> dict[str, Any]:
                 {
                     "description": t.get("description", ""),
                     "completed": t.get("output") is not None,
+                    "output": (t.get("output") or {}).get("raw", ""),
                 }
                 for t in tasks
             ]
         parsed_entities.append(info)
 
+    inputs: dict[str, Any] = {}
+    for entity in entities:
+        cp_inputs = entity.get("checkpoint_inputs")
+        if isinstance(cp_inputs, dict) and cp_inputs:
+            inputs = dict(cp_inputs)
+            break
+
+    for entity in entities:
+        for task in entity.get("tasks", []):
+            for field in (
+                "checkpoint_original_description",
+                "checkpoint_original_expected_output",
+            ):
+                text = task.get(field) or ""
+                for match in _PLACEHOLDER_RE.findall(text):
+                    if match not in inputs:
+                        inputs[match] = ""
+        for agent in entity.get("agents", []):
+            for field in ("role", "goal", "backstory"):
+                text = agent.get(field) or ""
+                for match in _PLACEHOLDER_RE.findall(text):
+                    if match not in inputs:
+                        inputs[match] = ""
+
+    branch = data.get("branch", "main")
+    parent_id = data.get("parent_id")
+
     return {
         "source": source,
         "event_count": event_count,
         "trigger": trigger_event,
         "entities": parsed_entities,
+        "branch": branch,
+        "parent_id": parent_id,
+        "inputs": inputs,
     }
 
 
@@ -189,6 +237,7 @@ def _list_sqlite(db_path: str) -> list[dict[str, Any]]:
                     "entities": [],
                     "source": checkpoint_id,
                 }
+            meta["db"] = db_path
             results.append(meta)
     return results
 
@@ -311,6 +360,10 @@ def _print_info(meta: dict[str, Any]) -> None:
     trigger = meta.get("trigger")
     if trigger:
         click.echo(f"Trigger: {trigger}")
+    click.echo(f"Branch:  {meta.get('branch', 'main')}")
+    parent_id = meta.get("parent_id")
+    if parent_id:
+        click.echo(f"Parent:  {parent_id}")
 
     for ent in meta.get("entities", []):
         eid = str(ent.get("id", ""))[:8]

lib/crewai/src/crewai/cli/checkpoint_tui.py

@@ -0,0 +1,622 @@
+"""Textual TUI for browsing checkpoint files."""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import Any, ClassVar
+
+from textual.app import App, ComposeResult
+from textual.binding import Binding
+from textual.containers import Horizontal, Vertical, VerticalScroll
+from textual.widgets import (
+    Button,
+    Footer,
+    Header,
+    Input,
+    Static,
+    TextArea,
+    Tree,
+)
+
+from crewai.cli.checkpoint_cli import (
+    _format_size,
+    _is_sqlite,
+    _list_json,
+    _list_sqlite,
+)
+
+
+_PRIMARY = "#eb6658"
+_SECONDARY = "#1F7982"
+_TERTIARY = "#ffffff"
+_DIM = "#888888"
+_BG_DARK = "#0d1117"
+_BG_PANEL = "#161b22"
+
+
+def _load_entries(location: str) -> list[dict[str, Any]]:
+    if _is_sqlite(location):
+        return _list_sqlite(location)
+    return _list_json(location)
+
+
+def _short_id(name: str) -> str:
+    """Shorten a checkpoint name for tree display."""
+    if len(name) > 30:
+        return name[:27] + "..."
+    return name
+
+
+def _entry_id(entry: dict[str, Any]) -> str:
+    """Normalize an entry's name into its checkpoint ID.
+
+    JSON filenames are ``{ts}_{uuid}_p-{parent}.json``; SQLite IDs
+    are already ``{ts}_{uuid}``. This strips the JSON suffix so
+    fork-parent lookups work in both providers.
+    """
+    name = str(entry.get("name", ""))
+    if name.endswith(".json"):
+        name = name[: -len(".json")]
+    idx = name.find("_p-")
+    if idx != -1:
+        name = name[:idx]
+    return name
+
+
+def _build_entity_header(ent: dict[str, Any]) -> str:
+    """Build rich text header for an entity (progress bar only)."""
+    lines: list[str] = []
+    tasks = ent.get("tasks")
+    if isinstance(tasks, list):
+        completed = ent.get("tasks_completed", 0)
+        total = ent.get("tasks_total", 0)
+        pct = int(completed / total * 100) if total else 0
+        bar_len = 20
+        filled = int(bar_len * completed / total) if total else 0
+        bar = f"[{_PRIMARY}]{'█' * filled}[/][{_DIM}]{'░' * (bar_len - filled)}[/]"
+        lines.append(f"{bar}  {completed}/{total} tasks ({pct}%)")
+    return "\n".join(lines)
+
+
+# Return type: (location, action, inputs, task_output_overrides)
+_TuiResult = tuple[str, str, dict[str, Any] | None, dict[int, str] | None] | None
+
+
+class CheckpointTUI(App[_TuiResult]):
+    """TUI to browse and inspect checkpoints.
+
+    Returns ``(location, action, inputs)`` where action is ``"resume"`` or
+    ``"fork"`` and inputs is a parsed dict or ``None``,
+    or ``None`` if the user quit without selecting.
+    """
+
+    TITLE = "CrewAI Checkpoints"
+
+    CSS = f"""
+    Screen {{
+        background: {_BG_DARK};
+    }}
+    Header {{
+        background: {_PRIMARY};
+        color: {_TERTIARY};
+    }}
+    Footer {{
+        background: {_SECONDARY};
+        color: {_TERTIARY};
+    }}
+    Footer > .footer-key--key {{
+        background: {_PRIMARY};
+        color: {_TERTIARY};
+    }}
+    #main-layout {{
+        height: 1fr;
+    }}
+    #tree-panel {{
+        width: 45%;
+        background: {_BG_PANEL};
+        border: round {_SECONDARY};
+        padding: 0 1;
+        scrollbar-color: {_PRIMARY};
+    }}
+    #tree-panel:focus-within {{
+        border: round {_PRIMARY};
+    }}
+    #detail-container {{
+        width: 55%;
+        height: 1fr;
+    }}
+    #detail-scroll {{
+        height: 1fr;
+        background: {_BG_PANEL};
+        border: round {_SECONDARY};
+        padding: 1 2;
+        scrollbar-color: {_PRIMARY};
+    }}
+    #detail-scroll:focus-within {{
+        border: round {_PRIMARY};
+    }}
+    #detail-header {{
+        margin-bottom: 1;
+    }}
+    #status {{
+        height: 1;
+        padding: 0 2;
+        color: {_DIM};
+    }}
+    #inputs-section {{
+        display: none;
+        height: auto;
+        max-height: 8;
+        padding: 0 1;
+    }}
+    #inputs-section.visible {{
+        display: block;
+    }}
+    #inputs-label {{
+        height: 1;
+        color: {_DIM};
+        padding: 0 1;
+    }}
+    .input-row {{
+        height: 3;
+        padding: 0 1;
+    }}
+    .input-row Static {{
+        width: auto;
+        min-width: 12;
+        padding: 1 1 0 0;
+        color: {_TERTIARY};
+    }}
+    .input-row Input {{
+        width: 1fr;
+    }}
+    #no-inputs-label {{
+        height: 1;
+        color: {_DIM};
+        padding: 0 1;
+    }}
+    #action-buttons {{
+        height: 3;
+        align: right middle;
+        padding: 0 1;
+        display: none;
+    }}
+    #action-buttons.visible {{
+        display: block;
+    }}
+    #action-buttons Button {{
+        margin: 0 0 0 1;
+        min-width: 10;
+    }}
+    #btn-resume {{
+        background: {_SECONDARY};
+        color: {_TERTIARY};
+    }}
+    #btn-resume:hover {{
+        background: {_PRIMARY};
+    }}
+    #btn-fork {{
+        background: {_PRIMARY};
+        color: {_TERTIARY};
+    }}
+    #btn-fork:hover {{
+        background: {_SECONDARY};
+    }}
+    .entity-title {{
+        padding: 1 1 0 1;
+    }}
+    .entity-detail {{
+        padding: 0 1;
+    }}
+    .task-output-editor {{
+        height: auto;
+        max-height: 10;
+        margin: 0 1 1 1;
+        border: round {_DIM};
+    }}
+    .task-output-editor:focus {{
+        border: round {_PRIMARY};
+    }}
+    .task-label {{
+        padding: 0 1;
+    }}
+    Tree {{
+        background: {_BG_PANEL};
+    }}
+    Tree > .tree--cursor {{
+        background: {_SECONDARY};
+        color: {_TERTIARY};
+    }}
+    """
+
+    BINDINGS: ClassVar[list[Binding | tuple[str, str] | tuple[str, str, str]]] = [
+        ("q", "quit", "Quit"),
+        ("r", "refresh", "Refresh"),
+    ]
+
+    def __init__(self, location: str = "./.checkpoints") -> None:
+        super().__init__()
+        self._location = location
+        self._entries: list[dict[str, Any]] = []
+        self._selected_entry: dict[str, Any] | None = None
+        self._input_keys: list[str] = []
+        self._task_output_ids: list[tuple[int, str, str]] = []
+
+    def compose(self) -> ComposeResult:
+        yield Header(show_clock=False)
+        with Horizontal(id="main-layout"):
+            tree: Tree[dict[str, Any]] = Tree("Checkpoints", id="tree-panel")
+            tree.show_root = True
+            tree.guide_depth = 3
+            yield tree
+            with Vertical(id="detail-container"):
+                yield Static("", id="status")
+                with VerticalScroll(id="detail-scroll"):
+                    yield Static(
+                        f"[{_DIM}]Select a checkpoint from the tree[/]",  # noqa: S608
+                        id="detail-header",
+                    )
+                with Vertical(id="inputs-section"):
+                    yield Static("Inputs", id="inputs-label")
+                with Horizontal(id="action-buttons"):
+                    yield Button("Resume", id="btn-resume")
+                    yield Button("Fork", id="btn-fork")
+        yield Footer()
+
+    async def on_mount(self) -> None:
+        self._refresh_tree()
+        self.query_one("#tree-panel", Tree).root.expand()
+
+    def _refresh_tree(self) -> None:
+        self._entries = _load_entries(self._location)
+        self._selected_entry = None
+
+        tree = self.query_one("#tree-panel", Tree)
+        tree.clear()
+
+        if not self._entries:
+            self.query_one("#detail-header", Static).update(
+                f"[{_DIM}]No checkpoints in {self._location}[/]"
+            )
+            self.query_one("#status", Static).update("")
+            self.sub_title = self._location
+            return
+
+        # Group by branch
+        branches: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for entry in self._entries:
+            branch = entry.get("branch", "main")
+            branches[branch].append(entry)
+
+        # Index checkpoint names to tree nodes so forks can attach
+        node_by_name: dict[str, Any] = {}
+
+        def _make_label(e: dict[str, Any]) -> str:
+            name = e.get("name", "")
+            ts = e.get("ts") or ""
+            trigger = e.get("trigger") or ""
+            parts = [f"[bold]{_short_id(name)}[/]"]
+            if ts:
+                time_part = ts.split(" ")[-1] if " " in ts else ts
+                parts.append(f"[{_DIM}]{time_part}[/]")
+            if trigger:
+                parts.append(f"[{_PRIMARY}]{trigger}[/]")
+            return "  ".join(parts)
+
+        fork_parents: set[str] = set()
+        for branch_name, entries in branches.items():
+            if branch_name == "main" or not entries:
+                continue
+            oldest = min(entries, key=lambda e: str(e.get("name", "")))
+            first_parent = oldest.get("parent_id")
+            if first_parent:
+                fork_parents.add(str(first_parent))
+
+        def _add_checkpoint(parent_node: Any, e: dict[str, Any]) -> None:
+            """Add a checkpoint node — expandable only if a fork attaches to it."""
+            cp_id = _entry_id(e)
+            if cp_id in fork_parents:
+                node = parent_node.add(
+                    _make_label(e), data=e, expand=False, allow_expand=True
+                )
+            else:
+                node = parent_node.add_leaf(_make_label(e), data=e)
+            node_by_name[cp_id] = node
+
+        if "main" in branches:
+            for entry in reversed(branches["main"]):
+                _add_checkpoint(tree.root, entry)
+
+        fork_branches = [
+            (name, sorted(entries, key=lambda e: str(e.get("name", ""))))
+            for name, entries in branches.items()
+            if name != "main"
+        ]
+        remaining = fork_branches
+        max_passes = len(remaining) + 1
+        while remaining and max_passes > 0:
+            max_passes -= 1
+            deferred = []
+            made_progress = False
+            for branch_name, entries in remaining:
+                first_parent = entries[0].get("parent_id") if entries else None
+                if first_parent and str(first_parent) not in node_by_name:
+                    deferred.append((branch_name, entries))
+                    continue
+                attach_to: Any = tree.root
+                if first_parent:
+                    attach_to = node_by_name.get(str(first_parent), tree.root)
+                branch_label = (
+                    f"[bold {_SECONDARY}]{branch_name}[/]  [{_DIM}]({len(entries)})[/]"
+                )
+                branch_node = attach_to.add(branch_label, expand=False)
+                for entry in entries:
+                    _add_checkpoint(branch_node, entry)
+                made_progress = True
+            remaining = deferred
+            if not made_progress:
+                break
+
+        for branch_name, entries in remaining:
+            branch_label = (
+                f"[bold {_SECONDARY}]{branch_name}[/]  "
+                f"[{_DIM}]({len(entries)})[/]  [{_DIM}](orphaned)[/]"
+            )
+            branch_node = tree.root.add(branch_label, expand=False)
+            for entry in entries:
+                _add_checkpoint(branch_node, entry)
+
+        count = len(self._entries)
+        storage = "SQLite" if _is_sqlite(self._location) else "JSON"
+        self.sub_title = self._location
+        self.query_one("#status", Static).update(f" {count} checkpoint(s) | {storage}")
+
+    async def _show_detail(self, entry: dict[str, Any]) -> None:
+        """Update the detail panel for a checkpoint entry."""
+        self._selected_entry = entry
+        self.query_one("#action-buttons").add_class("visible")
+
+        detail_scroll = self.query_one("#detail-scroll", VerticalScroll)
+
+        # Remove all dynamic children except the header — await so IDs are freed
+        to_remove = [c for c in detail_scroll.children if c.id != "detail-header"]
+        for child in to_remove:
+            await child.remove()
+
+        # Header
+        name = entry.get("name", "")
+        ts = entry.get("ts") or "unknown"
+        trigger = entry.get("trigger") or ""
+        branch = entry.get("branch", "main")
+        parent_id = entry.get("parent_id")
+
+        header_lines = [
+            f"[bold {_PRIMARY}]{name}[/]",
+            f"[{_DIM}]{'─' * 50}[/]",
+            "",
+            f"  [bold]Time[/]       {ts}",
+        ]
+        if "size" in entry:
+            header_lines.append(f"  [bold]Size[/]       {_format_size(entry['size'])}")
+        header_lines.append(f"  [bold]Events[/]     {entry.get('event_count', 0)}")
+        if trigger:
+            header_lines.append(f"  [bold]Trigger[/]    [{_PRIMARY}]{trigger}[/]")
+        header_lines.append(f"  [bold]Branch[/]     [{_SECONDARY}]{branch}[/]")
+        if parent_id:
+            header_lines.append(f"  [bold]Parent[/]     [{_DIM}]{parent_id}[/]")
+        if "path" in entry:
+            header_lines.append(f"  [bold]Path[/]       [{_DIM}]{entry['path']}[/]")
+        if "db" in entry:
+            header_lines.append(f"  [bold]Database[/]   [{_DIM}]{entry['db']}[/]")
+
+        self.query_one("#detail-header", Static).update("\n".join(header_lines))
+
+        # Entity details and editable task outputs — mounted flat for scrolling
+        self._task_output_ids = []
+        flat_task_idx = 0
+        for ent_idx, ent in enumerate(entry.get("entities", [])):
+            etype = ent.get("type", "unknown")
+            ename = ent.get("name", "unnamed")
+            completed = ent.get("tasks_completed")
+            total = ent.get("tasks_total")
+            entity_title = f"[bold {_SECONDARY}]{etype}: {ename}[/]"
+            if completed is not None and total is not None:
+                entity_title += f"  [{_DIM}]{completed}/{total} tasks[/]"
+            await detail_scroll.mount(Static(entity_title, classes="entity-title"))
+            await detail_scroll.mount(
+                Static(_build_entity_header(ent), classes="entity-detail")
+            )
+
+            tasks = ent.get("tasks", [])
+            for i, task in enumerate(tasks):
+                desc = str(task.get("description", ""))
+                if len(desc) > 55:
+                    desc = desc[:52] + "..."
+                if task.get("completed"):
+                    icon = "[green]✓[/]"
+                    await detail_scroll.mount(
+                        Static(f"  {icon}  {i + 1}. {desc}", classes="task-label")
+                    )
+                    output_text = task.get("output", "")
+                    editor_id = f"task-output-{ent_idx}-{i}"
+                    await detail_scroll.mount(
+                        TextArea(
+                            str(output_text),
+                            classes="task-output-editor",
+                            id=editor_id,
+                        )
+                    )
+                    self._task_output_ids.append(
+                        (flat_task_idx, editor_id, str(output_text))
+                    )
+                else:
+                    icon = "[yellow]○[/]"
+                    await detail_scroll.mount(
+                        Static(f"  {icon}  {i + 1}. {desc}", classes="task-label")
+                    )
+                flat_task_idx += 1
+
+        # Build input fields
+        await self._build_input_fields(entry.get("inputs", {}))
+
+    async def _build_input_fields(self, inputs: dict[str, Any]) -> None:
+        """Rebuild the inputs section with one field per input key."""
+        section = self.query_one("#inputs-section")
+
+        # Remove old dynamic children — await so IDs are freed
+        for widget in list(section.query(".input-row, .no-inputs")):
+            await widget.remove()
+
+        self._input_keys = []
+
+        if not inputs:
+            await section.mount(Static(f"[{_DIM}]No inputs[/]", classes="no-inputs"))
+            section.add_class("visible")
+            return
+
+        for key, value in inputs.items():
+            self._input_keys.append(key)
+            row = Horizontal(classes="input-row")
+            row.compose_add_child(Static(f"[bold]{key}[/]"))
+            row.compose_add_child(
+                Input(value=str(value), placeholder=key, id=f"input-{key}")
+            )
+            await section.mount(row)
+
+        section.add_class("visible")
+
+    def _collect_inputs(self) -> dict[str, Any] | None:
+        """Collect current values from input fields."""
+        if not self._input_keys:
+            return None
+        result: dict[str, Any] = {}
+        for key in self._input_keys:
+            widget = self.query_one(f"#input-{key}", Input)
+            result[key] = widget.value
+        return result
+
+    def _collect_task_overrides(self) -> dict[int, str] | None:
+        """Collect edited task outputs. Returns only changed values."""
+        if not self._task_output_ids or self._selected_entry is None:
+            return None
+        overrides: dict[int, str] = {}
+        for task_idx, editor_id, original in self._task_output_ids:
+            editor = self.query_one(f"#{editor_id}", TextArea)
+            if editor.text != original:
+                overrides[task_idx] = editor.text
+        return overrides or None
+
+    def _resolve_location(self, entry: dict[str, Any]) -> str:
+        """Get the restore location string for a checkpoint entry."""
+        if "path" in entry:
+            return str(entry["path"])
+        if _is_sqlite(self._location):
+            return f"{self._location}#{entry['name']}"
+        return str(entry.get("name", ""))
+
+    async def on_tree_node_highlighted(
+        self, event: Tree.NodeHighlighted[dict[str, Any]]
+    ) -> None:
+        if event.node.data is not None:
+            await self._show_detail(event.node.data)
+
+    def on_button_pressed(self, event: Button.Pressed) -> None:
+        if self._selected_entry is None:
+            return
+        inputs = self._collect_inputs()
+        overrides = self._collect_task_overrides()
+        loc = self._resolve_location(self._selected_entry)
+        if event.button.id == "btn-resume":
+            self.exit((loc, "resume", inputs, overrides))
+        elif event.button.id == "btn-fork":
+            self.exit((loc, "fork", inputs, overrides))
+
+    def action_refresh(self) -> None:
+        self._refresh_tree()
+
+
+async def _run_checkpoint_tui_async(location: str) -> None:
+    """Async implementation of the checkpoint TUI flow."""
+    import click
+
+    app = CheckpointTUI(location=location)
+    selection = await app.run_async()
+
+    if selection is None:
+        return
+
+    selected, action, inputs, task_overrides = selection
+
+    from crewai.crew import Crew
+    from crewai.state.checkpoint_config import CheckpointConfig
+
+    config = CheckpointConfig(restore_from=selected)
+
+    if action == "fork":
+        click.echo(f"\nForking from: {selected}\n")
+        crew = Crew.fork(config)
+    else:
+        click.echo(f"\nResuming from: {selected}\n")
+        crew = Crew.from_checkpoint(config)
+
+    if task_overrides:
+        click.echo("Modifications:")
+        overridden_agents: set[int] = set()
+        for task_idx, new_output in task_overrides.items():
+            if task_idx < len(crew.tasks) and crew.tasks[task_idx].output is not None:
+                desc = crew.tasks[task_idx].description or f"Task {task_idx + 1}"
+                if len(desc) > 60:
+                    desc = desc[:57] + "..."
+                crew.tasks[task_idx].output.raw = new_output  # type: ignore[union-attr]
+                preview = new_output.replace("\n", " ")
+                if len(preview) > 80:
+                    preview = preview[:77] + "..."
+                click.echo(f"  Task {task_idx + 1}: {desc}")
+                click.echo(f"    -> {preview}")
+                agent = crew.tasks[task_idx].agent
+                if agent and agent.agent_executor:
+                    nth = sum(1 for t in crew.tasks[:task_idx] if t.agent is agent)
+                    messages = agent.agent_executor.messages
+                    system_positions = [
+                        i for i, m in enumerate(messages) if m.get("role") == "system"
+                    ]
+                    if nth < len(system_positions):
+                        seg_start = system_positions[nth]
+                        seg_end = (
+                            system_positions[nth + 1]
+                            if nth + 1 < len(system_positions)
+                            else len(messages)
+                        )
+                        for j in range(seg_end - 1, seg_start, -1):
+                            if messages[j].get("role") == "assistant":
+                                messages[j]["content"] = new_output
+                                break
+                    overridden_agents.add(id(agent))
+
+        earliest = min(task_overrides)
+        for offset, subsequent in enumerate(
+            crew.tasks[earliest + 1 :], start=earliest + 1
+        ):
+            if subsequent.output and offset not in task_overrides:
+                subsequent.output = None
+            if subsequent.agent and subsequent.agent.agent_executor:
+                subsequent.agent.agent_executor._resuming = False
+                if id(subsequent.agent) not in overridden_agents:
+                    subsequent.agent.agent_executor.messages = []
+        click.echo()
+
+    if inputs:
+        click.echo("Inputs:")
+        for k, v in inputs.items():
+            click.echo(f"  {k}: {v}")
+        click.echo()
+
+    result = await crew.akickoff(inputs=inputs)
+    click.echo(f"\nResult: {getattr(result, 'raw', result)}")
+
+
+def run_checkpoint_tui(location: str = "./.checkpoints") -> None:
+    """Launch the checkpoint browser TUI."""
+    import asyncio
+
+    asyncio.run(_run_checkpoint_tui_async(location))

lib/crewai/src/crewai/cli/cli.py

@@ -392,10 +392,15 @@ def deploy() -> None:
 
 @deploy.command(name="create")
 @click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
-def deploy_create(yes: bool) -> None:
+@click.option(
+    "--skip-validate",
+    is_flag=True,
+    help="Skip the pre-deploy validation checks.",
+)
+def deploy_create(yes: bool, skip_validate: bool) -> None:
     """Create a Crew deployment."""
     deploy_cmd = DeployCommand()
-    deploy_cmd.create_crew(yes)
+    deploy_cmd.create_crew(yes, skip_validate=skip_validate)
 
 
 @deploy.command(name="list")
@@ -407,10 +412,28 @@ def deploy_list() -> None:
 
 @deploy.command(name="push")
 @click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_push(uuid: str | None) -> None:
+@click.option(
+    "--skip-validate",
+    is_flag=True,
+    help="Skip the pre-deploy validation checks.",
+)
+def deploy_push(uuid: str | None, skip_validate: bool) -> None:
     """Deploy the Crew."""
     deploy_cmd = DeployCommand()
-    deploy_cmd.deploy(uuid=uuid)
+    deploy_cmd.deploy(uuid=uuid, skip_validate=skip_validate)
+
+
+@deploy.command(name="validate")
+def deploy_validate() -> None:
+    """Validate the current project against common deployment failures.
+
+    Runs the same pre-deploy checks that `crewai deploy create` and
+    `crewai deploy push` run automatically, without contacting the platform.
+    Exits non-zero if any blocking issues are found.
+    """
+    from crewai.cli.deploy.validate import run_validate_command
+
+    run_validate_command()
 
 
 @deploy.command(name="status")
@@ -786,27 +809,40 @@ def traces_status() -> None:
     console.print(panel)
 
 
-@crewai.group()
-def checkpoint() -> None:
-    """Inspect checkpoint files."""
+@crewai.group(invoke_without_command=True)
+@click.option(
+    "--location", default="./.checkpoints", help="Checkpoint directory or SQLite file."
+)
+@click.pass_context
+def checkpoint(ctx: click.Context, location: str) -> None:
+    """Browse and inspect checkpoints. Launches a TUI when called without a subcommand."""
+    from crewai.cli.checkpoint_cli import _detect_location
+
+    location = _detect_location(location)
+    ctx.ensure_object(dict)
+    ctx.obj["location"] = location
+    if ctx.invoked_subcommand is None:
+        from crewai.cli.checkpoint_tui import run_checkpoint_tui
+
+        run_checkpoint_tui(location)
 
 
 @checkpoint.command("list")
 @click.argument("location", default="./.checkpoints")
 def checkpoint_list(location: str) -> None:
     """List checkpoints in a directory."""
-    from crewai.cli.checkpoint_cli import list_checkpoints
+    from crewai.cli.checkpoint_cli import _detect_location, list_checkpoints
 
-    list_checkpoints(location)
+    list_checkpoints(_detect_location(location))
 
 
 @checkpoint.command("info")
 @click.argument("path", default="./.checkpoints")
 def checkpoint_info(path: str) -> None:
     """Show details of a checkpoint. Pass a file or directory for latest."""
-    from crewai.cli.checkpoint_cli import info_checkpoint
+    from crewai.cli.checkpoint_cli import _detect_location, info_checkpoint
 
-    info_checkpoint(path)
+    info_checkpoint(_detect_location(path))
 
 
 if __name__ == "__main__":

lib/crewai/src/crewai/cli/crew_chat.py

@@ -13,7 +13,6 @@ from packaging import version
 import tomli
 
 from crewai.cli.utils import read_toml
-from crewai.cli.version import get_crewai_version
 from crewai.crew import Crew
 from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
@@ -21,6 +20,7 @@ from crewai.types.crew_chat import ChatInputField, ChatInputs
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.types import LLMMessage
+from crewai.utilities.version import get_crewai_version
 
 
 MIN_REQUIRED_VERSION: Final[Literal["0.98.0"]] = "0.98.0"

lib/crewai/src/crewai/cli/deploy/main.py

@@ -4,12 +4,35 @@ from rich.console import Console
 
 from crewai.cli import git
 from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.deploy.validate import validate_project
 from crewai.cli.utils import fetch_and_json_env_file, get_project_name
 
 
 console = Console()
 
 
+def _run_predeploy_validation(skip_validate: bool) -> bool:
+    """Run pre-deploy validation unless skipped.
+
+    Returns True if deployment should proceed, False if it should abort.
+    """
+    if skip_validate:
+        console.print(
+            "[yellow]Skipping pre-deploy validation (--skip-validate).[/yellow]"
+        )
+        return True
+
+    console.print("Running pre-deploy validation...", style="bold blue")
+    validator = validate_project()
+    if not validator.ok:
+        console.print(
+            "\n[bold red]Pre-deploy validation failed. "
+            "Fix the issues above or re-run with --skip-validate.[/bold red]"
+        )
+        return False
+    return True
+
+
 class DeployCommand(BaseCommand, PlusAPIMixin):
     """
     A class to handle deployment-related operations for CrewAI projects.
@@ -60,13 +83,16 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
                 f"{log_message['timestamp']} - {log_message['level']}: {log_message['message']}"
             )
 
-    def deploy(self, uuid: str | None = None) -> None:
+    def deploy(self, uuid: str | None = None, skip_validate: bool = False) -> None:
         """
         Deploy a crew using either UUID or project name.
 
         Args:
             uuid (Optional[str]): The UUID of the crew to deploy.
+            skip_validate (bool): Skip pre-deploy validation checks.
         """
+        if not _run_predeploy_validation(skip_validate):
+            return
         self._telemetry.start_deployment_span(uuid)
         console.print("Starting deployment...", style="bold blue")
         if uuid:
@@ -80,10 +106,16 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         self._validate_response(response)
         self._display_deployment_info(response.json())
 
-    def create_crew(self, confirm: bool = False) -> None:
+    def create_crew(self, confirm: bool = False, skip_validate: bool = False) -> None:
         """
         Create a new crew deployment.
+
+        Args:
+            confirm (bool): Whether to skip the interactive confirmation prompt.
+            skip_validate (bool): Skip pre-deploy validation checks.
         """
+        if not _run_predeploy_validation(skip_validate):
+            return
         self._telemetry.create_crew_deployment_span()
         console.print("Creating deployment...", style="bold blue")
         env_vars = fetch_and_json_env_file()

lib/crewai/src/crewai/cli/deploy/validate.py

@@ -0,0 +1,842 @@
+"""Pre-deploy validation for CrewAI projects.
+
+Catches locally what a deploy would reject at build or runtime so users
+don't burn deployment attempts on fixable project-structure problems.
+
+Each check is grouped into one of:
+- ERROR: will block a deployment; validator exits non-zero.
+- WARNING: may still deploy but is almost always a deployment bug; printed
+  but does not block.
+
+The individual checks mirror the categories observed in production
+deployment-failure logs:
+
+1. pyproject.toml present with ``[project].name``
+2. lockfile (``uv.lock`` or ``poetry.lock``) present and not stale
+3. package directory at ``src/<package>/`` exists (no empty name, no egg-info)
+4. standard crew files: ``crew.py``, ``config/agents.yaml``, ``config/tasks.yaml``
+5. flow entrypoint: ``main.py`` with a Flow subclass
+6. hatch wheel target resolves (packages = [...] or default dir matches name)
+7. crew/flow module imports cleanly (catches ``@CrewBase not found``,
+   ``No Flow subclass found``, provider import errors)
+8. environment variables referenced in code vs ``.env`` / deployment env
+9. installed crewai vs lockfile pin (catches missing-attribute failures from
+   stale pins)
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+import json
+import logging
+import os
+from pathlib import Path
+import re
+import shutil
+import subprocess
+import sys
+from typing import Any
+
+from rich.console import Console
+
+from crewai.cli.utils import parse_toml
+
+
+console = Console()
+logger = logging.getLogger(__name__)
+
+
+class Severity(str, Enum):
+    """Severity of a validation finding."""
+
+    ERROR = "error"
+    WARNING = "warning"
+
+
+@dataclass
+class ValidationResult:
+    """A single finding from a validation check.
+
+    Attributes:
+        severity: whether this blocks deploy or is advisory.
+        code: stable short identifier, used in tests and docs
+            (e.g. ``missing_pyproject``, ``stale_lockfile``).
+        title: one-line summary shown to the user.
+        detail: optional multi-line explanation.
+        hint: optional remediation suggestion.
+    """
+
+    severity: Severity
+    code: str
+    title: str
+    detail: str = ""
+    hint: str = ""
+
+
+# Maps known provider env var names → label used in hint messages.
+_KNOWN_API_KEY_HINTS: dict[str, str] = {
+    "OPENAI_API_KEY": "OpenAI",
+    "ANTHROPIC_API_KEY": "Anthropic",
+    "GOOGLE_API_KEY": "Google",
+    "GEMINI_API_KEY": "Gemini",
+    "AZURE_OPENAI_API_KEY": "Azure OpenAI",
+    "AZURE_API_KEY": "Azure",
+    "AWS_ACCESS_KEY_ID": "AWS",
+    "AWS_SECRET_ACCESS_KEY": "AWS",
+    "COHERE_API_KEY": "Cohere",
+    "GROQ_API_KEY": "Groq",
+    "MISTRAL_API_KEY": "Mistral",
+    "TAVILY_API_KEY": "Tavily",
+    "SERPER_API_KEY": "Serper",
+    "SERPLY_API_KEY": "Serply",
+    "PERPLEXITY_API_KEY": "Perplexity",
+    "DEEPSEEK_API_KEY": "DeepSeek",
+    "OPENROUTER_API_KEY": "OpenRouter",
+    "FIRECRAWL_API_KEY": "Firecrawl",
+    "EXA_API_KEY": "Exa",
+    "BROWSERBASE_API_KEY": "Browserbase",
+}
+
+
+def normalize_package_name(project_name: str) -> str:
+    """Normalize a pyproject project.name into a Python package directory name.
+
+    Mirrors the rules in ``crewai.cli.create_crew.create_crew`` so the
+    validator agrees with the scaffolder about where ``src/<pkg>/`` should
+    live.
+    """
+    folder = project_name.replace(" ", "_").replace("-", "_").lower()
+    return re.sub(r"[^a-zA-Z0-9_]", "", folder)
+
+
+class DeployValidator:
+    """Runs the full pre-deploy validation suite against a project directory."""
+
+    def __init__(self, project_root: Path | None = None) -> None:
+        self.project_root: Path = (project_root or Path.cwd()).resolve()
+        self.results: list[ValidationResult] = []
+        self._pyproject: dict[str, Any] | None = None
+        self._project_name: str | None = None
+        self._package_name: str | None = None
+        self._package_dir: Path | None = None
+        self._is_flow: bool = False
+
+    def _add(
+        self,
+        severity: Severity,
+        code: str,
+        title: str,
+        detail: str = "",
+        hint: str = "",
+    ) -> None:
+        self.results.append(
+            ValidationResult(
+                severity=severity,
+                code=code,
+                title=title,
+                detail=detail,
+                hint=hint,
+            )
+        )
+
+    @property
+    def errors(self) -> list[ValidationResult]:
+        return [r for r in self.results if r.severity is Severity.ERROR]
+
+    @property
+    def warnings(self) -> list[ValidationResult]:
+        return [r for r in self.results if r.severity is Severity.WARNING]
+
+    @property
+    def ok(self) -> bool:
+        return not self.errors
+
+    def run(self) -> list[ValidationResult]:
+        """Run all checks. Later checks are skipped when earlier ones make
+        them impossible (e.g. no pyproject.toml → no lockfile check)."""
+        if not self._check_pyproject():
+            return self.results
+
+        self._check_lockfile()
+
+        if not self._check_package_dir():
+            self._check_hatch_wheel_target()
+            return self.results
+
+        if self._is_flow:
+            self._check_flow_entrypoint()
+        else:
+            self._check_crew_entrypoint()
+            self._check_config_yamls()
+
+        self._check_hatch_wheel_target()
+        self._check_module_imports()
+        self._check_env_vars()
+        self._check_version_vs_lockfile()
+
+        return self.results
+
+    def _check_pyproject(self) -> bool:
+        pyproject_path = self.project_root / "pyproject.toml"
+        if not pyproject_path.exists():
+            self._add(
+                Severity.ERROR,
+                "missing_pyproject",
+                "Cannot find pyproject.toml",
+                detail=(
+                    f"Expected pyproject.toml at {pyproject_path}. "
+                    "CrewAI projects must be installable Python packages."
+                ),
+                hint="Run `crewai create crew <name>` to scaffold a valid project layout.",
+            )
+            return False
+
+        try:
+            self._pyproject = parse_toml(pyproject_path.read_text())
+        except Exception as e:
+            self._add(
+                Severity.ERROR,
+                "invalid_pyproject",
+                "pyproject.toml is not valid TOML",
+                detail=str(e),
+            )
+            return False
+
+        project = self._pyproject.get("project") or {}
+        name = project.get("name")
+        if not isinstance(name, str) or not name.strip():
+            self._add(
+                Severity.ERROR,
+                "missing_project_name",
+                "pyproject.toml is missing [project].name",
+                detail=(
+                    "Without a project name the platform cannot resolve your "
+                    "package directory (this produces errors like "
+                    "'Cannot find src//crew.py')."
+                ),
+                hint='Set a `name = "..."` field under `[project]` in pyproject.toml.',
+            )
+            return False
+
+        self._project_name = name
+        self._package_name = normalize_package_name(name)
+        self._is_flow = (self._pyproject.get("tool") or {}).get("crewai", {}).get(
+            "type"
+        ) == "flow"
+        return True
+
+    def _check_lockfile(self) -> None:
+        uv_lock = self.project_root / "uv.lock"
+        poetry_lock = self.project_root / "poetry.lock"
+        pyproject = self.project_root / "pyproject.toml"
+
+        if not uv_lock.exists() and not poetry_lock.exists():
+            self._add(
+                Severity.ERROR,
+                "missing_lockfile",
+                "Expected to find at least one of these files: uv.lock or poetry.lock",
+                hint=(
+                    "Run `uv lock` (recommended) or `poetry lock` in your project "
+                    "directory, commit the lockfile, then redeploy."
+                ),
+            )
+            return
+
+        lockfile = uv_lock if uv_lock.exists() else poetry_lock
+        try:
+            if lockfile.stat().st_mtime < pyproject.stat().st_mtime:
+                self._add(
+                    Severity.WARNING,
+                    "stale_lockfile",
+                    f"{lockfile.name} is older than pyproject.toml",
+                    detail=(
+                        "Your lockfile may not reflect recent dependency changes. "
+                        "The platform resolves from the lockfile, so deployed "
+                        "dependencies may differ from local."
+                    ),
+                    hint="Run `uv lock` (or `poetry lock`) and commit the result.",
+                )
+        except OSError:
+            pass
+
+    def _check_package_dir(self) -> bool:
+        if self._package_name is None:
+            return False
+
+        src_dir = self.project_root / "src"
+        if not src_dir.is_dir():
+            self._add(
+                Severity.ERROR,
+                "missing_src_dir",
+                "Missing src/ directory",
+                detail=(
+                    "CrewAI deployments expect a src-layout project: "
+                    f"src/{self._package_name}/crew.py (or main.py for flows)."
+                ),
+                hint="Run `crewai create crew <name>` to see the expected layout.",
+            )
+            return False
+
+        package_dir = src_dir / self._package_name
+        if not package_dir.is_dir():
+            siblings = [
+                p.name
+                for p in src_dir.iterdir()
+                if p.is_dir() and not p.name.endswith(".egg-info")
+            ]
+            egg_info = [
+                p.name for p in src_dir.iterdir() if p.name.endswith(".egg-info")
+            ]
+
+            hint_parts = [
+                f'Create src/{self._package_name}/ to match [project].name = "{self._project_name}".'
+            ]
+            if siblings:
+                hint_parts.append(
+                    f"Found other package directories: {', '.join(siblings)}. "
+                    f"Either rename one to '{self._package_name}' or update [project].name."
+                )
+            if egg_info:
+                hint_parts.append(
+                    f"Delete stale build artifacts: {', '.join(egg_info)} "
+                    "(these confuse the platform's package discovery)."
+                )
+
+            self._add(
+                Severity.ERROR,
+                "missing_package_dir",
+                f"Cannot find src/{self._package_name}/",
+                detail=(
+                    "The platform looks for your crew source under "
+                    "src/<package_name>/, derived from [project].name."
+                ),
+                hint=" ".join(hint_parts),
+            )
+            return False
+
+        for p in src_dir.iterdir():
+            if p.name.endswith(".egg-info"):
+                self._add(
+                    Severity.WARNING,
+                    "stale_egg_info",
+                    f"Stale build artifact in src/: {p.name}",
+                    detail=(
+                        ".egg-info directories can be mistaken for your package "
+                        "and cause 'Cannot find src/<name>.egg-info/crew.py' errors."
+                    ),
+                    hint=f"Delete {p} and add `*.egg-info/` to .gitignore.",
+                )
+
+        self._package_dir = package_dir
+        return True
+
+    def _check_crew_entrypoint(self) -> None:
+        if self._package_dir is None:
+            return
+        crew_py = self._package_dir / "crew.py"
+        if not crew_py.is_file():
+            self._add(
+                Severity.ERROR,
+                "missing_crew_py",
+                f"Cannot find {crew_py.relative_to(self.project_root)}",
+                detail=(
+                    "Standard crew projects must define a Crew class decorated "
+                    "with @CrewBase inside crew.py."
+                ),
+                hint=(
+                    "Create crew.py with an @CrewBase-annotated class, or set "
+                    '`[tool.crewai] type = "flow"` in pyproject.toml if this is a flow.'
+                ),
+            )
+
+    def _check_config_yamls(self) -> None:
+        if self._package_dir is None:
+            return
+        config_dir = self._package_dir / "config"
+        if not config_dir.is_dir():
+            self._add(
+                Severity.ERROR,
+                "missing_config_dir",
+                f"Cannot find {config_dir.relative_to(self.project_root)}",
+                hint="Create a config/ directory with agents.yaml and tasks.yaml.",
+            )
+            return
+
+        for yaml_name in ("agents.yaml", "tasks.yaml"):
+            yaml_path = config_dir / yaml_name
+            if not yaml_path.is_file():
+                self._add(
+                    Severity.ERROR,
+                    f"missing_{yaml_name.replace('.', '_')}",
+                    f"Cannot find {yaml_path.relative_to(self.project_root)}",
+                    detail=(
+                        "CrewAI loads agent and task config from these files; "
+                        "missing them causes empty-config warnings and runtime crashes."
+                    ),
+                )
+
+    def _check_flow_entrypoint(self) -> None:
+        if self._package_dir is None:
+            return
+        main_py = self._package_dir / "main.py"
+        if not main_py.is_file():
+            self._add(
+                Severity.ERROR,
+                "missing_flow_main",
+                f"Cannot find {main_py.relative_to(self.project_root)}",
+                detail=(
+                    "Flow projects must define a Flow subclass in main.py. "
+                    'This project has `[tool.crewai] type = "flow"` set.'
+                ),
+                hint="Create main.py with a `class MyFlow(Flow[...])`.",
+            )
+
+    def _check_hatch_wheel_target(self) -> None:
+        if not self._pyproject:
+            return
+
+        build_system = self._pyproject.get("build-system") or {}
+        backend = build_system.get("build-backend", "")
+        if "hatchling" not in backend:
+            return
+
+        hatch_wheel = (
+            (self._pyproject.get("tool") or {})
+            .get("hatch", {})
+            .get("build", {})
+            .get("targets", {})
+            .get("wheel", {})
+        )
+        if hatch_wheel.get("packages") or hatch_wheel.get("only-include"):
+            return
+
+        if self._package_dir and self._package_dir.is_dir():
+            return
+
+        self._add(
+            Severity.ERROR,
+            "hatch_wheel_target_missing",
+            "Hatchling cannot determine which files to ship",
+            detail=(
+                "Your pyproject uses hatchling but has no "
+                "[tool.hatch.build.targets.wheel] configuration and no "
+                "directory matching your project name."
+            ),
+            hint=(
+                "Add:\n"
+                "  [tool.hatch.build.targets.wheel]\n"
+                f'  packages = ["src/{self._package_name}"]'
+            ),
+        )
+
+    def _check_module_imports(self) -> None:
+        """Import the user's crew/flow via `uv run` so the check sees the same
+        package versions as `crewai run` would. Result is reported as JSON on
+        the subprocess's stdout."""
+        script = (
+            "import json, sys, traceback, os\n"
+            "os.chdir(sys.argv[1])\n"
+            "try:\n"
+            "    from crewai.cli.utils import get_crews, get_flows\n"
+            "    is_flow = sys.argv[2] == 'flow'\n"
+            "    if is_flow:\n"
+            "        instances = get_flows()\n"
+            "        kind = 'flow'\n"
+            "    else:\n"
+            "        instances = get_crews()\n"
+            "        kind = 'crew'\n"
+            "    print(json.dumps({'ok': True, 'kind': kind, 'count': len(instances)}))\n"
+            "except BaseException as e:\n"
+            "    print(json.dumps({\n"
+            "        'ok': False,\n"
+            "        'error_type': type(e).__name__,\n"
+            "        'error': str(e),\n"
+            "        'traceback': traceback.format_exc(),\n"
+            "    }))\n"
+        )
+
+        uv_path = shutil.which("uv")
+        if uv_path is None:
+            self._add(
+                Severity.WARNING,
+                "uv_not_found",
+                "Skipping import check: `uv` not installed",
+                hint="Install uv: https://docs.astral.sh/uv/",
+            )
+            return
+
+        try:
+            proc = subprocess.run(  # noqa: S603 - args constructed from trusted inputs
+                [
+                    uv_path,
+                    "run",
+                    "python",
+                    "-c",
+                    script,
+                    str(self.project_root),
+                    "flow" if self._is_flow else "crew",
+                ],
+                cwd=self.project_root,
+                capture_output=True,
+                text=True,
+                timeout=120,
+                check=False,
+            )
+        except subprocess.TimeoutExpired:
+            self._add(
+                Severity.ERROR,
+                "import_timeout",
+                "Importing your crew/flow module timed out after 120s",
+                detail=(
+                    "User code may be making network calls or doing heavy work "
+                    "at import time. Move that work into agent methods."
+                ),
+            )
+            return
+
+        # The payload is the last JSON object on stdout; user code may print
+        # other lines before it.
+        payload: dict[str, Any] | None = None
+        for line in reversed(proc.stdout.splitlines()):
+            line = line.strip()
+            if line.startswith("{") and line.endswith("}"):
+                try:
+                    payload = json.loads(line)
+                    break
+                except json.JSONDecodeError:
+                    continue
+
+        if payload is None:
+            self._add(
+                Severity.ERROR,
+                "import_failed",
+                "Could not import your crew/flow module",
+                detail=(proc.stderr or proc.stdout or "").strip()[:1500],
+                hint="Run `crewai run` locally first to reproduce the error.",
+            )
+            return
+
+        if payload.get("ok"):
+            if payload.get("count", 0) == 0:
+                kind = payload.get("kind", "crew")
+                if kind == "flow":
+                    self._add(
+                        Severity.ERROR,
+                        "no_flow_subclass",
+                        "No Flow subclass found in the module",
+                        hint=(
+                            "main.py must define a class extending "
+                            "`crewai.flow.Flow`, instantiable with no arguments."
+                        ),
+                    )
+                else:
+                    self._add(
+                        Severity.ERROR,
+                        "no_crewbase_class",
+                        "Crew class annotated with @CrewBase not found",
+                        hint=(
+                            "Decorate your crew class with @CrewBase from "
+                            "crewai.project (see `crewai create crew` template)."
+                        ),
+                    )
+            return
+
+        err_msg = str(payload.get("error", ""))
+        err_type = str(payload.get("error_type", "Exception"))
+        tb = str(payload.get("traceback", ""))
+        self._classify_import_error(err_type, err_msg, tb)
+
+    def _classify_import_error(self, err_type: str, err_msg: str, tb: str) -> None:
+        """Turn a raw import-time exception into a user-actionable finding."""
+        # Must be checked before the generic "native provider" branch below:
+        # the extras-missing message contains the same phrase.
+        m = re.search(
+            r"(?P<pkg>[A-Za-z0-9_ -]+?)\s+native provider not available.*?`([^`]+)`",
+            err_msg,
+        )
+        if m:
+            self._add(
+                Severity.ERROR,
+                "missing_provider_extra",
+                f"{m.group('pkg').strip()} provider extra not installed",
+                hint=f"Run: {m.group(2)}",
+            )
+            return
+
+        # crewai.llm.LLM.__new__ wraps provider init errors as
+        # ImportError("Error importing native provider: ...").
+        if "Error importing native provider" in err_msg or "native provider" in err_msg:
+            missing_key = self._extract_missing_api_key(err_msg)
+            if missing_key:
+                provider = _KNOWN_API_KEY_HINTS.get(missing_key, missing_key)
+                self._add(
+                    Severity.WARNING,
+                    "llm_init_missing_key",
+                    f"LLM is constructed at import time but {missing_key} is not set",
+                    detail=(
+                        f"Your crew instantiates a {provider} LLM during module "
+                        "load (e.g. in a class field default or @crew method). "
+                        f"The {provider} provider currently requires {missing_key} "
+                        "at construction time, so this will fail on the platform "
+                        "unless the key is set in your deployment environment."
+                    ),
+                    hint=(
+                        f"Add {missing_key} to your deployment's Environment "
+                        "Variables before deploying, or move LLM construction "
+                        "inside agent methods so it runs lazily."
+                    ),
+                )
+                return
+            self._add(
+                Severity.ERROR,
+                "llm_provider_init_failed",
+                "LLM native provider failed to initialize",
+                detail=err_msg,
+                hint=(
+                    "Check your LLM(model=...) configuration and provider-specific "
+                    "extras (e.g. `uv add 'crewai[azure-ai-inference]'` for Azure)."
+                ),
+            )
+            return
+
+        if err_type == "KeyError":
+            key = err_msg.strip("'\"")
+            if key in _KNOWN_API_KEY_HINTS or key.endswith("_API_KEY"):
+                self._add(
+                    Severity.WARNING,
+                    "env_var_read_at_import",
+                    f"{key} is read at import time via os.environ[...]",
+                    detail=(
+                        "Using os.environ[...] (rather than os.getenv(...)) "
+                        "at module scope crashes the build if the key isn't set."
+                    ),
+                    hint=(
+                        f"Either add {key} as a deployment env var, or switch "
+                        "to os.getenv() and move the access inside agent methods."
+                    ),
+                )
+                return
+
+        if "Crew class annotated with @CrewBase not found" in err_msg:
+            self._add(
+                Severity.ERROR,
+                "no_crewbase_class",
+                "Crew class annotated with @CrewBase not found",
+                detail=err_msg,
+            )
+            return
+        if "No Flow subclass found" in err_msg:
+            self._add(
+                Severity.ERROR,
+                "no_flow_subclass",
+                "No Flow subclass found in the module",
+                detail=err_msg,
+            )
+            return
+
+        if (
+            err_type == "AttributeError"
+            and "has no attribute '_load_response_format'" in err_msg
+        ):
+            self._add(
+                Severity.ERROR,
+                "stale_crewai_pin",
+                "Your lockfile pins a crewai version missing `_load_response_format`",
+                detail=err_msg,
+                hint=(
+                    "Run `uv lock --upgrade-package crewai` (or `poetry update crewai`) "
+                    "to pin a newer release."
+                ),
+            )
+            return
+
+        if "pydantic" in tb.lower() or "validation error" in err_msg.lower():
+            self._add(
+                Severity.ERROR,
+                "pydantic_validation_error",
+                "Pydantic validation failed while loading your crew",
+                detail=err_msg[:800],
+                hint=(
+                    "Check agent/task configuration fields. `crewai run` locally "
+                    "will show the full traceback."
+                ),
+            )
+            return
+
+        self._add(
+            Severity.ERROR,
+            "import_failed",
+            f"Importing your crew failed: {err_type}",
+            detail=err_msg[:800],
+            hint="Run `crewai run` locally to see the full traceback.",
+        )
+
+    @staticmethod
+    def _extract_missing_api_key(err_msg: str) -> str | None:
+        """Pull 'FOO_API_KEY' out of '... FOO_API_KEY is required ...'."""
+        m = re.search(r"([A-Z][A-Z0-9_]*_API_KEY)\s+is required", err_msg)
+        if m:
+            return m.group(1)
+        m = re.search(r"['\"]([A-Z][A-Z0-9_]*_API_KEY)['\"]", err_msg)
+        if m:
+            return m.group(1)
+        return None
+
+    def _check_env_vars(self) -> None:
+        """Warn about env vars referenced in user code but missing locally.
+        Best-effort only — the platform sets vars server-side, so we never error.
+        """
+        if not self._package_dir:
+            return
+
+        referenced: set[str] = set()
+        pattern = re.compile(
+            r"""(?x)
+            (?:os\.environ\s*(?:\[\s*|\.get\s*\(\s*)
+              |os\.getenv\s*\(\s*
+              |getenv\s*\(\s*)
+            ['"]([A-Z][A-Z0-9_]*)['"]
+            """
+        )
+
+        for path in self._package_dir.rglob("*.py"):
+            try:
+                text = path.read_text(encoding="utf-8", errors="ignore")
+            except OSError:
+                continue
+            referenced.update(pattern.findall(text))
+
+        for path in self._package_dir.rglob("*.yaml"):
+            try:
+                text = path.read_text(encoding="utf-8", errors="ignore")
+            except OSError:
+                continue
+            referenced.update(re.findall(r"\$\{?([A-Z][A-Z0-9_]+)\}?", text))
+
+        env_file = self.project_root / ".env"
+        env_keys: set[str] = set()
+        if env_file.exists():
+            for line in env_file.read_text(errors="ignore").splitlines():
+                line = line.strip()
+                if not line or line.startswith("#") or "=" not in line:
+                    continue
+                env_keys.add(line.split("=", 1)[0].strip())
+
+        missing_known: list[str] = sorted(
+            var
+            for var in referenced
+            if var in _KNOWN_API_KEY_HINTS
+            and var not in env_keys
+            and var not in os.environ
+        )
+        if missing_known:
+            self._add(
+                Severity.WARNING,
+                "env_vars_not_in_dotenv",
+                f"{len(missing_known)} referenced API key(s) not in .env",
+                detail=(
+                    "These env vars are referenced in your source but not set "
+                    f"locally: {', '.join(missing_known)}. Deploys will fail "
+                    "unless they are added to the deployment's Environment "
+                    "Variables in the CrewAI dashboard."
+                ),
+            )
+
+    def _check_version_vs_lockfile(self) -> None:
+        """Warn when the lockfile pins a crewai release older than 1.13.0,
+        which is where ``_load_response_format`` was introduced.
+        """
+        uv_lock = self.project_root / "uv.lock"
+        poetry_lock = self.project_root / "poetry.lock"
+        lockfile = (
+            uv_lock
+            if uv_lock.exists()
+            else poetry_lock
+            if poetry_lock.exists()
+            else None
+        )
+        if lockfile is None:
+            return
+
+        try:
+            text = lockfile.read_text(errors="ignore")
+        except OSError:
+            return
+
+        m = re.search(
+            r'name\s*=\s*"crewai"\s*\nversion\s*=\s*"([^"]+)"',
+            text,
+        )
+        if not m:
+            return
+        locked = m.group(1)
+
+        try:
+            from packaging.version import Version
+
+            if Version(locked) < Version("1.13.0"):
+                self._add(
+                    Severity.WARNING,
+                    "old_crewai_pin",
+                    f"Lockfile pins crewai=={locked} (older than 1.13.0)",
+                    detail=(
+                        "Older pinned versions are missing API surface the "
+                        "platform builder expects (e.g. `_load_response_format`)."
+                    ),
+                    hint="Run `uv lock --upgrade-package crewai` and redeploy.",
+                )
+        except Exception as e:
+            logger.debug("Could not parse crewai pin from lockfile: %s", e)
+
+
+def render_report(results: list[ValidationResult]) -> None:
+    """Pretty-print results to the shared rich console."""
+    if not results:
+        console.print("[bold green]Pre-deploy validation passed.[/bold green]")
+        return
+
+    errors = [r for r in results if r.severity is Severity.ERROR]
+    warnings = [r for r in results if r.severity is Severity.WARNING]
+
+    for result in errors:
+        console.print(f"[bold red]ERROR[/bold red] [{result.code}] {result.title}")
+        if result.detail:
+            console.print(f"  {result.detail}")
+        if result.hint:
+            console.print(f"  [dim]hint:[/dim] {result.hint}")
+
+    for result in warnings:
+        console.print(
+            f"[bold yellow]WARNING[/bold yellow] [{result.code}] {result.title}"
+        )
+        if result.detail:
+            console.print(f"  {result.detail}")
+        if result.hint:
+            console.print(f"  [dim]hint:[/dim] {result.hint}")
+
+    summary_parts: list[str] = []
+    if errors:
+        summary_parts.append(f"[bold red]{len(errors)} error(s)[/bold red]")
+    if warnings:
+        summary_parts.append(f"[bold yellow]{len(warnings)} warning(s)[/bold yellow]")
+    console.print(f"\n{' / '.join(summary_parts)}")
+
+
+def validate_project(project_root: Path | None = None) -> DeployValidator:
+    """Entrypoint: run validation, render results, return the validator.
+
+    The caller inspects ``validator.ok`` to decide whether to proceed with a
+    deploy.
+    """
+    validator = DeployValidator(project_root=project_root)
+    validator.run()
+    render_report(validator.results)
+    return validator
+
+
+def run_validate_command() -> None:
+    """Implementation of `crewai deploy validate`."""
+    validator = validate_project()
+    if not validator.ok:
+        sys.exit(1)

lib/crewai/src/crewai/cli/enterprise/main.py

@@ -7,7 +7,7 @@ from rich.console import Console
 from crewai.cli.authentication.main import Oauth2Settings, ProviderFactory
 from crewai.cli.command import BaseCommand
 from crewai.cli.settings.main import SettingsCommand
-from crewai.cli.version import get_crewai_version
+from crewai.utilities.version import get_crewai_version
 
 
 console = Console()

lib/crewai/src/crewai/cli/plus_api.py

@@ -6,7 +6,7 @@ import httpx
 
 from crewai.cli.config import Settings
 from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
-from crewai.cli.version import get_crewai_version
+from crewai.utilities.version import get_crewai_version
 
 
 class PlusAPI:

lib/crewai/src/crewai/cli/run_crew.py

@@ -5,7 +5,7 @@ import click
 from packaging import version
 
 from crewai.cli.utils import build_env_with_all_tool_credentials, read_toml
-from crewai.cli.version import get_crewai_version
+from crewai.utilities.version import get_crewai_version
 
 
 class CrewType(Enum):

lib/crewai/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]==1.14.0"
+    "crewai[tools]==1.14.2a2"
 ]
 
 [project.scripts]

lib/crewai/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]==1.14.0"
+    "crewai[tools]==1.14.2a2"
 ]
 
 [project.scripts]

lib/crewai/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]==1.14.0"
+    "crewai[tools]==1.14.2a2"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/cli/version.py

@@ -3,7 +3,6 @@
 from collections.abc import Mapping
 from datetime import datetime, timedelta
 from functools import lru_cache
-import importlib.metadata
 import json
 from pathlib import Path
 from typing import Any
@@ -13,6 +12,8 @@ from urllib.error import URLError
 import appdirs
 from packaging.version import InvalidVersion, Version, parse
 
+from crewai.utilities.version import get_crewai_version
+
 
 @lru_cache(maxsize=1)
 def _get_cache_file() -> Path:
@@ -25,11 +26,6 @@ def _get_cache_file() -> Path:
     return cache_dir / "version_cache.json"
 
 
-def get_crewai_version() -> str:
-    """Get the version number of CrewAI running the CLI."""
-    return importlib.metadata.version("crewai")
-
-
 def _is_cache_valid(cache_data: Mapping[str, Any]) -> bool:
     """Check if the cache is still valid, less than 24 hours old."""
     if "timestamp" not in cache_data:

lib/crewai/src/crewai/crew.py

@@ -42,7 +42,6 @@ if TYPE_CHECKING:
     from opentelemetry.trace import Span
 
     from crewai.context import ExecutionContext
-    from crewai.state.provider.core import BaseProvider
 
 try:
     from crewai_files import get_supported_content_types
@@ -104,7 +103,11 @@ from crewai.rag.types import SearchResult
 from crewai.security.fingerprint import Fingerprint
 from crewai.security.security_config import SecurityConfig
 from crewai.skills.models import Skill
-from crewai.state.checkpoint_config import CheckpointConfig, _coerce_checkpoint
+from crewai.state.checkpoint_config import (
+    CheckpointConfig,
+    _coerce_checkpoint,
+    apply_checkpoint,
+)
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
@@ -134,6 +137,7 @@ from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.streaming import (
     create_async_chunk_generator,
     create_chunk_generator,
+    register_cleanup,
     signal_end,
     signal_error,
 )
@@ -364,28 +368,21 @@ class Crew(FlowTrackable, BaseModel):
     checkpoint_kickoff_event_id: str | None = Field(default=None)
 
     @classmethod
-    def from_checkpoint(
-        cls, path: str, *, provider: BaseProvider | None = None
-    ) -> Crew:
-        """Restore a Crew from a checkpoint file, ready to resume via kickoff().
+    def from_checkpoint(cls, config: CheckpointConfig) -> Crew:
+        """Restore a Crew from a checkpoint, ready to resume via kickoff().
 
         Args:
-            path: Path to a checkpoint JSON file.
-            provider: Storage backend to read from. Defaults to JsonProvider.
+            config: Checkpoint configuration with ``restore_from`` set to
+                the path of the checkpoint to load.
 
         Returns:
             A Crew instance. Call kickoff() to resume from the last completed task.
         """
         from crewai.context import apply_execution_context
         from crewai.events.event_bus import crewai_event_bus
-        from crewai.state.provider.json_provider import JsonProvider
         from crewai.state.runtime import RuntimeState
 
-        state = RuntimeState.from_checkpoint(
-            path,
-            provider=provider or JsonProvider(),
-            context={"from_checkpoint": True},
-        )
+        state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
         crewai_event_bus.set_runtime_state(state)
         for entity in state.root:
             if isinstance(entity, cls):
@@ -393,7 +390,32 @@ class Crew(FlowTrackable, BaseModel):
                     apply_execution_context(entity.execution_context)
                 entity._restore_runtime()
                 return entity
-        raise ValueError(f"No Crew found in checkpoint: {path}")
+        raise ValueError(f"No Crew found in checkpoint: {config.restore_from}")
+
+    @classmethod
+    def fork(
+        cls,
+        config: CheckpointConfig,
+        branch: str | None = None,
+    ) -> Crew:
+        """Fork a Crew from a checkpoint, creating a new execution branch.
+
+        Args:
+            config: Checkpoint configuration with ``restore_from`` set.
+            branch: Branch label for the fork. Auto-generated if not provided.
+
+        Returns:
+            A Crew instance on the new branch. Call kickoff() to run.
+        """
+        crew = cls.from_checkpoint(config)
+        state = crewai_event_bus._runtime_state
+        if state is None:
+            raise RuntimeError(
+                "Cannot fork: no runtime state on the event bus. "
+                "Ensure from_checkpoint() succeeded before calling fork()."
+            )
+        state.fork(branch)
+        return crew
 
     def _restore_runtime(self) -> None:
         """Re-create runtime objects after restoring from a checkpoint."""
@@ -414,6 +436,13 @@ class Crew(FlowTrackable, BaseModel):
                         if agent.agent_executor is not None and task.output is None:
                             agent.agent_executor.task = task
                         break
+        for task in self.tasks:
+            if task.checkpoint_original_description is not None:
+                task._original_description = task.checkpoint_original_description
+            if task.checkpoint_original_expected_output is not None:
+                task._original_expected_output = (
+                    task.checkpoint_original_expected_output
+                )
         if self.checkpoint_inputs is not None:
             self._inputs = self.checkpoint_inputs
         if self.checkpoint_kickoff_event_id is not None:
@@ -849,16 +878,23 @@ class Crew(FlowTrackable, BaseModel):
         self,
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> CrewOutput | CrewStreamingOutput:
         """Execute the crew's workflow.
 
         Args:
             inputs: Optional input dictionary for task interpolation.
             input_files: Optional dict of named file inputs for the crew.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the crew resumes from that checkpoint. Remaining
+                config fields enable checkpointing for the run.
 
         Returns:
             CrewOutput or CrewStreamingOutput if streaming is enabled.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return restored.kickoff(inputs=inputs, input_files=input_files)  # type: ignore[no-any-return]
         get_env_context()
         if self.stream:
             enable_agent_streaming(self.agents)
@@ -882,6 +918,7 @@ class Crew(FlowTrackable, BaseModel):
                     ctx.state, run_crew, ctx.output_holder
                 )
             )
+            register_cleanup(streaming_output, ctx.state)
             ctx.output_holder.append(streaming_output)
             return streaming_output
 
@@ -970,12 +1007,15 @@ class Crew(FlowTrackable, BaseModel):
         self,
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> CrewOutput | CrewStreamingOutput:
         """Asynchronous kickoff method to start the crew execution.
 
         Args:
             inputs: Optional input dictionary for task interpolation.
             input_files: Optional dict of named file inputs for the crew.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the crew resumes from that checkpoint.
 
         Returns:
             CrewOutput or CrewStreamingOutput if streaming is enabled.
@@ -984,6 +1024,9 @@ class Crew(FlowTrackable, BaseModel):
         to get stream chunks. After iteration completes, access the final result
         via .result.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return await restored.kickoff_async(inputs=inputs, input_files=input_files)  # type: ignore[no-any-return]
         inputs = inputs or {}
 
         if self.stream:
@@ -1007,6 +1050,7 @@ class Crew(FlowTrackable, BaseModel):
                     ctx.state, run_crew, ctx.output_holder
                 )
             )
+            register_cleanup(streaming_output, ctx.state)
             ctx.output_holder.append(streaming_output)
 
             return streaming_output
@@ -1043,6 +1087,7 @@ class Crew(FlowTrackable, BaseModel):
         self,
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> CrewOutput | CrewStreamingOutput:
         """Native async kickoff method using async task execution throughout.
 
@@ -1053,10 +1098,15 @@ class Crew(FlowTrackable, BaseModel):
         Args:
             inputs: Optional input dictionary for task interpolation.
             input_files: Optional dict of named file inputs for the crew.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the crew resumes from that checkpoint.
 
         Returns:
             CrewOutput or CrewStreamingOutput if streaming is enabled.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return await restored.akickoff(inputs=inputs, input_files=input_files)  # type: ignore[no-any-return]
         if self.stream:
             enable_agent_streaming(self.agents)
             ctx = StreamingContext(use_async=True)
@@ -1078,6 +1128,7 @@ class Crew(FlowTrackable, BaseModel):
                     ctx.state, run_crew, ctx.output_holder
                 )
             )
+            register_cleanup(streaming_output, ctx.state)
             ctx.output_holder.append(streaming_output)
 
             return streaming_output

lib/crewai/src/crewai/crews/utils.py

@@ -431,6 +431,7 @@ async def run_for_each_async(
     from crewai.types.usage_metrics import UsageMetrics
     from crewai.utilities.streaming import (
         create_async_chunk_generator,
+        register_cleanup,
         signal_end,
         signal_error,
     )
@@ -480,6 +481,7 @@ async def run_for_each_async(
             streaming_output._set_results(result)
 
         streaming_output._set_result = set_results_wrapper  # type: ignore[method-assign]
+        register_cleanup(streaming_output, ctx.state)
         ctx.output_holder.append(streaming_output)
 
         return streaming_output

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

@@ -13,13 +13,13 @@ from crewai.cli.authentication.token import AuthError, get_auth_token
 from crewai.cli.config import Settings
 from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
 from crewai.cli.plus_api import PlusAPI
-from crewai.cli.version import get_crewai_version
 from crewai.events.listeners.tracing.types import TraceEvent
 from crewai.events.listeners.tracing.utils import (
     get_user_id,
     is_tracing_enabled_in_context,
     should_auto_collect_first_time_traces,
 )
+from crewai.utilities.version import get_crewai_version
 
 
 logger = getLogger(__name__)

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

@@ -7,7 +7,6 @@ import uuid
 from typing_extensions import Self
 
 from crewai.cli.authentication.token import AuthError, get_auth_token
-from crewai.cli.version import get_crewai_version
 from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.base_events import BaseEvent
 from crewai.events.event_bus import CrewAIEventsBus
@@ -127,6 +126,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageStartedEvent,
 )
 from crewai.events.utils.console_formatter import ConsoleFormatter
+from crewai.utilities.version import get_crewai_version
 
 
 class TraceCollectionListener(BaseEventListener):

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

@@ -91,7 +91,7 @@ from crewai.utilities.agent_utils import (
     track_delegation_if_needed,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.planning_types import (
     PlanStep,
     StepObservation,
@@ -189,7 +189,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
     )
     callbacks: list[Any] = Field(default_factory=list, exclude=True)
     response_model: type[BaseModel] | None = Field(default=None, exclude=True)
-    i18n: I18N | None = Field(default=None, exclude=True)
     log_error_after: int = Field(default=3, exclude=True)
     before_llm_call_hooks: list[BeforeLLMCallHookType | BeforeLLMCallHookCallable] = (
         Field(default_factory=list, exclude=True)
@@ -198,7 +197,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
         default_factory=list, exclude=True
     )
 
-    _i18n: I18N = PrivateAttr(default_factory=get_i18n)
     _console: Console = PrivateAttr(default_factory=Console)
     _last_parser_error: OutputParserError | None = PrivateAttr(default=None)
     _last_context_error: Exception | None = PrivateAttr(default=None)
@@ -214,7 +212,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
     @model_validator(mode="after")
     def _setup_executor(self) -> Self:
         """Configure executor after Pydantic field initialization."""
-        self._i18n = self.i18n or get_i18n()
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
 
@@ -363,7 +360,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
                 function_calling_llm=self.function_calling_llm,
                 request_within_rpm_limit=self.request_within_rpm_limit,
                 callbacks=self.callbacks,
-                i18n=self._i18n,
             )
         return self._step_executor
 
@@ -1203,7 +1199,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
         formatted_answer = handle_max_iterations_exceeded(
             formatted_answer=None,
             printer=PRINTER,
-            i18n=self._i18n,
             messages=list(self.state.messages),
             llm=self.llm,
             callbacks=self.callbacks,
@@ -1430,7 +1425,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
                 agent_action=action,
                 fingerprint_context=fingerprint_context,
                 tools=self.tools,
-                i18n=self._i18n,
                 agent_key=self.agent.key if self.agent else None,
                 agent_role=self.agent.role if self.agent else None,
                 tools_handler=self.tools_handler,
@@ -1450,7 +1444,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
             action.result = str(e)
             self._append_message_to_state(action.text)
 
-            reasoning_prompt = self._i18n.slice("post_tool_reasoning")
+            reasoning_prompt = I18N_DEFAULT.slice("post_tool_reasoning")
             reasoning_message: LLMMessage = {
                 "role": "user",
                 "content": reasoning_prompt,
@@ -1471,7 +1465,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
             self.state.is_finished = True
             return "tool_result_is_final"
 
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
+        reasoning_prompt = I18N_DEFAULT.slice("post_tool_reasoning")
         reasoning_message_post: LLMMessage = {
             "role": "user",
             "content": reasoning_prompt,
@@ -2222,10 +2216,10 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
         # Build synthesis prompt
         role = self.agent.role if self.agent else "Assistant"
 
-        system_prompt = self._i18n.retrieve(
+        system_prompt = I18N_DEFAULT.retrieve(
             "planning", "synthesis_system_prompt"
         ).format(role=role)
-        user_prompt = self._i18n.retrieve("planning", "synthesis_user_prompt").format(
+        user_prompt = I18N_DEFAULT.retrieve("planning", "synthesis_user_prompt").format(
             task_description=task_description,
             combined_steps=combined_steps,
         )
@@ -2472,7 +2466,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
             self.task.description if self.task else getattr(self, "_kickoff_input", "")
         )
 
-        enhancement = self._i18n.retrieve(
+        enhancement = I18N_DEFAULT.retrieve(
             "planning", "replan_enhancement_prompt"
         ).format(previous_context=previous_context)
 
@@ -2535,7 +2529,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
             messages=self.state.messages,
             llm=self.llm,
             callbacks=self.callbacks,
-            i18n=self._i18n,
             verbose=self.agent.verbose,
         )
 
@@ -2746,7 +2739,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):  # type: ignor
         Returns:
             Updated action or final answer.
         """
-        add_image_tool = self._i18n.tools("add_image")
+        add_image_tool = I18N_DEFAULT.tools("add_image")
         if (
             isinstance(add_image_tool, dict)
             and formatted_answer.tool.casefold().strip()

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

@@ -113,7 +113,11 @@ from crewai.flow.utils import (
 )
 from crewai.memory.memory_scope import MemoryScope, MemorySlice
 from crewai.memory.unified_memory import Memory
-from crewai.state.checkpoint_config import CheckpointConfig, _coerce_checkpoint
+from crewai.state.checkpoint_config import (
+    CheckpointConfig,
+    _coerce_checkpoint,
+    apply_checkpoint,
+)
 
 
 if TYPE_CHECKING:
@@ -122,7 +126,6 @@ if TYPE_CHECKING:
     from crewai.context import ExecutionContext
     from crewai.flow.async_feedback.types import PendingFeedbackContext
     from crewai.llms.base_llm import BaseLLM
-    from crewai.state.provider.core import BaseProvider
 
 from crewai.flow.visualization import build_flow_structure, render_interactive
 from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
@@ -132,6 +135,7 @@ from crewai.utilities.streaming import (
     create_async_chunk_generator,
     create_chunk_generator,
     create_streaming_state,
+    register_cleanup,
     signal_end,
     signal_error,
 )
@@ -927,20 +931,21 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
     ] = Field(default=None)
 
     @classmethod
-    def from_checkpoint(
-        cls, path: str, *, provider: BaseProvider | None = None
-    ) -> Flow:  # type: ignore[type-arg]
-        """Restore a Flow from a checkpoint file."""
+    def from_checkpoint(cls, config: CheckpointConfig) -> Flow:  # type: ignore[type-arg]
+        """Restore a Flow from a checkpoint.
+
+        Args:
+            config: Checkpoint configuration with ``restore_from`` set to
+                the path of the checkpoint to load.
+
+        Returns:
+            A Flow instance ready to resume.
+        """
         from crewai.context import apply_execution_context
         from crewai.events.event_bus import crewai_event_bus
-        from crewai.state.provider.json_provider import JsonProvider
         from crewai.state.runtime import RuntimeState
 
-        state = RuntimeState.from_checkpoint(
-            path,
-            provider=provider or JsonProvider(),
-            context={"from_checkpoint": True},
-        )
+        state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
         crewai_event_bus.set_runtime_state(state)
         for entity in state.root:
             if not isinstance(entity, Flow):
@@ -957,7 +962,32 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             instance.checkpoint_state = entity.checkpoint_state
             instance._restore_from_checkpoint()
             return instance
-        raise ValueError(f"No Flow found in checkpoint: {path}")
+        raise ValueError(f"No Flow found in checkpoint: {config.restore_from}")
+
+    @classmethod
+    def fork(
+        cls,
+        config: CheckpointConfig,
+        branch: str | None = None,
+    ) -> Flow:  # type: ignore[type-arg]
+        """Fork a Flow from a checkpoint, creating a new execution branch.
+
+        Args:
+            config: Checkpoint configuration with ``restore_from`` set.
+            branch: Branch label for the fork. Auto-generated if not provided.
+
+        Returns:
+            A Flow instance on the new branch. Call kickoff() to run.
+        """
+        flow = cls.from_checkpoint(config)
+        state = crewai_event_bus._runtime_state
+        if state is None:
+            raise RuntimeError(
+                "Cannot fork: no runtime state on the event bus. "
+                "Ensure from_checkpoint() succeeded before calling fork()."
+            )
+        state.fork(branch)
+        return flow
 
     checkpoint_completed_methods: set[str] | None = Field(default=None)
     checkpoint_method_outputs: list[Any] | None = Field(default=None)
@@ -1454,6 +1484,25 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 "No pending feedback context. Use from_pending() to restore a paused flow."
             )
 
+        if get_current_parent_id() is None:
+            reset_emission_counter()
+            reset_last_event_id()
+
+        if not self.suppress_flow_events:
+            future = crewai_event_bus.emit(
+                self,
+                FlowStartedEvent(
+                    type="flow_started",
+                    flow_name=self.name or self.__class__.__name__,
+                    inputs=None,
+                ),
+            )
+            if future and isinstance(future, Future):
+                try:
+                    await asyncio.wrap_future(future)
+                except Exception:
+                    logger.warning("FlowStartedEvent handler failed", exc_info=True)
+
         context = self._pending_feedback_context
         emit = context.emit
         default_outcome = context.default_outcome
@@ -1593,16 +1642,39 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
         final_result = self._method_outputs[-1] if self._method_outputs else result
 
-        # Emit flow finished
-        crewai_event_bus.emit(
-            self,
-            FlowFinishedEvent(
-                type="flow_finished",
-                flow_name=self.name or self.__class__.__name__,
-                result=final_result,
-                state=self._state,
-            ),
-        )
+        if self._event_futures:
+            await asyncio.gather(
+                *[
+                    asyncio.wrap_future(f)
+                    for f in self._event_futures
+                    if isinstance(f, Future)
+                ]
+            )
+            self._event_futures.clear()
+
+        if not self.suppress_flow_events:
+            future = crewai_event_bus.emit(
+                self,
+                FlowFinishedEvent(
+                    type="flow_finished",
+                    flow_name=self.name or self.__class__.__name__,
+                    result=final_result,
+                    state=self._copy_and_serialize_state(),
+                ),
+            )
+            if future and isinstance(future, Future):
+                try:
+                    await asyncio.wrap_future(future)
+                except Exception:
+                    logger.warning("FlowFinishedEvent handler failed", exc_info=True)
+
+            trace_listener = TraceCollectionListener()
+            if trace_listener.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:
+                    trace_listener.batch_manager.finalize_batch()
 
         return final_result
 
@@ -1913,6 +1985,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         self,
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> Any | FlowStreamingOutput:
         """Start the flow execution in a synchronous context.
 
@@ -1922,10 +1995,15 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         Args:
             inputs: Optional dictionary containing input values and/or a state ID.
             input_files: Optional dict of named file inputs for the flow.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the flow resumes from that checkpoint.
 
         Returns:
             The final output from the flow or FlowStreamingOutput if streaming.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return restored.kickoff(inputs=inputs, input_files=input_files)
         get_env_context()
         if self.stream:
             result_holder: list[Any] = []
@@ -1962,6 +2040,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             streaming_output = FlowStreamingOutput(
                 sync_iterator=create_chunk_generator(state, run_flow, output_holder)
             )
+            register_cleanup(streaming_output, state)
             output_holder.append(streaming_output)
 
             return streaming_output
@@ -1981,6 +2060,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         self,
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> Any | FlowStreamingOutput:
         """Start the flow execution asynchronously.
 
@@ -1992,10 +2072,15 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         Args:
             inputs: Optional dictionary containing input values and/or a state ID for restoration.
             input_files: Optional dict of named file inputs for the flow.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the flow resumes from that checkpoint.
 
         Returns:
             The final output from the flow, which is the result of the last executed method.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return await restored.kickoff_async(inputs=inputs, input_files=input_files)
         if self.stream:
             result_holder: list[Any] = []
             current_task_info: TaskInfo = {
@@ -2035,6 +2120,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                     state, run_flow, output_holder
                 )
             )
+            register_cleanup(streaming_output, state)
             output_holder.append(streaming_output)
 
             return streaming_output
@@ -2253,17 +2339,20 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         self,
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> Any | FlowStreamingOutput:
         """Native async method to start the flow execution. Alias for kickoff_async.
 
         Args:
             inputs: Optional dictionary containing input values and/or a state ID for restoration.
             input_files: Optional dict of named file inputs for the flow.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the flow resumes from that checkpoint.
 
         Returns:
             The final output from the flow, which is the result of the last executed method.
         """
-        return await self.kickoff_async(inputs, input_files)
+        return await self.kickoff_async(inputs, input_files, from_checkpoint)
 
     async def _execute_start_method(self, start_method_name: FlowMethodName) -> None:
         """Executes a flow's start method and its triggered listeners.
@@ -3191,7 +3280,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
         from crewai.llm import LLM
         from crewai.llms.base_llm import BaseLLM as BaseLLMClass
-        from crewai.utilities.i18n import get_i18n
+        from crewai.utilities.i18n import I18N_DEFAULT
 
         llm_instance: BaseLLMClass
         if isinstance(llm, str):
@@ -3211,9 +3300,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 description=f"The outcome that best matches the feedback. Must be one of: {', '.join(outcomes)}"
             )
 
-        # Load prompt from translations (using cached instance)
-        i18n = get_i18n()
-        prompt_template = i18n.slice("human_feedback_collapse")
+        prompt_template = I18N_DEFAULT.slice("human_feedback_collapse")
 
         prompt = prompt_template.format(
             feedback=feedback,

lib/crewai/src/crewai/flow/human_feedback.py

@@ -350,9 +350,9 @@ def human_feedback(
 
         def _get_hitl_prompt(key: str) -> str:
             """Read a HITL prompt from the i18n translations."""
-            from crewai.utilities.i18n import get_i18n
+            from crewai.utilities.i18n import I18N_DEFAULT
 
-            return get_i18n().slice(key)
+            return I18N_DEFAULT.slice(key)
 
         def _resolve_llm_instance() -> Any:
             """Resolve the ``llm`` parameter to a BaseLLM instance.

lib/crewai/src/crewai/hooks/decorators.py

@@ -5,6 +5,8 @@ from functools import wraps
 import inspect
 from typing import TYPE_CHECKING, Any, TypeVar, overload
 
+from crewai.utilities.string_utils import sanitize_tool_name
+
 
 if TYPE_CHECKING:
     from crewai.hooks.llm_hooks import LLMCallHookContext
@@ -37,6 +39,9 @@ def _create_hook_decorator(
         tools: list[str] | None = None,
         agents: list[str] | None = None,
     ) -> Callable[..., Any]:
+        if tools:
+            tools = [sanitize_tool_name(t) for t in tools]
+
         def decorator(f: Callable[..., Any]) -> Callable[..., Any]:
             setattr(f, marker_attribute, True)
 

lib/crewai/src/crewai/lite_agent.py

@@ -89,7 +89,7 @@ from crewai.utilities.converter import (
 )
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.pydantic_schema_utils import generate_model_description
@@ -227,9 +227,6 @@ class LiteAgent(FlowTrackable, BaseModel):
         default=None,
         description="Callback to check if the request is within the RPM8 limit",
     )
-    i18n: I18N = Field(
-        default_factory=get_i18n, description="Internationalization settings."
-    )
     response_format: type[BaseModel] | None = Field(
         default=None, description="Pydantic model for structured output"
     )
@@ -571,7 +568,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                     f"- {m.record.content}" for m in matches
                 )
             if memory_block:
-                formatted = self.i18n.slice("memory").format(memory=memory_block)
+                formatted = I18N_DEFAULT.slice("memory").format(memory=memory_block)
                 if self._messages and self._messages[0].get("role") == "system":
                     existing_content = self._messages[0].get("content", "")
                     if not isinstance(existing_content, str):
@@ -644,7 +641,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             try:
                 model_schema = generate_model_description(active_response_format)
                 schema = json.dumps(model_schema, indent=2)
-                instructions = self.i18n.slice("formatted_task_instructions").format(
+                instructions = I18N_DEFAULT.slice("formatted_task_instructions").format(
                     output_format=schema
                 )
 
@@ -793,7 +790,9 @@ class LiteAgent(FlowTrackable, BaseModel):
         base_prompt = ""
         if self._parsed_tools:
             # Use the prompt template for agents with tools
-            base_prompt = self.i18n.slice("lite_agent_system_prompt_with_tools").format(
+            base_prompt = I18N_DEFAULT.slice(
+                "lite_agent_system_prompt_with_tools"
+            ).format(
                 role=self.role,
                 backstory=self.backstory,
                 goal=self.goal,
@@ -802,7 +801,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
         else:
             # Use the prompt template for agents without tools
-            base_prompt = self.i18n.slice(
+            base_prompt = I18N_DEFAULT.slice(
                 "lite_agent_system_prompt_without_tools"
             ).format(
                 role=self.role,
@@ -814,7 +813,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         if active_response_format:
             model_description = generate_model_description(active_response_format)
             schema_json = json.dumps(model_description, indent=2)
-            base_prompt += self.i18n.slice("lite_agent_response_format").format(
+            base_prompt += I18N_DEFAULT.slice("lite_agent_response_format").format(
                 response_format=schema_json
             )
 
@@ -875,7 +874,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                     formatted_answer = handle_max_iterations_exceeded(
                         formatted_answer,
                         printer=PRINTER,
-                        i18n=self.i18n,
                         messages=self._messages,
                         llm=cast(LLM, self.llm),
                         callbacks=self._callbacks,
@@ -914,7 +912,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                         tool_result = execute_tool_and_check_finality(
                             agent_action=formatted_answer,
                             tools=self._parsed_tools,
-                            i18n=self.i18n,
                             agent_key=self.key,
                             agent_role=self.role,
                             agent=self.original_agent,
@@ -956,7 +953,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                         messages=self._messages,
                         llm=cast(LLM, self.llm),
                         callbacks=self._callbacks,
-                        i18n=self.i18n,
                         verbose=self.verbose,
                     )
                     continue

lib/crewai/src/crewai/llms/base_llm.py

@@ -172,6 +172,8 @@ class BaseLLM(BaseModel, ABC):
             "completion_tokens": 0,
             "successful_requests": 0,
             "cached_prompt_tokens": 0,
+            "reasoning_tokens": 0,
+            "cache_creation_tokens": 0,
         }
     )
 
@@ -808,14 +810,24 @@ class BaseLLM(BaseModel, ABC):
         cached_tokens = (
             usage_data.get("cached_tokens")
             or usage_data.get("cached_prompt_tokens")
+            or usage_data.get("cache_read_input_tokens")
             or 0
         )
+        if not cached_tokens:
+            prompt_details = usage_data.get("prompt_tokens_details")
+            if isinstance(prompt_details, dict):
+                cached_tokens = prompt_details.get("cached_tokens", 0) or 0
+
+        reasoning_tokens = usage_data.get("reasoning_tokens", 0) or 0
+        cache_creation_tokens = usage_data.get("cache_creation_tokens", 0) or 0
 
         self._token_usage["prompt_tokens"] += prompt_tokens
         self._token_usage["completion_tokens"] += completion_tokens
         self._token_usage["total_tokens"] += prompt_tokens + completion_tokens
         self._token_usage["successful_requests"] += 1
         self._token_usage["cached_prompt_tokens"] += cached_tokens
+        self._token_usage["reasoning_tokens"] += reasoning_tokens
+        self._token_usage["cache_creation_tokens"] += cache_creation_tokens
 
     def get_token_usage_summary(self) -> UsageMetrics:
         """Get summary of token usage for this LLM instance.

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -11,10 +11,14 @@ from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM, JsonResponseFormat, llm_call_context
 from crewai.llms.hooks.base import BaseInterceptor
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
+from crewai.llms.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
+from crewai.utilities.pydantic_schema_utils import (
+    sanitize_tool_params_for_anthropic_strict,
+)
 from crewai.utilities.types import LLMMessage
 
 
@@ -189,16 +193,41 @@ class AnthropicCompletion(BaseLLM):
 
     @model_validator(mode="after")
     def _init_clients(self) -> AnthropicCompletion:
-        self._client = Anthropic(**self._get_client_params())
+        """Eagerly build clients when the API key is available, otherwise
+        defer so ``LLM(model="anthropic/...")`` can be constructed at module
+        import time even before deployment env vars are set.
+        """
+        try:
+            self._client = self._build_sync_client()
+            self._async_client = self._build_async_client()
+        except ValueError:
+            pass
+        return self
 
-        async_client_params = self._get_client_params()
+    def _build_sync_client(self) -> Any:
+        return Anthropic(**self._get_client_params())
+
+    def _build_async_client(self) -> Any:
+        # Skip the sync httpx.Client that `_get_client_params` would
+        # otherwise construct under `interceptor`; we attach an async one
+        # below and would leak the sync one if both were built.
+        async_client_params = self._get_client_params(include_http_client=False)
         if self.interceptor:
             async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
-            async_http_client = httpx.AsyncClient(transport=async_transport)
-            async_client_params["http_client"] = async_http_client
+            async_client_params["http_client"] = httpx.AsyncClient(
+                transport=async_transport
+            )
+        return AsyncAnthropic(**async_client_params)
 
-        self._async_client = AsyncAnthropic(**async_client_params)
-        return self
+    def _get_sync_client(self) -> Any:
+        if self._client is None:
+            self._client = self._build_sync_client()
+        return self._client
+
+    def _get_async_client(self) -> Any:
+        if self._async_client is None:
+            self._async_client = self._build_async_client()
+        return self._async_client
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Anthropic-specific fields."""
@@ -213,8 +242,15 @@ class AnthropicCompletion(BaseLLM):
             config["timeout"] = self.timeout
         return config
 
-    def _get_client_params(self) -> dict[str, Any]:
-        """Get client parameters."""
+    def _get_client_params(self, include_http_client: bool = True) -> dict[str, Any]:
+        """Get client parameters.
+
+        Args:
+            include_http_client: When True (default) and an interceptor is
+                set, attach a sync ``httpx.Client``. The async builder
+                passes ``False`` so it can attach its own async client
+                without leaking a sync one.
+        """
 
         if self.api_key is None:
             self.api_key = os.getenv("ANTHROPIC_API_KEY")
@@ -228,7 +264,7 @@ class AnthropicCompletion(BaseLLM):
             "max_retries": self.max_retries,
         }
 
-        if self.interceptor:
+        if include_http_client and self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
             http_client = httpx.Client(transport=transport)
             client_params["http_client"] = http_client  # type: ignore[assignment]
@@ -473,10 +509,8 @@ class AnthropicCompletion(BaseLLM):
                 continue
 
             try:
-                from crewai.llms.providers.utils.common import safe_tool_conversion
-
                 name, description, parameters = safe_tool_conversion(tool, "Anthropic")
-            except (ImportError, KeyError, ValueError) as e:
+            except (KeyError, ValueError) as e:
                 logging.error(f"Error converting tool to Anthropic format: {e}")
                 raise e
 
@@ -485,8 +519,15 @@ class AnthropicCompletion(BaseLLM):
                 "description": description,
             }
 
+            func_info = tool.get("function", {})
+            strict_enabled = bool(func_info.get("strict"))
+
             if parameters and isinstance(parameters, dict):
-                anthropic_tool["input_schema"] = parameters
+                anthropic_tool["input_schema"] = (
+                    sanitize_tool_params_for_anthropic_strict(parameters)
+                    if strict_enabled
+                    else parameters
+                )
             else:
                 anthropic_tool["input_schema"] = {
                     "type": "object",
@@ -494,6 +535,9 @@ class AnthropicCompletion(BaseLLM):
                     "required": [],
                 }
 
+            if strict_enabled:
+                anthropic_tool["strict"] = True
+
             anthropic_tools.append(anthropic_tool)
 
         return anthropic_tools
@@ -786,11 +830,11 @@ class AnthropicCompletion(BaseLLM):
         try:
             if betas:
                 params["betas"] = betas
-                response = self._client.beta.messages.create(
+                response = self._get_sync_client().beta.messages.create(
                     **params, extra_body=extra_body
                 )
             else:
-                response = self._client.messages.create(**params)
+                response = self._get_sync_client().messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -938,9 +982,11 @@ class AnthropicCompletion(BaseLLM):
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
         stream_context = (
-            self._client.beta.messages.stream(**stream_params, extra_body=extra_body)
+            self._get_sync_client().beta.messages.stream(
+                **stream_params, extra_body=extra_body
+            )
             if betas
-            else self._client.messages.stream(**stream_params)
+            else self._get_sync_client().messages.stream(**stream_params)
         )
         with stream_context as stream:
             response_id = None
@@ -1219,7 +1265,9 @@ class AnthropicCompletion(BaseLLM):
 
         try:
             # Send tool results back to Claude for final response
-            final_response: Message = self._client.messages.create(**follow_up_params)
+            final_response: Message = self._get_sync_client().messages.create(
+                **follow_up_params
+            )
 
             # Track token usage for follow-up call
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
@@ -1315,11 +1363,11 @@ class AnthropicCompletion(BaseLLM):
         try:
             if betas:
                 params["betas"] = betas
-                response = await self._async_client.beta.messages.create(
+                response = await self._get_async_client().beta.messages.create(
                     **params, extra_body=extra_body
                 )
             else:
-                response = await self._async_client.messages.create(**params)
+                response = await self._get_async_client().messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -1453,11 +1501,11 @@ class AnthropicCompletion(BaseLLM):
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
         stream_context = (
-            self._async_client.beta.messages.stream(
+            self._get_async_client().beta.messages.stream(
                 **stream_params, extra_body=extra_body
             )
             if betas
-            else self._async_client.messages.stream(**stream_params)
+            else self._get_async_client().messages.stream(**stream_params)
         )
         async with stream_context as stream:
             response_id = None
@@ -1622,7 +1670,7 @@ class AnthropicCompletion(BaseLLM):
         ]
 
         try:
-            final_response: Message = await self._async_client.messages.create(
+            final_response: Message = await self._get_async_client().messages.create(
                 **follow_up_params
             )
 
@@ -1704,18 +1752,23 @@ class AnthropicCompletion(BaseLLM):
     def _extract_anthropic_token_usage(
         response: Message | BetaMessage,
     ) -> dict[str, Any]:
-        """Extract token usage from Anthropic response."""
+        """Extract token usage and response metadata from Anthropic response."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage
             input_tokens = getattr(usage, "input_tokens", 0)
             output_tokens = getattr(usage, "output_tokens", 0)
             cache_read_tokens = getattr(usage, "cache_read_input_tokens", 0) or 0
-            return {
+            cache_creation_tokens = (
+                getattr(usage, "cache_creation_input_tokens", 0) or 0
+            )
+            result: dict[str, Any] = {
                 "input_tokens": input_tokens,
                 "output_tokens": output_tokens,
                 "total_tokens": input_tokens + output_tokens,
                 "cached_prompt_tokens": cache_read_tokens,
+                "cache_creation_tokens": cache_creation_tokens,
             }
+            return result
         return {"total_tokens": 0}
 
     def supports_multimodal(self) -> bool:
@@ -1745,8 +1798,8 @@ class AnthropicCompletion(BaseLLM):
             from crewai_files.uploaders.anthropic import AnthropicFileUploader
 
             return AnthropicFileUploader(
-                client=self._client,
-                async_client=self._async_client,
+                client=self._get_sync_client(),
+                async_client=self._get_async_client(),
             )
         except ImportError:
             return None

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -116,43 +116,100 @@ class AzureCompletion(BaseLLM):
             data.get("api_version") or os.getenv("AZURE_API_VERSION") or "2024-06-01"
         )
 
-        if not data["api_key"]:
-            raise ValueError(
-                "Azure API key is required. Set AZURE_API_KEY environment variable or pass api_key parameter."
-            )
-        if not data["endpoint"]:
-            raise ValueError(
-                "Azure endpoint is required. Set AZURE_ENDPOINT environment variable or pass endpoint parameter."
-            )
-
+        # Credentials and endpoint are validated lazily in `_init_clients`
+        # so the LLM can be constructed before deployment env vars are set.
         model = data.get("model", "")
-        data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
-            data["endpoint"], model
+        if data["endpoint"]:
+            data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
+                data["endpoint"], model
+            )
+        data["is_azure_openai_endpoint"] = AzureCompletion._is_azure_openai_endpoint(
+            data["endpoint"]
         )
         data["is_openai_model"] = any(
             prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
         )
-        parsed = urlparse(data["endpoint"])
-        hostname = parsed.hostname or ""
-        data["is_azure_openai_endpoint"] = (
-            hostname == "openai.azure.com" or hostname.endswith(".openai.azure.com")
-        ) and "/openai/deployments/" in data["endpoint"]
         return data
 
+    @staticmethod
+    def _is_azure_openai_endpoint(endpoint: str | None) -> bool:
+        if not endpoint:
+            return False
+        hostname = urlparse(endpoint).hostname or ""
+        return (
+            hostname == "openai.azure.com" or hostname.endswith(".openai.azure.com")
+        ) and "/openai/deployments/" in endpoint
+
     @model_validator(mode="after")
     def _init_clients(self) -> AzureCompletion:
+        """Eagerly build clients when credentials are available, otherwise
+        defer so ``LLM(model="azure/...")`` can be constructed at module
+        import time even before deployment env vars are set.
+        """
+        try:
+            self._client = self._build_sync_client()
+            self._async_client = self._build_async_client()
+        except ValueError:
+            pass
+        return self
+
+    def _build_sync_client(self) -> Any:
+        return ChatCompletionsClient(**self._make_client_kwargs())
+
+    def _build_async_client(self) -> Any:
+        return AsyncChatCompletionsClient(**self._make_client_kwargs())
+
+    def _make_client_kwargs(self) -> dict[str, Any]:
+        # Re-read env vars so that a deferred build can pick up credentials
+        # that weren't set at instantiation time (e.g. LLM constructed at
+        # module import before deployment env vars were injected).
         if not self.api_key:
-            raise ValueError("Azure API key is required.")
+            self.api_key = os.getenv("AZURE_API_KEY")
+        if not self.endpoint:
+            endpoint = (
+                os.getenv("AZURE_ENDPOINT")
+                or os.getenv("AZURE_OPENAI_ENDPOINT")
+                or os.getenv("AZURE_API_BASE")
+            )
+            if endpoint:
+                self.endpoint = AzureCompletion._validate_and_fix_endpoint(
+                    endpoint, self.model
+                )
+                # Recompute the routing flag now that the endpoint is known —
+                # _prepare_completion_params uses it to decide whether to
+                # include `model` in the request body (Azure OpenAI endpoints
+                # embed the deployment name in the URL and reject it).
+                self.is_azure_openai_endpoint = (
+                    AzureCompletion._is_azure_openai_endpoint(self.endpoint)
+                )
+
+        if not self.api_key:
+            raise ValueError(
+                "Azure API key is required. Set AZURE_API_KEY environment "
+                "variable or pass api_key parameter."
+            )
+        if not self.endpoint:
+            raise ValueError(
+                "Azure endpoint is required. Set AZURE_ENDPOINT environment "
+                "variable or pass endpoint parameter."
+            )
         client_kwargs: dict[str, Any] = {
             "endpoint": self.endpoint,
             "credential": AzureKeyCredential(self.api_key),
         }
         if self.api_version:
             client_kwargs["api_version"] = self.api_version
+        return client_kwargs
 
-        self._client = ChatCompletionsClient(**client_kwargs)
-        self._async_client = AsyncChatCompletionsClient(**client_kwargs)
-        return self
+    def _get_sync_client(self) -> Any:
+        if self._client is None:
+            self._client = self._build_sync_client()
+        return self._client
+
+    def _get_async_client(self) -> Any:
+        if self._async_client is None:
+            self._async_client = self._build_async_client()
+        return self._async_client
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Azure-specific fields."""
@@ -713,8 +770,7 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle non-streaming chat completion."""
         try:
-            # Cast params to Any to avoid type checking issues with TypedDict unpacking
-            response: ChatCompletions = self._client.complete(**params)
+            response: ChatCompletions = self._get_sync_client().complete(**params)
             return self._process_completion_response(
                 response=response,
                 params=params,
@@ -913,7 +969,7 @@ class AzureCompletion(BaseLLM):
         tool_calls: dict[int, dict[str, Any]] = {}
 
         usage_data: dict[str, Any] | None = None
-        for update in self._client.complete(**params):
+        for update in self._get_sync_client().complete(**params):
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if update.usage:
                     usage = update.usage
@@ -953,8 +1009,9 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle non-streaming chat completion asynchronously."""
         try:
-            # Cast params to Any to avoid type checking issues with TypedDict unpacking
-            response: ChatCompletions = await self._async_client.complete(**params)
+            response: ChatCompletions = await self._get_async_client().complete(
+                **params
+            )
             return self._process_completion_response(
                 response=response,
                 params=params,
@@ -980,7 +1037,7 @@ class AzureCompletion(BaseLLM):
 
         usage_data: dict[str, Any] | None = None
 
-        stream = await self._async_client.complete(**params)
+        stream = await self._get_async_client().complete(**params)
         async for update in stream:
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if hasattr(update, "usage") and update.usage:
@@ -1076,28 +1133,39 @@ class AzureCompletion(BaseLLM):
 
     @staticmethod
     def _extract_azure_token_usage(response: ChatCompletions) -> dict[str, Any]:
-        """Extract token usage from Azure response."""
+        """Extract token usage and response metadata from Azure response."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage
             cached_tokens = 0
             prompt_details = getattr(usage, "prompt_tokens_details", None)
             if prompt_details:
                 cached_tokens = getattr(prompt_details, "cached_tokens", 0) or 0
-            return {
+            reasoning_tokens = 0
+            completion_details = getattr(usage, "completion_tokens_details", None)
+            if completion_details:
+                reasoning_tokens = (
+                    getattr(completion_details, "reasoning_tokens", 0) or 0
+                )
+            result: dict[str, Any] = {
                 "prompt_tokens": getattr(usage, "prompt_tokens", 0),
                 "completion_tokens": getattr(usage, "completion_tokens", 0),
                 "total_tokens": getattr(usage, "total_tokens", 0),
                 "cached_prompt_tokens": cached_tokens,
+                "reasoning_tokens": reasoning_tokens,
             }
+            return result
         return {"total_tokens": 0}
 
     async def aclose(self) -> None:
         """Close the async client and clean up resources.
 
         This ensures proper cleanup of the underlying aiohttp session
-        to avoid unclosed connector warnings.
+        to avoid unclosed connector warnings. Accesses the cached client
+        directly rather than going through `_get_async_client` so a
+        cleanup on an uninitialized LLM is a harmless no-op rather than
+        a credential-required error.
         """
-        if hasattr(self._async_client, "close"):
+        if self._async_client is not None and hasattr(self._async_client, "close"):
             await self._async_client.close()
 
     async def __aenter__(self) -> Self:

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -12,11 +12,15 @@ from typing_extensions import Required
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM, llm_call_context
+from crewai.llms.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
-from crewai.utilities.pydantic_schema_utils import generate_model_description
+from crewai.utilities.pydantic_schema_utils import (
+    generate_model_description,
+    sanitize_tool_params_for_bedrock_strict,
+)
 from crewai.utilities.types import LLMMessage
 
 
@@ -169,6 +173,7 @@ class ToolSpec(TypedDict, total=False):
     name: Required[str]
     description: Required[str]
     inputSchema: ToolInputSchema
+    strict: bool
 
 
 class ConverseToolTypeDef(TypedDict):
@@ -302,6 +307,22 @@ class BedrockCompletion(BaseLLM):
 
     @model_validator(mode="after")
     def _init_clients(self) -> BedrockCompletion:
+        """Eagerly build the sync client when AWS credentials resolve,
+        otherwise defer so ``LLM(model="bedrock/...")`` can be constructed
+        at module import time even before deployment env vars are set.
+
+        Only credential/SDK errors are caught — programming errors like
+        ``TypeError`` or ``AttributeError`` propagate so real bugs aren't
+        silently swallowed.
+        """
+        try:
+            self._client = self._build_sync_client()
+        except (BotoCoreError, ClientError, ValueError) as e:
+            logging.debug("Deferring Bedrock client construction: %s", e)
+        self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
+        return self
+
+    def _build_sync_client(self) -> Any:
         config = Config(
             read_timeout=300,
             retries={"max_attempts": 3, "mode": "adaptive"},
@@ -313,9 +334,17 @@ class BedrockCompletion(BaseLLM):
             aws_session_token=self.aws_session_token,
             region_name=self.region_name,
         )
-        self._client = session.client("bedrock-runtime", config=config)
-        self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
-        return self
+        return session.client("bedrock-runtime", config=config)
+
+    def _get_sync_client(self) -> Any:
+        if self._client is None:
+            self._client = self._build_sync_client()
+        return self._client
+
+    def _get_async_client(self) -> Any:
+        """Async client is set up separately by ``_ensure_async_client``
+        using ``aiobotocore`` inside an exit stack."""
+        return self._async_client
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Bedrock-specific fields."""
@@ -655,7 +684,7 @@ class BedrockCompletion(BaseLLM):
                     raise ValueError(f"Invalid message format at index {i}")
 
             # Call Bedrock Converse API with proper error handling
-            response = self._client.converse(
+            response = self._get_sync_client().converse(
                 modelId=self.model_id,
                 messages=cast(
                     "Sequence[MessageTypeDef | MessageOutputTypeDef]",
@@ -944,7 +973,7 @@ class BedrockCompletion(BaseLLM):
         usage_data: dict[str, Any] | None = None
 
         try:
-            response = self._client.converse_stream(
+            response = self._get_sync_client().converse_stream(
                 modelId=self.model_id,
                 messages=cast(
                     "Sequence[MessageTypeDef | MessageOutputTypeDef]",
@@ -1948,8 +1977,6 @@ class BedrockCompletion(BaseLLM):
         tools: list[dict[str, Any]],
     ) -> list[ConverseToolTypeDef]:
         """Convert CrewAI tools to Converse API format following AWS specification."""
-        from crewai.llms.providers.utils.common import safe_tool_conversion
-
         converse_tools: list[ConverseToolTypeDef] = []
 
         for tool in tools:
@@ -1961,10 +1988,21 @@ class BedrockCompletion(BaseLLM):
                     "description": description,
                 }
 
+                func_info = tool.get("function", {})
+                strict_enabled = bool(func_info.get("strict"))
+
                 if parameters and isinstance(parameters, dict):
-                    input_schema: ToolInputSchema = {"json": parameters}
+                    schema_params = (
+                        sanitize_tool_params_for_bedrock_strict(parameters)
+                        if strict_enabled
+                        else parameters
+                    )
+                    input_schema: ToolInputSchema = {"json": schema_params}
                     tool_spec["inputSchema"] = input_schema
 
+                if strict_enabled:
+                    tool_spec["strict"] = True
+
                 converse_tool: ConverseToolTypeDef = {"toolSpec": tool_spec}
 
                 converse_tools.append(converse_tool)
@@ -2025,11 +2063,18 @@ class BedrockCompletion(BaseLLM):
         input_tokens = usage.get("inputTokens", 0)
         output_tokens = usage.get("outputTokens", 0)
         total_tokens = usage.get("totalTokens", input_tokens + output_tokens)
+        raw_cached = (
+            usage.get("cacheReadInputTokenCount")
+            or usage.get("cacheReadInputTokens")
+            or 0
+        )
+        cached_tokens = raw_cached if isinstance(raw_cached, int) else 0
 
         self._token_usage["prompt_tokens"] += input_tokens
         self._token_usage["completion_tokens"] += output_tokens
         self._token_usage["total_tokens"] += total_tokens
         self._token_usage["successful_requests"] += 1
+        self._token_usage["cached_prompt_tokens"] += cached_tokens
 
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -118,9 +118,33 @@ class GeminiCompletion(BaseLLM):
 
     @model_validator(mode="after")
     def _init_client(self) -> GeminiCompletion:
-        self._client = self._initialize_client(self.use_vertexai)
+        """Eagerly build the client when credentials resolve, otherwise defer
+        so ``LLM(model="gemini/...")`` can be constructed at module import time
+        even before deployment env vars are set.
+        """
+        try:
+            self._client = self._initialize_client(self.use_vertexai)
+        except ValueError:
+            pass
         return self
 
+    def _get_sync_client(self) -> Any:
+        if self._client is None:
+            # Re-read env vars so a deferred build can pick up credentials
+            # that weren't set at instantiation time.
+            if not self.api_key:
+                self.api_key = os.getenv("GOOGLE_API_KEY") or os.getenv(
+                    "GEMINI_API_KEY"
+                )
+            if not self.project:
+                self.project = os.getenv("GOOGLE_CLOUD_PROJECT")
+            self._client = self._initialize_client(self.use_vertexai)
+        return self._client
+
+    def _get_async_client(self) -> Any:
+        """Gemini uses a single client for both sync and async calls."""
+        return self._get_sync_client()
+
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Gemini/Vertex-specific fields."""
         config = super().to_config_dict()
@@ -228,6 +252,7 @@ class GeminiCompletion(BaseLLM):
 
         if (
             hasattr(self, "client")
+            and self._client is not None
             and hasattr(self._client, "vertexai")
             and self._client.vertexai
         ):
@@ -1112,7 +1137,7 @@ class GeminiCompletion(BaseLLM):
         try:
             # The API accepts list[Content] but mypy is overly strict about variance
             contents_for_api: Any = contents
-            response = self._client.models.generate_content(
+            response = self._get_sync_client().models.generate_content(
                 model=self.model,
                 contents=contents_for_api,
                 config=config,
@@ -1153,7 +1178,7 @@ class GeminiCompletion(BaseLLM):
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
-        for chunk in self._client.models.generate_content_stream(
+        for chunk in self._get_sync_client().models.generate_content_stream(
             model=self.model,
             contents=contents_for_api,
             config=config,
@@ -1191,7 +1216,7 @@ class GeminiCompletion(BaseLLM):
         try:
             # The API accepts list[Content] but mypy is overly strict about variance
             contents_for_api: Any = contents
-            response = await self._client.aio.models.generate_content(
+            response = await self._get_async_client().aio.models.generate_content(
                 model=self.model,
                 contents=contents_for_api,
                 config=config,
@@ -1232,7 +1257,7 @@ class GeminiCompletion(BaseLLM):
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
-        stream = await self._client.aio.models.generate_content_stream(
+        stream = await self._get_async_client().aio.models.generate_content_stream(
             model=self.model,
             contents=contents_for_api,
             config=config,
@@ -1306,17 +1331,20 @@ class GeminiCompletion(BaseLLM):
 
     @staticmethod
     def _extract_token_usage(response: GenerateContentResponse) -> dict[str, Any]:
-        """Extract token usage from Gemini response."""
+        """Extract token usage and response metadata from Gemini response."""
         if response.usage_metadata:
             usage = response.usage_metadata
             cached_tokens = getattr(usage, "cached_content_token_count", 0) or 0
-            return {
+            thinking_tokens = getattr(usage, "thoughts_token_count", 0) or 0
+            result: dict[str, Any] = {
                 "prompt_token_count": getattr(usage, "prompt_token_count", 0),
                 "candidates_token_count": getattr(usage, "candidates_token_count", 0),
                 "total_token_count": getattr(usage, "total_token_count", 0),
                 "total_tokens": getattr(usage, "total_token_count", 0),
                 "cached_prompt_tokens": cached_tokens,
+                "reasoning_tokens": thinking_tokens,
             }
+            return result
         return {"total_tokens": 0}
 
     @staticmethod
@@ -1436,6 +1464,6 @@ class GeminiCompletion(BaseLLM):
         try:
             from crewai_files.uploaders.gemini import GeminiFileUploader
 
-            return GeminiFileUploader(client=self._client)
+            return GeminiFileUploader(client=self._get_sync_client())
         except ImportError:
             return None

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -32,11 +32,15 @@ from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM, JsonResponseFormat, llm_call_context
 from crewai.llms.hooks.base import BaseInterceptor
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
+from crewai.llms.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
-from crewai.utilities.pydantic_schema_utils import generate_model_description
+from crewai.utilities.pydantic_schema_utils import (
+    generate_model_description,
+    sanitize_tool_params_for_openai_strict,
+)
 from crewai.utilities.types import LLMMessage
 
 
@@ -253,22 +257,40 @@ class OpenAICompletion(BaseLLM):
 
     @model_validator(mode="after")
     def _init_clients(self) -> OpenAICompletion:
+        """Eagerly build clients when the API key is available, otherwise
+        defer so ``LLM(model="openai/...")`` can be constructed at module
+        import time even before deployment env vars are set.
+        """
+        try:
+            self._client = self._build_sync_client()
+            self._async_client = self._build_async_client()
+        except ValueError:
+            pass
+        return self
+
+    def _build_sync_client(self) -> Any:
         client_config = self._get_client_params()
         if self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
-            http_client = httpx.Client(transport=transport)
-            client_config["http_client"] = http_client
+            client_config["http_client"] = httpx.Client(transport=transport)
+        return OpenAI(**client_config)
 
-        self._client = OpenAI(**client_config)
-
-        async_client_config = self._get_client_params()
+    def _build_async_client(self) -> Any:
+        client_config = self._get_client_params()
         if self.interceptor:
-            async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
-            async_http_client = httpx.AsyncClient(transport=async_transport)
-            async_client_config["http_client"] = async_http_client
+            transport = AsyncHTTPTransport(interceptor=self.interceptor)
+            client_config["http_client"] = httpx.AsyncClient(transport=transport)
+        return AsyncOpenAI(**client_config)
 
-        self._async_client = AsyncOpenAI(**async_client_config)
-        return self
+    def _get_sync_client(self) -> Any:
+        if self._client is None:
+            self._client = self._build_sync_client()
+        return self._client
+
+    def _get_async_client(self) -> Any:
+        if self._async_client is None:
+            self._async_client = self._build_async_client()
+        return self._async_client
 
     @property
     def last_response_id(self) -> str | None:
@@ -764,8 +786,6 @@ class OpenAICompletion(BaseLLM):
             "function": {"name": "...", "description": "...", "parameters": {...}}
         }
         """
-        from crewai.llms.providers.utils.common import safe_tool_conversion
-
         responses_tools = []
 
         for tool in tools:
@@ -797,7 +817,7 @@ class OpenAICompletion(BaseLLM):
     ) -> str | ResponsesAPIResult | Any:
         """Handle non-streaming Responses API call."""
         try:
-            response: Response = self._client.responses.create(**params)
+            response: Response = self._get_sync_client().responses.create(**params)
 
             # Track response ID for auto-chaining
             if self.auto_chain and response.id:
@@ -933,7 +953,9 @@ class OpenAICompletion(BaseLLM):
     ) -> str | ResponsesAPIResult | Any:
         """Handle async non-streaming Responses API call."""
         try:
-            response: Response = await self._async_client.responses.create(**params)
+            response: Response = await self._get_async_client().responses.create(
+                **params
+            )
 
             # Track response ID for auto-chaining
             if self.auto_chain and response.id:
@@ -1069,7 +1091,7 @@ class OpenAICompletion(BaseLLM):
         final_response: Response | None = None
         usage: dict[str, Any] | None = None
 
-        stream = self._client.responses.create(**params)
+        stream = self._get_sync_client().responses.create(**params)
         response_id_stream = None
 
         for event in stream:
@@ -1197,7 +1219,7 @@ class OpenAICompletion(BaseLLM):
         final_response: Response | None = None
         usage: dict[str, Any] | None = None
 
-        stream = await self._async_client.responses.create(**params)
+        stream = await self._get_async_client().responses.create(**params)
         response_id_stream = None
 
         async for event in stream:
@@ -1324,19 +1346,23 @@ class OpenAICompletion(BaseLLM):
         ]
 
     def _extract_responses_token_usage(self, response: Response) -> dict[str, Any]:
-        """Extract token usage from Responses API response."""
+        """Extract token usage and response metadata from Responses API response."""
         if response.usage:
-            result = {
+            result: dict[str, Any] = {
                 "prompt_tokens": response.usage.input_tokens,
                 "completion_tokens": response.usage.output_tokens,
                 "total_tokens": response.usage.total_tokens,
             }
-            # Extract cached prompt tokens from input_tokens_details
             input_details = getattr(response.usage, "input_tokens_details", None)
             if input_details:
                 result["cached_prompt_tokens"] = (
                     getattr(input_details, "cached_tokens", 0) or 0
                 )
+            output_details = getattr(response.usage, "output_tokens_details", None)
+            if output_details:
+                result["reasoning_tokens"] = (
+                    getattr(output_details, "reasoning_tokens", 0) or 0
+                )
             return result
         return {"total_tokens": 0}
 
@@ -1544,11 +1570,6 @@ class OpenAICompletion(BaseLLM):
         self, tools: list[dict[str, BaseTool]]
     ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to OpenAI function calling format."""
-        from crewai.llms.providers.utils.common import safe_tool_conversion
-        from crewai.utilities.pydantic_schema_utils import (
-            force_additional_properties_false,
-        )
-
         openai_tools = []
 
         for tool in tools:
@@ -1567,8 +1588,9 @@ class OpenAICompletion(BaseLLM):
                 params_dict = (
                     parameters if isinstance(parameters, dict) else dict(parameters)
                 )
-                params_dict = force_additional_properties_false(params_dict)
-                openai_tool["function"]["parameters"] = params_dict
+                openai_tool["function"]["parameters"] = (
+                    sanitize_tool_params_for_openai_strict(params_dict)
+                )
 
             openai_tools.append(openai_tool)
         return openai_tools
@@ -1587,7 +1609,7 @@ class OpenAICompletion(BaseLLM):
                 parse_params = {
                     k: v for k, v in params.items() if k != "response_format"
                 }
-                parsed_response = self._client.beta.chat.completions.parse(
+                parsed_response = self._get_sync_client().beta.chat.completions.parse(
                     **parse_params,
                     response_format=response_model,
                 )
@@ -1611,7 +1633,9 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
-            response: ChatCompletion = self._client.chat.completions.create(**params)
+            response: ChatCompletion = self._get_sync_client().chat.completions.create(
+                **params
+            )
 
             usage = self._extract_openai_token_usage(response)
 
@@ -1838,7 +1862,7 @@ class OpenAICompletion(BaseLLM):
             }
 
             stream: ChatCompletionStream[BaseModel]
-            with self._client.beta.chat.completions.stream(
+            with self._get_sync_client().beta.chat.completions.stream(
                 **parse_params, response_format=response_model
             ) as stream:
                 for chunk in stream:
@@ -1875,7 +1899,7 @@ class OpenAICompletion(BaseLLM):
             return ""
 
         completion_stream: Stream[ChatCompletionChunk] = (
-            self._client.chat.completions.create(**params)
+            self._get_sync_client().chat.completions.create(**params)
         )
 
         usage_data: dict[str, Any] | None = None
@@ -1972,9 +1996,11 @@ class OpenAICompletion(BaseLLM):
                 parse_params = {
                     k: v for k, v in params.items() if k != "response_format"
                 }
-                parsed_response = await self._async_client.beta.chat.completions.parse(
-                    **parse_params,
-                    response_format=response_model,
+                parsed_response = (
+                    await self._get_async_client().beta.chat.completions.parse(
+                        **parse_params,
+                        response_format=response_model,
+                    )
                 )
                 math_reasoning = parsed_response.choices[0].message
 
@@ -1996,8 +2022,8 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
-            response: ChatCompletion = await self._async_client.chat.completions.create(
-                **params
+            response: ChatCompletion = (
+                await self._get_async_client().chat.completions.create(**params)
             )
 
             usage = self._extract_openai_token_usage(response)
@@ -2123,7 +2149,7 @@ class OpenAICompletion(BaseLLM):
         if response_model:
             completion_stream: AsyncIterator[
                 ChatCompletionChunk
-            ] = await self._async_client.chat.completions.create(**params)
+            ] = await self._get_async_client().chat.completions.create(**params)
 
             accumulated_content = ""
             usage_data: dict[str, Any] | None = None
@@ -2179,7 +2205,7 @@ class OpenAICompletion(BaseLLM):
 
         stream: AsyncIterator[
             ChatCompletionChunk
-        ] = await self._async_client.chat.completions.create(**params)
+        ] = await self._get_async_client().chat.completions.create(**params)
 
         usage_data = None
 
@@ -2307,20 +2333,24 @@ class OpenAICompletion(BaseLLM):
     def _extract_openai_token_usage(
         self, response: ChatCompletion | ChatCompletionChunk
     ) -> dict[str, Any]:
-        """Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
+        """Extract token usage and response metadata from OpenAI ChatCompletion."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage
-            result = {
+            result: dict[str, Any] = {
                 "prompt_tokens": getattr(usage, "prompt_tokens", 0),
                 "completion_tokens": getattr(usage, "completion_tokens", 0),
                 "total_tokens": getattr(usage, "total_tokens", 0),
             }
-            # Extract cached prompt tokens from prompt_tokens_details
             prompt_details = getattr(usage, "prompt_tokens_details", None)
             if prompt_details:
                 result["cached_prompt_tokens"] = (
                     getattr(prompt_details, "cached_tokens", 0) or 0
                 )
+            completion_details = getattr(usage, "completion_tokens_details", None)
+            if completion_details:
+                result["reasoning_tokens"] = (
+                    getattr(completion_details, "reasoning_tokens", 0) or 0
+                )
             return result
         return {"total_tokens": 0}
 
@@ -2371,8 +2401,8 @@ class OpenAICompletion(BaseLLM):
             from crewai_files.uploaders.openai import OpenAIFileUploader
 
             return OpenAIFileUploader(
-                client=self._client,
-                async_client=self._async_client,
+                client=self._get_sync_client(),
+                async_client=self._get_async_client(),
             )
         except ImportError:
             return None

lib/crewai/src/crewai/memory/analyze.py

@@ -8,8 +8,8 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
-from crewai.memory.types import MemoryPromptConfig, MemoryRecord, ScopeInfo
-from crewai.utilities.i18n import get_i18n
+from crewai.memory.types import MemoryRecord, ScopeInfo
+from crewai.utilities.i18n import I18N_DEFAULT
 
 
 _logger = logging.getLogger(__name__)
@@ -140,23 +140,19 @@ class ConsolidationPlan(BaseModel):
     )
 
 
-def _memory_prompt_line(
-    memory_prompt: MemoryPromptConfig | None,
-    key: str,
-) -> str:
-    """Resolve one memory prompt: override string or bundled translation."""
-    if memory_prompt is not None:
-        raw = getattr(memory_prompt, key, None)
-        if isinstance(raw, str) and raw.strip():
-            return raw
-    return get_i18n().memory(key)
+def _get_prompt(key: str) -> str:
+    """Retrieve a memory prompt from the i18n translations.
+
+    Args:
+        key: The prompt key under the "memory" section.
+
+    Returns:
+        The prompt string.
+    """
+    return I18N_DEFAULT.memory(key)
 
 
-def extract_memories_from_content(
-    content: str,
-    llm: Any,
-    memory_prompt: MemoryPromptConfig | None = None,
-) -> list[str]:
+def extract_memories_from_content(content: str, llm: Any) -> list[str]:
     """Use the LLM to extract discrete memory statements from raw content.
 
     This is a pure helper: it does NOT store anything. Callers should call
@@ -168,21 +164,15 @@ def extract_memories_from_content(
     Args:
         content: Raw text (e.g. task description + result dump).
         llm: The LLM instance to use.
-        memory_prompt: Optional per-step prompt strings (see ``MemoryPromptConfig``).
 
     Returns:
         List of short, self-contained memory statements (or [content] on failure).
     """
     if not (content or "").strip():
         return []
-    user = _memory_prompt_line(memory_prompt, "extract_memories_user").format(
-        content=content
-    )
+    user = _get_prompt("extract_memories_user").format(content=content)
     messages = [
-        {
-            "role": "system",
-            "content": _memory_prompt_line(memory_prompt, "extract_memories_system"),
-        },
+        {"role": "system", "content": _get_prompt("extract_memories_system")},
         {"role": "user", "content": user},
     ]
     try:
@@ -212,7 +202,6 @@ def analyze_query(
     available_scopes: list[str],
     scope_info: ScopeInfo | None,
     llm: Any,
-    memory_prompt: MemoryPromptConfig | None = None,
 ) -> QueryAnalysis:
     """Use the LLM to analyze a recall query.
 
@@ -223,7 +212,6 @@ def analyze_query(
         available_scopes: Scope paths that exist in the store.
         scope_info: Optional info about the current scope.
         llm: The LLM instance to use.
-        memory_prompt: Optional per-step prompt strings.
 
     Returns:
         QueryAnalysis with keywords, suggested_scopes, complexity, recall_queries, time_filter.
@@ -231,16 +219,13 @@ def analyze_query(
     scope_desc = ""
     if scope_info:
         scope_desc = f"Current scope has {scope_info.record_count} records, categories: {scope_info.categories}"
-    user = _memory_prompt_line(memory_prompt, "query_user").format(
+    user = _get_prompt("query_user").format(
         query=query,
         available_scopes=available_scopes or ["/"],
         scope_desc=scope_desc,
     )
     messages = [
-        {
-            "role": "system",
-            "content": _memory_prompt_line(memory_prompt, "query_system"),
-        },
+        {"role": "system", "content": _get_prompt("query_system")},
         {"role": "user", "content": user},
     ]
     try:
@@ -284,7 +269,6 @@ def analyze_for_save(
     existing_scopes: list[str],
     existing_categories: list[str],
     llm: Any,
-    memory_prompt: MemoryPromptConfig | None = None,
 ) -> MemoryAnalysis:
     """Infer scope, categories, importance, and metadata for a single memory.
 
@@ -296,21 +280,17 @@ def analyze_for_save(
         existing_scopes: Current scope paths in the memory store.
         existing_categories: Current categories in use.
         llm: The LLM instance to use.
-        memory_prompt: Optional per-step prompt strings.
 
     Returns:
         MemoryAnalysis with suggested_scope, categories, importance, extracted_metadata.
     """
-    user = _memory_prompt_line(memory_prompt, "save_user").format(
+    user = _get_prompt("save_user").format(
         content=content,
         existing_scopes=existing_scopes or ["/"],
         existing_categories=existing_categories or [],
     )
     messages = [
-        {
-            "role": "system",
-            "content": _memory_prompt_line(memory_prompt, "save_system"),
-        },
+        {"role": "system", "content": _get_prompt("save_system")},
         {"role": "user", "content": user},
     ]
     try:
@@ -342,7 +322,6 @@ def analyze_for_consolidation(
     new_content: str,
     existing_records: list[MemoryRecord],
     llm: Any,
-    memory_prompt: MemoryPromptConfig | None = None,
 ) -> ConsolidationPlan:
     """Decide insert/update/delete for a single memory against similar existing records.
 
@@ -353,7 +332,6 @@ def analyze_for_consolidation(
         new_content: The new content to store.
         existing_records: Existing records that are semantically similar.
         llm: The LLM instance to use.
-        memory_prompt: Optional per-step prompt strings.
 
     Returns:
         ConsolidationPlan with actions per record and whether to insert the new content.
@@ -367,15 +345,12 @@ def analyze_for_consolidation(
             f"- id={r.id} | scope={r.scope} | importance={r.importance:.2f} | created={created}\n"
             f"  content: {r.content[:200]}{'...' if len(r.content) > 200 else ''}"
         )
-    user = _memory_prompt_line(memory_prompt, "consolidation_user").format(
+    user = _get_prompt("consolidation_user").format(
         new_content=new_content,
         records_summary="\n\n".join(records_lines),
     )
     messages = [
-        {
-            "role": "system",
-            "content": _memory_prompt_line(memory_prompt, "consolidation_system"),
-        },
+        {"role": "system", "content": _get_prompt("consolidation_system")},
         {"role": "user", "content": user},
     ]
     try:

lib/crewai/src/crewai/memory/encoding_flow.py

@@ -314,7 +314,6 @@ class EncodingFlow(Flow[EncodingState]):
                         item.content,
                         list(item.similar_records),
                         self._llm,
-                        self._config.memory_prompt,
                     )
                 elif not fields_provided and not has_similar:
                     # Group C: field resolution only
@@ -325,7 +324,6 @@ class EncodingFlow(Flow[EncodingState]):
                         existing_scopes,
                         existing_categories,
                         self._llm,
-                        self._config.memory_prompt,
                     )
                 else:
                     # Group D: both in parallel
@@ -336,7 +334,6 @@ class EncodingFlow(Flow[EncodingState]):
                         existing_scopes,
                         existing_categories,
                         self._llm,
-                        self._config.memory_prompt,
                     )
                     consol_futures[i] = pool.submit(
                         contextvars.copy_context().run,
@@ -344,7 +341,6 @@ class EncodingFlow(Flow[EncodingState]):
                         item.content,
                         list(item.similar_records),
                         self._llm,
-                        self._config.memory_prompt,
                     )
 
             # Collect field-resolution results

lib/crewai/src/crewai/memory/recall_flow.py

@@ -227,7 +227,6 @@ class RecallFlow(Flow[RecallState]):
                 available,
                 scope_info,
                 self._llm,
-                self._config.memory_prompt,
             )
             self.state.query_analysis = analysis
 

lib/crewai/src/crewai/memory/types.py

@@ -6,7 +6,7 @@ from datetime import datetime
 from typing import Any
 from uuid import uuid4
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field
 
 
 # When searching the vector store, we ask for more results than the caller
@@ -132,28 +132,6 @@ class ScopeInfo(BaseModel):
     )
 
 
-class MemoryPromptConfig(BaseModel):
-    """Configuration for memory LLM prompts (like ``PlanningConfig`` for planning).
-
-    Field names match translation keys under ``memory`` in ``translations/en.json``.
-    When set, the string replaces the bundled prompt for that step; omitted keys
-    keep the default i18n text. Templates must include the same ``str.format``
-    placeholders as the defaults (e.g. ``save_user`` uses ``{content}``,
-    ``{existing_scopes}``, ``{existing_categories}``).
-    """
-
-    model_config = ConfigDict(extra="forbid")
-
-    save_system: str | None = None
-    save_user: str | None = None
-    query_system: str | None = None
-    query_user: str | None = None
-    extract_memories_system: str | None = None
-    extract_memories_user: str | None = None
-    consolidation_system: str | None = None
-    consolidation_user: str | None = None
-
-
 class MemoryConfig(BaseModel):
     """Internal configuration for memory scoring, consolidation, and recall behavior.
 
@@ -163,11 +141,6 @@ class MemoryConfig(BaseModel):
     compute_composite_score.
     """
 
-    memory_prompt: MemoryPromptConfig | None = Field(
-        default=None,
-        description="Per-step prompt strings overriding bundled memory prompts.",
-    )
-
     # -- Composite score weights --
     # The recall composite score is:
     #   semantic_weight * similarity + recency_weight * decay + importance_weight * importance

lib/crewai/src/crewai/memory/unified_memory.py

@@ -9,13 +9,7 @@ import threading
 import time
 from typing import TYPE_CHECKING, Annotated, Any, Literal
 
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    PlainValidator,
-    PrivateAttr,
-)
+from pydantic import BaseModel, ConfigDict, Field, PlainValidator, PrivateAttr
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.memory_events import (
@@ -32,7 +26,6 @@ from crewai.memory.storage.backend import StorageBackend
 from crewai.memory.types import (
     MemoryConfig,
     MemoryMatch,
-    MemoryPromptConfig,
     MemoryRecord,
     ScopeInfo,
     compute_composite_score,
@@ -66,10 +59,6 @@ class Memory(BaseModel):
     Works without agent/crew. Uses LLM to infer scope, categories, importance on save.
     Uses RecallFlow for adaptive-depth recall. Supports scope/slice views and
     pluggable storage (LanceDB default).
-
-    Override LLM prompts per step via ``memory_prompt`` (same idea as
-    ``PlanningConfig.system_prompt`` / ``plan_prompt``): set only the strings you
-    need; the rest stay on bundled translations.
     """
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -146,13 +135,6 @@ class Memory(BaseModel):
             "will store memories at '/crew/research/<inferred_scope>'."
         ),
     )
-    memory_prompt: MemoryPromptConfig | None = Field(
-        default=None,
-        description=(
-            "Optional prompt strings for save, query, extract, and consolidation steps. "
-            "See MemoryPromptConfig; unset fields use translations/en.json defaults."
-        ),
-    )
 
     _config: MemoryConfig = PrivateAttr()
     _llm_instance: BaseLLM | None = PrivateAttr(default=None)
@@ -199,7 +181,6 @@ class Memory(BaseModel):
     def model_post_init(self, __context: Any) -> None:
         """Initialize runtime state from field values."""
         self._config = MemoryConfig(
-            memory_prompt=self.memory_prompt,
             recency_weight=self.recency_weight,
             semantic_weight=self.semantic_weight,
             importance_weight=self.importance_weight,
@@ -657,9 +638,7 @@ class Memory(BaseModel):
         Returns:
             List of short, self-contained memory statements.
         """
-        return extract_memories_from_content(
-            content, self._llm, self._config.memory_prompt
-        )
+        return extract_memories_from_content(content, self._llm)
 
     def recall(
         self,

lib/crewai/src/crewai/state/checkpoint_config.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+from pathlib import Path
 from typing import Annotated, Any, Literal
 
 from pydantic import BaseModel, Field, model_validator
@@ -201,11 +202,20 @@ class CheckpointConfig(BaseModel):
         description="Maximum checkpoints to keep. Oldest are pruned after "
         "each write. None means keep all.",
     )
+    restore_from: Path | str | None = Field(
+        default=None,
+        description="Path or location of a checkpoint to restore from. "
+        "When passed via a kickoff method's from_checkpoint parameter, "
+        "the crew or flow resumes from this checkpoint.",
+    )
 
     @model_validator(mode="after")
     def _register_handlers(self) -> CheckpointConfig:
         from crewai.state.checkpoint_listener import _ensure_handlers_registered
 
+        if isinstance(self.provider, SqliteProvider) and not Path(self.location).suffix:
+            self.location = f"{self.location}.db"
+
         _ensure_handlers_registered()
         return self
 
@@ -216,3 +226,25 @@ class CheckpointConfig(BaseModel):
     @property
     def trigger_events(self) -> set[str]:
         return set(self.on_events)
+
+
+def apply_checkpoint(instance: Any, from_checkpoint: CheckpointConfig | None) -> Any:
+    """Handle checkpoint config for a kickoff method.
+
+    If *from_checkpoint* carries a ``restore_from`` path, builds and returns a
+    restored instance (with ``restore_from`` cleared).  The caller should
+    dispatch into its own kickoff variant on that restored instance.
+
+    If *from_checkpoint* is present but has no ``restore_from``, sets
+    ``instance.checkpoint`` and returns ``None`` (proceed normally).
+
+    If *from_checkpoint* is ``None``, returns ``None`` immediately.
+    """
+    if from_checkpoint is None:
+        return None
+    if from_checkpoint.restore_from is not None:
+        restored = type(instance).from_checkpoint(from_checkpoint)
+        restored.checkpoint = from_checkpoint.model_copy(update={"restore_from": None})
+        return restored
+    instance.checkpoint = from_checkpoint
+    return None

lib/crewai/src/crewai/state/checkpoint_listener.py

@@ -7,6 +7,7 @@ avoids per-event overhead when no entity uses checkpointing.
 
 from __future__ import annotations
 
+import json
 import logging
 import threading
 from typing import Any
@@ -102,14 +103,25 @@ def _find_checkpoint(source: Any) -> CheckpointConfig | None:
     return None
 
 
-def _do_checkpoint(state: RuntimeState, cfg: CheckpointConfig) -> None:
+def _do_checkpoint(
+    state: RuntimeState, cfg: CheckpointConfig, event: BaseEvent | None = None
+) -> None:
     """Write a checkpoint and prune old ones if configured."""
     _prepare_entities(state.root)
-    data = state.model_dump_json()
-    cfg.provider.checkpoint(data, cfg.location)
+    payload = state.model_dump(mode="json")
+    if event is not None:
+        payload["trigger"] = event.type
+    data = json.dumps(payload)
+    location = cfg.provider.checkpoint(
+        data,
+        cfg.location,
+        parent_id=state._parent_id,
+        branch=state._branch,
+    )
+    state._chain_lineage(cfg.provider, location)
 
     if cfg.max_checkpoints is not None:
-        cfg.provider.prune(cfg.location, cfg.max_checkpoints)
+        cfg.provider.prune(cfg.location, cfg.max_checkpoints, branch=state._branch)
 
 
 def _should_checkpoint(source: Any, event: BaseEvent) -> CheckpointConfig | None:
@@ -128,7 +140,7 @@ def _on_any_event(source: Any, event: BaseEvent, state: Any) -> None:
     if cfg is None:
         return
     try:
-        _do_checkpoint(state, cfg)
+        _do_checkpoint(state, cfg, event)
     except Exception:
         logger.warning("Auto-checkpoint failed for event %s", event.type, exc_info=True)
 

lib/crewai/src/crewai/state/provider/core.py

@@ -17,12 +17,21 @@ class BaseProvider(BaseModel, ABC):
     provider_type: str = "base"
 
     @abstractmethod
-    def checkpoint(self, data: str, location: str) -> str:
+    def checkpoint(
+        self,
+        data: str,
+        location: str,
+        *,
+        parent_id: str | None = None,
+        branch: str = "main",
+    ) -> str:
         """Persist a snapshot synchronously.
 
         Args:
             data: The serialized string to persist.
             location: Storage destination (directory, file path, URI, etc.).
+            parent_id: ID of the parent checkpoint for lineage tracking.
+            branch: Branch label for this checkpoint.
 
         Returns:
             A location identifier for the saved checkpoint.
@@ -30,12 +39,21 @@ class BaseProvider(BaseModel, ABC):
         ...
 
     @abstractmethod
-    async def acheckpoint(self, data: str, location: str) -> str:
+    async def acheckpoint(
+        self,
+        data: str,
+        location: str,
+        *,
+        parent_id: str | None = None,
+        branch: str = "main",
+    ) -> str:
         """Persist a snapshot asynchronously.
 
         Args:
             data: The serialized string to persist.
             location: Storage destination (directory, file path, URI, etc.).
+            parent_id: ID of the parent checkpoint for lineage tracking.
+            branch: Branch label for this checkpoint.
 
         Returns:
             A location identifier for the saved checkpoint.
@@ -43,12 +61,25 @@ class BaseProvider(BaseModel, ABC):
         ...
 
     @abstractmethod
-    def prune(self, location: str, max_keep: int) -> None:
-        """Remove old checkpoints, keeping at most *max_keep*.
+    def prune(self, location: str, max_keep: int, *, branch: str = "main") -> None:
+        """Remove old checkpoints, keeping at most *max_keep* per branch.
 
         Args:
             location: The storage destination passed to ``checkpoint``.
             max_keep: Maximum number of checkpoints to retain.
+            branch: Only prune checkpoints on this branch.
+        """
+        ...
+
+    @abstractmethod
+    def extract_id(self, location: str) -> str:
+        """Extract the checkpoint ID from a location string.
+
+        Args:
+            location: The identifier returned by a previous ``checkpoint`` call.
+
+        Returns:
+            The checkpoint ID.
         """
         ...
 

lib/crewai/src/crewai/state/provider/json_provider.py

@@ -19,48 +19,87 @@ from crewai.state.provider.core import BaseProvider
 logger = logging.getLogger(__name__)
 
 
+def _safe_branch(base: str, branch: str) -> None:
+    """Validate that a branch name doesn't escape the base directory.
+
+    Raises:
+        ValueError: If the branch resolves outside the base directory.
+    """
+    base_resolved = str(Path(base).resolve())
+    target_resolved = str((Path(base) / branch).resolve())
+    if (
+        not target_resolved.startswith(base_resolved + os.sep)
+        and target_resolved != base_resolved
+    ):
+        raise ValueError(f"Branch name escapes checkpoint directory: {branch!r}")
+
+
 class JsonProvider(BaseProvider):
     """Persists runtime state checkpoints as JSON files on the local filesystem."""
 
     provider_type: Literal["json"] = "json"
 
-    def checkpoint(self, data: str, location: str) -> str:
+    def checkpoint(
+        self,
+        data: str,
+        location: str,
+        *,
+        parent_id: str | None = None,
+        branch: str = "main",
+    ) -> str:
         """Write a JSON checkpoint file.
 
         Args:
             data: The serialized JSON string to persist.
-            location: Directory where the checkpoint will be saved.
+            location: Base directory where checkpoints are saved.
+            parent_id: ID of the parent checkpoint for lineage tracking.
+                Encoded in the filename for queryable lineage without
+                parsing the blob.
+            branch: Branch label. Files are stored under ``location/branch/``.
 
         Returns:
             The path to the written checkpoint file.
         """
-        file_path = _build_path(location)
+        file_path = _build_path(location, branch, parent_id)
         file_path.parent.mkdir(parents=True, exist_ok=True)
 
         with open(file_path, "w") as f:
             f.write(data)
         return str(file_path)
 
-    async def acheckpoint(self, data: str, location: str) -> str:
+    async def acheckpoint(
+        self,
+        data: str,
+        location: str,
+        *,
+        parent_id: str | None = None,
+        branch: str = "main",
+    ) -> str:
         """Write a JSON checkpoint file asynchronously.
 
         Args:
             data: The serialized JSON string to persist.
-            location: Directory where the checkpoint will be saved.
+            location: Base directory where checkpoints are saved.
+            parent_id: ID of the parent checkpoint for lineage tracking.
+                Encoded in the filename for queryable lineage without
+                parsing the blob.
+            branch: Branch label. Files are stored under ``location/branch/``.
 
         Returns:
             The path to the written checkpoint file.
         """
-        file_path = _build_path(location)
+        file_path = _build_path(location, branch, parent_id)
         await aiofiles.os.makedirs(str(file_path.parent), exist_ok=True)
 
         async with aiofiles.open(file_path, "w") as f:
             await f.write(data)
         return str(file_path)
 
-    def prune(self, location: str, max_keep: int) -> None:
-        """Remove oldest checkpoint files beyond *max_keep*."""
-        pattern = os.path.join(location, "*.json")
+    def prune(self, location: str, max_keep: int, *, branch: str = "main") -> None:
+        """Remove oldest checkpoint files beyond *max_keep* on a branch."""
+        _safe_branch(location, branch)
+        branch_dir = os.path.join(location, branch)
+        pattern = os.path.join(branch_dir, "*.json")
         files = sorted(glob.glob(pattern), key=os.path.getmtime)
         for path in files if max_keep == 0 else files[:-max_keep]:
             try:
@@ -68,6 +107,16 @@ class JsonProvider(BaseProvider):
             except OSError:  # noqa: PERF203
                 logger.debug("Failed to remove %s", path, exc_info=True)
 
+    def extract_id(self, location: str) -> str:
+        """Extract the checkpoint ID from a file path.
+
+        The filename format is ``{ts}_{uuid8}_p-{parent}.json``.
+        The checkpoint ID is the ``{ts}_{uuid8}`` prefix.
+        """
+        stem = Path(location).stem
+        idx = stem.find("_p-")
+        return stem[:idx] if idx != -1 else stem
+
     def from_checkpoint(self, location: str) -> str:
         """Read a JSON checkpoint file.
 
@@ -92,15 +141,24 @@ class JsonProvider(BaseProvider):
             return await f.read()
 
 
-def _build_path(directory: str) -> Path:
-    """Build a timestamped checkpoint file path.
+def _build_path(
+    directory: str, branch: str = "main", parent_id: str | None = None
+) -> Path:
+    """Build a timestamped checkpoint file path under a branch subdirectory.
+
+    Filename format: ``{ts}_{uuid8}_p-{parent_id}.json``
 
     Args:
-        directory: Parent directory for the checkpoint file.
+        directory: Base directory for checkpoints.
+        branch: Branch label used as a subdirectory name.
+        parent_id: Parent checkpoint ID to encode in the filename.
 
     Returns:
         The target file path.
     """
+    _safe_branch(directory, branch)
     ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S")
-    filename = f"{ts}_{uuid.uuid4().hex[:8]}.json"
-    return Path(directory) / filename
+    short_uuid = uuid.uuid4().hex[:8]
+    parent_suffix = parent_id or "none"
+    filename = f"{ts}_{short_uuid}_p-{parent_suffix}.json"
+    return Path(directory) / branch / filename

lib/crewai/src/crewai/state/provider/sqlite_provider.py

@@ -17,15 +17,20 @@ _CREATE_TABLE = """
 CREATE TABLE IF NOT EXISTS checkpoints (
     id TEXT PRIMARY KEY,
     created_at TEXT NOT NULL,
+    parent_id TEXT,
+    branch TEXT NOT NULL DEFAULT 'main',
     data JSONB NOT NULL
 )
 """
 
-_INSERT = "INSERT INTO checkpoints (id, created_at, data) VALUES (?, ?, jsonb(?))"
+_INSERT = (
+    "INSERT INTO checkpoints (id, created_at, parent_id, branch, data) "
+    "VALUES (?, ?, ?, ?, jsonb(?))"
+)
 _SELECT = "SELECT json(data) FROM checkpoints WHERE id = ?"
 _PRUNE = """
-DELETE FROM checkpoints WHERE rowid NOT IN (
-    SELECT rowid FROM checkpoints ORDER BY rowid DESC LIMIT ?
+DELETE FROM checkpoints WHERE branch = ? AND rowid NOT IN (
+    SELECT rowid FROM checkpoints WHERE branch = ? ORDER BY rowid DESC LIMIT ?
 )
 """
 
@@ -50,12 +55,21 @@ class SqliteProvider(BaseProvider):
 
     provider_type: Literal["sqlite"] = "sqlite"
 
-    def checkpoint(self, data: str, location: str) -> str:
+    def checkpoint(
+        self,
+        data: str,
+        location: str,
+        *,
+        parent_id: str | None = None,
+        branch: str = "main",
+    ) -> str:
         """Write a checkpoint to the SQLite database.
 
         Args:
             data: The serialized JSON string to persist.
             location: Path to the SQLite database file.
+            parent_id: ID of the parent checkpoint for lineage tracking.
+            branch: Branch label for this checkpoint.
 
         Returns:
             A location string in the format ``"db_path#checkpoint_id"``.
@@ -65,16 +79,25 @@ class SqliteProvider(BaseProvider):
         with sqlite3.connect(location) as conn:
             conn.execute("PRAGMA journal_mode=WAL")
             conn.execute(_CREATE_TABLE)
-            conn.execute(_INSERT, (checkpoint_id, ts, data))
+            conn.execute(_INSERT, (checkpoint_id, ts, parent_id, branch, data))
             conn.commit()
         return f"{location}#{checkpoint_id}"
 
-    async def acheckpoint(self, data: str, location: str) -> str:
+    async def acheckpoint(
+        self,
+        data: str,
+        location: str,
+        *,
+        parent_id: str | None = None,
+        branch: str = "main",
+    ) -> str:
         """Write a checkpoint to the SQLite database asynchronously.
 
         Args:
             data: The serialized JSON string to persist.
             location: Path to the SQLite database file.
+            parent_id: ID of the parent checkpoint for lineage tracking.
+            branch: Branch label for this checkpoint.
 
         Returns:
             A location string in the format ``"db_path#checkpoint_id"``.
@@ -84,16 +107,20 @@ class SqliteProvider(BaseProvider):
         async with aiosqlite.connect(location) as db:
             await db.execute("PRAGMA journal_mode=WAL")
             await db.execute(_CREATE_TABLE)
-            await db.execute(_INSERT, (checkpoint_id, ts, data))
+            await db.execute(_INSERT, (checkpoint_id, ts, parent_id, branch, data))
             await db.commit()
         return f"{location}#{checkpoint_id}"
 
-    def prune(self, location: str, max_keep: int) -> None:
-        """Remove oldest checkpoint rows beyond *max_keep*."""
+    def prune(self, location: str, max_keep: int, *, branch: str = "main") -> None:
+        """Remove oldest checkpoint rows beyond *max_keep* on a branch."""
         with sqlite3.connect(location) as conn:
-            conn.execute(_PRUNE, (max_keep,))
+            conn.execute(_PRUNE, (branch, branch, max_keep))
             conn.commit()
 
+    def extract_id(self, location: str) -> str:
+        """Extract the checkpoint ID from a ``db_path#id`` string."""
+        return location.rsplit("#", 1)[1]
+
     def from_checkpoint(self, location: str) -> str:
         """Read a checkpoint from the SQLite database.
 

lib/crewai/src/crewai/state/provider/utils.py

@@ -0,0 +1,34 @@
+"""Provider detection utilities."""
+
+from __future__ import annotations
+
+from crewai.state.provider.core import BaseProvider
+
+
+_SQLITE_MAGIC = b"SQLite format 3\x00"
+
+
+def detect_provider(path: str) -> BaseProvider:
+    """Detect the storage provider from a checkpoint path.
+
+    Reads the file's magic bytes to determine if it's a SQLite database.
+    For paths containing ``#``, checks the portion before the ``#``.
+    Falls back to JsonProvider.
+
+    Args:
+        path: A checkpoint file path, directory, or ``db_path#checkpoint_id``.
+
+    Returns:
+        The appropriate provider instance.
+    """
+    from crewai.state.provider.json_provider import JsonProvider
+    from crewai.state.provider.sqlite_provider import SqliteProvider
+
+    file_path = path.split("#")[0] if "#" in path else path
+    try:
+        with open(file_path, "rb") as f:
+            if f.read(16) == _SQLITE_MAGIC:
+                return SqliteProvider()
+    except OSError:
+        pass
+    return JsonProvider()

lib/crewai/src/crewai/state/runtime.py

@@ -9,8 +9,11 @@ via ``RuntimeState.model_rebuild()``.
 
 from __future__ import annotations
 
+import logging
 from typing import TYPE_CHECKING, Any
+import uuid
 
+from packaging.version import Version
 from pydantic import (
     ModelWrapValidatorHandler,
     PrivateAttr,
@@ -20,9 +23,14 @@ from pydantic import (
 )
 
 from crewai.context import capture_execution_context
+from crewai.state.checkpoint_config import CheckpointConfig
 from crewai.state.event_record import EventRecord
 from crewai.state.provider.core import BaseProvider
 from crewai.state.provider.json_provider import JsonProvider
+from crewai.utilities.version import get_crewai_version
+
+
+logger = logging.getLogger(__name__)
 
 
 if TYPE_CHECKING:
@@ -58,12 +66,51 @@ def _sync_checkpoint_fields(entity: object) -> None:
         entity.checkpoint_inputs = entity._inputs
         entity.checkpoint_train = entity._train
         entity.checkpoint_kickoff_event_id = entity._kickoff_event_id
+        for task in entity.tasks:
+            task.checkpoint_original_description = task._original_description
+            task.checkpoint_original_expected_output = task._original_expected_output
+
+
+def _migrate(data: dict[str, Any]) -> dict[str, Any]:
+    """Apply version-based migrations to checkpoint data.
+
+    Each block handles checkpoints older than a specific version,
+    transforming them forward to the current format. Blocks run in
+    version order so migrations compose.
+
+    Args:
+        data: The raw deserialized checkpoint dict.
+
+    Returns:
+        The migrated checkpoint dict.
+    """
+    raw = data.get("crewai_version")
+    current = Version(get_crewai_version())
+    stored = Version(raw) if raw else Version("0.0.0")
+
+    if raw is None:
+        logger.warning("Checkpoint has no crewai_version — treating as 0.0.0")
+    elif stored != current:
+        logger.debug(
+            "Migrating checkpoint from crewAI %s to %s",
+            stored,
+            current,
+        )
+
+    # --- migrations in version order ---
+    # if stored < Version("X.Y.Z"):
+    #     data.setdefault("some_field", "default")
+
+    return data
 
 
 class RuntimeState(RootModel):  # type: ignore[type-arg]
     root: list[Entity]
     _provider: BaseProvider = PrivateAttr(default_factory=JsonProvider)
     _event_record: EventRecord = PrivateAttr(default_factory=EventRecord)
+    _checkpoint_id: str | None = PrivateAttr(default=None)
+    _parent_id: str | None = PrivateAttr(default=None)
+    _branch: str = PrivateAttr(default="main")
 
     @property
     def event_record(self) -> EventRecord:
@@ -73,8 +120,11 @@ class RuntimeState(RootModel):  # type: ignore[type-arg]
     @model_serializer(mode="plain")
     def _serialize(self) -> dict[str, Any]:
         return {
+            "crewai_version": get_crewai_version(),
+            "parent_id": self._parent_id,
+            "branch": self._branch,
             "entities": [e.model_dump(mode="json") for e in self.root],
-            "event_record": self._event_record.model_dump(),
+            "event_record": self._event_record.model_dump(mode="json"),
         }
 
     @model_validator(mode="wrap")
@@ -83,13 +133,29 @@ class RuntimeState(RootModel):  # type: ignore[type-arg]
         cls, data: Any, handler: ModelWrapValidatorHandler[RuntimeState]
     ) -> RuntimeState:
         if isinstance(data, dict) and "entities" in data:
+            data = _migrate(data)
             record_data = data.get("event_record")
             state = handler(data["entities"])
             if record_data:
                 state._event_record = EventRecord.model_validate(record_data)
+            state._parent_id = data.get("parent_id")
+            state._branch = data.get("branch", "main")
             return state
         return handler(data)
 
+    def _chain_lineage(self, provider: BaseProvider, location: str) -> None:
+        """Update lineage fields after a successful checkpoint write.
+
+        Sets ``_checkpoint_id`` and ``_parent_id`` so the next write
+        records the correct parent in the lineage chain.
+
+        Args:
+            provider: The provider that performed the write.
+            location: The location string returned by the provider.
+        """
+        self._checkpoint_id = provider.extract_id(location)
+        self._parent_id = self._checkpoint_id
+
     def checkpoint(self, location: str) -> str:
         """Write a checkpoint.
 
@@ -101,7 +167,14 @@ class RuntimeState(RootModel):  # type: ignore[type-arg]
             A location identifier for the saved checkpoint.
         """
         _prepare_entities(self.root)
-        return self._provider.checkpoint(self.model_dump_json(), location)
+        result = self._provider.checkpoint(
+            self.model_dump_json(),
+            location,
+            parent_id=self._parent_id,
+            branch=self._branch,
+        )
+        self._chain_lineage(self._provider, result)
+        return result
 
     async def acheckpoint(self, location: str) -> str:
         """Async version of :meth:`checkpoint`.
@@ -114,41 +187,84 @@ class RuntimeState(RootModel):  # type: ignore[type-arg]
             A location identifier for the saved checkpoint.
         """
         _prepare_entities(self.root)
-        return await self._provider.acheckpoint(self.model_dump_json(), location)
+        result = await self._provider.acheckpoint(
+            self.model_dump_json(),
+            location,
+            parent_id=self._parent_id,
+            branch=self._branch,
+        )
+        self._chain_lineage(self._provider, result)
+        return result
+
+    def fork(self, branch: str | None = None) -> None:
+        """Create a new execution branch and write an initial checkpoint.
+
+        If this state was restored from a checkpoint, an initial checkpoint
+        is written on the new branch so the fork point is recorded.
+
+        Args:
+            branch: Branch label. Auto-generated from the current checkpoint
+                ID if not provided. Always unique — safe to call multiple
+                times without collisions.
+        """
+        if branch:
+            self._branch = branch
+        elif self._checkpoint_id:
+            self._branch = f"fork/{self._checkpoint_id}_{uuid.uuid4().hex[:6]}"
+        else:
+            self._branch = f"fork/{uuid.uuid4().hex[:8]}"
 
     @classmethod
-    def from_checkpoint(
-        cls, location: str, provider: BaseProvider, **kwargs: Any
-    ) -> RuntimeState:
+    def from_checkpoint(cls, config: CheckpointConfig, **kwargs: Any) -> RuntimeState:
         """Restore a RuntimeState from a checkpoint.
 
         Args:
-            location: The identifier returned by a previous ``checkpoint`` call.
-            provider: The storage backend to read from.
+            config: Checkpoint configuration with ``restore_from`` set.
             **kwargs: Passed to ``model_validate_json``.
 
         Returns:
             A restored RuntimeState.
         """
+        from crewai.state.provider.utils import detect_provider
+
+        if config.restore_from is None:
+            raise ValueError("CheckpointConfig.restore_from must be set")
+        location = str(config.restore_from)
+        provider = detect_provider(location)
         raw = provider.from_checkpoint(location)
-        return cls.model_validate_json(raw, **kwargs)
+        state = cls.model_validate_json(raw, **kwargs)
+        state._provider = provider
+        checkpoint_id = provider.extract_id(location)
+        state._checkpoint_id = checkpoint_id
+        state._parent_id = checkpoint_id
+        return state
 
     @classmethod
     async def afrom_checkpoint(
-        cls, location: str, provider: BaseProvider, **kwargs: Any
+        cls, config: CheckpointConfig, **kwargs: Any
     ) -> RuntimeState:
         """Async version of :meth:`from_checkpoint`.
 
         Args:
-            location: The identifier returned by a previous ``acheckpoint`` call.
-            provider: The storage backend to read from.
+            config: Checkpoint configuration with ``restore_from`` set.
             **kwargs: Passed to ``model_validate_json``.
 
         Returns:
             A restored RuntimeState.
         """
+        from crewai.state.provider.utils import detect_provider
+
+        if config.restore_from is None:
+            raise ValueError("CheckpointConfig.restore_from must be set")
+        location = str(config.restore_from)
+        provider = detect_provider(location)
         raw = await provider.afrom_checkpoint(location)
-        return cls.model_validate_json(raw, **kwargs)
+        state = cls.model_validate_json(raw, **kwargs)
+        state._provider = provider
+        checkpoint_id = provider.extract_id(location)
+        state._checkpoint_id = checkpoint_id
+        state._parent_id = checkpoint_id
+        return state
 
 
 def _prepare_entities(root: list[Entity]) -> None:

lib/crewai/src/crewai/task.py

@@ -80,7 +80,7 @@ from crewai.utilities.guardrail_types import (
     GuardrailType,
     GuardrailsType,
 )
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.string_utils import interpolate_only
 
@@ -115,7 +115,6 @@ class Task(BaseModel):
     used_tools: int = 0
     tools_errors: int = 0
     delegations: int = 0
-    i18n: I18N = Field(default_factory=get_i18n)
     name: str | None = Field(default=None)
     prompt_context: str | None = None
     description: str = Field(description="Description of the actual task.")
@@ -231,6 +230,8 @@ class Task(BaseModel):
     _original_description: str | None = PrivateAttr(default=None)
     _original_expected_output: str | None = PrivateAttr(default=None)
     _original_output_file: str | None = PrivateAttr(default=None)
+    checkpoint_original_description: str | None = Field(default=None, exclude=False)
+    checkpoint_original_expected_output: str | None = Field(default=None, exclude=False)
     _thread: threading.Thread | None = PrivateAttr(default=None)
     model_config = {"arbitrary_types_allowed": True}
 
@@ -896,7 +897,7 @@ class Task(BaseModel):
 
         tasks_slices = [description]
 
-        output = self.i18n.slice("expected_output").format(
+        output = I18N_DEFAULT.slice("expected_output").format(
             expected_output=self.expected_output
         )
         tasks_slices = [description, output]
@@ -968,7 +969,7 @@ Follow these guidelines:
                 raise ValueError(f"Error interpolating output_file path: {e!s}") from e
 
         if inputs.get("crew_chat_messages"):
-            conversation_instruction = self.i18n.slice(
+            conversation_instruction = I18N_DEFAULT.slice(
                 "conversation_history_instruction"
             )
 
@@ -1219,7 +1220,7 @@ Follow these guidelines:
                 self.retry_count += 1
                 current_retry_count = self.retry_count
 
-            context = self.i18n.errors("validation_error").format(
+            context = I18N_DEFAULT.errors("validation_error").format(
                 guardrail_result_error=guardrail_result.error,
                 task_output=task_output.raw,
             )
@@ -1316,7 +1317,7 @@ Follow these guidelines:
                 self.retry_count += 1
                 current_retry_count = self.retry_count
 
-            context = self.i18n.errors("validation_error").format(
+            context = I18N_DEFAULT.errors("validation_error").format(
                 guardrail_result_error=guardrail_result.error,
                 task_output=task_output.raw,
             )

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

@@ -51,8 +51,8 @@ from crewai.telemetry.utils import (
     add_crew_and_task_attributes,
     add_crew_attributes,
     close_span,
-    crew_memory_span_attribute_value,
 )
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.logger_utils import suppress_warnings
 from crewai.utilities.string_utils import sanitize_tool_name
 
@@ -281,11 +281,7 @@ class Telemetry:
             self._add_attribute(span, "python_version", platform.python_version())
             add_crew_attributes(span, crew, self._add_attribute)
             self._add_attribute(span, "crew_process", crew.process)
-            self._add_attribute(
-                span,
-                "crew_memory",
-                crew_memory_span_attribute_value(crew.memory),
-            )
+            self._add_attribute(span, "crew_memory", crew.memory)
             self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
             self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
 
@@ -319,7 +315,7 @@ class Telemetry:
                                 "verbose?": agent.verbose,
                                 "max_iter": agent.max_iter,
                                 "max_rpm": agent.max_rpm,
-                                "i18n": agent.i18n.prompt_file,
+                                "i18n": I18N_DEFAULT.prompt_file,
                                 "function_calling_llm": (
                                     getattr(
                                         getattr(agent, "function_calling_llm", None),
@@ -849,7 +845,7 @@ class Telemetry:
                             "verbose?": agent.verbose,
                             "max_iter": agent.max_iter,
                             "max_rpm": agent.max_rpm,
-                            "i18n": agent.i18n.prompt_file,
+                            "i18n": I18N_DEFAULT.prompt_file,
                             "llm": agent.llm.model
                             if isinstance(agent.llm, BaseLLM)
                             else str(agent.llm),

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

@@ -16,19 +16,6 @@ if TYPE_CHECKING:
     from crewai.task import Task
 
 
-def crew_memory_span_attribute_value(memory: Any) -> bool | str:
-    """Serialize ``Crew.memory`` for OpenTelemetry span attributes.
-
-    OTLP only allows bool, str, bytes, int, float, and homogeneous sequences
-    of those types — not arbitrary objects like :class:`~crewai.memory.unified_memory.Memory`.
-    """
-    if memory is None or memory is False:
-        return False
-    if memory is True:
-        return True
-    return type(memory).__name__
-
-
 def add_agent_fingerprint_to_span(
     span: Span, agent: Any, add_attribute_fn: Callable[[Span, str, Any], None]
 ) -> None:

lib/crewai/src/crewai/tools/agent_tools/add_image_tool.py

@@ -3,10 +3,7 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from crewai.tools.base_tool import BaseTool
-from crewai.utilities import I18N
-
-
-i18n = I18N()
+from crewai.utilities.i18n import I18N_DEFAULT
 
 
 class AddImageToolSchema(BaseModel):
@@ -19,9 +16,9 @@ class AddImageToolSchema(BaseModel):
 class AddImageTool(BaseTool):
     """Tool for adding images to the content"""
 
-    name: str = Field(default_factory=lambda: i18n.tools("add_image")["name"])  # type: ignore[index]
+    name: str = Field(default_factory=lambda: I18N_DEFAULT.tools("add_image")["name"])  # type: ignore[index]
     description: str = Field(
-        default_factory=lambda: i18n.tools("add_image")["description"]  # type: ignore[index]
+        default_factory=lambda: I18N_DEFAULT.tools("add_image")["description"]  # type: ignore[index]
     )
     args_schema: type[BaseModel] = AddImageToolSchema
 
@@ -31,7 +28,7 @@ class AddImageTool(BaseTool):
         action: str | None = None,
         **kwargs: Any,
     ) -> dict[str, Any]:
-        action = action or i18n.tools("add_image")["default_action"]  # type: ignore
+        action = action or I18N_DEFAULT.tools("add_image")["default_action"]  # type: ignore
         content = [
             {"type": "text", "text": action},
             {

lib/crewai/src/crewai/tools/agent_tools/agent_tools.py

@@ -5,21 +5,19 @@ from typing import TYPE_CHECKING
 
 from crewai.tools.agent_tools.ask_question_tool import AskQuestionTool
 from crewai.tools.agent_tools.delegate_work_tool import DelegateWorkTool
-from crewai.utilities.i18n import get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 
 
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.tools.base_tool import BaseTool
-    from crewai.utilities.i18n import I18N
 
 
 class AgentTools:
     """Manager class for agent-related tools"""
 
-    def __init__(self, agents: Sequence[BaseAgent], i18n: I18N | None = None) -> None:
+    def __init__(self, agents: Sequence[BaseAgent]) -> None:
         self.agents = agents
-        self.i18n = i18n if i18n is not None else get_i18n()
 
     def tools(self) -> list[BaseTool]:
         """Get all available agent tools"""
@@ -27,14 +25,12 @@ class AgentTools:
 
         delegate_tool = DelegateWorkTool(
             agents=self.agents,
-            i18n=self.i18n,
-            description=self.i18n.tools("delegate_work").format(coworkers=coworkers),  # type: ignore
+            description=I18N_DEFAULT.tools("delegate_work").format(coworkers=coworkers),  # type: ignore
         )
 
         ask_tool = AskQuestionTool(
             agents=self.agents,
-            i18n=self.i18n,
-            description=self.i18n.tools("ask_question").format(coworkers=coworkers),  # type: ignore
+            description=I18N_DEFAULT.tools("ask_question").format(coworkers=coworkers),  # type: ignore
         )
 
         return [delegate_tool, ask_tool]

lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py

@@ -6,7 +6,7 @@ from pydantic import Field
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
 from crewai.tools.base_tool import BaseTool
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 
 
 logger = logging.getLogger(__name__)
@@ -16,9 +16,6 @@ class BaseAgentTool(BaseTool):
     """Base class for agent-related tools"""
 
     agents: list[BaseAgent] = Field(description="List of available agents")
-    i18n: I18N = Field(
-        default_factory=get_i18n, description="Internationalization settings"
-    )
 
     def sanitize_agent_name(self, name: str) -> str:
         """
@@ -93,7 +90,7 @@ class BaseAgentTool(BaseTool):
             )
         except (AttributeError, ValueError) as e:
             # Handle specific exceptions that might occur during role name processing
-            return self.i18n.errors("agent_tool_unexisting_coworker").format(
+            return I18N_DEFAULT.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
                     [
                         f"- {self.sanitize_agent_name(agent.role)}"
@@ -105,7 +102,7 @@ class BaseAgentTool(BaseTool):
 
         if not agent:
             # No matching agent found after sanitization
-            return self.i18n.errors("agent_tool_unexisting_coworker").format(
+            return I18N_DEFAULT.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
                     [
                         f"- {self.sanitize_agent_name(agent.role)}"
@@ -120,8 +117,7 @@ class BaseAgentTool(BaseTool):
             task_with_assigned_agent = Task(
                 description=task,
                 agent=selected_agent,
-                expected_output=selected_agent.i18n.slice("manager_request"),
-                i18n=selected_agent.i18n,
+                expected_output=I18N_DEFAULT.slice("manager_request"),
             )
             logger.debug(
                 f"Created task for agent '{self.sanitize_agent_name(selected_agent.role)}': {task}"
@@ -129,6 +125,6 @@ class BaseAgentTool(BaseTool):
             return selected_agent.execute_task(task_with_assigned_agent, context)
         except Exception as e:
             # Handle task creation or execution errors
-            return self.i18n.errors("agent_tool_execution_error").format(
+            return I18N_DEFAULT.errors("agent_tool_execution_error").format(
                 agent_role=self.sanitize_agent_name(selected_agent.role), error=str(e)
             )

lib/crewai/src/crewai/tools/memory_tools.py

@@ -7,7 +7,7 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from crewai.tools.base_tool import BaseTool
-from crewai.utilities.i18n import get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 
 
 class RecallMemorySchema(BaseModel):
@@ -114,18 +114,17 @@ def create_memory_tools(memory: Any) -> list[BaseTool]:
     Returns:
         List containing a RecallMemoryTool and, if not read-only, a RememberTool.
     """
-    i18n = get_i18n()
     tools: list[BaseTool] = [
         RecallMemoryTool(
             memory=memory,
-            description=i18n.tools("recall_memory"),
+            description=I18N_DEFAULT.tools("recall_memory"),
         ),
     ]
     if not memory.read_only:
         tools.append(
             RememberTool(
                 memory=memory,
-                description=i18n.tools("save_to_memory"),
+                description=I18N_DEFAULT.tools("save_to_memory"),
             )
         )
     return tools

lib/crewai/src/crewai/tools/tool_usage.py

@@ -28,7 +28,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
 )
 from crewai.utilities.converter import Converter
-from crewai.utilities.i18n import I18N, get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.string_utils import sanitize_tool_name
 
@@ -93,7 +93,6 @@ class ToolUsage:
         action: Any = None,
         fingerprint_context: dict[str, str] | None = None,
     ) -> None:
-        self._i18n: I18N = agent.i18n if agent else get_i18n()
         self._telemetry: Telemetry = Telemetry()
         self._run_attempts: int = 1
         self._max_parsing_attempts: int = 3
@@ -146,7 +145,7 @@ class ToolUsage:
         if (
             isinstance(tool, CrewStructuredTool)
             and sanitize_tool_name(tool.name)
-            == sanitize_tool_name(self._i18n.tools("add_image")["name"])  # type: ignore
+            == sanitize_tool_name(I18N_DEFAULT.tools("add_image")["name"])  # type: ignore
         ):
             try:
                 return self._use(tool_string=tool_string, tool=tool, calling=calling)
@@ -194,7 +193,7 @@ class ToolUsage:
         if (
             isinstance(tool, CrewStructuredTool)
             and sanitize_tool_name(tool.name)
-            == sanitize_tool_name(self._i18n.tools("add_image")["name"])  # type: ignore
+            == sanitize_tool_name(I18N_DEFAULT.tools("add_image")["name"])  # type: ignore
         ):
             try:
                 return await self._ause(
@@ -230,7 +229,7 @@ class ToolUsage:
         """
         if self._check_tool_repeated_usage(calling=calling):
             try:
-                result = self._i18n.errors("task_repeated_usage").format(
+                result = I18N_DEFAULT.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
                 )
                 self._telemetry.tool_repeated_usage(
@@ -415,7 +414,7 @@ class ToolUsage:
                     self._run_attempts += 1
                     if self._run_attempts > self._max_parsing_attempts:
                         self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                        error_message = self._i18n.errors(
+                        error_message = I18N_DEFAULT.errors(
                             "tool_usage_exception"
                         ).format(
                             error=e,
@@ -423,7 +422,7 @@ class ToolUsage:
                             tool_inputs=tool.description,
                         )
                         result = ToolUsageError(
-                            f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                            f"\n{error_message}.\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
                         ).message
                         if self.task:
                             self.task.increment_tools_errors()
@@ -461,7 +460,7 @@ class ToolUsage:
         # Repeated usage check happens before event emission - safe to return early
         if self._check_tool_repeated_usage(calling=calling):
             try:
-                result = self._i18n.errors("task_repeated_usage").format(
+                result = I18N_DEFAULT.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
                 )
                 self._telemetry.tool_repeated_usage(
@@ -648,7 +647,7 @@ class ToolUsage:
                     self._run_attempts += 1
                     if self._run_attempts > self._max_parsing_attempts:
                         self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                        error_message = self._i18n.errors(
+                        error_message = I18N_DEFAULT.errors(
                             "tool_usage_exception"
                         ).format(
                             error=e,
@@ -656,7 +655,7 @@ class ToolUsage:
                             tool_inputs=tool.description,
                         )
                         result = ToolUsageError(
-                            f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                            f"\n{error_message}.\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
                         ).message
                         if self.task:
                             self.task.increment_tools_errors()
@@ -699,7 +698,7 @@ class ToolUsage:
 
     def _remember_format(self, result: str) -> str:
         result = str(result)
-        result += "\n\n" + self._i18n.slice("tools").format(
+        result += "\n\n" + I18N_DEFAULT.slice("tools").format(
             tools=self.tools_description, tool_names=self.tools_names
         )
         return result
@@ -825,12 +824,12 @@ class ToolUsage:
         except Exception:
             if raise_error:
                 raise
-            return ToolUsageError(f"{self._i18n.errors('tool_arguments_error')}")
+            return ToolUsageError(f"{I18N_DEFAULT.errors('tool_arguments_error')}")
 
         if not isinstance(arguments, dict):
             if raise_error:
                 raise
-            return ToolUsageError(f"{self._i18n.errors('tool_arguments_error')}")
+            return ToolUsageError(f"{I18N_DEFAULT.errors('tool_arguments_error')}")
 
         return ToolCalling(
             tool_name=sanitize_tool_name(tool.name),
@@ -856,7 +855,7 @@ class ToolUsage:
                 if self.agent and self.agent.verbose:
                     PRINTER.print(content=f"\n\n{e}\n", color="red")
                 return ToolUsageError(
-                    f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                    f"{I18N_DEFAULT.errors('tool_usage_error').format(error=e)}\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
                 )
             return self._tool_calling(tool_string)
 

lib/crewai/src/crewai/types/streaming.py

@@ -2,11 +2,12 @@
 
 from __future__ import annotations
 
-from collections.abc import AsyncIterator, Iterator
+from collections.abc import AsyncIterator, Callable, Iterator
 from enum import Enum
 from typing import TYPE_CHECKING, Any, Generic, TypeVar
 
 from pydantic import BaseModel, Field
+from typing_extensions import Self
 
 
 if TYPE_CHECKING:
@@ -78,12 +79,21 @@ class StreamingOutputBase(Generic[T]):
     via the .result property after streaming completes.
     """
 
-    def __init__(self) -> None:
+    def __init__(
+        self,
+        sync_iterator: Iterator[StreamChunk] | None = None,
+        async_iterator: AsyncIterator[StreamChunk] | None = None,
+    ) -> None:
         """Initialize streaming output base."""
         self._result: T | None = None
         self._completed: bool = False
         self._chunks: list[StreamChunk] = []
         self._error: Exception | None = None
+        self._cancelled: bool = False
+        self._exhausted: bool = False
+        self._on_cleanup: Callable[[], None] | None = None
+        self._sync_iterator = sync_iterator
+        self._async_iterator = async_iterator
 
     @property
     def result(self) -> T:
@@ -112,6 +122,11 @@ class StreamingOutputBase(Generic[T]):
         """Check if streaming has completed."""
         return self._completed
 
+    @property
+    def is_cancelled(self) -> bool:
+        """Check if streaming was cancelled."""
+        return self._cancelled
+
     @property
     def chunks(self) -> list[StreamChunk]:
         """Get all collected chunks so far."""
@@ -129,6 +144,98 @@ class StreamingOutputBase(Generic[T]):
             if chunk.chunk_type == StreamChunkType.TEXT
         )
 
+    async def __aenter__(self) -> Self:
+        """Enter async context manager."""
+        return self
+
+    async def __aexit__(self, *exc_info: Any) -> None:
+        """Exit async context manager, cancelling if still running."""
+        await self.aclose()
+
+    async def aclose(self) -> None:
+        """Cancel streaming and clean up resources.
+
+        Cancels any in-flight tasks and closes the underlying async iterator.
+        Safe to call multiple times. No-op if already cancelled or fully consumed.
+        """
+        if self._cancelled or self._exhausted or self._error is not None:
+            return
+        self._cancelled = True
+        self._completed = True
+        if self._async_iterator is not None and hasattr(self._async_iterator, "aclose"):
+            await self._async_iterator.aclose()
+        if self._on_cleanup is not None:
+            self._on_cleanup()
+            self._on_cleanup = None
+
+    def close(self) -> None:
+        """Cancel streaming and clean up resources (sync).
+
+        Closes the underlying sync iterator. Safe to call multiple times.
+        No-op if already cancelled, fully consumed, or errored.
+        """
+        if self._cancelled or self._exhausted or self._error is not None:
+            return
+        self._cancelled = True
+        self._completed = True
+        if self._sync_iterator is not None and hasattr(self._sync_iterator, "close"):
+            self._sync_iterator.close()
+        if self._on_cleanup is not None:
+            self._on_cleanup()
+            self._on_cleanup = None
+
+    def __iter__(self) -> Iterator[StreamChunk]:
+        """Iterate over stream chunks synchronously.
+
+        Yields:
+            StreamChunk objects as they arrive.
+
+        Raises:
+            RuntimeError: If sync iterator not available.
+        """
+        if self._sync_iterator is None:
+            raise RuntimeError("Sync iterator not available")
+        try:
+            for chunk in self._sync_iterator:
+                self._chunks.append(chunk)
+                yield chunk
+            self._exhausted = True
+        except Exception as e:
+            self._error = e
+            raise
+        finally:
+            self._completed = True
+
+    def __aiter__(self) -> AsyncIterator[StreamChunk]:
+        """Return async iterator for stream chunks.
+
+        Returns:
+            Async iterator for StreamChunk objects.
+        """
+        return self._async_iterate()
+
+    async def _async_iterate(self) -> AsyncIterator[StreamChunk]:
+        """Iterate over stream chunks asynchronously.
+
+        Yields:
+            StreamChunk objects as they arrive.
+
+        Raises:
+            RuntimeError: If async iterator not available.
+        """
+        if self._async_iterator is None:
+            raise RuntimeError("Async iterator not available")
+        try:
+            async for chunk in self._async_iterator:
+                self._chunks.append(chunk)
+                yield chunk
+            self._exhausted = True
+        except Exception as e:
+            self._error = e
+            raise
+        finally:
+            self._completed = True
+
 
 class CrewStreamingOutput(StreamingOutputBase["CrewOutput"]):
     """Streaming output wrapper for crew execution.
@@ -167,9 +274,7 @@ class CrewStreamingOutput(StreamingOutputBase["CrewOutput"]):
             sync_iterator: Synchronous iterator for chunks.
             async_iterator: Asynchronous iterator for chunks.
         """
-        super().__init__()
-        self._sync_iterator = sync_iterator
-        self._async_iterator = async_iterator
+        super().__init__(sync_iterator=sync_iterator, async_iterator=async_iterator)
         self._results: list[CrewOutput] | None = None
 
     @property
@@ -204,56 +309,6 @@ class CrewStreamingOutput(StreamingOutputBase["CrewOutput"]):
         self._results = results
         self._completed = True
 
-    def __iter__(self) -> Iterator[StreamChunk]:
-        """Iterate over stream chunks synchronously.
-
-        Yields:
-            StreamChunk objects as they arrive.
-
-        Raises:
-            RuntimeError: If sync iterator not available.
-        """
-        if self._sync_iterator is None:
-            raise RuntimeError("Sync iterator not available")
-        try:
-            for chunk in self._sync_iterator:
-                self._chunks.append(chunk)
-                yield chunk
-        except Exception as e:
-            self._error = e
-            raise
-        finally:
-            self._completed = True
-
-    def __aiter__(self) -> AsyncIterator[StreamChunk]:
-        """Return async iterator for stream chunks.
-
-        Returns:
-            Async iterator for StreamChunk objects.
-        """
-        return self._async_iterate()
-
-    async def _async_iterate(self) -> AsyncIterator[StreamChunk]:
-        """Iterate over stream chunks asynchronously.
-
-        Yields:
-            StreamChunk objects as they arrive.
-
-        Raises:
-            RuntimeError: If async iterator not available.
-        """
-        if self._async_iterator is None:
-            raise RuntimeError("Async iterator not available")
-        try:
-            async for chunk in self._async_iterator:
-                self._chunks.append(chunk)
-                yield chunk
-        except Exception as e:
-            self._error = e
-            raise
-        finally:
-            self._completed = True
-
     def _set_result(self, result: CrewOutput) -> None:
         """Set the final result after streaming completes.
 
@@ -286,71 +341,6 @@ class FlowStreamingOutput(StreamingOutputBase[Any]):
         ```
     """
 
-    def __init__(
-        self,
-        sync_iterator: Iterator[StreamChunk] | None = None,
-        async_iterator: AsyncIterator[StreamChunk] | None = None,
-    ) -> None:
-        """Initialize flow streaming output.
-
-        Args:
-            sync_iterator: Synchronous iterator for chunks.
-            async_iterator: Asynchronous iterator for chunks.
-        """
-        super().__init__()
-        self._sync_iterator = sync_iterator
-        self._async_iterator = async_iterator
-
-    def __iter__(self) -> Iterator[StreamChunk]:
-        """Iterate over stream chunks synchronously.
-
-        Yields:
-            StreamChunk objects as they arrive.
-
-        Raises:
-            RuntimeError: If sync iterator not available.
-        """
-        if self._sync_iterator is None:
-            raise RuntimeError("Sync iterator not available")
-        try:
-            for chunk in self._sync_iterator:
-                self._chunks.append(chunk)
-                yield chunk
-        except Exception as e:
-            self._error = e
-            raise
-        finally:
-            self._completed = True
-
-    def __aiter__(self) -> AsyncIterator[StreamChunk]:
-        """Return async iterator for stream chunks.
-
-        Returns:
-            Async iterator for StreamChunk objects.
-        """
-        return self._async_iterate()
-
-    async def _async_iterate(self) -> AsyncIterator[StreamChunk]:
-        """Iterate over stream chunks asynchronously.
-
-        Yields:
-            StreamChunk objects as they arrive.
-
-        Raises:
-            RuntimeError: If async iterator not available.
-        """
-        if self._async_iterator is None:
-            raise RuntimeError("Async iterator not available")
-        try:
-            async for chunk in self._async_iterator:
-                self._chunks.append(chunk)
-                yield chunk
-        except Exception as e:
-            self._error = e
-            raise
-        finally:
-            self._completed = True
-
     def _set_result(self, result: Any) -> None:
         """Set the final result after streaming completes.
 

lib/crewai/src/crewai/types/usage_metrics.py

@@ -29,6 +29,14 @@ class UsageMetrics(BaseModel):
     completion_tokens: int = Field(
         default=0, description="Number of tokens used in completions."
     )
+    reasoning_tokens: int = Field(
+        default=0,
+        description="Number of reasoning/thinking tokens (e.g. OpenAI o-series, Gemini thinking).",
+    )
+    cache_creation_tokens: int = Field(
+        default=0,
+        description="Number of cache creation tokens (e.g. Anthropic cache writes).",
+    )
     successful_requests: int = Field(
         default=0, description="Number of successful requests made."
     )
@@ -43,4 +51,6 @@ class UsageMetrics(BaseModel):
         self.prompt_tokens += usage_metrics.prompt_tokens
         self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
         self.completion_tokens += usage_metrics.completion_tokens
+        self.reasoning_tokens += usage_metrics.reasoning_tokens
+        self.cache_creation_tokens += usage_metrics.cache_creation_tokens
         self.successful_requests += usage_metrics.successful_requests

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

@@ -31,7 +31,7 @@ from crewai.utilities.errors import AgentRepositoryError
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.printer import PRINTER, ColoredText, Printer
 from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.string_utils import sanitize_tool_name
@@ -254,7 +254,6 @@ def has_reached_max_iterations(iterations: int, max_iterations: int) -> bool:
 def handle_max_iterations_exceeded(
     formatted_answer: AgentAction | AgentFinish | None,
     printer: Printer,
-    i18n: I18N,
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
@@ -265,7 +264,6 @@ def handle_max_iterations_exceeded(
     Args:
         formatted_answer: The last formatted answer from the agent.
         printer: Printer instance for output.
-        i18n: I18N instance for internationalization.
         messages: List of messages to send to the LLM.
         llm: The LLM instance to call.
         callbacks: List of callbacks for the LLM call.
@@ -282,10 +280,10 @@ def handle_max_iterations_exceeded(
 
     if formatted_answer and hasattr(formatted_answer, "text"):
         assistant_message = (
-            formatted_answer.text + f"\n{i18n.errors('force_final_answer')}"
+            formatted_answer.text + f"\n{I18N_DEFAULT.errors('force_final_answer')}"
         )
     else:
-        assistant_message = i18n.errors("force_final_answer")
+        assistant_message = I18N_DEFAULT.errors("force_final_answer")
 
     messages.append(format_message_for_llm(assistant_message, role="assistant"))
 
@@ -687,7 +685,6 @@ def handle_context_length(
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-    i18n: I18N,
     verbose: bool = True,
 ) -> None:
     """Handle context length exceeded by either summarizing or raising an error.
@@ -698,7 +695,6 @@ def handle_context_length(
         messages: List of messages to summarize
         llm: LLM instance for summarization
         callbacks: List of callbacks for LLM
-        i18n: I18N instance for messages
 
     Raises:
         SystemExit: If context length is exceeded and user opts not to summarize
@@ -710,7 +706,7 @@ def handle_context_length(
                 color="yellow",
             )
         summarize_messages(
-            messages=messages, llm=llm, callbacks=callbacks, i18n=i18n, verbose=verbose
+            messages=messages, llm=llm, callbacks=callbacks, verbose=verbose
         )
     else:
         if verbose:
@@ -863,7 +859,6 @@ async def _asummarize_chunks(
     chunks: list[list[LLMMessage]],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-    i18n: I18N,
 ) -> list[SummaryContent]:
     """Summarize multiple message chunks concurrently using asyncio.
 
@@ -871,7 +866,6 @@ async def _asummarize_chunks(
         chunks: List of message chunks to summarize.
         llm: LLM instance (must support ``acall``).
         callbacks: List of callbacks for the LLM.
-        i18n: I18N instance for prompt templates.
 
     Returns:
         Ordered list of summary contents, one per chunk.
@@ -881,10 +875,10 @@ async def _asummarize_chunks(
         conversation_text = _format_messages_for_summary(chunk)
         summarization_messages = [
             format_message_for_llm(
-                i18n.slice("summarizer_system_message"), role="system"
+                I18N_DEFAULT.slice("summarizer_system_message"), role="system"
             ),
             format_message_for_llm(
-                i18n.slice("summarize_instruction").format(
+                I18N_DEFAULT.slice("summarize_instruction").format(
                     conversation=conversation_text
                 ),
             ),
@@ -901,7 +895,6 @@ def summarize_messages(
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-    i18n: I18N,
     verbose: bool = True,
 ) -> None:
     """Summarize messages to fit within context window.
@@ -917,7 +910,6 @@ def summarize_messages(
         messages: List of messages to summarize (modified in-place)
         llm: LLM instance for summarization
         callbacks: List of callbacks for LLM
-        i18n: I18N instance for messages
         verbose: Whether to print progress.
     """
     # 1. Extract & preserve file attachments from user messages
@@ -953,10 +945,10 @@ def summarize_messages(
             conversation_text = _format_messages_for_summary(chunk)
             summarization_messages = [
                 format_message_for_llm(
-                    i18n.slice("summarizer_system_message"), role="system"
+                    I18N_DEFAULT.slice("summarizer_system_message"), role="system"
                 ),
                 format_message_for_llm(
-                    i18n.slice("summarize_instruction").format(
+                    I18N_DEFAULT.slice("summarize_instruction").format(
                         conversation=conversation_text
                     ),
                 ),
@@ -971,9 +963,7 @@ def summarize_messages(
                 content=f"Summarizing {total_chunks} chunks in parallel...",
                 color="yellow",
             )
-        coro = _asummarize_chunks(
-            chunks=chunks, llm=llm, callbacks=callbacks, i18n=i18n
-        )
+        coro = _asummarize_chunks(chunks=chunks, llm=llm, callbacks=callbacks)
         if is_inside_event_loop():
             ctx = contextvars.copy_context()
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
@@ -988,7 +978,7 @@ def summarize_messages(
     messages.extend(system_messages)
 
     summary_message = format_message_for_llm(
-        i18n.slice("summary").format(merged_summary=merged_summary)
+        I18N_DEFAULT.slice("summary").format(merged_summary=merged_summary)
     )
     if preserved_files:
         summary_message["files"] = preserved_files

lib/crewai/src/crewai/utilities/converter.py

@@ -8,7 +8,7 @@ from pydantic import BaseModel, ValidationError
 from typing_extensions import Unpack
 
 from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
-from crewai.utilities.i18n import get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.internal_instructor import InternalInstructor
 from crewai.utilities.printer import PRINTER
 from crewai.utilities.pydantic_schema_utils import generate_model_description
@@ -21,7 +21,7 @@ if TYPE_CHECKING:
     from crewai.llms.base_llm import BaseLLM
 
 _JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
-_I18N = get_i18n()
+_I18N = I18N_DEFAULT
 
 
 class ConverterError(Exception):

lib/crewai/src/crewai/utilities/evaluators/task_evaluator.py

@@ -8,7 +8,7 @@ from pydantic import BaseModel, Field
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.task_events import TaskEvaluationEvent
 from crewai.utilities.converter import Converter
-from crewai.utilities.i18n import get_i18n
+from crewai.utilities.i18n import I18N_DEFAULT
 from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.training_converter import TrainingConverter
 
@@ -98,11 +98,9 @@ class TaskEvaluator:
 
         if not self.llm.supports_function_calling():  # type: ignore[union-attr]
             schema_dict = generate_model_description(TaskEvaluation)
-            output_schema: str = (
-                get_i18n()
-                .slice("formatted_task_instructions")
-                .format(output_format=json.dumps(schema_dict, indent=2))
-            )
+            output_schema: str = I18N_DEFAULT.slice(
+                "formatted_task_instructions"
+            ).format(output_format=json.dumps(schema_dict, indent=2))
             instructions = f"{instructions}\n\n{output_schema}"
 
         converter = Converter(
@@ -174,11 +172,9 @@ class TaskEvaluator:
 
         if not self.llm.supports_function_calling():  # type: ignore[union-attr]
             schema_dict = generate_model_description(TrainingTaskEvaluation)
-            output_schema: str = (
-                get_i18n()
-                .slice("formatted_task_instructions")
-                .format(output_format=json.dumps(schema_dict, indent=2))
-            )
+            output_schema: str = I18N_DEFAULT.slice(
+                "formatted_task_instructions"
+            ).format(output_format=json.dumps(schema_dict, indent=2))
             instructions = f"{instructions}\n\n{output_schema}"
 
         converter = TrainingConverter(