feat(cli): send the project's own id with the evaluation

crewAI mints a project id into pyproject.toml and it is committed, so it is
the same id on every machine, in CI, and for a teammate. `crewai eval`
already read it on its way past and threw it away; it now travels with the
request, so a project's evaluations can be shown together rather than each
run standing alone.

Omitted entirely outside a crewAI project, where there is no id to send.

85 passed; ruff and mypy clean.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

.github/CONTRIBUTING.md

@@ -29,7 +29,7 @@ uv run pre-commit install
 
 ## Repository Structure
 
-This is a uv workspace with four packages under `lib/`:
+This is a uv workspace with six packages under `lib/`:
 
 | Package | Path | Description |
 |---------|------|-------------|
@@ -37,6 +37,8 @@ This is a uv workspace with four packages under `lib/`:
 | `crewai-tools` | `lib/crewai-tools/` | Tool integrations |
 | `crewai-files` | `lib/crewai-files/` | File handling |
 | `devtools` | `lib/devtools/` | Internal release tooling |
+| `crewai-cli` | `lib/cli/` | Command-line interface |
+| `crewai-core` | `lib/crewai-core/` | Shared core utilities |
 
 Documentation lives in `docs/` with translations under `docs/{en,ar,ko,pt-BR}/`.
 
@@ -103,7 +105,8 @@ chore(deps): bump pydantic to 2.11
 - Keep PRs focused — avoid bundling unrelated changes
 - PRs over 500 lines are labeled `size/XL` automatically
 - Title must follow the same conventional commit format
-- Link related issues where applicable
+- Link related issues where applicable (`#123`, `Fixes #123`, or the issue URL)
+- First-time contributors must open or pick an existing **open** issue first, then mention it in the PR title or body (for example `#123`). PRs without a linked open issue are closed automatically and labeled `needs-issue`.
 
 ## Testing
 

.github/pull_request_template.md

@@ -0,0 +1,24 @@
+## Related issue
+
+Fixes #
+
+<!--
+First-time contributors must mention an existing open issue in this repo
+(for example #123). PRs without a linked open issue are closed automatically
+and labeled needs-issue.
+-->
+
+## Summary
+
+<!-- Explain the solution and why. -->
+
+## Verification
+
+<!-- List the automated and manual checks used to verify the change. -->
+
+- [ ] Tests added or updated for the changed behavior
+- [ ] Relevant tests and quality checks pass locally
+
+## Additional context
+
+<!-- Include screenshots, compatibility notes, follow-up work, or "None". -->

.github/workflows/first-contributor-welcome.yml

@@ -0,0 +1,92 @@
+name: Welcome First-Time Contributors
+
+on:
+  pull_request_target:
+    types: [closed]
+
+permissions:
+  contents: read
+  issues: write
+  # GitHub accepts either Issues or Pull requests write access for PR comments.
+  # Request both scopes to match the first-time PR workflow and retain a
+  # supported permission path when GitHub issues the Actions token.
+  pull-requests: write
+
+jobs:
+  welcome:
+    # Match the first-time contributor definition used by ftc-require-issue.
+    if: >
+      github.event.pull_request.merged == true &&
+      github.event.pull_request.user.type != 'Bot' &&
+      !contains(fromJSON('["MEMBER","OWNER","COLLABORATOR","CONTRIBUTOR"]'),
+                github.event.pull_request.author_association)
+    runs-on: ubuntu-latest
+    steps:
+      - name: Thank first-time contributors
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          script: |
+            const pullRequest = context.payload.pull_request;
+            const owner = context.repo.owner;
+            const repo = context.repo.repo;
+            const marker = "<!-- crewai-first-merged-pr-welcome -->";
+
+            const existingComments = await github.paginate(
+              github.rest.issues.listComments,
+              {
+                owner,
+                repo,
+                issue_number: pullRequest.number,
+                per_page: 100,
+              },
+            );
+            if (existingComments.some((comment) => comment.body?.includes(marker))) {
+              core.info("Welcome comment already exists; skipping duplicate.");
+              return;
+            }
+
+            const prUrl = pullRequest.html_url;
+            const shareText = [
+              "I just made my first contribution to @crewAIInc!",
+              "",
+              "Excited to help build the future of AI agents with CrewAI.",
+              "",
+              prUrl,
+              "",
+              "#OpenSource #AI #CrewAI",
+            ].join("\n");
+            const xShareUrl = `https://x.com/intent/post?text=${encodeURIComponent(shareText)}`;
+            const linkedInShareUrl = `https://www.linkedin.com/sharing/share-offsite/?url=${encodeURIComponent(prUrl)}`;
+
+            const body = [
+              marker,
+              "",
+              `## Thanks for your first contribution to CrewAI, @${pullRequest.user.login}!`,
+              "",
+              "We really appreciate the time and care you put into this PR. Your work is now part of CrewAI — welcome to the contributor community.",
+              "",
+              "Want to share your contribution? Totally optional:",
+              "",
+              `[Share on X](${xShareUrl}) · [Share the PR on LinkedIn](${linkedInShareUrl})`,
+              "",
+              "If the links don't work, feel free to copy and personalize this:",
+              "",
+              "```text",
+              "I just made my first contribution to @crewAIInc!",
+              "",
+              "Excited to help build the future of AI agents with CrewAI.",
+              "",
+              prUrl,
+              "",
+              "#OpenSource #AI #CrewAI",
+              "```",
+              "",
+              "If you share, we'd love to see it — tag **@crewAIInc** on X and **CrewAI** on LinkedIn.",
+            ].join("\n");
+
+            await github.rest.issues.createComment({
+              owner,
+              repo,
+              issue_number: pullRequest.number,
+              body,
+            });

.github/workflows/ftc-require-issue.yml

@@ -0,0 +1,134 @@
+name: First-time contributor issue required
+
+on:
+  pull_request_target:
+    types: [opened, edited, reopened]
+
+permissions:
+  pull-requests: write
+  issues: write
+
+concurrency:
+  group: ftc-require-issue-${{ github.event.pull_request.number }}
+  cancel-in-progress: true
+
+jobs:
+  require-issue:
+    # Allow-list returning contributors. FIRST_TIMER / FIRST_TIME_CONTRIBUTOR
+    # are often NONE on pull_request_target at opened time, which skipped the
+    # previous deny-list and left first-timer PRs open.
+    if: >
+      github.event.pull_request.user.type != 'Bot' &&
+      !contains(fromJSON('["MEMBER","OWNER","COLLABORATOR","CONTRIBUTOR"]'),
+                github.event.pull_request.author_association)
+    runs-on: ubuntu-latest
+    steps:
+      - name: Require an open issue
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          REPO: ${{ github.repository }}
+          AUTHOR_ASSOCIATION: ${{ github.event.pull_request.author_association }}
+        run: |
+          python3 << 'PY'
+          import json
+          import os
+          import re
+          import subprocess
+          import sys
+
+          repo = os.environ["REPO"]
+          pr_number = os.environ["PR_NUMBER"]
+          owner, name = repo.split("/", 1)
+          print(
+              "author_association=",
+              os.environ.get("AUTHOR_ASSOCIATION", ""),
+              sep="",
+          )
+
+          patterns = (
+              re.compile(r"(?<![\w./-])#(\d+)\b"),
+              re.compile(rf"{re.escape(owner)}/{re.escape(name)}#(\d+)\b"),
+              re.compile(
+                  rf"https://github\.com/{re.escape(owner)}/{re.escape(name)}/issues/(\d+)\b"
+              ),
+          )
+
+          def gh_json(*args: str) -> dict:
+              return json.loads(
+                  subprocess.check_output(["gh", *args], text=True)
+              )
+
+          def is_open_repo_issue(number: int) -> bool:
+              result = subprocess.run(
+                  ["gh", "api", f"repos/{repo}/issues/{number}"],
+                  capture_output=True,
+                  text=True,
+              )
+              if result.returncode != 0:
+                  stderr = result.stderr or ""
+                  if "404" in stderr or "Not Found" in stderr:
+                      return False
+                  raise RuntimeError(
+                      f"GitHub API error looking up #{number}: {stderr}"
+                  )
+              payload = json.loads(result.stdout)
+              if "pull_request" in payload:
+                  return False
+              return (payload.get("state") or "").lower() == "open"
+
+          pr = gh_json(
+              "pr", "view", pr_number, "--repo", repo, "--json", "title,body,state"
+          )
+          text = f"{pr.get('title') or ''}\n{pr.get('body') or ''}"
+          candidates = {
+              int(match)
+              for pattern in patterns
+              for match in pattern.findall(text)
+          }
+          if any(is_open_repo_issue(number) for number in sorted(candidates)):
+              sys.exit(0)
+
+          if (pr.get("state") or "").upper() == "CLOSED":
+              sys.exit(0)
+
+          comment = f"""Thanks for the pull request.
+          
+          First-time contributors need an associated open issue before we can review a PR.
+          
+          1. Open an issue with a [template](https://github.com/{repo}/issues/new/choose), or pick an existing open one.
+          2. Open a new PR (or reopen this one) whose title or body mentions that issue, for example `#123`.
+          
+          See the [contributing guide](https://github.com/{repo}/blob/main/.github/CONTRIBUTING.md).
+          """
+          subprocess.run(
+              [
+                  "gh",
+                  "pr",
+                  "comment",
+                  pr_number,
+                  "--repo",
+                  repo,
+                  "--body",
+                  comment,
+              ],
+              check=True,
+          )
+          subprocess.run(
+              [
+                  "gh",
+                  "pr",
+                  "edit",
+                  pr_number,
+                  "--repo",
+                  repo,
+                  "--add-label",
+                  "needs-issue",
+              ],
+              check=True,
+          )
+          subprocess.run(
+              ["gh", "pr", "close", pr_number, "--repo", repo],
+              check=True,
+          )
+          PY

.github/workflows/vulnerability-scan.yml

@@ -100,6 +100,23 @@ jobs:
             # GHSA-xph7-9rjv-w5fr (CVE-2026-45831): SimpleRBACAuthorizationProvider
             # ignores tenant/database/collection scope.
             --ignore-vuln GHSA-xph7-9rjv-w5fr
+            # nltk <=3.10.3: GHSA-8mgp-746c-j5xp (CVE-2026-81726): model-artifact
+            # APIs bypass pathsec and read/write outside allowed roots. No patched
+            # PyPI release yet (fixes are on nltk develop only). Transitive via
+            # crewai-tools[xml] -> unstructured; CrewAI does not call those APIs.
+            # TODO: drop this ignore when bumping nltk past 3.10.3 to a patched
+            # release; keep the ignore list in sync with .pre-commit-config.yaml.
+            --ignore-vuln GHSA-8mgp-746c-j5xp
+            # accelerate <=1.14.0: GHSA-4j2p-28q2-5m79 (CVE-2026-69112): path
+            # traversal / FIFO DoS via unsanitized sharded-checkpoint weight_map
+            # entries. No patched PyPI release yet (fix is huggingface/accelerate
+            # #4138, not shipped). Transitive via docling extras
+            # (docling-ibm-models / docling-slim[standard]); CrewAI does not load
+            # untrusted checkpoints through those APIs.
+            # TODO: drop this ignore when bumping accelerate past 1.14.0 to a
+            # patched release; keep the ignore list in sync with
+            # .pre-commit-config.yaml.
+            --ignore-vuln GHSA-4j2p-28q2-5m79
           )
           uv run pip-audit "${pip_audit_args[@]}"
         continue-on-error: true

.pre-commit-config.yaml

@@ -3,19 +3,19 @@ repos:
     hooks:
       - id: ruff
         name: ruff
-        entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
+        entry: bash -c 'case "$OSTYPE" in msys*|cygwin*|win32*) source .venv/Scripts/activate ;; *) source .venv/bin/activate ;; esac && uv run ruff check --config pyproject.toml "$@"' --
         language: system
         pass_filenames: true
         types: [python]
       - id: ruff-format
         name: ruff-format
-        entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
+        entry: bash -c 'case "$OSTYPE" in msys*|cygwin*|win32*) source .venv/Scripts/activate ;; *) source .venv/bin/activate ;; esac && uv run ruff format --config pyproject.toml "$@"' --
         language: system
         pass_filenames: true
         types: [python]
       - id: mypy
         name: mypy
-        entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
+        entry: bash -c 'case "$OSTYPE" in msys*|cygwin*|win32*) source .venv/Scripts/activate ;; *) source .venv/bin/activate ;; esac && uv run mypy --config-file pyproject.toml "$@"' --
         language: system
         pass_filenames: true
         types: [python]
@@ -29,8 +29,10 @@ repos:
       - id: pip-audit
         name: pip-audit
         # Keep this ignore list in sync with .github/workflows/vulnerability-scan.yml.
+        # TODO: drop --ignore-vuln GHSA-8mgp-746c-j5xp when bumping nltk past 3.10.3.
+        # TODO: drop --ignore-vuln GHSA-4j2p-28q2-5m79 when bumping accelerate past 1.14.0.
         entry: >-
-          bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable
+          bash -c 'case "$OSTYPE" in msys*|cygwin*|win32*) source .venv/Scripts/activate ;; *) source .venv/bin/activate ;; esac && uv run pip-audit --skip-editable
           --ignore-vuln PYSEC-2024-277
           --ignore-vuln PYSEC-2026-89
           --ignore-vuln PYSEC-2026-97
@@ -59,7 +61,9 @@ repos:
           --ignore-vuln GHSA-f4j7-r4q5-qw2c
           --ignore-vuln GHSA-2wm9-hf6c-p5cr
           --ignore-vuln GHSA-36p7-vc44-83pf
-          --ignore-vuln GHSA-xph7-9rjv-w5fr' --
+          --ignore-vuln GHSA-xph7-9rjv-w5fr
+          --ignore-vuln GHSA-8mgp-746c-j5xp
+          --ignore-vuln GHSA-4j2p-28q2-5m79' --
         language: system
         pass_filenames: false
         stages: [pre-push, manual]

conftest.py

@@ -202,6 +202,38 @@ def cleanup_event_handlers() -> Generator[None, Any, None]:
         pass
 
 
+@pytest.fixture(autouse=True, scope="function")
+def reset_tracing_state() -> Generator[None, Any, None]:
+    """Drop the tracing singleton and its context after each test.
+
+    `TraceCollectionListener` is a singleton, so without this three things leak
+    for the rest of the xdist worker:
+
+    - `TraceBatchManager.trace_batch_id`, which moves later trace POSTs from
+      `/tracing/ephemeral/batches` to `/tracing/batches/<id>/events` until some
+      unrelated cassette stops matching, naming neither the leak nor its source.
+    - `_listeners_setup`, which makes `setup_listeners` return early
+      (`trace_listener.py:208`) after `cleanup_event_handlers` has wiped the bus,
+      so tracing silently registers nothing and collects no events.
+    - the `_tracing_enabled` context var, which leaves tracing on for later tests
+      and re-registers `on_task_failed` alongside telemetry's — breaking
+      `test_task_failure_instrumentation`, which requires one handler per event.
+
+    All three go together: clearing the context vars is what makes dropping the
+    singleton safe, because the replacement listener then sees tracing disabled
+    and registers nothing. Dropping the singleton alone re-registers handlers and
+    breaks the telemetry test.
+    """
+    yield
+
+    from crewai.events.listeners.tracing import utils as tracing_utils
+    from crewai.events.listeners.tracing.trace_listener import TraceCollectionListener
+
+    tracing_utils._tracing_enabled.set(None)
+    tracing_utils._tui_mode.set(False)
+    TraceCollectionListener._instance = None
+
+
 @pytest.fixture(autouse=True, scope="function")
 def reset_event_state() -> None:
     """Reset event system state before each test for isolation."""

docs/edge/ar/changelog.mdx

@@ -4,6 +4,145 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="16 سبتمبر 2026">
+  ## v1.15.22
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.22)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - دعم الأسماء المستعارة كمعرفات اتصال
+  - تسجيل أسباب فشل إنشاء النشر
+  - جمع ملاحظات بشرية وإيقاف الأحداث في التتبع
+  - إضافة متغير السياق `llm_overlay` لتوجيه أدوار الوكلاء إلى النماذج
+  - نقل `task_prompt` والإخراج في حمولات تنفيذ الوكلاء
+  - التحقق من تكاملات المنصة أثناء إعداد الطاقم
+  - إضافة أدوات المنصة إلى معالج JSON للطاقم
+  - كشف كتالوج تطبيق CrewAI Platform
+  - إضافة OpenRouter كمزود تضمين مدعوم
+
+  ### إصلاحات الأخطاء
+  - قبول CRLF في تعريفات المهارات المضمنة
+  - تحميل عناوين URL لملفات النصوص من خلال المستخرج الآمن
+  - إغلاق اتصالات SQLite في تخزين مخرجات مهمة البداية
+  - نقل `from_cache` في حدث `ToolUsageFinishedEvent` لمسار الأداة الأصلية
+  - رفع `ValueError` بدلاً من رفع عاري في المحمل
+  - دعم الأنواع غير الأولية في SQLiteFlowPersistence
+  - تحسين تعليمات وكلاء الترميز
+  - قراءة نقاط التحقق JSON كـ UTF-8
+  - الكتابة فوق النسخة الاحتياطية القديمة لـ `poetry.lock` على Windows
+  - مفتاح مكالمات الأداة المتدفقة بواسطة فهرس السلك في Azure
+  - الحفاظ على أجزاء محتوى بيانات الملف في Gemini
+  - احترام `read_only` في أوقات الوصول لـ `update()` و `recall()`
+  - إرسال `reasoning_effort` إلى كل نموذج استدلال OpenAI
+  - منع الأعطال في واجهة المستخدم عند تشغيلها عندما يحتوي الإخراج المتدفق على `[...]`
+  - رفض إعادة التشغيل عندما تختلف المهام المخزنة
+  - استخدام حراس `sys.platform` للتوافق مع mypy على Windows
+  - طلب الإجابة النهائية القسرية كدور مستخدم
+  - الاحتفاظ بالقيمة null في مخطط مخرجات المهمة المضمن في المطالبة
+  - محاذاة `DOCXSearchTool` مع نمط المخطط الثابت القياسي RAG
+
+  ### الوثائق
+  - إضافة `xpu` إلى خيارات جهاز التضمين
+  - سرد جميع حزم مساحة العمل
+
+  ## المساهمون
+
+  @ASTion24, @BlueX888, @HUAN2022A, @RaycarlLei, @Rohitkanithi, @SWAPI03, @SharoonSharif, @ShivangiRay, @Theater-ahyeon, @Vidit-Ostwal, @gamal1osama, @github-actions[bot], @joaomdmoura, @lorenzejay, @mhbuehler, @modusensus, @monkscode, @rohitkanithi, @roli-lpci, @vinibrsl, @wangtaotaotao95
+
+</Update>
+
+<Update label="9 سبتمبر 2026">
+  ## v1.15.21
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.21)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة بيانات تتبع لتتبع وقت تشغيل نقاط التحقق واستخدام واجهة سطر الأوامر.
+
+  ### إصلاحات الأخطاء
+  - إصلاح أخطاء البوابة المبلغ عنها داخل استجابة HTTP 200.
+  - الحفاظ على دفع النشر عند إنشاء مصدر AMP.
+  - الإبلاغ عن فشل السحب بدلاً من رفع IndexError في تكامل Oxylabs.
+  - توجيه جميع نماذج DashScope عبر المزود الأصلي.
+  - الاحتفاظ بنقاط تحقق JSON كـ UTF-8.
+  - إزالة $ الزائدة في عناوين URL الخاصة بالبحث في f-string لـ BrightData.
+  - دعم مصفوفات نوع القائمة في تحويل مخطط JSON.
+  - تصحيح نافذة السياق لـ gpt-4o-mini من 200000 إلى 128000.
+  - الحفاظ على وسائط استدعاء أداة البث عند contentBlockStop.
+  - جعل خطافات ما قبل الالتزام قابلة للنقل على Windows.
+
+  ### الوثائق
+  - توضيح أن تتبع البيانات يتم إدارته بشكل منفصل عن بيانات التتبع.
+  - إصلاح رابط مرجع البحث المتوازي.
+  - إزالة المعلمات القديمة من سلسلة توثيق handle_llm_stream_chunk.
+  - إصلاح أمثلة سلسلة توثيق مخرجات البث.
+  - استخدام UUIDs الخاصة بالمنظمة في مرجع تثبيت المهارة.
+
+  ## المساهمون
+
+  @Alphaxiaoteng, @DrewWhittleNZ, @Ghraven, @Shxiao101, @Vaishnavi220506, @Vidit-Ostwal, @danielfsbarreto, @georgeatparallel, @github-actions[bot], @jessemiller, @joaomdmoura, @kimnamu, @kiwoongyoon, @liang0417, @lorenzejay, @oxy-giedrius, @simpleqt, @uoparaji
+
+</Update>
+
+<Update label="4 سبتمبر 2026">
+  ## v1.15.20
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.20)
+
+  ## ما الذي تغير
+
+  ### إصلاحات الأخطاء
+  - إصلاح اكتشاف اسم مستعار لأداة المنصة القديمة
+
+  ### الوثائق
+  - تحديث اللقطة وسجل التغييرات للإصدار v1.15.19
+
+  ## المساهمون
+
+  @joaomdmoura, @vinibrsl
+
+</Update>
+
+<Update label="4 سبتمبر 2026">
+  ## v1.15.19
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.19)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة عميل تكامل Clipper
+  - إضافة `now()` إلى بيئة تعبير CEL
+  - تسجيل كيفية انتهاء تشغيل الطاقم لكل مستخدم
+  - الإبلاغ عن حجم الآلة كحزام خشن، وليس عدد النوى
+  - إضافة عميل قابل للحقن لأدوات منصة CrewAI
+
+  ### إصلاحات الأخطاء
+  - إصلاح قراءة عناوين octet-stream و xlsx في `urlreadtool`
+  - إصلاح إضافة دوران المستخدم المتأخر في موفر Gemini الأصلي
+  - تطبيع المخطط والمنفذ في عنوان URL الأساسي لـ Ollama
+  - الحفاظ على تكوينات النطاق القابلة لإعادة الاستخدام في الذاكرة
+  - تحديث `pypdf` إلى 6.16.2 لمعالجة ثغرة أمنية
+  - تحديث `nltk` إلى 3.10.3 لمعالجة ثغرة أمنية
+  - إصلاح المخرجات الهيكلية الأصلية لنماذج Claude الحالية وسقف CVE لـ Snowflake
+  - تشغيل روابط استدعاء النموذج على كل مسار ونشر رفض
+
+  ### الوثائق
+  - إزالة `CodeInterpreterTool` من أمثلة نظرة عامة على الذكاء الاصطناعي/التعلم الآلي
+  - توجيه رابط `prompt-template` إلى مساره الحالي
+  - تحديث دليل القنوات إلى واجهة برمجة التطبيقات الحالية لقنوات CopilotKit
+  - تحديث معرفات نماذج Gemini المتقاعدة
+
+  ## المساهمون
+
+  @Vidit-Ostwal, @a-yeyang, @github-actions[bot], @hvlcrs, @joaomdmoura, @kikifrost, @lorenzejay, @lucasgomide, @parthiban-sivakumar, @ranst91, @tandede, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="27 أغسطس 2026">
   ## v1.15.18
 

docs/edge/ar/concepts/cli.mdx

@@ -201,6 +201,7 @@ def crew(self) -> Crew:
   ```shell Terminal
   crewai deploy push
   ```
+  يحافظ الدفع على المصدر المستخدم عند الإنشاء. إضافة `origin` لاحقًا لا تحوّل نشر ZIP إلى git.
 
 - **حالة النشر**:
   ```shell Terminal
@@ -286,6 +287,7 @@ crewai traces [COMMAND]
    ```shell Terminal
    crewai traces enable
    ```
+   - يحدّث موجه التشغيل الأول (`Would you like to view your execution traces?`) هذا التفضيل أيضاً
 
 <Note>
 **لتفعيل التتبع**، استخدم أيًا من هذه الطرق:

docs/edge/ar/concepts/knowledge.mdx

@@ -636,6 +636,21 @@ agent = Agent(
         }
     }
 )
+
+# Option 4: Use OpenRouter embeddings (supports models across providers)
+# Set OPENROUTER_API_KEY environment variable if api_key is omitted in config
+crew = Crew(
+    agents=[agent],
+    tasks=[...],
+    knowledge_sources=[knowledge_source],
+    embedder={
+        "provider": "openrouter",
+        "config": {
+            "model_name": "openai/text-embedding-3-small",
+            "api_key": "your-openrouter-api-key"  # Optional if OPENROUTER_API_KEY is set
+        }
+    }
+)
 ```
 
 #### إعداد تضمينات Azure OpenAI

docs/edge/ar/concepts/llms.mdx

@@ -1339,6 +1339,38 @@ llm = LLM(
     llm = LLM(model="gpt-4")
     ```
   </Tab>
+  <Tab title="أخطاء البوابة">
+    <Tip>
+      تُعيد البوابات مثل OpenRouter الرمز `200 OK` بمجرد قبول المزود الأساسي للطلب، لذلك يصل انتهاء مهلة المزود داخل جسم الاستجابة بدلًا من رمز الحالة.
+    </Tip>
+
+    تُطلق CrewAI الاستثناء نفسه الذي كان رمز الحالة الأصلي سيُنتجه، ومن ثم يلتقط منطق إعادة المحاولة الموجود لديك هذا الفشل المُقنَّع:
+
+    ```python
+    import openai
+    from pydantic import BaseModel
+
+    from crewai import LLM
+
+
+    class Report(BaseModel):
+        summary: str
+
+
+    llm = LLM(model="openrouter/z-ai/glm-5.3", response_format=Report)
+
+    try:
+        result = llm.call("Summarize the incident", response_model=Report)
+    except openai.InternalServerError as e:
+        # "z-ai/glm-5.3 via openrouter.ai returned HTTP 200 with an upstream error
+        #  and no choices: The operation was aborted (upstream code 504)"
+        print(f"Upstream provider failed, safe to retry: {e}")
+    ```
+
+    <Warning>
+      إن استخدام `response_model` كبير أو متداخل بعمق يزيد احتمال انتهاء مهلة المزود. تعامل مع هذه الحالات كأعطال مؤقتة في المزود، وليس كإنتاج النموذج مخرجات منظمة تالفة.
+    </Warning>
+  </Tab>
   <Tab title="طول السياق">
     <Tip>
       استخدم نماذج سياق أكبر للمهام الواسعة

docs/edge/ar/concepts/memory.mdx

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Use Google Gemini
-memory = Memory(llm="gemini/gemini-2.0-flash")
+memory = Memory(llm="gemini/gemini-3.7-flash")
 
 # Pass a pre-configured LLM instance with custom settings
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/ar/concepts/skills.mdx

@@ -202,13 +202,17 @@ crewai skill publish
 
 ### التثبيت
 
-ثبّت مهارة منشورة عبر مرجعها `@org/name`:
+ثبّت مهارة منشورة عبر مرجعها `@org-uuid/name`:
 
 ```shell Terminal
-crewai skill install @acme/code-review
+crewai skill install @your-org-uuid/code-review
 ```
 
-داخل مشروع الطاقم تُثبَّت المهارة في `./skills/{name}/`؛ وخارج المشروع تذهب إلى ذاكرة التخزين المؤقتة المشتركة في `~/.crewai/skills/{org}/{name}/`.
+<Note>
+  استخدم **UUID** الخاص بمؤسستك وليس اسمها — فأسماء المؤسسات ليست فريدة، وقد يشير الاسم إلى مؤسسة خاطئة فيفشل التثبيت برسالة "غير موجود". شغّل `crewai org list` لعرض الـ UUID (عمود `ID`) لكل مؤسسة تنتمي إليها.
+</Note>
+
+داخل مشروع الطاقم تُثبَّت المهارة في `./skills/{name}/`؛ وخارج المشروع تذهب إلى ذاكرة التخزين المؤقتة المشتركة في `~/.crewai/skills/{org-uuid}/{name}/`.
 
 يمكن للوكلاء أيضًا الإشارة إلى مهارات السجل مباشرة — يتم حلّها من ذاكرة التخزين المؤقتة المحلية (أو من مجلد `skills/` في المشروع) وقت التشغيل:
 
@@ -217,7 +221,7 @@ agent = Agent(
     role="Senior Code Reviewer",
     goal="Review pull requests for quality and security issues",
     backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["@acme/code-review"],  # registry ref, resolved locally
+    skills=["@your-org-uuid/code-review"],  # registry ref, resolved locally
 )
 ```
 

docs/edge/ar/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ mode: "wide"
 - **معالجة الأخطاء** – توجيه كيفية استجابة الـ Agents للإخفاقات والاستثناءات وحالات انتهاء المهلة.
 - **مطالبات خاصة بالأدوات** – تعريف تعليمات مفصلة لكيفية استدعاء الأدوات أو استخدامها.
 
-اطلع على [قوالب المطالبات الأصلية في مستودع CrewAI](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) لمعرفة كيفية تنظيم هذه العناصر. من هناك، يمكنك تجاوزها أو تكييفها حسب الحاجة لفتح سلوكيات متقدمة.
+اطلع على [قوالب المطالبات الأصلية في مستودع CrewAI](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json) لمعرفة كيفية تنظيم هذه العناصر. من هناك، يمكنك تجاوزها أو تكييفها حسب الحاجة لفتح سلوكيات متقدمة.
 
 ## فهم تعليمات النظام الافتراضية
 

docs/edge/ar/guides/coding-tools/agents-md.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
 ## إنشاء مشروع باستخدام CLI
 
-استخدم CLI الخاص بـ CrewAI لإنشاء هيكل مشروع، وسيُضاف `AGENTS.md` تلقائيًا في الجذر.
+استخدم CLI الخاص بـ CrewAI لإنشاء هيكل مشروع. يُضاف `AGENTS.md` في الجذر، ومعه ملفا `CLAUDE.md` و`GEMINI.md` اللذان يستوردانه، بحيث يقرأ Claude Code وGemini CLI نفس التوجيهات التي يقرأها كل مساعد آخر.
 
 ```bash
 # Crew
@@ -32,24 +32,28 @@ crewai tool create my_tool
 
 ### Claude Code
 
-يخزّن Claude Code ذاكرة المشروع في `CLAUDE.md`. يمكنك تهيئته بـ `/init` وتحريره باستخدام `/memory`. يدعم Claude Code أيضًا الاستيرادات داخل `CLAUDE.md`، فيمكنك إضافة سطر واحد مثل `@AGENTS.md` لسحب التعليمات المشتركة دون تكرارها.
+يقرأ Claude Code ملف `CLAUDE.md` ويتجاهل `AGENTS.md`. تأتي المشاريع المُنشأة بملف `CLAUDE.md` تعليمته الوحيدة هي سطر الاستيراد `@AGENTS.md`، بحيث تُحمَّل التوجيهات المشتركة دون تكرارها. أضف الملاحظات الخاصة بـ Claude تحت هذا السطر واحتفظ بالاصطلاحات المشتركة في `AGENTS.md`.
 
-يمكنك ببساطة استخدام:
+لمشروع أُنشئ قبل أن يُضاف `CLAUDE.md` إلى الهيكل، أضف الاستيراد بنفسك:
 
 ```bash
-mv AGENTS.md CLAUDE.md
+printf '@AGENTS.md\n' > CLAUDE.md
 ```
 
+لا تُعِد تسمية `AGENTS.md` إلى `CLAUDE.md`: يقرأ Codex وCursor ملف `AGENTS.md`، وإعادة التسمية تخفيه عنهما.
+
 ### Gemini CLI وGoogle Antigravity
 
-يقوم Gemini CLI وAntigravity بتحميل ملف سياق المشروع (الافتراضي: `GEMINI.md`) من جذر المستودع والمجلدات الأصلية. يمكنك تهيئته لقراءة `AGENTS.md` بدلاً من ذلك (أو بالإضافة إليه) بتعيين `context.fileName` في إعدادات Gemini CLI. على سبيل المثال، عيّنه إلى `AGENTS.md` فقط، أو أدرج كلاً من `AGENTS.md` و`GEMINI.md` إذا أردت الاحتفاظ بتنسيق كل أداة.
+يقوم Gemini CLI وAntigravity بتحميل ملف سياق المشروع (الافتراضي: `GEMINI.md`) من جذر المستودع والمجلدات الأصلية. تأتي المشاريع المُنشأة بملف `GEMINI.md` تعليمته الوحيدة هي سطر الاستيراد `@./AGENTS.md`، بحيث تُحمَّل التوجيهات المشتركة دون تكرارها. أضف الملاحظات الخاصة بـ Gemini تحت هذا السطر واحتفظ بالاصطلاحات المشتركة في `AGENTS.md`.
 
-يمكنك ببساطة استخدام:
+لمشروع أُنشئ قبل أن يُضاف `GEMINI.md` إلى الهيكل، أضف الاستيراد بنفسك:
 
 ```bash
-mv AGENTS.md GEMINI.md
+printf '@./AGENTS.md\n' > GEMINI.md
 ```
 
+بدلاً من ذلك، عيّن `context.fileName` في إعدادات Gemini CLI ليشمل `AGENTS.md` فيقرأه Gemini مباشرة. لا تُعِد تسمية `AGENTS.md` إلى `GEMINI.md`: يقرأ Codex وCursor ملف `AGENTS.md`، وإعادة التسمية تخفيه عنهما.
+
 ### Cursor
 
 يدعم Cursor ملف `AGENTS.md` كملف تعليمات مشروع. ضعه في جذر المشروع لتوفير توجيهات لمساعد البرمجة في Cursor.

docs/edge/ar/guides/crews/first-crew.mdx

@@ -75,7 +75,7 @@ research_crew/
 }
 ```
 
-استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `anthropic/claude-sonnet-4-6` أو `gemini/gemini-2.0-flash-001`.
+استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `anthropic/claude-sonnet-4-6` أو `gemini/gemini-3.7-flash`.
 
 ## الخطوة 3: تعريف المهام وإعدادات الـ Crew
 

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

@@ -104,7 +104,7 @@ crewai flow add-crew content-crew
 }
 ```
 
-استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `gemini/gemini-2.0-flash-001` أو `anthropic/claude-sonnet-4-6`.
+استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `gemini/gemini-3.7-flash` أو `anthropic/claude-sonnet-4-6`.
 
 3. أنشئ `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
 

docs/edge/ar/guides/frontend/channels.mdx

@@ -0,0 +1,156 @@
+---
+title: القنوات
+description: شغّل نفس وكيل CrewAI كروبوت على Slack أو Teams باستخدام CopilotKit Channels SDK ومنصة Intelligence المُدارة.
+icon: messages
+mode: "wide"
+---
+
+## قابل مستخدميك حيث هم بالفعل
+
+وكيل CrewAI الذي بنيته في [النظرة العامة](/edge/ar/guides/frontend/overview) لا يجب أن يعيش خلف تطبيق ويب فقط. يمكن لنفس الـ Crew أو الـ Flow أن يعمل كروبوت داخل منصة مراسلة. لا حاجة لإعادة البناء ولا لنسخة ثانية من منطق وكيلك: يبقى الوكيل مكشوفًا عبر [بروتوكول AG-UI](https://docs.ag-ui.com)، وتقوم **قناة** بتشغيله من Slack أو Microsoft Teams.
+
+يوفّر [Channels SDK](https://docs.copilotkit.ai/slack) من CopilotKit تلك القناة. تُعرّف `createChannel` في وقت تشغيل صغير، وتوجّهه إلى وكيل CrewAI الخاص بك، وتتولى منصة **Intelligence** المُدارة من CopilotKit التوسّط في الاتصال مع مزوّد المراسلة.
+
+<Note>
+على خلاف بقية هذا القسم، فإن Channels **ليست ذاتية الاستضافة**. تعمل من خلال **CopilotKit Intelligence** — وهي سطح مطلوب لـ Channels، بحكم التصميم (تتوفر طبقة مجانية). تحتفظ Intelligence باتصال المنصة وبيانات الاعتماد، وتستقبل كل حدث من المنصة، وتسلّم الدور إلى عملية قناتك؛ تشغّل عمليتك الوكيل وتبثّ الرد مرة أخرى. تقوم بإعداد Slack مرة واحدة في لوحة تحكم Intelligence، ولا تدخل بيانات اعتماد المنصة عمليتك أبدًا. يبقى وكيلك وأدواتك وحالتك ملكًا لك.
+</Note>
+
+## كيف تتكامل الأجزاء معًا
+
+لا يتغير أي شيء بخصوص خادم وكيل CrewAI الخاص بك. يستمر في تقديم الـ Crew أو الـ Flow عبر AG-UI تمامًا كما في النظرة العامة. ما تضيفه هو عملية Node منفصلة طويلة الأمد مبنية باستخدام `@copilotkit/channels`: تسجّل قناة على `CopilotRuntime`، وتتصل بـ Intelligence، وتشغّل وكيلك كلما وصلت رسالة.
+
+```
+Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+```
+
+تحتفظ عملية القناة باتصال دائم مع بوابة Intelligence، لذا فهي تحتاج إلى مضيف طويل الأمد — لا يمكن لمعالج طلبات بلا خادم (serverless) أن يملك ذلك الاتصال. يمكن لخادم CrewAI الخاص بك أن يستمر في تقديم واجهة الويب الأمامية من النظرة العامة في الوقت نفسه: تطبيق الويب والقناة ما هما إلا عميلان لنقطة نهاية AG-UI واحدة.
+
+## دليل التكامل
+
+<Steps>
+
+<Step title="ثبّت حزم Channels">
+
+يأتي Channels SDK مكتمل العناصر — كل منصة تُشحن في الحزمة الواحدة، بلا محوّل خاص بكل منصة لتثبيته. أضفه إلى جانب وقت التشغيل الذي يستضيف القناة وعميل CrewAI AG-UI:
+
+```bash
+npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
+```
+
+</Step>
+
+<Step title="أنشئ قناة في Intelligence">
+
+في [لوحة تحكم CopilotKit](https://docs.copilotkit.ai/slack)، أنشئ قناة واربط Slack — ترشدك Intelligence خلال إنشاء تطبيق Slack وتحتفظ ببيانات اعتماده. يترك ذلك متغيّري بيئة لعمليتك، كلاهما من لوحة التحكم:
+
+```bash
+export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
+export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
+```
+
+</Step>
+
+<Step title="عرّف القناة">
+
+تُعرّف `createChannel` القناة وتربط وكيلك بها. ابنِ الوكيل كمصنع لكل خيط (thread) بحيث تحصل كل محادثة على جلستها الخاصة، مستخدمًا نفس `CrewAIAgent` الذي تستخدمه النظرة العامة في وقت تشغيل الويب، موجّهًا إلى نقطة نهاية AG-UI الخاصة بك. تتيح `identifyUser: "platform"` لـ Intelligence ربط كل مستخدم من المنصة بهوية ثابتة.
+
+```ts
+// channel.ts
+import { createChannel } from "@copilotkit/channels";
+import { CrewAIAgent } from "@ag-ui/crewai";
+
+const channel = createChannel({
+  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
+  identifyUser: "platform",
+  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
+  agent: (threadId) => {
+    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
+    agent.threadId = threadId;
+    return agent;
+  },
+});
+
+// A mention subscribes the thread and runs the agent; afterwards every message
+// in a subscribed thread runs it without needing another mention.
+channel.onMention(async ({ thread }) => {
+  await thread.subscribe();
+  await thread.runAgent();
+});
+
+channel.onMessage(async ({ thread }) => {
+  if (await thread.isSubscribed()) await thread.runAgent();
+});
+
+export { channel };
+```
+
+</Step>
+
+<Step title="سجّل القناة على وقت التشغيل">
+
+أنشئ `CopilotRuntime` مع بوابة Intelligence وقناتك، ثم قدّمه باستخدام `createCopilotNodeListener`. تبقى خريطة `agents` فارغة — القناة توفّر وكيلها الخاص. انتظر حتى تكون القناة جاهزة كي يفشل بدء التشغيل بصوت عالٍ عند وجود إعداد معطوب.
+
+```ts
+// server.ts
+import { createServer } from "node:http";
+import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
+import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
+import { channel } from "./channel";
+
+const runtime = new CopilotRuntime({
+  agents: {}, // the channel supplies its own agent; no web-facing agents needed
+  intelligence: new CopilotKitIntelligence({
+    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
+  }),
+  channels: [channel],
+});
+
+const listener = createCopilotNodeListener({ runtime });
+await listener.channels?.ready({ timeoutMs: 15_000 });
+
+createServer(listener).listen(3123, () => {
+  console.log("Channels runtime listening on port 3123");
+});
+```
+
+</Step>
+
+<Step title="شغّل وقت تشغيل القناة">
+
+ابدأه إلى جانب خادم وكيل CrewAI الخاص بك:
+
+```bash
+uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
+npx tsx server.ts                 # terminal 2 — Channels runtime
+```
+
+اذكر الروبوت في Slack أو Teams فيشغّل الـ Crew أو الـ Flow الخاص بك، ويبثّ الرد مرة أخرى داخل الخيط. يبقى الخيط مشتركًا، لذا تعمل رسائل المتابعة دون الحاجة إلى ذكر آخر.
+
+</Step>
+
+</Steps>
+
+## نموذج الأحداث
+
+تتفاعل القناة مع أحداث المنصة عبر معالِجات، ويستقبل كل معالِج خيطًا (`thread`) تديره بعدد قليل من الدوال:
+
+- **`channel.onMention`** يُطلَق عندما يذكر مستخدم الروبوت بـ @. استدعِ `thread.subscribe()` للانضمام إلى الخيط، ثم `thread.runAgent()` لتشغيل وكيل CrewAI الخاص بك عند الذكر.
+- **`channel.onMessage`** يُطلَق عند كل رسالة في خيط يمكن للروبوت رؤيته. قيّده بـ `thread.isSubscribed()` كي لا يستجيب الوكيل إلا حيث انضمّ، ثم `thread.runAgent()`.
+- **`thread.runAgent()`** يشغّل وكيل CrewAI المرفق للدور الحالي ويبثّ مخرجاته مرة أخرى داخل القناة. مرّر `{ prompt }` لتجاوز النص الذي يعمل عليه الوكيل.
+
+يستقبل وكيلك `RunAgentInput` عاديًا من AG-UI ويصدر أحداث AG-UI عادية؛ تبقى آليات المنصة خلف القناة، لذا يعمل نفس الـ Crew أو الـ Flow دون تغيير عبر كل منصة. تكشف القناة أيضًا معالِجات للترحيبات والمقاطعات والأوامر والتفاعلات والنوافذ (modals) — راجع [مرجع `Channel`](https://docs.copilotkit.ai/reference/channels/classes/Channel) للاطلاع على السطح الكامل.
+
+## دعم المنصات
+
+يغطي مسار Intelligence المُدار **Slack** و**Microsoft Teams** اليوم — يعمل نفس كود القناة على أيٍّ منهما، وتفيد `message.platform` / `thread.platform` بالأصل الأصلي. تُبلَغ المنصات الأخرى (Discord وTelegram وWhatsApp) عبر **محوّلات مباشرة** يشغّلها المطوّر بدلًا من المسار المُدار — تملك عمليتك الخاصة بيانات اعتماد المنصة والنقل. راجع [توثيق CopilotKit Channels](https://docs.copilotkit.ai/slack) للاطلاع على قائمة المنصات الحالية والإعداد الخاص بكل منصة.
+
+## ذات صلة
+
+<CardGroup cols={2}>
+  <Card title="النظرة العامة على الواجهة الأمامية" icon="browser" href="/edge/ar/guides/frontend/overview">
+    قدّم الـ Crew أو الـ Flow الخاص بك عبر AG-UI — الأساس الذي تُبنى عليه كل قناة.
+  </Card>
+  <Card title="التدخل البشري (Human-in-the-Loop)" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    أوقف الوكيل مؤقتًا لجمع موافقة المستخدم أو مدخلاته في منتصف التشغيل.
+  </Card>
+</CardGroup>

docs/edge/ar/guides/frontend/overview.mdx

@@ -0,0 +1,238 @@
+---
+title: النظرة العامة على الواجهة الأمامية
+description: ابنِ واجهات مستخدم تفاعلية لوكلاء CrewAI الخاصين بك باستخدام CopilotKit وبروتوكول AG-UI.
+icon: browser
+mode: "wide"
+---
+
+## امنح وكلاءك واجهة مستخدم
+
+يشغّل CrewAI وكلاءك. ويمنحهم [CopilotKit](https://copilotkit.ai) واجهة أمامية. معًا يتيحان لك بناء تطبيقات يحادث فيها المستخدمون Crew أو Flow، ويشاهدونه يعمل في الوقت الفعلي، ويوافقون على قراراته، ويرون مخرجاته معروضة كواجهة حيّة بدلًا من جدران من النص.
+
+يتصل الاثنان عبر [بروتوكول AG-UI](https://docs.ag-ui.com). تكشف حزمة `ag-ui-crewai` أي Crew أو Flow كنقطة نهاية AG-UI. وتستهلك خطافات (hooks) ومكوّنات React من CopilotKit تلك النقطة. يفتح ذلك تجارب تتجاوز بكثير صندوق المحادثة:
+
+<CardGroup cols={2}>
+  <Card title="واجهة المستخدم التوليدية (Generative UI)" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+    اعرض استدعاءات أدوات الوكيل وحالته كمكوّنات React خاصة بك.
+  </Card>
+  <Card title="التدخل البشري (Human-in-the-Loop)" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    أوقف الوكيل مؤقتًا لجمع موافقة المستخدم أو مدخلاته في منتصف التشغيل.
+  </Card>
+  <Card title="الحالة المشتركة (Shared State)" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+    أبقِ حالة الوكيل وواجهة تطبيقك متزامنتين في الاتجاهين.
+  </Card>
+  <Card title="القنوات (Channels)" icon="messages" href="/edge/ar/guides/frontend/channels">
+    شغّل نفس الوكيل كروبوت على Slack أو Discord أو Teams.
+  </Card>
+</CardGroup>
+
+يجعل هذا الدليل Crew أو Flow يتحدث مع واجهة أمامية بـ Next.js من البداية إلى النهاية. تبني بقية القسم على التطبيق الذي تعدّه هنا.
+
+## البنية
+
+هناك ثلاثة أجزاء:
+
+1. **خادم وكيل CrewAI** — عملية Python تقدّم الـ Crew أو الـ Flow الخاص بك عبر AG-UI (FastAPI + `ag-ui-crewai`).
+2. **وقت تشغيل CopilotKit** — مسار Next.js يسجّل وكيلك ويوكّل الطلبات إليه.
+3. **الواجهة الأمامية بـ React** — مزوّد `<CopilotKit>` إلى جانب مكوّنات المحادثة والواجهة التوليدية.
+
+```
+React app  ──►  CopilotKit runtime (/api/copilotkit)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+```
+
+<Note>
+يغطي هذا الدليل المسار **الذاتي الاستضافة**: تشغّل خادم وكيل CrewAI بنفسك باستخدام `ag-ui-crewai`، ويعمل محليًا دون أي خدمة مُدارة. يقدّم CopilotKit أيضًا مسارًا **مُدارًا** (CopilotKit Cloud / Enterprise Intelligence) بخيوط مستضافة وأداة فحص — راجع [دليل البدء السريع لـ CopilotKit مع CrewAI](https://docs.copilotkit.ai/crewai-crews/quickstart) إن أردت ذلك بدلًا منه. كود الواجهة الأمامية في هذا القسم هو نفسه في الحالتين؛ الاختلاف فقط في كيفية استضافة الوكيل وتسجيله.
+</Note>
+
+<Note>
+يعمل CrewAI خلف AG-UI بثلاثة أشكال: الـ **Flows** العادية (المستخدمة في هذه الأدلة)، و**[الـ Flows المحادثية (Conversational Flows)](/edge/en/guides/frontend/conversational-flows)** (أصلية، مدركة للجلسة، قائمة على الأدوار، بتكافؤ كامل في الميزات)، والـ **Crews** (محادثة أساسية). الواجهة الأمامية في هذا القسم متطابقة عبرها جميعًا — الاختلاف فقط في تأليف الخلفية وتسجيلها.
+</Note>
+
+## دليل التكامل
+
+<Steps>
+
+<Step title="قدّم وكيلك عبر AG-UI">
+
+ثبّت حزمة التكامل في مشروع CrewAI الخاص بك:
+
+```bash
+pip install ag-ui-crewai
+```
+
+اكشف وكيلك من تطبيق FastAPI. تستخدم الـ Flows دالة `add_crewai_flow_fastapi_endpoint`؛ وتستخدم الـ Crews دالة `add_crewai_crew_fastapi_endpoint`. يمكنك تسجيل ما تشاء منها، كلٌّ على مساره الخاص.
+
+<CodeGroup>
+
+```python Flow
+# server.py
+from fastapi import FastAPI
+from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
+from my_agents.recipe_flow import RecipeFlow
+
+app = FastAPI(title="CrewAI Agent Server")
+
+add_crewai_flow_fastapi_endpoint(
+    app=app,
+    flow=RecipeFlow(),
+    path="/recipe",
+)
+```
+
+```python Crew
+# server.py
+from fastapi import FastAPI
+from ag_ui_crewai.endpoint import add_crewai_crew_fastapi_endpoint
+from my_agents.research_crew import ResearchCrew
+
+app = FastAPI(title="CrewAI Agent Server")
+
+add_crewai_crew_fastapi_endpoint(
+    app=app,
+    crew=ResearchCrew().crew(),
+    path="/research",
+)
+```
+
+</CodeGroup>
+
+شغّله:
+
+```bash
+uvicorn server:app --port 8000
+```
+
+<Note>
+اضبط متغيّرات البيئة الخاصة بمزوّد الـ LLM الخاص بك (على سبيل المثال `OPENAI_API_KEY`) قبل بدء الخادم.
+</Note>
+
+</Step>
+
+<Step title="أنشئ تطبيق Next.js">
+
+إن لم تكن لديك واجهة أمامية بعد، أنشئ هيكلًا:
+
+```bash
+npx create-next-app@latest my-app
+cd my-app
+```
+
+ثبّت CopilotKit وعميل CrewAI AG-UI:
+
+```bash
+npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime @ag-ui/crewai
+```
+
+</Step>
+
+<Step title="أضف وقت تشغيل CopilotKit">
+
+أنشئ مسارًا يسجّل وكيل (أو وكلاء) CrewAI مع وقت تشغيل CopilotKit. يشير كل وكيل إلى مسار على خادم Python الخاص بك عبر `CrewAIAgent`.
+
+```ts
+// app/api/copilotkit/route.ts
+import {
+  CopilotRuntime,
+  InMemoryAgentRunner,
+  createCopilotEndpoint,
+} from "@copilotkit/runtime/v2";
+import { CrewAIAgent } from "@ag-ui/crewai";
+import { handle } from "hono/vercel";
+
+const runtime = new CopilotRuntime({
+  agents: {
+    recipe: new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
+  },
+  runner: new InMemoryAgentRunner(),
+});
+
+const app = createCopilotEndpoint({
+  runtime,
+  basePath: "/api/copilotkit",
+});
+
+const handler = handle(app);
+export const GET = handler;
+export const POST = handler;
+```
+
+</Step>
+
+<Step title="غلّف تطبيقك بالمزوّد">
+
+وجّه `<CopilotKit>` إلى مسار وقت التشغيل واذكر اسم الوكيل الذي سجّلته.
+
+```tsx
+// app/page.tsx
+"use client";
+import { CopilotKit } from "@copilotkit/react-core";
+import { CopilotSidebar } from "@copilotkit/react-core/v2";
+import "@copilotkit/react-core/v2/styles.css";
+
+export default function Page() {
+  return (
+    <CopilotKit runtimeUrl="/api/copilotkit" agent="recipe">
+      <YourApp />
+      <CopilotSidebar agentId="recipe" labels={{ modalHeaderTitle: "Assistant" }} />
+    </CopilotKit>
+  );
+}
+```
+
+</Step>
+
+<Step title="شغّله">
+
+ابدأ العمليتين وافتح التطبيق. تشغّل المحادثة في الشريط الجانبي الآن الـ Crew أو الـ Flow الخاص بك.
+
+```bash
+uvicorn server:app --port 8000   # terminal 1
+npm run dev                       # terminal 2
+```
+
+</Step>
+
+</Steps>
+
+## خيارات واجهة المحادثة
+
+يشحن CopilotKit ثلاثة أسطح محادثة قابلة للتبديل. بدّل المكوّن؛ يبقى التوصيل متطابقًا.
+
+<CodeGroup>
+
+```tsx Sidebar
+import { CopilotSidebar } from "@copilotkit/react-core/v2";
+
+<CopilotSidebar agentId="recipe" />
+```
+
+```tsx Popup
+import { CopilotPopup } from "@copilotkit/react-core/v2";
+
+<CopilotPopup agentId="recipe" />
+```
+
+```tsx Inline
+import { CopilotChat } from "@copilotkit/react-core/v2";
+
+<CopilotChat agentId="recipe" />
+```
+
+</CodeGroup>
+
+## إلى أين تذهب بعد ذلك
+
+<CardGroup cols={2}>
+  <Card title="واجهة المستخدم التوليدية (Generative UI)" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+    اعرض استدعاءات الأدوات وحالة الوكيل كمكوّنات مخصّصة.
+  </Card>
+  <Card title="إجراءات الواجهة الأمامية (Frontend Actions)" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
+    دع الوكيل يستدعي دوالًا تعمل في المتصفح.
+  </Card>
+  <Card title="التدخل البشري (Human-in-the-Loop)" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    قيّد إجراءات الوكيل خلف موافقة المستخدم.
+  </Card>
+  <Card title="الحالة التنبؤية (Predictive State)" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+    ابثّ الحالة قيد التنفيذ إلى الواجهة أثناء عمل الوكيل.
+  </Card>
+</CardGroup>

docs/edge/ar/learn/litellm-removal-guide.mdx

@@ -176,7 +176,7 @@ grep -r "llm:" --include="*.yaml" .
     # llm = LLM(model="mistral/mistral-large-latest")
 
     # After (Native):
-    llm = LLM(model="gemini/gemini-2.0-flash")
+    llm = LLM(model="gemini/gemini-3.7-flash")
     ```
 
     ```bash
@@ -312,7 +312,7 @@ llm = LLM(model="anthropic/claude-haiku-3-5")    # Fast & affordable
 # Together AI → OpenAI or Gemini
 # llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
 llm = LLM(model="openai/gpt-4o")                 # High quality
-llm = LLM(model="gemini/gemini-2.0-flash")       # Fast & capable
+llm = LLM(model="gemini/gemini-3.7-flash")       # Fast & capable
 
 # Mistral → Anthropic or OpenAI
 # llm = LLM(model="mistral/mistral-large-latest")

docs/edge/ar/learn/llm-connections.mdx

@@ -141,7 +141,7 @@ mode: "wide"
         # Example using Gemini's OpenAI-compatible API.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Should start with AIza...
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Add your Gemini model here, under openai/
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Add your Gemini model here, under openai/
         ```
         </CodeGroup>
     </Tab>
@@ -159,7 +159,7 @@ mode: "wide"
             ```python Google
             # Example using Gemini's OpenAI-compatible API
             llm = LLM(
-                model="openai/gemini-2.0-flash",
+                model="openai/gemini-3.7-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # Should start with AIza...
             )

docs/edge/ar/learn/llm-selection-guide.mdx

@@ -144,7 +144,7 @@ Planning agents benefit from reasoning models that can handle complex strategic
 from crewai import Agent, Task, Crew, LLM
 
 # High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
+manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
 
 # Creative model for content generation
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -409,7 +409,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
     # Manager or coordination agents
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # Premium for coordination
+        llm=LLM(model="gemini/gemini-3.7-flash"),  # Premium for coordination
         # ... rest of config
     )
 

docs/edge/ar/observability/tracing.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 يوفر CrewAI إمكانيات تتبع مدمجة تتيح لك مراقبة وتصحيح أخطاء الطواقم والتدفقات في الوقت الفعلي. يوضح هذا الدليل كيفية تفعيل التتبع لكل من **الطواقم** و**التدفقات** باستخدام منصة المراقبة المتكاملة في CrewAI.
 
-> **ما هو تتبع CrewAI؟** يوفر التتبع المدمج في CrewAI مراقبة شاملة لوكلاء الذكاء الاصطناعي، بما في ذلك قرارات الوكلاء وجداول تنفيذ المهام واستخدام الأدوات واستدعاءات LLM - كل ذلك متاح عبر [منصة CrewAI AMP](https://app.crewai.com).
+> **ما هو تتبع CrewAI؟** يوفر التتبع المدمج في CrewAI مراقبة شاملة لوكلاء الذكاء الاصطناعي، بما في ذلك قرارات الوكلاء وجداول تنفيذ المهام واستخدام الأدوات واستدعاءات LLM - كل ذلك متاح عبر [منصة CrewAI AMP](https://app.crewai.com). يتم إدارة التتبع بشكل مستقل عن [القياس عن بُعد](/ar/telemetry).
 
 ![واجهة تتبع CrewAI](/images/crewai-tracing.png)
 
@@ -150,8 +150,8 @@ result = flow.kickoff()
 
 ### الخطوة 5: عرض التتبعات في لوحة تحكم CrewAI AMP
 
-بعد تشغيل الطاقم أو التدفق، يمكنك عرض التتبعات التي أنشأها تطبيق CrewAI في لوحة تحكم CrewAI AMP. يجب أن ترى خطوات تفصيلية لتفاعلات الوكلاء واستخدامات الأدوات واستدعاءات LLM.
-ما عليك سوى النقر على الرابط أدناه لعرض التتبعات أو التوجه إلى علامة تبويب التتبعات في لوحة التحكم [هنا](https://app.crewai.com/crewai_plus/trace_batches)
+لا تُرفع التتبعات إلا بعد نجاح التصدير باستخدام المصادقة أو الرفع المجهول الذي وافقت عليه صراحةً. لا يوجد تتبع مرفوع لأي تشغيل حُذف مخزنه المؤقت المحلي.
+للتتبعات المرتبطة بحسابك، افتح [علامة تبويب التتبعات في لوحة تحكم CrewAI AMP](https://app.crewai.com/crewai_plus/trace_batches) لعرض تفاعلات الوكلاء واستخدام الأدوات واستدعاءات LLM.
 ![واجهة تتبع CrewAI](/images/view-traces.png)
 
 ### البديل: إعداد متغير البيئة
@@ -170,6 +170,49 @@ CREWAI_TRACING_ENABLED=true
 
 عند تعيين متغير البيئة هذا، ستُفعّل جميع الطواقم والتدفقات التتبع تلقائياً، حتى بدون تعيين `tracing=True` صراحةً.
 
+## عرض التتبعات بعد أول تشغيل
+
+في المرة الأولى التي تشغّل فيها طاقماً أو تدفقاً، قد يسألك طرف تفاعلي:
+
+```text
+Share this execution trace with CrewAI? [y/N]
+```
+
+اختر **yes** لرفع التتبع المخزّن مؤقتاً إلى CrewAI. قد تحتوي التتبعات على
+المطالبات والمدخلات والمخرجات. يُحذف المخزن المؤقت عند الرفض أو انتهاء المهلة
+أو التشغيل دون مطالبة تفاعلية بالموافقة. يمكنك تغيير إعداد التتبع لاحقاً باستخدام
+`crewai traces enable` أو `crewai traces disable`، أو بتعيين `tracing`
+على الطاقم أو التدفق.
+
+### التخزين المؤقت المحلي والتصدير بعد المصادقة
+
+يبقى جمع التتبعات في أول تشغيل داخل ذاكرة العملية إلى أن توافق على المشاركة،
+حتى إذا كانت لديك بيانات تسجيل دخول محفوظة. يستخدم التتبع دون مصادقة مسار
+الموافقة نفسه. قبل الموافقة، لا يطلب CrewAI تصريح رفع ولا يرسل أي مقاطع تنفيذ.
+
+يحتفظ المخزن المؤقت بحد أقصى **1,000 مقطع** و**8 MiB من بيانات OTLP المرمّزة**.
+اضبط `CREWAI_EPHEMERAL_TRACE_MAX_SPANS` و
+`CREWAI_EPHEMERAL_TRACE_MAX_BYTES` على أعداد صحيحة موجبة لتعديل هذين الحدّين.
+عند تجاوز السعة، تُحذف أقدم المقاطع؛ ويُحذف أي مقطع يتجاوز وحده حد البايتات.
+يُفرّغ المخزن المؤقت بعد مشاركته أو تجاهله.
+
+عند تفعيل التتبع وتوفّر بيانات الاعتماد، يستبدل CrewAI بيانات تسجيل دخول CLI
+أو `CREWAI_USER_PAT` أو بيانات اعتماد تكامل المنصة لدى AMP بتصريح خاص
+بالتنفيذ. ثم يصدّر مقاطع OpenTelemetry مباشرةً إلى Wharf باستخدام ذلك التصريح.
+لا تؤدي بيانات الاعتماد غير الصالحة إلى الرجوع إلى الرفع المجهول.
+
+### جلسات التنفيذ المستضافة
+
+يمكن للبيئات المضيفة إحاطة التنفيذ بـ `telemetry_session` من
+`crewai.telemetry.tracing`. تستخدم الجلسة أحداث دورة حياة CrewAI لإنشاء
+المقاطع وإنهائها، مع الحفاظ على طوابعها الزمنية وعلاقاتها بالمقاطع الأصل وروابط
+الإيقاف والاستئناف في HITL. مرّر موفّراً موجوداً عبر `providers=` للاحتفاظ
+بمتتبّع البيئة المضيفة وتكامل التسجيل لديها. مرّر معالجات المقاطع عبر
+`processors=` ودالة تسجيل للمضيف عبر `log_emitter=`. يتولى المضيف أي تنقيح
+للبيانات في هذه التكاملات.
+تدير كل جلسة دورة حياة التتبع الخاصة بها وتترك موفّر OpenTelemetry العام
+للتطبيق دون تغيير.
+
 ## عرض التتبعات
 
 ### الوصول إلى لوحة تحكم CrewAI AMP
@@ -210,5 +253,5 @@ CREWAI_TRACING_ENABLED=true
 
 1. تأكد من تعيين `tracing=True` في الطاقم/التدفق
 2. تحقق من `CREWAI_TRACING_ENABLED=true` إذا كنت تستخدم متغيرات البيئة
-3. تأكد من المصادقة عبر `crewai login`
-4. تحقق من أن الطاقم/التدفق قيد التنفيذ فعلاً
+3. للتصدير باستخدام المصادقة، تحقّق من تسجيل دخول CLI أو `CREWAI_USER_PAT` أو بيانات اعتماد تكامل المنصة. للمشاركة المجهولة، وافق صراحةً على مطالبة الموافقة؛ لا يلزم تسجيل الدخول
+4. تحقّق من تنفيذ الطاقم/التدفق ونجاح تصدير التتبع. يؤدي رفض الموافقة أو انتهاء المهلة أو التشغيل دون مطالبة تفاعلية بالموافقة إلى حذف المخزن المؤقت المحلي دون رفعه

docs/edge/ar/telemetry.mdx

@@ -23,7 +23,8 @@ mode: "wide"
 عند تفعيل ميزة `share_crew`، يتم جمع بيانات تفصيلية تشمل أوصاف المهام وخلفيات وأهداف الوكلاء وسمات محددة أخرى
 لتوفير رؤى أعمق. قد يتضمن جمع البيانات الموسع هذا معلومات شخصية إذا دمجها المستخدمون في طواقمهم أو مهامهم.
 يجب على المستخدمين النظر بعناية في محتوى طواقمهم ومهامهم قبل تفعيل `share_crew`.
-يمكن للمستخدمين تعطيل القياس عن بُعد عبر تعيين متغير البيئة `CREWAI_DISABLE_TELEMETRY` إلى `true` أو تعيين `OTEL_SDK_DISABLED` إلى `true` (لاحظ أن الأخير يعطل جميع أدوات OpenTelemetry عالمياً).
+يمكن للمستخدمين تعطيل القياس عن بُعد في CrewAI عبر تعيين `CREWAI_DISABLE_TELEMETRY` إلى `true` أو `1` أو `yes` أو `on` (بغض النظر عن حالة الأحرف). `OTEL_SDK_DISABLED` بنفس القيم يعطّل أيضاً مُصدِّر CrewAI. مجموعة أدوات OpenTelemetry نفسها ما تزال تقبل `true` فقط لتعطيل بقية أدوات القياس في العملية.
+تتبع AMP مشمول بشكل منفصل في [التتبع](/ar/observability/tracing).
 
 ### أمثلة:
 ```python
@@ -34,6 +35,8 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
+`CREWAI_DISABLE_TELEMETRY=1` (أو `yes` / `on`) يعمل بنفس طريقة `true`. تُتجاهل القيم غير المعروفة ويبقى القياس عن بُعد مفعّلاً.
+
 ### العزل عن إعداد OpenTelemetry الخاص بك
 
 يعمل القياس عن بُعد الخاص بـ CrewAI على `TracerProvider` خاص به ولا يسجل نفسه
@@ -60,8 +63,8 @@ os.environ['OTEL_SDK_DISABLED'] = 'true'
 | نعم       | بيانات دورة حياة المهمة | تشمل: أوقات الإنشاء وبدء/انتهاء التنفيذ، معرّفات الطاقم والمهمة، وما إذا نجحت المهمة أو فشلت. وعند فشل المهمة، يُسجَّل **اسم صنف** الاستثناء (مثل `TimeoutError`) بحيث يمكن عدّ حالات الفشل وتشخيصها — وليس رسالة الخطأ أبدًا، فهي قد تحتوي على مطالبات أو مخرجات نموذج أو مسارات ملفات أو بيانات اعتماد. مخزنة كنطاقات مع طوابع زمنية. لا بيانات شخصية. |
 | نعم       | سمات LLM | تشمل: الاسم، model_name، model، top_k، temperature، واسم فئة LLM. كلها بيانات تقنية غير شخصية. |
 | نعم       | إنشاء مشروع باستخدام CLI الخاص بـ CrewAI | تشمل: أن مشروعًا جديدًا أُنشئ عبر `crewai create`، ونوعه (`crew` أو `json_crew` أو `flow`)، ومعرّف المشروع الذي تم توليده لهذا المشروع الجديد وكُتب في ملف `pyproject.toml` الخاص به. وهو معرّف المشروع الجديد نفسه، ويُسجَّل بشكل منفصل عن `project_id` الخاص بالمجلد الذي شُغّل منه الأمر — وقد يختلفان. لا اسم مشروع، ولا محتويات ملفات، ولا شيفرة. لا بيانات شخصية. |
-| نعم       | محاولة نشر الطاقم باستخدام CLI الخاص بـ CrewAI | تشمل: حقيقة إجراء النشر ومعرّف الطاقم، وما إذا كان يحاول سحب السجلات، وما إذا بدأ النشر من أمر CLI أو من واجهة التشغيل TUI. لا تُسجَّل محتويات المشروع أو الطاقم. لا توجد بيانات شخصية. |
-| نعم       | بيئة التنفيذ | تشمل: مساعد البرمجة بالذكاء الاصطناعي الذي يشغّل العملية إن وُجد (واحد من قائمة ثابتة مثل `claude_code` أو `codex` أو `cursor` أو `unknown`)، ومكان تشغيل العملية (واحد من قائمة ثابتة مثل `ci` أو `container` أو `serverless` أو `interactive`)، و`project_id` من ملف `pyproject.toml` عند ضبطه. يتحقق الاكتشاف فقط مما إذا كانت متغيرات البيئة المعروفة مضبوطة، ولا يقرأ قيمها أبدًا. لا بيانات شخصية. |
+| نعم       | محاولة نشر الطاقم باستخدام CLI الخاص بـ CrewAI | تشمل: حقيقة إجراء النشر ومعرّف الطاقم، وما إذا كان يحاول سحب السجلات، وما إذا بدأ النشر من أمر CLI أو من واجهة التشغيل TUI. إذا فشل إنشاء النشر، تُسجَّل فئة الفشل (واحدة من قائمة ثابتة مثل `api_4xx` أو `network_error` أو `user_declined`) ورمز حالة HTTP لاستجابة API إن وُجدت — ولا تُسجَّل رسالة الخطأ أبدًا. لا تُسجَّل محتويات المشروع أو الطاقم. لا توجد بيانات شخصية. |
+| نعم       | بيئة التنفيذ | تشمل: مساعد البرمجة بالذكاء الاصطناعي الذي يشغّل العملية إن وُجد (واحد من قائمة ثابتة مثل `claude_code` أو `codex` أو `cursor` أو `unknown`)، ومكان تشغيل العملية (واحد من قائمة ثابتة مثل `ci` أو `container` أو `serverless` أو `interactive`)، و`project_id` من ملف `pyproject.toml` عند ضبطه، ونطاقًا تقريبيًا لحجم الجهاز (واحد من `1-2` أو `3-4` أو `5-8` أو `9-16` أو `17-32` أو `33+` أو `unknown`). النطاق مجال وليس العدد الدقيق للأنوية أبدًا — العدد الدقيق اختياري فقط، ضمن «معلومات البيئة» أدناه. تأتي فئة الحجم من عدد أنوية المضيف؛ ويتحقق اكتشاف المساعد وموقع التشغيل فقط مما إذا كانت متغيرات البيئة المعروفة مضبوطة، ولا يقرأ قيمها أبدًا. لا بيانات شخصية. |
 | نعم       | إشارات دورة حياة التدفق | تشمل: بدء التدفق، وما إذا اكتمل أو فشل، وما إذا فشلت إحدى طرقه، وما إذا توقف مؤقتًا لانتظار إدخال أو ملاحظات بشرية، وما إذا كان البدء تشغيلًا مستأنفًا، وما إذا فشل دور محادثة، ومدة تشغيل التدفق، وما إذا كان التدفق مما تشغّله CrewAI داخليًا أو مما كتبته أنت. ويُسجَّل اسم التدفق، كما هو الحال بالفعل لإنشاء التدفق وتنفيذه. وعند فشل تدفق أو إحدى طرقه، يُسجَّل **اسم فئة** الاستثناء (مثل `TimeoutError`) لتشخيص الأعطال — ولا تُسجَّل أبدًا رسالة الخطأ، التي قد تحتوي على مطالبات أو مخرجات النموذج أو مسارات ملفات أو بيانات اعتماد. ولا تُسجَّل أبدًا أسماء الطرق أو حالة التدفق. لا توجد بيانات شخصية. |
 | نعم       | إشارة مشاركة التتبع | تشمل: نجاح مشاركة دفعة من عمليات التتبع مع CrewAI AMP، وما إذا تمت المشاركة بشكل مجهول (قبل إنشاء حساب) أو مرتبطة بحسابك. ومثل كل span، تحمل أيضًا سمات بيئة التنفيذ الموضحة أعلاه (`project_id` عند تكوينه، ومساعد البرمجة، وبيئة التشغيل). يصف هذا الصف بيانات القياس عن بُعد الخاصة بالمشاركة فقط — وليس محتويات التتبع أو الوصول الذي تمنحه روابط التتبع المشتركة. لا تُسجَّل محتويات التتبع أو المدخلات أو المخرجات في هذه الإشارة. قبل مشاركة التتبعات، راجع الأسرار والبيانات الشخصية وإعدادات التنقيح والاحتفاظ في AMP. |
 | لا        | بيانات الوكيل الموسّعة | تشمل: وصف الهدف، نص الخلفية، معرّف ملف موجهات i18n. يجب على المستخدمين التأكد من عدم تضمين معلومات شخصية في حقول النص. |

docs/edge/ar/tools/ai-ml/overview.mdx

@@ -50,16 +50,15 @@ mode: "wide"
 - **سلامة الذكاء الاصطناعي**: تنفيذ فحوصات الإشراف على المحتوى والسلامة
 
 ```python
-from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
+from crewai_tools import DallETool, VisionTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
-code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor, code_executor],
+    tools=[image_generator, vision_processor],
     goal="Create and analyze content using AI capabilities"
 )

docs/edge/ar/tools/ai-ml/ragtool.mdx

@@ -460,7 +460,7 @@ rag_tool = RagTool(config=config, summarize=True)
 
     **خيارات الإعداد:**
     - `model_name` (str): معرّف نموذج HuggingFace. القيمة الافتراضية: `hkunlp/instructor-base`. الخيارات: `hkunlp/instructor-xl`، `hkunlp/instructor-large`، `hkunlp/instructor-base`
-    - `device` (str): الجهاز للتشغيل. القيمة الافتراضية: `cpu`. الخيارات: `cpu`، `cuda`، `mps`
+    - `device` (str): الجهاز للتشغيل. القيمة الافتراضية: `cpu`. الخيارات: `cpu`، `cuda`، `mps`، `xpu`
     - `instruction` (str): بادئة التعليمات للتضمينات
 
     **متغيرات البيئة:**
@@ -485,7 +485,7 @@ rag_tool = RagTool(config=config, summarize=True)
 
     **خيارات الإعداد:**
     - `model_name` (str): اسم نموذج Sentence Transformers. القيمة الافتراضية: `all-MiniLM-L6-v2`. الخيارات: `all-mpnet-base-v2`، `all-MiniLM-L6-v2`، `paraphrase-multilingual-MiniLM-L12-v2`
-    - `device` (str): الجهاز للتشغيل. القيمة الافتراضية: `cpu`. الخيارات: `cpu`، `cuda`، `mps`
+    - `device` (str): الجهاز للتشغيل. القيمة الافتراضية: `cpu`. الخيارات: `cpu`، `cuda`، `mps`، `xpu`
     - `normalize_embeddings` (bool): ما إذا كان يتم تطبيع التضمينات. القيمة الافتراضية: `False`
 
     **متغيرات البيئة:**

docs/edge/ar/tools/ai-ml/visiontool.mdx

@@ -12,6 +12,16 @@ mode: "wide"
 تُستخدم هذه الأداة لاستخراج النص من الصور. عند تمريرها إلى الوكيل، ستستخرج النص من الصورة ثم تستخدمه لتوليد استجابة أو تقرير أو أي مخرج آخر.
 يجب تمرير عنوان URL أو مسار الصورة إلى الوكيل.
 
+يمكنك أيضًا طرح استعلام `query` مخصص حول الصورة واختيار مستوى تعقيد `complexity_level` يقوم تلقائيًا باختيار النموذج الأنسب للطلب:
+
+| مستوى التعقيد | النموذج         |
+| :--------------- | :------------ |
+| `easy`           | `gpt-5.6-luna`  |
+| `medium` (افتراضي) | `gpt-5.6-terra` |
+| `hard`           | `gpt-5.6-sol`   |
+
+عند تمرير `llm` أو `model` بشكل صريح إلى الأداة، فإنه يأخذ الأولوية على اختيار النموذج المستند إلى مستوى التعقيد.
+
 ## التثبيت
 
 ثبّت حزمة crewai_tools
@@ -43,8 +53,10 @@ def researcher(self) -> Agent:
 
 ## المعاملات
 
-تتطلب VisionTool المعاملات التالية:
+تقبل VisionTool المعاملات التالية:
 
 | المعامل           | النوع     | الوصف                                                                      |
 | :----------------- | :------- | :------------------------------------------------------------------------------- |
-| **image_path_url** | `string` | **إلزامي**. مسار ملف الصورة المراد استخراج النص منها. |
+| **image_path_url** | `string` | **إلزامي**. مسار ملف الصورة (أو عنوان URL) المراد استخراج النص منها. |
+| **query**          | `string` | **اختياري**. السؤال أو التعليمات المراد طرحها على النموذج حول الصورة. القيمة الافتراضية هي `"What's in this image?"`. |
+| **complexity_level** | `string` | **اختياري**. مستوى تعقيد الطلب، والذي يحدد النموذج: `easy` أو `medium` أو `hard`. القيمة الافتراضية هي `medium`. |

docs/edge/ar/tools/web-scraping/oxylabsscraperstool.mdx

@@ -4,7 +4,7 @@ description: >
   تتيح أدوات استخراج Oxylabs الوصول بسهولة إلى المعلومات من المصادر المعنية. يرجى الاطلاع على قائمة المصادر المتاحة أدناه:
     - `Amazon Product`
     - `Amazon Search`
-    - `Google Seach`
+    - `Google Search`
     - `Universal`
 icon: globe
 mode: "wide"
@@ -87,7 +87,7 @@ print(result)
 ### المعاملات
 
 - `query` - مصطلح بحث Amazon.
-- `domain` - توطين النطاق لـ Bestbuy.
+- `domain` - توطين النطاق لـ Amazon.
 - `start_page` - رقم صفحة البداية.
 - `pages` - عدد الصفحات المراد استرجاعها.
 - `geo_location` - موقع _التوصيل إلى_.

docs/edge/en/changelog.mdx

@@ -4,6 +4,145 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Sep 16, 2026">
+  ## v1.15.22
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.22)
+
+  ## What's Changed
+
+  ### Features
+  - Support aliases as connection identifiers
+  - Record reasons for deployment creation failures
+  - Collect human feedback and pause events in tracing
+  - Add `llm_overlay` context variable to route agent roles to models
+  - Carry `task_prompt` and output in agent execution payloads
+  - Validate platform integrations during crew setup
+  - Add platform tools to JSON crew wizard
+  - Expose CrewAI Platform application catalog
+  - Add OpenRouter as a supported embedding provider
+
+  ### Bug Fixes
+  - Accept CRLF in inline skill definitions
+  - Load text file URLs through the safe fetcher
+  - Close SQLite connections in kickoff task outputs storage
+  - Carry `from_cache` on the native tool path's `ToolUsageFinishedEvent`
+  - Raise `ValueError` instead of bare raise in uploader
+  - Support non-primitive types in SQLiteFlowPersistence
+  - Improve coding agents instructions
+  - Read JSON checkpoints as UTF-8
+  - Overwrite stale `poetry.lock` backup on Windows
+  - Key streamed tool calls by wire index in Azure
+  - Preserve file data content parts in Gemini
+  - Honor `read_only` on `update()` and `recall()` access times
+  - Send `reasoning_effort` to every OpenAI reasoning model
+  - Prevent crashes in the run TUI when streamed output contains a literal `[...]`
+  - Reject replay when stored tasks differ
+  - Use `sys.platform` guards for mypy compatibility on Windows
+  - Request the forced final answer as a user turn
+  - Keep null in the task output schema embedded in the prompt
+  - Align `DOCXSearchTool` with standard RAG fixed schema pattern
+
+  ### Documentation
+  - Add `xpu` to embedding device options
+  - List all workspace packages
+
+  ## Contributors
+
+  @ASTion24, @BlueX888, @HUAN2022A, @RaycarlLei, @Rohitkanithi, @SWAPI03, @SharoonSharif, @ShivangiRay, @Theater-ahyeon, @Vidit-Ostwal, @gamal1osama, @github-actions[bot], @joaomdmoura, @lorenzejay, @mhbuehler, @modusensus, @monkscode, @rohitkanithi, @roli-lpci, @vinibrsl, @wangtaotaotao95
+
+</Update>
+
+<Update label="Sep 09, 2026">
+  ## v1.15.21
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.21)
+
+  ## What's Changed
+
+  ### Features
+  - Add telemetry to track checkpoint runtime and CLI usage.
+
+  ### Bug Fixes
+  - Fix gateway errors reported inside an HTTP 200 response.
+  - Keep deploy push on the AMP create source.
+  - Report scrape failures instead of raising IndexError in the Oxylabs integration.
+  - Route all DashScope models through the native provider.
+  - Persist JSON checkpoints as UTF-8.
+  - Drop stray $ in f-string search URLs for BrightData.
+  - Support list-form type arrays in JSON schema conversion.
+  - Correct gpt-4o-mini context window from 200000 to 128000.
+  - Preserve streaming tool call arguments at contentBlockStop.
+  - Make pre-commit hooks portable on Windows.
+
+  ### Documentation
+  - Clarify that tracing is managed separately from telemetry.
+  - Fix parallel search reference link.
+  - Remove stale parameters from handle_llm_stream_chunk docstring.
+  - Fix streaming output docstring examples.
+  - Use organization UUIDs in the skill install reference.
+
+  ## Contributors
+
+  @Alphaxiaoteng, @DrewWhittleNZ, @Ghraven, @Shxiao101, @Vaishnavi220506, @Vidit-Ostwal, @danielfsbarreto, @georgeatparallel, @github-actions[bot], @jessemiller, @joaomdmoura, @kimnamu, @kiwoongyoon, @liang0417, @lorenzejay, @oxy-giedrius, @simpleqt, @uoparaji
+
+</Update>
+
+<Update label="Sep 04, 2026">
+  ## v1.15.20
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.20)
+
+  ## What's Changed
+
+  ### Bug Fixes
+  - Fix legacy platform tool alias discovery
+
+  ### Documentation
+  - Update snapshot and changelog for v1.15.19
+
+  ## Contributors
+
+  @joaomdmoura, @vinibrsl
+
+</Update>
+
+<Update label="Sep 04, 2026">
+  ## v1.15.19
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.19)
+
+  ## What's Changed
+
+  ### Features
+  - Add Clipper integrations client
+  - Add `now()` to the CEL expression environment
+  - Record how a crew run ended for every user
+  - Report machine size as a coarse band, not a core count
+  - Add injectable client for CrewAI platform tools
+
+  ### Bug Fixes
+  - Fix reading of octet-stream and xlsx URLs in `urlreadtool`
+  - Fix appending trailing user turn in native Gemini provider
+  - Normalize scheme and port in Ollama base URL
+  - Preserve reusable scope configs in memory
+  - Bump `pypdf` to 6.16.2 for security vulnerability
+  - Bump `nltk` to 3.10.3 for security vulnerability
+  - Fix native structured outputs for current Claude models and Snowflake CVE floor
+  - Run model call hooks on every path and propagate a deny
+
+  ### Documentation
+  - Remove `CodeInterpreterTool` from AI/ML overview examples
+  - Point `prompt-template` link at its current path
+  - Update channels guide to current CopilotKit channels API
+  - Refresh retired Gemini model IDs
+
+  ## Contributors
+
+  @Vidit-Ostwal, @a-yeyang, @github-actions[bot], @hvlcrs, @joaomdmoura, @kikifrost, @lorenzejay, @lucasgomide, @parthiban-sivakumar, @ranst91, @tandede, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="Aug 27, 2026">
   ## v1.15.18
 

docs/edge/en/concepts/cli.mdx

@@ -338,6 +338,7 @@ crewai org switch <organization_id>
 
   - Initiates the deployment process on the CrewAI AMP platform.
   - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
+  - Push keeps the source used at create. Adding a git `origin` later does not switch a ZIP deployment to git.
 
 - **Deployment Status**: You can check the status of your deployment with:
 
@@ -578,6 +579,7 @@ Trace collection is controlled by checking three settings in priority order:
    ```
    - Checked only if `tracing` is not set in code and `CREWAI_TRACING_ENABLED` is not set to `true`
    - Running `crewai traces enable` is sufficient to enable tracing by itself
+   - The first-run prompt (`Would you like to view your execution traces?`) also updates this preference
 
 <Note>
 **To enable tracing**, use any one of these methods:

docs/edge/en/concepts/crews.mdx

@@ -37,7 +37,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Chat LLM** _(optional)_                      | `chat_llm`                    | The language model used to orchestrate `crewai chat` CLI interactions with the crew. Accepts a model name string or `LLM` instance. Defaults to `None`.                                                                                                    |
 | **Before Kickoff Callbacks** _(optional)_      | `before_kickoff_callbacks`    | A list of callable functions executed **before** the crew starts. Each callback receives and can modify the inputs dict. Distinct from the `@before_kickoff` decorator. Defaults to `[]`.                                                                   |
 | **After Kickoff Callbacks** _(optional)_       | `after_kickoff_callbacks`     | A list of callable functions executed **after** the crew finishes. Each callback receives and can modify the `CrewOutput`. Distinct from the `@after_kickoff` decorator. Defaults to `[]`.                                                                  |
-| **Tracing** _(optional)_                       | `tracing`                     | Controls OpenTelemetry tracing for the crew. `True` = always enable, `False` = always disable, `None` = inherit from environment / user settings. Defaults to `None`.                                                                                      |
+| **Tracing** _(optional)_                       | `tracing`                     | Controls tracing for the crew. `True` = always enable, `False` = always disable, `None` = inherit from environment / user settings. Defaults to `None`.                                                                                      |
 | **Skills** _(optional)_                        | `skills`                      | A list of `Path` objects (skill search directories) or pre-loaded `Skill` objects applied to all agents in the crew. Defaults to `None`.                                                                                                                    |
 | **Security Config** _(optional)_               | `security_config`              | A `SecurityConfig` instance managing crew fingerprinting and identity. Defaults to `SecurityConfig()`.                                                                                                                                                      |
 | **Checkpoint** _(optional)_                    | `checkpoint`                  | Enables automatic checkpointing. Pass `True` for sensible defaults, a `CheckpointConfig` for full control, `False` to opt out, or `None` to inherit. See the [Checkpointing](#checkpointing) section below. Defaults to `None`.                             |

docs/edge/en/concepts/knowledge.mdx

@@ -636,6 +636,21 @@ agent = Agent(
         }
     }
 )
+
+# Option 4: Use OpenRouter embeddings (supports models across providers)
+# Set OPENROUTER_API_KEY environment variable if api_key is omitted in config
+crew = Crew(
+    agents=[agent],
+    tasks=[...],
+    knowledge_sources=[knowledge_source],
+    embedder={
+        "provider": "openrouter",
+        "config": {
+            "model_name": "openai/text-embedding-3-small",
+            "api_key": "your-openrouter-api-key"  # Optional if OPENROUTER_API_KEY is set
+        }
+    }
+)
 ```
 
 #### Configuring Azure OpenAI Embeddings

docs/edge/en/concepts/llms.mdx

@@ -1487,6 +1487,38 @@ llm = LLM(
     llm = LLM(model="gpt-4")
     ```
   </Tab>
+  <Tab title="Gateway Errors">
+    <Tip>
+      Gateways such as OpenRouter return `200 OK` as soon as the upstream provider accepts the request, so a provider timeout arrives in the response body instead of the status code.
+    </Tip>
+
+    CrewAI raises the same exception the upstream code would have produced as a real HTTP status, so a masked failure is caught by the retry handling you already have:
+
+    ```python
+    import openai
+    from pydantic import BaseModel
+
+    from crewai import LLM
+
+
+    class Report(BaseModel):
+        summary: str
+
+
+    llm = LLM(model="openrouter/z-ai/glm-5.3", response_format=Report)
+
+    try:
+        result = llm.call("Summarize the incident", response_model=Report)
+    except openai.InternalServerError as e:
+        # "z-ai/glm-5.3 via openrouter.ai returned HTTP 200 with an upstream error
+        #  and no choices: The operation was aborted (upstream code 504)"
+        print(f"Upstream provider failed, safe to retry: {e}")
+    ```
+
+    <Warning>
+      A large or deeply nested `response_model` makes upstream timeouts more likely. Treat these as transient provider failures, not as the model producing malformed structured output.
+    </Warning>
+  </Tab>
   <Tab title="Context Length">
     <Tip>
       Use larger context models for extensive tasks

docs/edge/en/concepts/memory.mdx

@@ -740,7 +740,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Use Google Gemini
-memory = Memory(llm="gemini/gemini-2.0-flash")
+memory = Memory(llm="gemini/gemini-3.7-flash")
 
 # Pass a pre-configured LLM instance with custom settings
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/en/concepts/skills.mdx

@@ -210,13 +210,17 @@ Publishing reads `name`, `description`, and `metadata.version` from the `SKILL.m
 
 ### Install
 
-Install a published skill by its `@org/name` reference:
+Install a published skill by its `@org-uuid/name` reference:
 
 ```shell Terminal
-crewai skill install @acme/code-review
+crewai skill install @your-org-uuid/code-review
 ```
 
-Inside a crew project the skill lands in `./skills/{name}/`; outside a project it goes to the shared cache at `~/.crewai/skills/{org}/{name}/`.
+<Note>
+  Use your organization's **UUID**, not its name — organization names are not unique, so a name can resolve to the wrong organization and the install fails with a "not found" error. Run `crewai org list` to see the UUID (the `ID` column) of every organization you belong to.
+</Note>
+
+Inside a crew project the skill lands in `./skills/{name}/`; outside a project it goes to the shared cache at `~/.crewai/skills/{org-uuid}/{name}/`.
 
 Agents can also reference registry skills directly — they resolve from the local cache (or project `skills/` directory) at runtime:
 
@@ -225,7 +229,7 @@ agent = Agent(
     role="Senior Code Reviewer",
     goal="Review pull requests for quality and security issues",
     backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["@acme/code-review"],  # registry ref, resolved locally
+    skills=["@your-org-uuid/code-review"],  # registry ref, resolved locally
 )
 ```
 
@@ -240,7 +244,7 @@ agent = Agent(
     role="Senior Code Reviewer",
     goal="Review pull requests for quality and security issues",
     backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["@acme/code-review@1.2.0"],  # pinned; a leading "v" also works
+    skills=["@your-org-uuid/code-review@1.2.0"],  # pinned; a leading "v" also works
 )
 ```
 

docs/edge/en/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ Under the hood, CrewAI employs a modular prompt system that you can customize ex
 - **Error handling** – Direct how agents respond to failures, exceptions, or timeouts.
 - **Tool-specific prompts** – Define detailed instructions for how tools are invoked or utilized.
 
-Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
+Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
 
 ## Understanding Default System Instructions
 

docs/edge/en/guides/coding-tools/agents-md.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
 ## Create a Project with the CLI
 
-Use the CrewAI CLI to scaffold a project, then `AGENTS.md` will be automatically added at the root.
+Use the CrewAI CLI to scaffold a project. `AGENTS.md` is added at the root, together with a `CLAUDE.md` and a `GEMINI.md` that import it, so Claude Code and Gemini CLI read the same guidance as every other assistant.
 
 ```bash
 # Crew
@@ -36,24 +36,28 @@ Codex can be guided by `AGENTS.md` files placed in your repository. Use them to
 
 ### Claude Code
 
-Claude Code stores project memory in `CLAUDE.md`. You can bootstrap it with `/init` and edit it using `/memory`. Claude Code also supports imports inside `CLAUDE.md`, so you can add a single line like `@AGENTS.md` to pull in the shared instructions without duplicating them.
+Claude Code reads `CLAUDE.md` and ignores `AGENTS.md`. Scaffolded projects ship a `CLAUDE.md` whose only instruction is the import line `@AGENTS.md`, so the shared guidance is loaded without duplicating it. Add Claude-specific notes under that line and keep shared conventions in `AGENTS.md`.
 
-You can simply use:
+For a project created before `CLAUDE.md` was scaffolded, add the import yourself:
 
 ```bash
-mv AGENTS.md CLAUDE.md
+printf '@AGENTS.md\n' > CLAUDE.md
 ```
 
+Do not rename `AGENTS.md` to `CLAUDE.md`: Codex and Cursor read `AGENTS.md`, and the rename hides it from them.
+
 ### Gemini CLI and Google Antigravity
 
-Gemini CLI and Antigravity load a project context file (default: `GEMINI.md`) from the repo root and parent directories. You can configure it to read `AGENTS.md` instead (or in addition) by setting `context.fileName` in your Gemini CLI settings. For example, set it to `AGENTS.md` only, or include both `AGENTS.md` and `GEMINI.md` if you want to keep each tool’s format.
+Gemini CLI and Antigravity load a project context file (default: `GEMINI.md`) from the repo root and parent directories. Scaffolded projects ship a `GEMINI.md` whose only instruction is the import line `@./AGENTS.md`, so the shared guidance is loaded without duplicating it. Add Gemini-specific notes under that line and keep shared conventions in `AGENTS.md`.
 
-You can simply use:
+For a project created before `GEMINI.md` was scaffolded, add the import yourself:
 
 ```bash
-mv AGENTS.md GEMINI.md
+printf '@./AGENTS.md\n' > GEMINI.md
 ```
 
+Alternatively, set `context.fileName` in your Gemini CLI settings to include `AGENTS.md` and Gemini reads it directly. Do not rename `AGENTS.md` to `GEMINI.md`: Codex and Cursor read `AGENTS.md`, and the rename hides it from them.
+
 ### Cursor
 
 Cursor supports `AGENTS.md` as a project instruction file. Place it at the project root to provide guidance for Cursor’s coding assistant.

docs/edge/en/guides/crews/first-crew.mdx

@@ -77,7 +77,7 @@ Replace the generated `agents/researcher.jsonc` file and add `agents/analyst.jso
 }
 ```
 
-Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-2.0-flash-001`.
+Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-3.7-flash`.
 
 ## Step 3: Define Tasks and Crew Settings
 

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

@@ -136,7 +136,7 @@ Now, let's configure the content writer crew with JSONC. We'll set up two specia
 }
 ```
 
-Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `gemini/gemini-2.0-flash-001`, or `anthropic/claude-sonnet-4-6`.
+Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `gemini/gemini-3.7-flash`, or `anthropic/claude-sonnet-4-6`.
 
 3. Create `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
 
@@ -483,7 +483,7 @@ Flows allow you to make direct calls to language models when you need simple, st
 
 ```python
 llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    model="model-id-here",  # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
     response_format=GuideOutline
 )
 response = llm.call(messages=messages)

docs/edge/en/guides/frontend/channels.mdx

@@ -1,117 +1,148 @@
 ---
 title: Channels
-description: Run the same CrewAI agent as a chat bot on Slack and Discord with the CopilotKit Channels SDK.
-icon: slack
+description: Run the same CrewAI agent as a Slack or Teams bot with the CopilotKit Channels SDK and managed Intelligence platform.
+icon: messages
 mode: "wide"
 ---
 
 ## Meet your users where they already are
 
-The CrewAI agent you built in the [Overview](/edge/en/guides/frontend/overview) does not have to live behind a web app. The same Crew or Flow can run as a bot inside a messaging platform. No rebuild, no second copy of your agent logic: the agent stays exposed over the [AG-UI protocol](https://docs.ag-ui.com), and a bot process drives it.
+The CrewAI agent you built in the [Overview](/edge/en/guides/frontend/overview) does not have to live behind a web app. The same Crew or Flow can run as a bot inside a messaging platform. No rebuild, no second copy of your agent logic: the agent stays exposed over the [AG-UI protocol](https://docs.ag-ui.com), and a **channel** drives it from Slack or Microsoft Teams.
 
-CopilotKit's [Channels SDK](https://docs.copilotkit.ai/reference/channels) provides that bot process. It ships a platform-agnostic engine plus per-platform adapters.
+CopilotKit's [Channels SDK](https://docs.copilotkit.ai/slack) provides that channel. You declare a `createChannel` in a small runtime, point it at your CrewAI agent, and CopilotKit's managed **Intelligence** platform brokers the connection to the messaging provider.
+
+<Note>
+Unlike the rest of this section, Channels is **not self-hosted**. It runs through **CopilotKit Intelligence** — a required surface for Channels, by design (a free tier is available). Intelligence holds the platform connection and credentials, receives each platform event, and delivers the turn to your channel process; your process runs the agent and streams the reply back. You configure Slack once in the Intelligence dashboard, and platform credentials never enter your process. Your agent, tools, and state stay yours.
+</Note>
 
 ## How it fits together
 
-Nothing about your agent server changes. It keeps serving your Crew or Flow over AG-UI exactly as in the Overview. What you add is a separate **bot process**: it connects to a platform adapter, listens for messages, and runs your agent when it is messaged. The reply streams back into the channel.
+Nothing about your CrewAI agent server changes. It keeps serving your Crew or Flow over AG-UI exactly as in the Overview. What you add is a separate long-running Node process built with `@copilotkit/channels`: it registers a channel on the `CopilotRuntime`, connects to Intelligence, and runs your agent whenever a message arrives.
 
 ```
-Slack / Discord  ──►  Channels bot process  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
 ```
 
-Your agent server can keep serving the web frontend from the Overview at the same time. The web app and the bot are just two clients of one AG-UI endpoint.
+The channel process holds a persistent connection to the Intelligence gateway, so it needs a long-running host — a serverless request handler cannot own that connection. Your CrewAI server can keep serving the web frontend from the Overview at the same time: the web app and the channel are just two clients of one AG-UI endpoint.
 
-## Slack
+## Integration guide
 
 <Steps>
 
 <Step title="Install the Channels packages">
 
+The Channels SDK is batteries-included — every platform ships in the one package, with no per-platform adapter to install. Add it alongside the runtime that hosts the channel and the CrewAI AG-UI client:
+
 ```bash
-npm install @copilotkit/channels @copilotkit/channels-slack @ag-ui/crewai
+npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
 ```
 
 </Step>
 
-<Step title="Create a Slack app and get tokens">
+<Step title="Create a Channel in Intelligence">
 
-Create an app in the Slack API dashboard for your workspace, enable Socket Mode, and grant it the message and event scopes it needs to read and post in channels. Then expose its tokens to the bot process:
+In the [CopilotKit dashboard](https://docs.copilotkit.ai/slack), create a Channel and connect Slack — Intelligence walks you through creating the Slack app and holds its credentials. That leaves two environment variables for your process, both from the dashboard:
 
 ```bash
-export SLACK_BOT_TOKEN=xoxb-...   # bot user token
-export SLACK_APP_TOKEN=xapp-...   # app-level token (Socket Mode)
+export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
+export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
 ```
 
 </Step>
 
-<Step title="Point the bot at your CrewAI agent">
+<Step title="Define the channel">
 
-`createBot` wires a Slack adapter to your agent. The `agent` factory returns a `CrewAIAgent` pointed at the AG-UI path your server exposes (the same URL you registered in the runtime in the Overview).
+`createChannel` declares the channel and attaches your agent. Build the agent as a per-thread factory so each conversation gets its own session, using the same `CrewAIAgent` the Overview uses in the web runtime, pointed at your AG-UI endpoint. `identifyUser: "platform"` lets Intelligence map each platform user to a stable identity.
 
 ```ts
-// bot.ts
-import { createBot } from "@copilotkit/channels";
-import { slack, defaultSlackTools, defaultSlackContext } from "@copilotkit/channels-slack";
+// channel.ts
+import { createChannel } from "@copilotkit/channels";
 import { CrewAIAgent } from "@ag-ui/crewai";
 
-const bot = createBot({
-  adapters: [
-    slack({
-      botToken: process.env.SLACK_BOT_TOKEN!, // xoxb-…
-      appToken: process.env.SLACK_APP_TOKEN!, // xapp-… (Socket Mode)
-    }),
-  ],
-  agent: (threadId) => new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-  tools: [...defaultSlackTools],
-  context: [...defaultSlackContext],
+const channel = createChannel({
+  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
+  identifyUser: "platform",
+  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
+  agent: (threadId) => {
+    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
+    agent.threadId = threadId;
+    return agent;
+  },
 });
 
-bot.start();
+// A mention subscribes the thread and runs the agent; afterwards every message
+// in a subscribed thread runs it without needing another mention.
+channel.onMention(async ({ thread }) => {
+  await thread.subscribe();
+  await thread.runAgent();
+});
+
+channel.onMessage(async ({ thread }) => {
+  if (await thread.isSubscribed()) await thread.runAgent();
+});
+
+export { channel };
 ```
 
 </Step>
 
-<Step title="Run the bot">
+<Step title="Register the channel on the runtime">
 
-Start the bot process alongside your agent server:
+Create a `CopilotRuntime` with the Intelligence gateway and your channel, then serve it with `createCopilotNodeListener`. The `agents` map stays empty — the channel supplies its own agent. Wait for the channel to be ready so a broken config fails startup loudly.
+
+```ts
+// server.ts
+import { createServer } from "node:http";
+import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
+import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
+import { channel } from "./channel";
+
+const runtime = new CopilotRuntime({
+  agents: {}, // the channel supplies its own agent; no web-facing agents needed
+  intelligence: new CopilotKitIntelligence({
+    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
+  }),
+  channels: [channel],
+});
+
+const listener = createCopilotNodeListener({ runtime });
+await listener.channels?.ready({ timeoutMs: 15_000 });
+
+createServer(listener).listen(3123, () => {
+  console.log("Channels runtime listening on port 3123");
+});
+```
+
+</Step>
+
+<Step title="Run the channel runtime">
+
+Start it alongside your CrewAI agent server:
 
 ```bash
 uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
-node bot.ts                       # terminal 2 — Slack bot
+npx tsx server.ts                 # terminal 2 — Channels runtime
 ```
 
-Message the bot in Slack and it runs your Crew or Flow, streaming the reply back into the thread.
+Mention the bot in Slack or Teams and it runs your Crew or Flow, streaming the reply back into the thread. The thread stays subscribed, so follow-up messages run without another mention.
 
 </Step>
 
 </Steps>
 
-<Note>
-Slack app scopes, Socket Mode setup, and the full adapter options are maintained by CopilotKit. Follow the [Slack channel reference](https://docs.copilotkit.ai/reference/channels/slack) together with Slack's own app setup guide for the authoritative steps.
-</Note>
+## The event model
 
-## Discord
+A channel reacts to platform events with handlers, and each handler receives a `thread` you drive with a few methods:
 
-Discord uses the same `createBot` engine with the Discord adapter from `@copilotkit/channels-discord`:
+- **`channel.onMention`** fires when a user @-mentions the bot. Call `thread.subscribe()` to join the thread, then `thread.runAgent()` to run your CrewAI agent on the mention.
+- **`channel.onMessage`** fires on every message in a thread the bot can see. Gate it with `thread.isSubscribed()` so the agent only responds where it has joined, then `thread.runAgent()`.
+- **`thread.runAgent()`** runs the attached CrewAI agent for the current turn and streams its output back into the channel. Pass `{ prompt }` to override the text the agent runs on.
 
-```ts
-import { createBot } from "@copilotkit/channels";
-import { discord } from "@copilotkit/channels-discord";
-import { CrewAIAgent } from "@ag-ui/crewai";
-
-const bot = createBot({
-  adapters: [discord({ token: process.env.DISCORD_BOT_TOKEN! })],
-  agent: (threadId) => new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-});
-
-bot.start();
-```
-
-See the [Discord channel reference](https://docs.copilotkit.ai/reference/channels/discord) for the exact adapter options and bot setup.
+Your agent receives an ordinary AG-UI `RunAgentInput` and emits ordinary AG-UI events; the platform mechanics stay behind the channel, so the same Crew or Flow runs unchanged across every platform. The channel also exposes handlers for welcomes, interrupts, commands, reactions, and modals — see the [`Channel` reference](https://docs.copilotkit.ai/reference/channels/classes/Channel) for the full surface.
 
 ## Platform support
 
-Slack and Discord have official Channels adapters (`@copilotkit/channels-slack`, `@copilotkit/channels-discord`). Microsoft Teams is available through CopilotKit's managed offering (currently waitlisted). Check the [Channels reference](https://docs.copilotkit.ai/reference/channels) for the current list before promising a platform.
+The managed Intelligence path covers **Slack** and **Microsoft Teams** today — the same channel code runs on either, and `message.platform` / `thread.platform` report the native origin. Other platforms (Discord, Telegram, WhatsApp) are reached through developer-operated **direct adapters** rather than the managed path — your own process holds the platform credentials and transport. Check the [CopilotKit Channels documentation](https://docs.copilotkit.ai/slack) for the current platform list and per-platform setup.
 
 ## Related
 

docs/edge/en/guides/frontend/overview.mdx

@@ -21,7 +21,7 @@ The two connect through the [AG-UI protocol](https://docs.ag-ui.com). The `ag-ui
   <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
     Keep agent state and your app UI in two-way sync.
   </Card>
-  <Card title="Channels" icon="slack" href="/edge/en/guides/frontend/channels">
+  <Card title="Channels" icon="messages" href="/edge/en/guides/frontend/channels">
     Run the same agent as a Slack, Discord, or Teams bot.
   </Card>
 </CardGroup>

docs/edge/en/learn/litellm-removal-guide.mdx

@@ -176,7 +176,7 @@ grep -r "llm:" --include="*.yaml" .
     # llm = LLM(model="mistral/mistral-large-latest")
 
     # After (Native):
-    llm = LLM(model="gemini/gemini-2.0-flash")
+    llm = LLM(model="gemini/gemini-3.7-flash")
     ```
 
     ```bash
@@ -399,7 +399,7 @@ llm = LLM(model="anthropic/claude-haiku-3-5")    # Fast & affordable
 # Together AI → OpenAI or Gemini
 # llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
 llm = LLM(model="openai/gpt-4o")                 # High quality
-llm = LLM(model="gemini/gemini-2.0-flash")       # Fast & capable
+llm = LLM(model="gemini/gemini-3.7-flash")       # Fast & capable
 
 # Mistral → Anthropic or OpenAI
 # llm = LLM(model="mistral/mistral-large-latest")

docs/edge/en/learn/llm-connections.mdx

@@ -141,7 +141,7 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
         # Example using Gemini's OpenAI-compatible API.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Should start with AIza...
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Add your Gemini model here, under openai/
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Add your Gemini model here, under openai/
         ```
         </CodeGroup>
     </Tab>
@@ -159,7 +159,7 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
             ```python Google
             # Example using Gemini's OpenAI-compatible API
             llm = LLM(
-                model="openai/gemini-2.0-flash",
+                model="openai/gemini-3.7-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # Should start with AIza...
             )

docs/edge/en/learn/llm-selection-guide.mdx

@@ -147,7 +147,7 @@ Planning agents benefit from reasoning models that can handle complex strategic
 from crewai import Agent, Task, Crew, LLM
 
 # High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
+manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
 
 # Creative model for content generation
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -412,7 +412,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
     # Manager or coordination agents
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # Premium for coordination
+        llm=LLM(model="gemini/gemini-3.7-flash"),  # Premium for coordination
         # ... rest of config
     )
 

docs/edge/en/observability/tracing.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 CrewAI provides built-in tracing capabilities that allow you to monitor and debug your Crews and Flows in real-time. This guide demonstrates how to enable tracing for both **Crews** and **Flows** using CrewAI's integrated observability platform.
 
-> **What is CrewAI Tracing?** CrewAI's built-in tracing provides comprehensive observability for your AI agents, including agent decisions, task execution timelines, tool usage, and LLM calls - all accessible through the [CrewAI AMP platform](https://app.crewai.com).
+> **What is CrewAI Tracing?** CrewAI's built-in tracing provides comprehensive observability for your AI agents, including agent decisions, task execution timelines, tool usage, and LLM calls - all accessible through the [CrewAI AMP platform](https://app.crewai.com). Tracing is managed independently from [telemetry](/en/telemetry).
 
 ![CrewAI Tracing Interface](/images/crewai-tracing.png)
 
@@ -150,8 +150,8 @@ result = flow.kickoff()
 
 ### Step 5: View Traces in the CrewAI AMP Dashboard
 
-After running the crew or flow, you can view the traces generated by your CrewAI application in the CrewAI AMP dashboard. You should see detailed steps of the agent interactions, tool usages, and LLM calls.
-Just click on the link below to view the traces or head over to the traces tab in the dashboard [here](https://app.crewai.com/crewai_plus/trace_batches)
+Traces are uploaded only after a successful authenticated export or an explicitly approved anonymous upload. A run whose local buffer is discarded has no uploaded trace.
+For traces associated with your account, open the [Traces tab in the CrewAI AMP dashboard](https://app.crewai.com/crewai_plus/trace_batches) to view agent interactions, tool usage, and LLM calls.
 ![CrewAI Tracing Interface](/images/view-traces.png)
 
 ### Alternative: Environment Variable Configuration
@@ -170,6 +170,50 @@ CREWAI_TRACING_ENABLED=true
 
 When this environment variable is set, all Crews and Flows will automatically have tracing enabled, even without explicitly setting `tracing=True`.
 
+## Viewing traces after your first run
+
+The first time you run a Crew or Flow, an interactive terminal may ask:
+
+```text
+Share this execution trace with CrewAI? [y/N]
+```
+
+Choose **yes** to upload the buffered trace to CrewAI. Traces may contain
+prompts, inputs, and outputs. Declining, timing out, or running without an
+interactive consent prompt discards the buffer. You can change tracing later
+with `crewai traces enable` or `crewai traces disable`, or by setting `tracing`
+on the Crew or Flow.
+
+### Local buffering and authenticated export
+
+First-run trace collection stays in process memory until you agree to share,
+even if you have saved login credentials. Unauthenticated tracing uses the same
+consent flow. Before consent, CrewAI requests no upload grant and sends no
+execution spans.
+
+The buffer retains up to **1,000 spans** and **8 MiB of encoded OTLP data**.
+Set `CREWAI_EPHEMERAL_TRACE_MAX_SPANS` and
+`CREWAI_EPHEMERAL_TRACE_MAX_BYTES` to positive integers to adjust these limits.
+Overflow drops the oldest spans; a span larger than the byte limit is dropped.
+The buffer is cleared after sharing or discarding it.
+
+When tracing is enabled and credentials are available, CrewAI exchanges your
+CLI login, `CREWAI_USER_PAT`, or platform integration credential with AMP for
+an execution-specific grant. It then exports OpenTelemetry spans directly to
+Wharf using that grant. Invalid credentials do not fall back to anonymous upload.
+
+### Hosted execution sessions
+
+Hosts can wrap execution with `telemetry_session` from
+`crewai.telemetry.tracing`. The session uses CrewAI lifecycle events to create
+and finish spans, preserving their timestamps, parent relationships, and HITL
+pause/resume links. Pass an existing provider with `providers=` to retain the
+host's tracer and logging integration. Pass span processors with `processors=`
+and a host logging callback with `log_emitter=`. The host owns any redaction
+in these integrations.
+Each session owns its tracing lifecycle and leaves the application's global
+OpenTelemetry provider unchanged.
+
 ## Viewing Your Traces
 
 ### Access the CrewAI AMP Dashboard
@@ -210,5 +254,5 @@ If traces aren't showing up in the dashboard:
 
 1. Confirm `tracing=True` is set in your Crew/Flow
 2. Check that `CREWAI_TRACING_ENABLED=true` if using environment variables
-3. Ensure you're authenticated with `crewai login`
-4. Verify your crew/flow is actually executing
+3. For authenticated export, verify your CLI login, `CREWAI_USER_PAT`, or platform integration credential. For anonymous sharing, explicitly approve the consent prompt; login is not required
+4. Verify your crew/flow executed and the trace export succeeded. Declining consent, timing out, or running without an interactive consent prompt discards the local buffer without uploading it

docs/edge/en/telemetry.mdx

@@ -23,7 +23,8 @@ usage of tools, API calls, responses, any data processed by the agents, or secre
 When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected 
 to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks. 
 Users should carefully consider the content of their crews and tasks before enabling `share_crew`. 
-Users can disable telemetry by setting the environment variable `CREWAI_DISABLE_TELEMETRY` to `true` or by setting `OTEL_SDK_DISABLED` to `true` (note that the latter disables all OpenTelemetry instrumentation globally).
+Users can disable CrewAI telemetry by setting `CREWAI_DISABLE_TELEMETRY` to `true`, `1`, `yes`, or `on` (any case). `OTEL_SDK_DISABLED` with the same values also disables CrewAI's exporter. The OpenTelemetry SDK itself still only honors `true` for disabling other instrumentation in the process.
+AMP tracing is covered separately in [Tracing](/en/observability/tracing).
 
 ### Examples:
 ```python
@@ -34,6 +35,8 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
+`CREWAI_DISABLE_TELEMETRY=1` (or `yes` / `on`) works the same as `true`. Unrecognized values are ignored and leave telemetry on.
+
 ### Isolation from your own OpenTelemetry setup
 
 CrewAI's telemetry runs on its own private `TracerProvider` and never registers
@@ -60,8 +63,8 @@ own tracer provider, which is independent of the one described here.
 | Yes       | Task Lifecycle Data | Includes: creation and execution start/end times, crew and task identifiers, and whether the task succeeded or failed. When a task fails, the **class name** of the exception is recorded (for example `TimeoutError`) so failures can be counted and diagnosed — never the error message, which can contain prompts, model output, file paths or credentials. Stored as spans with timestamps. No personal data. |
 | Yes       | LLM Attributes | Includes: name, model_name, model, top_k, temperature, and class name of the LLM. All technical, non-personal data. |
 | Yes       | Project Creation using crewAI CLI | Includes: that a new project was scaffolded by `crewai create`, which kind it was (`crew`, `json_crew` or `flow`), and the project ID minted for that new project and written into its own `pyproject.toml`. That is the new project's own ID, recorded separately from the `project_id` of the directory the command was run from — the two can differ. No project name, no file contents, no code. No personal data. |
-| Yes       | Crew Deployment attempt using crewAI CLI | Includes: The fact a deploy is being made and crew id, whether it's trying to pull logs, and whether the deploy was started from a CLI command or from the run TUI. No project or crew contents. No personal data. |
-| Yes       | Execution Environment | Includes: which AI coding assistant is running the process, if any (one of a fixed list such as `claude_code`, `codex`, `cursor`, or `unknown`), where the process runs (one of a fixed list such as `ci`, `container`, `serverless`, `interactive`), and the `project_id` from your `pyproject.toml` when one is configured. Detection reads only whether known environment variables are set, never their values. No personal data. |
+| Yes       | Crew Deployment attempt using crewAI CLI | Includes: The fact a deploy is being made and crew id, whether it's trying to pull logs, and whether the deploy was started from a CLI command or from the run TUI. If creating a deployment fails, the failure category (one of a fixed list such as `api_4xx`, `network_error` or `user_declined`) and the HTTP status code of the API response, when there was one, are recorded — never the error message. No project or crew contents. No personal data. |
+| Yes       | Execution Environment | Includes: which AI coding assistant is running the process, if any (one of a fixed list such as `claude_code`, `codex`, `cursor`, or `unknown`), where the process runs (one of a fixed list such as `ci`, `container`, `serverless`, `interactive`), the `project_id` from your `pyproject.toml` when one is configured, and a coarse size band for the machine (one of `1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+`, or `unknown`). The band is a range, never the exact core count — the exact count is opt-in only, under Environment Information below. The size band comes from the host CPU count; assistant and location detection reads only whether known environment variables are set, never their values. No personal data. |
 | Yes       | Flow Lifecycle Signals | Includes: that a flow started, whether it completed or failed, whether one of its methods failed, whether it paused for human input or feedback, whether the start was a resumed run, whether a conversation turn failed, how long the flow ran, and whether the flow is one CrewAI runs internally or one you wrote. The flow name is recorded, as it already is for flow creation and execution. When a flow or one of its methods fails, the **class name** of the exception is recorded (for example `TimeoutError`) so that failures can be diagnosed — never the error message, which can contain prompts, model output, file paths or credentials. Method names and flow state are never recorded. No personal data. |
 | Yes       | Trace Sharing Signal | Includes: that a batch of traces was successfully shared with CrewAI AMP, and whether it was shared anonymously (before you have an account) or linked to your account. Like every span, it also carries the Execution Environment attributes described above (`project_id` when configured, the coding assistant, and the runtime). This row describes sharing telemetry only — not the trace contents or access granted by shared trace links. Trace contents, inputs, and outputs are never recorded on this signal. Before sharing traces, review secrets, personal data, and AMP redaction and retention settings. |
 | No        | Agent's Expanded Data | Includes: goal description, backstory text, i18n prompt file identifier. Users should ensure no personal info is included in text fields. |

docs/edge/en/tools/ai-ml/overview.mdx

@@ -50,16 +50,15 @@ These tools integrate with AI and machine learning services to enhance your agen
 - **AI Safety**: Implement content moderation and safety checks
 
 ```python
-from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
+from crewai_tools import DallETool, VisionTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
-code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor, code_executor],
+    tools=[image_generator, vision_processor],
     goal="Create and analyze content using AI capabilities"
 )

docs/edge/en/tools/ai-ml/ragtool.mdx

@@ -460,7 +460,7 @@ The `embedding_model` parameter accepts a `crewai.rag.embeddings.types.ProviderS
 
     **Config Options:**
     - `model_name` (str): HuggingFace model ID. Default: `hkunlp/instructor-base`. Options: `hkunlp/instructor-xl`, `hkunlp/instructor-large`, `hkunlp/instructor-base`
-    - `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`
+    - `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`, `xpu`
     - `instruction` (str): Instruction prefix for embeddings
 
     **Environment Variables:**
@@ -485,7 +485,7 @@ The `embedding_model` parameter accepts a `crewai.rag.embeddings.types.ProviderS
 
     **Config Options:**
     - `model_name` (str): Sentence Transformers model name. Default: `all-MiniLM-L6-v2`. Options: `all-mpnet-base-v2`, `all-MiniLM-L6-v2`, `paraphrase-multilingual-MiniLM-L12-v2`
-    - `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`
+    - `device` (str): Device to run on. Default: `cpu`. Options: `cpu`, `cuda`, `mps`, `xpu`
     - `normalize_embeddings` (bool): Whether to normalize embeddings. Default: `False`
 
     **Environment Variables:**

docs/edge/en/tools/ai-ml/visiontool.mdx

@@ -12,6 +12,16 @@ mode: "wide"
 This tool is used to extract text from images. When passed to the agent it will extract the text from the image and then use it to generate a response, report or any other output.
 The URL or the PATH of the image should be passed to the Agent.
 
+You can also ask a custom `query` about the image and pick a `complexity_level` that automatically selects the model best suited for the request:
+
+| Complexity level | Model         |
+| :--------------- | :------------ |
+| `easy`           | `gpt-5.6-luna`  |
+| `medium` (default) | `gpt-5.6-terra` |
+| `hard`           | `gpt-5.6-sol`   |
+
+When an explicit `llm` or `model` is provided to the tool, it takes precedence over the complexity-based model selection.
+
 ## Installation
 
 Install the crewai_tools package
@@ -43,8 +53,10 @@ def researcher(self) -> Agent:
 
 ## Arguments
 
-The VisionTool requires the following arguments:
+The VisionTool accepts the following arguments:
 
-| Argument           | Type     | Description                                                                      |
-| :----------------- | :------- | :------------------------------------------------------------------------------- |
-| **image_path_url** | `string` | **Mandatory**. The path to the image file from which text needs to be extracted. |
+| Argument             | Type     | Description                                                                                                   |
+| :------------------- | :------- | :----------------------------------------------------------------------------------------------------------- |
+| **image_path_url**   | `string` | **Mandatory**. The path to the image file (or URL) from which text needs to be extracted.                    |
+| **query**            | `string` | **Optional**. The question or instruction to ask the model about the image. Defaults to `"What's in this image?"`. |
+| **complexity_level** | `string` | **Optional**. The complexity of the request, which selects the model: `easy`, `medium`, or `hard`. Defaults to `medium`. |

docs/edge/en/tools/web-scraping/oxylabsscraperstool.mdx

@@ -4,7 +4,7 @@ description: >
   Oxylabs Scrapers allow to easily access the information from the respective sources. Please see the list of available sources below:
     - `Amazon Product`
     - `Amazon Search`
-    - `Google Seach`
+    - `Google Search`
     - `Universal`
 icon: globe
 mode: "wide"
@@ -87,7 +87,7 @@ print(result)
 ### Parameters
 
 - `query` - Amazon search term.
-- `domain` - Domain localization for Bestbuy.
+- `domain` - domain localization for Amazon.
 - `start_page` - starting page number.
 - `pages` - number of pages to retrieve.
 - `geo_location` - the _Deliver to_ location.

docs/edge/ko/changelog.mdx

@@ -4,6 +4,145 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 9월 16일">
+  ## v1.15.22
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.22)
+
+  ## 변경 사항
+
+  ### 기능
+  - 연결 식별자로서 별칭 지원
+  - 배포 생성 실패 사유 기록
+  - 추적에서 인간 피드백 및 일시 중지 이벤트 수집
+  - 에이전트 역할을 모델로 라우팅하기 위해 `llm_overlay` 컨텍스트 변수 추가
+  - 에이전트 실행 페이로드에 `task_prompt` 및 출력 포함
+  - 승무원 설정 중 플랫폼 통합 검증
+  - JSON 승무원 마법사에 플랫폼 도구 추가
+  - CrewAI 플랫폼 애플리케이션 카탈로그 노출
+  - OpenRouter를 지원되는 임베딩 제공자로 추가
+
+  ### 버그 수정
+  - 인라인 기술 정의에서 CRLF 수용
+  - 안전한 가져오기를 통해 텍스트 파일 URL 로드
+  - 시작 작업 출력 저장소에서 SQLite 연결 닫기
+  - 네이티브 도구 경로의 `ToolUsageFinishedEvent`에서 `from_cache` 전달
+  - 업로더에서 단순한 raise 대신 `ValueError` 발생
+  - SQLiteFlowPersistence에서 비원시 유형 지원
+  - 코딩 에이전트 지침 개선
+  - JSON 체크포인트를 UTF-8로 읽기
+  - Windows에서 오래된 `poetry.lock` 백업 덮어쓰기
+  - Azure에서 와이어 인덱스로 스트리밍된 도구 호출 키 지정
+  - Gemini에서 파일 데이터 콘텐츠 부분 보존
+  - `update()` 및 `recall()` 접근 시간에 대해 `read_only` 준수
+  - 모든 OpenAI 추론 모델에 `reasoning_effort` 전송
+  - 스트리밍된 출력에 리터럴 `[...]`가 포함될 때 실행 TUI에서 충돌 방지
+  - 저장된 작업이 다를 경우 재생 거부
+  - Windows에서 mypy 호환성을 위한 `sys.platform` 보호 사용
+  - 사용자 턴으로 강제 최종 답변 요청
+  - 프롬프트에 포함된 작업 출력 스키마에서 null 유지
+  - `DOCXSearchTool`을 표준 RAG 고정 스키마 패턴에 맞추기
+
+  ### 문서
+  - 임베딩 장치 옵션에 `xpu` 추가
+  - 모든 작업 공간 패키지 나열
+
+  ## 기여자
+
+  @ASTion24, @BlueX888, @HUAN2022A, @RaycarlLei, @Rohitkanithi, @SWAPI03, @SharoonSharif, @ShivangiRay, @Theater-ahyeon, @Vidit-Ostwal, @gamal1osama, @github-actions[bot], @joaomdmoura, @lorenzejay, @mhbuehler, @modusensus, @monkscode, @rohitkanithi, @roli-lpci, @vinibrsl, @wangtaotaotao95
+
+</Update>
+
+<Update label="2026년 9월 9일">
+  ## v1.15.21
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.21)
+
+  ## 변경 사항
+
+  ### 기능
+  - 체크포인트 실행 시간 및 CLI 사용을 추적하기 위한 텔레메트리 추가.
+
+  ### 버그 수정
+  - HTTP 200 응답 내에서 보고된 게이트웨이 오류 수정.
+  - AMP 소스 생성 시 배포 푸시 유지.
+  - Oxylabs 통합에서 IndexError를 발생시키는 대신 스크랩 실패 보고.
+  - 모든 DashScope 모델을 네이티브 제공자를 통해 라우팅.
+  - JSON 체크포인트를 UTF-8로 유지.
+  - BrightData의 f-string 검색 URL에서 이탈한 $ 제거.
+  - JSON 스키마 변환에서 리스트 형태의 배열 지원.
+  - gpt-4o-mini 컨텍스트 창 크기를 200000에서 128000으로 수정.
+  - contentBlockStop에서 스트리밍 도구 호출 인수 유지.
+  - Windows에서 pre-commit 훅을 휴대 가능하게 만듦.
+
+  ### 문서
+  - 추적이 텔레메트리와 별도로 관리됨을 명확히 함.
+  - 병렬 검색 참조 링크 수정.
+  - handle_llm_stream_chunk 문서 문자열에서 오래된 매개변수 제거.
+  - 스트리밍 출력 문서 문자열 예제 수정.
+  - 기술 설치 참조에서 조직 UUID 사용.
+
+  ## 기여자
+
+  @Alphaxiaoteng, @DrewWhittleNZ, @Ghraven, @Shxiao101, @Vaishnavi220506, @Vidit-Ostwal, @danielfsbarreto, @georgeatparallel, @github-actions[bot], @jessemiller, @joaomdmoura, @kimnamu, @kiwoongyoon, @liang0417, @lorenzejay, @oxy-giedrius, @simpleqt, @uoparaji
+
+</Update>
+
+<Update label="2026년 9월 4일">
+  ## v1.15.20
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.20)
+
+  ## 변경 사항
+
+  ### 버그 수정
+  - 레거시 플랫폼 도구 별칭 검색 수정
+
+  ### 문서
+  - v1.15.19에 대한 스냅샷 및 변경 로그 업데이트
+
+  ## 기여자
+
+  @joaomdmoura, @vinibrsl
+
+</Update>
+
+<Update label="2026년 9월 4일">
+  ## v1.15.19
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.19)
+
+  ## 변경 사항
+
+  ### 기능
+  - Clipper 통합 클라이언트 추가
+  - CEL 표현식 환경에 `now()` 추가
+  - 모든 사용자에 대해 크루 실행이 어떻게 종료되었는지 기록
+  - 기계 크기를 코어 수가 아닌 대략적인 범위로 보고
+  - CrewAI 플랫폼 도구를 위한 주입 가능한 클라이언트 추가
+
+  ### 버그 수정
+  - `urlreadtool`에서 octet-stream 및 xlsx URL 읽기 수정
+  - 네이티브 Gemini 제공자에서 후행 사용자 전환 추가 수정
+  - Ollama 기본 URL에서 스킴 및 포트 정규화
+  - 메모리에서 재사용 가능한 범위 구성 유지
+  - 보안 취약성을 위해 `pypdf`를 6.16.2로 업데이트
+  - 보안 취약성을 위해 `nltk`를 3.10.3으로 업데이트
+  - 현재 Claude 모델 및 Snowflake CVE 바닥을 위한 네이티브 구조화된 출력 수정
+  - 모든 경로에서 모델 호출 후크 실행 및 거부 전파
+
+  ### 문서
+  - AI/ML 개요 예제에서 `CodeInterpreterTool` 제거
+  - `prompt-template` 링크를 현재 경로로 지정
+  - 현재 CopilotKit 채널 API에 맞게 채널 가이드 업데이트
+  - 퇴역한 Gemini 모델 ID 새로 고침
+
+  ## 기여자
+
+  @Vidit-Ostwal, @a-yeyang, @github-actions[bot], @hvlcrs, @joaomdmoura, @kikifrost, @lorenzejay, @lucasgomide, @parthiban-sivakumar, @ranst91, @tandede, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="2026년 8월 27일">
   ## v1.15.18
 

docs/edge/ko/concepts/cli.mdx

@@ -286,6 +286,7 @@ crewai org switch <organization_id>
 
   - CrewAI AMP 플랫폼에서 배포 프로세스를 시작합니다.
   - 성공적으로 시작되면, Deployment created successfully! 메시지와 함께 Deployment Name 및 고유한 Deployment ID(UUID)가 출력됩니다.
+  - Push는 생성 시 사용한 소스를 유지합니다. 나중에 git `origin`을 추가해도 ZIP 배포가 git으로 바뀌지 않습니다.
 
 - **배포 상태**: 배포 상태를 확인하려면 다음을 사용합니다:
 

docs/edge/ko/concepts/knowledge.mdx

@@ -603,6 +603,21 @@ agent = Agent(
         }
     }
 )
+
+# Option 4: Use OpenRouter embeddings (supports models across providers)
+# Set OPENROUTER_API_KEY environment variable if api_key is omitted in config
+crew = Crew(
+    agents=[agent],
+    tasks=[...],
+    knowledge_sources=[knowledge_source],
+    embedder={
+        "provider": "openrouter",
+        "config": {
+            "model_name": "openai/text-embedding-3-small",
+            "api_key": "your-openrouter-api-key"  # Optional if OPENROUTER_API_KEY is set
+        }
+    }
+)
 ```
 
 #### Azure OpenAI 임베딩 구성

docs/edge/ko/concepts/llms.mdx

@@ -995,6 +995,38 @@ LLM 설정을 최대한 활용하는 방법을 알아보세요:
     llm = LLM(model="gpt-4")
     ```
   </Tab>
+  <Tab title="게이트웨이 오류">
+    <Tip>
+      OpenRouter와 같은 게이트웨이는 업스트림 공급자가 요청을 수락하는 즉시 `200 OK`를 반환하므로, 공급자 타임아웃은 상태 코드가 아니라 응답 본문에 담겨 도착합니다.
+    </Tip>
+
+    CrewAI는 해당 업스트림 코드가 실제 HTTP 상태로 왔을 때 발생시킬 예외와 동일한 예외를 발생시키므로, 이미 구성해 둔 재시도 처리로 가려진 실패를 잡을 수 있습니다:
+
+    ```python
+    import openai
+    from pydantic import BaseModel
+
+    from crewai import LLM
+
+
+    class Report(BaseModel):
+        summary: str
+
+
+    llm = LLM(model="openrouter/z-ai/glm-5.3", response_format=Report)
+
+    try:
+        result = llm.call("Summarize the incident", response_model=Report)
+    except openai.InternalServerError as e:
+        # "z-ai/glm-5.3 via openrouter.ai returned HTTP 200 with an upstream error
+        #  and no choices: The operation was aborted (upstream code 504)"
+        print(f"Upstream provider failed, safe to retry: {e}")
+    ```
+
+    <Warning>
+      크거나 깊게 중첩된 `response_model`은 업스트림 타임아웃 가능성을 높입니다. 이를 모델이 잘못된 구조화 출력을 생성한 것으로 보지 말고, 일시적인 공급자 장애로 처리하세요.
+    </Warning>
+  </Tab>
   <Tab title="컨텍스트 길이">
     <Tip>
       대규모 작업에는 더 큰 컨텍스트 모델을 사용하세요.

docs/edge/ko/concepts/memory.mdx

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Google Gemini 사용
-memory = Memory(llm="gemini/gemini-2.0-flash")
+memory = Memory(llm="gemini/gemini-3.7-flash")
 
 # 사용자 정의 설정이 있는 사전 구성된 LLM 인스턴스 전달
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/ko/concepts/skills.mdx

@@ -202,13 +202,17 @@ crewai skill publish
 
 ### 설치
 
-게시된 스킬을 `@org/name` 참조로 설치합니다:
+게시된 스킬을 `@org-uuid/name` 참조로 설치합니다:
 
 ```shell Terminal
-crewai skill install @acme/code-review
+crewai skill install @your-org-uuid/code-review
 ```
 
-크루 프로젝트 내부에서는 스킬이 `./skills/{name}/`에 설치되고, 프로젝트 외부에서는 공유 캐시인 `~/.crewai/skills/{org}/{name}/`에 저장됩니다.
+<Note>
+  조직 이름이 아니라 조직 **UUID**를 사용하세요 — 조직 이름은 고유하지 않아서 잘못된 조직으로 해석될 수 있고, 그러면 설치가 "찾을 수 없음" 오류로 실패합니다. `crewai org list`를 실행하면 소속된 각 조직의 UUID(`ID` 열)를 확인할 수 있습니다.
+</Note>
+
+크루 프로젝트 내부에서는 스킬이 `./skills/{name}/`에 설치되고, 프로젝트 외부에서는 공유 캐시인 `~/.crewai/skills/{org-uuid}/{name}/`에 저장됩니다.
 
 에이전트는 레지스트리 스킬을 직접 참조할 수도 있습니다 — 런타임에 로컬 캐시(또는 프로젝트 `skills/` 디렉터리)에서 해석됩니다:
 
@@ -217,7 +221,7 @@ agent = Agent(
     role="Senior Code Reviewer",
     goal="Review pull requests for quality and security issues",
     backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["@acme/code-review"],  # registry ref, resolved locally
+    skills=["@your-org-uuid/code-review"],  # registry ref, resolved locally
 )
 ```
 

docs/edge/ko/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ CrewAI의 기본 프롬프트는 많은 시나리오에서 잘 작동하지만,
 - **오류 처리** – agent가 실패, 예외, 또는 타임아웃에 어떻게 반응할지 지정합니다.
 - **도구별 prompt** – 도구가 호출되거나 사용되는 방법에 대한 상세 지침을 정의합니다.
 
-이 요소들이 어떻게 구성되어 있는지 보려면 [CrewAI 저장소의 원본 prompt 템플릿](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json)을 확인하세요. 여기서 필요에 따라 오버라이드하거나 수정하여 고급 동작을 구현할 수 있습니다.
+이 요소들이 어떻게 구성되어 있는지 보려면 [CrewAI 저장소의 원본 prompt 템플릿](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json)을 확인하세요. 여기서 필요에 따라 오버라이드하거나 수정하여 고급 동작을 구현할 수 있습니다.
 
 ## 기본 시스템 지침 이해하기
 

docs/edge/ko/guides/coding-tools/agents-md.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
 ## CLI로 프로젝트 생성
 
-CrewAI CLI를 사용하여 프로젝트를 스캐폴딩하면, `AGENTS.md`가 루트에 자동으로 추가됩니다.
+CrewAI CLI를 사용하여 프로젝트를 스캐폴딩하세요. `AGENTS.md`가 루트에 추가되며, 이를 임포트하는 `CLAUDE.md`와 `GEMINI.md`도 함께 추가되므로 Claude Code와 Gemini CLI가 다른 모든 어시스턴트와 동일한 안내를 읽습니다.
 
 ```bash
 # Crew
@@ -32,24 +32,28 @@ Codex는 저장소에 배치된 `AGENTS.md` 파일로 안내할 수 있습니다
 
 ### Claude Code
 
-Claude Code는 프로젝트 메모리를 `CLAUDE.md`에 저장합니다. `/init`으로 부트스트랩하고 `/memory`로 편집할 수 있습니다. Claude Code는 `CLAUDE.md` 내에서 임포트도 지원하므로, `@AGENTS.md`와 같은 한 줄을 추가하여 공유 지침을 중복 없이 가져올 수 있습니다.
+Claude Code는 `CLAUDE.md`를 읽고 `AGENTS.md`는 무시합니다. 스캐폴딩된 프로젝트에는 임포트 줄 `@AGENTS.md` 하나만 담긴 `CLAUDE.md`가 포함되어 있어, 공유 안내가 중복 없이 로드됩니다. Claude 전용 메모는 그 줄 아래에 추가하고, 공유 컨벤션은 `AGENTS.md`에 유지하세요.
 
-간단하게 다음과 같이 사용할 수 있습니다:
+`CLAUDE.md`가 스캐폴딩되기 전에 생성된 프로젝트라면 임포트를 직접 추가하세요:
 
 ```bash
-mv AGENTS.md CLAUDE.md
+printf '@AGENTS.md\n' > CLAUDE.md
 ```
 
+`AGENTS.md`를 `CLAUDE.md`로 이름을 바꾸지 마세요. Codex와 Cursor는 `AGENTS.md`를 읽으며, 이름을 바꾸면 이들에게 보이지 않게 됩니다.
+
 ### Gemini CLI와 Google Antigravity
 
-Gemini CLI와 Antigravity는 저장소 루트 및 상위 디렉토리에서 프로젝트 컨텍스트 파일(기본값: `GEMINI.md`)을 로드합니다. Gemini CLI 설정에서 `context.fileName`을 설정하여 `AGENTS.md`를 대신(또는 추가로) 읽도록 구성할 수 있습니다. 예를 들어, `AGENTS.md`만 설정하거나 각 도구의 형식을 유지하고 싶다면 `AGENTS.md`와 `GEMINI.md`를 모두 포함할 수 있습니다.
+Gemini CLI와 Antigravity는 저장소 루트 및 상위 디렉토리에서 프로젝트 컨텍스트 파일(기본값: `GEMINI.md`)을 로드합니다. 스캐폴딩된 프로젝트에는 임포트 줄 `@./AGENTS.md` 하나만 담긴 `GEMINI.md`가 포함되어 있어, 공유 안내가 중복 없이 로드됩니다. Gemini 전용 메모는 그 줄 아래에 추가하고, 공유 컨벤션은 `AGENTS.md`에 유지하세요.
 
-간단하게 다음과 같이 사용할 수 있습니다:
+`GEMINI.md`가 스캐폴딩되기 전에 생성된 프로젝트라면 임포트를 직접 추가하세요:
 
 ```bash
-mv AGENTS.md GEMINI.md
+printf '@./AGENTS.md\n' > GEMINI.md
 ```
 
+또는 Gemini CLI 설정의 `context.fileName`에 `AGENTS.md`를 포함시키면 Gemini가 이를 직접 읽습니다. `AGENTS.md`를 `GEMINI.md`로 이름을 바꾸지 마세요. Codex와 Cursor는 `AGENTS.md`를 읽으며, 이름을 바꾸면 이들에게 보이지 않게 됩니다.
+
 ### Cursor
 
 Cursor는 `AGENTS.md`를 프로젝트 지침 파일로 지원합니다. 프로젝트 루트에 배치하여 Cursor의 코딩 어시스턴트에 안내를 제공하세요.

docs/edge/ko/guides/crews/first-crew.mdx

@@ -75,7 +75,7 @@ research_crew/
 }
 ```
 
-`provider/model-id`를 `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, `gemini/gemini-2.0-flash-001` 같은 모델로 바꾸세요.
+`provider/model-id`를 `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, `gemini/gemini-3.7-flash` 같은 모델로 바꾸세요.
 
 ## 3단계: 태스크와 Crew 설정
 

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

@@ -135,7 +135,7 @@ crewai flow add-crew content-crew
 }
 ```
 
-`provider/model-id`를 사용하는 모델로 바꾸세요. 예: `openai/gpt-4o`, `gemini/gemini-2.0-flash-001`, `anthropic/claude-sonnet-4-6`.
+`provider/model-id`를 사용하는 모델로 바꾸세요. 예: `openai/gpt-4o`, `gemini/gemini-3.7-flash`, `anthropic/claude-sonnet-4-6`.
 
 3. `src/guide_creator_flow/crews/content_crew/crew.jsonc`를 만듭니다:
 
@@ -481,7 +481,7 @@ Flow를 사용하면 간단하고 구조화된 응답이 필요할 때 언어
 
 ```python
 llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    model="model-id-here",  # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
     response_format=GuideOutline
 )
 response = llm.call(messages=messages)

docs/edge/ko/guides/frontend/channels.mdx

@@ -0,0 +1,156 @@
+---
+title: Channels
+description: CopilotKit Channels SDK와 관리형 Intelligence 플랫폼으로 동일한 CrewAI 에이전트를 Slack 또는 Teams 봇으로 실행하세요.
+icon: messages
+mode: "wide"
+---
+
+## 사용자가 이미 있는 곳에서 만나세요
+
+[Overview](/edge/ko/guides/frontend/overview)에서 만든 CrewAI 에이전트는 반드시 웹 앱 뒤에서만 동작할 필요가 없습니다. 동일한 Crew 또는 Flow를 메시징 플랫폼 안에서 봇으로 실행할 수 있습니다. 다시 빌드할 필요도, 에이전트 로직을 두 번 복사할 필요도 없습니다. 에이전트는 [AG-UI 프로토콜](https://docs.ag-ui.com)을 통해 그대로 노출되고, **channel**이 Slack 또는 Microsoft Teams에서 이를 구동합니다.
+
+CopilotKit의 [Channels SDK](https://docs.copilotkit.ai/slack)가 그 channel을 제공합니다. 작은 런타임에 `createChannel`을 선언하고 이를 CrewAI 에이전트에 연결하면, CopilotKit의 관리형 **Intelligence** 플랫폼이 메시징 제공자와의 연결을 중개합니다.
+
+<Note>
+이 섹션의 나머지 내용과 달리 Channels는 **셀프 호스팅되지 않습니다**. Channels는 **CopilotKit Intelligence**를 통해 실행되며, 이는 설계상 Channels에 필수적인 서비스입니다(무료 티어 제공). Intelligence는 플랫폼 연결과 자격 증명을 보관하고, 각 플랫폼 이벤트를 수신하며, 해당 턴을 여러분의 channel 프로세스로 전달합니다. 여러분의 프로세스는 에이전트를 실행하고 응답을 다시 스트리밍합니다. Slack은 Intelligence 대시보드에서 한 번만 구성하면 되며, 플랫폼 자격 증명은 결코 여러분의 프로세스로 들어오지 않습니다. 에이전트, 도구, 상태는 온전히 여러분의 것으로 유지됩니다.
+</Note>
+
+## 어떻게 맞물리는가
+
+CrewAI 에이전트 서버에 관한 것은 아무것도 바뀌지 않습니다. Overview에서와 똑같이 AG-UI를 통해 Crew 또는 Flow를 계속 제공합니다. 여러분이 추가하는 것은 `@copilotkit/channels`로 빌드된 별도의 장시간 실행 Node 프로세스입니다. 이 프로세스는 `CopilotRuntime`에 channel을 등록하고, Intelligence에 연결하며, 메시지가 도착할 때마다 에이전트를 실행합니다.
+
+```
+Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+```
+
+channel 프로세스는 Intelligence 게이트웨이에 대한 지속적인 연결을 유지하므로, 장시간 실행되는 호스트가 필요합니다. 서버리스 요청 핸들러는 그 연결을 소유할 수 없습니다. CrewAI 서버는 동시에 Overview의 웹 프론트엔드를 계속 제공할 수 있습니다. 웹 앱과 channel은 하나의 AG-UI 엔드포인트에 연결된 두 개의 클라이언트일 뿐입니다.
+
+## 통합 가이드
+
+<Steps>
+
+<Step title="Channels 패키지 설치">
+
+Channels SDK는 모든 것이 포함되어 있습니다. 모든 플랫폼이 하나의 패키지로 제공되며, 플랫폼별로 설치할 어댑터가 없습니다. channel을 호스팅하는 런타임 및 CrewAI AG-UI 클라이언트와 함께 다음을 추가하세요:
+
+```bash
+npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
+```
+
+</Step>
+
+<Step title="Intelligence에서 Channel 생성">
+
+[CopilotKit 대시보드](https://docs.copilotkit.ai/slack)에서 Channel을 생성하고 Slack을 연결하세요. Intelligence가 Slack 앱 생성 과정을 안내하고 그 자격 증명을 보관합니다. 그러면 여러분의 프로세스를 위한 두 개의 환경 변수가 남으며, 둘 다 대시보드에서 얻습니다:
+
+```bash
+export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
+export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
+```
+
+</Step>
+
+<Step title="channel 정의">
+
+`createChannel`은 channel을 선언하고 에이전트를 연결합니다. 각 대화가 자신만의 세션을 갖도록 에이전트를 스레드별 팩토리로 빌드하되, Overview가 웹 런타임에서 사용하는 것과 동일한 `CrewAIAgent`를 여러분의 AG-UI 엔드포인트를 가리키도록 설정하세요. `identifyUser: "platform"`은 Intelligence가 각 플랫폼 사용자를 안정적인 신원에 매핑하도록 합니다.
+
+```ts
+// channel.ts
+import { createChannel } from "@copilotkit/channels";
+import { CrewAIAgent } from "@ag-ui/crewai";
+
+const channel = createChannel({
+  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
+  identifyUser: "platform",
+  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
+  agent: (threadId) => {
+    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
+    agent.threadId = threadId;
+    return agent;
+  },
+});
+
+// A mention subscribes the thread and runs the agent; afterwards every message
+// in a subscribed thread runs it without needing another mention.
+channel.onMention(async ({ thread }) => {
+  await thread.subscribe();
+  await thread.runAgent();
+});
+
+channel.onMessage(async ({ thread }) => {
+  if (await thread.isSubscribed()) await thread.runAgent();
+});
+
+export { channel };
+```
+
+</Step>
+
+<Step title="런타임에 channel 등록">
+
+Intelligence 게이트웨이와 여러분의 channel로 `CopilotRuntime`을 생성한 다음, `createCopilotNodeListener`로 이를 제공하세요. `agents` 맵은 비어 있는 상태로 둡니다. channel이 자신의 에이전트를 제공하기 때문입니다. 잘못된 구성이 시작 시 명확하게 실패하도록 channel이 준비될 때까지 기다리세요.
+
+```ts
+// server.ts
+import { createServer } from "node:http";
+import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
+import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
+import { channel } from "./channel";
+
+const runtime = new CopilotRuntime({
+  agents: {}, // the channel supplies its own agent; no web-facing agents needed
+  intelligence: new CopilotKitIntelligence({
+    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
+  }),
+  channels: [channel],
+});
+
+const listener = createCopilotNodeListener({ runtime });
+await listener.channels?.ready({ timeoutMs: 15_000 });
+
+createServer(listener).listen(3123, () => {
+  console.log("Channels runtime listening on port 3123");
+});
+```
+
+</Step>
+
+<Step title="channel 런타임 실행">
+
+CrewAI 에이전트 서버와 함께 시작하세요:
+
+```bash
+uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
+npx tsx server.ts                 # terminal 2 — Channels runtime
+```
+
+Slack 또는 Teams에서 봇을 멘션하면 Crew 또는 Flow를 실행하고 응답을 스레드로 다시 스트리밍합니다. 스레드는 구독된 상태로 유지되므로 후속 메시지는 또다시 멘션할 필요 없이 실행됩니다.
+
+</Step>
+
+</Steps>
+
+## 이벤트 모델
+
+channel은 핸들러로 플랫폼 이벤트에 반응하며, 각 핸들러는 몇 가지 메서드로 구동하는 `thread`를 받습니다:
+
+- **`channel.onMention`**은 사용자가 봇을 @-멘션할 때 발생합니다. `thread.subscribe()`를 호출해 스레드에 참여한 다음, `thread.runAgent()`로 멘션에 대해 CrewAI 에이전트를 실행하세요.
+- **`channel.onMessage`**는 봇이 볼 수 있는 스레드의 모든 메시지에서 발생합니다. `thread.isSubscribed()`로 게이트를 걸어 에이전트가 참여한 곳에서만 응답하도록 한 다음, `thread.runAgent()`를 호출하세요.
+- **`thread.runAgent()`**는 현재 턴에 대해 연결된 CrewAI 에이전트를 실행하고 그 출력을 channel로 다시 스트리밍합니다. 에이전트가 실행할 텍스트를 재정의하려면 `{ prompt }`를 전달하세요.
+
+여러분의 에이전트는 일반적인 AG-UI `RunAgentInput`을 받고 일반적인 AG-UI 이벤트를 방출합니다. 플랫폼 메커니즘은 channel 뒤에 머무르므로, 동일한 Crew 또는 Flow가 모든 플랫폼에서 변경 없이 실행됩니다. channel은 환영 인사, 인터럽트, 명령, 반응, 모달을 위한 핸들러도 노출합니다. 전체 표면은 [`Channel` 레퍼런스](https://docs.copilotkit.ai/reference/channels/classes/Channel)를 참조하세요.
+
+## 플랫폼 지원
+
+관리형 Intelligence 경로는 현재 **Slack**과 **Microsoft Teams**를 지원합니다. 동일한 channel 코드가 양쪽에서 실행되며, `message.platform` / `thread.platform`이 원래의 출처를 보고합니다. 다른 플랫폼(Discord, Telegram, WhatsApp)은 관리형 경로가 아니라 개발자가 운영하는 **direct adapters**를 통해 연결됩니다. 여러분 자신의 프로세스가 플랫폼 자격 증명과 전송을 보유합니다. 현재 지원 플랫폼 목록과 플랫폼별 설정은 [CopilotKit Channels 문서](https://docs.copilotkit.ai/slack)를 확인하세요.
+
+## 관련 항목
+
+<CardGroup cols={2}>
+  <Card title="Frontend Overview" icon="browser" href="/edge/ko/guides/frontend/overview">
+    Crew 또는 Flow를 AG-UI를 통해 제공하세요. 모든 channel이 그 위에 세워지는 토대입니다.
+  </Card>
+  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    실행 도중 사용자 승인이나 입력을 수집하기 위해 에이전트를 일시 중지하세요.
+  </Card>
+</CardGroup>

docs/edge/ko/guides/frontend/overview.mdx

@@ -0,0 +1,238 @@
+---
+title: Frontend Overview
+description: CopilotKit과 AG-UI 프로토콜로 CrewAI 에이전트를 위한 인터랙티브 사용자 인터페이스를 구축하세요.
+icon: browser
+mode: "wide"
+---
+
+## 에이전트에 사용자 인터페이스를 부여하세요
+
+CrewAI는 여러분의 에이전트를 실행합니다. [CopilotKit](https://copilotkit.ai)은 그 에이전트에 프론트엔드를 제공합니다. 이 둘을 함께 사용하면 사용자가 Crew 또는 Flow와 대화하고, 실시간으로 작동하는 모습을 지켜보고, 그 결정을 승인하며, 출력을 장황한 텍스트 대신 살아 있는 UI로 렌더링하여 볼 수 있는 애플리케이션을 구축할 수 있습니다.
+
+이 둘은 [AG-UI 프로토콜](https://docs.ag-ui.com)을 통해 연결됩니다. `ag-ui-crewai` 패키지는 어떤 Crew나 Flow든 AG-UI 엔드포인트로 노출합니다. CopilotKit의 React 훅과 컴포넌트가 그 엔드포인트를 소비합니다. 이를 통해 채팅 상자를 훨씬 뛰어넘는 경험이 열립니다:
+
+<CardGroup cols={2}>
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+    에이전트 도구 호출과 상태를 여러분만의 React 컴포넌트로 렌더링하세요.
+  </Card>
+  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    실행 도중 사용자 승인이나 입력을 수집하기 위해 에이전트를 일시 중지하세요.
+  </Card>
+  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+    에이전트 상태와 앱 UI를 양방향으로 동기화하세요.
+  </Card>
+  <Card title="Channels" icon="messages" href="/edge/ko/guides/frontend/channels">
+    동일한 에이전트를 Slack, Discord 또는 Teams 봇으로 실행하세요.
+  </Card>
+</CardGroup>
+
+이 가이드는 Crew 또는 Flow를 Next.js 프론트엔드와 처음부터 끝까지 연동시킵니다. 이 섹션의 나머지 내용은 여기서 설정한 앱을 기반으로 합니다.
+
+## 아키텍처
+
+세 가지 구성 요소가 있습니다:
+
+1. **CrewAI 에이전트 서버** — AG-UI를 통해 Crew 또는 Flow를 제공하는 Python 프로세스(FastAPI + `ag-ui-crewai`).
+2. **CopilotKit 런타임** — 에이전트를 등록하고 요청을 프록시하는 Next.js 라우트.
+3. **React 프론트엔드** — `<CopilotKit>` 프로바이더와 채팅 및 generative-UI 컴포넌트.
+
+```
+React app  ──►  CopilotKit runtime (/api/copilotkit)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+```
+
+<Note>
+이 가이드는 **셀프 호스팅** 경로를 다룹니다. `ag-ui-crewai`로 CrewAI 에이전트 서버를 직접 실행하며, 관리형 서비스 없이 로컬에서 동작합니다. CopilotKit은 호스팅된 스레드와 인스펙터를 갖춘 **관리형** 경로(CopilotKit Cloud / Enterprise Intelligence)도 제공합니다. 그 방식을 원한다면 [CopilotKit CrewAI 퀵스타트](https://docs.copilotkit.ai/crewai-crews/quickstart)를 참조하세요. 이 섹션의 프론트엔드 코드는 어느 쪽이든 동일합니다. 에이전트를 호스팅하고 등록하는 방식만 다릅니다.
+</Note>
+
+<Note>
+CrewAI는 AG-UI 뒤에서 세 가지 형태로 실행됩니다: 일반 **Flows**(이 가이드 전반에서 사용), **[Conversational Flows](/edge/en/guides/frontend/conversational-flows)**(네이티브, 세션 인식, 턴 기반, 완전한 기능 동등성), 그리고 **Crews**(기본 채팅). 이 섹션의 프론트엔드는 이들 전반에서 동일합니다. 백엔드 작성과 등록만 다릅니다.
+</Note>
+
+## 통합 가이드
+
+<Steps>
+
+<Step title="AG-UI를 통해 에이전트 제공">
+
+통합 패키지를 CrewAI 프로젝트에 설치하세요:
+
+```bash
+pip install ag-ui-crewai
+```
+
+FastAPI 앱에서 에이전트를 노출하세요. Flows는 `add_crewai_flow_fastapi_endpoint`를, Crews는 `add_crewai_crew_fastapi_endpoint`를 사용합니다. 원하는 만큼 등록할 수 있으며, 각각 자신의 경로에 배치됩니다.
+
+<CodeGroup>
+
+```python Flow
+# server.py
+from fastapi import FastAPI
+from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
+from my_agents.recipe_flow import RecipeFlow
+
+app = FastAPI(title="CrewAI Agent Server")
+
+add_crewai_flow_fastapi_endpoint(
+    app=app,
+    flow=RecipeFlow(),
+    path="/recipe",
+)
+```
+
+```python Crew
+# server.py
+from fastapi import FastAPI
+from ag_ui_crewai.endpoint import add_crewai_crew_fastapi_endpoint
+from my_agents.research_crew import ResearchCrew
+
+app = FastAPI(title="CrewAI Agent Server")
+
+add_crewai_crew_fastapi_endpoint(
+    app=app,
+    crew=ResearchCrew().crew(),
+    path="/research",
+)
+```
+
+</CodeGroup>
+
+실행하세요:
+
+```bash
+uvicorn server:app --port 8000
+```
+
+<Note>
+서버를 시작하기 전에 LLM 제공자를 위한 환경 변수(예: `OPENAI_API_KEY`)를 설정하세요.
+</Note>
+
+</Step>
+
+<Step title="Next.js 앱 생성">
+
+아직 프론트엔드가 없다면 하나를 스캐폴딩하세요:
+
+```bash
+npx create-next-app@latest my-app
+cd my-app
+```
+
+CopilotKit과 CrewAI AG-UI 클라이언트를 설치하세요:
+
+```bash
+npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime @ag-ui/crewai
+```
+
+</Step>
+
+<Step title="CopilotKit 런타임 추가">
+
+CrewAI 에이전트를 CopilotKit 런타임에 등록하는 라우트를 생성하세요. 각 에이전트는 `CrewAIAgent`를 통해 Python 서버의 경로를 가리킵니다.
+
+```ts
+// app/api/copilotkit/route.ts
+import {
+  CopilotRuntime,
+  InMemoryAgentRunner,
+  createCopilotEndpoint,
+} from "@copilotkit/runtime/v2";
+import { CrewAIAgent } from "@ag-ui/crewai";
+import { handle } from "hono/vercel";
+
+const runtime = new CopilotRuntime({
+  agents: {
+    recipe: new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
+  },
+  runner: new InMemoryAgentRunner(),
+});
+
+const app = createCopilotEndpoint({
+  runtime,
+  basePath: "/api/copilotkit",
+});
+
+const handler = handle(app);
+export const GET = handler;
+export const POST = handler;
+```
+
+</Step>
+
+<Step title="프로바이더로 앱 감싸기">
+
+`<CopilotKit>`을 런타임 라우트로 가리키고 등록한 에이전트의 이름을 지정하세요.
+
+```tsx
+// app/page.tsx
+"use client";
+import { CopilotKit } from "@copilotkit/react-core";
+import { CopilotSidebar } from "@copilotkit/react-core/v2";
+import "@copilotkit/react-core/v2/styles.css";
+
+export default function Page() {
+  return (
+    <CopilotKit runtimeUrl="/api/copilotkit" agent="recipe">
+      <YourApp />
+      <CopilotSidebar agentId="recipe" labels={{ modalHeaderTitle: "Assistant" }} />
+    </CopilotKit>
+  );
+}
+```
+
+</Step>
+
+<Step title="실행">
+
+두 프로세스를 모두 시작하고 앱을 여세요. 이제 사이드바에서 채팅하면 Crew 또는 Flow가 실행됩니다.
+
+```bash
+uvicorn server:app --port 8000   # terminal 1
+npm run dev                       # terminal 2
+```
+
+</Step>
+
+</Steps>
+
+## 채팅 UI 옵션
+
+CopilotKit은 서로 교체 가능한 세 가지 채팅 표면을 제공합니다. 컴포넌트만 바꾸면 되며, 연결 방식은 동일합니다.
+
+<CodeGroup>
+
+```tsx Sidebar
+import { CopilotSidebar } from "@copilotkit/react-core/v2";
+
+<CopilotSidebar agentId="recipe" />
+```
+
+```tsx Popup
+import { CopilotPopup } from "@copilotkit/react-core/v2";
+
+<CopilotPopup agentId="recipe" />
+```
+
+```tsx Inline
+import { CopilotChat } from "@copilotkit/react-core/v2";
+
+<CopilotChat agentId="recipe" />
+```
+
+</CodeGroup>
+
+## 다음으로 갈 곳
+
+<CardGroup cols={2}>
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+    도구 호출과 에이전트 상태를 커스텀 컴포넌트로 렌더링하세요.
+  </Card>
+  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
+    에이전트가 브라우저에서 실행되는 함수를 호출하도록 하세요.
+  </Card>
+  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    에이전트 동작을 사용자 승인 뒤에 두세요.
+  </Card>
+  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+    에이전트가 작동하는 동안 진행 중인 상태를 UI로 스트리밍하세요.
+  </Card>
+</CardGroup>

docs/edge/ko/learn/llm-connections.mdx

@@ -141,7 +141,7 @@ OpenAI 호환 LLM에 연결하려면 환경 변수를 사용하거나 LLM 클래
         # Gemini의 OpenAI 호환 API 예시입니다.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # AIza...로 시작해야 합니다.
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Gemini 모델을 여기에 추가하세요. openai/ 하위에 위치.
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Gemini 모델을 여기에 추가하세요. openai/ 하위에 위치.
         ```
         </CodeGroup>
     </Tab>
@@ -159,7 +159,7 @@ OpenAI 호환 LLM에 연결하려면 환경 변수를 사용하거나 LLM 클래
             ```python Google
             # Gemini의 OpenAI 호환 API 예시
             llm = LLM(
-                model="openai/gemini-2.0-flash",
+                model="openai/gemini-3.7-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # AIza...로 시작해야 합니다.
             )

docs/edge/ko/learn/llm-selection-guide.mdx

@@ -145,7 +145,7 @@ planning agent는 복잡한 전략적 사고와 다단계 분석을 처리할
 from crewai import Agent, Task, Crew, LLM
 
 # High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
+manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
 
 # Creative model for content generation
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -411,7 +411,7 @@ tech_writer = Agent(
     # Manager 또는 coordination agent
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # 조율을 위한 프리미엄
+        llm=LLM(model="gemini/gemini-3.7-flash"),  # 조율을 위한 프리미엄
         # ... 나머지 설정
     )
 

docs/edge/ko/observability/tracing.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 CrewAI는 Crews와 Flows를 실시간으로 모니터링하고 디버깅할 수 있는 내장 추적 기능을 제공합니다. 이 가이드는 CrewAI의 통합 관측 가능성 플랫폼을 사용하여 **Crews**와 **Flows** 모두에 대한 추적을 활성화하는 방법을 보여줍니다.
 
-> **CrewAI Tracing이란?** CrewAI의 내장 추적은 agent 결정, 작업 실행 타임라인, 도구 사용, LLM 호출을 포함한 AI agent에 대한 포괄적인 관측 가능성을 제공하며, 모두 [CrewAI AMP 플랫폼](https://app.crewai.com)을 통해 액세스할 수 있습니다.
+> **CrewAI Tracing이란?** CrewAI의 내장 추적은 agent 결정, 작업 실행 타임라인, 도구 사용, LLM 호출을 포함한 AI agent에 대한 포괄적인 관측 가능성을 제공하며, 모두 [CrewAI AMP 플랫폼](https://app.crewai.com)을 통해 액세스할 수 있습니다. 추적은 [텔레메트리](/ko/telemetry)와 별도로 관리됩니다.
 
 ![CrewAI Tracing Interface](/images/crewai-tracing.png)
 
@@ -150,8 +150,8 @@ result = flow.kickoff()
 
 ### 5단계: CrewAI AMP 대시보드에서 추적 보기
 
-crew 또는 flow를 실행한 후 CrewAI AMP 대시보드에서 CrewAI 애플리케이션이 생성한 추적을 볼 수 있습니다. agent 상호 작용, 도구 사용 및 LLM 호출의 세부 단계를 볼 수 있습니다.
-아래 링크를 클릭하여 추적을 보거나 대시보드의 추적 탭으로 이동하세요 [여기](https://app.crewai.com/crewai_plus/trace_batches)
+추적은 인증된 내보내기 또는 명시적으로 동의한 익명 업로드가 성공한 경우에만 업로드됩니다. 로컬 버퍼가 삭제된 실행에는 업로드된 추적이 없습니다.
+계정에 연결된 추적은 [CrewAI AMP 대시보드의 Traces 탭](https://app.crewai.com/crewai_plus/trace_batches)에서 agent 상호 작용, 도구 사용 및 LLM 호출을 확인하세요.
 ![CrewAI Tracing Interface](/images/view-traces.png)
 
 ### 대안: 환경 변수 구성
@@ -170,6 +170,51 @@ CREWAI_TRACING_ENABLED=true
 
 이 환경 변수가 설정되면 `tracing=True`를 명시적으로 설정하지 않아도 모든 Crews와 Flows에 자동으로 추적이 활성화됩니다.
 
+## 첫 실행 후 추적 보기
+
+Crew 또는 Flow를 처음 실행하면 대화형 터미널에서 다음을 물을 수 있습니다:
+
+```text
+Share this execution trace with CrewAI? [y/N]
+```
+
+버퍼에 저장된 추적을 CrewAI에 업로드하려면 **yes**를 선택하세요. 추적에는
+프롬프트, 입력, 출력이 포함될 수 있습니다. 거절하거나 시간이 초과되거나
+대화형 동의 프롬프트 없이 실행하면 버퍼가 삭제됩니다. 나중에
+`crewai traces enable` 또는 `crewai traces disable`을 사용하거나 Crew 또는
+Flow에서 `tracing`을 설정하여 추적 설정을 변경할 수 있습니다.
+
+### 로컬 버퍼링 및 인증된 내보내기
+
+첫 실행에서 수집한 추적은 저장된 로그인 자격 증명이 있어도 공유에 동의할
+때까지 프로세스 메모리에 보관됩니다. 인증되지 않은 추적도 같은 동의 절차를
+사용합니다. 동의하기 전에는 CrewAI가 업로드 권한을 요청하거나 실행 span을
+전송하지 않습니다.
+
+버퍼는 최대 **1,000개의 span**과 **8 MiB의 인코딩된 OTLP 데이터**를
+보관합니다. `CREWAI_EPHEMERAL_TRACE_MAX_SPANS`와
+`CREWAI_EPHEMERAL_TRACE_MAX_BYTES`를 양의 정수로 설정하여 한도를 조정할
+수 있습니다. 한도를 초과하면 가장 오래된 span부터 삭제하며, 개별 span이
+바이트 한도보다 크면 해당 span을 삭제합니다. 공유하거나 폐기한 후에는
+버퍼를 비웁니다.
+
+추적이 활성화되고 자격 증명을 사용할 수 있으면 CrewAI는 CLI 로그인,
+`CREWAI_USER_PAT` 또는 플랫폼 통합 자격 증명을 AMP에서 실행별 권한으로
+교환합니다. 그런 다음 해당 권한을 사용하여 OpenTelemetry span을 Wharf로
+직접 내보냅니다. 유효하지 않은 자격 증명으로는 익명 업로드로 전환하지 않습니다.
+
+### 호스팅된 실행 세션
+
+호스트는 `crewai.telemetry.tracing`의 `telemetry_session`으로 실행을 감쌀
+수 있습니다. 세션은 CrewAI 수명 주기 이벤트를 사용하여 span을 생성하고
+종료하며 타임스탬프, 부모 관계, HITL 일시 중지/재개 링크를 유지합니다.
+`providers=`에 기존 공급자를 전달하면 호스트의 tracer와 로깅 통합을
+유지할 수 있습니다. `processors=`로 span 프로세서를 전달하고
+`log_emitter=`로 호스트 로깅 콜백을 전달할 수 있습니다. 이러한 통합에서
+데이터 마스킹은 호스트가 담당합니다.
+각 세션은 자체 추적 수명 주기를 관리하며 애플리케이션의 전역
+OpenTelemetry 공급자를 변경하지 않습니다.
+
 ## 추적 보기
 
 ### CrewAI AMP 대시보드 액세스
@@ -210,5 +255,5 @@ CrewAI 추적은 다음에 대한 포괄적인 가시성을 제공합니다:
 
 1. Crew/Flow에서 `tracing=True`가 설정되어 있는지 확인하세요
 2. 환경 변수를 사용하는 경우 `CREWAI_TRACING_ENABLED=true`인지 확인하세요
-3. `crewai login`으로 인증되었는지 확인하세요
-4. crew/flow가 실제로 실행되고 있는지 확인하세요
+3. 인증된 내보내기의 경우 CLI 로그인, `CREWAI_USER_PAT` 또는 플랫폼 통합 자격 증명을 확인하세요. 익명으로 공유하려면 동의 프롬프트에서 명시적으로 동의하세요. 로그인은 필요하지 않습니다
+4. crew/flow가 실행되었고 추적 내보내기가 성공했는지 확인하세요. 동의를 거절하거나 시간이 초과되거나 대화형 동의 프롬프트 없이 실행하면 로컬 버퍼가 업로드되지 않고 삭제됩니다

docs/edge/ko/telemetry.mdx

@@ -22,7 +22,8 @@ CrewAI는 익명 텔레메트리를 활용하여 사용 통계를 수집하며,
 `share_crew` 기능이 활성화되면, 보다 심층적인 통찰을 제공하기 위해 작업 설명, 에이전트의 배경 이야기나 목표, 기타 특정 속성 등 상세한 데이터가 수집됩니다.
 이 확대된 데이터 수집에는 사용자가 crew나 작업에 개인정보를 포함한 경우, 개인정보가 포함될 수 있습니다.
 사용자는 `share_crew`를 활성화하기 전에 crew와 작업의 내용을 신중하게 검토해야 합니다.
-사용자는 환경 변수 `CREWAI_DISABLE_TELEMETRY`를 `true`로 설정하거나, `OTEL_SDK_DISABLED`를 `true`로 설정하여 텔레메트리를 비활성화할 수 있습니다(후자의 경우 전체 OpenTelemetry 계측이 전역에서 비활성화된다는 점에 유의하십시오).
+사용자는 `CREWAI_DISABLE_TELEMETRY`를 `true`, `1`, `yes`, `on` 중 하나로 설정하여 CrewAI 텔레메트리를 비활성화할 수 있습니다(대소문자 무관). 같은 값의 `OTEL_SDK_DISABLED`도 CrewAI exporter를 끕니다. 프로세스 내 다른 OpenTelemetry 계측을 끄려면 OpenTelemetry SDK는 여전히 `true`만 인식합니다.
+AMP 추적은 [Tracing](/ko/observability/tracing)에서 별도로 다룹니다.
 
 ### 예시:
 ```python
@@ -33,6 +34,8 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
+`CREWAI_DISABLE_TELEMETRY=1`(`yes` / `on`도 동일)은 `true`와 같습니다. 인식되지 않는 값은 무시되며 텔레메트리는 켜진 채로 남습니다.
+
 ### 사용자 OpenTelemetry 설정과의 격리
 
 CrewAI의 telemetry는 자체 전용 `TracerProvider`에서 실행되며 자신을 전역
@@ -58,8 +61,8 @@ provider로 등록하지 않습니다. 이를 통해 양방향이 분리됩니
 | 예      | 작업 라이프사이클 데이터                   | 생성 및 실행 시작/종료 시각, crew 및 작업 식별자, 그리고 작업의 성공 또는 실패 여부가 포함됩니다. 작업이 실패하면 실패를 집계하고 진단할 수 있도록 예외의 **클래스 이름**(예: `TimeoutError`)이 기록되며, 프롬프트·모델 출력·파일 경로·자격 증명이 포함될 수 있는 오류 메시지는 결코 기록되지 않습니다. 타임스탬프를 포함한 span으로 저장됩니다. 개인 정보 없음. |
 | 예      | LLM 속성                                  | LLM의 이름, model_name, 모델, top_k, temperature 및 클래스명이 포함됩니다. 모두 기술적이고 비개인 정보입니다. |
 | 예      | crewAI CLI를 통한 프로젝트 생성 | 포함 항목: `crewai create`로 새 프로젝트가 생성되었다는 사실, 그 종류(`crew`, `json_crew` 또는 `flow`), 그리고 그 새 프로젝트에 발급되어 해당 프로젝트의 `pyproject.toml`에 기록된 프로젝트 ID. 이는 새 프로젝트 자체의 ID이며, 명령을 실행한 디렉터리의 `project_id`와는 별개로 기록됩니다 — 두 값은 다를 수 있습니다. 프로젝트 이름, 파일 내용, 코드는 기록되지 않습니다. 개인 정보 없음. |
-| 예      | crewAI CLI를 통한 Crew 배포 시도 | 포함 항목: 배포가 시도되고 있다는 사실과 crew id, 로그를 가져오려고 하는지 여부, 그리고 배포가 CLI 명령에서 시작되었는지 실행 TUI에서 시작되었는지 여부. 프로젝트나 crew의 내용은 기록되지 않습니다. 개인 정보 없음. |
-| 예      | 실행 환경                                  | 포함: 프로세스를 실행 중인 AI 코딩 어시스턴트(있는 경우, `claude_code`, `codex`, `cursor`, `unknown` 등 고정 목록 중 하나), 프로세스가 실행되는 위치(`ci`, `container`, `serverless`, `interactive` 등 고정 목록 중 하나), 그리고 `pyproject.toml`에 설정된 경우 `project_id`. 감지는 알려진 환경 변수의 설정 여부만 확인하며 값은 읽지 않음. 개인 데이터 없음. |
+| 예      | crewAI CLI를 통한 Crew 배포 시도 | 포함 항목: 배포가 시도되고 있다는 사실과 crew id, 로그를 가져오려고 하는지 여부, 그리고 배포가 CLI 명령에서 시작되었는지 실행 TUI에서 시작되었는지 여부. 배포 생성이 실패하면 실패 범주(`api_4xx`, `network_error`, `user_declined` 등 고정된 목록 중 하나)와 API 응답의 HTTP 상태 코드(있는 경우)가 기록되며, 오류 메시지는 절대 기록되지 않습니다. 프로젝트나 crew의 내용은 기록되지 않습니다. 개인 정보 없음. |
+| 예      | 실행 환경                                  | 포함: 프로세스를 실행 중인 AI 코딩 어시스턴트(있는 경우, `claude_code`, `codex`, `cursor`, `unknown` 등 고정 목록 중 하나), 프로세스가 실행되는 위치(`ci`, `container`, `serverless`, `interactive` 등 고정 목록 중 하나), `pyproject.toml`에 설정된 경우 `project_id`, 그리고 머신 크기의 대략적인 구간(`1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+`, `unknown` 중 하나). 구간은 범위이며 정확한 코어 수는 절대 포함하지 않습니다 — 정확한 코어 수는 아래 환경 정보에서 옵트인한 경우에만 수집됩니다. 크기 구간은 호스트 CPU 수에서 가져오며, 어시스턴트와 실행 위치 감지는 알려진 환경 변수의 설정 여부만 확인하고 값은 읽지 않음. 개인 데이터 없음. |
 | 예      | Flow 라이프사이클 신호 | 포함 항목: flow의 시작, 완료 또는 실패 여부, 해당 메서드의 실패 여부, 사람의 입력이나 피드백을 위해 일시 중지되었는지 여부, 해당 시작이 재개된 실행이었는지 여부, 대화 턴의 실패 여부, flow 실행 시간, 그리고 해당 flow가 CrewAI가 내부적으로 실행하는 것인지 사용자가 작성한 것인지 여부. flow 이름은 flow 생성 및 실행에서와 마찬가지로 기록됩니다. flow 또는 해당 메서드가 실패하면 장애 진단을 위해 예외의 **클래스 이름**(예: `TimeoutError`)이 기록되며, 프롬프트·모델 출력·파일 경로·자격 증명이 포함될 수 있는 오류 메시지는 절대 기록되지 않습니다. 메서드 이름과 flow 상태는 절대 기록되지 않습니다. 개인 정보 없음. |
 | 예      | 트레이스 공유 신호 | 포함 항목: 트레이스 배치가 CrewAI AMP에 성공적으로 공유되었는지 여부와, 익명으로(계정 생성 전) 공유되었는지 또는 계정에 연결되어 공유되었는지 여부. 모든 span과 마찬가지로 위에서 설명한 실행 환경 속성(구성된 경우 `project_id`, 코딩 어시스턴트, 런타임)도 함께 기록됩니다. 이 행은 공유 텔레메트리만 설명하며 — 트레이스 내용이나 공유된 트레이스 링크로 부여되는 접근 권한은 설명하지 않습니다. 트레이스 내용, 입력, 출력은 이 신호에는 기록되지 않습니다. 트레이스를 공유하기 전에 비밀 정보, 개인 데이터, AMP 편집 및 보존 설정을 검토하세요. |
 | 아니오  | 에이전트 확장 데이터                      | 목표 설명, 배경 이야기 텍스트, i18n 프롬프트 파일 식별자가 포함됩니다. 사용자들은 텍스트 필드에 개인 정보가 포함되지 않도록 해야 합니다. |

docs/edge/ko/tools/ai-ml/overview.mdx

@@ -48,17 +48,16 @@ mode: "wide"
 - **AI 안전성**: 콘텐츠 모더레이션 및 안전성 점검 구현
 
 ```python
-from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
+from crewai_tools import DallETool, VisionTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
-code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor, code_executor],
+    tools=[image_generator, vision_processor],
     goal="Create and analyze content using AI capabilities"
 )
 ```

docs/edge/ko/tools/ai-ml/ragtool.mdx

@@ -1,6 +1,6 @@
 ---
 title: RAG 도구
-description: RagTool은 Retrieval-Augmented Generation을 사용하여 질문에 답변하는 동적 지식 기반 도구입니다.
+description: `RagTool`은 Retrieval-Augmented Generation을 사용하여 질문에 답변하는 동적 지식 기반 도구입니다.
 icon: vector-square
 mode: "wide"
 ---
@@ -9,7 +9,7 @@ mode: "wide"
 
 ## 설명
 
-`RagTool`은 EmbedChain을 통한 RAG(Retrieval-Augmented Generation)의 강력함을 활용하여 질문에 답하도록 설계되었습니다.
+`RagTool`은 CrewAI의 네이티브 RAG 시스템을 통해 RAG(Retrieval-Augmented Generation)의 강력함을 활용하여 질문에 답하도록 설계되었습니다.
 이는 다양한 데이터 소스에서 관련 정보를 검색할 수 있는 동적 지식 기반을 제공합니다.
 이 도구는 방대한 정보에 접근해야 하고 맥락에 맞는 답변을 제공해야 하는 애플리케이션에 특히 유용합니다.
 
@@ -76,24 +76,24 @@ def knowledge_expert(self) -> Agent:
 `RagTool`은 다음과 같은 매개변수를 허용합니다:
 
 - **summarize**: 선택 사항. 검색된 콘텐츠를 요약할지 여부입니다. 기본값은 `False`입니다.
-- **adapter**: 선택 사항. 지식 베이스에 대한 사용자 지정 어댑터입니다. 제공되지 않은 경우 EmbedchainAdapter가 사용됩니다.
-- **config**: 선택 사항. 내부 EmbedChain App의 구성입니다.
+- **adapter**: 선택 사항. 지식 기반을 위한 사용자 지정 어댑터입니다. 제공하지 않으면 CrewAIRagAdapter가 사용됩니다.
+- **config**: 선택 사항. 내부 CrewAI RAG 시스템에 대한 구성입니다. 선택적 `embedding_model`(ProviderSpec) 및 `vectordb`(VectorDbConfig) 키를 포함하는 `RagToolConfig` TypedDict를 허용합니다. 프로그래밍 방식으로 제공된 모든 구성 값은 환경 변수보다 우선합니다.
 
 ## 콘텐츠 추가
 
 `add` 메서드를 사용하여 지식 베이스에 콘텐츠를 추가할 수 있습니다:
 
 ```python Code
-# PDF 파일 추가
+# Add a PDF file
 rag_tool.add(data_type="file", path="path/to/your/document.pdf")
 
-# 웹 페이지 추가
+# Add a web page
 rag_tool.add(data_type="web_page", url="https://example.com")
 
-# YouTube 비디오 추가
+# Add a YouTube video
 rag_tool.add(data_type="youtube_video", url="https://www.youtube.com/watch?v=VIDEO_ID")
 
-# 파일이 있는 디렉터리 추가
+# Add a directory of files
 rag_tool.add(data_type="directory", path="path/to/your/directory")
 ```
 
@@ -123,51 +123,532 @@ def knowledge_expert(self) -> Agent:
 
 ## 고급 구성
 
-`RagTool`의 동작을 구성 사전을 제공하여 사용자 지정할 수 있습니다.
+구성 딕셔너리를 제공하여 `RagTool`의 동작을 사용자 지정할 수 있습니다.
 
 ```python Code
 from crewai_tools import RagTool
+from crewai_tools.tools.rag import RagToolConfig, VectorDbConfig, ProviderSpec
 
-# 사용자 지정 구성으로 RAG 도구 생성
-config = {
-    "app": {
-        "name": "custom_app",
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-4",
-        }
-    },
-    "embedding_model": {
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-ada-002"
-        }
-    },
-    "vectordb": {
-        "provider": "elasticsearch",
-        "config": {
-            "collection_name": "my-collection",
-            "cloud_id": "deployment-name:xxxx",
-            "api_key": "your-key",
-            "verify_certs": False
-        }
-    },
-    "chunker": {
-        "chunk_size": 400,
-        "chunk_overlap": 100,
-        "length_function": "len",
-        "min_chunk_size": 0
+# Create a RAG tool with custom configuration
+
+vectordb: VectorDbConfig = {
+    "provider": "qdrant",
+    "config": {
+        "collection_name": "my-collection"
     }
 }
 
+embedding_model: ProviderSpec = {
+    "provider": "openai",
+    "config": {
+        "model_name": "text-embedding-3-small"
+    }
+}
+
+config: RagToolConfig = {
+    "vectordb": vectordb,
+    "embedding_model": embedding_model
+}
+
 rag_tool = RagTool(config=config, summarize=True)
 ```
 
-내부 RAG 도구는 Embedchain 어댑터를 사용하므로 Embedchain에서 지원하는 모든 구성 옵션을 전달할 수 있습니다.
-자세한 내용은 [Embedchain 문서](https://docs.embedchain.ai/components/introduction)를 참조하세요.
-.yaml 파일에서 제공되는 구성 옵션을 반드시 검토하시기 바랍니다.
+## 임베딩 모델 구성
+
+`embedding_model` 매개변수는 다음 구조의 `crewai.rag.embeddings.types.ProviderSpec` 딕셔너리를 허용합니다.
+
+```python
+{
+    "provider": "provider-name",  # Required
+    "config": {                    # Optional
+        # Provider-specific configuration
+    }
+}
+```
+
+### 지원되는 프로바이더
+
+<AccordionGroup>
+  <Accordion title="OpenAI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
+
+    embedding_model: OpenAIProviderSpec = {
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "text-embedding-ada-002",
+            "dimensions": 1536,
+            "organization_id": "your-org-id",
+            "api_base": "https://api.openai.com/v1",
+            "api_version": "v1",
+            "default_headers": {"Custom-Header": "value"}
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `api_key` (str): OpenAI API 키
+    - `model_name` (str): 사용할 모델. 기본값: `text-embedding-ada-002`. 옵션: `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
+    - `dimensions` (int): 임베딩 차원 수
+    - `organization_id` (str): OpenAI 조직 ID
+    - `api_base` (str): 사용자 지정 API 기본 URL
+    - `api_version` (str): API 버전
+    - `default_headers` (dict): API 요청을 위한 사용자 지정 헤더
+
+    **환경 변수:**
+    - `OPENAI_API_KEY` 또는 `EMBEDDINGS_OPENAI_API_KEY`: `api_key`
+    - `OPENAI_ORGANIZATION_ID` 또는 `EMBEDDINGS_OPENAI_ORGANIZATION_ID`: `organization_id`
+    - `OPENAI_MODEL_NAME` 또는 `EMBEDDINGS_OPENAI_MODEL_NAME`: `model_name`
+    - `OPENAI_API_BASE` 또는 `EMBEDDINGS_OPENAI_API_BASE`: `api_base`
+    - `OPENAI_API_VERSION` 또는 `EMBEDDINGS_OPENAI_API_VERSION`: `api_version`
+    - `OPENAI_DIMENSIONS` 또는 `EMBEDDINGS_OPENAI_DIMENSIONS`: `dimensions`
+  </Accordion>
+
+  <Accordion title="Cohere">
+    ```python main.py
+    from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
+
+    embedding_model: CohereProviderSpec = {
+        "provider": "cohere",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "embed-english-v3.0"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `api_key` (str): Cohere API 키
+    - `model_name` (str): 사용할 모델. 기본값: `large`. 옵션: `embed-english-v3.0`, `embed-multilingual-v3.0`, `large`, `small`
+
+    **환경 변수:**
+    - `COHERE_API_KEY` 또는 `EMBEDDINGS_COHERE_API_KEY`: `api_key`
+    - `EMBEDDINGS_COHERE_MODEL_NAME`: `model_name`
+  </Accordion>
+
+  <Accordion title="VoyageAI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
+
+    embedding_model: VoyageAIProviderSpec = {
+        "provider": "voyageai",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "voyage-3",
+            "input_type": "document",
+            "truncation": True,
+            "output_dtype": "float32",
+            "output_dimension": 1024,
+            "max_retries": 3,
+            "timeout": 60.0
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `api_key` (str): VoyageAI API 키
+    - `model` (str): 사용할 모델. 기본값: `voyage-2`. 옵션: `voyage-3`, `voyage-3-lite`, `voyage-code-3`, `voyage-large-2`
+    - `input_type` (str): 입력 유형. 옵션: `document`(저장용), `query`(검색용)
+    - `truncation` (bool): 최대 길이를 초과하는 입력을 잘라낼지 여부. 기본값: `True`
+    - `output_dtype` (str): 출력 데이터 유형
+    - `output_dimension` (int): 출력 임베딩 차원
+    - `max_retries` (int): 최대 재시도 횟수. 기본값: `0`
+    - `timeout` (float): 요청 시간 제한(초)
+
+    **환경 변수:**
+    - `VOYAGEAI_API_KEY` 또는 `EMBEDDINGS_VOYAGEAI_API_KEY`: `api_key`
+    - `VOYAGEAI_MODEL` 또는 `EMBEDDINGS_VOYAGEAI_MODEL`: `model`
+    - `VOYAGEAI_INPUT_TYPE` 또는 `EMBEDDINGS_VOYAGEAI_INPUT_TYPE`: `input_type`
+    - `VOYAGEAI_TRUNCATION` 또는 `EMBEDDINGS_VOYAGEAI_TRUNCATION`: `truncation`
+    - `VOYAGEAI_OUTPUT_DTYPE` 또는 `EMBEDDINGS_VOYAGEAI_OUTPUT_DTYPE`: `output_dtype`
+    - `VOYAGEAI_OUTPUT_DIMENSION` 또는 `EMBEDDINGS_VOYAGEAI_OUTPUT_DIMENSION`: `output_dimension`
+    - `VOYAGEAI_MAX_RETRIES` 또는 `EMBEDDINGS_VOYAGEAI_MAX_RETRIES`: `max_retries`
+    - `VOYAGEAI_TIMEOUT` 또는 `EMBEDDINGS_VOYAGEAI_TIMEOUT`: `timeout`
+  </Accordion>
+
+  <Accordion title="Ollama">
+    ```python main.py
+    from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec
+
+    embedding_model: OllamaProviderSpec = {
+        "provider": "ollama",
+        "config": {
+            "model_name": "llama2",
+            "url": "http://localhost:11434/api/embeddings"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): Ollama 모델 이름(예: `llama2`, `mistral`, `nomic-embed-text`)
+    - `url` (str): Ollama API 엔드포인트 URL. 기본값: `http://localhost:11434/api/embeddings`
+
+    **환경 변수:**
+    - `OLLAMA_MODEL` 또는 `EMBEDDINGS_OLLAMA_MODEL`: `model_name`
+    - `OLLAMA_URL` 또는 `EMBEDDINGS_OLLAMA_URL`: `url`
+  </Accordion>
+
+  <Accordion title="Amazon Bedrock">
+    ```python main.py
+    from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
+
+    embedding_model: BedrockProviderSpec = {
+        "provider": "amazon-bedrock",
+        "config": {
+            "model_name": "amazon.titan-embed-text-v2:0",
+            "session": boto3_session
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): Bedrock 모델 ID. 기본값: `amazon.titan-embed-text-v1`. 옵션: `amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2:0`, `cohere.embed-english-v3`, `cohere.embed-multilingual-v3`
+    - `session` (Any): AWS 인증을 위한 Boto3 세션 객체
+
+    **환경 변수:**
+    - `AWS_ACCESS_KEY_ID`: AWS 액세스 키
+    - `AWS_SECRET_ACCESS_KEY`: AWS 비밀 키
+    - `AWS_REGION`: AWS 리전(예: `us-east-1`)
+  </Accordion>
+
+  <Accordion title="Azure OpenAI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
+
+    embedding_model: AzureProviderSpec = {
+        "provider": "azure",
+        "config": {
+            "deployment_id": "your-deployment-id",
+            "api_key": "your-api-key",
+            "api_base": "https://your-resource.openai.azure.com",
+            "api_version": "2024-02-01",
+            "model_name": "text-embedding-ada-002",
+            "api_type": "azure"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `deployment_id` (str): **필수** - Azure OpenAI 배포 ID
+    - `api_key` (str): Azure OpenAI API 키
+    - `api_base` (str): Azure OpenAI 리소스 엔드포인트
+    - `api_version` (str): API 버전. 예: `2024-02-01`
+    - `model_name` (str): 모델 이름. 기본값: `text-embedding-ada-002`
+    - `api_type` (str): API 유형. 기본값: `azure`
+    - `dimensions` (int): 출력 차원
+    - `default_headers` (dict): 사용자 지정 헤더
+
+    **환경 변수:**
+    - `AZURE_OPENAI_API_KEY` 또는 `EMBEDDINGS_AZURE_API_KEY`: `api_key`
+    - `AZURE_OPENAI_ENDPOINT` 또는 `EMBEDDINGS_AZURE_API_BASE`: `api_base`
+    - `EMBEDDINGS_AZURE_DEPLOYMENT_ID`: `deployment_id`
+    - `EMBEDDINGS_AZURE_API_VERSION`: `api_version`
+    - `EMBEDDINGS_AZURE_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_AZURE_API_TYPE`: `api_type`
+    - `EMBEDDINGS_AZURE_DIMENSIONS`: `dimensions`
+  </Accordion>
+
+  <Accordion title="Google Generative AI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.google.types import GenerativeAiProviderSpec
+
+    embedding_model: GenerativeAiProviderSpec = {
+        "provider": "google-generativeai",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "gemini-embedding-001",
+            "task_type": "RETRIEVAL_DOCUMENT"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `api_key` (str): Google AI API 키
+    - `model_name` (str): 모델 이름. 기본값: `gemini-embedding-001`. 옵션: `gemini-embedding-001`, `text-embedding-005`, `text-multilingual-embedding-002`
+    - `task_type` (str): 임베딩 작업 유형. 기본값: `RETRIEVAL_DOCUMENT`. 옵션: `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`
+
+    **환경 변수:**
+    - `GOOGLE_API_KEY`, `GEMINI_API_KEY` 또는 `EMBEDDINGS_GOOGLE_API_KEY`: `api_key`
+    - `EMBEDDINGS_GOOGLE_GENERATIVE_AI_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_GOOGLE_GENERATIVE_AI_TASK_TYPE`: `task_type`
+  </Accordion>
+
+  <Accordion title="Google Vertex AI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.google.types import VertexAIProviderSpec
+
+    embedding_model: VertexAIProviderSpec = {
+        "provider": "google-vertex",
+        "config": {
+            "model_name": "text-embedding-004",
+            "project_id": "your-project-id",
+            "region": "us-central1",
+            "api_key": "your-api-key"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): 모델 이름. 기본값: `textembedding-gecko`. 옵션: `text-embedding-004`, `textembedding-gecko`, `textembedding-gecko-multilingual`
+    - `project_id` (str): Google Cloud 프로젝트 ID. 기본값: `cloud-large-language-models`
+    - `region` (str): Google Cloud 리전. 기본값: `us-central1`
+    - `api_key` (str): 인증을 위한 API 키
+
+    **환경 변수:**
+    - `GOOGLE_APPLICATION_CREDENTIALS`: 서비스 계정 JSON 파일 경로
+    - `GOOGLE_CLOUD_PROJECT` 또는 `EMBEDDINGS_GOOGLE_VERTEX_PROJECT_ID`: `project_id`
+    - `EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_GOOGLE_VERTEX_REGION`: `region`
+    - `EMBEDDINGS_GOOGLE_VERTEX_API_KEY`: `api_key`
+  </Accordion>
+
+  <Accordion title="Jina AI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
+
+    embedding_model: JinaProviderSpec = {
+        "provider": "jina",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "jina-embeddings-v3"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `api_key` (str): Jina AI API 키
+    - `model_name` (str): 모델 이름. 기본값: `jina-embeddings-v2-base-en`. 옵션: `jina-embeddings-v3`, `jina-embeddings-v2-base-en`, `jina-embeddings-v2-small-en`
+
+    **환경 변수:**
+    - `JINA_API_KEY` 또는 `EMBEDDINGS_JINA_API_KEY`: `api_key`
+    - `EMBEDDINGS_JINA_MODEL_NAME`: `model_name`
+  </Accordion>
+
+  <Accordion title="HuggingFace">
+    ```python main.py
+    from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec
+
+    embedding_model: HuggingFaceProviderSpec = {
+        "provider": "huggingface",
+        "config": {
+            "url": "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `url` (str): HuggingFace 추론 API 엔드포인트의 전체 URL
+
+    **환경 변수:**
+    - `HUGGINGFACE_URL` 또는 `EMBEDDINGS_HUGGINGFACE_URL`: `url`
+  </Accordion>
+
+  <Accordion title="Instructor">
+    ```python main.py
+    from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
+
+    embedding_model: InstructorProviderSpec = {
+        "provider": "instructor",
+        "config": {
+            "model_name": "hkunlp/instructor-xl",
+            "device": "cuda",
+            "instruction": "Represent the document"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): HuggingFace 모델 ID. 기본값: `hkunlp/instructor-base`. 옵션: `hkunlp/instructor-xl`, `hkunlp/instructor-large`, `hkunlp/instructor-base`
+    - `device` (str): 실행할 장치. 기본값: `cpu`. 옵션: `cpu`, `cuda`, `mps`, `xpu`
+    - `instruction` (str): 임베딩을 위한 명령어 접두사
+
+    **환경 변수:**
+    - `EMBEDDINGS_INSTRUCTOR_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_INSTRUCTOR_DEVICE`: `device`
+    - `EMBEDDINGS_INSTRUCTOR_INSTRUCTION`: `instruction`
+  </Accordion>
+
+  <Accordion title="Sentence Transformer">
+    ```python main.py
+    from crewai.rag.embeddings.providers.sentence_transformer.types import SentenceTransformerProviderSpec
+
+    embedding_model: SentenceTransformerProviderSpec = {
+        "provider": "sentence-transformer",
+        "config": {
+            "model_name": "all-mpnet-base-v2",
+            "device": "cuda",
+            "normalize_embeddings": True
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): Sentence Transformers 모델 이름. 기본값: `all-MiniLM-L6-v2`. 옵션: `all-mpnet-base-v2`, `all-MiniLM-L6-v2`, `paraphrase-multilingual-MiniLM-L12-v2`
+    - `device` (str): 실행할 장치. 기본값: `cpu`. 옵션: `cpu`, `cuda`, `mps`, `xpu`
+    - `normalize_embeddings` (bool): 임베딩을 정규화할지 여부. 기본값: `False`
+
+    **환경 변수:**
+    - `EMBEDDINGS_SENTENCE_TRANSFORMER_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_SENTENCE_TRANSFORMER_DEVICE`: `device`
+    - `EMBEDDINGS_SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS`: `normalize_embeddings`
+  </Accordion>
+
+  <Accordion title="ONNX">
+    ```python main.py
+    from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec
+
+    embedding_model: ONNXProviderSpec = {
+        "provider": "onnx",
+        "config": {
+            "preferred_providers": ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `preferred_providers` (list[str]): 선호도 순으로 나열한 ONNX 실행 프로바이더 목록
+
+    **환경 변수:**
+    - `EMBEDDINGS_ONNX_PREFERRED_PROVIDERS`: `preferred_providers`(쉼표로 구분된 목록)
+  </Accordion>
+
+  <Accordion title="OpenCLIP">
+    ```python main.py
+    from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec
+
+    embedding_model: OpenCLIPProviderSpec = {
+        "provider": "openclip",
+        "config": {
+            "model_name": "ViT-B-32",
+            "checkpoint": "laion2b_s34b_b79k",
+            "device": "cuda"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): OpenCLIP 모델 아키텍처. 기본값: `ViT-B-32`. 옵션: `ViT-B-32`, `ViT-B-16`, `ViT-L-14`
+    - `checkpoint` (str): 사전 학습된 체크포인트 이름. 기본값: `laion2b_s34b_b79k`. 옵션: `laion2b_s34b_b79k`, `laion400m_e32`, `openai`
+    - `device` (str): 실행할 장치. 기본값: `cpu`. 옵션: `cpu`, `cuda`
+
+    **환경 변수:**
+    - `EMBEDDINGS_OPENCLIP_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_OPENCLIP_CHECKPOINT`: `checkpoint`
+    - `EMBEDDINGS_OPENCLIP_DEVICE`: `device`
+  </Accordion>
+
+  <Accordion title="Text2Vec">
+    ```python main.py
+    from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
+
+    embedding_model: Text2VecProviderSpec = {
+        "provider": "text2vec",
+        "config": {
+            "model_name": "shibing624/text2vec-base-multilingual"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_name` (str): HuggingFace의 Text2Vec 모델 이름. 기본값: `shibing624/text2vec-base-chinese`. 옵션: `shibing624/text2vec-base-multilingual`, `shibing624/text2vec-base-chinese`
+
+    **환경 변수:**
+    - `EMBEDDINGS_TEXT2VEC_MODEL_NAME`: `model_name`
+  </Accordion>
+
+  <Accordion title="Roboflow">
+    ```python main.py
+    from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec
+
+    embedding_model: RoboflowProviderSpec = {
+        "provider": "roboflow",
+        "config": {
+            "api_key": "your-api-key",
+            "api_url": "https://infer.roboflow.com"
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `api_key` (str): Roboflow API 키. 기본값: `""`(빈 문자열)
+    - `api_url` (str): Roboflow 추론 API URL. 기본값: `https://infer.roboflow.com`
+
+    **환경 변수:**
+    - `ROBOFLOW_API_KEY` 또는 `EMBEDDINGS_ROBOFLOW_API_KEY`: `api_key`
+    - `ROBOFLOW_API_URL` 또는 `EMBEDDINGS_ROBOFLOW_API_URL`: `api_url`
+  </Accordion>
+
+  <Accordion title="WatsonX (IBM)">
+    ```python main.py
+    from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderSpec
+
+    embedding_model: WatsonXProviderSpec = {
+        "provider": "watsonx",
+        "config": {
+            "model_id": "ibm/slate-125m-english-rtrvr",
+            "url": "https://us-south.ml.cloud.ibm.com",
+            "api_key": "your-api-key",
+            "project_id": "your-project-id",
+            "batch_size": 100,
+            "concurrency_limit": 10,
+            "persistent_connection": True
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `model_id` (str): WatsonX 모델 식별자
+    - `url` (str): WatsonX API 엔드포인트
+    - `api_key` (str): IBM Cloud API 키
+    - `project_id` (str): WatsonX 프로젝트 ID
+    - `space_id` (str): WatsonX 공간 ID(project_id의 대안)
+    - `batch_size` (int): 임베딩 배치 크기. 기본값: `100`
+    - `concurrency_limit` (int): 최대 동시 요청 수. 기본값: `10`
+    - `persistent_connection` (bool): 지속 연결 사용 여부. 기본값: `True`
+    - 그 외 20개 이상의 추가 인증 및 구성 옵션
+
+    **환경 변수:**
+    - `WATSONX_API_KEY` 또는 `EMBEDDINGS_WATSONX_API_KEY`: `api_key`
+    - `WATSONX_URL` 또는 `EMBEDDINGS_WATSONX_URL`: `url`
+    - `WATSONX_PROJECT_ID` 또는 `EMBEDDINGS_WATSONX_PROJECT_ID`: `project_id`
+    - `EMBEDDINGS_WATSONX_MODEL_ID`: `model_id`
+    - `EMBEDDINGS_WATSONX_SPACE_ID`: `space_id`
+    - `EMBEDDINGS_WATSONX_BATCH_SIZE`: `batch_size`
+    - `EMBEDDINGS_WATSONX_CONCURRENCY_LIMIT`: `concurrency_limit`
+    - `EMBEDDINGS_WATSONX_PERSISTENT_CONNECTION`: `persistent_connection`
+  </Accordion>
+
+  <Accordion title="사용자 지정">
+    ```python main.py
+    from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
+    from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
+
+    class MyEmbeddingFunction(EmbeddingFunction):
+        def __call__(self, input):
+            # Your custom embedding logic
+            return embeddings
+
+    embedding_model: CustomProviderSpec = {
+        "provider": "custom",
+        "config": {
+            "embedding_callable": MyEmbeddingFunction
+        }
+    }
+    ```
+
+    **구성 옵션:**
+    - `embedding_callable` (type[EmbeddingFunction]): 사용자 지정 임베딩 함수 클래스
+
+    **참고:** 사용자 지정 임베딩 함수는 `crewai.rag.core.base_embeddings_callable`에 정의된 `EmbeddingFunction` 프로토콜을 구현해야 합니다. `__call__` 메서드는 입력 데이터를 받아 numpy 배열 목록(또는 정규화할 수 있는 호환 형식)으로 임베딩을 반환해야 합니다. 반환된 임베딩은 자동으로 정규화되고 검증됩니다.
+  </Accordion>
+</AccordionGroup>
+
+### 참고
+- **필수**로 표시되지 않은 모든 구성 필드는 선택 사항입니다.
+- 일반적으로 API 키는 구성 대신 환경 변수를 통해 제공할 수 있습니다.
+- 해당하는 경우 기본값이 표시됩니다.
+
 
 ## 결론
 `RagTool`은 다양한 데이터 소스에서 지식 베이스를 생성하고 질의할 수 있는 강력한 방법을 제공합니다. Retrieval-Augmented Generation을 활용하여, 에이전트가 관련 정보를 효율적으로 접근하고 검색할 수 있게 하여, 보다 정확하고 상황에 맞는 응답을 제공하는 능력을 향상시킵니다.

docs/edge/ko/tools/ai-ml/visiontool.mdx

@@ -12,6 +12,16 @@ mode: "wide"
 이 도구는 이미지에서 텍스트를 추출하는 데 사용됩니다. 에이전트에 전달되면 이미지에서 텍스트를 추출한 후 이를 사용하여 응답, 보고서 또는 기타 출력을 생성합니다.
 이미지의 URL 또는 경로(PATH)를 에이전트에 전달해야 합니다.
 
+이미지에 대한 사용자 지정 `query`를 요청하고, 요청에 가장 적합한 모델을 자동으로 선택하는 `complexity_level`을 지정할 수도 있습니다:
+
+| 복잡도 수준 | 모델         |
+| :--------------- | :------------ |
+| `easy`           | `gpt-5.6-luna`  |
+| `medium` (기본값) | `gpt-5.6-terra` |
+| `hard`           | `gpt-5.6-sol`   |
+
+도구에 명시적인 `llm` 또는 `model`을 제공하면, 복잡도 기반 모델 선택보다 우선합니다.
+
 ## 설치
 
 crewai_tools 패키지를 설치하세요
@@ -43,8 +53,10 @@ def researcher(self) -> Agent:
 
 ## 인수
 
-VisionTool은 다음과 같은 인수가 필요합니다:
+VisionTool은 다음과 같은 인수를 받습니다:
 
 | 인수                | 타입     | 설명                                                                              |
 | :------------------ | :------- | :-------------------------------------------------------------------------------- |
-| **image_path_url**  | `string` | **필수**. 텍스트를 추출해야 하는 이미지 파일의 경로입니다.                         |
+| **image_path_url**  | `string` | **필수**. 텍스트를 추출해야 하는 이미지 파일의 경로(또는 URL)입니다.               |
+| **query**           | `string` | **선택**. 이미지에 대해 모델에 묻는 질문 또는 지시입니다. 기본값은 `"What's in this image?"`입니다. |
+| **complexity_level** | `string` | **선택**. 모델을 선택하는 요청의 복잡도입니다: `easy`, `medium`, `hard`. 기본값은 `medium`입니다. |

docs/edge/ko/tools/web-scraping/oxylabsscraperstool.mdx

@@ -4,7 +4,7 @@ description: >
   Oxylabs 스크래퍼를 사용하면 해당 소스에서 정보를 쉽게 접근할 수 있습니다. 아래에서 사용 가능한 소스 목록을 확인하세요:
     - `Amazon Product`
     - `Amazon Search`
-    - `Google Seach`
+    - `Google Search`
     - `Universal`
 icon: globe
 mode: "wide"
@@ -87,7 +87,7 @@ print(result)
 ### 파라미터
 
 - `query` - Amazon 검색어.
-- `domain` - Bestbuy의 도메인 로컬라이제이션.
+- `domain` - Amazon의 도메인 로컬라이제이션.
 - `start_page` - 시작 페이지 번호.
 - `pages` - 가져올 페이지 수.
 - `geo_location` - _배송지_ 위치.

docs/edge/pt-BR/changelog.mdx

@@ -4,6 +4,145 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="16 set 2026">
+  ## v1.15.22
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.22)
+
+  ## O que mudou
+
+  ### Recursos
+  - Suporte a aliases como identificadores de conexão
+  - Registrar razões para falhas na criação de implantações
+  - Coletar feedback humano e pausar eventos em rastreamento
+  - Adicionar variável de contexto `llm_overlay` para direcionar papéis de agentes a modelos
+  - Transportar `task_prompt` e saída nos payloads de execução de agentes
+  - Validar integrações de plataforma durante a configuração da equipe
+  - Adicionar ferramentas de plataforma ao assistente JSON da equipe
+  - Expor o catálogo de aplicações da CrewAI Platform
+  - Adicionar OpenRouter como um provedor de embedding suportado
+
+  ### Correções de Bugs
+  - Aceitar CRLF nas definições de habilidades inline
+  - Carregar URLs de arquivos de texto através do fetcher seguro
+  - Fechar conexões SQLite no armazenamento de saídas da tarefa de início
+  - Transportar `from_cache` no `ToolUsageFinishedEvent` do caminho da ferramenta nativa
+  - Levantar `ValueError` em vez de uma elevação genérica no uploader
+  - Suportar tipos não primitivos na SQLiteFlowPersistence
+  - Melhorar as instruções para agentes de codificação
+  - Ler pontos de verificação JSON como UTF-8
+  - Sobrescrever backup obsoleto de `poetry.lock` no Windows
+  - Chavear chamadas de ferramentas transmitidas por índice de fio no Azure
+  - Preservar partes do conteúdo dos dados do arquivo no Gemini
+  - Honrar `read_only` nos tempos de acesso de `update()` e `recall()`
+  - Enviar `reasoning_effort` para cada modelo de raciocínio da OpenAI
+  - Prevenir falhas na TUI de execução quando a saída transmitida contém um literal `[...]`
+  - Rejeitar replay quando as tarefas armazenadas diferem
+  - Usar guardas `sys.platform` para compatibilidade com mypy no Windows
+  - Solicitar a resposta final forçada como uma ação do usuário
+  - Manter nulo no esquema de saída da tarefa incorporado no prompt
+  - Alinhar `DOCXSearchTool` com o padrão de esquema fixo RAG padrão
+
+  ### Documentação
+  - Adicionar `xpu` às opções de dispositivo de embedding
+  - Listar todos os pacotes do workspace
+
+  ## Contribuidores
+
+  @ASTion24, @BlueX888, @HUAN2022A, @RaycarlLei, @Rohitkanithi, @SWAPI03, @SharoonSharif, @ShivangiRay, @Theater-ahyeon, @Vidit-Ostwal, @gamal1osama, @github-actions[bot], @joaomdmoura, @lorenzejay, @mhbuehler, @modusensus, @monkscode, @rohitkanithi, @roli-lpci, @vinibrsl, @wangtaotaotao95
+
+</Update>
+
+<Update label="09 set 2026">
+  ## v1.15.21
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.21)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar telemetria para rastrear o tempo de execução de checkpoints e o uso do CLI.
+
+  ### Correções de Bugs
+  - Corrigir erros de gateway relatados dentro de uma resposta HTTP 200.
+  - Manter o deploy push na criação de fonte AMP.
+  - Relatar falhas de raspagem em vez de levantar IndexError na integração com Oxylabs.
+  - Roteirizar todos os modelos DashScope através do provedor nativo.
+  - Persistir checkpoints JSON como UTF-8.
+  - Remover $ soltos nas URLs de busca de f-string para BrightData.
+  - Suportar arrays do tipo lista na conversão de esquema JSON.
+  - Corrigir a janela de contexto do gpt-4o-mini de 200000 para 128000.
+  - Preservar os argumentos de chamada da ferramenta de streaming em contentBlockStop.
+  - Tornar os hooks de pré-compromisso portáteis no Windows.
+
+  ### Documentação
+  - Esclarecer que o rastreamento é gerenciado separadamente da telemetria.
+  - Corrigir o link de referência de busca paralela.
+  - Remover parâmetros obsoletos da docstring de handle_llm_stream_chunk.
+  - Corrigir exemplos de docstring de saída de streaming.
+  - Usar UUIDs de organização na referência de instalação de habilidades.
+
+  ## Contribuidores
+
+  @Alphaxiaoteng, @DrewWhittleNZ, @Ghraven, @Shxiao101, @Vaishnavi220506, @Vidit-Ostwal, @danielfsbarreto, @georgeatparallel, @github-actions[bot], @jessemiller, @joaomdmoura, @kimnamu, @kiwoongyoon, @liang0417, @lorenzejay, @oxy-giedrius, @simpleqt, @uoparaji
+
+</Update>
+
+<Update label="04 set 2026">
+  ## v1.15.20
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.20)
+
+  ## O que Mudou
+
+  ### Correções de Bugs
+  - Corrigir a descoberta de alias da ferramenta da plataforma legada
+
+  ### Documentação
+  - Atualizar snapshot e changelog para v1.15.19
+
+  ## Contribuidores
+
+  @joaomdmoura, @vinibrsl
+
+</Update>
+
+<Update label="04 set 2026">
+  ## v1.15.19
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.19)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar cliente de integrações Clipper
+  - Adicionar `now()` ao ambiente de expressão CEL
+  - Registrar como uma execução de equipe terminou para cada usuário
+  - Relatar o tamanho da máquina como uma faixa grosseira, não uma contagem de núcleos
+  - Adicionar cliente injetável para ferramentas da plataforma CrewAI
+
+  ### Correções de Bugs
+  - Corrigir leitura de URLs de octet-stream e xlsx em `urlreadtool`
+  - Corrigir a adição de turno de usuário final em provedor nativo Gemini
+  - Normalizar esquema e porta na URL base do Ollama
+  - Preservar configurações de escopo reutilizáveis na memória
+  - Atualizar `pypdf` para 6.16.2 devido a vulnerabilidade de segurança
+  - Atualizar `nltk` para 3.10.3 devido a vulnerabilidade de segurança
+  - Corrigir saídas estruturadas nativas para os modelos atuais do Claude e piso CVE do Snowflake
+  - Executar ganchos de chamada de modelo em cada caminho e propagar uma negação
+
+  ### Documentação
+  - Remover `CodeInterpreterTool` dos exemplos de visão geral de IA/ML
+  - Apontar o link `prompt-template` para seu caminho atual
+  - Atualizar guia de canais para a API atual de canais do CopilotKit
+  - Atualizar IDs de modelos Gemini aposentados
+
+  ## Contribuidores
+
+  @Vidit-Ostwal, @a-yeyang, @github-actions[bot], @hvlcrs, @joaomdmoura, @kikifrost, @lorenzejay, @lucasgomide, @parthiban-sivakumar, @ranst91, @tandede, @thiagomoretto, @vinibrsl
+
+</Update>
+
 <Update label="27 ago 2026">
   ## v1.15.18
 

docs/edge/pt-BR/concepts/cli.mdx

@@ -307,6 +307,7 @@ crewai org switch <organization_id>
 
   - Inicia o processo de deployment na plataforma CrewAI AMP.
   - Após a iniciação bem-sucedida, será exibida a mensagem Deployment created successfully! juntamente com o Nome do Deployment e um Deployment ID (UUID) único.
+  - O push mantém a origem usada na criação. Adicionar um `origin` git depois não troca um deployment ZIP por git.
 
 - **Status do Deployment**: Você pode verificar o status do seu deployment com:
 

docs/edge/pt-BR/concepts/knowledge.mdx

@@ -601,6 +601,21 @@ agent = Agent(
         }
     }
 )
+
+# Option 4: Use OpenRouter embeddings (supports models across providers)
+# Set OPENROUTER_API_KEY environment variable if api_key is omitted in config
+crew = Crew(
+    agents=[agent],
+    tasks=[...],
+    knowledge_sources=[knowledge_source],
+    embedder={
+        "provider": "openrouter",
+        "config": {
+            "model_name": "openai/text-embedding-3-small",
+            "api_key": "your-openrouter-api-key"  # Optional if OPENROUTER_API_KEY is set
+        }
+    }
+)
 ```
 
 #### Configurando Embeddings do Azure OpenAI

docs/edge/pt-BR/concepts/llms.mdx

@@ -919,6 +919,38 @@ Saiba como obter o máximo da configuração do seu LLM:
     llm = LLM(model="gpt-4")
     ```
   </Tab>
+  <Tab title="Erros de Gateway">
+    <Tip>
+      Gateways como o OpenRouter retornam `200 OK` assim que o provedor upstream aceita a requisição, então um timeout do provedor chega no corpo da resposta em vez do código de status.
+    </Tip>
+
+    O CrewAI lança a mesma exceção que o código upstream produziria como um status HTTP real, portanto uma falha mascarada é capturada pelo tratamento de retry que você já possui:
+
+    ```python
+    import openai
+    from pydantic import BaseModel
+
+    from crewai import LLM
+
+
+    class Report(BaseModel):
+        summary: str
+
+
+    llm = LLM(model="openrouter/z-ai/glm-5.3", response_format=Report)
+
+    try:
+        result = llm.call("Summarize the incident", response_model=Report)
+    except openai.InternalServerError as e:
+        # "z-ai/glm-5.3 via openrouter.ai returned HTTP 200 with an upstream error
+        #  and no choices: The operation was aborted (upstream code 504)"
+        print(f"Upstream provider failed, safe to retry: {e}")
+    ```
+
+    <Warning>
+      Um `response_model` grande ou profundamente aninhado aumenta a chance de timeouts upstream. Trate esses casos como falhas transitórias do provedor, e não como o modelo produzindo saída estruturada malformada.
+    </Warning>
+  </Tab>
   <Tab title="Comprimento do Contexto">
     <Tip>
       Use modelos de contexto expandido para tarefas extensas

docs/edge/pt-BR/concepts/memory.mdx

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Usar Google Gemini
-memory = Memory(llm="gemini/gemini-2.0-flash")
+memory = Memory(llm="gemini/gemini-3.7-flash")
 
 # Passar uma instância LLM pré-configurada com configurações customizadas
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/pt-BR/concepts/skills.mdx

@@ -202,13 +202,17 @@ A publicação lê `name`, `description` e `metadata.version` do frontmatter do
 
 ### Instalar
 
-Instale uma skill publicada pela sua referência `@org/name`:
+Instale uma skill publicada pela sua referência `@org-uuid/name`:
 
 ```shell Terminal
-crewai skill install @acme/code-review
+crewai skill install @your-org-uuid/code-review
 ```
 
-Dentro de um projeto de crew, a skill é colocada em `./skills/{name}/`; fora de um projeto, vai para o cache compartilhado em `~/.crewai/skills/{org}/{name}/`.
+<Note>
+  Use o **UUID** da sua organização, não o nome — nomes de organização não são únicos, então um nome pode resolver para a organização errada e a instalação falha com um erro de "não encontrado". Execute `crewai org list` para ver o UUID (a coluna `ID`) de cada organização à qual você pertence.
+</Note>
+
+Dentro de um projeto de crew, a skill é colocada em `./skills/{name}/`; fora de um projeto, vai para o cache compartilhado em `~/.crewai/skills/{org-uuid}/{name}/`.
 
 Agentes também podem referenciar skills do registro diretamente — elas são resolvidas a partir do cache local (ou do diretório `skills/` do projeto) em tempo de execução:
 
@@ -217,7 +221,7 @@ agent = Agent(
     role="Senior Code Reviewer",
     goal="Review pull requests for quality and security issues",
     backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["@acme/code-review"],  # registry ref, resolved locally
+    skills=["@your-org-uuid/code-review"],  # registry ref, resolved locally
 )
 ```
 

docs/edge/pt-BR/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ Nos bastidores, o CrewAI adota um sistema de prompt modular que pode ser amplame
 - **Tratamento de erros** – Definem como os agentes respondem a falhas, exceções ou timeouts.
 - **Prompts específicos de ferramentas** – Definem instruções detalhadas para como as ferramentas são invocadas ou utilizadas.
 
-Confira os [templates de prompt originais no repositório do CrewAI](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) para ver como esses elementos são organizados. A partir daí, você pode sobrescrever ou adaptar conforme necessário para desbloquear comportamentos avançados.
+Confira os [templates de prompt originais no repositório do CrewAI](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json) para ver como esses elementos são organizados. A partir daí, você pode sobrescrever ou adaptar conforme necessário para desbloquear comportamentos avançados.
 
 ## Entendendo as Instruções de Sistema Padrão
 

docs/edge/pt-BR/guides/coding-tools/agents-md.mdx

@@ -11,7 +11,7 @@ mode: "wide"
 
 ## Criar um Projeto com o CLI
 
-Use o CLI do CrewAI para criar a estrutura de um projeto, e o `AGENTS.md` será automaticamente adicionado na raiz.
+Use o CLI do CrewAI para criar a estrutura de um projeto. O `AGENTS.md` é adicionado na raiz, junto com um `CLAUDE.md` e um `GEMINI.md` que o importam, para que o Claude Code e o Gemini CLI leiam a mesma orientação que todos os outros assistentes.
 
 ```bash
 # Crew
@@ -32,24 +32,28 @@ O Codex pode ser guiado por arquivos `AGENTS.md` colocados no seu repositório.
 
 ### Claude Code
 
-O Claude Code armazena a memória do projeto em `CLAUDE.md`. Você pode inicializá-lo com `/init` e editá-lo usando `/memory`. O Claude Code também suporta importações dentro do `CLAUDE.md`, então você pode adicionar uma única linha como `@AGENTS.md` para incluir as instruções compartilhadas sem duplicá-las.
+O Claude Code lê o `CLAUDE.md` e ignora o `AGENTS.md`. Projetos gerados pelo CLI já incluem um `CLAUDE.md` cuja única instrução é a linha de importação `@AGENTS.md`, para que a orientação compartilhada seja carregada sem duplicação. Adicione notas específicas do Claude abaixo dessa linha e mantenha as convenções compartilhadas no `AGENTS.md`.
 
-Você pode simplesmente usar:
+Para um projeto criado antes de o `CLAUDE.md` passar a ser gerado, adicione a importação você mesmo:
 
 ```bash
-mv AGENTS.md CLAUDE.md
+printf '@AGENTS.md\n' > CLAUDE.md
 ```
 
+Não renomeie o `AGENTS.md` para `CLAUDE.md`: o Codex e o Cursor leem o `AGENTS.md`, e a renomeação o esconde deles.
+
 ### Gemini CLI e Google Antigravity
 
-O Gemini CLI e o Antigravity carregam um arquivo de contexto do projeto (padrão: `GEMINI.md`) da raiz do repositório e diretórios pais. Você pode configurá-lo para ler o `AGENTS.md` em vez disso (ou além) definindo `context.fileName` nas configurações do Gemini CLI. Por exemplo, defina apenas para `AGENTS.md`, ou inclua tanto `AGENTS.md` quanto `GEMINI.md` se quiser manter o formato de cada ferramenta.
+O Gemini CLI e o Antigravity carregam um arquivo de contexto do projeto (padrão: `GEMINI.md`) da raiz do repositório e diretórios pais. Projetos gerados pelo CLI já incluem um `GEMINI.md` cuja única instrução é a linha de importação `@./AGENTS.md`, para que a orientação compartilhada seja carregada sem duplicação. Adicione notas específicas do Gemini abaixo dessa linha e mantenha as convenções compartilhadas no `AGENTS.md`.
 
-Você pode simplesmente usar:
+Para um projeto criado antes de o `GEMINI.md` passar a ser gerado, adicione a importação você mesmo:
 
 ```bash
-mv AGENTS.md GEMINI.md
+printf '@./AGENTS.md\n' > GEMINI.md
 ```
 
+Como alternativa, defina `context.fileName` nas configurações do Gemini CLI para incluir o `AGENTS.md` e o Gemini o lerá diretamente. Não renomeie o `AGENTS.md` para `GEMINI.md`: o Codex e o Cursor leem o `AGENTS.md`, e a renomeação o esconde deles.
+
 ### Cursor
 
 O Cursor suporta `AGENTS.md` como arquivo de instruções do projeto. Coloque-o na raiz do projeto para fornecer orientação ao assistente de codificação do Cursor.

docs/edge/pt-BR/guides/crews/first-crew.mdx

@@ -77,7 +77,7 @@ Substitua o arquivo gerado `agents/researcher.jsonc` e adicione `agents/analyst.
 }
 ```
 
-Substitua `provider/model-id` pelo modelo usado, como `openai/gpt-4o`, `anthropic/claude-sonnet-4-6` ou `gemini/gemini-2.0-flash-001`.
+Substitua `provider/model-id` pelo modelo usado, como `openai/gpt-4o`, `anthropic/claude-sonnet-4-6` ou `gemini/gemini-3.7-flash`.
 
 ## Etapa 3: Definir tarefas e configurações
 

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

@@ -135,7 +135,7 @@ Agora, vamos configurar o crew de redatores com JSONC. Vamos definir dois agente
 }
 ```
 
-Substitua `provider/model-id` pelo modelo que você usa, como `openai/gpt-4o`, `gemini/gemini-2.0-flash-001` ou `anthropic/claude-sonnet-4-6`.
+Substitua `provider/model-id` pelo modelo que você usa, como `openai/gpt-4o`, `gemini/gemini-3.7-flash` ou `anthropic/claude-sonnet-4-6`.
 
 3. Crie `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
 
@@ -481,7 +481,7 @@ Flows permitem que você faça chamadas diretas a modelos de linguagem quando pr
 
 ```python
 llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    model="model-id-here",  # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
     response_format=GuideOutline
 )
 response = llm.call(messages=messages)

docs/edge/pt-BR/guides/frontend/channels.mdx

@@ -0,0 +1,156 @@
+---
+title: Channels
+description: Execute o mesmo agente CrewAI como um bot do Slack ou Teams com o Channels SDK do CopilotKit e a plataforma gerenciada Intelligence.
+icon: messages
+mode: "wide"
+---
+
+## Encontre seus usuários onde eles já estão
+
+O agente CrewAI que você construiu na [Visão geral](/edge/pt-BR/guides/frontend/overview) não precisa viver por trás de um web app. O mesmo Crew ou Flow pode rodar como um bot dentro de uma plataforma de mensagens. Sem reconstruir, sem uma segunda cópia da lógica do seu agente: o agente permanece exposto pelo [protocolo AG-UI](https://docs.ag-ui.com), e um **channel** o aciona a partir do Slack ou do Microsoft Teams.
+
+O [Channels SDK](https://docs.copilotkit.ai/slack) do CopilotKit fornece esse channel. Você declara um `createChannel` em um pequeno runtime, aponta-o para o seu agente CrewAI, e a plataforma gerenciada **Intelligence** do CopilotKit intermedia a conexão com o provedor de mensagens.
+
+<Note>
+Diferentemente do restante desta seção, Channels **não é self-hosted**. Ele roda através do **CopilotKit Intelligence** — uma superfície obrigatória para Channels, por design (há um plano gratuito disponível). O Intelligence detém a conexão com a plataforma e as credenciais, recebe cada evento da plataforma e entrega o turno ao processo do seu channel; seu processo executa o agente e transmite a resposta de volta. Você configura o Slack uma vez no painel do Intelligence, e as credenciais da plataforma nunca entram no seu processo. Seu agente, suas tools e seu estado continuam sendo seus.
+</Note>
+
+## Como tudo se encaixa
+
+Nada muda no servidor do seu agente CrewAI. Ele continua servindo o seu Crew ou Flow por AG-UI exatamente como na Visão geral. O que você adiciona é um processo Node separado, de longa duração, construído com `@copilotkit/channels`: ele registra um channel no `CopilotRuntime`, conecta-se ao Intelligence e executa o seu agente sempre que chega uma mensagem.
+
+```
+Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+```
+
+O processo do channel mantém uma conexão persistente com o gateway do Intelligence, então ele precisa de um host de longa duração — um handler de requisições serverless não consegue ser dono dessa conexão. Seu servidor CrewAI pode continuar servindo o frontend web da Visão geral ao mesmo tempo: o web app e o channel são apenas dois clientes de um único endpoint AG-UI.
+
+## Guia de integração
+
+<Steps>
+
+<Step title="Instale os pacotes do Channels">
+
+O Channels SDK vem com tudo incluído — cada plataforma é entregue no mesmo pacote, sem nenhum adaptador por plataforma para instalar. Adicione-o junto ao runtime que hospeda o channel e ao cliente AG-UI do CrewAI:
+
+```bash
+npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
+```
+
+</Step>
+
+<Step title="Crie um Channel no Intelligence">
+
+No [painel do CopilotKit](https://docs.copilotkit.ai/slack), crie um Channel e conecte o Slack — o Intelligence guia você na criação do app do Slack e detém suas credenciais. Isso deixa duas variáveis de ambiente para o seu processo, ambas vindas do painel:
+
+```bash
+export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
+export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
+```
+
+</Step>
+
+<Step title="Defina o channel">
+
+`createChannel` declara o channel e anexa o seu agente. Construa o agente como uma factory por thread, para que cada conversa ganhe sua própria sessão, usando o mesmo `CrewAIAgent` que a Visão geral usa no runtime web, apontado para o seu endpoint AG-UI. `identifyUser: "platform"` permite que o Intelligence mapeie cada usuário da plataforma para uma identidade estável.
+
+```ts
+// channel.ts
+import { createChannel } from "@copilotkit/channels";
+import { CrewAIAgent } from "@ag-ui/crewai";
+
+const channel = createChannel({
+  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
+  identifyUser: "platform",
+  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
+  agent: (threadId) => {
+    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
+    agent.threadId = threadId;
+    return agent;
+  },
+});
+
+// A mention subscribes the thread and runs the agent; afterwards every message
+// in a subscribed thread runs it without needing another mention.
+channel.onMention(async ({ thread }) => {
+  await thread.subscribe();
+  await thread.runAgent();
+});
+
+channel.onMessage(async ({ thread }) => {
+  if (await thread.isSubscribed()) await thread.runAgent();
+});
+
+export { channel };
+```
+
+</Step>
+
+<Step title="Registre o channel no runtime">
+
+Crie um `CopilotRuntime` com o gateway do Intelligence e o seu channel, e então sirva-o com `createCopilotNodeListener`. O mapa `agents` permanece vazio — o channel fornece seu próprio agente. Aguarde o channel ficar pronto, para que uma configuração quebrada faça a inicialização falhar de forma visível.
+
+```ts
+// server.ts
+import { createServer } from "node:http";
+import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
+import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
+import { channel } from "./channel";
+
+const runtime = new CopilotRuntime({
+  agents: {}, // the channel supplies its own agent; no web-facing agents needed
+  intelligence: new CopilotKitIntelligence({
+    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
+  }),
+  channels: [channel],
+});
+
+const listener = createCopilotNodeListener({ runtime });
+await listener.channels?.ready({ timeoutMs: 15_000 });
+
+createServer(listener).listen(3123, () => {
+  console.log("Channels runtime listening on port 3123");
+});
+```
+
+</Step>
+
+<Step title="Execute o runtime do channel">
+
+Inicie-o junto ao servidor do seu agente CrewAI:
+
+```bash
+uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
+npx tsx server.ts                 # terminal 2 — Channels runtime
+```
+
+Mencione o bot no Slack ou no Teams e ele executa o seu Crew ou Flow, transmitindo a resposta de volta para a thread. A thread permanece inscrita, então mensagens de acompanhamento rodam sem outra menção.
+
+</Step>
+
+</Steps>
+
+## O modelo de eventos
+
+Um channel reage a eventos da plataforma com handlers, e cada handler recebe uma `thread` que você aciona com alguns métodos:
+
+- **`channel.onMention`** dispara quando um usuário @-menciona o bot. Chame `thread.subscribe()` para entrar na thread, e então `thread.runAgent()` para executar o seu agente CrewAI na menção.
+- **`channel.onMessage`** dispara em cada mensagem de uma thread que o bot consegue ver. Restrinja com `thread.isSubscribed()` para que o agente só responda onde tiver entrado, e então `thread.runAgent()`.
+- **`thread.runAgent()`** executa o agente CrewAI anexado para o turno atual e transmite a saída dele de volta para o channel. Passe `{ prompt }` para sobrescrever o texto sobre o qual o agente roda.
+
+Seu agente recebe um `RunAgentInput` comum do AG-UI e emite eventos comuns do AG-UI; as mecânicas da plataforma ficam por trás do channel, então o mesmo Crew ou Flow roda sem alterações em todas as plataformas. O channel também expõe handlers para boas-vindas, interrupções, comandos, reações e modais — consulte a [referência de `Channel`](https://docs.copilotkit.ai/reference/channels/classes/Channel) para conhecer toda a superfície.
+
+## Suporte a plataformas
+
+O caminho gerenciado do Intelligence cobre **Slack** e **Microsoft Teams** hoje — o mesmo código de channel roda em qualquer um dos dois, e `message.platform` / `thread.platform` reportam a origem nativa. Outras plataformas (Discord, Telegram, WhatsApp) são alcançadas através de **adaptadores diretos** operados pelo desenvolvedor, em vez do caminho gerenciado — o seu próprio processo detém as credenciais da plataforma e o transporte. Consulte a [documentação de Channels do CopilotKit](https://docs.copilotkit.ai/slack) para a lista atual de plataformas e a configuração por plataforma.
+
+## Relacionados
+
+<CardGroup cols={2}>
+  <Card title="Visão geral do Frontend" icon="browser" href="/edge/pt-BR/guides/frontend/overview">
+    Sirva o seu Crew ou Flow por AG-UI — a base sobre a qual todo channel é construído.
+  </Card>
+  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    Pause o agente para coletar aprovação ou input do usuário no meio da execução.
+  </Card>
+</CardGroup>

docs/edge/pt-BR/guides/frontend/overview.mdx

@@ -0,0 +1,238 @@
+---
+title: Frontend Overview
+description: Construa interfaces de usuário interativas para seus agentes CrewAI com o CopilotKit e o protocolo AG-UI.
+icon: browser
+mode: "wide"
+---
+
+## Dê uma interface de usuário aos seus agentes
+
+O CrewAI executa seus agentes. O [CopilotKit](https://copilotkit.ai) dá a eles um frontend. Juntos, eles permitem que você construa aplicações em que os usuários conversam com um Crew ou Flow, o observam trabalhar em tempo real, aprovam suas decisões e veem sua saída renderizada como UI ao vivo, em vez de paredes de texto.
+
+Os dois se conectam através do [protocolo AG-UI](https://docs.ag-ui.com). O pacote `ag-ui-crewai` expõe qualquer Crew ou Flow como um endpoint AG-UI. Os hooks e componentes React do CopilotKit consomem esse endpoint. Isso desbloqueia experiências que vão muito além de uma caixa de chat:
+
+<CardGroup cols={2}>
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+    Renderize as chamadas de tool e o estado do agente como seus próprios componentes React.
+  </Card>
+  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    Pause o agente para coletar aprovação ou input do usuário no meio da execução.
+  </Card>
+  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+    Mantenha o estado do agente e a UI do seu app em sincronia bidirecional.
+  </Card>
+  <Card title="Channels" icon="messages" href="/edge/pt-BR/guides/frontend/channels">
+    Execute o mesmo agente como um bot do Slack, Discord ou Teams.
+  </Card>
+</CardGroup>
+
+Este guia coloca um Crew ou Flow conversando com um frontend Next.js de ponta a ponta. O restante da seção se apoia no app que você configura aqui.
+
+## Arquitetura
+
+Há três peças:
+
+1. **CrewAI agent server** — um processo Python que serve o seu Crew ou Flow por AG-UI (FastAPI + `ag-ui-crewai`).
+2. **CopilotKit runtime** — uma rota Next.js que registra o seu agente e faz o proxy das requisições para ele.
+3. **React frontend** — o provider `<CopilotKit>` mais os componentes de chat e de generative UI.
+
+```
+React app  ──►  CopilotKit runtime (/api/copilotkit)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+```
+
+<Note>
+Este guia cobre o caminho **self-hosted**: você mesmo executa o servidor do agente CrewAI com `ag-ui-crewai`, e ele funciona localmente sem nenhum serviço gerenciado. O CopilotKit também oferece um caminho **gerenciado** (CopilotKit Cloud / Enterprise Intelligence) com threads hospedadas e um inspetor — consulte o [quickstart de CrewAI do CopilotKit](https://docs.copilotkit.ai/crewai-crews/quickstart) se preferir isso. O código do frontend nesta seção é o mesmo de qualquer forma; apenas como o agente é hospedado e registrado é que muda.
+</Note>
+
+<Note>
+O CrewAI roda por trás do AG-UI em três formatos: **Flows** comuns (usados ao longo destes guias), **[Conversational Flows](/edge/en/guides/frontend/conversational-flows)** (nativos, cientes de sessão, baseados em turnos, com paridade total de recursos) e **Crews** (chat básico). O frontend nesta seção é idêntico entre eles — apenas a autoria e o registro no backend é que diferem.
+</Note>
+
+## Guia de integração
+
+<Steps>
+
+<Step title="Sirva seu agente por AG-UI">
+
+Instale o pacote de integração no seu projeto CrewAI:
+
+```bash
+pip install ag-ui-crewai
+```
+
+Exponha o seu agente a partir de um app FastAPI. Flows usam `add_crewai_flow_fastapi_endpoint`; Crews usam `add_crewai_crew_fastapi_endpoint`. Você pode registrar quantos quiser, cada um em seu próprio path.
+
+<CodeGroup>
+
+```python Flow
+# server.py
+from fastapi import FastAPI
+from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
+from my_agents.recipe_flow import RecipeFlow
+
+app = FastAPI(title="CrewAI Agent Server")
+
+add_crewai_flow_fastapi_endpoint(
+    app=app,
+    flow=RecipeFlow(),
+    path="/recipe",
+)
+```
+
+```python Crew
+# server.py
+from fastapi import FastAPI
+from ag_ui_crewai.endpoint import add_crewai_crew_fastapi_endpoint
+from my_agents.research_crew import ResearchCrew
+
+app = FastAPI(title="CrewAI Agent Server")
+
+add_crewai_crew_fastapi_endpoint(
+    app=app,
+    crew=ResearchCrew().crew(),
+    path="/research",
+)
+```
+
+</CodeGroup>
+
+Execute:
+
+```bash
+uvicorn server:app --port 8000
+```
+
+<Note>
+Defina as variáveis de ambiente do seu provedor de LLM (por exemplo `OPENAI_API_KEY`) antes de iniciar o servidor.
+</Note>
+
+</Step>
+
+<Step title="Crie um app Next.js">
+
+Se você ainda não tem um frontend, gere um:
+
+```bash
+npx create-next-app@latest my-app
+cd my-app
+```
+
+Instale o CopilotKit e o cliente AG-UI do CrewAI:
+
+```bash
+npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime @ag-ui/crewai
+```
+
+</Step>
+
+<Step title="Adicione o runtime do CopilotKit">
+
+Crie uma rota que registre o(s) seu(s) agente(s) CrewAI no runtime do CopilotKit. Cada agente aponta para um path no seu servidor Python via `CrewAIAgent`.
+
+```ts
+// app/api/copilotkit/route.ts
+import {
+  CopilotRuntime,
+  InMemoryAgentRunner,
+  createCopilotEndpoint,
+} from "@copilotkit/runtime/v2";
+import { CrewAIAgent } from "@ag-ui/crewai";
+import { handle } from "hono/vercel";
+
+const runtime = new CopilotRuntime({
+  agents: {
+    recipe: new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
+  },
+  runner: new InMemoryAgentRunner(),
+});
+
+const app = createCopilotEndpoint({
+  runtime,
+  basePath: "/api/copilotkit",
+});
+
+const handler = handle(app);
+export const GET = handler;
+export const POST = handler;
+```
+
+</Step>
+
+<Step title="Envolva seu app com o provider">
+
+Aponte `<CopilotKit>` para a rota do runtime e nomeie o agente que você registrou.
+
+```tsx
+// app/page.tsx
+"use client";
+import { CopilotKit } from "@copilotkit/react-core";
+import { CopilotSidebar } from "@copilotkit/react-core/v2";
+import "@copilotkit/react-core/v2/styles.css";
+
+export default function Page() {
+  return (
+    <CopilotKit runtimeUrl="/api/copilotkit" agent="recipe">
+      <YourApp />
+      <CopilotSidebar agentId="recipe" labels={{ modalHeaderTitle: "Assistant" }} />
+    </CopilotKit>
+  );
+}
+```
+
+</Step>
+
+<Step title="Execute">
+
+Inicie os dois processos e abra o app. Conversar na sidebar agora executa o seu Crew ou Flow.
+
+```bash
+uvicorn server:app --port 8000   # terminal 1
+npm run dev                       # terminal 2
+```
+
+</Step>
+
+</Steps>
+
+## Opções de UI de chat
+
+O CopilotKit entrega três superfícies de chat intercambiáveis. Troque o componente; a fiação é idêntica.
+
+<CodeGroup>
+
+```tsx Sidebar
+import { CopilotSidebar } from "@copilotkit/react-core/v2";
+
+<CopilotSidebar agentId="recipe" />
+```
+
+```tsx Popup
+import { CopilotPopup } from "@copilotkit/react-core/v2";
+
+<CopilotPopup agentId="recipe" />
+```
+
+```tsx Inline
+import { CopilotChat } from "@copilotkit/react-core/v2";
+
+<CopilotChat agentId="recipe" />
+```
+
+</CodeGroup>
+
+## Para onde ir em seguida
+
+<CardGroup cols={2}>
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+    Renderize chamadas de tool e o estado do agente como componentes personalizados.
+  </Card>
+  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
+    Permita que o agente chame funções que rodam no navegador.
+  </Card>
+  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+    Restrinja ações do agente por trás da aprovação do usuário.
+  </Card>
+  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+    Transmita o estado em andamento para a UI enquanto o agente trabalha.
+  </Card>
+</CardGroup>

docs/edge/pt-BR/learn/llm-connections.mdx

@@ -140,7 +140,7 @@ Você pode se conectar a LLMs compatíveis com a OpenAI usando variáveis de amb
         # Exemplo usando a API compatível com OpenAI do Gemini.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Deve começar com AIza...
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Adicione aqui seu modelo do Gemini, sob openai/
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Adicione aqui seu modelo do Gemini, sob openai/
         ```
         </CodeGroup>
     </Tab>
@@ -158,7 +158,7 @@ Você pode se conectar a LLMs compatíveis com a OpenAI usando variáveis de amb
             ```python Google
             # Exemplo usando a API compatível com OpenAI do Gemini
             llm = LLM(
-                model="openai/gemini-2.0-flash",
+                model="openai/gemini-3.7-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # Deve começar com AIza...
             )

docs/edge/pt-BR/learn/llm-selection-guide.mdx

@@ -148,7 +148,7 @@ Agentes de planejamento se beneficiam de modelos de raciocínio para pensamento
 from crewai import Agent, Task, Crew, LLM
 
 # Modelo de raciocínio para planejamento estratégico
-manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
+manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
 
 # Modelo criativo para gerar conteúdo
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -413,7 +413,7 @@ Em vez de repetir o framework estratégico, segue um checklist tático para impl
     # Agentes gerenciadores ou de coordenação
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini-2.5-flash-preview-05-20"),
+        llm=LLM(model="gemini/gemini-3.7-flash"),
         # ... demais configs
     )
 

docs/edge/pt-BR/observability/tracing.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 O CrewAI fornece recursos de rastreamento integrados que permitem monitorar e depurar seus Crews e Flows em tempo real. Este guia demonstra como habilitar o rastreamento para **Crews** e **Flows** usando a plataforma de observabilidade integrada do CrewAI.
 
-> **O que é o CrewAI Tracing?** O rastreamento integrado do CrewAI fornece observabilidade abrangente para seus agentes de IA, incluindo decisões de agentes, cronogramas de execução de tarefas, uso de ferramentas e chamadas de LLM - tudo acessível através da [plataforma CrewAI AMP](https://app.crewai.com).
+> **O que é o CrewAI Tracing?** O rastreamento integrado do CrewAI fornece observabilidade abrangente para seus agentes de IA, incluindo decisões de agentes, cronogramas de execução de tarefas, uso de ferramentas e chamadas de LLM - tudo acessível através da [plataforma CrewAI AMP](https://app.crewai.com). O rastreamento é gerenciado de forma independente da [telemetria](/pt-BR/telemetry).
 
 ![CrewAI Tracing Interface](/images/crewai-tracing.png)
 
@@ -150,8 +150,8 @@ result = flow.kickoff()
 
 ### Passo 5: Visualize os Rastreamentos no Painel CrewAI AMP
 
-Após executar o crew ou flow, você pode visualizar os rastreamentos gerados pela sua aplicação CrewAI no painel CrewAI AMP. Você verá etapas detalhadas das interações dos agentes, usos de ferramentas e chamadas de LLM.
-Basta clicar no link abaixo para visualizar os rastreamentos ou ir para a aba de rastreamentos no painel [aqui](https://app.crewai.com/crewai_plus/trace_batches)
+Os rastreamentos são enviados somente após uma exportação autenticada bem-sucedida ou um upload anônimo explicitamente aprovado e concluído com sucesso. Uma execução cujo buffer local foi descartado não tem rastreamento enviado.
+Para rastreamentos associados à sua conta, abra a [aba Traces no painel CrewAI AMP](https://app.crewai.com/crewai_plus/trace_batches) para visualizar interações dos agentes, uso de ferramentas e chamadas de LLM.
 ![CrewAI Tracing Interface](/images/view-traces.png)
 
 ### Alternativa: Configuração de Variável de Ambiente
@@ -170,6 +170,52 @@ CREWAI_TRACING_ENABLED=true
 
 Quando esta variável de ambiente estiver definida, todos os Crews e Flows terão automaticamente o rastreamento habilitado, mesmo sem definir explicitamente `tracing=True`.
 
+## Visualizando rastreamentos após a primeira execução
+
+Na primeira vez que você executa um Crew ou Flow, um terminal interativo pode perguntar:
+
+```text
+Share this execution trace with CrewAI? [y/N]
+```
+
+Escolha **yes** para enviar o rastreamento armazenado no buffer ao CrewAI.
+Os rastreamentos podem conter prompts, entradas e saídas. Recusar, deixar o
+prazo expirar ou executar sem uma solicitação interativa de consentimento
+descarta o buffer. Você pode alterar o rastreamento depois com
+`crewai traces enable` ou `crewai traces disable`, ou definindo `tracing`
+no Crew ou Flow.
+
+### Buffer local e exportação autenticada
+
+A coleta de rastreamentos da primeira execução permanece na memória do processo
+até você concordar em compartilhar, mesmo com credenciais de login salvas.
+O rastreamento sem autenticação usa o mesmo fluxo de consentimento. Antes do
+consentimento, o CrewAI não solicita autorização de upload nem envia spans de execução.
+
+O buffer retém até **1.000 spans** e **8 MiB de dados OTLP codificados**.
+Defina `CREWAI_EPHEMERAL_TRACE_MAX_SPANS` e
+`CREWAI_EPHEMERAL_TRACE_MAX_BYTES` como inteiros positivos para ajustar esses limites.
+Ao exceder o limite, os spans mais antigos são descartados; um span maior que o
+limite de bytes é descartado. O buffer é esvaziado após o compartilhamento ou descarte.
+
+Quando o rastreamento está habilitado e há credenciais disponíveis, o CrewAI troca
+seu login da CLI, `CREWAI_USER_PAT` ou credencial de integração da plataforma
+com o AMP por uma autorização específica para a execução. Em seguida, exporta
+spans OpenTelemetry diretamente para o Wharf usando essa autorização.
+Credenciais inválidas não resultam em upload anônimo como alternativa.
+
+### Sessões de execução hospedadas
+
+Os hosts podem envolver a execução com `telemetry_session` de
+`crewai.telemetry.tracing`. A sessão usa eventos do ciclo de vida do CrewAI
+para criar e finalizar spans, preservando timestamps, relações de parentesco e
+links de pausa/retomada de HITL. Passe um provedor existente com `providers=`
+para manter o tracer e a integração de logs do host. Passe processadores de
+spans com `processors=` e um callback de logs do host com `log_emitter=`.
+O host é responsável por qualquer remoção de dados sensíveis nessas integrações. Cada sessão gerencia
+seu próprio ciclo de vida de rastreamento e mantém o provedor OpenTelemetry
+global da aplicação inalterado.
+
 ## Visualizando seus Rastreamentos
 
 ### Acesse o Painel CrewAI AMP
@@ -210,5 +256,5 @@ Se os rastreamentos não estiverem aparecendo no painel:
 
 1. Confirme que `tracing=True` está definido em seu Crew/Flow
 2. Verifique se `CREWAI_TRACING_ENABLED=true` se estiver usando variáveis de ambiente
-3. Certifique-se de estar autenticado com `crewai login`
-4. Verifique se seu crew/flow está realmente executando
+3. Para exportação autenticada, verifique seu login da CLI, `CREWAI_USER_PAT` ou credencial de integração da plataforma. Para compartilhamento anônimo, aprove explicitamente a solicitação de consentimento; não é necessário fazer login
+4. Verifique se seu crew/flow foi executado e se a exportação do rastreamento foi bem-sucedida. Recusar o consentimento, deixar o prazo expirar ou executar sem uma solicitação interativa de consentimento descarta o buffer local sem enviá-lo

docs/edge/pt-BR/telemetry.mdx

@@ -23,7 +23,8 @@ uso de ferramentas, chamadas de API, respostas, quaisquer dados processados pelo
 Quando o recurso `share_crew` está ativado, dados detalhados, incluindo descrições das tarefas, histórias ou objetivos dos agentes e outros atributos específicos são coletados
 para fornecer insights mais detalhados. Essa coleta expandida pode incluir informações pessoais caso o usuário as tenha inserido em seus crews ou tarefas.
 Usuários devem considerar cuidadosamente o conteúdo de seus crews e tarefas antes de habilitar o `share_crew`.
-A telemetria pode ser desabilitada ao definir a variável de ambiente `CREWAI_DISABLE_TELEMETRY` como `true` ou ao definir `OTEL_SDK_DISABLED` como `true` (observe que esta última desabilita toda instrumentação OpenTelemetry globalmente).
+A telemetria do CrewAI pode ser desabilitada ao definir `CREWAI_DISABLE_TELEMETRY` como `true`, `1`, `yes` ou `on` (qualquer capitalização). `OTEL_SDK_DISABLED` com os mesmos valores também desabilita o exportador do CrewAI. O SDK do OpenTelemetry em si ainda só reconhece `true` para desabilitar as demais instrumentações do processo.
+O rastreamento AMP é tratado em [Tracing](/pt-BR/observability/tracing).
 
 ### Exemplos:
 ```python
@@ -34,6 +35,8 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
+`CREWAI_DISABLE_TELEMETRY=1` (ou `yes` / `on`) funciona como `true`. Valores não reconhecidos são ignorados e a telemetria permanece ligada.
+
 ### Isolamento da sua própria configuração do OpenTelemetry
 
 A telemetria do CrewAI roda em seu próprio `TracerProvider` privado e nunca se
@@ -60,8 +63,8 @@ por meio do próprio tracer provider, que é independente do descrito aqui.
 | Sim    | Dados do Ciclo de Vida da Tarefa           | Inclui: horários de criação, início/fim de execução, identificadores de crew e tarefa, e se a tarefa foi bem-sucedida ou falhou. Quando uma tarefa falha, o **nome da classe** da exceção é registrado (por exemplo `TimeoutError`) para que as falhas possam ser contadas e diagnosticadas — nunca a mensagem de erro, que pode conter prompts, saída do modelo, caminhos de arquivos ou credenciais. Armazenado como spans com timestamps. Sem dados pessoais.            |
 | Sim    | Atributos do LLM                           | Inclui: nome, model_name, model, top_k, temperatura e nome da classe do LLM. Todos técnicos, sem dados pessoais.                  |
 | Sim    | Criação de Projeto pelo CLI do crewAI | Inclui: o fato de um novo projeto ter sido criado por `crewai create`, de qual tipo ele é (`crew`, `json_crew` ou `flow`) e o ID de projeto gerado para esse novo projeto e gravado no `pyproject.toml` dele. É o ID do próprio projeto novo, registrado separadamente do `project_id` do diretório de onde o comando foi executado — os dois podem diferir. Sem nome de projeto, sem conteúdo de arquivos, sem código. Sem dados pessoais. |
-| Sim    | Tentativa de Deploy do Crew pelo CLI do crewAI | Inclui: O fato de um deploy estar sendo realizado e o crew id, se está tentando buscar logs, e se o deploy foi iniciado por um comando do CLI ou pela TUI de execução. Não inclui conteúdo do projeto ou do crew nem dados pessoais. |
-| Sim    | Ambiente de Execução                       | Inclui: qual assistente de código com IA está executando o processo, se houver (um de uma lista fixa como `claude_code`, `codex`, `cursor` ou `unknown`), onde o processo é executado (um de uma lista fixa como `ci`, `container`, `serverless`, `interactive`) e o `project_id` do seu `pyproject.toml` quando houver um configurado. A detecção lê apenas se variáveis de ambiente conhecidas estão definidas, nunca seus valores. Sem dados pessoais. |
+| Sim    | Tentativa de Deploy do Crew pelo CLI do crewAI | Inclui: O fato de um deploy estar sendo realizado e o crew id, se está tentando buscar logs, e se o deploy foi iniciado por um comando do CLI ou pela TUI de execução. Se a criação de um deploy falhar, são registradas a categoria da falha (uma de uma lista fixa, como `api_4xx`, `network_error` ou `user_declined`) e o código de status HTTP da resposta da API, quando houver — nunca a mensagem de erro. Não inclui conteúdo do projeto ou do crew nem dados pessoais. |
+| Sim    | Ambiente de Execução                       | Inclui: qual assistente de código com IA está executando o processo, se houver (um valor de uma lista fixa como `claude_code`, `codex`, `cursor` ou `unknown`), onde o processo é executado (um valor de uma lista fixa como `ci`, `container`, `serverless`, `interactive`), o `project_id` do seu `pyproject.toml` quando houver um configurado e uma faixa aproximada de tamanho da máquina (uma de `1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+` ou `unknown`). A faixa é um intervalo, nunca a contagem exata de núcleos — a contagem exata é opcional, em Informações de Ambiente abaixo. A faixa de tamanho vem da contagem de núcleos do host; a detecção do assistente e do local de execução lê apenas se variáveis de ambiente conhecidas estão definidas, nunca seus valores. Sem dados pessoais. |
 | Sim    | Sinais de Ciclo de Vida do Flow | Inclui: que um flow iniciou, se foi concluído ou falhou, se um de seus métodos falhou, se pausou para entrada ou feedback humano, se o início foi uma execução retomada, se um turno de conversa falhou, quanto tempo o flow executou, e se o flow é um que a CrewAI executa internamente ou um que você escreveu. O nome do flow é registrado, como já é para criação e execução de flow. Quando um flow ou um de seus métodos falha, o **nome da classe** da exceção é registrado (por exemplo `TimeoutError`) para permitir o diagnóstico de falhas — nunca a mensagem de erro, que pode conter prompts, saída do modelo, caminhos de arquivo ou credenciais. Nomes de métodos e estado do flow nunca são registrados. Nenhum dado pessoal. |
 | Sim    | Sinal de Compartilhamento de Trace | Inclui: que um lote de traces foi compartilhado com sucesso com o CrewAI AMP, e se foi compartilhado anonimamente (antes de você ter uma conta) ou vinculado à sua conta. Como todo span, também carrega os atributos de Ambiente de Execução descritos acima (`project_id` quando configurado, o assistente de programação e o runtime). Esta linha descreve apenas a telemetria do compartilhamento — não o conteúdo dos traces nem o acesso concedido por links de traces compartilhados. O conteúdo dos traces, entradas e saídas nunca são registrados neste sinal. Antes de compartilhar traces, revise segredos, dados pessoais e as configurações de redação e retenção do AMP. |
 | Não    | Dados Expandidos do Agente                 | Inclui: descrição do objetivo, texto da história, identificador de arquivo i18n prompt. Usuários devem garantir que não haja info pessoal nesses campos de texto.            |

docs/edge/pt-BR/tools/ai-ml/overview.mdx

@@ -50,17 +50,16 @@ Essas ferramentas se integram com serviços de IA e machine learning para aprimo
 - **Segurança em IA**: Implemente moderação de conteúdo e checagens de segurança
 
 ```python
-from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
+from crewai_tools import DallETool, VisionTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
-code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor, code_executor],
+    tools=[image_generator, vision_processor],
     goal="Create and analyze content using AI capabilities"
 )
 ```

docs/edge/pt-BR/tools/ai-ml/ragtool.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 ## Descrição
 
-O `RagTool` foi desenvolvido para responder perguntas aproveitando o poder da Geração Aumentada por Recuperação (RAG) através do EmbedChain.
+O `RagTool` foi desenvolvido para responder perguntas aproveitando o poder da Geração Aumentada por Recuperação (RAG) por meio do sistema RAG nativo da CrewAI.
 Ele fornece uma base de conhecimento dinâmica que pode ser consultada para recuperar informações relevantes de várias fontes de dados.
 Esta ferramenta é particularmente útil para aplicações que exigem acesso a uma ampla variedade de informações e precisam fornecer respostas contextualmente relevantes.
 
@@ -76,8 +76,8 @@ O `RagTool` pode ser utilizado com uma grande variedade de fontes de dados, incl
 O `RagTool` aceita os seguintes parâmetros:
 
 - **summarize**: Opcional. Indica se o conteúdo recuperado deve ser resumido. O padrão é `False`.
-- **adapter**: Opcional. Um adaptador personalizado para a base de conhecimento. Se não for fornecido, será utilizado o EmbedchainAdapter.
-- **config**: Opcional. Configuração para o aplicativo EmbedChain subjacente.
+- **adapter**: Opcional. Um adaptador personalizado para a base de conhecimento. Se não for fornecido, será utilizado o CrewAIRagAdapter.
+- **config**: Opcional. Configuração do sistema RAG subjacente da CrewAI. Aceita um TypedDict `RagToolConfig` com as chaves opcionais `embedding_model` (ProviderSpec) e `vectordb` (VectorDbConfig). Todos os valores de configuração fornecidos programaticamente têm precedência sobre as variáveis de ambiente.
 
 ## Adicionando Conteúdo
 
@@ -127,47 +127,528 @@ def knowledge_expert(self) -> Agent:
 
 ```python Code
 from crewai_tools import RagTool
+from crewai_tools.tools.rag import RagToolConfig, VectorDbConfig, ProviderSpec
 
 # Create a RAG tool with custom configuration
-config = {
-    "app": {
-        "name": "custom_app",
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-4",
-        }
-    },
-    "embedding_model": {
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-ada-002"
-        }
-    },
-    "vectordb": {
-        "provider": "elasticsearch",
-        "config": {
-            "collection_name": "my-collection",
-            "cloud_id": "deployment-name:xxxx",
-            "api_key": "your-key",
-            "verify_certs": False
-        }
-    },
-    "chunker": {
-        "chunk_size": 400,
-        "chunk_overlap": 100,
-        "length_function": "len",
-        "min_chunk_size": 0
+
+vectordb: VectorDbConfig = {
+    "provider": "qdrant",
+    "config": {
+        "collection_name": "my-collection"
     }
 }
 
+embedding_model: ProviderSpec = {
+    "provider": "openai",
+    "config": {
+        "model_name": "text-embedding-3-small"
+    }
+}
+
+config: RagToolConfig = {
+    "vectordb": vectordb,
+    "embedding_model": embedding_model
+}
+
 rag_tool = RagTool(config=config, summarize=True)
 ```
 
-A ferramenta RAG interna utiliza o adaptador Embedchain, possibilitando que você forneça quaisquer opções de configuração suportadas pelo Embedchain.
-Você pode consultar a [documentação do Embedchain](https://docs.embedchain.ai/components/introduction) para mais detalhes.
-Certifique-se de revisar as opções de configuração disponíveis no arquivo .yaml.
+## Configuração do Modelo de Embedding
+
+O parâmetro `embedding_model` aceita um dicionário `crewai.rag.embeddings.types.ProviderSpec` com a seguinte estrutura:
+
+```python
+{
+    "provider": "provider-name",  # Required
+    "config": {                    # Optional
+        # Provider-specific configuration
+    }
+}
+```
+
+### Provedores Suportados
+
+<AccordionGroup>
+  <Accordion title="OpenAI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
+
+    embedding_model: OpenAIProviderSpec = {
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "text-embedding-ada-002",
+            "dimensions": 1536,
+            "organization_id": "your-org-id",
+            "api_base": "https://api.openai.com/v1",
+            "api_version": "v1",
+            "default_headers": {"Custom-Header": "value"}
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `api_key` (str): Chave da API OpenAI
+    - `model_name` (str): Modelo a ser utilizado. Padrão: `text-embedding-ada-002`. Opções: `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
+    - `dimensions` (int): Número de dimensões do embedding
+    - `organization_id` (str): ID da organização OpenAI
+    - `api_base` (str): URL base personalizada da API
+    - `api_version` (str): Versão da API
+    - `default_headers` (dict): Cabeçalhos personalizados para solicitações à API
+
+    **Variáveis de Ambiente:**
+    - `OPENAI_API_KEY` ou `EMBEDDINGS_OPENAI_API_KEY`: `api_key`
+    - `OPENAI_ORGANIZATION_ID` ou `EMBEDDINGS_OPENAI_ORGANIZATION_ID`: `organization_id`
+    - `OPENAI_MODEL_NAME` ou `EMBEDDINGS_OPENAI_MODEL_NAME`: `model_name`
+    - `OPENAI_API_BASE` ou `EMBEDDINGS_OPENAI_API_BASE`: `api_base`
+    - `OPENAI_API_VERSION` ou `EMBEDDINGS_OPENAI_API_VERSION`: `api_version`
+    - `OPENAI_DIMENSIONS` ou `EMBEDDINGS_OPENAI_DIMENSIONS`: `dimensions`
+  </Accordion>
+
+  <Accordion title="Cohere">
+    ```python main.py
+    from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
+
+    embedding_model: CohereProviderSpec = {
+        "provider": "cohere",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "embed-english-v3.0"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `api_key` (str): Chave da API Cohere
+    - `model_name` (str): Modelo a ser utilizado. Padrão: `large`. Opções: `embed-english-v3.0`, `embed-multilingual-v3.0`, `large`, `small`
+
+    **Variáveis de Ambiente:**
+    - `COHERE_API_KEY` ou `EMBEDDINGS_COHERE_API_KEY`: `api_key`
+    - `EMBEDDINGS_COHERE_MODEL_NAME`: `model_name`
+  </Accordion>
+
+  <Accordion title="VoyageAI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
+
+    embedding_model: VoyageAIProviderSpec = {
+        "provider": "voyageai",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "voyage-3",
+            "input_type": "document",
+            "truncation": True,
+            "output_dtype": "float32",
+            "output_dimension": 1024,
+            "max_retries": 3,
+            "timeout": 60.0
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `api_key` (str): Chave da API VoyageAI
+    - `model` (str): Modelo a ser utilizado. Padrão: `voyage-2`. Opções: `voyage-3`, `voyage-3-lite`, `voyage-code-3`, `voyage-large-2`
+    - `input_type` (str): Tipo de entrada. Opções: `document` (para armazenamento), `query` (para pesquisa)
+    - `truncation` (bool): Indica se entradas que excedem o comprimento máximo devem ser truncadas. Padrão: `True`
+    - `output_dtype` (str): Tipo de dados da saída
+    - `output_dimension` (int): Dimensão dos embeddings de saída
+    - `max_retries` (int): Número máximo de tentativas. Padrão: `0`
+    - `timeout` (float): Tempo limite da solicitação em segundos
+
+    **Variáveis de Ambiente:**
+    - `VOYAGEAI_API_KEY` ou `EMBEDDINGS_VOYAGEAI_API_KEY`: `api_key`
+    - `VOYAGEAI_MODEL` ou `EMBEDDINGS_VOYAGEAI_MODEL`: `model`
+    - `VOYAGEAI_INPUT_TYPE` ou `EMBEDDINGS_VOYAGEAI_INPUT_TYPE`: `input_type`
+    - `VOYAGEAI_TRUNCATION` ou `EMBEDDINGS_VOYAGEAI_TRUNCATION`: `truncation`
+    - `VOYAGEAI_OUTPUT_DTYPE` ou `EMBEDDINGS_VOYAGEAI_OUTPUT_DTYPE`: `output_dtype`
+    - `VOYAGEAI_OUTPUT_DIMENSION` ou `EMBEDDINGS_VOYAGEAI_OUTPUT_DIMENSION`: `output_dimension`
+    - `VOYAGEAI_MAX_RETRIES` ou `EMBEDDINGS_VOYAGEAI_MAX_RETRIES`: `max_retries`
+    - `VOYAGEAI_TIMEOUT` ou `EMBEDDINGS_VOYAGEAI_TIMEOUT`: `timeout`
+  </Accordion>
+
+  <Accordion title="Ollama">
+    ```python main.py
+    from crewai.rag.embeddings.providers.ollama.types import OllamaProviderSpec
+
+    embedding_model: OllamaProviderSpec = {
+        "provider": "ollama",
+        "config": {
+            "model_name": "llama2",
+            "url": "http://localhost:11434/api/embeddings"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): Nome do modelo Ollama (por exemplo, `llama2`, `mistral`, `nomic-embed-text`)
+    - `url` (str): URL do endpoint da API Ollama. Padrão: `http://localhost:11434/api/embeddings`
+
+    **Variáveis de Ambiente:**
+    - `OLLAMA_MODEL` ou `EMBEDDINGS_OLLAMA_MODEL`: `model_name`
+    - `OLLAMA_URL` ou `EMBEDDINGS_OLLAMA_URL`: `url`
+  </Accordion>
+
+  <Accordion title="Amazon Bedrock">
+    ```python main.py
+    from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
+
+    embedding_model: BedrockProviderSpec = {
+        "provider": "amazon-bedrock",
+        "config": {
+            "model_name": "amazon.titan-embed-text-v2:0",
+            "session": boto3_session
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): ID do modelo Bedrock. Padrão: `amazon.titan-embed-text-v1`. Opções: `amazon.titan-embed-text-v1`, `amazon.titan-embed-text-v2:0`, `cohere.embed-english-v3`, `cohere.embed-multilingual-v3`
+    - `session` (Any): Objeto de sessão Boto3 para autenticação da AWS
+
+    **Variáveis de Ambiente:**
+    - `AWS_ACCESS_KEY_ID`: Chave de acesso da AWS
+    - `AWS_SECRET_ACCESS_KEY`: Chave secreta da AWS
+    - `AWS_REGION`: Região da AWS (por exemplo, `us-east-1`)
+  </Accordion>
+
+  <Accordion title="Azure OpenAI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.microsoft.types import AzureProviderSpec
+
+    embedding_model: AzureProviderSpec = {
+        "provider": "azure",
+        "config": {
+            "deployment_id": "your-deployment-id",
+            "api_key": "your-api-key",
+            "api_base": "https://your-resource.openai.azure.com",
+            "api_version": "2024-02-01",
+            "model_name": "text-embedding-ada-002",
+            "api_type": "azure"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `deployment_id` (str): **Obrigatório** - ID de implantação do Azure OpenAI
+    - `api_key` (str): Chave da API Azure OpenAI
+    - `api_base` (str): Endpoint do recurso Azure OpenAI
+    - `api_version` (str): Versão da API. Exemplo: `2024-02-01`
+    - `model_name` (str): Nome do modelo. Padrão: `text-embedding-ada-002`
+    - `api_type` (str): Tipo de API. Padrão: `azure`
+    - `dimensions` (int): Dimensões da saída
+    - `default_headers` (dict): Cabeçalhos personalizados
+
+    **Variáveis de Ambiente:**
+    - `AZURE_OPENAI_API_KEY` ou `EMBEDDINGS_AZURE_API_KEY`: `api_key`
+    - `AZURE_OPENAI_ENDPOINT` ou `EMBEDDINGS_AZURE_API_BASE`: `api_base`
+    - `EMBEDDINGS_AZURE_DEPLOYMENT_ID`: `deployment_id`
+    - `EMBEDDINGS_AZURE_API_VERSION`: `api_version`
+    - `EMBEDDINGS_AZURE_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_AZURE_API_TYPE`: `api_type`
+    - `EMBEDDINGS_AZURE_DIMENSIONS`: `dimensions`
+  </Accordion>
+
+  <Accordion title="Google Generative AI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.google.types import GenerativeAiProviderSpec
+
+    embedding_model: GenerativeAiProviderSpec = {
+        "provider": "google-generativeai",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "gemini-embedding-001",
+            "task_type": "RETRIEVAL_DOCUMENT"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `api_key` (str): Chave da API Google AI
+    - `model_name` (str): Nome do modelo. Padrão: `gemini-embedding-001`. Opções: `gemini-embedding-001`, `text-embedding-005`, `text-multilingual-embedding-002`
+    - `task_type` (str): Tipo de tarefa para embeddings. Padrão: `RETRIEVAL_DOCUMENT`. Opções: `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`
+
+    **Variáveis de Ambiente:**
+    - `GOOGLE_API_KEY`, `GEMINI_API_KEY` ou `EMBEDDINGS_GOOGLE_API_KEY`: `api_key`
+    - `EMBEDDINGS_GOOGLE_GENERATIVE_AI_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_GOOGLE_GENERATIVE_AI_TASK_TYPE`: `task_type`
+  </Accordion>
+
+  <Accordion title="Google Vertex AI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.google.types import VertexAIProviderSpec
+
+    embedding_model: VertexAIProviderSpec = {
+        "provider": "google-vertex",
+        "config": {
+            "model_name": "text-embedding-004",
+            "project_id": "your-project-id",
+            "region": "us-central1",
+            "api_key": "your-api-key"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): Nome do modelo. Padrão: `textembedding-gecko`. Opções: `text-embedding-004`, `textembedding-gecko`, `textembedding-gecko-multilingual`
+    - `project_id` (str): ID do projeto Google Cloud. Padrão: `cloud-large-language-models`
+    - `region` (str): Região do Google Cloud. Padrão: `us-central1`
+    - `api_key` (str): Chave de API para autenticação
+
+    **Variáveis de Ambiente:**
+    - `GOOGLE_APPLICATION_CREDENTIALS`: Caminho para o arquivo JSON da conta de serviço
+    - `GOOGLE_CLOUD_PROJECT` ou `EMBEDDINGS_GOOGLE_VERTEX_PROJECT_ID`: `project_id`
+    - `EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_GOOGLE_VERTEX_REGION`: `region`
+    - `EMBEDDINGS_GOOGLE_VERTEX_API_KEY`: `api_key`
+  </Accordion>
+
+  <Accordion title="Jina AI">
+    ```python main.py
+    from crewai.rag.embeddings.providers.jina.types import JinaProviderSpec
+
+    embedding_model: JinaProviderSpec = {
+        "provider": "jina",
+        "config": {
+            "api_key": "your-api-key",
+            "model_name": "jina-embeddings-v3"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `api_key` (str): Chave da API Jina AI
+    - `model_name` (str): Nome do modelo. Padrão: `jina-embeddings-v2-base-en`. Opções: `jina-embeddings-v3`, `jina-embeddings-v2-base-en`, `jina-embeddings-v2-small-en`
+
+    **Variáveis de Ambiente:**
+    - `JINA_API_KEY` ou `EMBEDDINGS_JINA_API_KEY`: `api_key`
+    - `EMBEDDINGS_JINA_MODEL_NAME`: `model_name`
+  </Accordion>
+
+  <Accordion title="HuggingFace">
+    ```python main.py
+    from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec
+
+    embedding_model: HuggingFaceProviderSpec = {
+        "provider": "huggingface",
+        "config": {
+            "url": "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `url` (str): URL completa do endpoint da API de inferência do HuggingFace
+
+    **Variáveis de Ambiente:**
+    - `HUGGINGFACE_URL` ou `EMBEDDINGS_HUGGINGFACE_URL`: `url`
+  </Accordion>
+
+  <Accordion title="Instructor">
+    ```python main.py
+    from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec
+
+    embedding_model: InstructorProviderSpec = {
+        "provider": "instructor",
+        "config": {
+            "model_name": "hkunlp/instructor-xl",
+            "device": "cuda",
+            "instruction": "Represent the document"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): ID do modelo HuggingFace. Padrão: `hkunlp/instructor-base`. Opções: `hkunlp/instructor-xl`, `hkunlp/instructor-large`, `hkunlp/instructor-base`
+    - `device` (str): Dispositivo no qual executar. Padrão: `cpu`. Opções: `cpu`, `cuda`, `mps`, `xpu`
+    - `instruction` (str): Prefixo de instrução para embeddings
+
+    **Variáveis de Ambiente:**
+    - `EMBEDDINGS_INSTRUCTOR_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_INSTRUCTOR_DEVICE`: `device`
+    - `EMBEDDINGS_INSTRUCTOR_INSTRUCTION`: `instruction`
+  </Accordion>
+
+  <Accordion title="Sentence Transformer">
+    ```python main.py
+    from crewai.rag.embeddings.providers.sentence_transformer.types import SentenceTransformerProviderSpec
+
+    embedding_model: SentenceTransformerProviderSpec = {
+        "provider": "sentence-transformer",
+        "config": {
+            "model_name": "all-mpnet-base-v2",
+            "device": "cuda",
+            "normalize_embeddings": True
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): Nome do modelo Sentence Transformers. Padrão: `all-MiniLM-L6-v2`. Opções: `all-mpnet-base-v2`, `all-MiniLM-L6-v2`, `paraphrase-multilingual-MiniLM-L12-v2`
+    - `device` (str): Dispositivo no qual executar. Padrão: `cpu`. Opções: `cpu`, `cuda`, `mps`, `xpu`
+    - `normalize_embeddings` (bool): Indica se os embeddings devem ser normalizados. Padrão: `False`
+
+    **Variáveis de Ambiente:**
+    - `EMBEDDINGS_SENTENCE_TRANSFORMER_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_SENTENCE_TRANSFORMER_DEVICE`: `device`
+    - `EMBEDDINGS_SENTENCE_TRANSFORMER_NORMALIZE_EMBEDDINGS`: `normalize_embeddings`
+  </Accordion>
+
+  <Accordion title="ONNX">
+    ```python main.py
+    from crewai.rag.embeddings.providers.onnx.types import ONNXProviderSpec
+
+    embedding_model: ONNXProviderSpec = {
+        "provider": "onnx",
+        "config": {
+            "preferred_providers": ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `preferred_providers` (list[str]): Lista de provedores de execução ONNX em ordem de preferência
+
+    **Variáveis de Ambiente:**
+    - `EMBEDDINGS_ONNX_PREFERRED_PROVIDERS`: `preferred_providers` (lista separada por vírgulas)
+  </Accordion>
+
+  <Accordion title="OpenCLIP">
+    ```python main.py
+    from crewai.rag.embeddings.providers.openclip.types import OpenCLIPProviderSpec
+
+    embedding_model: OpenCLIPProviderSpec = {
+        "provider": "openclip",
+        "config": {
+            "model_name": "ViT-B-32",
+            "checkpoint": "laion2b_s34b_b79k",
+            "device": "cuda"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): Arquitetura do modelo OpenCLIP. Padrão: `ViT-B-32`. Opções: `ViT-B-32`, `ViT-B-16`, `ViT-L-14`
+    - `checkpoint` (str): Nome do checkpoint pré-treinado. Padrão: `laion2b_s34b_b79k`. Opções: `laion2b_s34b_b79k`, `laion400m_e32`, `openai`
+    - `device` (str): Dispositivo no qual executar. Padrão: `cpu`. Opções: `cpu`, `cuda`
+
+    **Variáveis de Ambiente:**
+    - `EMBEDDINGS_OPENCLIP_MODEL_NAME`: `model_name`
+    - `EMBEDDINGS_OPENCLIP_CHECKPOINT`: `checkpoint`
+    - `EMBEDDINGS_OPENCLIP_DEVICE`: `device`
+  </Accordion>
+
+  <Accordion title="Text2Vec">
+    ```python main.py
+    from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
+
+    embedding_model: Text2VecProviderSpec = {
+        "provider": "text2vec",
+        "config": {
+            "model_name": "shibing624/text2vec-base-multilingual"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_name` (str): Nome do modelo Text2Vec do HuggingFace. Padrão: `shibing624/text2vec-base-chinese`. Opções: `shibing624/text2vec-base-multilingual`, `shibing624/text2vec-base-chinese`
+
+    **Variáveis de Ambiente:**
+    - `EMBEDDINGS_TEXT2VEC_MODEL_NAME`: `model_name`
+  </Accordion>
+
+  <Accordion title="Roboflow">
+    ```python main.py
+    from crewai.rag.embeddings.providers.roboflow.types import RoboflowProviderSpec
+
+    embedding_model: RoboflowProviderSpec = {
+        "provider": "roboflow",
+        "config": {
+            "api_key": "your-api-key",
+            "api_url": "https://infer.roboflow.com"
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `api_key` (str): Chave da API Roboflow. Padrão: `""` (string vazia)
+    - `api_url` (str): URL da API de inferência do Roboflow. Padrão: `https://infer.roboflow.com`
+
+    **Variáveis de Ambiente:**
+    - `ROBOFLOW_API_KEY` ou `EMBEDDINGS_ROBOFLOW_API_KEY`: `api_key`
+    - `ROBOFLOW_API_URL` ou `EMBEDDINGS_ROBOFLOW_API_URL`: `api_url`
+  </Accordion>
+
+  <Accordion title="WatsonX (IBM)">
+    ```python main.py
+    from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderSpec
+
+    embedding_model: WatsonXProviderSpec = {
+        "provider": "watsonx",
+        "config": {
+            "model_id": "ibm/slate-125m-english-rtrvr",
+            "url": "https://us-south.ml.cloud.ibm.com",
+            "api_key": "your-api-key",
+            "project_id": "your-project-id",
+            "batch_size": 100,
+            "concurrency_limit": 10,
+            "persistent_connection": True
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `model_id` (str): Identificador do modelo WatsonX
+    - `url` (str): Endpoint da API WatsonX
+    - `api_key` (str): Chave da API IBM Cloud
+    - `project_id` (str): ID do projeto WatsonX
+    - `space_id` (str): ID do espaço WatsonX (alternativa ao project_id)
+    - `batch_size` (int): Tamanho do lote para embeddings. Padrão: `100`
+    - `concurrency_limit` (int): Número máximo de solicitações simultâneas. Padrão: `10`
+    - `persistent_connection` (bool): Utilizar conexões persistentes. Padrão: `True`
+    - Mais de 20 opções adicionais de autenticação e configuração
+
+    **Variáveis de Ambiente:**
+    - `WATSONX_API_KEY` ou `EMBEDDINGS_WATSONX_API_KEY`: `api_key`
+    - `WATSONX_URL` ou `EMBEDDINGS_WATSONX_URL`: `url`
+    - `WATSONX_PROJECT_ID` ou `EMBEDDINGS_WATSONX_PROJECT_ID`: `project_id`
+    - `EMBEDDINGS_WATSONX_MODEL_ID`: `model_id`
+    - `EMBEDDINGS_WATSONX_SPACE_ID`: `space_id`
+    - `EMBEDDINGS_WATSONX_BATCH_SIZE`: `batch_size`
+    - `EMBEDDINGS_WATSONX_CONCURRENCY_LIMIT`: `concurrency_limit`
+    - `EMBEDDINGS_WATSONX_PERSISTENT_CONNECTION`: `persistent_connection`
+  </Accordion>
+
+  <Accordion title="Personalizado">
+    ```python main.py
+    from crewai.rag.core.base_embeddings_callable import EmbeddingFunction
+    from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
+
+    class MyEmbeddingFunction(EmbeddingFunction):
+        def __call__(self, input):
+            # Your custom embedding logic
+            return embeddings
+
+    embedding_model: CustomProviderSpec = {
+        "provider": "custom",
+        "config": {
+            "embedding_callable": MyEmbeddingFunction
+        }
+    }
+    ```
+
+    **Opções de Configuração:**
+    - `embedding_callable` (type[EmbeddingFunction]): Classe da função de embedding personalizada
+
+    **Observação:** As funções de embedding personalizadas devem implementar o protocolo `EmbeddingFunction` definido em `crewai.rag.core.base_embeddings_callable`. O método `__call__` deve aceitar dados de entrada e retornar embeddings como uma lista de arrays numpy (ou um formato compatível que será normalizado). Os embeddings retornados são normalizados e validados automaticamente.
+  </Accordion>
+</AccordionGroup>
+
+### Observações
+- Todos os campos de configuração são opcionais, a menos que estejam marcados como **Obrigatório**
+- Normalmente, as chaves de API podem ser fornecidas por meio de variáveis de ambiente em vez da configuração
+- Os valores padrão são exibidos quando aplicável
+
 
 ## Conclusão
-O `RagTool` oferece uma maneira poderosa de criar e consultar bases de conhecimento a partir de diversas fontes de dados. Ao explorar a Geração Aumentada por Recuperação, ele permite que agentes acessem e recuperem informações relevantes de forma eficiente, ampliando a capacidade de fornecer respostas precisas e contextualmente apropriadas.
+O `RagTool` oferece uma maneira poderosa de criar e consultar bases de conhecimento a partir de diversas fontes de dados. Ao explorar a Geração Aumentada por Recuperação, ele permite que agentes acessem e recuperem informações relevantes de forma eficiente, ampliando a capacidade de fornecer respostas precisas e contextualmente apropriadas.

docs/edge/pt-BR/tools/ai-ml/visiontool.mdx

@@ -12,6 +12,16 @@ mode: "wide"
 Esta ferramenta é utilizada para extrair texto de imagens. Quando passada para o agente, ela extrai o texto da imagem e depois o utiliza para gerar uma resposta, relatório ou qualquer outra saída.
 A URL ou o CAMINHO da imagem deve ser passado para o Agente.
 
+Você também pode fazer uma `query` personalizada sobre a imagem e escolher um `complexity_level` que seleciona automaticamente o modelo mais adequado para a solicitação:
+
+| Nível de complexidade | Modelo         |
+| :--------------- | :------------ |
+| `easy`           | `gpt-5.6-luna`  |
+| `medium` (padrão) | `gpt-5.6-terra` |
+| `hard`           | `gpt-5.6-sol`   |
+
+Quando um `llm` ou `model` explícito é fornecido à ferramenta, ele tem precedência sobre a seleção de modelo baseada na complexidade.
+
 ## Instalação
 
 Instale o pacote crewai_tools
@@ -43,8 +53,10 @@ def researcher(self) -> Agent:
 
 ## Argumentos
 
-O VisionTool requer os seguintes argumentos:
+O VisionTool aceita os seguintes argumentos:
 
 | Argumento           | Tipo     | Descrição                                                                        |
 | :------------------ | :------- | :------------------------------------------------------------------------------- |
-| **image_path_url**  | `string` | **Obrigatório**. O caminho para o arquivo de imagem do qual o texto será extraído. |
+| **image_path_url**  | `string` | **Obrigatório**. O caminho para o arquivo de imagem (ou URL) do qual o texto será extraído. |
+| **query**           | `string` | **Opcional**. A pergunta ou instrução a ser feita ao modelo sobre a imagem. O padrão é `"What's in this image?"`. |
+| **complexity_level** | `string` | **Opcional**. A complexidade da solicitação, que seleciona o modelo: `easy`, `medium` ou `hard`. O padrão é `medium`. |

docs/edge/pt-BR/tools/web-scraping/oxylabsscraperstool.mdx

@@ -4,7 +4,7 @@ description: >
   Os Scrapers da Oxylabs permitem acessar facilmente informações de fontes específicas. Veja abaixo a lista de fontes disponíveis:
     - `Amazon Product`
     - `Amazon Search`
-    - `Google Seach`
+    - `Google Search`
     - `Universal`
 icon: globe
 mode: "wide"
@@ -87,7 +87,7 @@ print(result)
 ### Parâmetros
 
 - `query` - termo de busca da Amazon.
-- `domain` - Domínio de localização para Bestbuy.
+- `domain` - domínio de localização da Amazon.
 - `start_page` - número da página inicial.
 - `pages` - quantidade de páginas a ser recuperada.
 - `geo_location` - local de entrega (_Deliver to_).

docs/v1.15.16/en/guides/frontend/channels.mdx

@@ -1,117 +1,148 @@
 ---
 title: Channels
-description: Run the same CrewAI agent as a chat bot on Slack and Discord with the CopilotKit Channels SDK.
-icon: slack
+description: Run the same CrewAI agent as a Slack or Teams bot with the CopilotKit Channels SDK and managed Intelligence platform.
+icon: messages
 mode: "wide"
 ---
 
 ## Meet your users where they already are
 
-The CrewAI agent you built in the [Overview](/edge/en/guides/frontend/overview) does not have to live behind a web app. The same Crew or Flow can run as a bot inside a messaging platform. No rebuild, no second copy of your agent logic: the agent stays exposed over the [AG-UI protocol](https://docs.ag-ui.com), and a bot process drives it.
+The CrewAI agent you built in the [Overview](/edge/en/guides/frontend/overview) does not have to live behind a web app. The same Crew or Flow can run as a bot inside a messaging platform. No rebuild, no second copy of your agent logic: the agent stays exposed over the [AG-UI protocol](https://docs.ag-ui.com), and a **channel** drives it from Slack or Microsoft Teams.
 
-CopilotKit's [Channels SDK](https://docs.copilotkit.ai/reference/channels) provides that bot process. It ships a platform-agnostic engine plus per-platform adapters.
+CopilotKit's [Channels SDK](https://docs.copilotkit.ai/slack) provides that channel. You declare a `createChannel` in a small runtime, point it at your CrewAI agent, and CopilotKit's managed **Intelligence** platform brokers the connection to the messaging provider.
+
+<Note>
+Unlike the rest of this section, Channels is **not self-hosted**. It runs through **CopilotKit Intelligence** — a required surface for Channels, by design (a free tier is available). Intelligence holds the platform connection and credentials, receives each platform event, and delivers the turn to your channel process; your process runs the agent and streams the reply back. You configure Slack once in the Intelligence dashboard, and platform credentials never enter your process. Your agent, tools, and state stay yours.
+</Note>
 
 ## How it fits together
 
-Nothing about your agent server changes. It keeps serving your Crew or Flow over AG-UI exactly as in the Overview. What you add is a separate **bot process**: it connects to a platform adapter, listens for messages, and runs your agent when it is messaged. The reply streams back into the channel.
+Nothing about your CrewAI agent server changes. It keeps serving your Crew or Flow over AG-UI exactly as in the Overview. What you add is a separate long-running Node process built with `@copilotkit/channels`: it registers a channel on the `CopilotRuntime`, connects to Intelligence, and runs your agent whenever a message arrives.
 
 ```
-Slack / Discord  ──►  Channels bot process  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
 ```
 
-Your agent server can keep serving the web frontend from the Overview at the same time. The web app and the bot are just two clients of one AG-UI endpoint.
+The channel process holds a persistent connection to the Intelligence gateway, so it needs a long-running host — a serverless request handler cannot own that connection. Your CrewAI server can keep serving the web frontend from the Overview at the same time: the web app and the channel are just two clients of one AG-UI endpoint.
 
-## Slack
+## Integration guide
 
 <Steps>
 
 <Step title="Install the Channels packages">
 
+The Channels SDK is batteries-included — every platform ships in the one package, with no per-platform adapter to install. Add it alongside the runtime that hosts the channel and the CrewAI AG-UI client:
+
 ```bash
-npm install @copilotkit/channels @copilotkit/channels-slack @ag-ui/crewai
+npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
 ```
 
 </Step>
 
-<Step title="Create a Slack app and get tokens">
+<Step title="Create a Channel in Intelligence">
 
-Create an app in the Slack API dashboard for your workspace, enable Socket Mode, and grant it the message and event scopes it needs to read and post in channels. Then expose its tokens to the bot process:
+In the [CopilotKit dashboard](https://docs.copilotkit.ai/slack), create a Channel and connect Slack — Intelligence walks you through creating the Slack app and holds its credentials. That leaves two environment variables for your process, both from the dashboard:
 
 ```bash
-export SLACK_BOT_TOKEN=xoxb-...   # bot user token
-export SLACK_APP_TOKEN=xapp-...   # app-level token (Socket Mode)
+export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
+export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
 ```
 
 </Step>
 
-<Step title="Point the bot at your CrewAI agent">
+<Step title="Define the channel">
 
-`createBot` wires a Slack adapter to your agent. The `agent` factory returns a `CrewAIAgent` pointed at the AG-UI path your server exposes (the same URL you registered in the runtime in the Overview).
+`createChannel` declares the channel and attaches your agent. Build the agent as a per-thread factory so each conversation gets its own session, using the same `CrewAIAgent` the Overview uses in the web runtime, pointed at your AG-UI endpoint. `identifyUser: "platform"` lets Intelligence map each platform user to a stable identity.
 
 ```ts
-// bot.ts
-import { createBot } from "@copilotkit/channels";
-import { slack, defaultSlackTools, defaultSlackContext } from "@copilotkit/channels-slack";
+// channel.ts
+import { createChannel } from "@copilotkit/channels";
 import { CrewAIAgent } from "@ag-ui/crewai";
 
-const bot = createBot({
-  adapters: [
-    slack({
-      botToken: process.env.SLACK_BOT_TOKEN!, // xoxb-…
-      appToken: process.env.SLACK_APP_TOKEN!, // xapp-… (Socket Mode)
-    }),
-  ],
-  agent: (threadId) => new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-  tools: [...defaultSlackTools],
-  context: [...defaultSlackContext],
+const channel = createChannel({
+  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
+  identifyUser: "platform",
+  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
+  agent: (threadId) => {
+    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
+    agent.threadId = threadId;
+    return agent;
+  },
 });
 
-bot.start();
+// A mention subscribes the thread and runs the agent; afterwards every message
+// in a subscribed thread runs it without needing another mention.
+channel.onMention(async ({ thread }) => {
+  await thread.subscribe();
+  await thread.runAgent();
+});
+
+channel.onMessage(async ({ thread }) => {
+  if (await thread.isSubscribed()) await thread.runAgent();
+});
+
+export { channel };
 ```
 
 </Step>
 
-<Step title="Run the bot">
+<Step title="Register the channel on the runtime">
 
-Start the bot process alongside your agent server:
+Create a `CopilotRuntime` with the Intelligence gateway and your channel, then serve it with `createCopilotNodeListener`. The `agents` map stays empty — the channel supplies its own agent. Wait for the channel to be ready so a broken config fails startup loudly.
+
+```ts
+// server.ts
+import { createServer } from "node:http";
+import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
+import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
+import { channel } from "./channel";
+
+const runtime = new CopilotRuntime({
+  agents: {}, // the channel supplies its own agent; no web-facing agents needed
+  intelligence: new CopilotKitIntelligence({
+    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
+  }),
+  channels: [channel],
+});
+
+const listener = createCopilotNodeListener({ runtime });
+await listener.channels?.ready({ timeoutMs: 15_000 });
+
+createServer(listener).listen(3123, () => {
+  console.log("Channels runtime listening on port 3123");
+});
+```
+
+</Step>
+
+<Step title="Run the channel runtime">
+
+Start it alongside your CrewAI agent server:
 
 ```bash
 uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
-node bot.ts                       # terminal 2 — Slack bot
+npx tsx server.ts                 # terminal 2 — Channels runtime
 ```
 
-Message the bot in Slack and it runs your Crew or Flow, streaming the reply back into the thread.
+Mention the bot in Slack or Teams and it runs your Crew or Flow, streaming the reply back into the thread. The thread stays subscribed, so follow-up messages run without another mention.
 
 </Step>
 
 </Steps>
 
-<Note>
-Slack app scopes, Socket Mode setup, and the full adapter options are maintained by CopilotKit. Follow the [Slack channel reference](https://docs.copilotkit.ai/reference/channels/slack) together with Slack's own app setup guide for the authoritative steps.
-</Note>
+## The event model
 
-## Discord
+A channel reacts to platform events with handlers, and each handler receives a `thread` you drive with a few methods:
 
-Discord uses the same `createBot` engine with the Discord adapter from `@copilotkit/channels-discord`:
+- **`channel.onMention`** fires when a user @-mentions the bot. Call `thread.subscribe()` to join the thread, then `thread.runAgent()` to run your CrewAI agent on the mention.
+- **`channel.onMessage`** fires on every message in a thread the bot can see. Gate it with `thread.isSubscribed()` so the agent only responds where it has joined, then `thread.runAgent()`.
+- **`thread.runAgent()`** runs the attached CrewAI agent for the current turn and streams its output back into the channel. Pass `{ prompt }` to override the text the agent runs on.
 
-```ts
-import { createBot } from "@copilotkit/channels";
-import { discord } from "@copilotkit/channels-discord";
-import { CrewAIAgent } from "@ag-ui/crewai";
-
-const bot = createBot({
-  adapters: [discord({ token: process.env.DISCORD_BOT_TOKEN! })],
-  agent: (threadId) => new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-});
-
-bot.start();
-```
-
-See the [Discord channel reference](https://docs.copilotkit.ai/reference/channels/discord) for the exact adapter options and bot setup.
+Your agent receives an ordinary AG-UI `RunAgentInput` and emits ordinary AG-UI events; the platform mechanics stay behind the channel, so the same Crew or Flow runs unchanged across every platform. The channel also exposes handlers for welcomes, interrupts, commands, reactions, and modals — see the [`Channel` reference](https://docs.copilotkit.ai/reference/channels/classes/Channel) for the full surface.
 
 ## Platform support
 
-Slack and Discord have official Channels adapters (`@copilotkit/channels-slack`, `@copilotkit/channels-discord`). Microsoft Teams is available through CopilotKit's managed offering (currently waitlisted). Check the [Channels reference](https://docs.copilotkit.ai/reference/channels) for the current list before promising a platform.
+The managed Intelligence path covers **Slack** and **Microsoft Teams** today — the same channel code runs on either, and `message.platform` / `thread.platform` report the native origin. Other messaging platforms connect the same managed way, with your channel code unchanged. Check the [CopilotKit Channels documentation](https://docs.copilotkit.ai/slack) for the current platform list and per-platform setup.
 
 ## Related
 

docs/v1.15.16/en/guides/frontend/overview.mdx

@@ -21,7 +21,7 @@ The two connect through the [AG-UI protocol](https://docs.ag-ui.com). The `ag-ui
   <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
     Keep agent state and your app UI in two-way sync.
   </Card>
-  <Card title="Channels" icon="slack" href="/edge/en/guides/frontend/channels">
+  <Card title="Channels" icon="messages" href="/edge/en/guides/frontend/channels">
     Run the same agent as a Slack, Discord, or Teams bot.
   </Card>
 </CardGroup>

docs/v1.15.19/ar/api-reference/inputs.mdx

@@ -0,0 +1,8 @@
+---
+title: "GET /inputs"
+description: "الحصول على المدخلات المطلوبة لطاقمك"
+openapi: "/v1.15.19/enterprise-api.en.yaml GET /inputs"
+mode: "wide"
+---
+
+

docs/v1.15.19/ar/api-reference/introduction.mdx

@@ -0,0 +1,135 @@
+---
+title: "مقدمة"
+description: "المرجع الكامل لواجهة برمجة تطبيقات CrewAI AMP REST"
+icon: "code"
+mode: "wide"
+---
+
+# واجهة برمجة تطبيقات CrewAI AMP
+
+مرحبًا بك في مرجع واجهة برمجة تطبيقات CrewAI AMP. تتيح لك هذه الواجهة التفاعل برمجيًا مع الأطقم المنشورة، مما يمكّنك من دمجها مع تطبيقاتك وسير عملك وخدماتك.
+
+## البدء السريع
+
+<Steps>
+  <Step title="الحصول على بيانات اعتماد API">
+    انتقل إلى صفحة تفاصيل طاقمك في لوحة تحكم CrewAI AMP وانسخ رمز Bearer من علامة تبويب الحالة.
+  </Step>
+
+<Step title="اكتشاف المدخلات المطلوبة">
+  استخدم نقطة النهاية `GET /inputs` لمعرفة المعاملات التي يتوقعها طاقمك.
+</Step>
+
+<Step title="بدء تنفيذ الطاقم">
+  استدعِ `POST /kickoff` مع مدخلاتك لبدء تنفيذ الطاقم واستلام
+  `kickoff_id`.
+</Step>
+
+  <Step title="مراقبة التقدم">
+    استخدم `GET /status/{kickoff_id}` للتحقق من حالة التنفيذ واسترجاع النتائج.
+  </Step>
+</Steps>
+
+## المصادقة
+
+تتطلب جميع طلبات API المصادقة باستخدام رمز Bearer. أدرج رمزك في ترويسة `Authorization`:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/inputs
+```
+
+### أنواع الرموز
+
+| نوع الرمز            | النطاق                     | حالة الاستخدام                                                     |
+| :-------------------- | :------------------------ | :----------------------------------------------------------- |
+| **Bearer Token**      | وصول على مستوى المؤسسة | عمليات الطاقم الكاملة، مثالي للتكامل بين الخوادم |
+| **User Bearer Token** | وصول محدد بالمستخدم        | صلاحيات محدودة، مناسب للعمليات الخاصة بالمستخدم   |
+
+<Tip>
+  يمكنك العثور على كلا نوعي الرموز في علامة تبويب الحالة من صفحة تفاصيل طاقمك في
+  لوحة تحكم CrewAI AMP.
+</Tip>
+
+## عنوان URL الأساسي
+
+لكل طاقم منشور نقطة نهاية API فريدة خاصة به:
+
+```
+https://your-crew-name.crewai.com
+```
+
+استبدل `your-crew-name` بعنوان URL الفعلي لطاقمك من لوحة التحكم.
+
+## سير العمل النموذجي
+
+1. **الاكتشاف**: استدعِ `GET /inputs` لفهم ما يحتاجه طاقمك
+2. **التنفيذ**: أرسل المدخلات عبر `POST /kickoff` لبدء المعالجة
+3. **المراقبة**: استعلم عن `GET /status/{kickoff_id}` حتى الاكتمال
+4. **النتائج**: استخرج المخرجات النهائية من الاستجابة المكتملة
+
+## معالجة الأخطاء
+
+تستخدم الواجهة أكواد حالة HTTP القياسية:
+
+| الكود  | المعنى                                    |
+| ----- | :----------------------------------------- |
+| `200` | نجاح                                    |
+| `400` | طلب غير صالح - تنسيق مدخلات غير صحيح         |
+| `401` | غير مصرّح - رمز bearer غير صالح        |
+| `404` | غير موجود - المورد غير موجود         |
+| `422` | خطأ في التحقق - مدخلات مطلوبة مفقودة |
+| `500` | خطأ في الخادم - تواصل مع الدعم             |
+
+## الاختبار التفاعلي
+
+<Info>
+  **لماذا لا يوجد زر "إرسال"؟** نظرًا لأن كل مستخدم CrewAI AMP لديه عنوان URL
+  فريد للطاقم، نستخدم **وضع المرجع** بدلاً من بيئة تفاعلية لتجنب
+  الالتباس. يوضح لك هذا بالضبط كيف يجب أن تبدو الطلبات بدون
+  أزرار إرسال غير فعالة.
+</Info>
+
+تعرض لك كل صفحة نقطة نهاية:
+
+- **تنسيق الطلب الدقيق** مع جميع المعاملات
+- **أمثلة الاستجابة** لحالات النجاح والخطأ
+- **عينات الكود** بلغات متعددة (cURL، Python، JavaScript، إلخ)
+- **أمثلة المصادقة** بتنسيق رمز Bearer الصحيح
+
+### **لاختبار واجهتك الفعلية:**
+
+<CardGroup cols={2}>
+  <Card title="نسخ أمثلة cURL" icon="terminal">
+    انسخ أمثلة cURL واستبدل العنوان URL + الرمز بقيمك الحقيقية
+  </Card>
+  <Card title="استخدام Postman/Insomnia" icon="play">
+    استورد الأمثلة في أداة اختبار API المفضلة لديك
+  </Card>
+</CardGroup>
+
+**مثال على سير العمل:**
+
+1. **انسخ مثال cURL هذا** من أي صفحة نقطة نهاية
+2. **استبدل `your-actual-crew-name.crewai.com`** بعنوان URL الحقيقي لطاقمك
+3. **استبدل رمز Bearer** برمزك الحقيقي من لوحة التحكم
+4. **نفّذ الطلب** في طرفيتك أو عميل API
+
+## هل تحتاج مساعدة؟
+
+<CardGroup cols={2}>
+  <Card
+    title="دعم المؤسسات"
+    icon="headset"
+    href="mailto:support@crewai.com"
+  >
+    احصل على مساعدة في تكامل API واستكشاف الأخطاء وإصلاحها
+  </Card>
+  <Card
+    title="لوحة تحكم المؤسسات"
+    icon="chart-line"
+    href="https://app.crewai.com"
+  >
+    إدارة أطقمك وعرض سجلات التنفيذ
+  </Card>
+</CardGroup>

docs/v1.15.19/ar/api-reference/kickoff.mdx

@@ -0,0 +1,8 @@
+---
+title: "POST /kickoff"
+description: "بدء تنفيذ الطاقم"
+openapi: "/v1.15.19/enterprise-api.en.yaml POST /kickoff"
+mode: "wide"
+---
+
+

docs/v1.15.19/ar/api-reference/resume.mdx

@@ -0,0 +1,6 @@
+---
+title: "POST /resume"
+description: "استئناف تنفيذ الطاقم مع التغذية الراجعة البشرية"
+openapi: "/v1.15.19/enterprise-api.en.yaml POST /resume"
+mode: "wide"
+---

docs/v1.15.19/ar/api-reference/status.mdx

@@ -0,0 +1,6 @@
+---
+title: "GET /status/{kickoff_id}"
+description: "الحصول على حالة التنفيذ"
+openapi: "/v1.15.19/enterprise-api.en.yaml GET /status/{kickoff_id}"
+mode: "wide"
+---

docs/v1.15.19/ar/concepts/agent-capabilities.mdx

@@ -0,0 +1,147 @@
+---
+title: "قدرات الوكيل"
+description: "فهم الطرق الخمس لتوسيع وكلاء CrewAI: الأدوات، MCP، التطبيقات، المهارات، والمعرفة."
+icon: puzzle-piece
+mode: "wide"
+---
+
+## نظرة عامة
+
+يمكن توسيع وكلاء CrewAI بـ **خمسة أنواع مميزة من القدرات**، كل منها يخدم غرضًا مختلفًا. فهم متى تستخدم كل نوع — وكيف يعملون معًا — هو المفتاح لبناء وكلاء فعّالين.
+
+<CardGroup cols={2}>
+  <Card title="الأدوات" icon="wrench" href="/ar/concepts/tools" color="#3B82F6">
+    **دوال قابلة للاستدعاء** — تمنح الوكلاء القدرة على اتخاذ إجراءات. البحث على الويب، عمليات الملفات، استدعاءات API، تنفيذ الكود.
+  </Card>
+  <Card title="خوادم MCP" icon="plug" href="/ar/mcp/overview" color="#8B5CF6">
+    **خوادم أدوات عن بُعد** — تربط الوكلاء بخوادم أدوات خارجية عبر Model Context Protocol. نفس تأثير الأدوات، لكن مستضافة خارجيًا.
+  </Card>
+  <Card title="التطبيقات" icon="grid-2" color="#EC4899">
+    **تكاملات المنصة** — تربط الوكلاء بتطبيقات SaaS (Gmail، Slack، Jira، Salesforce) عبر منصة CrewAI. تعمل محليًا مع رمز تكامل المنصة.
+  </Card>
+  <Card title="المهارات" icon="bolt" href="/ar/concepts/skills" color="#F59E0B">
+    **خبرة المجال** — تحقن التعليمات والإرشادات والمواد المرجعية في إرشادات الوكلاء. المهارات تخبر الوكلاء *كيف يفكرون*.
+  </Card>
+  <Card title="المعرفة" icon="book" href="/ar/concepts/knowledge" color="#10B981">
+    **حقائق مُسترجعة** — توفر للوكلاء بيانات من المستندات والملفات وعناوين URL عبر البحث الدلالي (RAG). المعرفة تعطي الوكلاء *ما يحتاجون معرفته*.
+  </Card>
+</CardGroup>
+
+---
+
+## التمييز الأساسي
+
+أهم شيء يجب فهمه: **هذه القدرات تنقسم إلى فئتين**.
+
+### قدرات الإجراء (الأدوات، MCP، التطبيقات)
+
+تمنح الوكلاء القدرة على **فعل أشياء** — استدعاء APIs، قراءة الملفات، البحث على الويب، إرسال رسائل البريد الإلكتروني. عند التنفيذ، تتحول الأنواع الثلاثة إلى نفس التنسيق الداخلي (مثيلات `BaseTool`) وتظهر في قائمة أدوات موحدة يمكن للوكيل استدعاؤها.
+
+```python
+from crewai import Agent
+from crewai_tools import SerperDevTool, FileReadTool
+
+agent = Agent(
+    role="Researcher",
+    goal="Find and compile market data",
+    backstory="Expert market analyst",
+    tools=[SerperDevTool(), FileReadTool()],  # أدوات محلية
+    mcps=["https://mcp.example.com/sse"],     # أدوات خادم MCP عن بُعد
+    apps=["gmail", "google_sheets"],           # تكاملات المنصة
+)
+```
+
+### قدرات السياق (المهارات، المعرفة)
+
+تُعدّل **إرشادات** الوكيل — بحقن الخبرة أو التعليمات أو البيانات المُسترجعة قبل أن يبدأ الوكيل في التفكير. لا تمنح الوكلاء إجراءات جديدة؛ بل تُشكّل كيف يفكر الوكلاء وما هي المعلومات التي يمكنهم الوصول إليها.
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Security Auditor",
+    goal="Audit cloud infrastructure for vulnerabilities",
+    backstory="Expert in cloud security with 10 years of experience",
+    skills=["./skills/security-audit"],        # تعليمات المجال
+    knowledge_sources=[pdf_source, url_source], # حقائق مُسترجعة
+)
+```
+
+---
+
+## متى تستخدم ماذا
+
+| تحتاج إلى...                                            | استخدم            | مثال                                     |
+| :------------------------------------------------------- | :---------------- | :--------------------------------------- |
+| الوكيل يبحث على الويب                                    | **الأدوات**       | `tools=[SerperDevTool()]`                |
+| الوكيل يستدعي API عن بُعد عبر MCP                        | **MCP**           | `mcps=["https://api.example.com/sse"]`   |
+| الوكيل يرسل بريد إلكتروني عبر Gmail                      | **التطبيقات**     | `apps=["gmail"]`                         |
+| الوكيل يتبع إجراءات محددة                                | **المهارات**      | `skills=["./skills/code-review"]`        |
+| الوكيل يرجع لمستندات الشركة                              | **المعرفة**       | `knowledge_sources=[pdf_source]`         |
+| الوكيل يبحث على الويب ويتبع إرشادات المراجعة              | **الأدوات + المهارات** | استخدم كليهما معًا                  |
+
+---
+
+## دمج القدرات
+
+في الممارسة العملية، غالبًا ما يستخدم الوكلاء **أنواعًا متعددة من القدرات معًا**. إليك مثال واقعي:
+
+```python
+from crewai import Agent
+from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
+
+# وكيل بحث مجهز بالكامل
+researcher = Agent(
+    role="Senior Research Analyst",
+    goal="Produce comprehensive market analysis reports",
+    backstory="Expert analyst with deep industry knowledge",
+
+    # الإجراء: ما يمكن للوكيل فعله
+    tools=[
+        SerperDevTool(),         # البحث على الويب
+        FileReadTool(),          # قراءة الملفات المحلية
+        CodeInterpreterTool(),   # تشغيل كود Python للتحليل
+    ],
+    mcps=["https://data-api.example.com/sse"],  # الوصول لـ API بيانات عن بُعد
+    apps=["google_sheets"],                      # الكتابة في Google Sheets
+
+    # السياق: ما يعرفه الوكيل
+    skills=["./skills/research-methodology"],    # كيفية إجراء البحث
+    knowledge_sources=[company_docs],            # بيانات خاصة بالشركة
+)
+```
+
+---
+
+## جدول المقارنة
+
+| الميزة | الأدوات | MCP | التطبيقات | المهارات | المعرفة |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| **يمنح الوكيل إجراءات** | ✅ | ✅ | ✅ | ❌ | ❌ |
+| **يُعدّل الإرشادات** | ❌ | ❌ | ❌ | ✅ | ✅ |
+| **يتطلب كود** | نعم | إعداد فقط | إعداد فقط | Markdown فقط | إعداد فقط |
+| **يعمل محليًا** | نعم | يعتمد | نعم (مع متغير بيئة) | غير متاح | نعم |
+| **يحتاج مفاتيح API** | لكل أداة | لكل خادم | رمز التكامل | لا | المُضمّن فقط |
+| **يُعيَّن على Agent** | `tools=[]` | `mcps=[]` | `apps=[]` | `skills=[]` | `knowledge_sources=[]` |
+| **يُعيَّن على Crew** | ❌ | ❌ | ❌ | `skills=[]` | `knowledge_sources=[]` |
+
+---
+
+## تعمّق أكثر
+
+هل أنت مستعد لمعرفة المزيد عن كل نوع من أنواع القدرات؟
+
+<CardGroup cols={2}>
+  <Card title="الأدوات" icon="wrench" href="/ar/concepts/tools">
+    إنشاء أدوات مخصصة، استخدام كتالوج OSS مع أكثر من 75 خيارًا، تكوين التخزين المؤقت والتنفيذ غير المتزامن.
+  </Card>
+  <Card title="تكامل MCP" icon="plug" href="/ar/mcp/overview">
+    الاتصال بخوادم MCP عبر stdio أو SSE أو HTTP. تصفية الأدوات، تكوين المصادقة.
+  </Card>
+  <Card title="المهارات" icon="bolt" href="/ar/concepts/skills">
+    بناء حزم المهارات مع SKILL.md، حقن خبرة المجال، استخدام الكشف التدريجي.
+  </Card>
+  <Card title="المعرفة" icon="book" href="/ar/concepts/knowledge">
+    إضافة المعرفة من ملفات PDF وCSV وعناوين URL والمزيد. تكوين المُضمّنات والاسترجاع.
+  </Card>
+</CardGroup>