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Author SHA1 Message Date
Iris Clawd
2aa9942f57 docs: add top-level Security Policy page across all languages
Create a dedicated Security Policy page (docs/{en,pt-BR,ko,ar}/security.mdx)
with vulnerability reporting instructions pointing to the Bugcrowd VDP
(crewai-vdp-ess@submit.bugcrowd.com), consistent with the updated security
policy from PR #5096.

The page is added to the Documentation tab navigation (after Telemetry)
across all versions and languages in docs.json.

This is a top-level security page, not buried inside MCP docs.
2026-03-26 17:55:27 +00:00
iris-clawd
52249683a7 docs: comprehensive RBAC permissions matrix and deployment guide (#5112)
- Add full feature permissions matrix (11 features × permission levels)
- Document Owner vs Member default permissions
- Add deployment guide: what permissions are needed to deploy from GitHub or Zip
- Document entity-level permissions (deployment permission types: run, traces, manage_settings, HITL, full_access)
- Document entity RBAC for env vars, LLM connections, and Git repositories
- Add common role patterns: Developer, Viewer/Stakeholder, Ops/Platform Admin
- Add quick-reference table for minimum deployment permissions

Addresses user feedback that RBAC was too restrictive and unclear:
members didn't know which permissions to configure for a developer profile.
2026-03-26 12:30:17 -04:00
João Moura
6193e082e1 docs: update changelog and version for v1.12.2 (#5103)
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2026-03-26 03:54:26 -03:00
João Moura
33f33c6fcc feat: bump versions to 1.12.2 (#5101) 2026-03-26 03:33:10 -03:00
alex-clawd
74976b157d fix: preserve method return value as flow output for @human_feedback with emit (#5099)
* fix: preserve method return value as flow output for @human_feedback with emit

When a @human_feedback decorated method with emit= is the final method in a
flow (no downstream listeners triggered), the flow's final output was
incorrectly set to the collapsed outcome string (e.g., 'approved') instead
of the method's actual return value (e.g., a state dict).

Root cause: _process_feedback() returns the collapsed_outcome string when
emit is set, and this string was being stored as the method's result in
_method_outputs.

The fix:
1. In human_feedback.py: After _process_feedback, stash the real method_output
   on the flow instance as _human_feedback_method_output when emit is set.

2. In flow.py: After appending a method result to _method_outputs, check if
   _human_feedback_method_output is set. If so, replace the last entry with
   the stashed real output and clear the stash.

This ensures:
- Routing still works correctly (collapsed outcome used for @listen matching)
- The flow's final result is the actual method return value
- If downstream listeners execute, their results become the final output

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* style: ruff format flow.py

* fix: use per-method dict stash for concurrency safety and None returns

Addresses review comments:
- Replace single flow-level slot with dict keyed by method name,
  safe under concurrent @human_feedback+emit execution
- Dict key presence (not value) indicates stashed output,
  correctly preserving None return values
- Added test for None return value preservation

---------

Co-authored-by: Joao Moura <joao@crewai.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-03-26 03:28:17 -03:00
Greyson LaLonde
bd03f6cf64 feat: add enterprise release phase to devtools release
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2026-03-26 12:22:37 +08:00
Rip&Tear
a91cd1a7d7 Revise security policy and reporting instructions (#5096)
* Revise security policy and reporting instructions

Updated the security reporting process and contact details.

* Update .github/security.md
---------
2026-03-26 10:50:21 +08:00
João Moura
66dee3195f docs: update changelog and version for v1.12.1 (#5095) 2026-03-25 22:52:11 -03:00
João Moura
034f576dc0 feat: bump versions to 1.12.1 (#5094)
* chore: bump version to 1.12.1 across all modules

* feat: bump versions to 1.12.1
2026-03-25 22:45:33 -03:00
Lucas Gomide
918654318b feat: add request_id to HumanFeedbackRequestedEvent (#5092)
* feat: add request_id to HumanFeedbackRequestedEvent

Allow platforms to attach a correlation identifier to human feedback requests so downstream consumers can deterministically match spans to their corresponding feedback records

* feat: add request_id to HumanFeedbackReceivedEvent for correlation

Without request_id on the received event, consumers cannot correlate
a feedback response back to its originating request. Both sides of the
request/response pair need the correlation identifier.

---------

Co-authored-by: Alex <alex@crewai.com>
2026-03-25 22:43:24 -03:00
João Moura
371e6cfd11 docs: update changelog and version for v1.12.0 (#5091) 2026-03-25 22:07:28 -03:00
João Moura
6fd70ce6e5 chore: bump version to 1.14.0 across all modules (#5090)
* chore: bump version to 1.14.0 across all modules

* chore: downgrade version to 1.12.0 across all modules
2026-03-25 22:03:37 -03:00
alex-clawd
c183b77991 fix: address Copilot review on OpenAI-compatible providers (#5042) (#5089)
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- Delegate supports_function_calling() to parent (handles o1 models via OpenRouter)
- Guard empty env vars in base_url resolution
- Fix misleading comment about model validation rules
- Remove unused MagicMock import
- Use 'is not None' for env var restoration in tests

Co-authored-by: Joao Moura <joao@crewai.com>
2026-03-25 18:22:13 -03:00
Greyson LaLonde
b5a0d6e709 docs: update changelog and version for v1.12.0a3 2026-03-26 04:17:37 +08:00
Greyson LaLonde
454156cff9 feat: bump versions to 1.12.0a3 2026-03-26 04:12:49 +08:00
Tiago Freire
d86707da3d Fix: bad credentials for traces batch push (404) (#4947)
## Summary

### Core fixes

<details>
<summary><b>Fix silent 404 cascade on trace event send</b></summary>

When `_initialize_backend_batch` failed, `trace_batch_id` was left populated with a client-generated UUID never registered server-side. All subsequent event sends hit a non-existent batch endpoint and returned 404. Now all three failure paths (None response, non-2xx status, exception) clear `trace_batch_id`.
</details>

<details>
<summary><b>Fix first-time deferred batch init silently skipped</b></summary>

First-time users have `is_tracing_enabled_in_context() = False` by design. This caused `_initialize_backend_batch` to return early without creating the batch, and `finalize_batch` to skip finalization (same guard). The first-time handler now passes `skip_context_check=True` to bypass both guards, calls `_finalize_backend_batch` directly, gates `backend_initialized` on actual success, checks `_send_events_to_backend` return status (marking batch as failed on 500), captures event count/duration/batch ID before they're consumed by send/finalize, and cleans up all singleton state via `_reset_batch_state()` on every exit path.
</details>

<details>
<summary><b>Sync <code>is_current_batch_ephemeral</code> on batch creation success</b></summary>

When the batch is successfully created on the server, `is_current_batch_ephemeral` is now synced with the actual `use_ephemeral` value used. This prevents endpoint mismatches where the batch was created on one endpoint but events and finalization were sent to a different one, resulting in 404.
</details>

<details>
<summary><b>Route <code>mark_trace_batch_as_failed</code> to correct endpoint for ephemeral batches</b></summary>

`mark_trace_batch_as_failed` always routed to the non-ephemeral endpoint (`/tracing/batches/{id}`), causing 404s when called on ephemeral batches — the same class of endpoint mismatch this PR aims to fix. Added `mark_ephemeral_trace_batch_as_failed` to `PlusAPI` and a `_mark_batch_as_failed` helper on `TraceBatchManager` that routes based on `is_current_batch_ephemeral`.
</details>

<details>
<summary><b>Gate <code>backend_initialized</code> on actual init success (non-first-time path)</b></summary>

On the non-first-time path, `backend_initialized` was set to `True` unconditionally after `_initialize_backend_batch` returned. With the new failure-path cleanup that clears `trace_batch_id`, this created an inconsistent state: `backend_initialized=True` + `trace_batch_id=None`. Now set via `self.trace_batch_id is not None`.
</details>

### Resilience improvements

<details>
<summary><b>Retry transient failures on batch creation</b></summary>

`_initialize_backend_batch` now retries up to 2 times with 200ms backoff on transient failures (None response, 5xx, network errors). Non-transient 4xx errors are not retried. The short backoff minimizes lock hold time on the non-first-time path where `_batch_ready_cv` is held.
</details>

<details>
<summary><b>Fall back to ephemeral on server auth rejection</b></summary>

When the non-ephemeral endpoint returns 401/403 (expired token, revoked credentials, key rotation), the client automatically switches to ephemeral tracing instead of losing traces. The fallback forwards `skip_context_check` and is guarded against infinite recursion — if ephemeral also fails, `trace_batch_id` is cleared normally.
</details>

<details>
<summary><b>Fix action-event race initializing batch as non-ephemeral</b></summary>

`_handle_action_event` called `batch_manager.initialize_batch()` directly, defaulting `use_ephemeral=False`. When a `DefaultEnvEvent` or `LLMCallStartedEvent` fired before `CrewKickoffStartedEvent` in the thread pool, the batch was locked in as non-ephemeral. Now routes through `_initialize_batch()` which computes `use_ephemeral` from `_check_authenticated()`.
</details>

<details>
<summary><b>Guard <code>_mark_batch_as_failed</code> against cascading network errors</b></summary>

When `_finalize_backend_batch` failed with a network error (e.g. `[Errno 54] Connection reset by peer`), the exception handler called `_mark_batch_as_failed` — which also makes an HTTP request on the same dead connection. That second failure was unhandled. Now wrapped in a try/except so it logs at debug level instead of propagating.
</details>

<details>
<summary><b>Design decision: first-time users always use ephemeral</b></summary>

First-time trace collection **always creates ephemeral batches**, regardless of authentication status. This is intentional:

1. **The first-time handler UX is built around ephemeral traces** — it displays an access code, a 24-hour expiry link, and opens the browser to the ephemeral trace viewer. Non-ephemeral batches don't produce these artifacts, so the handler would fall through to the "Local Traces Collected" fallback even when traces were successfully sent.

2. **The server handles account linking automatically** — `LinkEphemeralTracesJob` runs on user signup and migrates ephemeral traces to permanent records. Logged-in users can access their traces via their dashboard regardless.

3. **Checking auth during batch setup broke event collection** — moving `_check_authenticated()` into `_initialize_batch` caused the batch initialization to fail silently during the flow/crew start event handler, preventing all event collection. Keeping the first-time path fast and side-effect-free preserves event collection.

The auth check is deferred to the non-first-time path (second run onwards), where `is_tracing_enabled_in_context()` is `True` and the normal tracing pipeline handles everything — including the 401/403 ephemeral fallback.
</details>


### Manual tests


<details>
<summary><b>Matrix</b></summary>

| Scenario | First run | Second run |
|----------|-----------|------------|
| Logged out, fresh `.crewai_user.json` | Ephemeral trace created, URL returned | Ephemeral trace created, URL returned |
| Logged in, fresh `.crewai_user.json` | Ephemeral trace created, URL returned | Trace batch finalized, URL returned |
| Flow execution | Tested with `poem_flow` | Tested with `poem_flow` |
| Crew execution | Tested with `hitl_crew` | Tested with `hitl_crew` |
</details>
2026-03-25 16:00:05 -04:00
Greyson LaLonde
1956471086 fix: resolve multiple bugs in HITL flow system 2026-03-26 03:33:03 +08:00
42 changed files with 8153 additions and 178 deletions

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.github/security.md vendored
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@@ -1,50 +1,12 @@
## CrewAI Security Policy
We are committed to protecting the confidentiality, integrity, and availability of the CrewAI ecosystem. This policy explains how to report potential vulnerabilities and what you can expect from us when you do.
### Scope
We welcome reports for vulnerabilities that could impact:
- CrewAI-maintained source code and repositories
- CrewAI-operated infrastructure and services
- Official CrewAI releases, packages, and distributions
Issues affecting clearly unaffiliated third-party services or user-generated content are out of scope, unless you can demonstrate a direct impact on CrewAI systems or customers.
We are committed to protecting the confidentiality, integrity, and availability of the
CrewAI ecosystem.
### How to Report
- **Please do not** disclose vulnerabilities via public GitHub issues, pull requests, or social media.
- Email detailed reports to **security@crewai.com** with the subject line `Security Report`.
- If you need to share large files or sensitive artifacts, mention it in your email and we will coordinate a secure transfer method.
Please submit reports to **crewai-vdp-ess@submit.bugcrowd.com**
### What to Include
Providing comprehensive information enables us to validate the issue quickly:
- **Vulnerability overview** — a concise description and classification (e.g., RCE, privilege escalation)
- **Affected components** — repository, branch, tag, or deployed service along with relevant file paths or endpoints
- **Reproduction steps** — detailed, step-by-step instructions; include logs, screenshots, or screen recordings when helpful
- **Proof-of-concept** — exploit details or code that demonstrates the impact (if available)
- **Impact analysis** — severity assessment, potential exploitation scenarios, and any prerequisites or special configurations
### Our Commitment
- **Acknowledgement:** We aim to acknowledge your report within two business days.
- **Communication:** We will keep you informed about triage results, remediation progress, and planned release timelines.
- **Resolution:** Confirmed vulnerabilities will be prioritized based on severity and fixed as quickly as possible.
- **Recognition:** We currently do not run a bug bounty program; any rewards or recognition are issued at CrewAI's discretion.
### Coordinated Disclosure
We ask that you allow us a reasonable window to investigate and remediate confirmed issues before any public disclosure. We will coordinate publication timelines with you whenever possible.
### Safe Harbor
We will not pursue or support legal action against individuals who, in good faith:
- Follow this policy and refrain from violating any applicable laws
- Avoid privacy violations, data destruction, or service disruption
- Limit testing to systems in scope and respect rate limits and terms of service
If you are unsure whether your testing is covered, please contact us at **security@crewai.com** before proceeding.
- **Please do not** disclose vulnerabilities via public GitHub issues, pull requests,
or social media
- Reports submitted via channels other than this Bugcrowd submission email will not be reviewed and will be dismissed

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@@ -4,6 +4,134 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
icon: "clock"
mode: "wide"
---
<Update label="25 مارس 2026">
## v1.12.2
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.2)
## ما الذي تغير
### الميزات
- إضافة مرحلة إصدار المؤسسات إلى إصدار أدوات المطورين
### إصلاحات الأخطاء
- الحفاظ على قيمة إرجاع الطريقة كإخراج تدفق لـ @human_feedback مع emit
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.12.1
- مراجعة سياسة الأمان وتعليمات الإبلاغ
## المساهمون
@alex-clawd, @greysonlalonde, @joaomdmoura, @theCyberTech
</Update>
<Update label="25 مارس 2026">
## v1.12.1
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.1)
## ما الذي تغير
### الميزات
- إضافة request_id إلى HumanFeedbackRequestedEvent
- إضافة Qdrant Edge كخلفية تخزين لنظام الذاكرة
- إضافة أمر docs-check لتحليل التغييرات وتوليد الوثائق مع الترجمات
- إضافة دعم اللغة العربية إلى سجل التغييرات وأدوات الإصدار
- إضافة ترجمة باللغة العربية الفصحى لجميع الوثائق
- إضافة أمر تسجيل الخروج في واجهة سطر الأوامر
- إضافة مهارات الوكيل
- تنفيذ root_scope تلقائيًا لعزل الذاكرة الهيكلية
- تنفيذ مزودين متوافقين مع OpenAI (OpenRouter، DeepSeek، Ollama، vLLM، Cerebras، Dashscope)
### إصلاحات الأخطاء
- إصلاح بيانات اعتماد غير صحيحة لدفع دفعات التتبع (404)
- حل العديد من الأخطاء في نظام تدفق HITL
- إصلاح حفظ ذاكرة الوكيل
- حل جميع أخطاء mypy الصارمة عبر حزمة crewai
- إصلاح استخدام __router_paths__ لطرق المستمع + الموجه في FlowMeta
- إصلاح خطأ القيمة عند عدم دعم الملفات
- تصحيح صياغة الحجر الصحي لـ litellm في الوثائق
- إصلاح جميع أخطاء mypy في crewai-files وإضافة جميع الحزم إلى فحوصات النوع في CI
- تثبيت الحد الأعلى لـ litellm على آخر إصدار تم اختباره (1.82.6)
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.12.0
- إضافة CONTRIBUTING.md
- إضافة دليل لاستخدام CrewAI بدون LiteLLM
## المساهمون
@akaKuruma، @alex-clawd، @greysonlalonde، @iris-clawd، @joaomdmoura، @lorenzejay، @lucasgomide، @nicoferdi96
</Update>
<Update label="25 مارس 2026">
## v1.12.0
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0)
## ما الذي تغير
### الميزات
- إضافة واجهة تخزين Qdrant Edge لنظام الذاكرة
- إضافة أمر docs-check لتحليل التغييرات وتوليد الوثائق مع الترجمات
- إضافة دعم اللغة العربية لسجل التغييرات وأدوات الإصدار
- إضافة ترجمة اللغة العربية الفصحى لجميع الوثائق
- إضافة أمر تسجيل الخروج في واجهة سطر الأوامر
- تنفيذ مهارات الوكيل
- تنفيذ نطاق الجذر التلقائي لعزل الذاكرة الهرمية
- تنفيذ موفري خدمات متوافقين مع OpenAI (OpenRouter، DeepSeek، Ollama، vLLM، Cerebras، Dashscope)
### إصلاح الأخطاء
- إصلاح بيانات الاعتماد السيئة لدفع دفعات التتبع (404)
- حل العديد من الأخطاء في نظام تدفق HITL
- حل أخطاء mypy في crewai-files وإضافة جميع الحزم إلى فحوصات نوع CI
- حل جميع أخطاء mypy الصارمة عبر حزمة crewai-tools
- حل جميع أخطاء mypy عبر حزمة crewai
- إصلاح حفظ الذاكرة في الوكيل
- إصلاح استخدام __router_paths__ لطرق المستمع + الموجه في FlowMeta
- رفع خطأ القيمة عند عدم دعم الملفات
- تصحيح صياغة الحجر الصحي لـ litellm في الوثائق
- استخدام فحص None بدلاً من isinstance للذاكرة في تعلم التغذية الراجعة البشرية
- تثبيت الحد الأعلى لـ litellm على آخر إصدار تم اختباره (1.82.6)
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.12.0
- إضافة CONTRIBUTING.md
- إضافة دليل لاستخدام CrewAI بدون LiteLLM
### إعادة الهيكلة
- إعادة هيكلة لتجنب تكرار تنفيذ المهام المتزامنة / غير المتزامنة وبدء التشغيل في الوكيل
- تبسيط الأنابيب الداخلية من litellm (عد الرموز، ردود النداء، اكتشاف الميزات، الأخطاء)
## المساهمون
@akaKuruma، @alex-clawd، @greysonlalonde، @iris-clawd، @joaomdmoura، @lorenzejay، @nicoferdi96
</Update>
<Update label="26 مارس 2026">
## v1.12.0a3
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0a3)
## ما الذي تغير
### إصلاحات الأخطاء
- إصلاح بيانات الاعتماد الخاطئة لدفع دفعات التتبع (404)
- حل العديد من الأخطاء في نظام تدفق HITL
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.12.0a2
## المساهمون
@akaKuruma, @greysonlalonde
</Update>
<Update label="25 مارس 2026">
## v1.12.0a2

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- **الالتزام بمواصفات ترخيص MCP**: إذا كنت تنفذ المصادقة والترخيص، اتبع بدقة [مواصفات ترخيص MCP](https://modelcontextprotocol.io/specification/draft/basic/authorization).
- **تدقيقات أمنية منتظمة**: إذا كان خادم MCP يتعامل مع بيانات حساسة، فكر في إجراء تدقيقات أمنية دورية.
## 5. قراءة إضافية
## 5. الإبلاغ عن الثغرات الأمنية
إذا اكتشفت ثغرة أمنية في CrewAI، يرجى الإبلاغ عنها بشكل مسؤول من خلال برنامج الكشف عن الثغرات (VDP) الخاص بنا على Bugcrowd:
**أرسل التقارير إلى:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
**لا تكشف** عن الثغرات عبر issues العامة على GitHub أو pull requests أو وسائل التواصل الاجتماعي. لن تتم مراجعة التقارير المقدمة عبر قنوات غير Bugcrowd.
</Warning>
لمزيد من التفاصيل، راجع [سياسة الأمان](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md) الخاصة بنا.
## 6. قراءة إضافية
لمزيد من المعلومات التفصيلية حول أمان MCP، راجع التوثيق الرسمي:
- **[أمان نقل MCP](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**

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---
title: سياسة الأمان
description: تعرف على كيفية الإبلاغ عن الثغرات الأمنية وممارسات الأمان في CrewAI.
icon: shield
mode: "wide"
---
## الإبلاغ عن الثغرات الأمنية
إذا اكتشفت ثغرة أمنية في CrewAI، يرجى الإبلاغ عنها بشكل مسؤول من خلال برنامج الكشف عن الثغرات (VDP) الخاص بنا على Bugcrowd:
**أرسل التقارير إلى:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
**لا تكشف** عن الثغرات عبر issues العامة على GitHub أو pull requests أو وسائل التواصل الاجتماعي. لن تتم مراجعة التقارير المقدمة عبر قنوات غير Bugcrowd.
</Warning>
لمزيد من التفاصيل، راجع [سياسة الأمان على GitHub](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
## موارد الأمان
- **[اعتبارات أمان MCP](/mcp/security)** — أفضل الممارسات لدمج خوادم MCP بأمان مع وكلاء CrewAI، بما في ذلك أمان النقل ومخاطر حقن الأوامر ونصائح تنفيذ الخادم.

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@@ -4,6 +4,134 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="Mar 25, 2026">
## v1.12.2
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.2)
## What's Changed
### Features
- Add enterprise release phase to devtools release
### Bug Fixes
- Preserve method return value as flow output for @human_feedback with emit
### Documentation
- Update changelog and version for v1.12.1
- Revise security policy and reporting instructions
## Contributors
@alex-clawd, @greysonlalonde, @joaomdmoura, @theCyberTech
</Update>
<Update label="Mar 25, 2026">
## v1.12.1
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.1)
## What's Changed
### Features
- Add request_id to HumanFeedbackRequestedEvent
- Add Qdrant Edge storage backend for memory system
- Add docs-check command to analyze changes and generate docs with translations
- Add Arabic language support to changelog and release tooling
- Add modern standard Arabic translation of all documentation
- Add logout command in CLI
- Add agent skills
- Implement automatic root_scope for hierarchical memory isolation
- Implement native OpenAI-compatible providers (OpenRouter, DeepSeek, Ollama, vLLM, Cerebras, Dashscope)
### Bug Fixes
- Fix bad credentials for traces batch push (404)
- Resolve multiple bugs in HITL flow system
- Fix agent memory saving
- Resolve all strict mypy errors across crewai package
- Fix use of __router_paths__ for listener+router methods in FlowMeta
- Fix value error on no file support
- Correct litellm quarantine wording in docs
- Fix all mypy errors in crewai-files and add all packages to CI type checks
- Pin litellm upper bound to last tested version (1.82.6)
### Documentation
- Update changelog and version for v1.12.0
- Add CONTRIBUTING.md
- Add guide for using CrewAI without LiteLLM
## Contributors
@akaKuruma, @alex-clawd, @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @nicoferdi96
</Update>
<Update label="Mar 25, 2026">
## v1.12.0
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0)
## What's Changed
### Features
- Add Qdrant Edge storage backend for memory system
- Add docs-check command to analyze changes and generate docs with translations
- Add Arabic language support to changelog and release tooling
- Add modern standard Arabic translation of all documentation
- Add logout command in CLI
- Implement agent skills
- Implement automatic root_scope for hierarchical memory isolation
- Implement native OpenAI-compatible providers (OpenRouter, DeepSeek, Ollama, vLLM, Cerebras, Dashscope)
### Bug Fixes
- Fix bad credentials for traces batch push (404)
- Resolve multiple bugs in HITL flow system
- Resolve mypy errors in crewai-files and add all packages to CI type checks
- Resolve all strict mypy errors across crewai-tools package
- Resolve all mypy errors across crewai package
- Fix memory saving in agent
- Fix usage of __router_paths__ for listener+router methods in FlowMeta
- Raise value error on no file support
- Correct litellm quarantine wording in docs
- Use None check instead of isinstance for memory in human feedback learn
- Pin litellm upper bound to last tested version (1.82.6)
### Documentation
- Update changelog and version for v1.12.0
- Add CONTRIBUTING.md
- Add guide for using CrewAI without LiteLLM
### Refactoring
- Refactor to deduplicate sync/async task execution and kickoff in agent
- Simplify internal plumbing from litellm (token counting, callbacks, feature detection, errors)
## Contributors
@akaKuruma, @alex-clawd, @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @nicoferdi96
</Update>
<Update label="Mar 26, 2026">
## v1.12.0a3
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0a3)
## What's Changed
### Bug Fixes
- Fix bad credentials for traces batch push (404)
- Resolve multiple bugs in HITL flow system
### Documentation
- Update changelog and version for v1.12.0a2
## Contributors
@akaKuruma, @greysonlalonde
</Update>
<Update label="Mar 25, 2026">
## v1.12.0a2

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@@ -7,11 +7,13 @@ mode: "wide"
## Overview
RBAC in CrewAI AMP enables secure, scalable access management through a combination of organizationlevel roles and automationlevel visibility controls.
RBAC in CrewAI AMP enables secure, scalable access management through two layers:
1. **Feature permissions** — control what each role can do across the platform (manage, read, or no access)
2. **Entity-level permissions** — fine-grained access on individual automations, environment variables, LLM connections, and Git repositories
<Frame>
<img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI AMP" />
</Frame>
## Users and Roles
@@ -39,6 +41,13 @@ You can configure users and roles in Settings → Roles.
</Step>
</Steps>
### Predefined Roles
| Role | Description |
| :--------- | :-------------------------------------------------------------------------- |
| **Owner** | Full access to all features and settings. Cannot be restricted. |
| **Member** | Read access to most features, manage access to Studio projects. Cannot modify organization or default settings. |
### Configuration summary
| Area | Where to configure | Options |
@@ -46,23 +55,80 @@ You can configure users and roles in Settings → Roles.
| Users & Roles | Settings → Roles | Predefined: Owner, Member; Custom roles |
| Automation visibility | Automation → Settings → Visibility | Private; Whitelist users/roles |
## Automationlevel Access Control
---
In addition to organizationwide roles, CrewAI Automations support finegrained visibility settings that let you restrict access to specific automations by user or role.
## Feature Permissions Matrix
This is useful for:
Every role has a permission level for each feature area. The three levels are:
- **Manage** — full read/write access (create, edit, delete)
- **Read** — view-only access
- **No access** — feature is hidden/inaccessible
| Feature | Owner | Member (default) | Description |
| :------------------------ | :------ | :--------------- | :-------------------------------------------------------------- |
| `usage_dashboards` | Manage | Read | View usage metrics and analytics |
| `crews_dashboards` | Manage | Read | View deployment dashboards, access automation details |
| `invitations` | Manage | Read | Invite new members to the organization |
| `training_ui` | Manage | Read | Access training/fine-tuning interfaces |
| `tools` | Manage | Read | Create and manage tools |
| `agents` | Manage | Read | Create and manage agents |
| `environment_variables` | Manage | Read | Create and manage environment variables |
| `llm_connections` | Manage | Read | Configure LLM provider connections |
| `default_settings` | Manage | No access | Modify organization-wide default settings |
| `organization_settings` | Manage | No access | Manage billing, plans, and organization configuration |
| `studio_projects` | Manage | Manage | Create and edit projects in Studio |
<Tip>
When creating a custom role, you can set each feature independently to **Manage**, **Read**, or **No access** to match your team's needs.
</Tip>
---
## Deploying from GitHub or Zip
One of the most common RBAC questions is: _"What permissions does a team member need to deploy?"_
### Deploy from GitHub
To deploy an automation from a GitHub repository, a user needs:
1. **`crews_dashboards`**: at least `Read` — required to access the automations dashboard where deployments are created
2. **Git repository access** (if entity-level RBAC for Git repositories is enabled): the user's role must be granted access to the specific Git repository via entity-level permissions
3. **`studio_projects`: `Manage`** — if building the crew in Studio before deploying
### Deploy from Zip
To deploy an automation from a Zip file upload, a user needs:
1. **`crews_dashboards`**: at least `Read` — required to access the automations dashboard
2. **Zip deployments enabled**: the organization must not have disabled zip deployments in organization settings
### Quick Reference: Minimum Permissions for Deployment
| Action | Required feature permissions | Additional requirements |
| :------------------- | :------------------------------------ | :----------------------------------------------- |
| Deploy from GitHub | `crews_dashboards: Read` | Git repo entity access (if Git RBAC is enabled) |
| Deploy from Zip | `crews_dashboards: Read` | Zip deployments must be enabled at the org level |
| Build in Studio | `studio_projects: Manage` | — |
| Configure LLM keys | `llm_connections: Manage` | — |
| Set environment vars | `environment_variables: Manage` | Entity-level access (if entity RBAC is enabled) |
---
## Automationlevel Access Control (Entity Permissions)
In addition to organizationwide roles, CrewAI supports finegrained entity-level permissions that restrict access to individual resources.
### Automation Visibility
Automations support visibility settings that restrict access by user or role. This is useful for:
- Keeping sensitive or experimental automations private
- Managing visibility across large teams or external collaborators
- Testing automations in isolated contexts
Deployments can be configured as private, meaning only whitelisted users and roles will be able to:
- View the deployment
- Run it or interact with its API
- Access its logs, metrics, and settings
The organization owner always has access, regardless of visibility settings.
Deployments can be configured as private, meaning only whitelisted users and roles will be able to interact with them.
You can configure automationlevel access control in Automation → Settings → Visibility tab.
@@ -99,9 +165,92 @@ You can configure automationlevel access control in Automation → Settings
<Frame>
<img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI AMP" />
</Frame>
### Deployment Permission Types
When granting entity-level access to a specific automation, you can assign these permission types:
| Permission | What it allows |
| :------------------- | :-------------------------------------------------- |
| `run` | Execute the automation and use its API |
| `traces` | View execution traces and logs |
| `manage_settings` | Edit, redeploy, rollback, or delete the automation |
| `human_in_the_loop` | Respond to human-in-the-loop (HITL) requests |
| `full_access` | All of the above |
### Entity-level RBAC for Other Resources
When entity-level RBAC is enabled, access to these resources can also be controlled per user or role:
| Resource | Controlled by | Description |
| :--------------------- | :------------------------------- | :---------------------------------------------------- |
| Environment variables | Entity RBAC feature flag | Restrict which roles/users can view or manage specific env vars |
| LLM connections | Entity RBAC feature flag | Restrict access to specific LLM provider configurations |
| Git repositories | Git repositories RBAC org setting | Restrict which roles/users can access specific connected repos |
---
## Common Role Patterns
While CrewAI ships with Owner and Member roles, most teams benefit from creating custom roles. Here are common patterns:
### Developer Role
A role for team members who build and deploy automations but don't manage organization settings.
| Feature | Permission |
| :------------------------ | :--------- |
| `usage_dashboards` | Read |
| `crews_dashboards` | Manage |
| `invitations` | Read |
| `training_ui` | Read |
| `tools` | Manage |
| `agents` | Manage |
| `environment_variables` | Manage |
| `llm_connections` | Read |
| `default_settings` | No access |
| `organization_settings` | No access |
| `studio_projects` | Manage |
### Viewer / Stakeholder Role
A role for non-technical stakeholders who need to monitor automations and view results.
| Feature | Permission |
| :------------------------ | :--------- |
| `usage_dashboards` | Read |
| `crews_dashboards` | Read |
| `invitations` | No access |
| `training_ui` | Read |
| `tools` | Read |
| `agents` | Read |
| `environment_variables` | No access |
| `llm_connections` | No access |
| `default_settings` | No access |
| `organization_settings` | No access |
| `studio_projects` | Read |
### Ops / Platform Admin Role
A role for platform operators who manage infrastructure settings but may not build agents.
| Feature | Permission |
| :------------------------ | :--------- |
| `usage_dashboards` | Manage |
| `crews_dashboards` | Manage |
| `invitations` | Manage |
| `training_ui` | Read |
| `tools` | Read |
| `agents` | Read |
| `environment_variables` | Manage |
| `llm_connections` | Manage |
| `default_settings` | Manage |
| `organization_settings` | Read |
| `studio_projects` | Read |
---
<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
Contact our support team for assistance with RBAC questions.
</Card>

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@@ -156,7 +156,19 @@ If you are developing an MCP server that CrewAI agents might connect to, conside
- **Adherence to MCP Authorization Spec**: If implementing authentication and authorization, strictly follow the [MCP Authorization specification](https://modelcontextprotocol.io/specification/draft/basic/authorization) and relevant [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700).
- **Regular Security Audits**: If your MCP server handles sensitive data, performs critical operations, or is publicly exposed, consider periodic security audits by qualified professionals.
## 5. Further Reading
## 5. Reporting Security Vulnerabilities
If you discover a security vulnerability in CrewAI, please report it responsibly through our Bugcrowd Vulnerability Disclosure Program (VDP):
**Submit reports to:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
**Do not** disclose vulnerabilities via public GitHub issues, pull requests, or social media. Reports submitted via channels other than Bugcrowd will not be reviewed.
</Warning>
For full details, see our [Security Policy](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
## 6. Further Reading
For more detailed information on MCP security, refer to the official documentation:
- **[MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**

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---
title: Security Policy
description: Learn how to report security vulnerabilities and about CrewAI's security practices.
icon: shield
mode: "wide"
---
## Reporting Security Vulnerabilities
If you discover a security vulnerability in CrewAI, please report it responsibly through our Bugcrowd Vulnerability Disclosure Program (VDP):
**Submit reports to:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
**Do not** disclose vulnerabilities via public GitHub issues, pull requests, or social media. Reports submitted via channels other than Bugcrowd will not be reviewed.
</Warning>
For full details, see our [Security Policy on GitHub](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
## Security Resources
- **[MCP Security Considerations](/mcp/security)** — Best practices for securely integrating MCP servers with your CrewAI agents, including transport security, prompt injection risks, and server implementation advice.

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@@ -4,6 +4,134 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
icon: "clock"
mode: "wide"
---
<Update label="2026년 3월 25일">
## v1.12.2
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.12.2)
## 변경 사항
### 기능
- devtools 릴리스에 기업 릴리스 단계 추가
### 버그 수정
- @human_feedback과 함께 emit을 사용할 때 메서드 반환 값을 흐름 출력으로 유지
### 문서
- v1.12.1에 대한 변경 로그 및 버전 업데이트
- 보안 정책 및 보고 지침 수정
## 기여자
@alex-clawd, @greysonlalonde, @joaomdmoura, @theCyberTech
</Update>
<Update label="2026년 3월 25일">
## v1.12.1
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.12.1)
## 변경 사항
### 기능
- HumanFeedbackRequestedEvent에 request_id 추가
- 메모리 시스템을 위한 Qdrant Edge 저장소 백엔드 추가
- 변경 사항을 분석하고 번역된 문서와 함께 문서를 생성하는 docs-check 명령어 추가
- 변경 로그 및 릴리스 도구에 아랍어 지원 추가
- 모든 문서에 대한 현대 표준 아랍어 번역 추가
- CLI에 로그아웃 명령어 추가
- 에이전트 기술 추가
- 계층적 메모리 격리를 위한 자동 root_scope 구현
- OpenAI 호환 네이티브 제공자 구현 (OpenRouter, DeepSeek, Ollama, vLLM, Cerebras, Dashscope)
### 버그 수정
- 트레이스 배치 푸시에 대한 잘못된 자격 증명 수정 (404)
- HITL 흐름 시스템의 여러 버그 해결
- 에이전트 메모리 저장 수정
- crewai 패키지 전반에 걸쳐 모든 엄격한 mypy 오류 해결
- FlowMeta의 listener+router 메서드에 대한 __router_paths__ 사용 수정
- 파일 지원이 없는 경우 값 오류 수정
- 문서에서 litellm 격리 단어 수정
- crewai-files의 모든 mypy 오류 수정 및 모든 패키지를 CI 유형 검사에 추가
- litellm의 상한을 마지막 테스트된 버전 (1.82.6)으로 고정
### 문서
- v1.12.0에 대한 변경 로그 및 버전 업데이트
- CONTRIBUTING.md 추가
- LiteLLM 없이 CrewAI를 사용하는 가이드 추가
## 기여자
@akaKuruma, @alex-clawd, @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @nicoferdi96
</Update>
<Update label="2026년 3월 25일">
## v1.12.0
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0)
## 변경 사항
### 기능
- 메모리 시스템을 위한 Qdrant Edge 스토리지 백엔드 추가
- 변경 사항을 분석하고 번역된 문서와 함께 문서를 생성하는 docs-check 명령어 추가
- 변경 로그 및 릴리스 도구에 아랍어 지원 추가
- 모든 문서의 현대 표준 아랍어 번역 추가
- CLI에 로그아웃 명령어 추가
- 에이전트 기술 구현
- 계층적 메모리 격리를 위한 자동 root_scope 구현
- OpenAI 호환 네이티브 제공자 구현 (OpenRouter, DeepSeek, Ollama, vLLM, Cerebras, Dashscope)
### 버그 수정
- 트레이스 배치 푸시에 대한 잘못된 자격 증명 수정 (404)
- HITL 흐름 시스템의 여러 버그 해결
- crewai-files의 mypy 오류 해결 및 모든 패키지를 CI 타입 검사에 추가
- crewai-tools 패키지 전반의 모든 엄격한 mypy 오류 해결
- crewai 패키지 전반의 모든 mypy 오류 해결
- 에이전트의 메모리 절약 수정
- FlowMeta에서 listener+router 메서드의 __router_paths__ 사용 수정
- 파일 지원이 없을 때 값 오류 발생
- 문서에서 litellm 격리 단어 수정
- 인간 피드백 학습에서 메모리에 대한 isinstance 대신 None 체크 사용
- litellm의 상한을 마지막 테스트된 버전(1.82.6)으로 고정
### 문서
- v1.12.0에 대한 변경 로그 및 버전 업데이트
- CONTRIBUTING.md 추가
- LiteLLM 없이 CrewAI를 사용하는 가이드 추가
### 리팩토링
- 에이전트에서 동기/비동기 작업 실행 및 시작을 중복 제거하도록 리팩토링
- litellm의 내부 플러밍 단순화 (토큰 카운팅, 콜백, 기능 감지, 오류)
## 기여자
@akaKuruma, @alex-clawd, @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @nicoferdi96
</Update>
<Update label="2026년 3월 26일">
## v1.12.0a3
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0a3)
## 변경 사항
### 버그 수정
- 트레이스 배치 푸시에 대한 잘못된 자격 증명 수정 (404)
- HITL 흐름 시스템의 여러 버그 해결
### 문서
- v1.12.0a2에 대한 변경 로그 및 버전 업데이트
## 기여자
@akaKuruma, @greysonlalonde
</Update>
<Update label="2026년 3월 25일">
## v1.12.0a2

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@@ -156,7 +156,19 @@ CrewAI 에이전트가 연결할 수 있는 MCP 서버를 개발하고 있다면
- **MCP 인증 사양 준수**: 인증 및 권한 부여를 구현할 경우, [MCP Authorization specification](https://modelcontextprotocol.io/specification/draft/basic/authorization) 및 관련 [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700)를 엄격히 준수하세요.
- **정기적인 보안 감사**: MCP 서버가 민감한 데이터를 처리하거나, 중요한 작업을 수행하거나, 대외적으로 노출된 경우 자격을 갖춘 전문가의 정기적인 보안 감사를 고려하세요.
## 5. 추가 참고 자료
## 5. 보안 취약점 보고
CrewAI에서 보안 취약점을 발견하셨다면, Bugcrowd 취약점 공개 프로그램(VDP)을 통해 책임감 있게 보고해 주세요:
**보고서 제출:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
공개 GitHub 이슈, 풀 리퀘스트 또는 소셜 미디어를 통해 취약점을 공개하지 **마세요**. Bugcrowd 이외의 채널로 제출된 보고서는 검토되지 않습니다.
</Warning>
자세한 내용은 [보안 정책](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md)을 참조하세요.
## 6. 추가 참고 자료
MCP 보안에 대한 자세한 내용은 공식 문서를 참고하세요:
- **[MCP 전송 보안](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**

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---
title: 보안 정책
description: CrewAI의 보안 취약점 보고 방법과 보안 관행에 대해 알아보세요.
icon: shield
mode: "wide"
---
## 보안 취약점 보고
CrewAI에서 보안 취약점을 발견하셨다면, Bugcrowd 취약점 공개 프로그램(VDP)을 통해 책임감 있게 보고해 주세요:
**보고서 제출:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
공개 GitHub 이슈, 풀 리퀘스트 또는 소셜 미디어를 통해 취약점을 공개하지 **마세요**. Bugcrowd 이외의 채널로 제출된 보고서는 검토되지 않습니다.
</Warning>
자세한 내용은 [GitHub 보안 정책](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md)을 참조하세요.
## 보안 리소스
- **[MCP 보안 고려사항](/mcp/security)** — MCP 서버를 CrewAI 에이전트와 안전하게 통합하기 위한 모범 사례로, 전송 보안, 프롬프트 인젝션 위험 및 서버 구현 권장 사항을 포함합니다.

View File

@@ -4,6 +4,134 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="25 mar 2026">
## v1.12.2
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.2)
## O que Mudou
### Recursos
- Adicionar fase de lançamento empresarial ao lançamento do devtools
### Correções de Bugs
- Preservar o valor de retorno do método como saída de fluxo para @human_feedback com emit
### Documentação
- Atualizar changelog e versão para v1.12.1
- Revisar política de segurança e instruções de relatório
## Contributors
@alex-clawd, @greysonlalonde, @joaomdmoura, @theCyberTech
</Update>
<Update label="25 mar 2026">
## v1.12.1
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.1)
## O que Mudou
### Recursos
- Adicionar request_id ao HumanFeedbackRequestedEvent
- Adicionar backend de armazenamento Qdrant Edge para sistema de memória
- Adicionar comando docs-check para analisar mudanças e gerar documentação com traduções
- Adicionar suporte ao idioma árabe para changelog e ferramentas de lançamento
- Adicionar tradução em árabe padrão moderno de toda a documentação
- Adicionar comando de logout na CLI
- Adicionar habilidades de agente
- Implementar root_scope automático para isolamento hierárquico de memória
- Implementar provedores nativos compatíveis com OpenAI (OpenRouter, DeepSeek, Ollama, vLLM, Cerebras, Dashscope)
### Correções de Bugs
- Corrigir credenciais incorretas para envio em lote de traces (404)
- Resolver múltiplos bugs no sistema de fluxo HITL
- Corrigir salvamento de memória do agente
- Resolver todos os erros estritos do mypy no pacote crewai
- Corrigir uso de __router_paths__ para métodos listener+router em FlowMeta
- Corrigir erro de valor em caso de suporte a nenhum arquivo
- Corrigir redação da quarentena do litellm na documentação
- Corrigir todos os erros do mypy em crewai-files e adicionar todos os pacotes às verificações de tipo do CI
- Fixar limite superior do litellm na última versão testada (1.82.6)
### Documentação
- Atualizar changelog e versão para v1.12.0
- Adicionar CONTRIBUTING.md
- Adicionar guia para usar CrewAI sem LiteLLM
## Contribuidores
@akaKuruma, @alex-clawd, @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @nicoferdi96
</Update>
<Update label="25 mar 2026">
## v1.12.0
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0)
## O que Mudou
### Funcionalidades
- Adicionar backend de armazenamento Qdrant Edge para sistema de memória
- Adicionar comando docs-check para analisar mudanças e gerar documentos com traduções
- Adicionar suporte ao idioma árabe para changelog e ferramentas de lançamento
- Adicionar tradução em árabe padrão moderno de toda a documentação
- Adicionar comando de logout na CLI
- Implementar habilidades de agente
- Implementar root_scope automático para isolamento hierárquico de memória
- Implementar provedores nativos compatíveis com OpenAI (OpenRouter, DeepSeek, Ollama, vLLM, Cerebras, Dashscope)
### Correções de Bugs
- Corrigir credenciais inválidas para envio em lote de rastros (404)
- Resolver múltiplos bugs no sistema de fluxo HITL
- Resolver erros do mypy em crewai-files e adicionar todos os pacotes às verificações de tipo do CI
- Resolver todos os erros estritos do mypy no pacote crewai-tools
- Resolver todos os erros do mypy no pacote crewai
- Corrigir economia de memória no agente
- Corrigir uso de __router_paths__ para métodos listener+router em FlowMeta
- Levantar erro de valor em caso de suporte a arquivos inexistente
- Corrigir a redação da quarentena do litellm na documentação
- Usar verificação de None em vez de isinstance para memória no aprendizado de feedback humano
- Fixar limite superior do litellm na última versão testada (1.82.6)
### Documentação
- Atualizar changelog e versão para v1.12.0
- Adicionar CONTRIBUTING.md
- Adicionar guia para usar CrewAI sem LiteLLM
### Refatoração
- Refatorar para desduplicar execução de tarefas síncronas/assíncronas e início no agente
- Simplificar a infraestrutura interna do litellm (contagem de tokens, callbacks, detecção de recursos, erros)
## Contribuidores
@akaKuruma, @alex-clawd, @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @nicoferdi96
</Update>
<Update label="26 mar 2026">
## v1.12.0a3
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.12.0a3)
## O que Mudou
### Correções de Bugs
- Corrigir credenciais inválidas para envio em lote de rastros (404)
- Resolver múltiplos bugs no sistema de fluxo HITL
### Documentação
- Atualizar changelog e versão para v1.12.0a2
## Contributors
@akaKuruma, @greysonlalonde
</Update>
<Update label="25 mar 2026">
## v1.12.0a2

View File

@@ -156,7 +156,19 @@ Se você está desenvolvendo um servidor MCP ao qual agentes CrewAI possam se co
- **Aderência à Especificação de Autorização MCP**: Caso implemente autenticação e autorização, siga estritamente a [especificação de autorização MCP](https://modelcontextprotocol.io/specification/draft/basic/authorization) e as [melhores práticas de segurança OAuth 2.0](https://datatracker.ietf.org/doc/html/rfc9700) relevantes.
- **Auditorias de Segurança Regulares**: Caso seu servidor MCP manipule dados sensíveis, realize operações críticas ou seja exposto publicamente, considere auditorias de segurança periódicas conduzidas por profissionais qualificados.
## 5. Leituras Adicionais
## 5. Reportando Vulnerabilidades de Segurança
Se você descobrir uma vulnerabilidade de segurança no CrewAI, por favor reporte de forma responsável através do nosso Programa de Divulgação de Vulnerabilidades (VDP) no Bugcrowd:
**Envie relatórios para:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
**Não** divulgue vulnerabilidades por meio de issues públicas no GitHub, pull requests ou redes sociais. Relatórios enviados por outros canais que não o Bugcrowd não serão analisados.
</Warning>
Para mais detalhes, consulte nossa [Política de Segurança](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
## 6. Leituras Adicionais
Para informações mais detalhadas sobre segurança MCP, consulte a documentação oficial:
- **[Segurança de Transporte MCP](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**

22
docs/pt-BR/security.mdx Normal file
View File

@@ -0,0 +1,22 @@
---
title: Política de Segurança
description: Saiba como reportar vulnerabilidades de segurança e sobre as práticas de segurança do CrewAI.
icon: shield
mode: "wide"
---
## Reportando Vulnerabilidades de Segurança
Se você descobrir uma vulnerabilidade de segurança no CrewAI, por favor reporte de forma responsável através do nosso Programa de Divulgação de Vulnerabilidades (VDP) no Bugcrowd:
**Envie relatórios para:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
<Warning>
**Não** divulgue vulnerabilidades por meio de issues públicas no GitHub, pull requests ou redes sociais. Relatórios enviados por outros canais que não o Bugcrowd não serão analisados.
</Warning>
Para mais detalhes, consulte nossa [Política de Segurança no GitHub](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
## Recursos de Segurança
- **[Considerações de Segurança MCP](/mcp/security)** — Melhores práticas para integrar servidores MCP com segurança aos seus agentes CrewAI, incluindo segurança de transporte, riscos de injeção de prompt e conselhos de implementação de servidor.

View File

@@ -152,4 +152,4 @@ __all__ = [
"wrap_file_source",
]
__version__ = "1.12.0a2"
__version__ = "1.12.2"

View File

@@ -11,7 +11,7 @@ dependencies = [
"pytube~=15.0.0",
"requests~=2.32.5",
"docker~=7.1.0",
"crewai==1.12.0a2",
"crewai==1.12.2",
"tiktoken~=0.8.0",
"beautifulsoup4~=4.13.4",
"python-docx~=1.2.0",

View File

@@ -309,4 +309,4 @@ __all__ = [
"ZapierActionTools",
]
__version__ = "1.12.0a2"
__version__ = "1.12.2"

View File

@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
[project.optional-dependencies]
tools = [
"crewai-tools==1.12.0a2",
"crewai-tools==1.12.2",
]
embeddings = [
"tiktoken~=0.8.0"

View File

@@ -42,7 +42,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
_suppress_pydantic_deprecation_warnings()
__version__ = "1.12.0a2"
__version__ = "1.12.2"
_telemetry_submitted = False

View File

@@ -196,6 +196,16 @@ class PlusAPI:
timeout=30,
)
def mark_ephemeral_trace_batch_as_failed(
self, trace_batch_id: str, error_message: str
) -> httpx.Response:
return self._make_request(
"PATCH",
f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}",
json={"status": "failed", "failure_reason": error_message},
timeout=30,
)
def get_mcp_configs(self, slugs: list[str]) -> httpx.Response:
"""Get MCP server configurations for the given slugs."""
return self._make_request(

View File

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.12.0a2"
"crewai[tools]==1.12.2"
]
[project.scripts]

View File

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.12.0a2"
"crewai[tools]==1.12.2"
]
[project.scripts]

View File

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
readme = "README.md"
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.12.0a2"
"crewai[tools]==1.12.2"
]
[tool.crewai]

View File

@@ -1,3 +1,4 @@
from datetime import datetime, timezone
import logging
import uuid
import webbrowser
@@ -100,20 +101,50 @@ class FirstTimeTraceHandler:
user_context=user_context,
execution_metadata=execution_metadata,
use_ephemeral=True,
skip_context_check=True,
)
if not self.batch_manager.trace_batch_id:
self._gracefully_fail(
"Backend batch creation failed, cannot send events."
)
self._reset_batch_state()
return
self.batch_manager.backend_initialized = True
if self.batch_manager.event_buffer:
self.batch_manager._send_events_to_backend()
# Capture values before send/finalize consume them
events_count = len(self.batch_manager.event_buffer)
batch_id = self.batch_manager.trace_batch_id
# Read duration non-destructively — _finalize_backend_batch will consume it
start_time = self.batch_manager.execution_start_times.get("execution")
duration_ms = (
int((datetime.now(timezone.utc) - start_time).total_seconds() * 1000)
if start_time
else 0
)
self.batch_manager.finalize_batch()
if self.batch_manager.event_buffer:
send_status = self.batch_manager._send_events_to_backend()
if send_status == 500 and self.batch_manager.trace_batch_id:
self.batch_manager._mark_batch_as_failed(
self.batch_manager.trace_batch_id,
"Error sending events to backend",
)
self._reset_batch_state()
return
self.batch_manager._finalize_backend_batch(events_count)
self.ephemeral_url = self.batch_manager.ephemeral_trace_url
if not self.ephemeral_url:
self._show_local_trace_message()
self._show_local_trace_message(events_count, duration_ms, batch_id)
self._reset_batch_state()
except Exception as e:
self._gracefully_fail(f"Backend initialization failed: {e}")
self._reset_batch_state()
def _display_ephemeral_trace_link(self) -> None:
"""Display the ephemeral trace link to the user and automatically open browser."""
@@ -185,6 +216,19 @@ To enable tracing later, do any one of these:
console.print(panel)
console.print()
def _reset_batch_state(self) -> None:
"""Reset batch manager state to allow future executions to re-initialize."""
if not self.batch_manager:
return
self.batch_manager.batch_owner_type = None
self.batch_manager.batch_owner_id = None
self.batch_manager.current_batch = None
self.batch_manager.event_buffer.clear()
self.batch_manager.trace_batch_id = None
self.batch_manager.is_current_batch_ephemeral = False
self.batch_manager.backend_initialized = False
self.batch_manager._cleanup_batch_data()
def _gracefully_fail(self, error_message: str) -> None:
"""Handle errors gracefully without disrupting user experience."""
console = Console()
@@ -192,7 +236,9 @@ To enable tracing later, do any one of these:
logger.debug(f"First-time trace error: {error_message}")
def _show_local_trace_message(self) -> None:
def _show_local_trace_message(
self, events_count: int = 0, duration_ms: int = 0, batch_id: str | None = None
) -> None:
"""Show message when traces were collected locally but couldn't be uploaded."""
if self.batch_manager is None:
return
@@ -203,9 +249,9 @@ To enable tracing later, do any one of these:
📊 Your execution traces were collected locally!
Unfortunately, we couldn't upload them to the server right now, but here's what we captured:
{len(self.batch_manager.event_buffer)} trace events
• Execution duration: {self.batch_manager.calculate_duration("execution")}ms
• Batch ID: {self.batch_manager.trace_batch_id}
{events_count} trace events
• Execution duration: {duration_ms}ms
• Batch ID: {batch_id}
✅ Tracing has been enabled for future runs!
Your preference has been saved. Future Crew/Flow executions will automatically collect traces.

View File

@@ -2,6 +2,7 @@ from dataclasses import dataclass, field
from datetime import datetime, timezone
from logging import getLogger
from threading import Condition, Lock
import time
from typing import Any
import uuid
@@ -98,7 +99,7 @@ class TraceBatchManager:
self._initialize_backend_batch(
user_context, execution_metadata, use_ephemeral
)
self.backend_initialized = True
self.backend_initialized = self.trace_batch_id is not None
self._batch_ready_cv.notify_all()
return self.current_batch
@@ -108,14 +109,15 @@ class TraceBatchManager:
user_context: dict[str, str],
execution_metadata: dict[str, Any],
use_ephemeral: bool = False,
skip_context_check: bool = False,
) -> None:
"""Send batch initialization to backend"""
if not is_tracing_enabled_in_context():
return
if not skip_context_check and not is_tracing_enabled_in_context():
return None
if not self.plus_api or not self.current_batch:
return
return None
try:
payload = {
@@ -142,19 +144,53 @@ class TraceBatchManager:
payload["ephemeral_trace_id"] = self.current_batch.batch_id
payload["user_identifier"] = get_user_id()
response = (
self.plus_api.initialize_ephemeral_trace_batch(payload)
if use_ephemeral
else self.plus_api.initialize_trace_batch(payload)
)
max_retries = 1
response = None
try:
for attempt in range(max_retries + 1):
response = (
self.plus_api.initialize_ephemeral_trace_batch(payload)
if use_ephemeral
else self.plus_api.initialize_trace_batch(payload)
)
if response is not None and response.status_code < 500:
break
if attempt < max_retries:
logger.debug(
f"Trace batch init attempt {attempt + 1} failed "
f"(status={response.status_code if response else 'None'}), retrying..."
)
time.sleep(0.2)
except Exception as e:
logger.warning(
f"Error initializing trace batch: {e}. Continuing without tracing."
)
self.trace_batch_id = None
return None
if response is None:
logger.warning(
"Trace batch initialization failed gracefully. Continuing without tracing."
)
return
self.trace_batch_id = None
return None
# Fall back to ephemeral on auth failure (expired/revoked token)
if response.status_code in [401, 403] and not use_ephemeral:
logger.warning(
"Auth rejected by server, falling back to ephemeral tracing."
)
self.is_current_batch_ephemeral = True
return self._initialize_backend_batch(
user_context,
execution_metadata,
use_ephemeral=True,
skip_context_check=skip_context_check,
)
if response.status_code in [201, 200]:
self.is_current_batch_ephemeral = use_ephemeral
response_data = response.json()
self.trace_batch_id = (
response_data["trace_id"]
@@ -165,11 +201,22 @@ class TraceBatchManager:
logger.warning(
f"Trace batch initialization returned status {response.status_code}. Continuing without tracing."
)
self.trace_batch_id = None
except Exception as e:
logger.warning(
f"Error initializing trace batch: {e}. Continuing without tracing."
)
self.trace_batch_id = None
def _mark_batch_as_failed(self, trace_batch_id: str, error_message: str) -> None:
"""Mark a trace batch as failed, routing to the correct endpoint."""
if self.is_current_batch_ephemeral:
self.plus_api.mark_ephemeral_trace_batch_as_failed(
trace_batch_id, error_message
)
else:
self.plus_api.mark_trace_batch_as_failed(trace_batch_id, error_message)
def begin_event_processing(self) -> None:
"""Mark that an event handler started processing (for synchronization)."""
@@ -260,7 +307,7 @@ class TraceBatchManager:
logger.error(
"Event handler timeout - marking batch as failed due to incomplete events"
)
self.plus_api.mark_trace_batch_as_failed(
self._mark_batch_as_failed(
self.trace_batch_id,
"Timeout waiting for event handlers - events incomplete",
)
@@ -284,7 +331,7 @@ class TraceBatchManager:
events_sent_to_backend_status = self._send_events_to_backend()
self.event_buffer = original_buffer
if events_sent_to_backend_status == 500 and self.trace_batch_id:
self.plus_api.mark_trace_batch_as_failed(
self._mark_batch_as_failed(
self.trace_batch_id, "Error sending events to backend"
)
return None
@@ -364,13 +411,16 @@ class TraceBatchManager:
logger.error(
f"❌ Failed to finalize trace batch: {response.status_code} - {response.text}"
)
self.plus_api.mark_trace_batch_as_failed(
self.trace_batch_id, response.text
)
self._mark_batch_as_failed(self.trace_batch_id, response.text)
except Exception as e:
logger.error(f"❌ Error finalizing trace batch: {e}")
self.plus_api.mark_trace_batch_as_failed(self.trace_batch_id, str(e))
try:
self._mark_batch_as_failed(self.trace_batch_id, str(e))
except Exception:
logger.debug(
"Could not mark trace batch as failed (network unavailable)"
)
def _cleanup_batch_data(self) -> None:
"""Clean up batch data after successful finalization to free memory"""

View File

@@ -235,8 +235,11 @@ class TraceCollectionListener(BaseEventListener):
@event_bus.on(FlowStartedEvent)
def on_flow_started(source: Any, event: FlowStartedEvent) -> None:
if not self.batch_manager.is_batch_initialized():
self._initialize_flow_batch(source, event)
# Always call _initialize_flow_batch to claim ownership.
# If batch was already initialized by a concurrent action event
# (race condition), initialize_batch() returns early but
# batch_owner_type is still correctly set to "flow".
self._initialize_flow_batch(source, event)
self._handle_trace_event("flow_started", source, event)
@event_bus.on(MethodExecutionStartedEvent)
@@ -266,7 +269,12 @@ class TraceCollectionListener(BaseEventListener):
@event_bus.on(CrewKickoffStartedEvent)
def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
if not self.batch_manager.is_batch_initialized():
if self.batch_manager.batch_owner_type != "flow":
# Always call _initialize_crew_batch to claim ownership.
# If batch was already initialized by a concurrent action event
# (race condition with DefaultEnvEvent), initialize_batch() returns
# early but batch_owner_type is still correctly set to "crew".
# Skip only when a parent flow already owns the batch.
self._initialize_crew_batch(source, event)
self._handle_trace_event("crew_kickoff_started", source, event)
@@ -772,7 +780,7 @@ class TraceCollectionListener(BaseEventListener):
"crew_name": getattr(source, "name", "Unknown Crew"),
"crewai_version": get_crewai_version(),
}
self.batch_manager.initialize_batch(user_context, execution_metadata)
self._initialize_batch(user_context, execution_metadata)
self.batch_manager.begin_event_processing()
try:

View File

@@ -178,12 +178,15 @@ class HumanFeedbackRequestedEvent(FlowEvent):
output: The method output shown to the human for review.
message: The message displayed when requesting feedback.
emit: Optional list of possible outcomes for routing.
request_id: Platform-assigned identifier for this feedback request,
used for correlating the request across system boundaries.
"""
method_name: str
output: Any
message: str
emit: list[str] | None = None
request_id: str | None = None
type: str = "human_feedback_requested"
@@ -198,9 +201,12 @@ class HumanFeedbackReceivedEvent(FlowEvent):
method_name: Name of the method that received feedback.
feedback: The raw text feedback provided by the human.
outcome: The collapsed outcome string (if emit was specified).
request_id: Platform-assigned identifier for this feedback request,
used for correlating the response back to its originating request.
"""
method_name: str
feedback: str
outcome: str | None = None
request_id: str | None = None
type: str = "human_feedback_received"

View File

@@ -127,6 +127,9 @@ To update, run: uv sync --upgrade-package crewai"""
def _show_tracing_disabled_message_if_needed(self) -> None:
"""Show tracing disabled message if tracing is not enabled."""
from crewai.events.listeners.tracing.trace_listener import (
TraceCollectionListener,
)
from crewai.events.listeners.tracing.utils import (
has_user_declined_tracing,
is_tracing_enabled_in_context,
@@ -136,6 +139,12 @@ To update, run: uv sync --upgrade-package crewai"""
if should_suppress_tracing_messages():
return
# Don't show "disabled" message when the first-time handler will show
# the trace prompt after execution completes (avoids confusing mid-flow messages)
listener = TraceCollectionListener._instance # type: ignore[misc]
if listener and listener.first_time_handler.is_first_time:
return
if not is_tracing_enabled_in_context():
if has_user_declined_tracing():
message = """Info: Tracing is disabled.

View File

@@ -182,7 +182,7 @@ class ConsoleProvider:
console.print(message, style="yellow")
console.print()
response = input(">>> \n").strip()
response = input(">>> ").strip()
else:
response = input(f"{message} ").strip()

View File

@@ -63,6 +63,32 @@ class PendingFeedbackContext:
llm: dict[str, Any] | str | None = None
requested_at: datetime = field(default_factory=datetime.now)
@staticmethod
def _make_json_safe(value: Any) -> Any:
"""Convert a value to a JSON-serializable form.
Handles Pydantic models, dataclasses, and arbitrary objects by
progressively falling back to string representation.
"""
if value is None or isinstance(value, (str, int, float, bool)):
return value
if isinstance(value, (list, tuple)):
return [PendingFeedbackContext._make_json_safe(v) for v in value]
if isinstance(value, dict):
return {
k: PendingFeedbackContext._make_json_safe(v) for k, v in value.items()
}
from pydantic import BaseModel
if isinstance(value, BaseModel):
return value.model_dump(mode="json")
import dataclasses
if dataclasses.is_dataclass(value) and not isinstance(value, type):
return PendingFeedbackContext._make_json_safe(dataclasses.asdict(value))
return str(value)
def to_dict(self) -> dict[str, Any]:
"""Serialize context to a dictionary for persistence.
@@ -73,11 +99,11 @@ class PendingFeedbackContext:
"flow_id": self.flow_id,
"flow_class": self.flow_class,
"method_name": self.method_name,
"method_output": self.method_output,
"method_output": self._make_json_safe(self.method_output),
"message": self.message,
"emit": self.emit,
"default_outcome": self.default_outcome,
"metadata": self.metadata,
"metadata": self._make_json_safe(self.metadata),
"llm": self.llm,
"requested_at": self.requested_at.isoformat(),
}

View File

@@ -883,6 +883,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
self.human_feedback_history: list[HumanFeedbackResult] = []
self.last_human_feedback: HumanFeedbackResult | None = None
self._pending_feedback_context: PendingFeedbackContext | None = None
# Per-method stash for real @human_feedback output (keyed by method name)
# Used to decouple routing outcome from method return value when emit is set
self._human_feedback_method_outputs: dict[str, Any] = {}
self.suppress_flow_events: bool = suppress_flow_events
# User input history (for self.ask())
@@ -1223,9 +1226,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
# Mark that we're resuming execution
instance._is_execution_resuming = True
# Mark the method as completed (it ran before pausing)
instance._completed_methods.add(FlowMethodName(pending_context.method_name))
return instance
@property
@@ -1380,7 +1380,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
self.human_feedback_history.append(result)
self.last_human_feedback = result
# Clear pending context after processing
self._completed_methods.add(FlowMethodName(context.method_name))
self._pending_feedback_context = None
# Clear pending feedback from persistence
@@ -1403,7 +1404,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
# This allows methods to re-execute in loops (e.g., implement_changes → suggest_changes → implement_changes)
self._is_execution_resuming = False
final_result: Any = result
if emit and collapsed_outcome is None:
collapsed_outcome = default_outcome or emit[0]
result.outcome = collapsed_outcome
try:
if emit and collapsed_outcome:
self._method_outputs.append(collapsed_outcome)
@@ -1421,7 +1425,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
from crewai.flow.async_feedback.types import HumanFeedbackPending
if isinstance(e, HumanFeedbackPending):
# Auto-save pending feedback (create default persistence if needed)
self._pending_feedback_context = e.context
if self._persistence is None:
from crewai.flow.persistence import SQLiteFlowPersistence
@@ -1455,6 +1460,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
return e
raise
final_result = self._method_outputs[-1] if self._method_outputs else result
# Emit flow finished
crewai_event_bus.emit(
self,
@@ -2286,6 +2293,17 @@ class Flow(Generic[T], metaclass=FlowMeta):
result = await result
self._method_outputs.append(result)
# For @human_feedback methods with emit, the result is the collapsed outcome
# (e.g., "approved") used for routing. But we want the actual method output
# to be the stored result (for final flow output). Replace the last entry
# if a stashed output exists. Dict-based stash is concurrency-safe and
# handles None return values (presence in dict = stashed, not value).
if method_name in self._human_feedback_method_outputs:
self._method_outputs[-1] = self._human_feedback_method_outputs.pop(
method_name
)
self._method_execution_counts[method_name] = (
self._method_execution_counts.get(method_name, 0) + 1
)
@@ -2314,7 +2332,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
if isinstance(e, HumanFeedbackPending):
e.context.method_name = method_name
# Auto-save pending feedback (create default persistence if needed)
if self._persistence is None:
from crewai.flow.persistence import SQLiteFlowPersistence
@@ -3133,10 +3150,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
if outcome.lower() == response_clean.lower():
return outcome
# Partial match
# Partial match (longest wins, first on length ties)
response_lower = response_clean.lower()
best_outcome: str | None = None
best_len = -1
for outcome in outcomes:
if outcome.lower() in response_clean.lower():
return outcome
if outcome.lower() in response_lower and len(outcome) > best_len:
best_outcome = outcome
best_len = len(outcome)
if best_outcome is not None:
return best_outcome
# Fallback to first outcome
logger.warning(

View File

@@ -116,10 +116,11 @@ def _deserialize_llm_from_context(
return LLM(model=llm_data)
if isinstance(llm_data, dict):
model = llm_data.pop("model", None)
data = dict(llm_data)
model = data.pop("model", None)
if not model:
return None
return LLM(model=model, **llm_data)
return LLM(model=model, **data)
return None
@@ -450,12 +451,12 @@ def human_feedback(
# -- Core feedback helpers ------------------------------------
def _request_feedback(flow_instance: Flow[Any], method_output: Any) -> str:
"""Request feedback using provider or default console."""
def _build_feedback_context(
flow_instance: Flow[Any], method_output: Any
) -> tuple[Any, Any]:
"""Build the PendingFeedbackContext and resolve the effective provider."""
from crewai.flow.async_feedback.types import PendingFeedbackContext
# Build context for provider
# Use flow_id property which handles both dict and BaseModel states
context = PendingFeedbackContext(
flow_id=flow_instance.flow_id or "unknown",
flow_class=f"{flow_instance.__class__.__module__}.{flow_instance.__class__.__name__}",
@@ -468,15 +469,53 @@ def human_feedback(
llm=llm if isinstance(llm, str) else _serialize_llm_for_context(llm),
)
# Determine effective provider:
effective_provider = provider
if effective_provider is None:
from crewai.flow.flow_config import flow_config
effective_provider = flow_config.hitl_provider
return context, effective_provider
def _request_feedback(flow_instance: Flow[Any], method_output: Any) -> str:
"""Request feedback using provider or default console (sync)."""
context, effective_provider = _build_feedback_context(
flow_instance, method_output
)
if effective_provider is not None:
return effective_provider.request_feedback(context, flow_instance)
feedback_result = effective_provider.request_feedback(
context, flow_instance
)
if asyncio.iscoroutine(feedback_result):
raise TypeError(
f"Provider {type(effective_provider).__name__}.request_feedback() "
"returned a coroutine in a sync flow method. Use an async flow "
"method or a synchronous provider."
)
return str(feedback_result)
return flow_instance._request_human_feedback(
message=message,
output=method_output,
metadata=metadata,
emit=emit,
)
async def _request_feedback_async(
flow_instance: Flow[Any], method_output: Any
) -> str:
"""Request feedback, awaiting the provider if it returns a coroutine."""
context, effective_provider = _build_feedback_context(
flow_instance, method_output
)
if effective_provider is not None:
feedback_result = effective_provider.request_feedback(
context, flow_instance
)
if asyncio.iscoroutine(feedback_result):
return str(await feedback_result)
return str(feedback_result)
return flow_instance._request_human_feedback(
message=message,
output=method_output,
@@ -524,10 +563,11 @@ def human_feedback(
flow_instance.human_feedback_history.append(result)
flow_instance.last_human_feedback = result
# Return based on mode
if emit:
# Return outcome for routing
return collapsed_outcome # type: ignore[return-value]
if collapsed_outcome is None:
collapsed_outcome = default_outcome or emit[0]
result.outcome = collapsed_outcome
return collapsed_outcome
return result
if asyncio.iscoroutinefunction(func):
@@ -540,7 +580,7 @@ def human_feedback(
if learn and getattr(self, "memory", None) is not None:
method_output = _pre_review_with_lessons(self, method_output)
raw_feedback = _request_feedback(self, method_output)
raw_feedback = await _request_feedback_async(self, method_output)
result = _process_feedback(self, method_output, raw_feedback)
# Distill: extract lessons from output + feedback, store in memory
@@ -551,6 +591,13 @@ def human_feedback(
):
_distill_and_store_lessons(self, method_output, raw_feedback)
# Stash the real method output for final flow result when emit is set
# (result is the collapsed outcome string for routing, but we want to
# preserve the actual method output as the flow's final result)
# Uses per-method dict for concurrency safety and to handle None returns
if emit:
self._human_feedback_method_outputs[func.__name__] = method_output
return result
wrapper: Any = async_wrapper
@@ -575,6 +622,13 @@ def human_feedback(
):
_distill_and_store_lessons(self, method_output, raw_feedback)
# Stash the real method output for final flow result when emit is set
# (result is the collapsed outcome string for routing, but we want to
# preserve the actual method output as the flow's final result)
# Uses per-method dict for concurrency safety and to handle None returns
if emit:
self._human_feedback_method_outputs[func.__name__] = method_output
return result
wrapper = sync_wrapper

View File

@@ -483,8 +483,8 @@ class LLM(BaseLLM):
for prefix in ["gpt-", "gpt-35-", "o1", "o3", "o4", "azure-"]
)
# OpenAI-compatible providers - accept any model name since these
# providers host many different models with varied naming conventions
# OpenAI-compatible providers - most accept any model name, but some
# (DeepSeek, Dashscope) restrict to their own model prefixes
if provider == "deepseek":
return model_lower.startswith("deepseek")

View File

@@ -239,7 +239,8 @@ class OpenAICompatibleCompletion(OpenAICompletion):
if base_url:
resolved = base_url
elif config.base_url_env:
resolved = os.getenv(config.base_url_env, config.base_url)
env_value = os.getenv(config.base_url_env)
resolved = env_value if env_value else config.base_url
else:
resolved = config.base_url
@@ -274,9 +275,11 @@ class OpenAICompatibleCompletion(OpenAICompletion):
def supports_function_calling(self) -> bool:
"""Check if the provider supports function calling.
All modern OpenAI-compatible providers support function calling.
Delegates to the parent OpenAI implementation which handles
edge cases like o1 models (which may be routed through
OpenRouter or other compatible providers).
Returns:
True, as all supported providers have function calling support.
Whether the model supports function calling.
"""
return True
return super().supports_function_calling()

View File

@@ -1,7 +1,7 @@
"""Tests for OpenAI-compatible providers."""
import os
from unittest.mock import MagicMock, patch
from unittest.mock import patch
import pytest
@@ -133,7 +133,7 @@ class TestOpenAICompatibleCompletion:
with pytest.raises(ValueError, match="API key required"):
OpenAICompatibleCompletion(model="deepseek-chat", provider="deepseek")
finally:
if original:
if original is not None:
os.environ[env_key] = original
def test_api_key_from_env(self):

View File

@@ -246,7 +246,7 @@ class TestHumanFeedbackExecution:
@patch("builtins.input", return_value="")
@patch("builtins.print")
def test_empty_feedback_with_default_outcome(self, mock_print, mock_input):
"""Test empty feedback uses default_outcome."""
"""Test empty feedback uses default_outcome for routing, but flow returns method output."""
class TestFlow(Flow):
@start()
@@ -264,14 +264,16 @@ class TestHumanFeedbackExecution:
with patch.object(flow, "_request_human_feedback", return_value=""):
result = flow.kickoff()
assert result == "needs_work"
# Flow result is the method's return value, NOT the collapsed outcome
assert result == "Content"
assert flow.last_human_feedback is not None
# But the outcome is still correctly set for routing purposes
assert flow.last_human_feedback.outcome == "needs_work"
@patch("builtins.input", return_value="Approved!")
@patch("builtins.print")
def test_feedback_collapsing(self, mock_print, mock_input):
"""Test that feedback is collapsed to an outcome."""
"""Test that feedback is collapsed to an outcome for routing, but flow returns method output."""
class TestFlow(Flow):
@start()
@@ -291,8 +293,10 @@ class TestHumanFeedbackExecution:
):
result = flow.kickoff()
assert result == "approved"
# Flow result is the method's return value, NOT the collapsed outcome
assert result == "Content"
assert flow.last_human_feedback is not None
# But the outcome is still correctly set for routing purposes
assert flow.last_human_feedback.outcome == "approved"
@@ -591,3 +595,162 @@ class TestHumanFeedbackLearn:
assert config.learn is True
# llm defaults to "gpt-4o-mini" at the function level
assert config.llm == "gpt-4o-mini"
class TestHumanFeedbackFinalOutputPreservation:
"""Tests for preserving method return value as flow's final output when @human_feedback with emit is terminal.
This addresses the bug where the flow's final output was the collapsed outcome string (e.g., 'approved')
instead of the method's actual return value when a @human_feedback method with emit is the final method.
"""
@patch("builtins.input", return_value="Looks good!")
@patch("builtins.print")
def test_final_output_is_method_return_not_collapsed_outcome(
self, mock_print, mock_input
):
"""When @human_feedback with emit is the final method, flow output is the method's return value."""
class FinalHumanFeedbackFlow(Flow):
@start()
@human_feedback(
message="Review this content:",
emit=["approved", "rejected"],
llm="gpt-4o-mini",
)
def generate_and_review(self):
# This dict should be the final output, NOT the string 'approved'
return {"title": "My Article", "content": "Article content here", "status": "ready"}
flow = FinalHumanFeedbackFlow()
with (
patch.object(flow, "_request_human_feedback", return_value="Looks great, approved!"),
patch.object(flow, "_collapse_to_outcome", return_value="approved"),
):
result = flow.kickoff()
# The final output should be the actual method return value, not the collapsed outcome
assert isinstance(result, dict), f"Expected dict, got {type(result).__name__}: {result}"
assert result == {"title": "My Article", "content": "Article content here", "status": "ready"}
# But the outcome should still be tracked in last_human_feedback
assert flow.last_human_feedback is not None
assert flow.last_human_feedback.outcome == "approved"
@patch("builtins.input", return_value="approved")
@patch("builtins.print")
def test_routing_still_works_with_downstream_listener(self, mock_print, mock_input):
"""When @human_feedback has a downstream listener, routing still triggers the listener."""
publish_called = []
class RoutingFlow(Flow):
@start()
@human_feedback(
message="Review:",
emit=["approved", "rejected"],
llm="gpt-4o-mini",
)
def review(self):
return {"content": "original content"}
@listen("approved")
def publish(self):
publish_called.append(True)
return {"published": True, "timestamp": "2024-01-01"}
flow = RoutingFlow()
with (
patch.object(flow, "_request_human_feedback", return_value="LGTM"),
patch.object(flow, "_collapse_to_outcome", return_value="approved"),
):
result = flow.kickoff()
# The downstream listener should have been triggered
assert len(publish_called) == 1, "publish() should have been called"
# The final output should be from the listener, not the human_feedback method
assert result == {"published": True, "timestamp": "2024-01-01"}
@patch("builtins.input", return_value="")
@patch("builtins.print")
@pytest.mark.asyncio
async def test_async_human_feedback_final_output_preserved(self, mock_print, mock_input):
"""Async @human_feedback methods also preserve the real return value."""
class AsyncFinalFlow(Flow):
@start()
@human_feedback(
message="Review async content:",
emit=["approved", "rejected"],
llm="gpt-4o-mini",
default_outcome="approved",
)
async def async_generate(self):
return {"async_data": "value", "computed": 42}
flow = AsyncFinalFlow()
with (
patch.object(flow, "_request_human_feedback", return_value=""),
):
result = await flow.kickoff_async()
# The final output should be the dict, not "approved"
assert isinstance(result, dict), f"Expected dict, got {type(result).__name__}: {result}"
assert result == {"async_data": "value", "computed": 42}
assert flow.last_human_feedback.outcome == "approved"
@patch("builtins.input", return_value="feedback")
@patch("builtins.print")
def test_method_outputs_contains_real_output(self, mock_print, mock_input):
"""The _method_outputs list should contain the real method output, not the collapsed outcome."""
class OutputTrackingFlow(Flow):
@start()
@human_feedback(
message="Review:",
emit=["approved", "rejected"],
llm="gpt-4o-mini",
)
def generate(self):
return {"data": "real output"}
flow = OutputTrackingFlow()
with (
patch.object(flow, "_request_human_feedback", return_value="approved"),
patch.object(flow, "_collapse_to_outcome", return_value="approved"),
):
flow.kickoff()
# _method_outputs should contain the real output
assert len(flow._method_outputs) == 1
assert flow._method_outputs[0] == {"data": "real output"}
@patch("builtins.input", return_value="looks good")
@patch("builtins.print")
def test_none_return_value_is_preserved(self, mock_print, mock_input):
"""A method returning None should preserve None as flow output, not the outcome string."""
class NoneReturnFlow(Flow):
@start()
@human_feedback(
message="Review:",
emit=["approved", "rejected"],
llm="gpt-4o-mini",
)
def process(self):
# Method does work but returns None (implicit)
pass
flow = NoneReturnFlow()
with (
patch.object(flow, "_request_human_feedback", return_value=""),
patch.object(flow, "_collapse_to_outcome", return_value="approved"),
):
result = flow.kickoff()
# Final output should be None (the method's real return), not "approved"
assert result is None, f"Expected None, got {result!r}"
assert flow.last_human_feedback.outcome == "approved"

View File

@@ -708,7 +708,7 @@ class TestEdgeCases:
@patch("builtins.input", return_value="")
@patch("builtins.print")
def test_empty_feedback_first_outcome_fallback(self, mock_print, mock_input):
"""Test that empty feedback without default uses first outcome."""
"""Test that empty feedback without default uses first outcome for routing, but returns method output."""
class FallbackFlow(Flow):
@start()
@@ -726,12 +726,15 @@ class TestEdgeCases:
with patch.object(flow, "_request_human_feedback", return_value=""):
result = flow.kickoff()
assert result == "first" # Falls back to first outcome
# Flow result is the method's return value, NOT the collapsed outcome
assert result == "content"
# But outcome is still set to first for routing purposes
assert flow.last_human_feedback.outcome == "first"
@patch("builtins.input", return_value="whitespace only ")
@patch("builtins.print")
def test_whitespace_only_feedback_treated_as_empty(self, mock_print, mock_input):
"""Test that whitespace-only feedback is treated as empty."""
"""Test that whitespace-only feedback is treated as empty for routing, but returns method output."""
class WhitespaceFlow(Flow):
@start()
@@ -749,7 +752,10 @@ class TestEdgeCases:
with patch.object(flow, "_request_human_feedback", return_value=" "):
result = flow.kickoff()
assert result == "reject" # Uses default because feedback is empty after strip
# Flow result is the method's return value, NOT the collapsed outcome
assert result == "content"
# But outcome is set to default because feedback is empty after strip
assert flow.last_human_feedback.outcome == "reject"
@patch("builtins.input", return_value="feedback")
@patch("builtins.print")

View File

@@ -7,6 +7,7 @@ from crewai.events.listeners.tracing.first_time_trace_handler import (
FirstTimeTraceHandler,
)
from crewai.events.listeners.tracing.trace_batch_manager import (
TraceBatch,
TraceBatchManager,
)
from crewai.events.listeners.tracing.trace_listener import (
@@ -657,6 +658,16 @@ class TestTraceListenerSetup:
trace_listener.first_time_handler.collected_events = True
mock_batch_response = MagicMock()
mock_batch_response.status_code = 201
mock_batch_response.json.return_value = {
"trace_id": "mock-trace-id",
"ephemeral_trace_id": "mock-ephemeral-trace-id",
"access_code": "TRACE-mock",
}
mock_events_response = MagicMock()
mock_events_response.status_code = 200
with (
patch.object(
trace_listener.first_time_handler,
@@ -666,6 +677,40 @@ class TestTraceListenerSetup:
patch.object(
trace_listener.first_time_handler, "_display_ephemeral_trace_link"
) as mock_display_link,
patch.object(
trace_listener.batch_manager.plus_api,
"initialize_trace_batch",
return_value=mock_batch_response,
),
patch.object(
trace_listener.batch_manager.plus_api,
"initialize_ephemeral_trace_batch",
return_value=mock_batch_response,
),
patch.object(
trace_listener.batch_manager.plus_api,
"send_trace_events",
return_value=mock_events_response,
),
patch.object(
trace_listener.batch_manager.plus_api,
"send_ephemeral_trace_events",
return_value=mock_events_response,
),
patch.object(
trace_listener.batch_manager.plus_api,
"finalize_trace_batch",
return_value=mock_events_response,
),
patch.object(
trace_listener.batch_manager.plus_api,
"finalize_ephemeral_trace_batch",
return_value=mock_events_response,
),
patch.object(
trace_listener.batch_manager,
"_cleanup_batch_data",
),
):
crew.kickoff()
wait_for_event_handlers()
@@ -918,3 +963,676 @@ class TestTraceListenerSetup:
mock_init.assert_called_once()
payload = mock_init.call_args[0][0]
assert "user_identifier" not in payload
class TestTraceBatchIdClearedOnFailure:
"""Tests: trace_batch_id is cleared when _initialize_backend_batch fails."""
def _make_batch_manager(self):
"""Create a TraceBatchManager with a pre-set trace_batch_id (simulating first-time user)."""
with patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
):
bm = TraceBatchManager()
bm.current_batch = TraceBatch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew", "crew_name": "test"},
)
bm.trace_batch_id = bm.current_batch.batch_id # simulate line 96
bm.is_current_batch_ephemeral = True
return bm
def test_trace_batch_id_cleared_on_exception(self):
"""trace_batch_id must be None when the API call raises an exception."""
bm = self._make_batch_manager()
assert bm.trace_batch_id is not None
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
side_effect=ConnectionError("network down"),
),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id is None
def test_trace_batch_id_set_on_success(self):
"""trace_batch_id must be set from the server response on success."""
bm = self._make_batch_manager()
server_id = "server-ephemeral-trace-id-999"
mock_response = MagicMock(
status_code=201,
json=MagicMock(return_value={"ephemeral_trace_id": server_id}),
)
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=mock_response,
),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id == server_id
def test_send_events_skipped_when_trace_batch_id_none(self):
"""_send_events_to_backend must return early when trace_batch_id is None."""
bm = self._make_batch_manager()
bm.trace_batch_id = None
bm.event_buffer = [MagicMock()] # has events
with patch.object(
bm.plus_api, "send_ephemeral_trace_events"
) as mock_send:
result = bm._send_events_to_backend()
assert result == 500
mock_send.assert_not_called()
class TestInitializeBackendBatchRetry:
"""Tests for retry logic in _initialize_backend_batch."""
def _make_batch_manager(self):
"""Create a TraceBatchManager with a pre-set trace_batch_id."""
with patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
):
bm = TraceBatchManager()
bm.current_batch = TraceBatch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew", "crew_name": "test"},
)
bm.trace_batch_id = bm.current_batch.batch_id
bm.is_current_batch_ephemeral = True
return bm
def test_retries_on_none_response_then_succeeds(self):
"""Retries when API returns None, succeeds on second attempt."""
bm = self._make_batch_manager()
server_id = "server-id-after-retry"
success_response = MagicMock(
status_code=201,
json=MagicMock(return_value={"ephemeral_trace_id": server_id}),
)
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
side_effect=[None, success_response],
) as mock_init,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep") as mock_sleep,
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id == server_id
assert mock_init.call_count == 2
mock_sleep.assert_called_once_with(0.2)
def test_retries_on_5xx_then_succeeds(self):
"""Retries on 500 server error, succeeds on second attempt."""
bm = self._make_batch_manager()
server_id = "server-id-after-5xx"
error_response = MagicMock(status_code=500, text="Internal Server Error")
success_response = MagicMock(
status_code=201,
json=MagicMock(return_value={"ephemeral_trace_id": server_id}),
)
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
side_effect=[error_response, success_response],
) as mock_init,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep"),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id == server_id
assert mock_init.call_count == 2
def test_no_retry_on_exception(self):
"""Exceptions (e.g. timeout, connection error) abort immediately without retry."""
bm = self._make_batch_manager()
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
side_effect=ConnectionError("network down"),
) as mock_init,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep") as mock_sleep,
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id is None
assert mock_init.call_count == 1
mock_sleep.assert_not_called()
def test_no_retry_on_4xx(self):
"""Does NOT retry on 422 — client error is not transient."""
bm = self._make_batch_manager()
error_response = MagicMock(status_code=422, text="Unprocessable Entity")
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=error_response,
) as mock_init,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep") as mock_sleep,
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id is None
assert mock_init.call_count == 1
mock_sleep.assert_not_called()
def test_exhausts_retries_then_clears_batch_id(self):
"""After all retries fail, trace_batch_id is None."""
bm = self._make_batch_manager()
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=None,
) as mock_init,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep"),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id is None
assert mock_init.call_count == 2 # initial + 1 retry
class TestFirstTimeHandlerBackendInitGuard:
"""Tests: backend_initialized gated on actual batch creation success."""
def _make_handler_with_manager(self):
"""Create a FirstTimeTraceHandler wired to a TraceBatchManager."""
with patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
):
bm = TraceBatchManager()
bm.current_batch = TraceBatch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew", "crew_name": "test"},
)
bm.trace_batch_id = bm.current_batch.batch_id
bm.is_current_batch_ephemeral = True
handler = FirstTimeTraceHandler()
handler.is_first_time = True
handler.collected_events = True
handler.batch_manager = bm
return handler, bm
def test_backend_initialized_true_on_success(self):
"""Events are sent when batch creation succeeds, then state is cleaned up."""
handler, bm = self._make_handler_with_manager()
server_id = "server-id-abc"
mock_init_response = MagicMock(
status_code=201,
json=MagicMock(return_value={"ephemeral_trace_id": server_id}),
)
mock_send_response = MagicMock(status_code=200)
trace_batch_id_during_send = None
def capture_send(*args, **kwargs):
nonlocal trace_batch_id_during_send
trace_batch_id_during_send = bm.trace_batch_id
return mock_send_response
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=mock_init_response,
),
patch.object(
bm.plus_api,
"send_ephemeral_trace_events",
side_effect=capture_send,
),
patch.object(bm, "_finalize_backend_batch"),
):
bm.event_buffer = [MagicMock(to_dict=MagicMock(return_value={}))]
handler._initialize_backend_and_send_events()
# trace_batch_id was set correctly during send
assert trace_batch_id_during_send == server_id
# State cleaned up after completion (singleton reuse)
assert bm.backend_initialized is False
assert bm.trace_batch_id is None
assert bm.current_batch is None
def test_backend_initialized_false_on_failure(self):
"""backend_initialized stays False and events are NOT sent when batch creation fails."""
handler, bm = self._make_handler_with_manager()
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=None, # server call fails
),
patch.object(bm, "_send_events_to_backend") as mock_send,
patch.object(bm, "_finalize_backend_batch") as mock_finalize,
patch.object(handler, "_gracefully_fail") as mock_fail,
):
bm.event_buffer = [MagicMock()]
handler._initialize_backend_and_send_events()
assert bm.backend_initialized is False
assert bm.trace_batch_id is None
mock_send.assert_not_called()
mock_finalize.assert_not_called()
mock_fail.assert_called_once()
def test_backend_initialized_false_on_non_2xx(self):
"""backend_initialized stays False when server returns non-2xx."""
handler, bm = self._make_handler_with_manager()
mock_response = MagicMock(status_code=500, text="Internal Server Error")
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=mock_response,
),
patch.object(bm, "_send_events_to_backend") as mock_send,
patch.object(bm, "_finalize_backend_batch") as mock_finalize,
patch.object(handler, "_gracefully_fail") as mock_fail,
):
bm.event_buffer = [MagicMock()]
handler._initialize_backend_and_send_events()
assert bm.backend_initialized is False
assert bm.trace_batch_id is None
mock_send.assert_not_called()
mock_finalize.assert_not_called()
mock_fail.assert_called_once()
class TestFirstTimeHandlerAlwaysEphemeral:
"""Tests that first-time handler always uses ephemeral with skip_context_check."""
def _make_handler_with_manager(self):
with patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
):
bm = TraceBatchManager()
bm.current_batch = TraceBatch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew", "crew_name": "test"},
)
bm.trace_batch_id = bm.current_batch.batch_id
bm.is_current_batch_ephemeral = True
handler = FirstTimeTraceHandler()
handler.is_first_time = True
handler.collected_events = True
handler.batch_manager = bm
return handler, bm
def test_deferred_init_uses_ephemeral_and_skip_context_check(self):
"""Deferred backend init always uses ephemeral=True and skip_context_check=True."""
handler, bm = self._make_handler_with_manager()
with (
patch.object(bm, "_initialize_backend_batch") as mock_init,
patch.object(bm, "_send_events_to_backend"),
patch.object(bm, "_finalize_backend_batch"),
):
mock_init.side_effect = lambda **kwargs: None
bm.event_buffer = [MagicMock()]
handler._initialize_backend_and_send_events()
mock_init.assert_called_once()
assert mock_init.call_args.kwargs["use_ephemeral"] is True
assert mock_init.call_args.kwargs["skip_context_check"] is True
class TestAuthFailbackToEphemeral:
"""Tests for ephemeral fallback when server rejects auth (401/403)."""
def _make_batch_manager(self):
"""Create a TraceBatchManager with a pre-set trace_batch_id."""
with patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
):
bm = TraceBatchManager()
bm.current_batch = TraceBatch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew", "crew_name": "test"},
)
bm.trace_batch_id = bm.current_batch.batch_id
bm.is_current_batch_ephemeral = False # authenticated path
return bm
def test_401_non_ephemeral_falls_back_to_ephemeral(self):
"""A 401 on the non-ephemeral endpoint should retry as ephemeral."""
bm = self._make_batch_manager()
server_id = "ephemeral-fallback-id"
auth_rejected = MagicMock(status_code=401, text="Bad credentials")
ephemeral_success = MagicMock(
status_code=201,
json=MagicMock(return_value={"ephemeral_trace_id": server_id}),
)
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_trace_batch",
return_value=auth_rejected,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=ephemeral_success,
) as mock_ephemeral,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep"),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=False,
)
assert bm.trace_batch_id == server_id
assert bm.is_current_batch_ephemeral is True
mock_ephemeral.assert_called_once()
def test_403_non_ephemeral_falls_back_to_ephemeral(self):
"""A 403 on the non-ephemeral endpoint should also fall back."""
bm = self._make_batch_manager()
server_id = "ephemeral-fallback-403"
forbidden = MagicMock(status_code=403, text="Forbidden")
ephemeral_success = MagicMock(
status_code=201,
json=MagicMock(return_value={"ephemeral_trace_id": server_id}),
)
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_trace_batch",
return_value=forbidden,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=ephemeral_success,
),
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep"),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=False,
)
assert bm.trace_batch_id == server_id
assert bm.is_current_batch_ephemeral is True
def test_401_on_ephemeral_does_not_recurse(self):
"""A 401 on the ephemeral endpoint should NOT try to fall back again."""
bm = self._make_batch_manager()
bm.is_current_batch_ephemeral = True
auth_rejected = MagicMock(status_code=401, text="Bad credentials")
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=auth_rejected,
) as mock_ephemeral,
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep"),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=True,
)
assert bm.trace_batch_id is None
# Called only once — no recursive fallback
mock_ephemeral.assert_called()
def test_401_fallback_ephemeral_also_fails(self):
"""If ephemeral fallback also fails, trace_batch_id is cleared."""
bm = self._make_batch_manager()
auth_rejected = MagicMock(status_code=401, text="Bad credentials")
ephemeral_fail = MagicMock(status_code=422, text="Validation failed")
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch.object(
bm.plus_api,
"initialize_trace_batch",
return_value=auth_rejected,
),
patch.object(
bm.plus_api,
"initialize_ephemeral_trace_batch",
return_value=ephemeral_fail,
),
patch("crewai.events.listeners.tracing.trace_batch_manager.time.sleep"),
):
bm._initialize_backend_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
use_ephemeral=False,
)
assert bm.trace_batch_id is None
class TestMarkBatchAsFailedRouting:
"""Tests: _mark_batch_as_failed routes to the correct endpoint."""
def _make_batch_manager(self, ephemeral: bool = False):
with patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
):
bm = TraceBatchManager()
bm.is_current_batch_ephemeral = ephemeral
return bm
def test_routes_to_ephemeral_endpoint_when_ephemeral(self):
"""Ephemeral batches must use mark_ephemeral_trace_batch_as_failed."""
bm = self._make_batch_manager(ephemeral=True)
with patch.object(
bm.plus_api, "mark_ephemeral_trace_batch_as_failed"
) as mock_ephemeral, patch.object(
bm.plus_api, "mark_trace_batch_as_failed"
) as mock_non_ephemeral:
bm._mark_batch_as_failed("batch-123", "some error")
mock_ephemeral.assert_called_once_with("batch-123", "some error")
mock_non_ephemeral.assert_not_called()
def test_routes_to_non_ephemeral_endpoint_when_not_ephemeral(self):
"""Non-ephemeral batches must use mark_trace_batch_as_failed."""
bm = self._make_batch_manager(ephemeral=False)
with patch.object(
bm.plus_api, "mark_ephemeral_trace_batch_as_failed"
) as mock_ephemeral, patch.object(
bm.plus_api, "mark_trace_batch_as_failed"
) as mock_non_ephemeral:
bm._mark_batch_as_failed("batch-456", "another error")
mock_non_ephemeral.assert_called_once_with("batch-456", "another error")
mock_ephemeral.assert_not_called()
class TestBackendInitializedGatedOnSuccess:
"""Tests: backend_initialized reflects actual init success on non-first-time path."""
def test_backend_initialized_true_on_success(self):
"""backend_initialized is True when _initialize_backend_batch succeeds."""
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch(
"crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
return_value=False,
),
patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
),
):
bm = TraceBatchManager()
mock_response = MagicMock(
status_code=201,
json=MagicMock(return_value={"trace_id": "server-id"}),
)
with patch.object(
bm.plus_api, "initialize_trace_batch", return_value=mock_response
):
bm.initialize_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
)
assert bm.backend_initialized is True
assert bm.trace_batch_id == "server-id"
def test_backend_initialized_false_on_failure(self):
"""backend_initialized is False when _initialize_backend_batch fails."""
with (
patch(
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
return_value=True,
),
patch(
"crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
return_value=False,
),
patch(
"crewai.events.listeners.tracing.trace_batch_manager.get_auth_token",
return_value="mock_token",
),
):
bm = TraceBatchManager()
with patch.object(
bm.plus_api, "initialize_trace_batch", return_value=None
):
bm.initialize_batch(
user_context={"privacy_level": "standard"},
execution_metadata={"execution_type": "crew"},
)
assert bm.backend_initialized is False
assert bm.trace_batch_id is None

View File

@@ -8,18 +8,22 @@ Installed automatically via the workspace (`uv sync`). Requires:
- [GitHub CLI](https://cli.github.com/) (`gh`) — authenticated
- `OPENAI_API_KEY` env var — for release note generation and translation
- `ENTERPRISE_REPO` env var — GitHub repo for enterprise releases
- `ENTERPRISE_VERSION_DIRS` env var — comma-separated directories to bump in the enterprise repo
- `ENTERPRISE_CREWAI_DEP_PATH` env var — path to the pyproject.toml with the `crewai[tools]` pin in the enterprise repo
## Commands
### `devtools release <version>`
Full end-to-end release. Bumps versions, creates PRs, tags, and publishes a GitHub release.
Full end-to-end release. Bumps versions, creates PRs, tags, publishes a GitHub release, and releases the enterprise repo.
```
devtools release 1.10.3
devtools release 1.10.3a1 # pre-release
devtools release 1.10.3 --no-edit # skip editing release notes
devtools release 1.10.3 --dry-run # preview without changes
devtools release 1.10.3a1 # pre-release
devtools release 1.10.3 --no-edit # skip editing release notes
devtools release 1.10.3 --dry-run # preview without changes
devtools release 1.10.3 --skip-enterprise # skip enterprise release phase
```
**Flow:**
@@ -31,6 +35,10 @@ devtools release 1.10.3 --dry-run # preview without changes
5. Updates changelogs (en, pt-BR, ko) and docs version switcher
6. Creates docs PR against main, polls until merged
7. Tags main and creates GitHub release
8. Triggers PyPI publish workflow
9. Clones enterprise repo, bumps versions and `crewai[tools]` dep, runs `uv sync`
10. Creates enterprise bump PR, polls until merged
11. Tags and creates GitHub release on enterprise repo
### `devtools bump <version>`

View File

@@ -1,3 +1,3 @@
"""CrewAI development tools."""
__version__ = "1.12.0a2"
__version__ = "1.12.2"

View File

@@ -2,10 +2,13 @@
import os
from pathlib import Path
import re
import subprocess
import sys
import tempfile
import time
from typing import Final, Literal
from urllib.request import urlopen
import click
from dotenv import load_dotenv
@@ -153,12 +156,24 @@ def update_version_in_file(file_path: Path, new_version: str) -> bool:
return False
def update_pyproject_dependencies(file_path: Path, new_version: str) -> bool:
_DEFAULT_WORKSPACE_PACKAGES: Final[list[str]] = [
"crewai",
"crewai-tools",
"crewai-devtools",
]
def update_pyproject_dependencies(
file_path: Path,
new_version: str,
extra_packages: list[str] | None = None,
) -> bool:
"""Update workspace dependency versions in pyproject.toml.
Args:
file_path: Path to pyproject.toml file.
new_version: New version string.
extra_packages: Additional package names to update beyond the defaults.
Returns:
True if any dependencies were updated, False otherwise.
@@ -170,7 +185,7 @@ def update_pyproject_dependencies(file_path: Path, new_version: str) -> bool:
lines = content.splitlines()
updated = False
workspace_packages = ["crewai", "crewai-tools", "crewai-devtools"]
workspace_packages = _DEFAULT_WORKSPACE_PACKAGES + (extra_packages or [])
for i, line in enumerate(lines):
for pkg in workspace_packages:
@@ -431,12 +446,29 @@ def update_changelog(
return True
def update_template_dependencies(templates_dir: Path, new_version: str) -> list[Path]:
"""Update crewai dependency versions in CLI template pyproject.toml files.
def _pin_crewai_deps(content: str, version: str) -> str:
"""Replace crewai dependency version pins in a pyproject.toml string.
Handles both pinned (==) and minimum (>=) version specifiers,
as well as extras like [tools].
Args:
content: File content to transform.
version: New version string.
Returns:
Transformed content.
"""
return re.sub(
r'"crewai(\[tools\])?(==|>=)[^"]*"',
lambda m: f'"crewai{(m.group(1) or "")!s}=={version}"',
content,
)
def update_template_dependencies(templates_dir: Path, new_version: str) -> list[Path]:
"""Update crewai dependency versions in CLI template pyproject.toml files.
Args:
templates_dir: Path to the CLI templates directory.
new_version: New version string.
@@ -444,16 +476,10 @@ def update_template_dependencies(templates_dir: Path, new_version: str) -> list[
Returns:
List of paths that were updated.
"""
import re
updated = []
for pyproject in templates_dir.rglob("pyproject.toml"):
content = pyproject.read_text()
new_content = re.sub(
r'"crewai(\[tools\])?(==|>=)[^"]*"',
lambda m: f'"crewai{(m.group(1) or "")!s}=={new_version}"',
content,
)
new_content = _pin_crewai_deps(content, new_version)
if new_content != content:
pyproject.write_text(new_content)
updated.append(pyproject)
@@ -607,24 +633,26 @@ def get_github_contributors(commit_range: str) -> list[str]:
# ---------------------------------------------------------------------------
def _poll_pr_until_merged(branch_name: str, label: str) -> None:
"""Poll a GitHub PR until it is merged. Exit if closed without merging."""
def _poll_pr_until_merged(
branch_name: str, label: str, repo: str | None = None
) -> None:
"""Poll a GitHub PR until it is merged. Exit if closed without merging.
Args:
branch_name: Branch name to look up the PR.
label: Human-readable label for status messages.
repo: Optional GitHub repo (owner/name) for cross-repo PRs.
"""
console.print(f"[cyan]Waiting for {label} to be merged...[/cyan]")
cmd = ["gh", "pr", "view", branch_name]
if repo:
cmd.extend(["--repo", repo])
cmd.extend(["--json", "state", "--jq", ".state"])
while True:
time.sleep(10)
try:
state = run_command(
[
"gh",
"pr",
"view",
branch_name,
"--json",
"state",
"--jq",
".state",
]
)
state = run_command(cmd)
except subprocess.CalledProcessError:
state = ""
@@ -984,8 +1012,252 @@ def _create_tag_and_release(
console.print(f"[green]✓[/green] Created GitHub {release_type} for {tag_name}")
def _trigger_pypi_publish(tag_name: str) -> None:
"""Trigger the PyPI publish GitHub Actions workflow."""
_ENTERPRISE_REPO: Final[str | None] = os.getenv("ENTERPRISE_REPO")
_ENTERPRISE_VERSION_DIRS: Final[tuple[str, ...]] = tuple(
d.strip() for d in os.getenv("ENTERPRISE_VERSION_DIRS", "").split(",") if d.strip()
)
_ENTERPRISE_CREWAI_DEP_PATH: Final[str | None] = os.getenv("ENTERPRISE_CREWAI_DEP_PATH")
_ENTERPRISE_EXTRA_PACKAGES: Final[tuple[str, ...]] = tuple(
p.strip()
for p in os.getenv("ENTERPRISE_EXTRA_PACKAGES", "").split(",")
if p.strip()
)
def _update_enterprise_crewai_dep(pyproject_path: Path, version: str) -> bool:
"""Update the crewai[tools] pin in an enterprise pyproject.toml.
Args:
pyproject_path: Path to the pyproject.toml file.
version: New crewai version string.
Returns:
True if the file was modified.
"""
if not pyproject_path.exists():
return False
content = pyproject_path.read_text()
new_content = _pin_crewai_deps(content, version)
if new_content != content:
pyproject_path.write_text(new_content)
return True
return False
_PYPI_POLL_INTERVAL: Final[int] = 15
_PYPI_POLL_TIMEOUT: Final[int] = 600
def _wait_for_pypi(package: str, version: str) -> None:
"""Poll PyPI until a specific package version is available.
Args:
package: PyPI package name.
version: Version string to wait for.
"""
url = f"https://pypi.org/pypi/{package}/{version}/json"
deadline = time.monotonic() + _PYPI_POLL_TIMEOUT
console.print(f"[cyan]Waiting for {package}=={version} to appear on PyPI...[/cyan]")
while time.monotonic() < deadline:
try:
with urlopen(url) as resp: # noqa: S310
if resp.status == 200:
console.print(
f"[green]✓[/green] {package}=={version} is available on PyPI"
)
return
except Exception: # noqa: S110
pass
time.sleep(_PYPI_POLL_INTERVAL)
console.print(
f"[red]Error:[/red] Timed out waiting for {package}=={version} on PyPI"
)
sys.exit(1)
def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> None:
"""Clone the enterprise repo, bump versions, and create a release PR.
Expects ENTERPRISE_REPO, ENTERPRISE_VERSION_DIRS, and
ENTERPRISE_CREWAI_DEP_PATH to be validated before calling.
Args:
version: New version string.
is_prerelease: Whether this is a pre-release version.
dry_run: Show what would be done without making changes.
"""
if (
not _ENTERPRISE_REPO
or not _ENTERPRISE_VERSION_DIRS
or not _ENTERPRISE_CREWAI_DEP_PATH
):
console.print("[red]Error:[/red] Enterprise env vars not configured")
sys.exit(1)
enterprise_repo: str = _ENTERPRISE_REPO
enterprise_dep_path: str = _ENTERPRISE_CREWAI_DEP_PATH
console.print(
f"\n[bold cyan]Phase 3: Releasing {enterprise_repo} {version}[/bold cyan]"
)
if dry_run:
console.print(f"[dim][DRY RUN][/dim] Would clone {enterprise_repo}")
for d in _ENTERPRISE_VERSION_DIRS:
console.print(f"[dim][DRY RUN][/dim] Would update versions in {d}")
console.print(
f"[dim][DRY RUN][/dim] Would update crewai[tools] dep in "
f"{enterprise_dep_path}"
)
console.print(
"[dim][DRY RUN][/dim] Would create bump PR, wait for merge, "
"then tag and release"
)
return
with tempfile.TemporaryDirectory() as tmp:
repo_dir = Path(tmp) / enterprise_repo.split("/")[-1]
console.print(f"Cloning {enterprise_repo}...")
run_command(["gh", "repo", "clone", enterprise_repo, str(repo_dir)])
console.print(f"[green]✓[/green] Cloned {enterprise_repo}")
# --- bump versions ---
for rel_dir in _ENTERPRISE_VERSION_DIRS:
pkg_dir = repo_dir / rel_dir
if not pkg_dir.exists():
console.print(
f"[yellow]Warning:[/yellow] {rel_dir} not found, skipping"
)
continue
for vfile in find_version_files(pkg_dir):
if update_version_in_file(vfile, version):
console.print(
f"[green]✓[/green] Updated: {vfile.relative_to(repo_dir)}"
)
pyproject = pkg_dir / "pyproject.toml"
if pyproject.exists():
if update_pyproject_dependencies(
pyproject, version, extra_packages=list(_ENTERPRISE_EXTRA_PACKAGES)
):
console.print(
f"[green]✓[/green] Updated deps in: "
f"{pyproject.relative_to(repo_dir)}"
)
# --- update crewai[tools] pin ---
enterprise_pyproject = repo_dir / enterprise_dep_path
if _update_enterprise_crewai_dep(enterprise_pyproject, version):
console.print(
f"[green]✓[/green] Updated crewai[tools] dep in {enterprise_dep_path}"
)
_wait_for_pypi("crewai", version)
console.print("\nSyncing workspace...")
run_command(["uv", "sync"], cwd=repo_dir)
console.print("[green]✓[/green] Workspace synced")
# --- branch, commit, push, PR ---
branch_name = f"feat/bump-version-{version}"
run_command(["git", "checkout", "-b", branch_name], cwd=repo_dir)
run_command(["git", "add", "."], cwd=repo_dir)
run_command(
["git", "commit", "-m", f"feat: bump versions to {version}"],
cwd=repo_dir,
)
console.print("[green]✓[/green] Changes committed")
run_command(["git", "push", "-u", "origin", branch_name], cwd=repo_dir)
console.print("[green]✓[/green] Branch pushed")
run_command(
[
"gh",
"pr",
"create",
"--repo",
enterprise_repo,
"--base",
"main",
"--title",
f"feat: bump versions to {version}",
"--body",
"",
],
cwd=repo_dir,
)
console.print("[green]✓[/green] Enterprise bump PR created")
_poll_pr_until_merged(branch_name, "enterprise bump PR", repo=enterprise_repo)
# --- tag and release ---
run_command(["git", "checkout", "main"], cwd=repo_dir)
run_command(["git", "pull"], cwd=repo_dir)
tag_name = version
run_command(
["git", "tag", "-a", tag_name, "-m", f"Release {version}"],
cwd=repo_dir,
)
run_command(["git", "push", "origin", tag_name], cwd=repo_dir)
console.print(f"[green]✓[/green] Pushed tag {tag_name}")
gh_cmd = [
"gh",
"release",
"create",
tag_name,
"--repo",
enterprise_repo,
"--title",
tag_name,
"--notes",
f"Release {version}",
]
if is_prerelease:
gh_cmd.append("--prerelease")
run_command(gh_cmd)
release_type = "prerelease" if is_prerelease else "release"
console.print(
f"[green]✓[/green] Created GitHub {release_type} for "
f"{enterprise_repo} {tag_name}"
)
def _trigger_pypi_publish(tag_name: str, wait: bool = False) -> None:
"""Trigger the PyPI publish GitHub Actions workflow.
Args:
tag_name: The release tag to publish.
wait: Block until the workflow run completes.
"""
# Capture the latest run ID before triggering so we can detect the new one
prev_run_id = ""
if wait:
try:
prev_run_id = run_command(
[
"gh",
"run",
"list",
"--workflow=publish.yml",
"--limit=1",
"--json=databaseId",
"--jq=.[0].databaseId",
]
)
except subprocess.CalledProcessError:
console.print(
"[yellow]Note:[/yellow] Could not determine previous workflow run; "
"continuing without previous run ID"
)
with console.status("[cyan]Triggering PyPI publish workflow..."):
try:
run_command(
@@ -1003,6 +1275,42 @@ def _trigger_pypi_publish(tag_name: str) -> None:
sys.exit(1)
console.print("[green]✓[/green] Triggered PyPI publish workflow")
if wait:
console.print("[cyan]Waiting for PyPI publish workflow to complete...[/cyan]")
run_id = ""
deadline = time.monotonic() + 120
while time.monotonic() < deadline:
time.sleep(5)
try:
run_id = run_command(
[
"gh",
"run",
"list",
"--workflow=publish.yml",
"--limit=1",
"--json=databaseId",
"--jq=.[0].databaseId",
]
)
except subprocess.CalledProcessError:
continue
if run_id and run_id != prev_run_id:
break
if not run_id or run_id == prev_run_id:
console.print(
"[red]Error:[/red] Could not find the PyPI publish workflow run"
)
sys.exit(1)
try:
run_command(["gh", "run", "watch", run_id, "--exit-status"])
except subprocess.CalledProcessError as e:
console.print(f"[red]✗[/red] PyPI publish workflow failed: {e}")
sys.exit(1)
console.print("[green]✓[/green] PyPI publish workflow completed")
# ---------------------------------------------------------------------------
# CLI commands
@@ -1032,6 +1340,15 @@ def bump(version: str, dry_run: bool, no_push: bool, no_commit: bool) -> None:
no_push: Don't push changes to remote.
no_commit: Don't commit changes (just update files).
"""
console.print(
f"\n[yellow]Note:[/yellow] [bold]devtools bump[/bold] only bumps versions "
f"in this repo. It will not tag, publish to PyPI, or release enterprise.\n"
f"If you want a full end-to-end release, run "
f"[bold]devtools release {version}[/bold] instead."
)
if not Confirm.ask("Continue with bump only?", default=True):
sys.exit(0)
try:
check_gh_installed()
@@ -1136,6 +1453,16 @@ def tag(dry_run: bool, no_edit: bool) -> None:
dry_run: Show what would be done without making changes.
no_edit: Skip editing release notes.
"""
console.print(
"\n[yellow]Note:[/yellow] [bold]devtools tag[/bold] only tags and creates "
"a GitHub release for this repo. It will not bump versions, publish to "
"PyPI, or release enterprise.\n"
"If you want a full end-to-end release, run "
"[bold]devtools release <version>[/bold] instead."
)
if not Confirm.ask("Continue with tag only?", default=True):
sys.exit(0)
try:
cwd = Path.cwd()
lib_dir = cwd / "lib"
@@ -1226,21 +1553,44 @@ def tag(dry_run: bool, no_edit: bool) -> None:
"--dry-run", is_flag=True, help="Show what would be done without making changes"
)
@click.option("--no-edit", is_flag=True, help="Skip editing release notes")
def release(version: str, dry_run: bool, no_edit: bool) -> None:
@click.option(
"--skip-enterprise",
is_flag=True,
help="Skip the enterprise release phase",
)
def release(version: str, dry_run: bool, no_edit: bool, skip_enterprise: bool) -> None:
"""Full release: bump versions, tag, and publish a GitHub release.
Combines bump and tag into a single workflow. Creates a version bump PR,
waits for it to be merged, then generates release notes, updates docs,
creates the tag, and publishes a GitHub release.
creates the tag, and publishes a GitHub release. Then bumps versions and
releases the enterprise repo.
Args:
version: New version to set (e.g., 1.0.0, 1.0.0a1).
dry_run: Show what would be done without making changes.
no_edit: Skip editing release notes.
skip_enterprise: Skip the enterprise release phase.
"""
try:
check_gh_installed()
if not skip_enterprise:
missing: list[str] = []
if not _ENTERPRISE_REPO:
missing.append("ENTERPRISE_REPO")
if not _ENTERPRISE_VERSION_DIRS:
missing.append("ENTERPRISE_VERSION_DIRS")
if not _ENTERPRISE_CREWAI_DEP_PATH:
missing.append("ENTERPRISE_CREWAI_DEP_PATH")
if missing:
console.print(
f"[red]Error:[/red] Missing required environment variable(s): "
f"{', '.join(missing)}\n"
f"Set them or pass --skip-enterprise to skip the enterprise release."
)
sys.exit(1)
cwd = Path.cwd()
lib_dir = cwd / "lib"
@@ -1337,7 +1687,10 @@ def release(version: str, dry_run: bool, no_edit: bool) -> None:
if not dry_run:
_create_tag_and_release(tag_name, release_notes, is_prerelease)
_trigger_pypi_publish(tag_name)
_trigger_pypi_publish(tag_name, wait=not skip_enterprise)
if not skip_enterprise:
_release_enterprise(version, is_prerelease, dry_run)
console.print(f"\n[green]✓[/green] Release [bold]{version}[/bold] complete!")