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| Author | SHA1 | Date | |
|---|---|---|---|
|
565592c36e |
@@ -4,38 +4,6 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
|
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icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="3 يونيو 2026">
|
||||
## v1.14.7a1
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الميزات
|
||||
- إضافة دعم ملفات الوكلاء المدربين
|
||||
- إضافة مزود LLM الأصلي لـ Snowflake Cortex
|
||||
- إضافة دليل تكامل Databricks
|
||||
- إضافة دليل تكامل Snowflake
|
||||
|
||||
### إصلاحات الأخطاء
|
||||
- إصلاح CLI عن طريق استعادة `[project.scripts]` في حزمة crewai لتثبيت أداة UV
|
||||
- حل مشكلات موثوقية إدخال الملفات
|
||||
- إصلاح تاريخ نتائج الأدوات غير المكتملة في Snowflake Claude
|
||||
- التعامل مع استدعاءات الأدوات الممثلة كسلاسل لـ Snowflake Claude
|
||||
- إعادة تفعيل مستمعي `or_` متعدد المصادر عبر دورات مدفوعة بالموجه
|
||||
|
||||
### الأداء
|
||||
- تحسين سرعة استيراد crewai عن طريق تحميل استيرادات docling بشكل كسول
|
||||
|
||||
### إعادة هيكلة
|
||||
- تقسيم `flow.py` إلى DSL، تعريف، وتشغيل
|
||||
|
||||
## المساهمون
|
||||
|
||||
@Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
|
||||
|
||||
</Update>
|
||||
|
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<Update label="28 مايو 2026">
|
||||
## v1.14.6
|
||||
|
||||
|
||||
@@ -164,12 +164,6 @@ crewai deploy remove <deployment_id>
|
||||
|
||||
</Frame>
|
||||
|
||||
<Tip>
|
||||
إذا كان Crew أو Flow داخل مجلد فرعي في monorepo، فوسّع **Advanced**
|
||||
وعيّن دليل عمل قبل النشر. راجع
|
||||
[النشر من Monorepo](/ar/enterprise/guides/monorepo-deployments).
|
||||
</Tip>
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="تعيين متغيرات البيئة">
|
||||
|
||||
@@ -1,220 +0,0 @@
|
||||
---
|
||||
title: "النشر من Monorepo"
|
||||
description: "انشر Crew أو Flow من مجلد فرعي داخل مستودع أكبر"
|
||||
icon: "folder-tree"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
استخدم دليل عمل عندما يكون Crew أو Flow داخل مستودع أكبر. يتحقق CrewAI AMP
|
||||
من الأتمتة ويبنيها ويشغلها من ذلك المجلد الفرعي بدلاً من جذر المستودع.
|
||||
</Note>
|
||||
|
||||
## متى تستخدم ذلك
|
||||
|
||||
يكون النشر من monorepo مفيداً عندما يحتوي مستودع واحد على عدة أتمتات أو حزم
|
||||
مشتركة أو كود تطبيقات آخر:
|
||||
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
|-- support_agent/
|
||||
| |-- pyproject.toml
|
||||
| `-- src/
|
||||
| `-- support_agent/
|
||||
| |-- main.py
|
||||
| `-- crew.py
|
||||
`-- research_flow/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- research_flow/
|
||||
`-- main.py
|
||||
```
|
||||
|
||||
لنشر `support_agent`، اضبط دليل العمل على:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
لا يزال AMP يجلب المستودع كاملاً أو يرفعه، لكنه يتعامل مع المجلد المحدد كجذر
|
||||
مشروع الأتمتة.
|
||||
|
||||
## ما الذي يتحكم به دليل العمل
|
||||
|
||||
عند تعيين دليل عمل، يستخدم AMP ذلك المجلد من أجل:
|
||||
|
||||
- التحقق من المشروع، بما في ذلك `pyproject.toml` و`src/` ونقطة دخول Crew أو Flow
|
||||
- تثبيت الاعتماديات باستخدام `uv`
|
||||
- دليل العمل للعملية قيد التشغيل
|
||||
- متغير البيئة `CREW_ROOT_DIR`
|
||||
|
||||
ترك الحقل فارغاً يحافظ على السلوك الحالي ويستخدم جذر المستودع.
|
||||
|
||||
## المصادر المدعومة
|
||||
|
||||
يمكنك تعيين دليل عمل عند إنشاء نشر من:
|
||||
|
||||
- مستودع GitHub متصل
|
||||
- مستودع Git مكوّن في AMP
|
||||
- رفع ملف ZIP
|
||||
|
||||
<Info>
|
||||
اضبط أدلة العمل من واجهة AMP على الويب. لا يطلب تدفق CLI
|
||||
`crewai deploy create` هذا الحقل.
|
||||
</Info>
|
||||
|
||||
يمكنك أيضاً إضافة دليل العمل أو تغييره في نشر موجود من صفحة **Settings** الخاصة
|
||||
بالنشر. يسري التغيير في النشر التالي.
|
||||
|
||||
<Warning>
|
||||
لا يمكن استخدام أدلة العمل وauto-deploy معاً. إذا كان للنشر دليل عمل، يتم
|
||||
تعطيل auto-deploy لذلك النشر. أوقف auto-deploy قبل تعيين دليل عمل.
|
||||
</Warning>
|
||||
|
||||
## إعداد نشر جديد
|
||||
|
||||
<Steps>
|
||||
<Step title="افتح Deploy from Code">
|
||||
في CrewAI AMP، أنشئ نشراً جديداً واختر المصدر: GitHub أو Git Repository أو
|
||||
رفع ZIP.
|
||||
</Step>
|
||||
|
||||
<Step title="اختر المستودع أو الفرع أو ملف ZIP">
|
||||
اختر المستودع والفرع اللذين يحتويان على monorepo، أو ارفع ملف ZIP يحتوي
|
||||
جذره على محتويات monorepo.
|
||||
</Step>
|
||||
|
||||
<Step title="افتح الإعدادات المتقدمة">
|
||||
وسّع قسم **Advanced** في نموذج النشر.
|
||||
</Step>
|
||||
|
||||
<Step title="أدخل دليل العمل">
|
||||
أدخل المسار من جذر المستودع إلى مشروع Crew أو Flow:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
لا تضف شرطة مائلة في البداية.
|
||||
</Step>
|
||||
|
||||
<Step title="انشر">
|
||||
أضف أي متغيرات بيئة مطلوبة، ثم ابدأ النشر.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## إعداد نشر موجود
|
||||
|
||||
<Steps>
|
||||
<Step title="افتح إعدادات النشر">
|
||||
انتقل إلى الأتمتة في AMP وافتح **Settings**.
|
||||
</Step>
|
||||
|
||||
<Step title="أوقف auto-deploy إذا لزم الأمر">
|
||||
إذا كان auto-deploy مفعلاً، أوقفه أولاً. لا يكون حقل دليل العمل متاحاً
|
||||
أثناء تشغيل auto-deploy.
|
||||
</Step>
|
||||
|
||||
<Step title="عيّن دليل العمل">
|
||||
في **Basic settings**، أدخل مسار المجلد الفرعي، مثل:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="أعد النشر">
|
||||
احفظ الإعداد وأعد نشر الأتمتة. سيتم استخدام دليل العمل الجديد في النشر
|
||||
التالي.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## قواعد المسار
|
||||
|
||||
يجب أن يكون دليل العمل مساراً نسبياً داخل جذر المستودع أو ZIP.
|
||||
|
||||
| القاعدة | المثال |
|
||||
|---------|--------|
|
||||
| استخدم مساراً نسبياً | `crews/support_agent` |
|
||||
| لا تبدأ بـ `/` | `/crews/support_agent` غير صالح |
|
||||
| لا تستخدم مقاطع المسار `.` أو `..` | `crews/../support_agent` غير صالح |
|
||||
| استخدم الأحرف والأرقام والشرطات والشرطات السفلية والنقاط والشرطات المائلة فقط | `crews/support agent` غير صالح |
|
||||
| اجعل المسار 255 حرفاً أو أقل | يتم رفض المسارات الأطول |
|
||||
|
||||
يزيل AMP المسافات البيضاء في البداية والنهاية، ويضغط الشرطات المائلة المتكررة،
|
||||
ويزيل الشرطة المائلة النهائية. تستخدم القيمة الفارغة جذر المستودع.
|
||||
|
||||
## ملفات القفل وUV Workspaces
|
||||
|
||||
يجب أن يحتوي المجلد المحدد على `pyproject.toml` ودليل `src/` الخاصين بالأتمتة.
|
||||
يمكن أن يوجد ملف `uv.lock` أو `poetry.lock` إما في المجلد المحدد أو في جذر
|
||||
المستودع.
|
||||
|
||||
يدعم هذا التخطيطين الشائعين في monorepo:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="ملف قفل المشروع">
|
||||
```text
|
||||
company-ai/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
|-- uv.lock
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
|
||||
<Tab title="ملف قفل workspace">
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Tip>
|
||||
إذا كانت الأتمتة تستورد حزماً مشتركة من مكان آخر في monorepo، فصرّح بهذه
|
||||
الحزم في `pyproject.toml` باستخدام إعدادات UV workspace أو path أو source.
|
||||
يشغل AMP الأتمتة من المجلد المحدد، لذلك يجب تثبيت الكود المشترك كاعتمادية
|
||||
بدلاً من الاعتماد على وجود جذر المستودع في Python path.
|
||||
</Tip>
|
||||
|
||||
## استكشاف الأخطاء وإصلاحها
|
||||
|
||||
### لم يتم العثور على دليل العمل
|
||||
|
||||
تحقق من أن المسار نسبي إلى جذر المستودع أو ZIP. بالنسبة لرفع ZIP، يجب أن
|
||||
تتضمن محتويات ZIP مسار دليل العمل تماماً كما أدخلته.
|
||||
|
||||
### pyproject.toml مفقود
|
||||
|
||||
يجب أن يشير دليل العمل إلى مجلد مشروع Crew أو Flow، وليس فقط إلى مجلد أب
|
||||
يحتوي على عدة مشاريع.
|
||||
|
||||
### uv.lock أو poetry.lock مفقود
|
||||
|
||||
اعمل commit لملف قفل إما في مجلد المشروع المحدد أو في جذر المستودع. بالنسبة
|
||||
إلى UV workspaces، يتم دعم إبقاء `uv.lock` في جذر workspace.
|
||||
|
||||
### Auto-Deploy غير متاح
|
||||
|
||||
يتم تعطيل auto-deploy أثناء تعيين دليل عمل. استخدم إعادة النشر اليدوية أو شغّل
|
||||
إعادة النشر من CI/CD باستخدام AMP API.
|
||||
|
||||
<Card title="النشر على AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
|
||||
تابع دليل النشر بعد اختيار دليل عمل monorepo.
|
||||
</Card>
|
||||
@@ -1,451 +0,0 @@
|
||||
---
|
||||
title: تدفقات المحادثة
|
||||
description: أنشئ تطبيقات دردشة متعددة الجولات مع kickoff لكل جولة وسجل الرسائل وتوجيه النية والتتبع وجسور WebSocket.
|
||||
icon: comments
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## نظرة عامة
|
||||
|
||||
تعامل التطبيقات المحادثية مع كل سطر من المستخدم كـ **تشغيل flow جديد** بنفس **معرّف الجلسة**. توفر CrewAI مساعدات لسجل الرسائل وتصنيف النية الاختياري وتأجيل التتبع وجسور الواجهة — دون API منفصل `chat()` على `Flow`.
|
||||
|
||||
| المفهوم | التنفيذ |
|
||||
|---------|---------|
|
||||
| معرّف الجلسة | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
|
||||
| سطر المستخدم | `kickoff(user_message=...)` يُضاف إلى `state.messages` قبل تشغيل الرسم |
|
||||
| اكتمال الجولة | `FlowFinished` لهذا **التشغيل** فقط؛ تستمر المحادثة في `kickoff` التالي |
|
||||
| تتبع الجلسة | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
|
||||
|
||||
## نقطة دخول واحدة: `kickoff`
|
||||
|
||||
استخدم **`flow.kickoff(user_message=..., session_id=...)`** لكل رسالة مستخدم (REST أو WebSocket أو CLI). لا تنشئ غلاف `chat()` مخصصاً على `Flow`.
|
||||
|
||||
| API | الاستخدام |
|
||||
|-----|-----------|
|
||||
| `kickoff(user_message=..., session_id=...)` | كل رسالة مستخدم |
|
||||
| `kickoff_async(...)` | نفس المعاملات؛ دخول async أصلي |
|
||||
| `ask()` | مطالبة حاجزة **داخل** خطوة واحدة |
|
||||
| `@human_feedback` | الموافقة/الرفض على **مخرجات خطوة** — وليس السطر التالي |
|
||||
| `ChatSession.handle_turn(...)` | طبقة نقل فوق `kickoff` |
|
||||
|
||||
## بداية سريعة
|
||||
|
||||
```python
|
||||
from uuid import uuid4
|
||||
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
Flow,
|
||||
listen,
|
||||
or_,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
|
||||
class SupportFlow(Flow[ChatState]):
|
||||
conversational_config = ConversationalConfig(
|
||||
default_intents=["order", "help", "goodbye"],
|
||||
intent_llm="gpt-4o-mini",
|
||||
defer_trace_finalization=True,
|
||||
)
|
||||
|
||||
@start()
|
||||
def bootstrap(self):
|
||||
if not self.state.session_ready:
|
||||
self.state.session_ready = True
|
||||
return "ready"
|
||||
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
return self.state.last_intent or "help"
|
||||
|
||||
@listen("order")
|
||||
def handle_order(self):
|
||||
reply = "طلبك في الطريق."
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("help")
|
||||
def handle_help(self):
|
||||
reply = "كيف يمكنني المساعدة؟"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("goodbye")
|
||||
def handle_goodbye(self):
|
||||
reply = "وداعاً!"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@persist(SQLiteFlowPersistence("support.db"))
|
||||
@listen(or_(handle_order, handle_help, handle_goodbye))
|
||||
def finalize(self):
|
||||
return self.state.model_dump()
|
||||
|
||||
|
||||
session_id = str(uuid4())
|
||||
flow = SupportFlow()
|
||||
|
||||
flow.kickoff(user_message="أين طلبي؟", session_id=session_id)
|
||||
flow.kickoff(user_message="وماذا عن الإرجاع؟", session_id=session_id)
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
## دورة حياة الجولة
|
||||
|
||||
كل `kickoff` مع `user_message` يشغّل:
|
||||
|
||||
1. **`_configure_conversational_kickoff`** — دمج `session_id` / `user_message` في `inputs` وتطبيق `ConversationalConfig`.
|
||||
2. **استعادة الحالة** — عند وجود `inputs["id"]` و`@persist`.
|
||||
3. **`FlowStarted`** — في أول جولة للجلسة المؤجلة فقط.
|
||||
4. **`prepare_conversational_turn`** — إضافة رسالة المستخدم و`last_user_message` وتصنيف اختياري.
|
||||
5. **تنفيذ الرسم** — `@start` → `@router` → معالجات `@listen`.
|
||||
6. **نهاية التشغيل** — يُتخطى `flow_finished` والتتبع لكل جولة عند التأجيل؛ `Agent.kickoff()` / crews لا تغلق دفعة الأب.
|
||||
|
||||
استدعِ **`append_message("assistant", reply)`** في المعالجات. سطر المستخدم محفوظ عند kickoff — لا تُضفه مرة أخرى.
|
||||
|
||||
## `ConversationalConfig` (افتراضيات على مستوى الصنف)
|
||||
|
||||
عيّن على صنف `Flow` كـ `conversational_config: ClassVar[ConversationalConfig | None]`.
|
||||
|
||||
| الحقل | الافتراضي | الغرض |
|
||||
|-------|-----------|--------|
|
||||
| `default_intents` | `None` | تسميات outcome للتصنيف التلقائي قبل kickoff |
|
||||
| `intent_llm` | `None` | نموذج التصنيف (مطلوب عند وجود intents) |
|
||||
| `interactive_prompt` | `"You: "` | مطالبة `kickoff(interactive=True)` |
|
||||
| `interactive_timeout` | `None` | مهلة لكل سطر في الوضع التفاعلي |
|
||||
| `exit_commands` | `exit`, `quit` | كلمات إنهاء الوضع التفاعلي |
|
||||
| `defer_trace_finalization` | `True` | إبقاء دفعة trace واحدة مفتوحة بين الجولات |
|
||||
|
||||
يمكن التجاوز لكل kickoff عبر `intents=` و`intent_llm=`.
|
||||
|
||||
## `ChatState` (شكل الحالة الموصى به للحفظ)
|
||||
|
||||
```python
|
||||
from crewai.flow import ChatState
|
||||
|
||||
|
||||
class MyChatState(ChatState):
|
||||
# موروث: id, messages, last_user_message, last_intent, session_ready
|
||||
research_turn_count: int = 0
|
||||
custom_flag: bool = False
|
||||
```
|
||||
|
||||
| الحقل | الدور |
|
||||
|-------|------|
|
||||
| `id` | UUID الجلسة (مثل `session_id` / `inputs["id"]`) |
|
||||
| `messages` | قائمة `{role, content}` لسجل LLM |
|
||||
| `last_user_message` | آخر سطر مستخدم في هذه الجولة |
|
||||
| `last_intent` | تسمية المسار بعد التصنيف (إن وُجد) |
|
||||
| `session_ready` | علم bootstrap لمرة واحدة |
|
||||
|
||||
`ConversationalInputs` هو `TypedDict` لـ `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
|
||||
|
||||
## API المحادثة على `Flow`
|
||||
|
||||
### معاملات `kickoff` / `kickoff_async`
|
||||
|
||||
| المعامل | الغرض |
|
||||
|---------|--------|
|
||||
| `user_message` | نص هذه الجولة (أو `{"role": "user", "content": "..."}`) |
|
||||
| `session_id` | UUID المحادثة → `inputs["id"]` / `state.id` |
|
||||
| `intents` | تسميات outcome لـ `classify_intent` قبل kickoff |
|
||||
| `intent_llm` | LLM للتصنيف (مطلوب مع `intents`) |
|
||||
| `interactive` | حلقة CLI عبر `ask()` (للعروض المحلية فقط) |
|
||||
| `interactive_prompt` | مطالبة الوضع التفاعلي |
|
||||
| `interactive_timeout` | مهلة `ask()` لكل سطر |
|
||||
| `exit_commands` | كلمات إنهاء الوضع التفاعلي |
|
||||
| `inputs` | حقول حالة إضافية |
|
||||
| `restore_from_state_id` | استنساخ من flow محفوظ آخر |
|
||||
|
||||
### سمات المثيل
|
||||
|
||||
| السمة | الغرض |
|
||||
|-------|--------|
|
||||
| `conversational_config` | افتراضيات `ConversationalConfig` على مستوى الصنف |
|
||||
| `defer_trace_finalization` | علم المثيل؛ يُضبط تلقائياً من config عند kickoff |
|
||||
| `suppress_flow_events` | يخفي لوحات console؛ **التتبع يُسجّل** |
|
||||
| `stream` | بث؛ مع `ChatSession.handle_turn(..., stream=True)` |
|
||||
|
||||
### طرق وخصائص
|
||||
|
||||
| الاسم | الوصف |
|
||||
|------|--------|
|
||||
| `append_message(role, content, **extra)` | إضافة إلى `state.messages` |
|
||||
| `conversation_messages` | سجل للقراءة فقط لاستدعاءات LLM |
|
||||
| `classify_intent(text, outcomes, *, llm, context=None)` | تعيين outcome |
|
||||
| `receive_user_message(text, *, outcomes=None, llm=None)` | إضافة رسالة مستخدم؛ `last_intent` اختياري |
|
||||
| `finalize_session_traces()` | إصدار `flow_finished` المؤجل وإنهاء دفعة trace |
|
||||
| `_should_defer_trace_finalization()` | هل يُؤجل إنهاء trace لكل جولة |
|
||||
| `input_history` | سجل تدقيق مطالبات وردود `ask()` |
|
||||
|
||||
### مساعدات الوحدة (`crewai.flow.conversation`)
|
||||
|
||||
| الدالة | الوصف |
|
||||
|--------|--------|
|
||||
| `normalize_kickoff_inputs(...)` | دمج kwargs المحادثة في `inputs` |
|
||||
| `get_conversation_messages(flow)` | قراءة الرسائل من الحالة أو المخزن |
|
||||
| `append_message(flow, ...)` | مثل طريقة المثيل |
|
||||
| `prepare_conversational_turn(flow, ...)` | تهيئة الجولة (عادةً kickoff يستدعيها) |
|
||||
| `receive_user_message(flow, ...)` | مثل طريقة المثيل |
|
||||
| `set_state_field(flow, name, value)` | تعيين حقل dict أو Pydantic |
|
||||
| `get_conversational_config(flow)` | قراءة `conversational_config` |
|
||||
| `input_history_to_messages(entries)` | تحويل `input_history` لصيغة رسائل LLM |
|
||||
|
||||
## أنماط توجيه النية
|
||||
|
||||
### أ. تصنيف مسبق عبر `ConversationalConfig` (الأبسط)
|
||||
|
||||
عيّن `default_intents` و`intent_llm`. كل kickoff يصنّف قبل `@router`؛ اقرأ `self.state.last_intent` في `route()`.
|
||||
|
||||
### ب. تصنيف داخل `@router` (مطالبات أغنى)
|
||||
|
||||
عيّن `default_intents=None` ليضيف kickoff الرسالة فقط. في `route()` استدعِ `classify_intent`:
|
||||
|
||||
```python
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
intent = self.classify_intent(
|
||||
self._routing_prompt(self.state.last_user_message),
|
||||
("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
|
||||
llm=self.conversational_config.intent_llm or "gpt-4o-mini",
|
||||
)
|
||||
self.state.last_intent = intent
|
||||
return intent
|
||||
```
|
||||
|
||||
للبحث على الويب أو أدوات متعددة الخطوات استخدم **`@listen("RESEARCH")`** مع `Agent.kickoff()` وأدوات — وليس `LLM.call()` فقط.
|
||||
|
||||
## عندما ينتهي الـ flow ويستمر المستخدم
|
||||
|
||||
`FlowFinished` يعني أن **تنفيذ الرسم هذا** اكتمل. تستمر المحادثة بـ `kickoff` آخر ونفس `session_id`. `@persist` يستعيد `messages` والأعلام والسياق.
|
||||
|
||||
**نمط الحفظ:** يُفضّل `@persist` على **خطوة نهائية واحدة** (مثل `finalize`) وليس على صنف `Flow` بالكامل. الحفظ على مستوى الصنف بعد كل method قد يفقد تحديثات المعالجات في نفس الجولة.
|
||||
|
||||
لا تستخدم `@human_feedback` لأسطر المتابعة في الدردشة إلا عند الحاجة لموافقة بشرية على مخرجات خطوة محددة.
|
||||
|
||||
## `Flow` المحادثاتي (تجريبي)
|
||||
|
||||
<Warning>
|
||||
**ميزة تجريبية.** سطح `Flow` المحادثاتي (`conversational = True`،
|
||||
`handle_turn`، `ConversationConfig`، `RouterConfig`،
|
||||
`ConversationState`، الرسم البياني المدمج والمساعدات) يقع تحت
|
||||
`crewai.experimental` وقد يتغير شكله قبل التخرج. ثبّت إصدار CrewAI إذا
|
||||
كنت تعتمد على سلوك محدد، وراقب changelog للتحديثات الكاسرة. الملاحظات
|
||||
والمشاكل مرحب بها.
|
||||
</Warning>
|
||||
|
||||
فعّل الرسم المحادثاتي بتعيين `conversational = True` على صنف فرعي من `Flow`. عندئذٍ يُظهر `Flow` الأساسي رسم `@start` / `@router` / `converse_turn` / `end_conversation` مدمجاً، ويدير `state.messages`، ويُشغّل LLM التوجيه، ويبقي دفعة trace مفتوحة عبر الجولات. أنت تكتب **المسارات المخصصة** فقط؛ والإطار يتولى الباقي.
|
||||
|
||||
استخدمه عندما تريد دردشة متعددة الجولات مع موجّه قائم على LLM ومعالجات لكل مسار دون توصيل دورة الحياة يدوياً. استخدم `Flow[ChatState]` (النمط الأدنى مستوى في الأعلى) عندما تحتاج تحكماً كاملاً.
|
||||
|
||||
### مثال سريع
|
||||
|
||||
```python
|
||||
from crewai import LLM, Flow
|
||||
from crewai.flow import listen
|
||||
from crewai.experimental.conversational import (
|
||||
ConversationConfig,
|
||||
ConversationState,
|
||||
RouterConfig,
|
||||
)
|
||||
|
||||
|
||||
ROUTER_LLM = LLM(model="gpt-4o-mini")
|
||||
|
||||
|
||||
@ConversationConfig(
|
||||
system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
|
||||
llm=ROUTER_LLM,
|
||||
router=RouterConfig(), # المسارات + الأوصاف تُكتشف تلقائياً من معالجات @listen
|
||||
)
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
@listen("CREWAI_DOCS")
|
||||
def handle_crewai_docs(self) -> str:
|
||||
"""Look up the CrewAI documentation for framework/API questions."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
|
||||
flow = SupportFlow()
|
||||
try:
|
||||
flow.handle_turn("ماذا يمكنك أن تفعل؟") # يوجَّه إلى converse (مدمج)
|
||||
flow.handle_turn("ابحث في الويب عن أخبار الذكاء الاصطناعي.") # يوجَّه إلى INTERNET_SEARCH
|
||||
flow.handle_turn("لخص النتيجة الأولى.") # يعود إلى converse
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
### `ConversationConfig`
|
||||
|
||||
مزخرف صنف يُلحق افتراضيات الدردشة على مستوى الصنف.
|
||||
|
||||
| الحقل | الافتراضي | الغرض |
|
||||
|-------|-----------|-------|
|
||||
| `system_prompt` | `slices.conversational_system_prompt` من i18n | رسالة system يستخدمها `converse_turn` المدمج. مرر `""` للتعطيل التام. |
|
||||
| `llm` | `None` | LLM المحادثة (يستخدمه `converse_turn` وكاحتياطي للموجّه). |
|
||||
| `router` | `None` | `RouterConfig` للتوجيه عبر LLM. بدونه، يسقط الـ flow دائماً إلى `converse`. |
|
||||
| `answer_from_history_prompt` | افتراضي الإطار | رسالة system للمسار الاختياري `answer_from_history`. |
|
||||
| `answer_from_history_llm` | `None` | يُفعّل الاختصار `answer_from_history` عند تعيينه. |
|
||||
| `intent_llm` | `None` | LLM لمسار التصنيف المسبق القديم `intents=`/`default_intents`. |
|
||||
| `default_intents` | `None` | تسميات النتائج للتصنيف المسبق القديم. |
|
||||
| `visible_agent_outputs` | `None` | `"all"` أو قائمة بأسماء الـ agents الذين تُرفع مخرجاتهم من `append_agent_result()` إلى رسائل عامة. |
|
||||
| `defer_trace_finalization` | `True` | يبقي دفعة trace واحدة مفتوحة عبر استدعاءات `handle_turn()`. |
|
||||
|
||||
### `RouterConfig` وفهرس المسارات المُولَّد تلقائياً
|
||||
|
||||
```python
|
||||
RouterConfig(
|
||||
prompt="تأطير اختياري للنطاق (سياسة، صوت، شخصية).",
|
||||
response_format=MyRoute, # اختياري؛ يُولَّد تلقائياً عند الإغفال
|
||||
llm=ROUTER_LLM, # يسقط إلى ConversationConfig.llm
|
||||
routes=["INTERNET_SEARCH", "CREWAI_DOCS"], # اختياري؛ يُستنتج من المستمعين
|
||||
route_descriptions={
|
||||
"INTERNET_SEARCH": "تجاوز الـ docstring لهذا المسار فقط.",
|
||||
},
|
||||
default_intent="converse", # يُستخدم عند فشل LLM أو غيابه
|
||||
fallback_intent="converse", # يُستخدم عندما يعيد LLM مساراً غير صالح
|
||||
intent_field="intent",
|
||||
)
|
||||
```
|
||||
|
||||
تُبنى رسالة الموجّه إلى LLM تلقائياً. لكل مسار يختار الإطار وصفاً بهذا الترتيب من الأولوية:
|
||||
|
||||
1. `RouterConfig.route_descriptions[label]` — تجاوز صريح.
|
||||
2. `Flow.builtin_route_descriptions[label]` — نص جاهز من الإطار لـ `converse` و`end` و`answer_from_history` (مصاغ لـ LLM التوجيه).
|
||||
3. أول سطر غير فارغ من docstring معالج `@listen(label)`.
|
||||
4. فارغ (المسار يظهر في الفهرس بلا وصف).
|
||||
|
||||
عملياً، **إضافة مسار جديد = `@listen("X")` + docstring من سطر واحد**:
|
||||
|
||||
```python
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
```
|
||||
|
||||
…وسيرى LLM التوجيه:
|
||||
|
||||
```
|
||||
Routes:
|
||||
- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
|
||||
- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
|
||||
- converse: Ordinary chat, follow-ups, summaries, clarifications…
|
||||
- end: User signals the conversation is finished (goodbye, exit, done).
|
||||
```
|
||||
|
||||
`RouterConfig.prompt` مخصص لـ **تأطير النطاق** (شخصية المساعد، قواعد العمل، النبرة). فهرس المسارات يُبنى تلقائياً — لا تُدرج المسارات في `prompt`؛ سيختل التزامن لحظة إضافة معالج جديد.
|
||||
|
||||
### المسارات المدمجة
|
||||
|
||||
| المسار | المعالج | الغرض |
|
||||
|--------|---------|-------|
|
||||
| `converse` | `converse_turn` | معالج الدردشة الافتراضي. يستدعي `ConversationConfig.llm` بـ system prompt + التاريخ القانوني للرسائل. |
|
||||
| `end` | `end_conversation` | يضبط `state.ended = True` ويُصدر رد إنهاء. |
|
||||
| `answer_from_history` | `answer_from_history_turn` | اختياري. يُوجَّه إليه عندما يكون `ConversationConfig.answer_from_history_llm` مُعيَّناً ويمكن الإجابة على الرسالة من التاريخ فقط. |
|
||||
|
||||
يمكنك تجاوز أي من هذه بتعريف معالج بنفس الاسم في الصنف الفرعي.
|
||||
|
||||
### دلالات `handle_turn()`
|
||||
|
||||
`flow.handle_turn(message)` يُشغّل جولة واحدة:
|
||||
|
||||
1. يعيد ضبط تعقّب التنفيذ لكل جولة (`_completed_methods`, `_method_outputs`) ليُعاد تشغيل الرسم — بدون ذلك، استدعاءات `kickoff` المتكررة على نفس النسخة ستُحدث دائرة قصر من الجولة الثانية لأن `Flow.kickoff_async` يعتبر `inputs={"id": ...}` استعادة من نقطة تفتيش.
|
||||
2. يُلحق رسالة المستخدم بـ `state.messages` ويضبط `current_user_message` / `last_user_message`. يُحافَظ على `last_intent` **من الجولة السابقة** كي يستخدمها LLM التوجيه كإشارة.
|
||||
3. يُشغّل `conversation_start` → `route_conversation` → معالج `@listen` المختار.
|
||||
4. يخزّن الموجّه قراره في `state.last_intent` (يكون مرئياً لسياق التوجيه في الجولة التالية).
|
||||
5. إذا أعاد معالجك سلسلة نصية ولم يستدعِ `append_assistant_message`، فإن `handle_turn` يُلحقها نيابةً عنك.
|
||||
|
||||
يمكنك أيضاً استدعاء `flow.kickoff(user_message=..., session_id=...)` مباشرةً — نفس منطق الإعادة والتشغيل يعمل. `handle_turn` هو الغلاف المريح.
|
||||
|
||||
### سلوك موجّه مخصص
|
||||
|
||||
لتشغيل آثار جانبية (إعداد ناقل أحداث، قياس عن بُعد) في كل قرار توجيه، تجاوز `route_turn`:
|
||||
|
||||
```python
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
def route_turn(self, context: dict[str, Any]) -> str | None:
|
||||
self.event_bus = MyBus(self)
|
||||
return super().route_turn(context)
|
||||
```
|
||||
|
||||
لتجاوز موجّه LLM واختيار مسار برمجياً، أعد سلسلة نصية من `route_turn`؛ إعادة `None` تسقط إلى `_route_with_config(...)`.
|
||||
|
||||
### `append_assistant_message` و`append_agent_result`
|
||||
|
||||
داخل معالج `@listen(label)`، اختر:
|
||||
|
||||
- `self.append_assistant_message(text)` — يضيف جولة مساعد مرئية للمستخدم إلى `state.messages`. سيراها `converse_turn` في الجولة التالية.
|
||||
- `self.append_agent_result(agent_name, result, visibility="private")` — يسجّل حدثاً منظماً في `state.events` وموضوعاً في `state.agent_threads[agent_name]`. الرؤية العامة تستدعي `append_assistant_message` أيضاً. استخدم النتائج الخاصة للعمل الجانبي الذي يجب ألا يلوث التاريخ القانوني.
|
||||
|
||||
يمكن لـ `ConversationConfig.visible_agent_outputs` رفع النتائج الخاصة لـ agents محددين إلى عامة عالمياً (`"all"` أو قائمة بالأسماء).
|
||||
|
||||
## التتبع عبر الجولات
|
||||
|
||||
مع `defer_trace_finalization=True` (افتراضي في `ConversationalConfig`):
|
||||
|
||||
- **دفعة trace واحدة** لجلسة الدردشة.
|
||||
- **`flow_started`** في الجولة الأولى فقط؛ **`flow_finished`** مرة في `finalize_session_traces()`.
|
||||
- **`kickoff` لكل جولة** لا يطبع "Trace batch finalized".
|
||||
- **العمل المتداخل** (`Agent.kickoff()`, crews, Exa) يُلحق بدفعة **الأب**؛ flow داخلي من `AgentExecutor` لا يغلق دفعة الجلسة مبكراً.
|
||||
|
||||
```python
|
||||
try:
|
||||
while True:
|
||||
line = input("You: ").strip()
|
||||
if not line:
|
||||
break
|
||||
flow.kickoff(user_message=line, session_id=session_id)
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
`ChatSession.close()` يستدعي `finalize_session_traces()` عند التأجيل.
|
||||
|
||||
`suppress_flow_events=True` يخفي لوحات Rich فقط؛ أحداث trace والـ methods تُصدر.
|
||||
|
||||
### دورة حياة trace لـ `Flow` المحادثاتي
|
||||
|
||||
يستخدم [`Flow` المحادثاتي](#flow-المحادثاتي-تجريبي) التجريبي نفس دورة حياة tracing: `defer_trace_finalization` افتراضياً `True`، فيبقي كل `handle_turn()` أثر الجلسة مفتوحاً. أنهِ دوماً عند نهاية الجلسة — لُف حلقتك بـ `try/finally` واستدعِ `flow.finalize_session_traces()` عند الخروج. بدون ذلك، تبقى الدفعة مفتوحة وقد لا تُصدَّر آخر محادثة أبداً.
|
||||
|
||||
## البث
|
||||
|
||||
اضبط `stream = True` على صنف `Flow`. عندئذٍ يُصدر `kickoff(...)` أحداث `assistant_delta` (وما يرتبط بها) عبر ناقل الأحداث القياسي.
|
||||
|
||||
## الاستيراد
|
||||
|
||||
```python
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
ConversationalInputs,
|
||||
Flow,
|
||||
listen,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
```
|
||||
|
||||
## مراجع
|
||||
|
||||
- [إتقان إدارة حالة Flow](/ar/guides/flows/mastering-flow-state)
|
||||
- [أنشئ أول Flow](/ar/guides/flows/first-flow)
|
||||
- Demo: `lib/crewai/runner_conversational_flow_simple.py` — REPL بسيط مع `RESEARCH` ووكيل Exa
|
||||
@@ -272,7 +272,6 @@ crewai flow plot
|
||||
3. استكشف دوال `and_` و`or_` لتنفيذ متوازٍ أكثر تعقيدًا
|
||||
4. اربط Flow بواجهات API خارجية وقواعد بيانات وواجهات مستخدم
|
||||
5. ادمج عدة Crews متخصصة في Flow واحد
|
||||
6. أنشئ تطبيقات دردشة متعددة الجولات مع [تدفقات المحادثة](/ar/guides/flows/conversational-flows) (`kickoff` لكل رسالة، `ChatSession`، تأجيل التتبع)
|
||||
|
||||
<Check>
|
||||
تهانينا! لقد بنيت بنجاح أول CrewAI Flow يجمع بين الكود العادي واستدعاءات LLM المباشرة ومعالجة Crew لإنشاء دليل شامل. هذه المهارات الأساسية تمكّنك من إنشاء تطبيقات AI متطورة بشكل متزايد.
|
||||
|
||||
@@ -20,8 +20,6 @@ mode: "wide"
|
||||
5. **توسيع تطبيقاتك** - دعم سير العمل المعقدة بتنظيم بيانات مناسب
|
||||
6. **تمكين التطبيقات الحوارية** - تخزين والوصول إلى سجل المحادثات للتفاعلات الواعية بالسياق
|
||||
|
||||
للدردشة متعددة الجولات (`kickoff` لكل سطر مستخدم، `ChatState`، توجيه النية، تأجيل التتبع، و`ChatSession`)، راجع [تدفقات المحادثة](/ar/guides/flows/conversational-flows).
|
||||
|
||||
## أساسيات إدارة الحالة
|
||||
|
||||
### نهجان لإدارة الحالة
|
||||
|
||||
116
docs/docs.json
116
docs/docs.json
@@ -115,7 +115,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -510,7 +509,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -1030,7 +1028,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -1156,7 +1153,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -1518,7 +1514,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -1644,7 +1639,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -2005,7 +1999,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -2131,7 +2124,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -2492,7 +2484,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -2617,7 +2608,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -2989,7 +2979,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -3114,7 +3103,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -3486,7 +3474,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -3611,7 +3598,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -3983,7 +3969,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -4108,7 +4093,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -4480,7 +4464,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -4605,7 +4588,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -4966,7 +4948,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -5091,7 +5072,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -5452,7 +5432,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/update-crew",
|
||||
@@ -5577,7 +5556,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -5938,7 +5916,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/training-crews",
|
||||
@@ -6064,7 +6041,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -6426,7 +6402,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/training-crews",
|
||||
@@ -6552,7 +6527,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -6912,7 +6886,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/training-crews",
|
||||
@@ -7038,7 +7011,6 @@
|
||||
"pages": [
|
||||
"en/guides/flows/first-flow",
|
||||
"en/guides/flows/mastering-flow-state",
|
||||
"en/guides/flows/conversational-flows",
|
||||
"en/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -7401,7 +7373,6 @@
|
||||
"en/enterprise/guides/build-crew",
|
||||
"en/enterprise/guides/prepare-for-deployment",
|
||||
"en/enterprise/guides/deploy-to-amp",
|
||||
"en/enterprise/guides/monorepo-deployments",
|
||||
"en/enterprise/guides/private-package-registry",
|
||||
"en/enterprise/guides/kickoff-crew",
|
||||
"en/enterprise/guides/training-crews",
|
||||
@@ -7557,7 +7528,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -7913,7 +7883,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -8410,7 +8379,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -8552,7 +8520,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -8875,7 +8842,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -9017,7 +8983,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -9340,7 +9305,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -9482,7 +9446,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -9804,7 +9767,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -9946,7 +9908,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -10278,7 +10239,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -10420,7 +10380,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -10752,7 +10711,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -10894,7 +10852,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -11226,7 +11183,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -11368,7 +11324,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -11700,7 +11655,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -11842,7 +11796,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -12164,7 +12117,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -12306,7 +12258,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -12628,7 +12579,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -12770,7 +12720,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -13092,7 +13041,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -13234,7 +13182,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -13555,7 +13502,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -13697,7 +13643,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -14018,7 +13963,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -14160,7 +14104,6 @@
|
||||
"pages": [
|
||||
"pt-BR/guides/flows/first-flow",
|
||||
"pt-BR/guides/flows/mastering-flow-state",
|
||||
"pt-BR/guides/flows/conversational-flows",
|
||||
"pt-BR/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -14482,7 +14425,6 @@
|
||||
"pt-BR/enterprise/guides/build-crew",
|
||||
"pt-BR/enterprise/guides/prepare-for-deployment",
|
||||
"pt-BR/enterprise/guides/deploy-to-amp",
|
||||
"pt-BR/enterprise/guides/monorepo-deployments",
|
||||
"pt-BR/enterprise/guides/private-package-registry",
|
||||
"pt-BR/enterprise/guides/kickoff-crew",
|
||||
"pt-BR/enterprise/guides/training-crews",
|
||||
@@ -14654,7 +14596,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -15022,7 +14963,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -15531,7 +15471,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -15673,7 +15612,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -16008,7 +15946,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -16150,7 +16087,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -16485,7 +16421,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -16627,7 +16562,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -16962,7 +16896,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -17104,7 +17037,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -17449,7 +17381,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -17591,7 +17522,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -17936,7 +17866,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -18078,7 +18007,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -18423,7 +18351,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -18565,7 +18492,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -18910,7 +18836,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -19052,7 +18977,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -19387,7 +19311,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -19529,7 +19452,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -19864,7 +19786,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -20006,7 +19927,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -20341,7 +20261,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -20483,7 +20402,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -20817,7 +20735,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -20959,7 +20876,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -21293,7 +21209,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -21435,7 +21350,6 @@
|
||||
"pages": [
|
||||
"ko/guides/flows/first-flow",
|
||||
"ko/guides/flows/mastering-flow-state",
|
||||
"ko/guides/flows/conversational-flows",
|
||||
"ko/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -21770,7 +21684,6 @@
|
||||
"ko/enterprise/guides/build-crew",
|
||||
"ko/enterprise/guides/prepare-for-deployment",
|
||||
"ko/enterprise/guides/deploy-to-amp",
|
||||
"ko/enterprise/guides/monorepo-deployments",
|
||||
"ko/enterprise/guides/private-package-registry",
|
||||
"ko/enterprise/guides/kickoff-crew",
|
||||
"ko/enterprise/guides/training-crews",
|
||||
@@ -21942,7 +21855,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -22310,7 +22222,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -22819,7 +22730,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -22961,7 +22871,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -23296,7 +23205,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -23438,7 +23346,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -23773,7 +23680,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -23915,7 +23821,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -24250,7 +24155,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -24392,7 +24296,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -24737,7 +24640,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -24879,7 +24781,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -25224,7 +25125,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -25366,7 +25266,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -25711,7 +25610,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -25853,7 +25751,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -26198,7 +26095,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -26340,7 +26236,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -26675,7 +26570,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -26817,7 +26711,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -27152,7 +27045,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -27294,7 +27186,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -27629,7 +27520,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -27771,7 +27661,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -28105,7 +27994,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -28247,7 +28135,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -28581,7 +28468,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
@@ -28723,7 +28609,6 @@
|
||||
"pages": [
|
||||
"ar/guides/flows/first-flow",
|
||||
"ar/guides/flows/mastering-flow-state",
|
||||
"ar/guides/flows/conversational-flows",
|
||||
"ar/guides/flows/inputs-id-deprecation"
|
||||
]
|
||||
},
|
||||
@@ -29058,7 +28943,6 @@
|
||||
"ar/enterprise/guides/build-crew",
|
||||
"ar/enterprise/guides/prepare-for-deployment",
|
||||
"ar/enterprise/guides/deploy-to-amp",
|
||||
"ar/enterprise/guides/monorepo-deployments",
|
||||
"ar/enterprise/guides/private-package-registry",
|
||||
"ar/enterprise/guides/kickoff-crew",
|
||||
"ar/enterprise/guides/training-crews",
|
||||
|
||||
@@ -4,38 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="Jun 03, 2026">
|
||||
## v1.14.7a1
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add crew trained agents file support
|
||||
- Add native Snowflake Cortex LLM provider
|
||||
- Add Databricks integration guide
|
||||
- Add Snowflake integration guide
|
||||
|
||||
### Bug Fixes
|
||||
- Fix CLI by restoring `[project.scripts]` in crewai package for UV tool install
|
||||
- Resolve file input reliability issues
|
||||
- Fix incomplete tool result histories in Snowflake Claude
|
||||
- Handle stringified tool calls for Snowflake Claude
|
||||
- Re-arm multi-source `or_` listeners across router-driven cycles
|
||||
|
||||
### Performance
|
||||
- Improve crewai import speed by lazy-loading docling imports
|
||||
|
||||
### Refactoring
|
||||
- Split `flow.py` into DSL, definition, and runtime
|
||||
|
||||
## Contributors
|
||||
|
||||
@Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="May 28, 2026">
|
||||
## v1.14.6
|
||||
|
||||
|
||||
@@ -952,61 +952,6 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="NVIDIA Nemotron">
|
||||
NVIDIA Nemotron models are designed for demanding agentic workloads, including complex reasoning, long-context analysis, tool use, multilingual tasks, and high-stakes RAG.
|
||||
|
||||
The `NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` model is a frontier-scale open-weight model from NVIDIA with 550B total parameters and 55B active parameters. It uses a LatentMoE architecture that combines Mamba-2, MoE, Attention, and Multi-Token Prediction (MTP), and supports context lengths up to 1M tokens.
|
||||
|
||||
<Info>
|
||||
`NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` is a very large model. NVIDIA lists minimum serving requirements of 4x GB200, 4x B200, 4x GB300, 4x B300, or 8x H100 GPUs. For most CrewAI users, the recommended path is to use NVIDIA NIM or another OpenAI-compatible hosted endpoint rather than running it locally.
|
||||
</Info>
|
||||
|
||||
**Hosted NVIDIA NIM usage:**
|
||||
```toml Code
|
||||
NVIDIA_API_KEY=<your-api-key>
|
||||
```
|
||||
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="nvidia_nim/nvidia/nvidia-nemotron-3-ultra-550b-a55b",
|
||||
temperature=0.2,
|
||||
max_tokens=4096,
|
||||
)
|
||||
```
|
||||
|
||||
**Self-hosted OpenAI-compatible endpoint:**
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="openai/nvidia-nemotron-3-ultra-550b-a55b-nvfp4",
|
||||
base_url="https://your-nemotron-endpoint.example.com/v1",
|
||||
api_key="your-api-key",
|
||||
temperature=0.2,
|
||||
max_tokens=4096,
|
||||
)
|
||||
```
|
||||
|
||||
**Model details:**
|
||||
|
||||
| Model | Context Window | Best For |
|
||||
|-------|----------------|----------|
|
||||
| `nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4` | Up to 1M tokens | Frontier reasoning, complex agentic workflows, long-context analysis, tool use, multilingual reasoning, and high-stakes RAG |
|
||||
|
||||
**Supported languages:** English, French, Spanish, Italian, German, Japanese, Korean, Hindi, Brazilian Portuguese, and Chinese.
|
||||
|
||||
**Reasoning mode:** Nemotron 3 Ultra supports configurable reasoning via its chat template using `enable_thinking=True` or `enable_thinking=False`. If you are using a hosted endpoint, check your provider's documentation for how that flag is exposed.
|
||||
|
||||
For model details, license, and deployment guidance, see the [NVIDIA Nemotron 3 Ultra model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4).
|
||||
|
||||
**Note:** Hosted NVIDIA NIM usage uses LiteLLM. Add it as a dependency to your project:
|
||||
```bash
|
||||
uv add 'crewai[litellm]'
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local NVIDIA NIM Deployed using WSL2">
|
||||
|
||||
NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
|
||||
|
||||
@@ -164,12 +164,6 @@ You need to push your crew to a GitHub repository. If you haven't created a crew
|
||||
|
||||
</Frame>
|
||||
|
||||
<Tip>
|
||||
If your Crew or Flow is inside a monorepo subfolder, expand **Advanced**
|
||||
and set a working directory before deploying. See
|
||||
[Monorepo Deployments](/en/enterprise/guides/monorepo-deployments).
|
||||
</Tip>
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="Set Environment Variables">
|
||||
|
||||
@@ -1,225 +0,0 @@
|
||||
---
|
||||
title: "Monorepo Deployments"
|
||||
description: "Deploy a Crew or Flow from a subfolder in a larger repository"
|
||||
icon: "folder-tree"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
Use a working directory when your Crew or Flow lives inside a larger
|
||||
repository. CrewAI AMP validates, builds, tests, and runs the automation from
|
||||
that subfolder instead of the repository root.
|
||||
</Note>
|
||||
|
||||
## When to Use This
|
||||
|
||||
Monorepo deployments are useful when one repository contains multiple
|
||||
automations, shared packages, or other application code:
|
||||
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
|-- support_agent/
|
||||
| |-- pyproject.toml
|
||||
| `-- src/
|
||||
| `-- support_agent/
|
||||
| |-- main.py
|
||||
| `-- crew.py
|
||||
`-- research_flow/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- research_flow/
|
||||
`-- main.py
|
||||
```
|
||||
|
||||
To deploy `support_agent`, set the working directory to:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
AMP still pulls or uploads the whole repository, but it treats the selected
|
||||
folder as the automation project root.
|
||||
|
||||
## What the Working Directory Controls
|
||||
|
||||
When a working directory is set, AMP uses that folder for:
|
||||
|
||||
- Project validation, including `pyproject.toml`, `src/`, and the Crew or Flow entry point
|
||||
- Dependency installation with `uv`
|
||||
- The running process working directory
|
||||
- The `CREW_ROOT_DIR` environment variable
|
||||
|
||||
Leaving the field empty keeps the existing behavior and uses the repository
|
||||
root.
|
||||
|
||||
## Supported Sources
|
||||
|
||||
You can set a working directory when creating a deployment from:
|
||||
|
||||
- A connected GitHub repository
|
||||
- A Git repository configured in AMP
|
||||
- A ZIP upload
|
||||
|
||||
<Info>
|
||||
Configure working directories in the AMP web interface. The
|
||||
`crewai deploy create` CLI flow does not prompt for this field.
|
||||
</Info>
|
||||
|
||||
You can also add or change the working directory on an existing deployment from
|
||||
the deployment's **Settings** page. The change takes effect on the next deploy.
|
||||
|
||||
<Warning>
|
||||
Working directories and auto-deploy cannot be used together. If a deployment
|
||||
has a working directory, auto-deploy is disabled for that deployment. Turn
|
||||
auto-deploy off before setting a working directory.
|
||||
</Warning>
|
||||
|
||||
## Configure a New Deployment
|
||||
|
||||
<Steps>
|
||||
<Step title="Open Deploy from Code">
|
||||
In CrewAI AMP, create a new deployment and choose your source: GitHub, Git
|
||||
Repository, or ZIP upload.
|
||||
</Step>
|
||||
|
||||
<Step title="Select the repository, branch, or ZIP file">
|
||||
Choose the repository and branch that contain your monorepo, or upload a ZIP
|
||||
file whose root contains the monorepo contents.
|
||||
</Step>
|
||||
|
||||
<Step title="Open Advanced settings">
|
||||
Expand the **Advanced** section in the deploy form.
|
||||
</Step>
|
||||
|
||||
<Step title="Enter the working directory">
|
||||
Enter the path from the repository root to the Crew or Flow project:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
Do not include a leading slash.
|
||||
</Step>
|
||||
|
||||
<Step title="Deploy">
|
||||
Add any required environment variables, then start the deployment.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Configure an Existing Deployment
|
||||
|
||||
<Steps>
|
||||
<Step title="Open the deployment settings">
|
||||
Go to your automation in AMP and open **Settings**.
|
||||
</Step>
|
||||
|
||||
<Step title="Turn off auto-deploy if needed">
|
||||
If auto-deploy is enabled, disable it first. The working directory field is
|
||||
unavailable while auto-deploy is on.
|
||||
</Step>
|
||||
|
||||
<Step title="Set the working directory">
|
||||
In **Basic settings**, enter the subfolder path, such as:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Redeploy">
|
||||
Save the setting and redeploy the automation. The new working directory is
|
||||
used on the next deploy.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Path Rules
|
||||
|
||||
The working directory must be a relative path inside the repository or ZIP root.
|
||||
|
||||
| Rule | Example |
|
||||
|------|---------|
|
||||
| Use a relative path | `crews/support_agent` |
|
||||
| Do not start with `/` | `/crews/support_agent` is invalid |
|
||||
| Do not use `.` or `..` path segments | `crews/../support_agent` is invalid |
|
||||
| Use only letters, numbers, dashes, underscores, dots, and forward slashes | `crews/support agent` is invalid |
|
||||
| Keep the path at 255 characters or fewer | Longer paths are rejected |
|
||||
|
||||
AMP trims leading and trailing whitespace, collapses repeated slashes, and
|
||||
removes trailing slashes. A blank value uses the repository root.
|
||||
|
||||
## Lock Files and UV Workspaces
|
||||
|
||||
The selected folder must contain the automation's `pyproject.toml` and `src/`
|
||||
directory. A `uv.lock` or `poetry.lock` file can live either in the selected
|
||||
folder or at the repository root.
|
||||
|
||||
This supports both common monorepo layouts:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Project lock file">
|
||||
```text
|
||||
company-ai/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
|-- uv.lock
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
|
||||
<Tab title="Workspace lock file">
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Tip>
|
||||
If your automation imports shared packages from elsewhere in the monorepo,
|
||||
declare those packages in `pyproject.toml` using UV workspace, path, or source
|
||||
configuration. AMP runs the automation from the selected folder, so shared
|
||||
code should be installed as a dependency instead of relying on the repository
|
||||
root being on the Python path.
|
||||
</Tip>
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Working Directory Not Found
|
||||
|
||||
Check that the path is relative to the repository or ZIP root. For ZIP uploads,
|
||||
the ZIP contents must include the working directory path exactly as entered.
|
||||
|
||||
### Missing pyproject.toml
|
||||
|
||||
The working directory should point to the Crew or Flow project folder, not just
|
||||
to a parent folder that contains several projects.
|
||||
|
||||
### Missing uv.lock or poetry.lock
|
||||
|
||||
Commit a lock file either in the selected project folder or in the repository
|
||||
root. For UV workspaces, keeping `uv.lock` at the workspace root is supported.
|
||||
|
||||
### Auto-Deploy Is Unavailable
|
||||
|
||||
Auto-deploy is disabled while a working directory is set. Use manual redeploys
|
||||
or trigger redeployments from CI/CD with the AMP API instead.
|
||||
|
||||
<Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
|
||||
Continue with the deployment guide after choosing your monorepo working
|
||||
directory.
|
||||
</Card>
|
||||
@@ -1,454 +0,0 @@
|
||||
---
|
||||
title: Conversational Flows
|
||||
description: Build multi-turn chat apps with kickoff per turn, message history, intent routing, tracing, and WebSocket bridges.
|
||||
icon: comments
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Conversational apps treat each user line as a **new flow run** with the **same session id**. CrewAI adds helpers for message history, optional intent classification, deferred tracing, and UI bridges — without a separate `chat()` API on `Flow`.
|
||||
|
||||
| Concept | Implementation |
|
||||
|---------|----------------|
|
||||
| Session id | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
|
||||
| User line | `kickoff(user_message=...)` appends to `state.messages` before the graph runs |
|
||||
| Turn complete | `FlowFinished` for **this run** only; chat continues on the next `kickoff` |
|
||||
| Full-session trace | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
|
||||
|
||||
## One entry point: `kickoff`
|
||||
|
||||
Use **`flow.kickoff(user_message=..., session_id=...)`** for every user message (REST, WebSocket, CLI). Do not add a custom `chat()` wrapper on `Flow`.
|
||||
|
||||
| API | Use for |
|
||||
|-----|---------|
|
||||
| `kickoff(user_message=..., session_id=...)` | Each user message |
|
||||
| `kickoff_async(...)` | Same parameters; native async entry |
|
||||
| `ask()` | Blocking prompt **inside** one step (wizard, clarification) |
|
||||
| `@human_feedback` | Approve/reject **a step output** — not the next chat line |
|
||||
| `ChatSession.handle_turn(...)` | Transport layer over `kickoff` (SSE / WebSocket) |
|
||||
|
||||
## Quick start
|
||||
|
||||
```python
|
||||
from uuid import uuid4
|
||||
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
Flow,
|
||||
listen,
|
||||
or_,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
|
||||
class SupportFlow(Flow[ChatState]):
|
||||
conversational_config = ConversationalConfig(
|
||||
default_intents=["order", "help", "goodbye"],
|
||||
intent_llm="gpt-4o-mini",
|
||||
defer_trace_finalization=True,
|
||||
)
|
||||
|
||||
@start()
|
||||
def bootstrap(self):
|
||||
if not self.state.session_ready:
|
||||
self.state.session_ready = True
|
||||
return "ready"
|
||||
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
# last_intent set in prepare_conversational_turn when default_intents is set
|
||||
return self.state.last_intent or "help"
|
||||
|
||||
@listen("order")
|
||||
def handle_order(self):
|
||||
reply = "Your order is on the way."
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("help")
|
||||
def handle_help(self):
|
||||
reply = "How can I help?"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("goodbye")
|
||||
def handle_goodbye(self):
|
||||
reply = "Goodbye!"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@persist(SQLiteFlowPersistence("support.db"))
|
||||
@listen(or_(handle_order, handle_help, handle_goodbye))
|
||||
def finalize(self):
|
||||
return self.state.model_dump()
|
||||
|
||||
|
||||
session_id = str(uuid4())
|
||||
flow = SupportFlow()
|
||||
|
||||
flow.kickoff(user_message="Where is my order?", session_id=session_id)
|
||||
flow.kickoff(user_message="What about returns?", session_id=session_id)
|
||||
flow.finalize_session_traces() # one trace link for the whole chat
|
||||
```
|
||||
|
||||
## Turn lifecycle
|
||||
|
||||
Each `kickoff` with `user_message` runs this pipeline:
|
||||
|
||||
1. **`_configure_conversational_kickoff`** — merges `session_id` / `user_message` into `inputs`, applies `ConversationalConfig`, enables deferred tracing when configured.
|
||||
2. **State restore** — if `inputs["id"]` exists and `@persist` is configured, loads the latest snapshot.
|
||||
3. **`FlowStarted`** — emitted on the first deferred session turn only.
|
||||
4. **`prepare_conversational_turn`** — appends the user message to `state.messages`, sets `last_user_message`, clears `last_intent`, optionally classifies when `intents` / `default_intents` + `intent_llm` are set.
|
||||
5. **Graph execution** — `@start` → `@router` → `@listen` handlers.
|
||||
6. **End of run** — per-turn `flow_finished` and trace finalization are **skipped** when deferral is enabled; nested `Agent.kickoff()` / crews do not close the parent batch either.
|
||||
|
||||
Handlers should call **`append_message("assistant", reply)`** so the next turn’s `conversation_messages` includes assistant text. The user line is already stored at kickoff — do not append it again in handlers.
|
||||
|
||||
## `ConversationalConfig` (class-level defaults)
|
||||
|
||||
Set on your `Flow` subclass as `conversational_config: ClassVar[ConversationalConfig | None]`.
|
||||
|
||||
| Field | Default | Purpose |
|
||||
|-------|---------|---------|
|
||||
| `default_intents` | `None` | Outcome labels for automatic pre-kickoff classification |
|
||||
| `intent_llm` | `None` | Model for classification (required when intents are used) |
|
||||
| `interactive_prompt` | `"You: "` | Prompt for `kickoff(interactive=True)` |
|
||||
| `interactive_timeout` | `None` | Per-line timeout in interactive mode |
|
||||
| `exit_commands` | `exit`, `quit` | Words that end interactive mode |
|
||||
| `defer_trace_finalization` | `True` | Keep one trace batch open across turns |
|
||||
|
||||
Override per kickoff with `intents=` and `intent_llm=` keyword arguments.
|
||||
|
||||
## `ChatState` (recommended persisted shape)
|
||||
|
||||
```python
|
||||
from crewai.flow import ChatState
|
||||
|
||||
|
||||
class MyChatState(ChatState):
|
||||
# Inherited: id, messages, last_user_message, last_intent, session_ready
|
||||
research_turn_count: int = 0
|
||||
custom_flag: bool = False
|
||||
```
|
||||
|
||||
| Field | Role |
|
||||
|-------|------|
|
||||
| `id` | Session UUID (same as `session_id` / `inputs["id"]`) |
|
||||
| `messages` | `list` of `{role, content}` for LLM history |
|
||||
| `last_user_message` | Latest user line for this turn |
|
||||
| `last_intent` | Route label after classification (if used) |
|
||||
| `session_ready` | One-time bootstrap flag (permissions, caches, etc.) |
|
||||
|
||||
`ConversationalInputs` is a `TypedDict` for conventional `kickoff(inputs={...})` keys: `id`, `user_message`, `last_intent`.
|
||||
|
||||
## `Flow` conversational API
|
||||
|
||||
### `kickoff` / `kickoff_async` parameters
|
||||
|
||||
| Parameter | Purpose |
|
||||
|-----------|---------|
|
||||
| `user_message` | This turn’s text (or `{"role": "user", "content": "..."}`) |
|
||||
| `session_id` | Conversation UUID → `inputs["id"]` / `state.id` |
|
||||
| `intents` | Outcome labels for pre-kickoff `classify_intent` |
|
||||
| `intent_llm` | LLM for classification (required with `intents`) |
|
||||
| `interactive` | CLI loop via `ask()` (local demos only) |
|
||||
| `interactive_prompt` | Override prompt in interactive mode |
|
||||
| `interactive_timeout` | Per-line `ask()` timeout |
|
||||
| `exit_commands` | Words that end interactive mode |
|
||||
| `inputs` | Additional state fields (merged with conversational keys) |
|
||||
| `restore_from_state_id` | Fork hydration from another persisted flow |
|
||||
|
||||
### Instance attributes
|
||||
|
||||
| Attribute | Purpose |
|
||||
|-----------|---------|
|
||||
| `conversational_config` | Class-level `ConversationalConfig` defaults |
|
||||
| `defer_trace_finalization` | Instance flag; set automatically from config on kickoff |
|
||||
| `suppress_flow_events` | Hides console flow panels; **tracing still records** method/flow events |
|
||||
| `stream` | Enable streaming; use with `ChatSession.handle_turn(..., stream=True)` |
|
||||
|
||||
### Methods and properties
|
||||
|
||||
| Name | Description |
|
||||
|------|-------------|
|
||||
| `append_message(role, content, **extra)` | Append to `state.messages` (roles: `user`, `assistant`, `system`, `tool`) |
|
||||
| `conversation_messages` | Read-only history for LLM calls |
|
||||
| `classify_intent(text, outcomes, *, llm, context=None)` | Map text to one outcome (same collapse logic as `@human_feedback`) |
|
||||
| `receive_user_message(text, *, outcomes=None, llm=None)` | Append user message; optionally set `last_intent` |
|
||||
| `finalize_session_traces()` | Emit deferred `flow_finished` and finalize the session trace batch |
|
||||
| `_should_defer_trace_finalization()` | Whether this flow defers per-turn trace finalization |
|
||||
| `input_history` | Audit trail of `ask()` prompts and responses |
|
||||
|
||||
### Module helpers (`crewai.flow.conversation`)
|
||||
|
||||
Importable for tests or custom orchestration:
|
||||
|
||||
| Function | Description |
|
||||
|----------|-------------|
|
||||
| `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | Merge conversational kwargs into `inputs` |
|
||||
| `get_conversation_messages(flow)` | Read messages from state or internal buffer |
|
||||
| `append_message(flow, role, content, **extra)` | Same as instance method |
|
||||
| `prepare_conversational_turn(flow, user_message=..., intents=..., intent_llm=..., config=...)` | Turn hydration (usually called by kickoff) |
|
||||
| `receive_user_message(flow, text, ...)` | Same as instance method |
|
||||
| `set_state_field(flow, name, value)` | Set a field on dict or Pydantic state |
|
||||
| `get_conversational_config(flow)` | Read class `conversational_config` |
|
||||
| `input_history_to_messages(entries)` | Convert `input_history` to LLM message format |
|
||||
|
||||
## Intent routing patterns
|
||||
|
||||
### A. Pre-classify via `ConversationalConfig` (simplest)
|
||||
|
||||
Set `default_intents` and `intent_llm`. Each kickoff runs classification before your `@router`; read `self.state.last_intent` in `route()`.
|
||||
|
||||
### B. Classify inside `@router` (richer prompts)
|
||||
|
||||
Set `default_intents=None` so kickoff only appends the user message. In `route()`, call `classify_intent` with a custom prompt or descriptions:
|
||||
|
||||
```python
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
intent = self.classify_intent(
|
||||
self._routing_prompt(self.state.last_user_message),
|
||||
("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
|
||||
llm=self.conversational_config.intent_llm or "gpt-4o-mini",
|
||||
)
|
||||
self.state.last_intent = intent
|
||||
return intent
|
||||
```
|
||||
|
||||
Use **`@listen("RESEARCH")`** (or similar) for steps that run `Agent.kickoff()` with tools — not bare `LLM.call()` — when you need web research or multi-step tool use.
|
||||
|
||||
## When the flow finishes but the user keeps chatting
|
||||
|
||||
`FlowFinished` means **this graph run** completed. The conversation continues with another `kickoff` and the same `session_id`. `@persist` restores `messages`, flags, and context.
|
||||
|
||||
**Persist pattern:** prefer `@persist` on a **single terminal step** (for example `finalize`) rather than on the whole `Flow` class. Class-level persist saves after every method; `load_state` uses the latest row, which may be a mid-run snapshot (for example right after `bootstrap`) and miss handler updates from the same turn.
|
||||
|
||||
Do **not** use `@human_feedback` for follow-up chat lines unless a human must approve a specific step output before it is shown.
|
||||
|
||||
## Conversational `Flow` (experimental)
|
||||
|
||||
<Warning>
|
||||
**This is an experimental feature.** The conversational `Flow` surface
|
||||
(`conversational = True`, `handle_turn`, `ConversationConfig`,
|
||||
`RouterConfig`, `ConversationState`, the built-in graph + helpers) lives
|
||||
under `crewai.experimental` and may change shape before it graduates.
|
||||
Pin your CrewAI version if you depend on specific behavior, and watch the
|
||||
changelog for breaking updates. Open issues / feedback welcome.
|
||||
</Warning>
|
||||
|
||||
Opt into the conversational chat graph by setting `conversational = True` on a `Flow` subclass. The base `Flow` then ships a built-in `@start` / `@router` / `converse_turn` / `end_conversation` graph, manages `state.messages`, drives the router LLM, and keeps the trace batch open across turns. You write the **custom routes**; the framework owns the rest.
|
||||
|
||||
Use this when you want a multi-turn chat with an LLM-driven router and per-route handlers without wiring the lifecycle yourself. Use `Flow[ChatState]` (the lower-level pattern above) when you need full control.
|
||||
|
||||
### Quick example
|
||||
|
||||
```python
|
||||
from crewai import LLM, Flow
|
||||
from crewai.flow import listen
|
||||
from crewai.experimental.conversational import (
|
||||
ConversationConfig,
|
||||
ConversationState,
|
||||
RouterConfig,
|
||||
)
|
||||
|
||||
|
||||
ROUTER_LLM = LLM(model="gpt-4o-mini")
|
||||
|
||||
|
||||
@ConversationConfig(
|
||||
system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
|
||||
llm=ROUTER_LLM,
|
||||
router=RouterConfig(), # routes + descriptions auto-discovered from @listen handlers
|
||||
)
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
@listen("CREWAI_DOCS")
|
||||
def handle_crewai_docs(self) -> str:
|
||||
"""Look up the CrewAI documentation for framework/API questions."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
|
||||
flow = SupportFlow()
|
||||
try:
|
||||
flow.handle_turn("What can you do?") # routes to converse (built-in)
|
||||
flow.handle_turn("Search the web for AI news.") # routes to INTERNET_SEARCH
|
||||
flow.handle_turn("Summarize the first result.") # routes back to converse
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
### `ConversationConfig`
|
||||
|
||||
Class decorator that attaches per-class chat defaults.
|
||||
|
||||
| Field | Default | Purpose |
|
||||
|-------|---------|---------|
|
||||
| `system_prompt` | `slices.conversational_system_prompt` from i18n | System message used by the built-in `converse_turn`. Pass `""` to opt out entirely. |
|
||||
| `llm` | `None` | Conversation LLM (used by `converse_turn` and as router fallback). |
|
||||
| `router` | `None` | `RouterConfig` for LLM-driven routing. Without it, the flow always falls through to `converse`. |
|
||||
| `answer_from_history_prompt` | Framework default | System message for the optional `answer_from_history` route. |
|
||||
| `answer_from_history_llm` | `None` | Enables the `answer_from_history` short-circuit when set. |
|
||||
| `intent_llm` | `None` | LLM for legacy `intents=`/`default_intents` pre-classification. |
|
||||
| `default_intents` | `None` | Outcome labels for legacy pre-classification. |
|
||||
| `visible_agent_outputs` | `None` | `"all"`, or a list of agent names whose `append_agent_result()` calls should be promoted to public assistant messages. |
|
||||
| `defer_trace_finalization` | `True` | Keep one trace batch open across `handle_turn()` calls. |
|
||||
|
||||
### `RouterConfig` and the auto-built route catalog
|
||||
|
||||
```python
|
||||
RouterConfig(
|
||||
prompt="Optional domain framing (policy, voice, persona).",
|
||||
response_format=MyRoute, # optional; auto-generated otherwise
|
||||
llm=ROUTER_LLM, # falls back to ConversationConfig.llm
|
||||
routes=["INTERNET_SEARCH", "CREWAI_DOCS"], # optional; inferred from listeners
|
||||
route_descriptions={
|
||||
"INTERNET_SEARCH": "Override the docstring for this one route.",
|
||||
},
|
||||
default_intent="converse", # used when LLM call fails or no LLM available
|
||||
fallback_intent="converse", # used when LLM returns an invalid route
|
||||
intent_field="intent",
|
||||
)
|
||||
```
|
||||
|
||||
The router prompt that gets sent to the LLM is built automatically. For each route the framework picks a description with this precedence:
|
||||
|
||||
1. `RouterConfig.route_descriptions[label]` — explicit override.
|
||||
2. `Flow.builtin_route_descriptions[label]` — framework-canned text for `converse`, `end`, `answer_from_history` (phrased for the router LLM).
|
||||
3. First non-empty line of the `@listen(label)` handler's docstring.
|
||||
4. Empty (the route is listed without a description).
|
||||
|
||||
So in practice, **adding a new route is `@listen("X")` + a one-line docstring**:
|
||||
|
||||
```python
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
```
|
||||
|
||||
…and the router LLM sees:
|
||||
|
||||
```
|
||||
Routes:
|
||||
- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
|
||||
- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
|
||||
- converse: Ordinary chat, follow-ups, summaries, clarifications…
|
||||
- end: User signals the conversation is finished (goodbye, exit, done).
|
||||
```
|
||||
|
||||
`RouterConfig.prompt` is for **domain framing** (assistant persona, business rules, voice). The route catalog is auto-built — don't list routes in `prompt`; they'll drift the moment you add a handler.
|
||||
|
||||
### Built-in routes
|
||||
|
||||
| Route | Handler | Purpose |
|
||||
|-------|---------|---------|
|
||||
| `converse` | `converse_turn` | Default chat handler. Calls `ConversationConfig.llm` with the system prompt + canonical message history. |
|
||||
| `end` | `end_conversation` | Sets `state.ended = True` and emits a terminator reply. |
|
||||
| `answer_from_history` | `answer_from_history_turn` | Optional. Routes here when `ConversationConfig.answer_from_history_llm` is set and the message can be answered from existing history. |
|
||||
|
||||
You can override any of these by defining a same-named handler in your subclass.
|
||||
|
||||
### `handle_turn()` semantics
|
||||
|
||||
`flow.handle_turn(message)` runs one turn:
|
||||
|
||||
1. Resets per-execution tracking (`_completed_methods`, `_method_outputs`) so the graph re-runs — without this, repeated `kickoff` calls on the same flow instance would short-circuit on turn 2+ because `Flow.kickoff_async` treats `inputs={"id": ...}` as a checkpoint restore.
|
||||
2. Appends the user message to `state.messages`, sets `current_user_message` / `last_user_message`. `last_intent` is **preserved from the prior turn** so the router LLM can use it as a signal.
|
||||
3. Runs `conversation_start` → `route_conversation` → the chosen `@listen` handler.
|
||||
4. The router stores its decision in `state.last_intent` (visible to the next turn's router context).
|
||||
5. If your handler returned a string and didn't already call `append_assistant_message`, `handle_turn` appends it for you.
|
||||
|
||||
You can also call `flow.kickoff(user_message=..., session_id=...)` directly — the same reset/run logic fires. `handle_turn` is the ergonomic wrapper.
|
||||
|
||||
### Custom router behavior
|
||||
|
||||
To run side effects (event bus setup, telemetry) on every routing decision, override `route_turn`:
|
||||
|
||||
```python
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
def route_turn(self, context: dict[str, Any]) -> str | None:
|
||||
self.event_bus = MyBus(self)
|
||||
return super().route_turn(context)
|
||||
```
|
||||
|
||||
To bypass the LLM router entirely and pick a route programmatically, return a string from `route_turn`; returning `None` falls back to `_route_with_config(...)`.
|
||||
|
||||
### `append_assistant_message` and `append_agent_result`
|
||||
|
||||
Inside a `@listen(label)` handler, choose:
|
||||
|
||||
- `self.append_assistant_message(text)` — adds a user-visible assistant turn to `state.messages`. The next turn's `converse_turn` sees it.
|
||||
- `self.append_agent_result(agent_name, result, visibility="private")` — records a structured event in `state.events` and a thread in `state.agent_threads[agent_name]`. Public visibility also calls `append_assistant_message` for you. Use private results for scratch work that shouldn't pollute the canonical history.
|
||||
|
||||
`ConversationConfig.visible_agent_outputs` can promote specific agents' private results to public globally (`"all"`, or a list of agent names).
|
||||
|
||||
## Tracing across turns
|
||||
|
||||
With `defer_trace_finalization=True` (default in `ConversationalConfig`):
|
||||
|
||||
- **One trace batch** for the whole chat session.
|
||||
- **`flow_started`** on the first turn only; **`flow_finished`** once in `finalize_session_traces()`.
|
||||
- **Per-turn** `kickoff` does not print “Trace batch finalized”.
|
||||
- **Nested work** (`Agent.kickoff()`, crews, Exa tools) appends to the **parent** batch; inner `AgentExecutor` flows do not close the session batch early.
|
||||
|
||||
```python
|
||||
try:
|
||||
while True:
|
||||
line = input("You: ").strip()
|
||||
if not line:
|
||||
break
|
||||
flow.kickoff(user_message=line, session_id=session_id)
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
`ChatSession.close()` calls `finalize_session_traces()` when deferral is enabled.
|
||||
|
||||
`suppress_flow_events=True` only hides Rich console panels; trace and method events still emit for observability.
|
||||
|
||||
### Conversational `Flow` trace lifecycle
|
||||
|
||||
The experimental [conversational `Flow`](#conversational-flow-experimental) uses the same tracing lifecycle: `defer_trace_finalization` defaults to `True`, so each `handle_turn()` keeps the session trace open. Always finalize at the end of the session — wrap your REPL/loop in `try/finally` and call `flow.finalize_session_traces()` on exit. Without it, the trace batch stays open and the final conversation may never export.
|
||||
|
||||
## Streaming
|
||||
|
||||
Set `stream = True` on the `Flow` class. `kickoff(...)` will then emit `assistant_delta` (and related) events through the standard event bus.
|
||||
|
||||
## Imports
|
||||
|
||||
```python
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
ConversationalInputs,
|
||||
Flow,
|
||||
listen,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
```
|
||||
|
||||
## See also
|
||||
|
||||
- [Mastering Flow State Management](/en/guides/flows/mastering-flow-state) — persistence, Pydantic state, `@persist`
|
||||
- [Build Your First Flow](/en/guides/flows/first-flow) — flow basics
|
||||
- Demo: `lib/crewai/runner_conversational_flow_simple.py` — minimal REPL with `RESEARCH` + Exa agent
|
||||
@@ -617,7 +617,6 @@ Now that you've built your first flow, you can:
|
||||
3. Explore the `and_` and `or_` functions for more complex parallel execution
|
||||
4. Connect your flow to external APIs, databases, or user interfaces
|
||||
5. Combine multiple specialized crews in a single flow
|
||||
6. Build multi-turn chat apps with [Conversational Flows](/en/guides/flows/conversational-flows) (`kickoff` per message, `ChatSession`, deferred tracing)
|
||||
|
||||
<Check>
|
||||
Congratulations! You've successfully built your first CrewAI Flow that combines regular code, direct LLM calls, and crew-based processing to create a comprehensive guide. These foundational skills enable you to create increasingly sophisticated AI applications that can tackle complex, multi-stage problems through a combination of procedural control and collaborative intelligence.
|
||||
|
||||
@@ -22,8 +22,6 @@ Effective state management enables you to:
|
||||
5. **Scale your applications** - Support complex workflows with proper data organization
|
||||
6. **Enable conversational applications** - Store and access conversation history for context-aware AI interactions
|
||||
|
||||
For multi-turn chat (`kickoff` per user line, `ChatState`, intent routing, deferred tracing, and `ChatSession`), see [Conversational Flows](/en/guides/flows/conversational-flows).
|
||||
|
||||
Let's explore how to leverage these capabilities effectively.
|
||||
|
||||
## State Management Fundamentals
|
||||
|
||||
@@ -4,38 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="2026년 6월 3일">
|
||||
## v1.14.7a1
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- 승무원 교육을 받은 에이전트 파일 지원 추가
|
||||
- 네이티브 Snowflake Cortex LLM 공급자 추가
|
||||
- Databricks 통합 가이드 추가
|
||||
- Snowflake 통합 가이드 추가
|
||||
|
||||
### 버그 수정
|
||||
- UV 도구 설치를 위한 crewai 패키지에서 `[project.scripts]` 복원하여 CLI 수정
|
||||
- 파일 입력 신뢰성 문제 해결
|
||||
- Snowflake Claude에서 불완전한 도구 결과 기록 수정
|
||||
- Snowflake Claude를 위한 문자열화된 도구 호출 처리
|
||||
- 라우터 주도 사이클 전반에 걸쳐 다중 소스 `or_` 리스너 재장착
|
||||
|
||||
### 성능
|
||||
- docling 가져오기를 지연 로딩하여 crewai 가져오기 속도 개선
|
||||
|
||||
### 리팩토링
|
||||
- `flow.py`를 DSL, 정의 및 런타임으로 분할
|
||||
|
||||
## 기여자
|
||||
|
||||
@Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 5월 28일">
|
||||
## v1.14.6
|
||||
|
||||
|
||||
@@ -163,12 +163,6 @@ Crew를 GitHub 저장소에 푸시해야 합니다. 아직 Crew를 만들지 않
|
||||
|
||||
</Frame>
|
||||
|
||||
<Tip>
|
||||
Crew 또는 Flow가 모노레포 하위 폴더 안에 있다면 배포 전에
|
||||
**Advanced**를 펼치고 작업 디렉터리를 설정하세요.
|
||||
[모노레포 배포](/ko/enterprise/guides/monorepo-deployments)를 참조하세요.
|
||||
</Tip>
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="환경 변수 설정하기">
|
||||
@@ -446,4 +440,4 @@ type = "flow"
|
||||
|
||||
<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
|
||||
배포 문제 또는 AMP 플랫폼에 대한 문의 사항이 있으시면 지원팀에 연락해 주세요.
|
||||
</Card>
|
||||
</Card>
|
||||
@@ -1,222 +0,0 @@
|
||||
---
|
||||
title: "모노레포 배포"
|
||||
description: "더 큰 저장소의 하위 폴더에서 Crew 또는 Flow 배포하기"
|
||||
icon: "folder-tree"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
Crew 또는 Flow가 더 큰 저장소 안에 있을 때 작업 디렉터리를 사용하세요.
|
||||
CrewAI AMP는 저장소 루트 대신 해당 하위 폴더에서 자동화를 검증, 빌드,
|
||||
실행합니다.
|
||||
</Note>
|
||||
|
||||
## 사용 시점
|
||||
|
||||
모노레포 배포는 하나의 저장소에 여러 자동화, 공유 패키지 또는 다른 애플리케이션
|
||||
코드가 함께 있을 때 유용합니다:
|
||||
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
|-- support_agent/
|
||||
| |-- pyproject.toml
|
||||
| `-- src/
|
||||
| `-- support_agent/
|
||||
| |-- main.py
|
||||
| `-- crew.py
|
||||
`-- research_flow/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- research_flow/
|
||||
`-- main.py
|
||||
```
|
||||
|
||||
`support_agent`를 배포하려면 작업 디렉터리를 다음과 같이 설정합니다:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
AMP는 여전히 전체 저장소를 가져오거나 업로드하지만, 선택한 폴더를 자동화
|
||||
프로젝트 루트로 처리합니다.
|
||||
|
||||
## 작업 디렉터리가 제어하는 항목
|
||||
|
||||
작업 디렉터리가 설정되면 AMP는 해당 폴더를 다음 용도로 사용합니다:
|
||||
|
||||
- `pyproject.toml`, `src/`, Crew 또는 Flow 진입점을 포함한 프로젝트 검증
|
||||
- `uv`를 사용한 종속성 설치
|
||||
- 실행 중인 프로세스의 작업 디렉터리
|
||||
- `CREW_ROOT_DIR` 환경 변수
|
||||
|
||||
필드를 비워 두면 기존 동작이 유지되며 저장소 루트를 사용합니다.
|
||||
|
||||
## 지원되는 소스
|
||||
|
||||
다음 소스에서 배포를 만들 때 작업 디렉터리를 설정할 수 있습니다:
|
||||
|
||||
- 연결된 GitHub 저장소
|
||||
- AMP에 구성된 Git 저장소
|
||||
- ZIP 업로드
|
||||
|
||||
<Info>
|
||||
작업 디렉터리는 AMP 웹 인터페이스에서 구성하세요.
|
||||
`crewai deploy create` CLI 흐름은 이 필드를 묻지 않습니다.
|
||||
</Info>
|
||||
|
||||
기존 배포의 **Settings** 페이지에서도 작업 디렉터리를 추가하거나 변경할 수
|
||||
있습니다. 변경 사항은 다음 배포부터 적용됩니다.
|
||||
|
||||
<Warning>
|
||||
작업 디렉터리와 auto-deploy는 함께 사용할 수 없습니다. 배포에 작업
|
||||
디렉터리가 설정되어 있으면 해당 배포의 auto-deploy가 비활성화됩니다.
|
||||
작업 디렉터리를 설정하기 전에 auto-deploy를 끄세요.
|
||||
</Warning>
|
||||
|
||||
## 새 배포 구성
|
||||
|
||||
<Steps>
|
||||
<Step title="Deploy from Code 열기">
|
||||
CrewAI AMP에서 새 배포를 만들고 소스를 선택합니다: GitHub, Git
|
||||
Repository 또는 ZIP 업로드.
|
||||
</Step>
|
||||
|
||||
<Step title="저장소, 브랜치 또는 ZIP 파일 선택">
|
||||
모노레포가 들어 있는 저장소와 브랜치를 선택하거나, 루트에 모노레포 내용이
|
||||
포함된 ZIP 파일을 업로드합니다.
|
||||
</Step>
|
||||
|
||||
<Step title="고급 설정 열기">
|
||||
배포 양식에서 **Advanced** 섹션을 펼칩니다.
|
||||
</Step>
|
||||
|
||||
<Step title="작업 디렉터리 입력">
|
||||
저장소 루트에서 Crew 또는 Flow 프로젝트까지의 경로를 입력합니다:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
앞에 슬래시를 붙이지 마세요.
|
||||
</Step>
|
||||
|
||||
<Step title="배포">
|
||||
필요한 환경 변수를 추가한 다음 배포를 시작합니다.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## 기존 배포 구성
|
||||
|
||||
<Steps>
|
||||
<Step title="배포 설정 열기">
|
||||
AMP에서 자동화로 이동한 뒤 **Settings**를 엽니다.
|
||||
</Step>
|
||||
|
||||
<Step title="필요한 경우 auto-deploy 끄기">
|
||||
auto-deploy가 활성화되어 있으면 먼저 끄세요. auto-deploy가 켜져 있는
|
||||
동안에는 작업 디렉터리 필드를 사용할 수 없습니다.
|
||||
</Step>
|
||||
|
||||
<Step title="작업 디렉터리 설정">
|
||||
**Basic settings**에서 다음과 같은 하위 폴더 경로를 입력합니다:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="다시 배포">
|
||||
설정을 저장하고 자동화를 다시 배포합니다. 새 작업 디렉터리는 다음 배포부터
|
||||
사용됩니다.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## 경로 규칙
|
||||
|
||||
작업 디렉터리는 저장소 또는 ZIP 루트 안의 상대 경로여야 합니다.
|
||||
|
||||
| 규칙 | 예시 |
|
||||
|------|------|
|
||||
| 상대 경로를 사용합니다 | `crews/support_agent` |
|
||||
| `/`로 시작하지 않습니다 | `/crews/support_agent`는 유효하지 않습니다 |
|
||||
| `.` 또는 `..` 경로 세그먼트를 사용하지 않습니다 | `crews/../support_agent`는 유효하지 않습니다 |
|
||||
| 문자, 숫자, 하이픈, 밑줄, 점, 슬래시만 사용합니다 | `crews/support agent`는 유효하지 않습니다 |
|
||||
| 경로는 255자 이하로 유지합니다 | 더 긴 경로는 거부됩니다 |
|
||||
|
||||
AMP는 앞뒤 공백을 제거하고, 반복된 슬래시를 하나로 줄이며, 끝의 슬래시를
|
||||
제거합니다. 빈 값은 저장소 루트를 사용합니다.
|
||||
|
||||
## Lock 파일과 UV 워크스페이스
|
||||
|
||||
선택한 폴더에는 자동화의 `pyproject.toml`과 `src/` 디렉터리가 있어야
|
||||
합니다. `uv.lock` 또는 `poetry.lock` 파일은 선택한 폴더나 저장소 루트에
|
||||
둘 수 있습니다.
|
||||
|
||||
이 방식은 일반적인 두 가지 모노레포 레이아웃을 모두 지원합니다:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="프로젝트 lock 파일">
|
||||
```text
|
||||
company-ai/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
|-- uv.lock
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
|
||||
<Tab title="워크스페이스 lock 파일">
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Tip>
|
||||
자동화가 모노레포의 다른 위치에 있는 공유 패키지를 가져온다면, UV
|
||||
workspace, path 또는 source 설정을 사용해 해당 패키지를 `pyproject.toml`에
|
||||
선언하세요. AMP는 선택한 폴더에서 자동화를 실행하므로, 저장소 루트가
|
||||
Python path에 있다고 가정하기보다 공유 코드를 종속성으로 설치해야 합니다.
|
||||
</Tip>
|
||||
|
||||
## 문제 해결
|
||||
|
||||
### 작업 디렉터리를 찾을 수 없음
|
||||
|
||||
경로가 저장소 또는 ZIP 루트를 기준으로 한 상대 경로인지 확인하세요. ZIP
|
||||
업로드의 경우 ZIP 내용에 입력한 작업 디렉터리 경로가 정확히 포함되어야 합니다.
|
||||
|
||||
### pyproject.toml 누락
|
||||
|
||||
작업 디렉터리는 여러 프로젝트를 담은 상위 폴더가 아니라 Crew 또는 Flow 프로젝트
|
||||
폴더를 가리켜야 합니다.
|
||||
|
||||
### uv.lock 또는 poetry.lock 누락
|
||||
|
||||
선택한 프로젝트 폴더 또는 저장소 루트에 lock 파일을 커밋하세요. UV
|
||||
워크스페이스의 경우 `uv.lock`을 워크스페이스 루트에 두는 방식이 지원됩니다.
|
||||
|
||||
### Auto-Deploy를 사용할 수 없음
|
||||
|
||||
작업 디렉터리가 설정되어 있으면 auto-deploy가 비활성화됩니다. 수동 재배포를
|
||||
사용하거나 AMP API로 CI/CD에서 재배포를 트리거하세요.
|
||||
|
||||
<Card title="AMP에 배포하기" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
|
||||
모노레포 작업 디렉터리를 선택한 뒤 배포 가이드를 계속 진행하세요.
|
||||
</Card>
|
||||
@@ -1,453 +0,0 @@
|
||||
---
|
||||
title: 대화형 Flow
|
||||
description: 턴마다 kickoff, 메시지 기록, 의도 라우팅, 트레이싱, WebSocket 브리지로 멀티턴 채팅 앱을 만듭니다.
|
||||
icon: comments
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## 개요
|
||||
|
||||
대화형 앱은 각 사용자 입력을 **동일한 세션 id**로 **새 flow 실행**으로 처리합니다. CrewAI는 메시지 기록, 선택적 의도 분류, 지연 트레이싱, UI 브리지를 제공하며, `Flow`에 별도 `chat()` API는 없습니다.
|
||||
|
||||
| 개념 | 구현 |
|
||||
|------|------|
|
||||
| 세션 id | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
|
||||
| 사용자 입력 | `kickoff(user_message=...)`가 그래프 실행 전 `state.messages`에 추가 |
|
||||
| 턴 완료 | `FlowFinished`는 **이번 실행**만 의미; 다음 `kickoff`로 대화 계속 |
|
||||
| 세션 전체 트레이스 | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
|
||||
|
||||
## 단일 진입점: `kickoff`
|
||||
|
||||
모든 사용자 메시지에 **`flow.kickoff(user_message=..., session_id=...)`**를 사용하세요 (REST, WebSocket, CLI). `Flow`에 커스텀 `chat()` 래퍼를 만들지 마세요.
|
||||
|
||||
| API | 용도 |
|
||||
|-----|------|
|
||||
| `kickoff(user_message=..., session_id=...)` | 각 사용자 메시지 |
|
||||
| `kickoff_async(...)` | 동일 파라미터; 네이티브 async 진입 |
|
||||
| `ask()` | 한 스텝 **내부** 블로킹 프롬프트 (마법사, 확인) |
|
||||
| `@human_feedback` | **스텝 출력** 승인/거부 — 다음 채팅 줄이 아님 |
|
||||
| `ChatSession.handle_turn(...)` | `kickoff` 위의 전송 계층 (SSE / WebSocket) |
|
||||
|
||||
## 빠른 시작
|
||||
|
||||
```python
|
||||
from uuid import uuid4
|
||||
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
Flow,
|
||||
listen,
|
||||
or_,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
|
||||
class SupportFlow(Flow[ChatState]):
|
||||
conversational_config = ConversationalConfig(
|
||||
default_intents=["order", "help", "goodbye"],
|
||||
intent_llm="gpt-4o-mini",
|
||||
defer_trace_finalization=True,
|
||||
)
|
||||
|
||||
@start()
|
||||
def bootstrap(self):
|
||||
if not self.state.session_ready:
|
||||
self.state.session_ready = True
|
||||
return "ready"
|
||||
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
# default_intents 설정 시 prepare_conversational_turn에서 last_intent 설정
|
||||
return self.state.last_intent or "help"
|
||||
|
||||
@listen("order")
|
||||
def handle_order(self):
|
||||
reply = "주문이 배송 중입니다."
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("help")
|
||||
def handle_help(self):
|
||||
reply = "무엇을 도와드릴까요?"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("goodbye")
|
||||
def handle_goodbye(self):
|
||||
reply = "안녕히 가세요!"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@persist(SQLiteFlowPersistence("support.db"))
|
||||
@listen(or_(handle_order, handle_help, handle_goodbye))
|
||||
def finalize(self):
|
||||
return self.state.model_dump()
|
||||
|
||||
|
||||
session_id = str(uuid4())
|
||||
flow = SupportFlow()
|
||||
|
||||
flow.kickoff(user_message="주문 어디까지 왔나요?", session_id=session_id)
|
||||
flow.kickoff(user_message="반품은 어떻게 하나요?", session_id=session_id)
|
||||
flow.finalize_session_traces() # 전체 대화에 대한 단일 trace 링크
|
||||
```
|
||||
|
||||
## 턴 생명주기
|
||||
|
||||
`user_message`가 있는 각 `kickoff`는 다음 파이프라인을 실행합니다:
|
||||
|
||||
1. **`_configure_conversational_kickoff`** — `session_id` / `user_message`를 `inputs`에 병합, `ConversationalConfig` 적용, 설정 시 지연 트레이싱 활성화.
|
||||
2. **상태 복원** — `inputs["id"]`가 있고 `@persist`가 설정되면 최신 스냅샷 로드.
|
||||
3. **`FlowStarted`** — 지연 세션의 첫 턴에서만 발생.
|
||||
4. **`prepare_conversational_turn`** — 사용자 메시지를 `state.messages`에 추가, `last_user_message` 설정, `last_intent` 초기화, `intents` / `default_intents` + `intent_llm` 설정 시 분류.
|
||||
5. **그래프 실행** — `@start` → `@router` → `@listen` 핸들러.
|
||||
6. **실행 종료** — 지연 활성화 시 턴별 `flow_finished` 및 trace 종료 **건너뜀**; 중첩 `Agent.kickoff()` / crew도 부모 batch를 닫지 않음.
|
||||
|
||||
핸들러는 **`append_message("assistant", reply)`**를 호출해 다음 턴의 `conversation_messages`에 어시스턴트 응답이 포함되게 하세요. 사용자 입력은 kickoff 시 이미 저장됩니다 — 핸들러에서 다시 추가하지 마세요.
|
||||
|
||||
## `ConversationalConfig` (클래스 수준 기본값)
|
||||
|
||||
`Flow` 서브클래스에 `conversational_config: ClassVar[ConversationalConfig | None]`로 설정합니다.
|
||||
|
||||
| 필드 | 기본값 | 목적 |
|
||||
|------|--------|------|
|
||||
| `default_intents` | `None` | kickoff 전 자동 분류용 outcome 라벨 |
|
||||
| `intent_llm` | `None` | 분류용 모델 (intent 사용 시 필수) |
|
||||
| `interactive_prompt` | `"You: "` | `kickoff(interactive=True)` 프롬프트 |
|
||||
| `interactive_timeout` | `None` | 대화형 모드 줄 단위 타임아웃 |
|
||||
| `exit_commands` | `exit`, `quit` | 대화형 모드 종료 단어 |
|
||||
| `defer_trace_finalization` | `True` | 턴 간 하나의 trace batch 유지 |
|
||||
|
||||
`intents=` 및 `intent_llm=` 키워드로 kickoff마다 재정의할 수 있습니다.
|
||||
|
||||
## `ChatState` (권장 persist 형태)
|
||||
|
||||
```python
|
||||
from crewai.flow import ChatState
|
||||
|
||||
|
||||
class MyChatState(ChatState):
|
||||
# 상속: id, messages, last_user_message, last_intent, session_ready
|
||||
research_turn_count: int = 0
|
||||
custom_flag: bool = False
|
||||
```
|
||||
|
||||
| 필드 | 역할 |
|
||||
|------|------|
|
||||
| `id` | 세션 UUID (`session_id` / `inputs["id"]`와 동일) |
|
||||
| `messages` | LLM 기록용 `{role, content}` 리스트 |
|
||||
| `last_user_message` | 이번 턴의 최신 사용자 입력 |
|
||||
| `last_intent` | 분류 후 라우트 라벨 (사용 시) |
|
||||
| `session_ready` | 일회성 bootstrap 플래그 |
|
||||
|
||||
`ConversationalInputs`는 `kickoff(inputs={...})`용 `TypedDict`: `id`, `user_message`, `last_intent`.
|
||||
|
||||
## `Flow` 대화 API
|
||||
|
||||
### `kickoff` / `kickoff_async` 파라미터
|
||||
|
||||
| 파라미터 | 목적 |
|
||||
|----------|------|
|
||||
| `user_message` | 이번 턴 텍스트 (또는 `{"role": "user", "content": "..."}`) |
|
||||
| `session_id` | 대화 UUID → `inputs["id"]` / `state.id` |
|
||||
| `intents` | kickoff 전 `classify_intent`용 outcome 라벨 |
|
||||
| `intent_llm` | 분류 LLM (`intents`와 함께 필수) |
|
||||
| `interactive` | `ask()` CLI 루프 (로컬 데모 전용) |
|
||||
| `interactive_prompt` | 대화형 모드 프롬프트 |
|
||||
| `interactive_timeout` | 줄 단위 `ask()` 타임아웃 |
|
||||
| `exit_commands` | 대화형 모드 종료 단어 |
|
||||
| `inputs` | 추가 상태 필드 |
|
||||
| `restore_from_state_id` | 다른 persist flow에서 fork 복원 |
|
||||
|
||||
### 인스턴스 속성
|
||||
|
||||
| 속성 | 목적 |
|
||||
|------|------|
|
||||
| `conversational_config` | 클래스 수준 `ConversationalConfig` |
|
||||
| `defer_trace_finalization` | 인스턴스 플래그; kickoff 시 config에서 자동 설정 |
|
||||
| `suppress_flow_events` | 콘솔 flow 패널 숨김; **트레이싱은 계속 기록** |
|
||||
| `stream` | 스트리밍; `ChatSession.handle_turn(..., stream=True)`와 함께 |
|
||||
|
||||
### 메서드 및 프로퍼티
|
||||
|
||||
| 이름 | 설명 |
|
||||
|------|------|
|
||||
| `append_message(role, content, **extra)` | `state.messages`에 추가 |
|
||||
| `conversation_messages` | LLM 호출용 읽기 전용 기록 |
|
||||
| `classify_intent(text, outcomes, *, llm, context=None)` | outcome 매핑 (`@human_feedback`와 동일 collapse) |
|
||||
| `receive_user_message(text, *, outcomes=None, llm=None)` | 사용자 메시지 추가; 선택적 `last_intent` |
|
||||
| `finalize_session_traces()` | 지연 `flow_finished` 발생 및 세션 trace batch 종료 |
|
||||
| `_should_defer_trace_finalization()` | 턴별 trace 종료 지연 여부 |
|
||||
| `input_history` | `ask()` 프롬프트/응답 감사 기록 |
|
||||
|
||||
### 모듈 헬퍼 (`crewai.flow.conversation`)
|
||||
|
||||
테스트 또는 커스텀 오케스트레이션용:
|
||||
|
||||
| 함수 | 설명 |
|
||||
|------|------|
|
||||
| `normalize_kickoff_inputs(...)` | 대화 kwargs를 `inputs`에 병합 |
|
||||
| `get_conversation_messages(flow)` | 상태 또는 내부 버퍼에서 메시지 읽기 |
|
||||
| `append_message(flow, ...)` | 인스턴스 메서드와 동일 |
|
||||
| `prepare_conversational_turn(flow, ...)` | 턴 수화 (보통 kickoff가 호출) |
|
||||
| `receive_user_message(flow, ...)` | 인스턴스 메서드와 동일 |
|
||||
| `set_state_field(flow, name, value)` | dict 또는 Pydantic 상태 필드 설정 |
|
||||
| `get_conversational_config(flow)` | 클래스 `conversational_config` 읽기 |
|
||||
| `input_history_to_messages(entries)` | `input_history`를 LLM 메시지 형식으로 |
|
||||
|
||||
## 의도 라우팅 패턴
|
||||
|
||||
### A. `ConversationalConfig`로 사전 분류 (가장 단순)
|
||||
|
||||
`default_intents`와 `intent_llm` 설정. 각 kickoff가 `@router` 전에 분류; `route()`에서 `self.state.last_intent` 읽기.
|
||||
|
||||
### B. `@router` 내부에서 분류 (풍부한 프롬프트)
|
||||
|
||||
`default_intents=None`으로 kickoff는 메시지만 추가. `route()`에서 커스텀 프롬프트로 `classify_intent` 호출:
|
||||
|
||||
```python
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
intent = self.classify_intent(
|
||||
self._routing_prompt(self.state.last_user_message),
|
||||
("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
|
||||
llm=self.conversational_config.intent_llm or "gpt-4o-mini",
|
||||
)
|
||||
self.state.last_intent = intent
|
||||
return intent
|
||||
```
|
||||
|
||||
웹 리서치나 다단계 tool이 필요하면 **`@listen("RESEARCH")`** 등에서 `Agent.kickoff()`와 tool 사용 — 단순 `LLM.call()` 대신.
|
||||
|
||||
## flow가 끝났지만 사용자는 계속 대화할 때
|
||||
|
||||
`FlowFinished`는 **이번 그래프 실행**이 완료됨을 의미합니다. 같은 `session_id`로 또 다른 `kickoff`로 대화가 이어집니다. `@persist`가 `messages`, 플래그, 컨텍스트를 복원합니다.
|
||||
|
||||
**Persist 패턴:** 전체 `Flow` 클래스보다 **단일 종료 스텝**(예: `finalize`)에 `@persist`를 두는 것이 좋습니다. 클래스 수준 persist는 매 메서드 후 저장하며, `load_state`는 최신 행을 사용해 같은 턴의 핸들러 업데이트를 놓칠 수 있습니다.
|
||||
|
||||
후속 채팅 줄에 `@human_feedback`를 쓰지 마세요. 특정 스텝 출력을 사람이 승인해야 할 때만 사용하세요.
|
||||
|
||||
## 대화형 `Flow` (실험적)
|
||||
|
||||
<Warning>
|
||||
**실험적 기능입니다.** 대화형 `Flow`의 API 표면(`conversational = True`,
|
||||
`handle_turn`, `ConversationConfig`, `RouterConfig`, `ConversationState`,
|
||||
내장 그래프와 헬퍼)은 `crewai.experimental` 하위에 있으며 정식 출시
|
||||
전까지 변경될 수 있습니다. 특정 동작에 의존한다면 CrewAI 버전을 고정하고
|
||||
변경 사항이 있는지 changelog를 확인하세요. 피드백과 이슈 환영합니다.
|
||||
</Warning>
|
||||
|
||||
`Flow` 서브클래스에 `conversational = True`를 지정하면 대화형 챗 그래프가 활성화됩니다. 베이스 `Flow`가 `@start` / `@router` / `converse_turn` / `end_conversation` 그래프를 노출하고, `state.messages`를 관리하며, router LLM을 구동하고, 턴 간 trace 배치를 열린 상태로 유지합니다. 여러분은 **커스텀 라우트**만 작성하면 되고, 나머지는 프레임워크가 담당합니다.
|
||||
|
||||
LLM 기반 라우터와 라우트별 핸들러로 멀티턴 챗을 만들고 싶지만 라이프사이클을 직접 배선하고 싶지 않을 때 사용하세요. 완전한 제어가 필요하면 위의 `Flow[ChatState]`로 내려가세요.
|
||||
|
||||
### 빠른 예제
|
||||
|
||||
```python
|
||||
from crewai import LLM, Flow
|
||||
from crewai.flow import listen
|
||||
from crewai.experimental.conversational import (
|
||||
ConversationConfig,
|
||||
ConversationState,
|
||||
RouterConfig,
|
||||
)
|
||||
|
||||
|
||||
ROUTER_LLM = LLM(model="gpt-4o-mini")
|
||||
|
||||
|
||||
@ConversationConfig(
|
||||
system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
|
||||
llm=ROUTER_LLM,
|
||||
router=RouterConfig(), # 라우트 + 설명은 @listen 핸들러에서 자동 발견
|
||||
)
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
@listen("CREWAI_DOCS")
|
||||
def handle_crewai_docs(self) -> str:
|
||||
"""Look up the CrewAI documentation for framework/API questions."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
|
||||
flow = SupportFlow()
|
||||
try:
|
||||
flow.handle_turn("뭘 할 수 있어?") # converse(빌트인)로 라우팅
|
||||
flow.handle_turn("AI 뉴스를 웹에서 찾아줘.") # INTERNET_SEARCH로 라우팅
|
||||
flow.handle_turn("첫 번째 결과를 요약해줘.") # 다시 converse로 라우팅
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
### `ConversationConfig`
|
||||
|
||||
클래스 단위의 챗 기본값을 부착하는 클래스 데코레이터입니다.
|
||||
|
||||
| 필드 | 기본값 | 목적 |
|
||||
|------|--------|------|
|
||||
| `system_prompt` | i18n `slices.conversational_system_prompt` | 빌트인 `converse_turn`이 사용하는 system 메시지. 빈 문자열(`""`)을 전달하면 system 메시지를 끕니다. |
|
||||
| `llm` | `None` | 대화용 LLM (빌트인 `converse_turn`이 사용하고 router 폴백도 됨). |
|
||||
| `router` | `None` | LLM 기반 라우팅을 위한 `RouterConfig`. 없으면 항상 `converse`로 떨어집니다. |
|
||||
| `answer_from_history_prompt` | 프레임워크 기본값 | 선택적인 `answer_from_history` 라우트용 system 메시지. |
|
||||
| `answer_from_history_llm` | `None` | 설정되면 `answer_from_history` 단축 경로가 활성화됩니다. |
|
||||
| `intent_llm` | `None` | 레거시 `intents=`/`default_intents` 사전 분류용 LLM. |
|
||||
| `default_intents` | `None` | 레거시 사전 분류용 outcome 레이블. |
|
||||
| `visible_agent_outputs` | `None` | `"all"` 또는 `append_agent_result()` 결과를 사용자에게 공개로 승격할 에이전트 이름 목록. |
|
||||
| `defer_trace_finalization` | `True` | `handle_turn()` 호출들 사이에서 하나의 trace 배치를 열어 둡니다. |
|
||||
|
||||
### `RouterConfig`와 자동 생성되는 라우트 카탈로그
|
||||
|
||||
```python
|
||||
RouterConfig(
|
||||
prompt="선택적인 도메인 프레이밍 (정책, 톤, 페르소나).",
|
||||
response_format=MyRoute, # 선택; 없으면 자동 생성
|
||||
llm=ROUTER_LLM, # ConversationConfig.llm으로 폴백
|
||||
routes=["INTERNET_SEARCH", "CREWAI_DOCS"], # 선택; 리스너에서 추론
|
||||
route_descriptions={
|
||||
"INTERNET_SEARCH": "이 라우트만 docstring 대신 사용할 설명.",
|
||||
},
|
||||
default_intent="converse", # LLM 호출 실패 또는 LLM 없음일 때 사용
|
||||
fallback_intent="converse", # LLM이 잘못된 라우트를 반환할 때 사용
|
||||
intent_field="intent",
|
||||
)
|
||||
```
|
||||
|
||||
router에 전달되는 프롬프트는 자동으로 만들어집니다. 각 라우트의 설명은 다음 우선순위로 결정됩니다:
|
||||
|
||||
1. `RouterConfig.route_descriptions[label]` — 명시적 오버라이드.
|
||||
2. `Flow.builtin_route_descriptions[label]` — `converse`, `end`, `answer_from_history`용 프레임워크 캐닝 텍스트 (router LLM용으로 다듬어진 문구).
|
||||
3. `@listen(label)` 핸들러 docstring의 첫 줄(비어있지 않은 줄).
|
||||
4. 빈 문자열 (라우트만 카탈로그에 등장하고 설명은 없음).
|
||||
|
||||
실제 사용에서 **새 라우트를 추가하는 방법은 `@listen("X")` + 한 줄짜리 docstring**입니다:
|
||||
|
||||
```python
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
```
|
||||
|
||||
…그러면 router LLM은 다음을 봅니다:
|
||||
|
||||
```
|
||||
Routes:
|
||||
- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
|
||||
- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
|
||||
- converse: Ordinary chat, follow-ups, summaries, clarifications…
|
||||
- end: User signals the conversation is finished (goodbye, exit, done).
|
||||
```
|
||||
|
||||
`RouterConfig.prompt`는 **도메인 프레이밍** (어시스턴트 페르소나, 비즈니스 규칙, 톤)을 위한 자리입니다. 라우트 카탈로그는 자동 생성되니 `prompt` 안에 라우트 목록을 넣지 마세요. 핸들러를 추가하는 순간 동기화가 깨집니다.
|
||||
|
||||
### 빌트인 라우트
|
||||
|
||||
| 라우트 | 핸들러 | 목적 |
|
||||
|--------|--------|------|
|
||||
| `converse` | `converse_turn` | 기본 챗 핸들러. system prompt + 정식 메시지 히스토리와 함께 `ConversationConfig.llm`을 호출합니다. |
|
||||
| `end` | `end_conversation` | `state.ended = True`로 설정하고 종료 응답을 보냅니다. |
|
||||
| `answer_from_history` | `answer_from_history_turn` | 선택적. `ConversationConfig.answer_from_history_llm`이 설정되어 있고 메시지를 히스토리만으로 답할 수 있을 때 라우팅됩니다. |
|
||||
|
||||
서브클래스에 같은 이름의 핸들러를 정의하면 어떤 것이든 오버라이드할 수 있습니다.
|
||||
|
||||
### `handle_turn()` 시맨틱
|
||||
|
||||
`flow.handle_turn(message)`는 한 턴을 실행합니다:
|
||||
|
||||
1. 그래프가 다시 실행되도록 턴 단위 실행 추적(`_completed_methods`, `_method_outputs`)을 초기화합니다 — 이게 없으면 동일 인스턴스에서 반복 `kickoff` 호출 시 `Flow.kickoff_async`가 `inputs={"id": ...}`를 체크포인트 복원으로 간주해 2번째 턴부터 단락 회로가 발생합니다.
|
||||
2. 사용자 메시지를 `state.messages`에 추가하고 `current_user_message` / `last_user_message`를 설정합니다. `last_intent`는 **이전 턴 값이 유지**되어 router LLM이 신호로 활용할 수 있습니다.
|
||||
3. `conversation_start` → `route_conversation` → 선택된 `@listen` 핸들러 순으로 실행됩니다.
|
||||
4. router는 결정을 `state.last_intent`에 저장합니다 (다음 턴의 router 컨텍스트에서 보입니다).
|
||||
5. 핸들러가 문자열을 반환했지만 `append_assistant_message`를 직접 호출하지 않았다면, `handle_turn`이 대신 추가해 줍니다.
|
||||
|
||||
`flow.kickoff(user_message=..., session_id=...)`를 직접 호출해도 동일한 reset/run 로직이 동작합니다. `handle_turn`은 그 위에 얹은 편의 래퍼입니다.
|
||||
|
||||
### 커스텀 router 동작
|
||||
|
||||
매 라우팅 결정마다 사이드 이펙트(이벤트 버스 셋업, 텔레메트리)를 실행하려면 `route_turn`을 오버라이드하세요:
|
||||
|
||||
```python
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
def route_turn(self, context: dict[str, Any]) -> str | None:
|
||||
self.event_bus = MyBus(self)
|
||||
return super().route_turn(context)
|
||||
```
|
||||
|
||||
LLM router를 우회해 프로그램적으로 라우트를 선택하려면 `route_turn`에서 문자열을 반환하세요. `None`을 반환하면 `_route_with_config(...)`로 떨어집니다.
|
||||
|
||||
### `append_assistant_message`와 `append_agent_result`
|
||||
|
||||
`@listen(label)` 핸들러 안에서 두 가지 중 선택하세요:
|
||||
|
||||
- `self.append_assistant_message(text)` — 사용자에게 보이는 어시스턴트 턴을 `state.messages`에 추가합니다. 다음 턴의 `converse_turn`이 이 내용을 보게 됩니다.
|
||||
- `self.append_agent_result(agent_name, result, visibility="private")` — 구조화된 이벤트를 `state.events`에, 스레드를 `state.agent_threads[agent_name]`에 기록합니다. public 가시성은 자동으로 `append_assistant_message`도 호출합니다. 정식 히스토리를 더럽히지 말아야 할 임시 작업에는 private을 쓰세요.
|
||||
|
||||
`ConversationConfig.visible_agent_outputs`로 특정 에이전트의 private 결과를 전역적으로 public으로 승격할 수 있습니다 (`"all"` 또는 이름 리스트).
|
||||
|
||||
## 턴 간 트레이싱
|
||||
|
||||
`defer_trace_finalization=True` (`ConversationalConfig` 기본값):
|
||||
|
||||
- 채팅 세션 전체에 **하나의 trace batch**.
|
||||
- 첫 턴에만 **`flow_started`**; `finalize_session_traces()`에서 **`flow_finished`** 한 번.
|
||||
- 턴별 `kickoff`는 “Trace batch finalized”를 출력하지 않음.
|
||||
- **중첩 작업** (`Agent.kickoff()`, crew, Exa tool)은 **부모** batch에 추가; 내부 `AgentExecutor` flow가 세션 batch를 조기 종료하지 않음.
|
||||
|
||||
```python
|
||||
try:
|
||||
while True:
|
||||
line = input("You: ").strip()
|
||||
if not line:
|
||||
break
|
||||
flow.kickoff(user_message=line, session_id=session_id)
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
지연 활성화 시 `ChatSession.close()`가 `finalize_session_traces()`를 호출합니다.
|
||||
|
||||
`suppress_flow_events=True`는 Rich 콘솔 패널만 숨깁니다. trace 및 method 이벤트는 계속 발생합니다.
|
||||
|
||||
### 대화형 `Flow` trace 수명 주기
|
||||
|
||||
실험적 [대화형 `Flow`](#대화형-flow-실험적)는 동일한 tracing 수명 주기를 따릅니다. `defer_trace_finalization` 기본값이 `True`이므로 각 `handle_turn()`이 세션 trace를 열어 둡니다. 세션 끝에서 항상 finalize하세요 — REPL/루프를 `try/finally`로 감싸고 종료 시 `flow.finalize_session_traces()`를 호출하세요. 호출하지 않으면 batch가 열린 채 남아 마지막 대화가 export되지 않을 수 있습니다.
|
||||
|
||||
## 스트리밍
|
||||
|
||||
`Flow` 클래스에 `stream = True`. `kickoff(...)`가 표준 이벤트 버스를 통해 `assistant_delta` 등 이벤트를 발생시킵니다.
|
||||
|
||||
## import
|
||||
|
||||
```python
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
ConversationalInputs,
|
||||
Flow,
|
||||
listen,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
```
|
||||
|
||||
## 참고
|
||||
|
||||
- [Flow 상태 관리 마스터하기](/ko/guides/flows/mastering-flow-state)
|
||||
- [첫 Flow 만들기](/ko/guides/flows/first-flow)
|
||||
- 데모: `lib/crewai/runner_conversational_flow_simple.py`
|
||||
@@ -607,7 +607,6 @@ result = ContentCrew().crew().kickoff(inputs={
|
||||
3. 더 복잡한 병렬 실행을 위해 `and_` 및 `or_` 함수를 탐색해 보세요.
|
||||
4. flow를 외부 API, 데이터베이스 또는 사용자 인터페이스에 연결해 보세요.
|
||||
5. 여러 전문화된 crew를 하나의 flow에서 결합해 보세요.
|
||||
6. [대화형 Flow](/ko/guides/flows/conversational-flows)로 멀티턴 채팅 앱 구축 (`kickoff` per message, `ChatSession`, 지연 트레이싱)
|
||||
|
||||
<Check>
|
||||
축하합니다! 정규 코드, 직접적인 LLM 호출, crew 기반 처리를 결합하여 포괄적인 가이드를 생성하는 첫 번째 CrewAI Flow를 성공적으로 구축하셨습니다. 이러한 기초적인 역량을 바탕으로 절차적 제어와 협업적 인텔리전스를 결합하여 복잡하고 다단계의 문제를 해결할 수 있는 점점 더 정교한 AI 애플리케이션을 만들 수 있습니다.
|
||||
|
||||
@@ -22,8 +22,6 @@ State 관리는 모든 고급 AI 워크플로우의 중추입니다. CrewAI Flow
|
||||
5. **애플리케이션 확장** - 적절한 데이터 조직을 통해 복잡한 워크플로를 지원할 수 있습니다.
|
||||
6. **대화형 애플리케이션 활성화** - 컨텍스트 기반 AI 상호작용을 위해 대화 내역을 저장하고 접근할 수 있습니다.
|
||||
|
||||
멀티턴 채팅(`kickoff` per user line, `ChatState`, 의도 라우팅, 지연 트레이싱, `ChatSession`)은 [대화형 Flow](/ko/guides/flows/conversational-flows)를 참고하세요.
|
||||
|
||||
이러한 기능을 효과적으로 활용하는 방법을 살펴보겠습니다.
|
||||
|
||||
## 상태 관리 기본 사항
|
||||
|
||||
@@ -4,38 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="03 jun 2026">
|
||||
## v1.14.7a1
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.7a1)
|
||||
|
||||
## O Que Mudou
|
||||
|
||||
### Funcionalidades
|
||||
- Adicionar suporte a arquivos de agentes treinados da equipe
|
||||
- Adicionar provedor nativo Snowflake Cortex LLM
|
||||
- Adicionar guia de integração com Databricks
|
||||
- Adicionar guia de integração com Snowflake
|
||||
|
||||
### Correções de Bugs
|
||||
- Corrigir CLI restaurando `[project.scripts]` no pacote crewai para instalação da ferramenta UV
|
||||
- Resolver problemas de confiabilidade na entrada de arquivos
|
||||
- Corrigir históricos de resultados de ferramentas incompletos no Snowflake Claude
|
||||
- Lidar com chamadas de ferramentas em formato de string para Snowflake Claude
|
||||
- Re-armar ouvintes `or_` de múltiplas fontes em ciclos controlados por roteadores
|
||||
|
||||
### Desempenho
|
||||
- Melhorar a velocidade de importação do crewai através do carregamento preguiçoso de importações do docling
|
||||
|
||||
### Refatoração
|
||||
- Dividir `flow.py` em DSL, definição e tempo de execução
|
||||
|
||||
## Contributors
|
||||
|
||||
@Luzk, @alex-clawd, @devin-ai-integration[bot], @greysonlalonde, @jessemiller, @lorenzejay, @vinibrsl
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="28 mai 2026">
|
||||
## v1.14.6
|
||||
|
||||
|
||||
@@ -163,12 +163,6 @@ Você precisa enviar seu crew para um repositório do GitHub. Caso ainda não te
|
||||
|
||||
</Frame>
|
||||
|
||||
<Tip>
|
||||
Se seu Crew ou Flow estiver dentro de uma subpasta de monorepo, expanda
|
||||
**Advanced** e defina um diretório de trabalho antes de implantar. Consulte
|
||||
[Implantações em Monorepo](/pt-BR/enterprise/guides/monorepo-deployments).
|
||||
</Tip>
|
||||
|
||||
</Step>
|
||||
|
||||
<Step title="Definir as Variáveis de Ambiente">
|
||||
@@ -447,4 +441,4 @@ type = "flow"
|
||||
<Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
|
||||
Entre em contato com nossa equipe de suporte para ajuda com questões de
|
||||
implantação ou dúvidas sobre a plataforma AMP.
|
||||
</Card>
|
||||
</Card>
|
||||
@@ -1,230 +0,0 @@
|
||||
---
|
||||
title: "Implantações em Monorepo"
|
||||
description: "Implante um Crew ou Flow a partir de uma subpasta em um repositório maior"
|
||||
icon: "folder-tree"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Note>
|
||||
Use um diretório de trabalho quando seu Crew ou Flow estiver dentro de um
|
||||
repositório maior. O CrewAI AMP valida, faz o build e executa a automação a
|
||||
partir dessa subpasta em vez da raiz do repositório.
|
||||
</Note>
|
||||
|
||||
## Quando Usar
|
||||
|
||||
Implantações em monorepo são úteis quando um repositório contém múltiplas
|
||||
automações, pacotes compartilhados ou outro código de aplicação:
|
||||
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
|-- support_agent/
|
||||
| |-- pyproject.toml
|
||||
| `-- src/
|
||||
| `-- support_agent/
|
||||
| |-- main.py
|
||||
| `-- crew.py
|
||||
`-- research_flow/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- research_flow/
|
||||
`-- main.py
|
||||
```
|
||||
|
||||
Para implantar `support_agent`, defina o diretório de trabalho como:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
O AMP ainda baixa ou recebe o repositório inteiro, mas trata a pasta
|
||||
selecionada como a raiz do projeto da automação.
|
||||
|
||||
## O Que o Diretório de Trabalho Controla
|
||||
|
||||
Quando um diretório de trabalho é definido, o AMP usa essa pasta para:
|
||||
|
||||
- Validação do projeto, incluindo `pyproject.toml`, `src/` e o ponto de entrada do Crew ou Flow
|
||||
- Instalação de dependências com `uv`
|
||||
- O diretório de trabalho do processo em execução
|
||||
- A variável de ambiente `CREW_ROOT_DIR`
|
||||
|
||||
Deixar o campo vazio mantém o comportamento existente e usa a raiz do
|
||||
repositório.
|
||||
|
||||
## Fontes Suportadas
|
||||
|
||||
Você pode definir um diretório de trabalho ao criar uma implantação a partir de:
|
||||
|
||||
- Um repositório GitHub conectado
|
||||
- Um repositório Git configurado no AMP
|
||||
- Um upload de ZIP
|
||||
|
||||
<Info>
|
||||
Configure diretórios de trabalho na interface web do AMP. O fluxo
|
||||
`crewai deploy create` da CLI não solicita esse campo.
|
||||
</Info>
|
||||
|
||||
Você também pode adicionar ou alterar o diretório de trabalho de uma implantação
|
||||
existente pela página **Settings** da implantação. A alteração passa a valer no
|
||||
próximo deploy.
|
||||
|
||||
<Warning>
|
||||
Diretórios de trabalho e auto-deploy não podem ser usados juntos. Se uma
|
||||
implantação tiver um diretório de trabalho, o auto-deploy fica desabilitado
|
||||
para ela. Desative o auto-deploy antes de definir um diretório de trabalho.
|
||||
</Warning>
|
||||
|
||||
## Configurar uma Nova Implantação
|
||||
|
||||
<Steps>
|
||||
<Step title="Abra Deploy from Code">
|
||||
No CrewAI AMP, crie uma nova implantação e escolha sua fonte: GitHub, Git
|
||||
Repository ou upload de ZIP.
|
||||
</Step>
|
||||
|
||||
<Step title="Selecione o repositório, branch ou arquivo ZIP">
|
||||
Escolha o repositório e a branch que contêm seu monorepo, ou envie um ZIP
|
||||
cuja raiz contenha os arquivos do monorepo.
|
||||
</Step>
|
||||
|
||||
<Step title="Abra as configurações avançadas">
|
||||
Expanda a seção **Advanced** no formulário de deploy.
|
||||
</Step>
|
||||
|
||||
<Step title="Informe o diretório de trabalho">
|
||||
Informe o caminho da raiz do repositório até o projeto Crew ou Flow:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
|
||||
Não inclua uma barra inicial.
|
||||
</Step>
|
||||
|
||||
<Step title="Implante">
|
||||
Adicione as variáveis de ambiente necessárias e inicie a implantação.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Configurar uma Implantação Existente
|
||||
|
||||
<Steps>
|
||||
<Step title="Abra as configurações da implantação">
|
||||
Acesse sua automação no AMP e abra **Settings**.
|
||||
</Step>
|
||||
|
||||
<Step title="Desative o auto-deploy, se necessário">
|
||||
Se o auto-deploy estiver habilitado, desative-o primeiro. O campo de
|
||||
diretório de trabalho fica indisponível enquanto o auto-deploy está ativo.
|
||||
</Step>
|
||||
|
||||
<Step title="Defina o diretório de trabalho">
|
||||
Em **Basic settings**, informe o caminho da subpasta, como:
|
||||
|
||||
```text
|
||||
crews/support_agent
|
||||
```
|
||||
</Step>
|
||||
|
||||
<Step title="Reimplante">
|
||||
Salve a configuração e reimplante a automação. O novo diretório de trabalho
|
||||
será usado no próximo deploy.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## Regras de Caminho
|
||||
|
||||
O diretório de trabalho deve ser um caminho relativo dentro da raiz do
|
||||
repositório ou do ZIP.
|
||||
|
||||
| Regra | Exemplo |
|
||||
|-------|---------|
|
||||
| Use um caminho relativo | `crews/support_agent` |
|
||||
| Não comece com `/` | `/crews/support_agent` é inválido |
|
||||
| Não use segmentos de caminho `.` ou `..` | `crews/../support_agent` é inválido |
|
||||
| Use apenas letras, números, hifens, underscores, pontos e barras | `crews/support agent` é inválido |
|
||||
| Mantenha o caminho com 255 caracteres ou menos | Caminhos maiores são rejeitados |
|
||||
|
||||
O AMP remove espaços em branco no início e no fim, reduz barras repetidas e
|
||||
remove barras finais. Um valor em branco usa a raiz do repositório.
|
||||
|
||||
## Arquivos Lock e Workspaces UV
|
||||
|
||||
A pasta selecionada deve conter o `pyproject.toml` e o diretório `src/` da
|
||||
automação. Um arquivo `uv.lock` ou `poetry.lock` pode ficar na pasta selecionada
|
||||
ou na raiz do repositório.
|
||||
|
||||
Isso oferece suporte aos dois layouts comuns de monorepo:
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Arquivo lock do projeto">
|
||||
```text
|
||||
company-ai/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
|-- uv.lock
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
|
||||
<Tab title="Arquivo lock do workspace">
|
||||
```text
|
||||
company-ai/
|
||||
|-- uv.lock
|
||||
|-- packages/
|
||||
| `-- shared_tools/
|
||||
`-- crews/
|
||||
`-- support_agent/
|
||||
|-- pyproject.toml
|
||||
`-- src/
|
||||
`-- support_agent/
|
||||
`-- main.py
|
||||
```
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Tip>
|
||||
Se sua automação importar pacotes compartilhados de outro lugar do monorepo,
|
||||
declare esses pacotes no `pyproject.toml` usando configuração de workspace,
|
||||
caminho ou source do UV. O AMP executa a automação a partir da pasta
|
||||
selecionada, então o código compartilhado deve ser instalado como dependência
|
||||
em vez de depender da raiz do repositório no Python path.
|
||||
</Tip>
|
||||
|
||||
## Solução de Problemas
|
||||
|
||||
### Diretório de Trabalho Não Encontrado
|
||||
|
||||
Verifique se o caminho é relativo à raiz do repositório ou do ZIP. Para uploads
|
||||
de ZIP, o conteúdo do ZIP deve incluir exatamente o caminho informado como
|
||||
diretório de trabalho.
|
||||
|
||||
### pyproject.toml Ausente
|
||||
|
||||
O diretório de trabalho deve apontar para a pasta do projeto Crew ou Flow, não
|
||||
apenas para uma pasta pai que contém vários projetos.
|
||||
|
||||
### uv.lock ou poetry.lock Ausente
|
||||
|
||||
Faça commit de um arquivo lock na pasta do projeto selecionada ou na raiz do
|
||||
repositório. Para workspaces UV, manter `uv.lock` na raiz do workspace é
|
||||
suportado.
|
||||
|
||||
### Auto-Deploy Indisponível
|
||||
|
||||
O auto-deploy fica desabilitado enquanto um diretório de trabalho está definido.
|
||||
Use reimplantações manuais ou acione reimplantações a partir de CI/CD com a API
|
||||
do AMP.
|
||||
|
||||
<Card title="Deploy para AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
|
||||
Continue com o guia de implantação depois de escolher o diretório de trabalho
|
||||
do monorepo.
|
||||
</Card>
|
||||
@@ -1,454 +0,0 @@
|
||||
---
|
||||
title: Flows Conversacionais
|
||||
description: Crie apps de chat multi-turno com kickoff por turno, histórico de mensagens, roteamento de intenção, tracing e pontes WebSocket.
|
||||
icon: comments
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Visão geral
|
||||
|
||||
Apps conversacionais tratam cada linha do usuário como uma **nova execução do flow** com o **mesmo id de sessão**. A CrewAI oferece helpers para histórico de mensagens, classificação opcional de intenção, tracing adiado e pontes para UI — sem uma API `chat()` separada em `Flow`.
|
||||
|
||||
| Conceito | Implementação |
|
||||
|---------|----------------|
|
||||
| Id de sessão | `kickoff(session_id=...)` → `inputs["id"]` → `state.id` |
|
||||
| Linha do usuário | `kickoff(user_message=...)` acrescenta em `state.messages` antes do grafo rodar |
|
||||
| Fim do turno | `FlowFinished` só para **esta execução**; o chat segue no próximo `kickoff` |
|
||||
| Trace da sessão | `ConversationalConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
|
||||
|
||||
## Um ponto de entrada: `kickoff`
|
||||
|
||||
Use **`flow.kickoff(user_message=..., session_id=...)`** para cada mensagem (REST, WebSocket, CLI). Não crie um wrapper `chat()` customizado em `Flow`.
|
||||
|
||||
| API | Uso |
|
||||
|-----|-----|
|
||||
| `kickoff(user_message=..., session_id=...)` | Cada mensagem do usuário |
|
||||
| `kickoff_async(...)` | Mesmos parâmetros; entrada async nativa |
|
||||
| `ask()` | Prompt bloqueante **dentro** de um passo (wizard, esclarecimento) |
|
||||
| `@human_feedback` | Aprovar/rejeitar **saída de um passo** — não a próxima linha do chat |
|
||||
| `ChatSession.handle_turn(...)` | Camada de transporte sobre `kickoff` (SSE / WebSocket) |
|
||||
|
||||
## Início rápido
|
||||
|
||||
```python
|
||||
from uuid import uuid4
|
||||
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
Flow,
|
||||
listen,
|
||||
or_,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
from crewai.flow.persistence import SQLiteFlowPersistence
|
||||
|
||||
|
||||
class SupportFlow(Flow[ChatState]):
|
||||
conversational_config = ConversationalConfig(
|
||||
default_intents=["order", "help", "goodbye"],
|
||||
intent_llm="gpt-4o-mini",
|
||||
defer_trace_finalization=True,
|
||||
)
|
||||
|
||||
@start()
|
||||
def bootstrap(self):
|
||||
if not self.state.session_ready:
|
||||
self.state.session_ready = True
|
||||
return "ready"
|
||||
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
# last_intent definido em prepare_conversational_turn quando default_intents está setado
|
||||
return self.state.last_intent or "help"
|
||||
|
||||
@listen("order")
|
||||
def handle_order(self):
|
||||
reply = "Seu pedido está a caminho."
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("help")
|
||||
def handle_help(self):
|
||||
reply = "Como posso ajudar?"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@listen("goodbye")
|
||||
def handle_goodbye(self):
|
||||
reply = "Até logo!"
|
||||
self.append_message("assistant", reply)
|
||||
return reply
|
||||
|
||||
@persist(SQLiteFlowPersistence("support.db"))
|
||||
@listen(or_(handle_order, handle_help, handle_goodbye))
|
||||
def finalize(self):
|
||||
return self.state.model_dump()
|
||||
|
||||
|
||||
session_id = str(uuid4())
|
||||
flow = SupportFlow()
|
||||
|
||||
flow.kickoff(user_message="Onde está meu pedido?", session_id=session_id)
|
||||
flow.kickoff(user_message="E as devoluções?", session_id=session_id)
|
||||
flow.finalize_session_traces() # um link de trace para o chat inteiro
|
||||
```
|
||||
|
||||
## Ciclo de vida do turno
|
||||
|
||||
Cada `kickoff` com `user_message` executa este pipeline:
|
||||
|
||||
1. **`_configure_conversational_kickoff`** — mescla `session_id` / `user_message` em `inputs`, aplica `ConversationalConfig`, habilita tracing adiado quando configurado.
|
||||
2. **Restauração de estado** — se `inputs["id"]` existe e `@persist` está configurado, carrega o snapshot mais recente.
|
||||
3. **`FlowStarted`** — emitido apenas no primeiro turno da sessão adiada.
|
||||
4. **`prepare_conversational_turn`** — acrescenta a mensagem do usuário em `state.messages`, define `last_user_message`, limpa `last_intent`, classifica opcionalmente quando `intents` / `default_intents` + `intent_llm` estão definidos.
|
||||
5. **Execução do grafo** — `@start` → `@router` → handlers `@listen`.
|
||||
6. **Fim da execução** — `flow_finished` por turno e finalização de trace são **ignorados** com adiamento; `Agent.kickoff()` / crews aninhados também não fecham o batch pai.
|
||||
|
||||
Os handlers devem chamar **`append_message("assistant", reply)`** para que o próximo turno inclua a resposta do assistente. A linha do usuário já é salva no kickoff — não acrescente de novo nos handlers.
|
||||
|
||||
## `ConversationalConfig` (padrões em nível de classe)
|
||||
|
||||
Defina na subclasse de `Flow` como `conversational_config: ClassVar[ConversationalConfig | None]`.
|
||||
|
||||
| Campo | Padrão | Propósito |
|
||||
|-------|---------|-----------|
|
||||
| `default_intents` | `None` | Rótulos de outcome para classificação automática antes do kickoff |
|
||||
| `intent_llm` | `None` | Modelo para classificação (obrigatório quando há intents) |
|
||||
| `interactive_prompt` | `"You: "` | Prompt para `kickoff(interactive=True)` |
|
||||
| `interactive_timeout` | `None` | Timeout por linha no modo interativo |
|
||||
| `exit_commands` | `exit`, `quit` | Palavras que encerram o modo interativo |
|
||||
| `defer_trace_finalization` | `True` | Manter um batch de trace aberto entre turnos |
|
||||
|
||||
Sobrescreva por kickoff com `intents=` e `intent_llm=`.
|
||||
|
||||
## `ChatState` (formato persistido recomendado)
|
||||
|
||||
```python
|
||||
from crewai.flow import ChatState
|
||||
|
||||
|
||||
class MyChatState(ChatState):
|
||||
# Herdados: id, messages, last_user_message, last_intent, session_ready
|
||||
research_turn_count: int = 0
|
||||
custom_flag: bool = False
|
||||
```
|
||||
|
||||
| Campo | Função |
|
||||
|-------|--------|
|
||||
| `id` | UUID da sessão (igual a `session_id` / `inputs["id"]`) |
|
||||
| `messages` | `list` de `{role, content}` para histórico de LLM |
|
||||
| `last_user_message` | Última linha do usuário neste turno |
|
||||
| `last_intent` | Rótulo de rota após classificação (se usado) |
|
||||
| `session_ready` | Flag de bootstrap único (permissões, caches, etc.) |
|
||||
|
||||
`ConversationalInputs` é um `TypedDict` para `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
|
||||
|
||||
## API conversacional em `Flow`
|
||||
|
||||
### Parâmetros de `kickoff` / `kickoff_async`
|
||||
|
||||
| Parâmetro | Propósito |
|
||||
|-----------|-----------|
|
||||
| `user_message` | Texto deste turno (ou `{"role": "user", "content": "..."}`) |
|
||||
| `session_id` | UUID da conversa → `inputs["id"]` / `state.id` |
|
||||
| `intents` | Rótulos de outcome para `classify_intent` antes do kickoff |
|
||||
| `intent_llm` | LLM para classificação (obrigatório com `intents`) |
|
||||
| `interactive` | Loop CLI via `ask()` (só demos locais) |
|
||||
| `interactive_prompt` | Prompt no modo interativo |
|
||||
| `interactive_timeout` | Timeout de `ask()` por linha |
|
||||
| `exit_commands` | Palavras que encerram o modo interativo |
|
||||
| `inputs` | Campos extras de estado (mesclados com chaves conversacionais) |
|
||||
| `restore_from_state_id` | Hidratação fork de outro flow persistido |
|
||||
|
||||
### Atributos de instância
|
||||
|
||||
| Atributo | Propósito |
|
||||
|-----------|-----------|
|
||||
| `conversational_config` | Padrões `ConversationalConfig` em nível de classe |
|
||||
| `defer_trace_finalization` | Flag de instância; definida automaticamente a partir do config no kickoff |
|
||||
| `suppress_flow_events` | Oculta painéis Rich no console; **tracing ainda registra** eventos |
|
||||
| `stream` | Habilita streaming; use com `ChatSession.handle_turn(..., stream=True)` |
|
||||
|
||||
### Métodos e propriedades
|
||||
|
||||
| Nome | Descrição |
|
||||
|------|-------------|
|
||||
| `append_message(role, content, **extra)` | Acrescenta em `state.messages` (roles: `user`, `assistant`, `system`, `tool`) |
|
||||
| `conversation_messages` | Histórico somente leitura para chamadas LLM |
|
||||
| `classify_intent(text, outcomes, *, llm, context=None)` | Mapeia texto a um outcome (mesma lógica de `@human_feedback`) |
|
||||
| `receive_user_message(text, *, outcomes=None, llm=None)` | Acrescenta mensagem do usuário; opcionalmente define `last_intent` |
|
||||
| `finalize_session_traces()` | Emite `flow_finished` adiado e finaliza o batch de trace da sessão |
|
||||
| `_should_defer_trace_finalization()` | Se este flow adia finalização de trace por turno |
|
||||
| `input_history` | Trilha de auditoria de prompts e respostas de `ask()` |
|
||||
|
||||
### Helpers do módulo (`crewai.flow.conversation`)
|
||||
|
||||
Importáveis para testes ou orquestração customizada:
|
||||
|
||||
| Função | Descrição |
|
||||
|----------|-------------|
|
||||
| `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | Mescla kwargs conversacionais em `inputs` |
|
||||
| `get_conversation_messages(flow)` | Lê mensagens do estado ou buffer interno |
|
||||
| `append_message(flow, role, content, **extra)` | Igual ao método de instância |
|
||||
| `prepare_conversational_turn(flow, ...)` | Hidratação do turno (geralmente chamado pelo kickoff) |
|
||||
| `receive_user_message(flow, text, ...)` | Igual ao método de instância |
|
||||
| `set_state_field(flow, name, value)` | Define campo em estado dict ou Pydantic |
|
||||
| `get_conversational_config(flow)` | Lê `conversational_config` da classe |
|
||||
| `input_history_to_messages(entries)` | Converte `input_history` para formato de mensagens LLM |
|
||||
|
||||
## Padrões de roteamento de intenção
|
||||
|
||||
### A. Pré-classificar via `ConversationalConfig` (mais simples)
|
||||
|
||||
Defina `default_intents` e `intent_llm`. Cada kickoff classifica antes do `@router`; leia `self.state.last_intent` em `route()`.
|
||||
|
||||
### B. Classificar dentro do `@router` (prompts mais ricos)
|
||||
|
||||
Defina `default_intents=None` para o kickoff só acrescentar a mensagem. Em `route()`, chame `classify_intent` com prompt ou descrições customizadas:
|
||||
|
||||
```python
|
||||
@router(bootstrap)
|
||||
def route(self):
|
||||
intent = self.classify_intent(
|
||||
self._routing_prompt(self.state.last_user_message),
|
||||
("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
|
||||
llm=self.conversational_config.intent_llm or "gpt-4o-mini",
|
||||
)
|
||||
self.state.last_intent = intent
|
||||
return intent
|
||||
```
|
||||
|
||||
Use **`@listen("RESEARCH")`** (ou similar) para passos com `Agent.kickoff()` e ferramentas — não `LLM.call()` puro — quando precisar de pesquisa web ou uso multi-etapa de tools.
|
||||
|
||||
## Quando o flow termina mas o usuário continua conversando
|
||||
|
||||
`FlowFinished` significa que **esta execução do grafo** terminou. A conversa segue com outro `kickoff` e o mesmo `session_id`. `@persist` restaura `messages`, flags e contexto.
|
||||
|
||||
**Padrão de persistência:** prefira `@persist` em um **único passo terminal** (por exemplo `finalize`) em vez de na classe `Flow` inteira. Persist em nível de classe salva após cada método; `load_state` usa a linha mais recente, que pode ser snapshot no meio da execução e perder atualizações dos handlers no mesmo turno.
|
||||
|
||||
Não use `@human_feedback` para linhas de chat de follow-up, a menos que um humano precise aprovar uma saída específica antes de exibi-la.
|
||||
|
||||
## `Flow` conversacional (experimental)
|
||||
|
||||
<Warning>
|
||||
**Funcionalidade experimental.** A superfície do `Flow` conversacional
|
||||
(`conversational = True`, `handle_turn`, `ConversationConfig`,
|
||||
`RouterConfig`, `ConversationState`, o grafo embutido + helpers) vive em
|
||||
`crewai.experimental` e pode mudar de formato antes de graduar. Fixe a
|
||||
versão do CrewAI se depende de comportamento específico e acompanhe o
|
||||
changelog para mudanças quebradoras. Feedback / issues bem-vindos.
|
||||
</Warning>
|
||||
|
||||
Habilite o grafo conversacional definindo `conversational = True` em uma subclasse de `Flow`. O `Flow` base passa a expor um grafo embutido `@start` / `@router` / `converse_turn` / `end_conversation`, gerencia `state.messages`, dirige o LLM de roteamento e mantém o batch de trace aberto entre os turnos. Você escreve as **rotas customizadas**; o framework cuida do resto.
|
||||
|
||||
Use isto quando quiser um chat multi-turno com router LLM e handlers por rota sem cablar o ciclo de vida na mão. Use `Flow[ChatState]` (o padrão de mais baixo nível acima) quando precisar de controle total.
|
||||
|
||||
### Exemplo rápido
|
||||
|
||||
```python
|
||||
from crewai import LLM, Flow
|
||||
from crewai.flow import listen
|
||||
from crewai.experimental.conversational import (
|
||||
ConversationConfig,
|
||||
ConversationState,
|
||||
RouterConfig,
|
||||
)
|
||||
|
||||
|
||||
ROUTER_LLM = LLM(model="gpt-4o-mini")
|
||||
|
||||
|
||||
@ConversationConfig(
|
||||
system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
|
||||
llm=ROUTER_LLM,
|
||||
router=RouterConfig(), # rotas + descrições auto-descobertas pelos handlers @listen
|
||||
)
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
@listen("CREWAI_DOCS")
|
||||
def handle_crewai_docs(self) -> str:
|
||||
"""Look up the CrewAI documentation for framework/API questions."""
|
||||
...
|
||||
self.append_assistant_message(reply)
|
||||
return reply
|
||||
|
||||
|
||||
flow = SupportFlow()
|
||||
try:
|
||||
flow.handle_turn("O que você pode fazer?") # roteia para converse (built-in)
|
||||
flow.handle_turn("Pesquise na web por notícias de IA.") # roteia para INTERNET_SEARCH
|
||||
flow.handle_turn("Resuma o primeiro resultado.") # volta para converse
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
### `ConversationConfig`
|
||||
|
||||
Decorador de classe que anexa os defaults de chat por classe.
|
||||
|
||||
| Campo | Padrão | Propósito |
|
||||
|-------|--------|-----------|
|
||||
| `system_prompt` | `slices.conversational_system_prompt` (i18n) | System message usado pelo `converse_turn` embutido. Passe `""` para desativar totalmente. |
|
||||
| `llm` | `None` | LLM de conversa (usado pelo `converse_turn` e como fallback do router). |
|
||||
| `router` | `None` | `RouterConfig` para roteamento por LLM. Sem ele, o flow sempre cai em `converse`. |
|
||||
| `answer_from_history_prompt` | padrão do framework | System message para a rota opcional `answer_from_history`. |
|
||||
| `answer_from_history_llm` | `None` | Habilita o atalho `answer_from_history` quando definido. |
|
||||
| `intent_llm` | `None` | LLM para o caminho legado `intents=`/`default_intents`. |
|
||||
| `default_intents` | `None` | Labels de outcome para pré-classificação legada. |
|
||||
| `visible_agent_outputs` | `None` | `"all"` ou lista de nomes de agentes cujos `append_agent_result()` devem virar mensagens públicas. |
|
||||
| `defer_trace_finalization` | `True` | Mantém um único batch de trace aberto entre chamadas de `handle_turn()`. |
|
||||
|
||||
### `RouterConfig` e o catálogo de rotas auto-gerado
|
||||
|
||||
```python
|
||||
RouterConfig(
|
||||
prompt="Enquadramento de domínio opcional (política, voz, persona).",
|
||||
response_format=MyRoute, # opcional; auto-gerado caso contrário
|
||||
llm=ROUTER_LLM, # usa ConversationConfig.llm como fallback
|
||||
routes=["INTERNET_SEARCH", "CREWAI_DOCS"], # opcional; inferido dos listeners
|
||||
route_descriptions={
|
||||
"INTERNET_SEARCH": "Sobrescreve a docstring só desta rota.",
|
||||
},
|
||||
default_intent="converse", # usado quando a chamada ao LLM falha ou não há LLM
|
||||
fallback_intent="converse", # usado quando o LLM retorna rota inválida
|
||||
intent_field="intent",
|
||||
)
|
||||
```
|
||||
|
||||
O prompt do router é montado automaticamente. Para cada rota o framework escolhe a descrição nesta precedência:
|
||||
|
||||
1. `RouterConfig.route_descriptions[label]` — override explícito.
|
||||
2. `Flow.builtin_route_descriptions[label]` — texto canônico do framework para `converse`, `end`, `answer_from_history` (otimizado para o LLM de routing).
|
||||
3. Primeira linha não vazia da docstring do handler `@listen(label)`.
|
||||
4. Vazio (a rota aparece no catálogo sem descrição).
|
||||
|
||||
Na prática, **adicionar uma rota é `@listen("X")` + uma docstring de uma linha**:
|
||||
|
||||
```python
|
||||
@listen("INTERNET_SEARCH")
|
||||
def handle_internet_search(self) -> str:
|
||||
"""Fresh web research, current news, real-time lookups."""
|
||||
...
|
||||
```
|
||||
|
||||
…e o LLM de routing vê:
|
||||
|
||||
```
|
||||
Routes:
|
||||
- CREWAI_DOCS: Look up the CrewAI documentation for framework/API questions.
|
||||
- INTERNET_SEARCH: Fresh web research, current news, real-time lookups.
|
||||
- converse: Ordinary chat, follow-ups, summaries, clarifications…
|
||||
- end: User signals the conversation is finished (goodbye, exit, done).
|
||||
```
|
||||
|
||||
`RouterConfig.prompt` é para **enquadramento de domínio** (persona do assistente, regras de negócio, voz). O catálogo de rotas é auto-gerado — não liste rotas em `prompt`; elas vão sair de sincronia assim que você adicionar um handler.
|
||||
|
||||
### Rotas embutidas
|
||||
|
||||
| Rota | Handler | Propósito |
|
||||
|------|---------|-----------|
|
||||
| `converse` | `converse_turn` | Handler de chat padrão. Chama `ConversationConfig.llm` com o system prompt + histórico canônico. |
|
||||
| `end` | `end_conversation` | Define `state.ended = True` e emite uma resposta de encerramento. |
|
||||
| `answer_from_history` | `answer_from_history_turn` | Opcional. Cai aqui quando `ConversationConfig.answer_from_history_llm` está definido e a mensagem pode ser respondida só pelo histórico. |
|
||||
|
||||
Você pode sobrescrever qualquer uma definindo um handler com o mesmo nome na subclasse.
|
||||
|
||||
### Semântica de `handle_turn()`
|
||||
|
||||
`flow.handle_turn(message)` roda um turno:
|
||||
|
||||
1. Reseta o tracking por execução (`_completed_methods`, `_method_outputs`) para o grafo re-rodar — sem isso, chamadas repetidas de `kickoff` na mesma instância dariam curto-circuito no turno 2+ porque `Flow.kickoff_async` trata `inputs={"id": ...}` como restauração de checkpoint.
|
||||
2. Anexa a mensagem do usuário em `state.messages`, define `current_user_message` / `last_user_message`. `last_intent` é **preservado do turno anterior** para que o LLM de routing possa usá-lo como sinal.
|
||||
3. Roda `conversation_start` → `route_conversation` → o handler `@listen` escolhido.
|
||||
4. O router grava sua decisão em `state.last_intent` (visível para o contexto de routing do próximo turno).
|
||||
5. Se seu handler retornou uma string e ainda não chamou `append_assistant_message`, `handle_turn` anexa para você.
|
||||
|
||||
Você também pode chamar `flow.kickoff(user_message=..., session_id=...)` diretamente — a mesma lógica de reset/run é acionada. `handle_turn` é o wrapper ergonômico.
|
||||
|
||||
### Comportamento customizado do router
|
||||
|
||||
Para rodar efeitos colaterais (setup de event bus, telemetria) em toda decisão de routing, sobrescreva `route_turn`:
|
||||
|
||||
```python
|
||||
class SupportFlow(Flow[ConversationState]):
|
||||
conversational = True
|
||||
|
||||
def route_turn(self, context: dict[str, Any]) -> str | None:
|
||||
self.event_bus = MyBus(self)
|
||||
return super().route_turn(context)
|
||||
```
|
||||
|
||||
Para ignorar o router LLM e escolher uma rota programaticamente, retorne uma string de `route_turn`; retornar `None` cai no `_route_with_config(...)`.
|
||||
|
||||
### `append_assistant_message` e `append_agent_result`
|
||||
|
||||
Dentro de um handler `@listen(label)`, escolha:
|
||||
|
||||
- `self.append_assistant_message(text)` — adiciona um turno de assistente visível ao usuário em `state.messages`. O `converse_turn` do próximo turno vai vê-lo.
|
||||
- `self.append_agent_result(agent_name, result, visibility="private")` — registra um evento estruturado em `state.events` e uma thread em `state.agent_threads[agent_name]`. Visibilidade pública também chama `append_assistant_message` automaticamente. Use resultados privados para trabalho de bastidor que não deve poluir o histórico canônico.
|
||||
|
||||
`ConversationConfig.visible_agent_outputs` pode promover globalmente os resultados privados de agentes específicos para públicos (`"all"` ou lista de nomes).
|
||||
|
||||
## Tracing entre turnos
|
||||
|
||||
Com `defer_trace_finalization=True` (padrão em `ConversationalConfig`):
|
||||
|
||||
- **Um batch de trace** para toda a sessão de chat.
|
||||
- **`flow_started`** só no primeiro turno; **`flow_finished`** uma vez em `finalize_session_traces()`.
|
||||
- **`kickoff` por turno** não exibe “Trace batch finalized”.
|
||||
- **Trabalho aninhado** (`Agent.kickoff()`, crews, tools Exa) acrescenta ao batch **pai**; flows internos de `AgentExecutor` não fecham o batch da sessão cedo.
|
||||
|
||||
```python
|
||||
try:
|
||||
while True:
|
||||
line = input("You: ").strip()
|
||||
if not line:
|
||||
break
|
||||
flow.kickoff(user_message=line, session_id=session_id)
|
||||
finally:
|
||||
flow.finalize_session_traces()
|
||||
```
|
||||
|
||||
`ChatSession.close()` chama `finalize_session_traces()` quando o adiamento está habilitado.
|
||||
|
||||
`suppress_flow_events=True` só oculta painéis do console; eventos de trace e método ainda são emitidos.
|
||||
|
||||
### Ciclo de vida de trace do `Flow` conversacional
|
||||
|
||||
O [`Flow` conversacional](#flow-conversacional-experimental) experimental usa o mesmo ciclo de vida de tracing: `defer_trace_finalization` é `True` por padrão, então cada `handle_turn()` mantém o trace da sessão aberto. Sempre finalize ao fim da sessão — envolva seu loop em `try/finally` e chame `flow.finalize_session_traces()` na saída. Sem isso, o batch fica aberto e a última conversa pode nunca ser exportada.
|
||||
|
||||
## Streaming
|
||||
|
||||
Defina `stream = True` na classe `Flow`. `kickoff(...)` então emitirá `assistant_delta` (e eventos relacionados) pelo event bus padrão.
|
||||
|
||||
## Imports
|
||||
|
||||
```python
|
||||
from crewai.flow import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
ConversationalInputs,
|
||||
Flow,
|
||||
listen,
|
||||
persist,
|
||||
router,
|
||||
start,
|
||||
)
|
||||
```
|
||||
|
||||
## Veja também
|
||||
|
||||
- [Dominando o Gerenciamento de Estado em Flows](/pt-BR/guides/flows/mastering-flow-state) — persistência, estado Pydantic, `@persist`
|
||||
- [Construa Seu Primeiro Flow](/pt-BR/guides/flows/first-flow) — fundamentos de flow
|
||||
- Demo: `lib/crewai/runner_conversational_flow_simple.py` — REPL mínimo com `RESEARCH` + agente Exa
|
||||
@@ -614,7 +614,6 @@ Agora que você construiu seu primeiro flow, pode:
|
||||
3. Explorar as funções `and_` e `or_` para execuções paralelas e mais complexas
|
||||
4. Conectar seu flow a APIs externas, bancos de dados ou interfaces de usuário
|
||||
5. Combinar múltiplos crews especializados em um único flow
|
||||
6. Criar apps de chat multi-turn com [Flows conversacionais](/pt-BR/guides/flows/conversational-flows) (`kickoff` por mensagem, `ChatSession`, tracing adiado)
|
||||
|
||||
<Check>
|
||||
Parabéns! Você construiu seu primeiro CrewAI Flow que combina código regular, chamadas diretas a LLM e processamento baseado em crews para criar um guia abrangente. Essas habilidades fundamentais permitem criar aplicações de IA cada vez mais sofisticadas, capazes de resolver problemas complexos de múltiplas etapas por meio de controle procedural e inteligência colaborativa.
|
||||
|
||||
@@ -22,8 +22,6 @@ Um gerenciamento de estado efetivo possibilita que você:
|
||||
5. **Escalone suas aplicações** – Ofereça suporte a workflows complexos com organização apropriada dos dados
|
||||
6. **Habilite aplicações conversacionais** – Armazene e acesse o histórico da conversa para interações de IA com contexto
|
||||
|
||||
Para chat multi-turn (`kickoff` por linha do usuário, `ChatState`, roteamento por intenção, tracing adiado e `ChatSession`), veja [Flows conversacionais](/pt-BR/guides/flows/conversational-flows).
|
||||
|
||||
Vamos explorar como aproveitar essas capacidades de forma eficiente.
|
||||
|
||||
## Fundamentos do Gerenciamento de Estado
|
||||
|
||||
@@ -8,7 +8,7 @@ authors = [
|
||||
]
|
||||
requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
"crewai-core==1.14.7a1",
|
||||
"crewai-core==1.14.6",
|
||||
"click>=8.1.7,<9",
|
||||
"pydantic>=2.11.9,<2.13",
|
||||
"pydantic-settings~=2.10.1",
|
||||
|
||||
@@ -1 +1 @@
|
||||
__version__ = "1.14.7a1"
|
||||
__version__ = "1.14.6"
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.7a1"
|
||||
"crewai[tools]==1.14.6"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.7a1"
|
||||
"crewai[tools]==1.14.6"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.7a1"
|
||||
"crewai[tools]==1.14.6"
|
||||
]
|
||||
|
||||
[tool.crewai]
|
||||
|
||||
@@ -1 +1 @@
|
||||
__version__ = "1.14.7a1"
|
||||
__version__ = "1.14.6"
|
||||
|
||||
@@ -152,4 +152,4 @@ __all__ = [
|
||||
"wrap_file_source",
|
||||
]
|
||||
|
||||
__version__ = "1.14.7a1"
|
||||
__version__ = "1.14.6"
|
||||
|
||||
@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
"pytube~=15.0.0",
|
||||
"requests>=2.33.0,<3",
|
||||
"crewai==1.14.7a1",
|
||||
"crewai==1.14.6",
|
||||
"tiktoken>=0.8.0,<0.13",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
"python-docx~=1.2.0",
|
||||
|
||||
@@ -330,4 +330,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.14.7a1"
|
||||
__version__ = "1.14.6"
|
||||
|
||||
158
lib/crewai-tools/src/crewai_tools/security/safe_requests.py
Normal file
158
lib/crewai-tools/src/crewai_tools/security/safe_requests.py
Normal file
@@ -0,0 +1,158 @@
|
||||
"""SSRF-safe HTTP fetching for crewai-tools.
|
||||
|
||||
:func:`validate_url` checks the URL it is handed, but it cannot protect a
|
||||
fetch on its own: ``requests`` re-resolves DNS at connect time and follows
|
||||
redirects automatically, so a public-looking host that 302-redirects to an
|
||||
internal address (or that rebinds DNS between validation and connect) reaches
|
||||
the internal target without ever being re-checked.
|
||||
|
||||
This module closes both gaps at the connection layer:
|
||||
|
||||
* :class:`SSRFProtectedAdapter` re-runs :func:`validate_url` for every request
|
||||
it sends. ``requests.Session.send`` invokes the adapter once per redirect
|
||||
hop, so each ``Location`` target is validated before it is followed.
|
||||
* The adapter's connections validate the *actual* peer IP immediately after
|
||||
the socket connects. The IP that was authorised is therefore the IP the
|
||||
connection uses, removing the DNS time-of-check/time-of-use gap that
|
||||
:func:`validate_url`'s own ``getaddrinfo`` call leaves open.
|
||||
|
||||
Use :func:`safe_get` (or :func:`create_safe_session`) instead of calling
|
||||
``requests.get`` directly from any tool that fetches a user- or
|
||||
LLM-controlled URL.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
import requests
|
||||
from requests.adapters import DEFAULT_POOLBLOCK, HTTPAdapter
|
||||
from urllib3.connection import HTTPConnection, HTTPSConnection
|
||||
from urllib3.connectionpool import HTTPConnectionPool, HTTPSConnectionPool
|
||||
from urllib3.poolmanager import PoolManager
|
||||
|
||||
from crewai_tools.security.safe_path import (
|
||||
_is_escape_hatch_enabled,
|
||||
_is_private_or_reserved,
|
||||
validate_url,
|
||||
)
|
||||
|
||||
|
||||
def _assert_safe_peer(sock: Any) -> None:
|
||||
"""Raise if a connected socket's peer is a private/reserved address.
|
||||
|
||||
Validating the real peer (rather than a separately resolved IP) is what
|
||||
defeats DNS rebinding: the address we connected to is the address we check.
|
||||
"""
|
||||
if _is_escape_hatch_enabled():
|
||||
return
|
||||
try:
|
||||
peer = sock.getpeername()
|
||||
except OSError:
|
||||
return
|
||||
ip_str = str(peer[0])
|
||||
if _is_private_or_reserved(ip_str):
|
||||
raise ValueError(
|
||||
f"Connection resolved to private/reserved IP {ip_str}. "
|
||||
f"Access to internal networks is not allowed (possible SSRF via "
|
||||
f"redirect or DNS rebinding)."
|
||||
)
|
||||
|
||||
|
||||
class _SafeHTTPConnection(HTTPConnection):
|
||||
def connect(self) -> None:
|
||||
super().connect()
|
||||
_assert_safe_peer(self.sock)
|
||||
|
||||
|
||||
class _SafeHTTPSConnection(HTTPSConnection):
|
||||
def connect(self) -> None:
|
||||
super().connect()
|
||||
_assert_safe_peer(self.sock)
|
||||
|
||||
|
||||
class _SafeHTTPConnectionPool(HTTPConnectionPool):
|
||||
ConnectionCls = _SafeHTTPConnection
|
||||
|
||||
|
||||
class _SafeHTTPSConnectionPool(HTTPSConnectionPool):
|
||||
ConnectionCls = _SafeHTTPSConnection
|
||||
|
||||
|
||||
_SAFE_POOL_CLASSES = {
|
||||
"http": _SafeHTTPConnectionPool,
|
||||
"https": _SafeHTTPSConnectionPool,
|
||||
}
|
||||
|
||||
|
||||
class _SafePoolManager(PoolManager):
|
||||
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
self.pool_classes_by_scheme = _SAFE_POOL_CLASSES
|
||||
|
||||
|
||||
class SSRFProtectedAdapter(HTTPAdapter):
|
||||
"""Transport adapter that re-validates every hop and pins the peer IP.
|
||||
|
||||
``validate_url`` runs on each ``send`` — including every redirect hop
|
||||
``requests`` follows — and the underlying connections reject any socket
|
||||
that ends up connected to a private/reserved address.
|
||||
"""
|
||||
|
||||
def init_poolmanager(
|
||||
self,
|
||||
connections: int,
|
||||
maxsize: int,
|
||||
block: bool = DEFAULT_POOLBLOCK,
|
||||
**pool_kwargs: Any,
|
||||
) -> None:
|
||||
self.poolmanager = _SafePoolManager(
|
||||
num_pools=connections,
|
||||
maxsize=maxsize,
|
||||
block=block,
|
||||
**pool_kwargs,
|
||||
)
|
||||
|
||||
def send(self, request: Any, *args: Any, **kwargs: Any) -> Any:
|
||||
# Re-validate the target of every request the session sends. Because
|
||||
# Session.send calls this once per redirect hop, each Location is
|
||||
# checked before it is followed.
|
||||
validate_url(request.url)
|
||||
return super().send(request, *args, **kwargs)
|
||||
|
||||
|
||||
def create_safe_session() -> requests.Session:
|
||||
"""Return a ``requests.Session`` that is hardened against SSRF.
|
||||
|
||||
The session validates every request (and redirect hop) and pins
|
||||
connections to the validated peer IP.
|
||||
"""
|
||||
session = requests.Session()
|
||||
adapter = SSRFProtectedAdapter()
|
||||
session.mount("http://", adapter)
|
||||
session.mount("https://", adapter)
|
||||
return session
|
||||
|
||||
|
||||
def safe_get(url: str, **kwargs: Any) -> requests.Response:
|
||||
"""Perform an SSRF-safe ``GET``.
|
||||
|
||||
Drop-in replacement for ``requests.get`` for tools that fetch a
|
||||
user- or LLM-controlled URL. Validates the initial URL and every redirect
|
||||
hop, and rejects connections that land on private/reserved addresses.
|
||||
|
||||
Args:
|
||||
url: The URL to fetch.
|
||||
**kwargs: Forwarded to ``Session.get`` (``headers``, ``cookies``,
|
||||
``timeout``, ...).
|
||||
|
||||
Returns:
|
||||
The ``requests.Response``.
|
||||
|
||||
Raises:
|
||||
ValueError: If the URL, a redirect target, or the connected peer is
|
||||
not allowed.
|
||||
"""
|
||||
validate_url(url)
|
||||
with create_safe_session() as session:
|
||||
return session.get(url, **kwargs)
|
||||
@@ -3,9 +3,8 @@ from typing import Any
|
||||
|
||||
from crewai.tools import BaseTool
|
||||
from pydantic import BaseModel, Field
|
||||
import requests
|
||||
|
||||
from crewai_tools.security.safe_path import validate_url
|
||||
from crewai_tools.security.safe_requests import safe_get
|
||||
|
||||
|
||||
try:
|
||||
@@ -83,8 +82,7 @@ class ScrapeElementFromWebsiteTool(BaseTool):
|
||||
if website_url is None or css_element is None:
|
||||
raise ValueError("Both website_url and css_element must be provided.")
|
||||
|
||||
website_url = validate_url(website_url)
|
||||
page = requests.get(
|
||||
page = safe_get(
|
||||
website_url,
|
||||
headers=self.headers,
|
||||
cookies=self.cookies if self.cookies else {},
|
||||
|
||||
@@ -3,9 +3,8 @@ import re
|
||||
from typing import Any
|
||||
|
||||
from pydantic import Field
|
||||
import requests
|
||||
|
||||
from crewai_tools.security.safe_path import validate_url
|
||||
from crewai_tools.security.safe_requests import safe_get
|
||||
|
||||
|
||||
try:
|
||||
@@ -75,8 +74,7 @@ class ScrapeWebsiteTool(BaseTool):
|
||||
if website_url is None:
|
||||
raise ValueError("Website URL must be provided.")
|
||||
|
||||
website_url = validate_url(website_url)
|
||||
page = requests.get(
|
||||
page = safe_get(
|
||||
website_url,
|
||||
timeout=15,
|
||||
headers=self.headers,
|
||||
|
||||
124
lib/crewai-tools/tests/utilities/test_safe_requests.py
Normal file
124
lib/crewai-tools/tests/utilities/test_safe_requests.py
Normal file
@@ -0,0 +1,124 @@
|
||||
"""Tests for SSRF-safe HTTP fetching (redirect + DNS-rebinding protection)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import http.server
|
||||
import socketserver
|
||||
import threading
|
||||
|
||||
import pytest
|
||||
import requests
|
||||
|
||||
from crewai_tools.security import safe_requests
|
||||
from crewai_tools.security.safe_requests import (
|
||||
SSRFProtectedAdapter,
|
||||
create_safe_session,
|
||||
safe_get,
|
||||
)
|
||||
|
||||
|
||||
INTERNAL_BODY = b"INTERNAL-ONLY-SECRET"
|
||||
|
||||
|
||||
class _InternalHandler(http.server.BaseHTTPRequestHandler):
|
||||
def do_GET(self):
|
||||
self.send_response(200)
|
||||
self.send_header("Content-Type", "text/plain")
|
||||
self.end_headers()
|
||||
self.wfile.write(INTERNAL_BODY)
|
||||
|
||||
def log_message(self, *args): # silence
|
||||
pass
|
||||
|
||||
|
||||
def _serve(handler):
|
||||
"""Start a localhost server on an ephemeral port; return (server, port)."""
|
||||
server = socketserver.TCPServer(("127.0.0.1", 0), handler)
|
||||
port = server.server_address[1]
|
||||
threading.Thread(target=server.serve_forever, daemon=True).start()
|
||||
return server, port
|
||||
|
||||
|
||||
class TestRedirectRevalidation:
|
||||
"""Layer 1: validate_url runs on every send, including each redirect hop.
|
||||
|
||||
``requests.Session.send`` calls ``adapter.send`` once per redirect hop, so
|
||||
re-validating in ``send`` is what blocks a 302 to an internal target.
|
||||
"""
|
||||
|
||||
def test_adapter_revalidates_before_any_network_call(self, monkeypatch):
|
||||
calls: list[str] = []
|
||||
|
||||
def spy(url: str) -> str:
|
||||
calls.append(url)
|
||||
if "internal.target" in url:
|
||||
raise ValueError("URL resolves to private/reserved IP")
|
||||
return url
|
||||
|
||||
monkeypatch.setattr(safe_requests, "validate_url", spy)
|
||||
|
||||
adapter = SSRFProtectedAdapter()
|
||||
# Internal redirect target: send() must reject it before ever calling
|
||||
# the real transport (super().send is never reached).
|
||||
req = requests.Request("GET", "http://internal.target/").prepare()
|
||||
with pytest.raises(ValueError, match="private/reserved"):
|
||||
adapter.send(req)
|
||||
assert calls == ["http://internal.target/"]
|
||||
|
||||
def test_session_mounts_protected_adapter(self):
|
||||
session = create_safe_session()
|
||||
assert isinstance(session.get_adapter("http://x"), SSRFProtectedAdapter)
|
||||
assert isinstance(session.get_adapter("https://x"), SSRFProtectedAdapter)
|
||||
|
||||
|
||||
class _FakeSock:
|
||||
def __init__(self, peer):
|
||||
self._peer = peer
|
||||
|
||||
def getpeername(self):
|
||||
return self._peer
|
||||
|
||||
|
||||
class TestConnectionPeerGuard:
|
||||
"""Layer 2: the connection rejects an internal peer IP at connect time.
|
||||
|
||||
This is what closes the validate-then-connect DNS-rebinding gap — the IP
|
||||
the socket actually connected to is the IP that gets checked, so a host
|
||||
that resolved public at validation time but connects internal is blocked.
|
||||
"""
|
||||
|
||||
def test_safe_get_blocks_direct_internal(self):
|
||||
# No network: validate_url rejects 127.0.0.1 at the URL layer first.
|
||||
with pytest.raises(ValueError, match="private/reserved"):
|
||||
safe_get("http://127.0.0.1:9/", timeout=10)
|
||||
|
||||
def test_assert_safe_peer_blocks_private(self):
|
||||
with pytest.raises(ValueError, match="private/reserved"):
|
||||
safe_requests._assert_safe_peer(_FakeSock(("127.0.0.1", 80)))
|
||||
|
||||
def test_assert_safe_peer_blocks_metadata(self):
|
||||
with pytest.raises(ValueError, match="private/reserved"):
|
||||
safe_requests._assert_safe_peer(_FakeSock(("169.254.169.254", 80)))
|
||||
|
||||
def test_assert_safe_peer_allows_public(self):
|
||||
# A public IP must not raise.
|
||||
safe_requests._assert_safe_peer(_FakeSock(("93.184.216.34", 80)))
|
||||
|
||||
def test_assert_safe_peer_respects_escape_hatch(self, monkeypatch):
|
||||
monkeypatch.setenv("CREWAI_TOOLS_ALLOW_UNSAFE_PATHS", "true")
|
||||
# No raise even for a private peer when the escape hatch is on.
|
||||
safe_requests._assert_safe_peer(_FakeSock(("127.0.0.1", 80)))
|
||||
|
||||
def test_connection_validates_peer_after_connect(self, monkeypatch):
|
||||
"""_SafeHTTPConnection.connect runs the peer guard after connecting."""
|
||||
conn = safe_requests._SafeHTTPConnection("example.com")
|
||||
|
||||
def fake_super_connect(self):
|
||||
# Simulate a rebind: we connected to an internal address.
|
||||
self.sock = _FakeSock(("127.0.0.1", 80))
|
||||
|
||||
monkeypatch.setattr(
|
||||
safe_requests.HTTPConnection, "connect", fake_super_connect
|
||||
)
|
||||
with pytest.raises(ValueError, match="private/reserved"):
|
||||
conn.connect()
|
||||
@@ -8,8 +8,8 @@ authors = [
|
||||
]
|
||||
requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
"crewai-core==1.14.7a1",
|
||||
"crewai-cli==1.14.7a1",
|
||||
"crewai-core==1.14.6",
|
||||
"crewai-cli==1.14.6",
|
||||
# Core Dependencies
|
||||
"pydantic>=2.11.9,<2.13",
|
||||
"openai>=2.30.0,<3",
|
||||
@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.14.7a1",
|
||||
"crewai-tools==1.14.6",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken>=0.8.0,<0.13"
|
||||
@@ -138,9 +138,6 @@ torchvision = [
|
||||
crewai-files = { workspace = true }
|
||||
|
||||
|
||||
[project.scripts]
|
||||
crewai = "crewai_cli.cli:crewai"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
@@ -48,7 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.14.7a1"
|
||||
__version__ = "1.14.6"
|
||||
|
||||
_LAZY_IMPORTS: dict[str, tuple[str, str]] = {
|
||||
"Memory": ("crewai.memory.unified_memory", "Memory"),
|
||||
|
||||
@@ -306,24 +306,20 @@ class EventListener(BaseEventListener):
|
||||
self._telemetry.flow_execution_span(
|
||||
event.flow_name, list(source._methods.keys())
|
||||
)
|
||||
if not getattr(source, "suppress_flow_events", False):
|
||||
self.formatter.handle_flow_created(event.flow_name, str(source.flow_id))
|
||||
self.formatter.handle_flow_started(event.flow_name, str(source.flow_id))
|
||||
self.formatter.handle_flow_created(event.flow_name, str(source.flow_id))
|
||||
self.formatter.handle_flow_started(event.flow_name, str(source.flow_id))
|
||||
|
||||
@crewai_event_bus.on(FlowFinishedEvent)
|
||||
def on_flow_finished(source: Any, event: FlowFinishedEvent) -> None:
|
||||
if not getattr(source, "suppress_flow_events", False):
|
||||
self.formatter.handle_flow_status(
|
||||
event.flow_name,
|
||||
source.flow_id,
|
||||
)
|
||||
self.formatter.handle_flow_status(
|
||||
event.flow_name,
|
||||
source.flow_id,
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(MethodExecutionStartedEvent)
|
||||
def on_method_execution_started(
|
||||
source: Any, event: MethodExecutionStartedEvent
|
||||
_: Any, event: MethodExecutionStartedEvent
|
||||
) -> None:
|
||||
if getattr(source, "suppress_flow_events", False):
|
||||
return
|
||||
self.formatter.handle_method_status(
|
||||
event.method_name,
|
||||
"running",
|
||||
@@ -331,10 +327,8 @@ class EventListener(BaseEventListener):
|
||||
|
||||
@crewai_event_bus.on(MethodExecutionFinishedEvent)
|
||||
def on_method_execution_finished(
|
||||
source: Any, event: MethodExecutionFinishedEvent
|
||||
_: Any, event: MethodExecutionFinishedEvent
|
||||
) -> None:
|
||||
if getattr(source, "suppress_flow_events", False):
|
||||
return
|
||||
self.formatter.handle_method_status(
|
||||
event.method_name,
|
||||
"completed",
|
||||
|
||||
@@ -222,8 +222,6 @@ To enable tracing later, do any one of these:
|
||||
return
|
||||
self.batch_manager.batch_owner_type = None
|
||||
self.batch_manager.batch_owner_id = None
|
||||
self.batch_manager.defer_session_finalization = False
|
||||
self.batch_manager._batch_finalized = False
|
||||
self.batch_manager.current_batch = None
|
||||
self.batch_manager.event_buffer.clear()
|
||||
self.batch_manager.trace_batch_id = None
|
||||
|
||||
@@ -62,7 +62,6 @@ class TraceBatchManager:
|
||||
self._pending_events_lock = Lock()
|
||||
self._pending_events_cv = Condition(self._pending_events_lock)
|
||||
self._pending_events_count = 0
|
||||
self._finalize_lock = Lock()
|
||||
|
||||
self.is_current_batch_ephemeral = False
|
||||
self.trace_batch_id: str | None = None
|
||||
@@ -71,8 +70,6 @@ class TraceBatchManager:
|
||||
self.execution_start_times: dict[str, datetime] = {}
|
||||
self.batch_owner_type: str | None = None
|
||||
self.batch_owner_id: str | None = None
|
||||
self.defer_session_finalization: bool = False
|
||||
self._batch_finalized: bool = False
|
||||
self.backend_initialized: bool = False
|
||||
self.ephemeral_trace_url: str | None = None
|
||||
try:
|
||||
@@ -104,7 +101,6 @@ class TraceBatchManager:
|
||||
user_context=user_context, execution_metadata=execution_metadata
|
||||
)
|
||||
self.is_current_batch_ephemeral = use_ephemeral
|
||||
self._batch_finalized = False
|
||||
|
||||
self.record_start_time("execution")
|
||||
|
||||
@@ -316,9 +312,6 @@ class TraceBatchManager:
|
||||
def finalize_batch(self) -> TraceBatch | None:
|
||||
"""Finalize batch and return it for sending"""
|
||||
|
||||
if self._batch_finalized:
|
||||
return None
|
||||
|
||||
if not self.current_batch or not is_tracing_enabled_in_context():
|
||||
return None
|
||||
|
||||
@@ -347,15 +340,16 @@ class TraceBatchManager:
|
||||
self.current_batch.events = sorted_events
|
||||
events_sent_count = len(sorted_events)
|
||||
if sorted_events:
|
||||
original_buffer = self.event_buffer
|
||||
self.event_buffer = sorted_events
|
||||
events_sent_to_backend_status = self._send_events_to_backend()
|
||||
self.event_buffer = original_buffer
|
||||
if events_sent_to_backend_status == 500 and self.trace_batch_id:
|
||||
self._mark_batch_as_failed(
|
||||
self.trace_batch_id, "Error sending events to backend"
|
||||
)
|
||||
return None
|
||||
if not self._finalize_backend_batch(events_sent_count):
|
||||
return None
|
||||
self._finalize_backend_batch(events_sent_count)
|
||||
|
||||
finalized_batch = self.current_batch
|
||||
|
||||
@@ -366,87 +360,80 @@ class TraceBatchManager:
|
||||
self.event_buffer.clear()
|
||||
self.trace_batch_id = None
|
||||
self.is_current_batch_ephemeral = False
|
||||
self._batch_finalized = True
|
||||
|
||||
self._cleanup_batch_data()
|
||||
|
||||
return finalized_batch
|
||||
|
||||
def _finalize_backend_batch(self, events_count: int = 0) -> bool:
|
||||
def _finalize_backend_batch(self, events_count: int = 0) -> None:
|
||||
"""Send batch finalization to backend
|
||||
|
||||
Args:
|
||||
events_count: Number of events that were successfully sent
|
||||
"""
|
||||
with self._finalize_lock:
|
||||
batch_id = self.trace_batch_id
|
||||
is_ephemeral = self.is_current_batch_ephemeral
|
||||
if self._batch_finalized or not self.plus_api or not batch_id:
|
||||
return True
|
||||
if not self.plus_api or not self.trace_batch_id:
|
||||
return
|
||||
|
||||
try:
|
||||
payload: TraceFinalizePayload = {
|
||||
"status": "completed",
|
||||
"duration_ms": self.calculate_duration("execution"),
|
||||
"final_event_count": events_count,
|
||||
}
|
||||
try:
|
||||
payload: TraceFinalizePayload = {
|
||||
"status": "completed",
|
||||
"duration_ms": self.calculate_duration("execution"),
|
||||
"final_event_count": events_count,
|
||||
}
|
||||
|
||||
response = (
|
||||
self.plus_api.finalize_ephemeral_trace_batch(batch_id, payload)
|
||||
if is_ephemeral
|
||||
else self.plus_api.finalize_trace_batch(batch_id, payload)
|
||||
response = (
|
||||
self.plus_api.finalize_ephemeral_trace_batch(
|
||||
self.trace_batch_id, payload
|
||||
)
|
||||
if self.is_current_batch_ephemeral
|
||||
else self.plus_api.finalize_trace_batch(self.trace_batch_id, payload)
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
access_code = response.json().get("access_code", None)
|
||||
console = Console()
|
||||
settings = Settings()
|
||||
base_url = settings.enterprise_base_url or DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
return_link = (
|
||||
f"{base_url}/crewai_plus/trace_batches/{self.trace_batch_id}"
|
||||
if not self.is_current_batch_ephemeral and access_code is None
|
||||
else f"{base_url}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
self._batch_finalized = True
|
||||
access_code = response.json().get("access_code", None)
|
||||
console = Console()
|
||||
settings = Settings()
|
||||
base_url = (
|
||||
settings.enterprise_base_url or DEFAULT_CREWAI_ENTERPRISE_URL
|
||||
)
|
||||
return_link = (
|
||||
f"{base_url}/crewai_plus/trace_batches/{batch_id}"
|
||||
if not is_ephemeral and access_code is None
|
||||
else f"{base_url}/crewai_plus/ephemeral_trace_batches/{batch_id}?access_code={access_code}"
|
||||
)
|
||||
if self.is_current_batch_ephemeral:
|
||||
self.ephemeral_trace_url = return_link
|
||||
|
||||
if is_ephemeral:
|
||||
self.ephemeral_trace_url = return_link
|
||||
message_parts = [
|
||||
f"✅ Trace batch finalized with session ID: {self.trace_batch_id}",
|
||||
"",
|
||||
f"🔗 View here: {return_link}",
|
||||
]
|
||||
|
||||
message_parts = [
|
||||
f"✅ Trace batch finalized with session ID: {batch_id}",
|
||||
"",
|
||||
f"🔗 View here: {return_link}",
|
||||
]
|
||||
if access_code:
|
||||
message_parts.append(f"🔑 Access Code: {access_code}")
|
||||
|
||||
if access_code:
|
||||
message_parts.append(f"🔑 Access Code: {access_code}")
|
||||
|
||||
panel = Panel(
|
||||
"\n".join(message_parts),
|
||||
title="Trace Batch Finalization",
|
||||
border_style="green",
|
||||
)
|
||||
if not should_auto_collect_first_time_traces():
|
||||
console.print(panel)
|
||||
return True
|
||||
panel = Panel(
|
||||
"\n".join(message_parts),
|
||||
title="Trace Batch Finalization",
|
||||
border_style="green",
|
||||
)
|
||||
if not should_auto_collect_first_time_traces():
|
||||
console.print(panel)
|
||||
|
||||
else:
|
||||
logger.error(
|
||||
f"❌ Failed to finalize trace batch: {response.status_code} - {response.text}"
|
||||
)
|
||||
self._mark_batch_as_failed(batch_id, response.text)
|
||||
return False
|
||||
self._mark_batch_as_failed(self.trace_batch_id, response.text)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error finalizing trace batch: {e}")
|
||||
try:
|
||||
self._mark_batch_as_failed(batch_id, str(e))
|
||||
except Exception:
|
||||
logger.debug(
|
||||
"Could not mark trace batch as failed (network unavailable)"
|
||||
)
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error finalizing trace batch: {e}")
|
||||
try:
|
||||
self._mark_batch_as_failed(self.trace_batch_id, str(e))
|
||||
except Exception:
|
||||
logger.debug(
|
||||
"Could not mark trace batch as failed (network unavailable)"
|
||||
)
|
||||
|
||||
def _cleanup_batch_data(self) -> None:
|
||||
"""Clean up batch data after successful finalization to free memory"""
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
"""Trace collection listener for orchestrating trace collection."""
|
||||
|
||||
from datetime import datetime, timezone
|
||||
import os
|
||||
from typing import Any, ClassVar
|
||||
import uuid
|
||||
@@ -231,14 +230,11 @@ class TraceCollectionListener(BaseEventListener):
|
||||
|
||||
@event_bus.on(FlowStartedEvent)
|
||||
def on_flow_started(source: Any, event: FlowStartedEvent) -> None:
|
||||
# Only the first execution to open the session batch owns it. A flow
|
||||
# that starts while a batch already exists is nested -- inside a crew
|
||||
# (e.g. an agent's Flow-based executor), a conversational Flow, or a
|
||||
# parent flow -- and must NOT re-claim ownership. Re-claiming would
|
||||
# mark batch_owner_type="flow" and cause the nested flow to finalize
|
||||
# the parent's batch prematurely when it completes.
|
||||
if not self.batch_manager.is_batch_initialized():
|
||||
self._initialize_flow_batch(source, event)
|
||||
# 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)
|
||||
@@ -268,20 +264,18 @@ class TraceCollectionListener(BaseEventListener):
|
||||
|
||||
@event_bus.on(CrewKickoffStartedEvent)
|
||||
def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
|
||||
# Nested crew inside Flow.kickoff: never claim an existing flow session batch.
|
||||
if not self._nested_in_flow_execution() and (
|
||||
not self.batch_manager.is_batch_initialized()
|
||||
):
|
||||
if self.batch_manager.batch_owner_type != "flow":
|
||||
# Always call _initialize_crew_batch to claim ownership.
|
||||
# If batch was already initialized by a concurrent action event
|
||||
# (e.g. LLM/tool before crew_kickoff_started), initialize_batch()
|
||||
# returns early but batch_owner_type is still correctly set to "crew".
|
||||
# Skip only when a parent flow already owns the batch.
|
||||
self._initialize_crew_batch(source, event)
|
||||
self._handle_trace_event("crew_kickoff_started", source, event)
|
||||
|
||||
@event_bus.on(CrewKickoffCompletedEvent)
|
||||
def on_crew_completed(source: Any, event: CrewKickoffCompletedEvent) -> None:
|
||||
self._handle_trace_event("crew_kickoff_completed", source, event)
|
||||
if self.batch_manager.defer_session_finalization:
|
||||
return
|
||||
if self._nested_in_flow_execution():
|
||||
return
|
||||
if self.batch_manager.batch_owner_type == "crew":
|
||||
if self.first_time_handler.is_first_time:
|
||||
self.first_time_handler.mark_events_collected()
|
||||
@@ -292,14 +286,10 @@ class TraceCollectionListener(BaseEventListener):
|
||||
@event_bus.on(CrewKickoffFailedEvent)
|
||||
def on_crew_failed(source: Any, event: CrewKickoffFailedEvent) -> None:
|
||||
self._handle_trace_event("crew_kickoff_failed", source, event)
|
||||
if self.batch_manager.defer_session_finalization:
|
||||
return
|
||||
if self._nested_in_flow_execution():
|
||||
return
|
||||
if self.first_time_handler.is_first_time:
|
||||
self.first_time_handler.mark_events_collected()
|
||||
self.first_time_handler.handle_execution_completion()
|
||||
elif self.batch_manager.batch_owner_type == "crew":
|
||||
else:
|
||||
self.batch_manager.finalize_batch()
|
||||
|
||||
@event_bus.on(TaskStartedEvent)
|
||||
@@ -717,32 +707,8 @@ class TraceCollectionListener(BaseEventListener):
|
||||
@on_signal
|
||||
def handle_signal(source: Any, event: SignalEvent) -> None:
|
||||
"""Flush trace batch on system signals to prevent data loss."""
|
||||
if not self.batch_manager.is_batch_initialized():
|
||||
return
|
||||
# Multi-turn flows defer batch finalization to finalize_session_traces().
|
||||
if self.batch_manager.defer_session_finalization:
|
||||
return
|
||||
self.batch_manager.finalize_batch()
|
||||
|
||||
@staticmethod
|
||||
def _is_inside_active_flow_context() -> bool:
|
||||
"""True when ``kickoff_async`` has set ``current_flow_id`` (nested crew)."""
|
||||
from crewai.flow.flow_context import current_flow_id
|
||||
|
||||
return current_flow_id.get() is not None
|
||||
|
||||
def _flow_owns_trace_batch(self) -> bool:
|
||||
"""True when an in-flight conversational flow already owns the trace batch."""
|
||||
if self.batch_manager.batch_owner_type == "flow":
|
||||
return True
|
||||
batch = self.batch_manager.current_batch
|
||||
if batch is not None:
|
||||
return batch.execution_metadata.get("execution_type") == "flow"
|
||||
return False
|
||||
|
||||
def _nested_in_flow_execution(self) -> bool:
|
||||
"""True when a crew runs inside a flow session (context or batch ownership)."""
|
||||
return self._is_inside_active_flow_context() or self._flow_owns_trace_batch()
|
||||
if self.batch_manager.is_batch_initialized():
|
||||
self.batch_manager.finalize_batch()
|
||||
|
||||
def _initialize_crew_batch(self, source: Any, event: BaseEvent) -> None:
|
||||
"""Initialize trace batch.
|
||||
@@ -763,33 +729,6 @@ class TraceCollectionListener(BaseEventListener):
|
||||
|
||||
self._initialize_batch(user_context, execution_metadata)
|
||||
|
||||
def _try_initialize_flow_batch_from_context(self, event: Any) -> bool:
|
||||
"""Claim a flow trace batch when an action event fires inside kickoff.
|
||||
|
||||
When ``suppress_flow_events=True``, console panels are hidden but
|
||||
``FlowStartedEvent`` and method lifecycle events still emit; if no
|
||||
batch exists yet, LLM/tool events must not fall back to implicit crew
|
||||
batches.
|
||||
"""
|
||||
from crewai.flow.flow_context import current_flow_id, current_flow_name
|
||||
|
||||
flow_id = current_flow_id.get()
|
||||
if flow_id is None:
|
||||
return False
|
||||
|
||||
started_at = getattr(event, "timestamp", None) or datetime.now(timezone.utc)
|
||||
user_context = self._get_user_context()
|
||||
execution_metadata = {
|
||||
"flow_name": current_flow_name.get() or "Unknown Flow",
|
||||
"execution_start": started_at,
|
||||
"crewai_version": get_crewai_version(),
|
||||
"execution_type": "flow",
|
||||
}
|
||||
self.batch_manager.batch_owner_type = "flow"
|
||||
self.batch_manager.batch_owner_id = flow_id
|
||||
self._initialize_batch(user_context, execution_metadata)
|
||||
return True
|
||||
|
||||
def _initialize_flow_batch(self, source: Any, event: BaseEvent) -> None:
|
||||
"""Initialize trace batch for Flow execution.
|
||||
|
||||
@@ -854,19 +793,12 @@ class TraceCollectionListener(BaseEventListener):
|
||||
event: Event object.
|
||||
"""
|
||||
if not self.batch_manager.is_batch_initialized():
|
||||
if self._try_initialize_flow_batch_from_context(event):
|
||||
pass
|
||||
elif not self._nested_in_flow_execution():
|
||||
user_context = self._get_user_context()
|
||||
execution_metadata = {
|
||||
"crew_name": getattr(source, "name", "Unknown Crew"),
|
||||
"crewai_version": get_crewai_version(),
|
||||
}
|
||||
self.batch_manager.batch_owner_type = "crew"
|
||||
self.batch_manager.batch_owner_id = getattr(
|
||||
source, "id", str(uuid.uuid4())
|
||||
)
|
||||
self._initialize_batch(user_context, execution_metadata)
|
||||
user_context = self._get_user_context()
|
||||
execution_metadata = {
|
||||
"crew_name": getattr(source, "name", "Unknown Crew"),
|
||||
"crewai_version": get_crewai_version(),
|
||||
}
|
||||
self._initialize_batch(user_context, execution_metadata)
|
||||
|
||||
self.batch_manager.begin_event_processing()
|
||||
try:
|
||||
|
||||
@@ -1,88 +1,30 @@
|
||||
"""Experimental CrewAI surface — APIs here may change without major-version bumps."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
# ``crewai.experimental.conversational`` is pure data shapes — no Flow or Task
|
||||
# imports — so it's safe to eager-import. Everything else is resolved lazily
|
||||
# below; otherwise the chain
|
||||
# crewai → Flow → experimental.conversational → experimental.__init__
|
||||
# → experimental.agent_executor / experimental.evaluation
|
||||
# → Flow / Task (mid-load)
|
||||
# would deadlock with "partially initialized module" ImportErrors.
|
||||
from crewai.experimental.conversational import (
|
||||
AgentMessage,
|
||||
ConversationConfig,
|
||||
ConversationEvent,
|
||||
ConversationMessage,
|
||||
ConversationState,
|
||||
RouterConfig,
|
||||
from crewai.experimental.agent_executor import AgentExecutor, CrewAgentExecutorFlow
|
||||
from crewai.experimental.evaluation import (
|
||||
AgentEvaluationResult,
|
||||
AgentEvaluator,
|
||||
BaseEvaluator,
|
||||
EvaluationScore,
|
||||
EvaluationTraceCallback,
|
||||
ExperimentResult,
|
||||
ExperimentResults,
|
||||
ExperimentRunner,
|
||||
GoalAlignmentEvaluator,
|
||||
MetricCategory,
|
||||
ParameterExtractionEvaluator,
|
||||
ReasoningEfficiencyEvaluator,
|
||||
SemanticQualityEvaluator,
|
||||
ToolInvocationEvaluator,
|
||||
ToolSelectionEvaluator,
|
||||
create_default_evaluator,
|
||||
create_evaluation_callbacks,
|
||||
)
|
||||
|
||||
|
||||
_LAZY_FROM_AGENT_EXECUTOR = {"AgentExecutor", "CrewAgentExecutorFlow"}
|
||||
|
||||
_LAZY_FROM_EVALUATION = {
|
||||
"AgentEvaluationResult",
|
||||
"AgentEvaluator",
|
||||
"BaseEvaluator",
|
||||
"EvaluationScore",
|
||||
"EvaluationTraceCallback",
|
||||
"ExperimentResult",
|
||||
"ExperimentResults",
|
||||
"ExperimentRunner",
|
||||
"GoalAlignmentEvaluator",
|
||||
"MetricCategory",
|
||||
"ParameterExtractionEvaluator",
|
||||
"ReasoningEfficiencyEvaluator",
|
||||
"SemanticQualityEvaluator",
|
||||
"ToolInvocationEvaluator",
|
||||
"ToolSelectionEvaluator",
|
||||
"create_default_evaluator",
|
||||
"create_evaluation_callbacks",
|
||||
}
|
||||
|
||||
|
||||
def __getattr__(name: str) -> Any:
|
||||
"""Lazily resolve symbols whose modules import ``Flow`` or ``Task``.
|
||||
|
||||
Eager re-exports would deadlock when ``Flow`` itself is the consumer that
|
||||
triggered ``crewai.experimental.__init__`` (``Flow`` imports types from
|
||||
:mod:`crewai.experimental.conversational`). Callers like
|
||||
``from crewai.experimental import AgentExecutor`` still work — the
|
||||
real import just runs lazily, after the original loader finishes.
|
||||
"""
|
||||
if name in _LAZY_FROM_AGENT_EXECUTOR:
|
||||
from crewai.experimental.agent_executor import (
|
||||
AgentExecutor,
|
||||
CrewAgentExecutorFlow,
|
||||
)
|
||||
|
||||
globals()["AgentExecutor"] = AgentExecutor
|
||||
globals()["CrewAgentExecutorFlow"] = CrewAgentExecutorFlow
|
||||
return globals()[name]
|
||||
|
||||
if name in _LAZY_FROM_EVALUATION:
|
||||
from crewai.experimental import evaluation as _evaluation_mod
|
||||
|
||||
for attr in _LAZY_FROM_EVALUATION:
|
||||
globals()[attr] = getattr(_evaluation_mod, attr)
|
||||
return globals()[name]
|
||||
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
|
||||
|
||||
__all__ = [
|
||||
"AgentEvaluationResult",
|
||||
"AgentEvaluator",
|
||||
"AgentExecutor",
|
||||
"AgentMessage",
|
||||
"BaseEvaluator",
|
||||
"ConversationConfig",
|
||||
"ConversationEvent",
|
||||
"ConversationMessage",
|
||||
"ConversationState",
|
||||
"CrewAgentExecutorFlow", # Deprecated alias for AgentExecutor
|
||||
"EvaluationScore",
|
||||
"EvaluationTraceCallback",
|
||||
@@ -93,7 +35,6 @@ __all__ = [
|
||||
"MetricCategory",
|
||||
"ParameterExtractionEvaluator",
|
||||
"ReasoningEfficiencyEvaluator",
|
||||
"RouterConfig",
|
||||
"SemanticQualityEvaluator",
|
||||
"ToolInvocationEvaluator",
|
||||
"ToolSelectionEvaluator",
|
||||
|
||||
@@ -1,184 +0,0 @@
|
||||
"""Conversational types and helpers shared by ``Flow`` (experimental).
|
||||
|
||||
The conversational chat surface (``Flow`` with ``conversational = True``) is
|
||||
EXPERIMENTAL. APIs in this module and the conversational methods on ``Flow``
|
||||
may change without a major-version bump until the feature graduates.
|
||||
|
||||
This module hosts the **data shapes** — ``ConversationConfig``,
|
||||
``RouterConfig``, ``ConversationState`` and its message types — plus the
|
||||
``_conversational_only`` decorator used to gate built-in conversational
|
||||
methods on the base ``Flow`` class. The methods themselves live on ``Flow``
|
||||
directly.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable, Sequence
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Literal, TypeVar, cast
|
||||
from uuid import uuid4
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
ConversationMessageRole = Literal["user", "assistant", "system", "tool"]
|
||||
ConversationEventVisibility = Literal["private", "public"]
|
||||
|
||||
F = TypeVar("F", bound=Callable[..., Any])
|
||||
|
||||
|
||||
def _conversational_only(func: F) -> F:
|
||||
"""Mark a method as part of the conversational built-in graph.
|
||||
|
||||
Methods carrying this marker only register on a ``Flow`` subclass when
|
||||
``conversational = True``. Subclasses that don't opt in see them as
|
||||
inert attributes — they don't fire and don't pollute the listener graph.
|
||||
"""
|
||||
func.__conversational_only__ = True # type: ignore[attr-defined]
|
||||
return func
|
||||
|
||||
|
||||
@dataclass
|
||||
class RouterConfig:
|
||||
"""LLM router configuration for the experimental conversational ``Flow``.
|
||||
|
||||
.. warning::
|
||||
|
||||
**EXPERIMENTAL.** Part of the conversational ``Flow`` surface. Fields
|
||||
and defaults may change before the feature graduates from
|
||||
``crewai.experimental``. Pin your CrewAI version if you depend on
|
||||
a specific shape.
|
||||
|
||||
``route_descriptions`` overrides the per-route descriptions used to build
|
||||
the router LLM's "available routes" catalog. Routes without an entry fall
|
||||
back to the handler's docstring first line (or, for built-in routes, the
|
||||
framework's canned description). ``prompt`` is reserved for domain
|
||||
policy/voice, not the route catalog — that's auto-built.
|
||||
"""
|
||||
|
||||
prompt: str | None = None
|
||||
response_format: type[BaseModel] | None = None
|
||||
llm: Any | None = None
|
||||
routes: Sequence[str] | None = None
|
||||
route_descriptions: dict[str, str] | None = None
|
||||
default_intent: str | None = "converse"
|
||||
fallback_intent: str | None = "converse"
|
||||
intent_field: str = "intent"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConversationConfig:
|
||||
"""Class-level configuration for the experimental conversational ``Flow``.
|
||||
|
||||
.. warning::
|
||||
|
||||
**EXPERIMENTAL.** Part of the conversational ``Flow`` surface. Fields
|
||||
and defaults may change before the feature graduates from
|
||||
``crewai.experimental``. Pin your CrewAI version if you depend on
|
||||
a specific shape.
|
||||
|
||||
``system_prompt`` defaults to the ``slices.conversational_system_prompt``
|
||||
translation when left as ``None``. Pass an empty string to opt out of any
|
||||
system prompt for ``converse_turn``. ``answer_from_history_prompt`` falls
|
||||
back to ``slices.conversational_answer_from_history_prompt`` when ``None``.
|
||||
"""
|
||||
|
||||
system_prompt: str | None = None
|
||||
llm: Any | None = None
|
||||
router: RouterConfig | None = None
|
||||
answer_from_history_prompt: str | None = None
|
||||
default_intents: Sequence[str] | None = None
|
||||
intent_llm: Any | None = None
|
||||
answer_from_history_llm: Any | None = None
|
||||
visible_agent_outputs: Sequence[str] | Literal["all"] | None = None
|
||||
defer_trace_finalization: bool = True
|
||||
|
||||
def __call__(self, flow_cls: type[Any]) -> type[Any]:
|
||||
"""Use this config as a class decorator."""
|
||||
flow_cls.conversational_config = self
|
||||
return flow_cls
|
||||
|
||||
|
||||
class ConversationMessage(BaseModel):
|
||||
"""Canonical user-facing message shared across conversational turns."""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
role: ConversationMessageRole
|
||||
content: str | list[dict[str, Any]] | None
|
||||
name: str | None = None
|
||||
tool_call_id: str | None = None
|
||||
tool_calls: list[dict[str, Any]] | None = None
|
||||
files: dict[str, Any] | None = None
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class AgentMessage(BaseModel):
|
||||
"""Private per-agent message or scratch result."""
|
||||
|
||||
role: ConversationMessageRole | str = "assistant"
|
||||
content: Any
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class ConversationEvent(BaseModel):
|
||||
"""Structured trace/event that is separate from user-visible messages."""
|
||||
|
||||
type: str
|
||||
payload: dict[str, Any] = Field(default_factory=dict)
|
||||
agent_name: str | None = None
|
||||
visibility: ConversationEventVisibility = "private"
|
||||
|
||||
|
||||
class ConversationState(BaseModel):
|
||||
"""Structured state for the experimental conversational ``Flow``.
|
||||
|
||||
.. warning::
|
||||
|
||||
**EXPERIMENTAL.** Field shape and defaults may change before the
|
||||
conversational ``Flow`` graduates from ``crewai.experimental``.
|
||||
|
||||
``messages`` is the canonical user-facing history. Agent/tool scratch work
|
||||
belongs in ``events`` or ``agent_threads`` unless explicitly made public.
|
||||
"""
|
||||
|
||||
id: str = Field(default_factory=lambda: str(uuid4()))
|
||||
messages: list[ConversationMessage] = Field(default_factory=list)
|
||||
current_user_message: str | None = None
|
||||
last_user_message: str | None = None
|
||||
last_intent: str | None = None
|
||||
ended: bool = False
|
||||
events: list[ConversationEvent] = Field(default_factory=list)
|
||||
agent_threads: dict[str, list[AgentMessage]] = Field(default_factory=dict)
|
||||
session_ready: bool = False
|
||||
|
||||
|
||||
def message_to_llm_dict(message: Any) -> LLMMessage:
|
||||
"""Coerce a stored ``ConversationMessage`` (or dict) into an ``LLMMessage``."""
|
||||
if isinstance(message, BaseModel):
|
||||
data = message.model_dump(exclude_none=True)
|
||||
elif isinstance(message, dict):
|
||||
data = dict(message)
|
||||
else:
|
||||
data = {"role": "user", "content": str(message)}
|
||||
|
||||
return cast(
|
||||
LLMMessage,
|
||||
{key: value for key, value in data.items() if key != "metadata"},
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"AgentMessage",
|
||||
"ConversationConfig",
|
||||
"ConversationEvent",
|
||||
"ConversationEventVisibility",
|
||||
"ConversationMessage",
|
||||
"ConversationMessageRole",
|
||||
"ConversationState",
|
||||
"RouterConfig",
|
||||
"_conversational_only",
|
||||
"message_to_llm_dict",
|
||||
]
|
||||
@@ -1,814 +0,0 @@
|
||||
"""Conversational graph + helpers as a mixin for ``Flow`` (experimental).
|
||||
|
||||
The experimental conversational chat surface lives here as a mixin so that
|
||||
``crewai.flow.runtime`` stays focused on the execution engine. ``Flow``
|
||||
inherits from ``_ConversationalMixin``; the methods only register on
|
||||
subclasses that opt in via ``conversational = True`` (enforced by the
|
||||
``_conversational_only`` marker + ``FlowMeta`` gating in
|
||||
``crewai.flow.runtime``).
|
||||
|
||||
Import surface:
|
||||
- :class:`_ConversationalMixin` — internal; ``Flow`` mixes it in. Users
|
||||
don't import it directly.
|
||||
- The data types this mixin uses live in
|
||||
:mod:`crewai.experimental.conversational`.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Mapping, Sequence
|
||||
from enum import Enum
|
||||
import json
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any, ClassVar, Literal, cast
|
||||
|
||||
from pydantic import BaseModel, Field, create_model
|
||||
|
||||
from crewai.experimental.conversational import (
|
||||
AgentMessage,
|
||||
ConversationConfig,
|
||||
ConversationEvent,
|
||||
ConversationMessage,
|
||||
ConversationState,
|
||||
RouterConfig,
|
||||
_conversational_only,
|
||||
message_to_llm_dict,
|
||||
)
|
||||
from crewai.flow.conversation import (
|
||||
append_message as _append_conversation_message,
|
||||
get_conversation_messages,
|
||||
receive_user_message as _receive_user_message,
|
||||
)
|
||||
from crewai.flow.dsl import listen, router, start
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.runtime import Flow
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class _ConversationalMixin:
|
||||
"""Built-in conversational graph for ``Flow`` (gated on ``conversational``).
|
||||
|
||||
Mixed into ``Flow`` so its execution engine (``runtime.py``) stays focused
|
||||
on running graphs. The methods here only register on subclasses that set
|
||||
``conversational = True``; non-chat flows see them as inert attributes.
|
||||
"""
|
||||
|
||||
# The metaclass + state attributes referenced below live on ``Flow`` —
|
||||
# this mixin is never instantiated standalone. These type-only
|
||||
# declarations exist so static analyzers don't flag attribute access.
|
||||
# Class-level slots use ``ClassVar`` to match Flow's actual declarations
|
||||
# (otherwise mypy flags "Cannot override instance variable with class
|
||||
# variable" when Flow declares them as ``ClassVar``).
|
||||
if TYPE_CHECKING:
|
||||
conversational: ClassVar[bool]
|
||||
conversational_config: ClassVar[ConversationConfig | None]
|
||||
builtin_routes: ClassVar[tuple[str, ...]]
|
||||
internal_routes: ClassVar[tuple[str, ...]]
|
||||
builtin_route_descriptions: ClassVar[dict[str, str]]
|
||||
# Registry ClassVars populated by ``FlowMeta`` at class creation.
|
||||
_listeners: ClassVar[dict[Any, Any]]
|
||||
|
||||
# Instance attrs from ``Flow``.
|
||||
state: Any
|
||||
name: str | None
|
||||
_completed_methods: set[Any]
|
||||
_method_outputs: list[Any]
|
||||
_pending_and_listeners: dict[Any, Any]
|
||||
_method_call_counts: dict[Any, int]
|
||||
_is_execution_resuming: bool
|
||||
_pending_user_message: str | dict[str, Any] | None
|
||||
_pending_intents: Sequence[str] | None
|
||||
_pending_intent_llm: str | BaseLLM | None
|
||||
|
||||
def _clear_or_listeners(self) -> None:
|
||||
pass
|
||||
|
||||
def _collapse_to_outcome(
|
||||
self,
|
||||
feedback: str,
|
||||
outcomes: tuple[str, ...],
|
||||
llm: str | BaseLLM | Any,
|
||||
) -> str:
|
||||
pass
|
||||
|
||||
def _copy_and_serialize_state(self) -> dict[str, Any]:
|
||||
pass
|
||||
|
||||
def kickoff(self, *args: Any, **kwargs: Any) -> Any:
|
||||
pass
|
||||
|
||||
@start()
|
||||
@_conversational_only
|
||||
def conversation_start(self) -> str | None:
|
||||
"""Internal Flow entrypoint that hands the user message to the router.
|
||||
|
||||
In conversational mode, ``Flow.kickoff_async`` runs all ``@start``
|
||||
methods sequentially and this one is registered last, so any user
|
||||
``@start`` methods (e.g. permission loading) have already finished
|
||||
before the returned value triggers ``route_conversation``.
|
||||
"""
|
||||
state = cast(ConversationState, self.state)
|
||||
return state.current_user_message
|
||||
|
||||
@router(conversation_start)
|
||||
@_conversational_only
|
||||
def route_conversation(self) -> str:
|
||||
"""Route the current turn to a listener label."""
|
||||
state = cast(ConversationState, self.state)
|
||||
context = self.build_router_context()
|
||||
configured_route = self.route_turn(context)
|
||||
if configured_route:
|
||||
state.last_intent = configured_route
|
||||
return configured_route
|
||||
|
||||
if state.last_intent:
|
||||
return state.last_intent
|
||||
|
||||
if self.can_answer_from_history(context):
|
||||
state.last_intent = "answer_from_history"
|
||||
return "answer_from_history"
|
||||
|
||||
state.last_intent = "converse"
|
||||
return "converse"
|
||||
|
||||
@listen("converse")
|
||||
@_conversational_only
|
||||
def converse_turn(self) -> str:
|
||||
"""Built-in chat handler over canonical conversation history."""
|
||||
llm = self._default_conversation_llm()
|
||||
if llm is None:
|
||||
content = "I can continue the conversation once an LLM is configured."
|
||||
self.append_assistant_message(content)
|
||||
return content
|
||||
|
||||
messages: list[LLMMessage] = []
|
||||
system_prompt = self._resolve_system_prompt()
|
||||
if system_prompt:
|
||||
messages.append({"role": "system", "content": system_prompt})
|
||||
messages.extend(self.conversation_messages)
|
||||
|
||||
response = self._coerce_llm(llm).call(messages=messages)
|
||||
content = self._stringify_result(response)
|
||||
self.append_assistant_message(content)
|
||||
return content
|
||||
|
||||
@listen("end")
|
||||
@_conversational_only
|
||||
def end_conversation(self) -> str:
|
||||
"""Built-in conversation terminator."""
|
||||
cast(ConversationState, self.state).ended = True
|
||||
content = "Conversation ended."
|
||||
self.append_assistant_message(content)
|
||||
return content
|
||||
|
||||
@listen("answer_from_history")
|
||||
@_conversational_only
|
||||
def answer_from_history_turn(self) -> str | None:
|
||||
"""Answer directly from canonical conversation history when configured."""
|
||||
config = self._conversation_config
|
||||
if config is None:
|
||||
return None
|
||||
llm = config.answer_from_history_llm
|
||||
if llm is None:
|
||||
return None
|
||||
|
||||
llm_instance = self._coerce_llm(llm)
|
||||
messages: list[LLMMessage] = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": self._resolve_answer_from_history_prompt(),
|
||||
},
|
||||
*self.build_agent_context("answer_from_history"),
|
||||
]
|
||||
response = llm_instance.call(messages=messages)
|
||||
content = self._stringify_result(response)
|
||||
self.append_assistant_message(content)
|
||||
return content
|
||||
|
||||
def handle_turn(
|
||||
self,
|
||||
message: str,
|
||||
*,
|
||||
session_id: str | None = None,
|
||||
intents: Sequence[str] | None = None,
|
||||
intent_llm: str | BaseLLM | None = None,
|
||||
**kickoff_kwargs: Any,
|
||||
) -> Any:
|
||||
"""Append a user message, run one conversational turn, and return output.
|
||||
|
||||
.. warning::
|
||||
|
||||
**EXPERIMENTAL.** This is the public entry point for the
|
||||
conversational ``Flow``. Signature and semantics may change before
|
||||
the feature graduates from ``crewai.experimental``.
|
||||
|
||||
Available only when ``conversational = True`` is set on the subclass.
|
||||
Stashes the message + session_id as pending turn state, runs kickoff
|
||||
(which restores from persist and then applies the pending turn), and
|
||||
promotes the result to an assistant message when the handler didn't.
|
||||
"""
|
||||
state = cast(ConversationState, self.state)
|
||||
sid = session_id or state.id
|
||||
|
||||
# Stash the pending turn so ``_apply_pending_conversational_turn``
|
||||
# picks it up AFTER persist restore.
|
||||
self._pending_user_message = message
|
||||
self._pending_intents = list(intents) if intents else None
|
||||
self._pending_intent_llm = intent_llm
|
||||
|
||||
# Each turn is a fresh execution; clear graph tracking so the second
|
||||
# turn re-runs instead of being treated as a checkpoint restore.
|
||||
if "from_checkpoint" not in kickoff_kwargs:
|
||||
self._reset_turn_execution_state()
|
||||
|
||||
assistant_count = self._assistant_message_count()
|
||||
try:
|
||||
result = self.kickoff(inputs={"id": sid}, **kickoff_kwargs)
|
||||
finally:
|
||||
self._pending_user_message = None
|
||||
self._pending_intents = None
|
||||
self._pending_intent_llm = None
|
||||
|
||||
if (
|
||||
result is not None
|
||||
and self._assistant_message_count() == assistant_count
|
||||
and self._is_public_turn_result(result)
|
||||
):
|
||||
self.append_assistant_message(self._stringify_result(result))
|
||||
return result
|
||||
|
||||
def build_router_context(self) -> dict[str, Any]:
|
||||
"""Build context used by the routing policy for the current turn."""
|
||||
state = cast(ConversationState, self.state)
|
||||
return {
|
||||
"system_prompt": self._resolve_system_prompt(),
|
||||
"current_user_message": state.current_user_message,
|
||||
"message_history": self.conversation_messages,
|
||||
"events": [event.model_dump() for event in state.events],
|
||||
"last_intent": state.last_intent,
|
||||
}
|
||||
|
||||
def build_agent_context(self, agent_name: str) -> list[LLMMessage]:
|
||||
"""Build canonical message context for an agent or direct LLM call."""
|
||||
state = cast(ConversationState, self.state)
|
||||
messages = list(self.conversation_messages)
|
||||
thread = state.agent_threads.get(agent_name, [])
|
||||
messages.extend(
|
||||
cast(
|
||||
LLMMessage,
|
||||
{
|
||||
"role": msg.role,
|
||||
"content": self._stringify_result(msg.content),
|
||||
},
|
||||
)
|
||||
for msg in thread
|
||||
)
|
||||
return messages
|
||||
|
||||
def route_turn(self, context: dict[str, Any]) -> str | None:
|
||||
"""Route the current turn via the LLM router.
|
||||
|
||||
When ``ConversationConfig.router`` is omitted, the router is
|
||||
auto-enabled with default settings as long as the flow declares
|
||||
custom ``@listen`` handlers (anything beyond the built-in
|
||||
``converse`` / ``end`` routes). ``@ConversationConfig(llm=ROUTER_LLM)``
|
||||
is enough to dispatch to your custom handlers — no explicit
|
||||
``RouterConfig()`` needed.
|
||||
|
||||
Pass an explicit ``RouterConfig`` only to override the routing prompt,
|
||||
supply per-route descriptions, or change the default/fallback intent.
|
||||
Override this method to bypass the LLM router entirely (e.g.,
|
||||
permission gates before the LLM decision).
|
||||
"""
|
||||
config = self._conversation_config
|
||||
if config is None:
|
||||
return None
|
||||
|
||||
router_config = config.router
|
||||
if router_config is None:
|
||||
if config.default_intents:
|
||||
return None
|
||||
custom_routes = self._effective_routes(None) - set(self.builtin_routes)
|
||||
if not custom_routes:
|
||||
return None
|
||||
router_config = RouterConfig()
|
||||
|
||||
return self._route_with_config(router_config, context)
|
||||
|
||||
def can_answer_from_history(self, context: dict[str, Any]) -> bool:
|
||||
"""Return whether this turn can be answered from message history."""
|
||||
config = self._conversation_config
|
||||
if config is None or config.answer_from_history_llm is None:
|
||||
return False
|
||||
if len(self.conversation_messages) < 2:
|
||||
return False
|
||||
|
||||
feedback = (
|
||||
f"{self._resolve_answer_from_history_prompt()}\n\n"
|
||||
f"Current user message: {context.get('current_user_message')}\n\n"
|
||||
f"Message history:\n{self._format_messages(self.conversation_messages)}"
|
||||
)
|
||||
outcome = self._collapse_to_outcome(
|
||||
feedback,
|
||||
("answer_from_history", "route_to_flow"),
|
||||
config.answer_from_history_llm,
|
||||
)
|
||||
return outcome == "answer_from_history"
|
||||
|
||||
def append_agent_result(
|
||||
self,
|
||||
agent_name: str,
|
||||
result: Any,
|
||||
*,
|
||||
visibility: Literal["private", "public"] = "private",
|
||||
metadata: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
"""Record an agent result, optionally making it visible to the user."""
|
||||
content = self._stringify_result(result)
|
||||
event_visibility = self._resolve_visibility(agent_name, visibility)
|
||||
event = ConversationEvent(
|
||||
type="agent_result",
|
||||
agent_name=agent_name,
|
||||
visibility=event_visibility,
|
||||
payload={"content": content, **(metadata or {})},
|
||||
)
|
||||
state = cast(ConversationState, self.state)
|
||||
state.events.append(event)
|
||||
state.agent_threads.setdefault(agent_name, []).append(
|
||||
AgentMessage(content=content, metadata=metadata or {})
|
||||
)
|
||||
if event_visibility == "public":
|
||||
self.append_assistant_message(content)
|
||||
|
||||
def append_assistant_message(
|
||||
self,
|
||||
content: str,
|
||||
*,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
"""Append a final user-visible assistant message."""
|
||||
cast(ConversationState, self.state).messages.append(
|
||||
ConversationMessage(
|
||||
role="assistant",
|
||||
content=content,
|
||||
metadata=metadata or {},
|
||||
)
|
||||
)
|
||||
|
||||
def append_message(
|
||||
self,
|
||||
role: Literal["user", "assistant", "system", "tool"],
|
||||
content: str,
|
||||
**extra: Any,
|
||||
) -> None:
|
||||
"""Append a message to conversation history (legacy ChatState path)."""
|
||||
_append_conversation_message(cast("Flow[Any]", self), role, content, **extra)
|
||||
|
||||
@property
|
||||
def conversation_messages(self) -> list[LLMMessage]:
|
||||
"""Message history from state, coerced to LLM-shaped dicts."""
|
||||
return [
|
||||
message_to_llm_dict(message)
|
||||
for message in get_conversation_messages(cast("Flow[Any]", self))
|
||||
]
|
||||
|
||||
def receive_user_message(
|
||||
self,
|
||||
text: str,
|
||||
*,
|
||||
outcomes: Sequence[str] | None = None,
|
||||
llm: str | BaseLLM | None = None,
|
||||
) -> str:
|
||||
"""Append a user message and optionally classify intent.
|
||||
|
||||
Conversational flows push a ``ConversationMessage`` onto
|
||||
``state.messages`` and preserve ``last_intent`` across turns.
|
||||
Non-conversational flows fall through to the legacy helper.
|
||||
"""
|
||||
if self.conversational:
|
||||
state = cast(ConversationState, self.state)
|
||||
state.messages.append(ConversationMessage(role="user", content=text))
|
||||
state.current_user_message = text
|
||||
state.last_user_message = text
|
||||
if outcomes and llm is not None:
|
||||
intent = self.classify_intent(
|
||||
text,
|
||||
outcomes,
|
||||
llm=llm,
|
||||
context=self.conversation_messages,
|
||||
)
|
||||
state.last_intent = intent
|
||||
return intent
|
||||
return text
|
||||
|
||||
return _receive_user_message(
|
||||
cast("Flow[Any]", self), text, outcomes=outcomes, llm=llm
|
||||
)
|
||||
|
||||
def classify_intent(
|
||||
self,
|
||||
text: str,
|
||||
outcomes: Sequence[str],
|
||||
*,
|
||||
llm: str | BaseLLM,
|
||||
context: Sequence[Mapping[str, Any]] | None = None,
|
||||
) -> str:
|
||||
"""Map user text to one of the given outcomes using an LLM."""
|
||||
if context:
|
||||
context_blob = "\n".join(
|
||||
f"{m.get('role', 'user')}: {m.get('content', '')}" for m in context
|
||||
)
|
||||
feedback = f"{context_blob}\n\nLatest user message: {text}"
|
||||
else:
|
||||
feedback = text
|
||||
return self._collapse_to_outcome(feedback, tuple(outcomes), llm)
|
||||
|
||||
@property
|
||||
def _conversation_config(self) -> ConversationConfig | None:
|
||||
return getattr(type(self), "conversational_config", None)
|
||||
|
||||
def _should_defer_trace_finalization(self) -> bool:
|
||||
"""Whether per-turn ``FlowFinished`` + ``finalize_batch`` should be skipped.
|
||||
|
||||
True when either:
|
||||
- ``flow.defer_trace_finalization`` is set on the instance, OR
|
||||
- the class-level ``ConversationConfig.defer_trace_finalization``
|
||||
on a conversational subclass is True.
|
||||
|
||||
Either source enables the deferred-session pattern. The caller
|
||||
eventually invokes ``finalize_session_traces()`` to close the batch.
|
||||
"""
|
||||
if getattr(self, "defer_trace_finalization", False):
|
||||
return True
|
||||
config = self._conversation_config
|
||||
return bool(config and config.defer_trace_finalization)
|
||||
|
||||
def _reset_turn_execution_state(self) -> None:
|
||||
"""Clear per-execution tracking so the next turn re-runs the graph."""
|
||||
self._completed_methods.clear()
|
||||
self._method_outputs.clear()
|
||||
self._pending_and_listeners.clear()
|
||||
self._method_call_counts.clear()
|
||||
self._clear_or_listeners()
|
||||
self._is_execution_resuming = False
|
||||
|
||||
def _apply_pending_conversational_turn(self) -> None:
|
||||
"""Drain the stashed user message + classify if intents configured.
|
||||
|
||||
Called from ``Flow.kickoff_async`` AFTER persist state restore so
|
||||
the appended message survives ``self.persistence.load_state(...)``.
|
||||
"""
|
||||
if self._pending_user_message is None:
|
||||
return
|
||||
|
||||
text = self._coerce_user_message_text(self._pending_user_message)
|
||||
if not text.strip():
|
||||
return
|
||||
|
||||
cfg = self._conversation_config
|
||||
outcomes = self._pending_intents
|
||||
if outcomes is None and cfg is not None:
|
||||
outcomes = cfg.default_intents
|
||||
llm = self._pending_intent_llm
|
||||
if llm is None and cfg is not None:
|
||||
llm = cfg.intent_llm
|
||||
|
||||
if outcomes:
|
||||
if llm is None:
|
||||
raise ValueError("intent_llm is required when intents are provided")
|
||||
self.receive_user_message(text, outcomes=outcomes, llm=llm)
|
||||
else:
|
||||
self.receive_user_message(text)
|
||||
|
||||
def _resolve_system_prompt(self) -> str | None:
|
||||
"""Return the effective conversational system prompt."""
|
||||
from crewai.utilities.i18n import I18N_DEFAULT
|
||||
|
||||
config = self._conversation_config
|
||||
if config is None or config.system_prompt is None:
|
||||
return I18N_DEFAULT.slice("conversational_system_prompt")
|
||||
return config.system_prompt or None
|
||||
|
||||
def _resolve_answer_from_history_prompt(self) -> str:
|
||||
"""Return the effective ``answer_from_history`` prompt."""
|
||||
from crewai.utilities.i18n import I18N_DEFAULT
|
||||
|
||||
config = self._conversation_config
|
||||
if config is None or not config.answer_from_history_prompt:
|
||||
return I18N_DEFAULT.slice("conversational_answer_from_history_prompt")
|
||||
return config.answer_from_history_prompt
|
||||
|
||||
def _route_with_config(
|
||||
self,
|
||||
router_config: RouterConfig,
|
||||
context: dict[str, Any],
|
||||
) -> str | None:
|
||||
router_llm = self._default_router_llm(router_config)
|
||||
if router_llm is None:
|
||||
return router_config.default_intent
|
||||
|
||||
try:
|
||||
llm = self._coerce_llm(router_llm)
|
||||
response = self._call_router_llm(
|
||||
llm,
|
||||
messages=self._build_router_messages(router_config, context),
|
||||
response_format=self._router_response_format(router_config),
|
||||
)
|
||||
intent = self._extract_router_intent(response, router_config.intent_field)
|
||||
except Exception:
|
||||
return router_config.fallback_intent or router_config.default_intent
|
||||
|
||||
if intent is None:
|
||||
return router_config.fallback_intent or router_config.default_intent
|
||||
|
||||
valid_labels = self._effective_routes(router_config)
|
||||
if valid_labels and intent not in valid_labels:
|
||||
return router_config.fallback_intent or router_config.default_intent
|
||||
|
||||
return intent
|
||||
|
||||
def _default_router_llm(self, router_config: RouterConfig) -> Any | None:
|
||||
config = self._conversation_config
|
||||
return (
|
||||
router_config.llm
|
||||
or (config.intent_llm if config else None)
|
||||
or (config.llm if config else None)
|
||||
)
|
||||
|
||||
def _router_response_format(
|
||||
self,
|
||||
router_config: RouterConfig,
|
||||
) -> type[BaseModel]:
|
||||
if router_config.response_format is not None:
|
||||
return router_config.response_format
|
||||
|
||||
routes = sorted(self._effective_routes(router_config))
|
||||
field_definitions: dict[str, Any] = {
|
||||
router_config.intent_field: (
|
||||
str,
|
||||
Field(description=f"One of: {', '.join(routes)}"),
|
||||
)
|
||||
}
|
||||
return cast(
|
||||
type[BaseModel],
|
||||
create_model("ConversationRoute", **field_definitions),
|
||||
)
|
||||
|
||||
def _call_router_llm(
|
||||
self,
|
||||
llm: Any,
|
||||
*,
|
||||
messages: list[LLMMessage],
|
||||
response_format: type[BaseModel],
|
||||
) -> Any:
|
||||
try:
|
||||
return llm.call(messages=messages, response_format=response_format)
|
||||
except TypeError as exc:
|
||||
if "response_format" not in str(exc):
|
||||
raise
|
||||
return llm.call(messages=messages, response_model=response_format)
|
||||
|
||||
def _build_router_messages(
|
||||
self,
|
||||
router_config: RouterConfig,
|
||||
context: dict[str, Any],
|
||||
) -> list[LLMMessage]:
|
||||
catalog = self._build_route_catalog(router_config)
|
||||
context = {**context, "available_routes": sorted(catalog.keys())}
|
||||
domain_prompt = f"{router_config.prompt}\n\n" if router_config.prompt else ""
|
||||
routes_section = "Routes:\n" + "\n".join(
|
||||
f"- {label}: {description}" if description else f"- {label}"
|
||||
for label, description in sorted(catalog.items())
|
||||
)
|
||||
routing_prompt = (
|
||||
domain_prompt
|
||||
+ routes_section
|
||||
+ "\n\nChoose exactly one route from the list above. Prefer "
|
||||
"'converse' for follow-ups, summaries, and clarifications about "
|
||||
"prior turns — even if they touch on a topic the user previously "
|
||||
"invoked a custom route for. Use a custom route only when the user "
|
||||
"is making a fresh request for that tool or workflow."
|
||||
)
|
||||
return [
|
||||
{"role": "system", "content": routing_prompt},
|
||||
{"role": "user", "content": json.dumps(context, default=str)},
|
||||
]
|
||||
|
||||
def _build_route_catalog(
|
||||
self,
|
||||
router_config: RouterConfig | None,
|
||||
) -> dict[str, str]:
|
||||
label_to_method: dict[str, str] = {}
|
||||
for listener_name, condition in self._listeners.items():
|
||||
if isinstance(condition, tuple):
|
||||
_, trigger_labels = condition
|
||||
for trigger_label in trigger_labels:
|
||||
label_to_method.setdefault(str(trigger_label), str(listener_name))
|
||||
|
||||
routes = self._effective_routes(router_config)
|
||||
overrides = (
|
||||
router_config.route_descriptions
|
||||
if router_config and router_config.route_descriptions
|
||||
else {}
|
||||
)
|
||||
|
||||
catalog: dict[str, str] = {}
|
||||
for route_label in routes:
|
||||
if route_label in overrides:
|
||||
catalog[route_label] = overrides[route_label]
|
||||
continue
|
||||
if route_label in self.builtin_route_descriptions:
|
||||
catalog[route_label] = self.builtin_route_descriptions[route_label]
|
||||
continue
|
||||
handler_name = label_to_method.get(route_label)
|
||||
description = ""
|
||||
if handler_name:
|
||||
method = getattr(type(self), handler_name, None)
|
||||
doc = getattr(method, "__doc__", None)
|
||||
if doc:
|
||||
description = doc.strip().split("\n", 1)[0].strip()
|
||||
catalog[route_label] = description
|
||||
|
||||
return catalog
|
||||
|
||||
def _extract_router_intent(self, response: Any, intent_field: str) -> str | None:
|
||||
if isinstance(response, BaseModel):
|
||||
value = getattr(response, intent_field, None)
|
||||
elif isinstance(response, dict):
|
||||
value = response.get(intent_field)
|
||||
elif isinstance(response, str):
|
||||
try:
|
||||
parsed = json.loads(response)
|
||||
except json.JSONDecodeError:
|
||||
value = response.strip()
|
||||
else:
|
||||
value = parsed.get(intent_field)
|
||||
else:
|
||||
value = getattr(response, intent_field, None)
|
||||
|
||||
if value is None:
|
||||
return None
|
||||
if isinstance(value, Enum):
|
||||
return str(value.value)
|
||||
return str(value)
|
||||
|
||||
def _valid_route_labels(self) -> set[str]:
|
||||
labels: set[str] = set()
|
||||
for condition in self._listeners.values():
|
||||
if isinstance(condition, tuple):
|
||||
_, methods = condition
|
||||
labels.update(str(method) for method in methods)
|
||||
return labels
|
||||
|
||||
def _effective_routes(self, router_config: RouterConfig | None = None) -> set[str]:
|
||||
custom_routes = set(router_config.routes or ()) if router_config else set()
|
||||
if not custom_routes:
|
||||
custom_routes = (
|
||||
self._valid_route_labels()
|
||||
- set(self.builtin_routes)
|
||||
- set(self.internal_routes)
|
||||
)
|
||||
return custom_routes | set(self.builtin_routes)
|
||||
|
||||
def _default_conversation_llm(self) -> Any | None:
|
||||
config = self._conversation_config
|
||||
if config is None:
|
||||
return None
|
||||
if config.llm is not None:
|
||||
return config.llm
|
||||
if config.answer_from_history_llm is not None:
|
||||
return config.answer_from_history_llm
|
||||
if config.router is not None:
|
||||
return config.router.llm
|
||||
return config.intent_llm
|
||||
|
||||
def _resolve_visibility(
|
||||
self,
|
||||
agent_name: str,
|
||||
visibility: Literal["private", "public"],
|
||||
) -> Literal["private", "public"]:
|
||||
if visibility == "public":
|
||||
return "public"
|
||||
config = self._conversation_config
|
||||
visible = config.visible_agent_outputs if config else None
|
||||
if visible == "all" or (visible is not None and agent_name in visible):
|
||||
return "public"
|
||||
return "private"
|
||||
|
||||
def _assistant_message_count(self) -> int:
|
||||
state = cast(ConversationState, self.state)
|
||||
return sum(1 for message in state.messages if message.role == "assistant")
|
||||
|
||||
def _is_public_turn_result(self, result: Any) -> bool:
|
||||
if not isinstance(result, str):
|
||||
return False
|
||||
if result in {
|
||||
"conversation",
|
||||
"converse",
|
||||
"end",
|
||||
"answer_from_history",
|
||||
"route_to_flow",
|
||||
}:
|
||||
return False
|
||||
return result != cast(ConversationState, self.state).last_intent
|
||||
|
||||
@staticmethod
|
||||
def _coerce_user_message_text(user_message: str | dict[str, Any] | Any) -> str:
|
||||
if isinstance(user_message, str):
|
||||
return user_message
|
||||
if isinstance(user_message, dict) and user_message.get("content") is not None:
|
||||
return str(user_message["content"])
|
||||
return str(user_message)
|
||||
|
||||
@staticmethod
|
||||
def _stringify_result(result: Any) -> str:
|
||||
if hasattr(result, "raw"):
|
||||
return str(result.raw)
|
||||
if isinstance(result, BaseModel):
|
||||
return result.model_dump_json()
|
||||
return str(result)
|
||||
|
||||
@staticmethod
|
||||
def _format_messages(messages: Sequence[Mapping[str, Any]]) -> str:
|
||||
return "\n".join(
|
||||
f"{message.get('role', 'user')}: {message.get('content', '')}"
|
||||
for message in messages
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _coerce_llm(llm: str | BaseLLM | Any) -> Any:
|
||||
from crewai.llm import LLM
|
||||
from crewai.llms.base_llm import BaseLLM as BaseLLMClass
|
||||
|
||||
if isinstance(llm, str):
|
||||
return LLM(model=llm)
|
||||
if isinstance(llm, BaseLLMClass) or callable(getattr(llm, "call", None)):
|
||||
return llm
|
||||
raise ValueError(f"Invalid llm type: {type(llm)}. Expected str or BaseLLM.")
|
||||
|
||||
def finalize_session_traces(self) -> None:
|
||||
"""Emit a final ``FlowFinishedEvent`` and finalize the trace batch.
|
||||
|
||||
Pairs with ``flow.defer_trace_finalization = True`` (or
|
||||
``ConversationConfig(defer_trace_finalization=True)``): per-turn
|
||||
``handle_turn()`` skips the close, then a single call here at
|
||||
session end emits one ``FlowFinishedEvent`` + ``finalize_batch()``
|
||||
so the whole conversation lands as one trace.
|
||||
|
||||
Safe to call when not deferring — it's a no-op if the trace batch
|
||||
was already finalized per-turn or never started.
|
||||
"""
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.event_context import restore_event_scope
|
||||
from crewai.events.listeners.tracing.trace_listener import (
|
||||
TraceCollectionListener,
|
||||
)
|
||||
from crewai.events.types.flow_events import FlowFinishedEvent
|
||||
|
||||
# Only emit the session-end event when a deferred flow_started is
|
||||
# actually pending. ``_deferred_flow_started_event_id`` is set only by
|
||||
# deferred kickoffs; when finalization was not deferred, each per-turn
|
||||
# kickoff already emitted its own flow_finished, so emitting here would
|
||||
# duplicate the session-end event and confuse tracing. Restoring the
|
||||
# stashed scope also pairs this flow_finished with its opener instead
|
||||
# of warning about an empty scope stack.
|
||||
started_id = getattr(self, "_deferred_flow_started_event_id", None)
|
||||
if started_id:
|
||||
last_output = self._method_outputs[-1] if self._method_outputs else None
|
||||
restore_event_scope(((started_id, "flow_started"),))
|
||||
try:
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
FlowFinishedEvent(
|
||||
type="flow_finished",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
result=last_output,
|
||||
state=self._copy_and_serialize_state(),
|
||||
),
|
||||
)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"FlowFinishedEvent emission failed during finalize_session_traces",
|
||||
exc_info=True,
|
||||
)
|
||||
finally:
|
||||
restore_event_scope(())
|
||||
object.__setattr__(self, "_deferred_flow_started_event_id", None)
|
||||
|
||||
trace_listener = TraceCollectionListener()
|
||||
batch_manager = trace_listener.batch_manager
|
||||
if batch_manager.batch_owner_type == "flow":
|
||||
if trace_listener.first_time_handler.is_first_time:
|
||||
trace_listener.first_time_handler.mark_events_collected()
|
||||
trace_listener.first_time_handler.handle_execution_completion()
|
||||
else:
|
||||
batch_manager.finalize_batch()
|
||||
|
||||
|
||||
__all__ = ["_ConversationalMixin"]
|
||||
@@ -4,14 +4,10 @@ from crewai.flow.async_feedback import (
|
||||
HumanFeedbackProvider,
|
||||
PendingFeedbackContext,
|
||||
)
|
||||
from crewai.flow.conversation import (
|
||||
ChatState,
|
||||
ConversationalConfig,
|
||||
ConversationalInputs,
|
||||
)
|
||||
from crewai.flow.dsl import HumanFeedbackResult, human_feedback
|
||||
from crewai.flow.flow import Flow, and_, listen, or_, router, start
|
||||
from crewai.flow.flow_config import flow_config
|
||||
from crewai.flow.flow_serializer import flow_structure
|
||||
from crewai.flow.human_feedback import HumanFeedbackResult, human_feedback
|
||||
from crewai.flow.input_provider import InputProvider, InputResponse
|
||||
from crewai.flow.persistence import persist
|
||||
from crewai.flow.visualization import (
|
||||
@@ -22,10 +18,7 @@ from crewai.flow.visualization import (
|
||||
|
||||
|
||||
__all__ = [
|
||||
"ChatState",
|
||||
"ConsoleProvider",
|
||||
"ConversationalConfig",
|
||||
"ConversationalInputs",
|
||||
"Flow",
|
||||
"FlowStructure",
|
||||
"HumanFeedbackPending",
|
||||
@@ -37,6 +30,7 @@ __all__ = [
|
||||
"and_",
|
||||
"build_flow_structure",
|
||||
"flow_config",
|
||||
"flow_structure",
|
||||
"human_feedback",
|
||||
"listen",
|
||||
"or_",
|
||||
|
||||
@@ -1,246 +0,0 @@
|
||||
"""Conversational turn helpers for CrewAI Flows.
|
||||
|
||||
Provides message history utilities, kickoff input normalization, and optional
|
||||
class-level defaults via ``ConversationalConfig``. Session identity is ``state.id``
|
||||
(``inputs["id"]`` / ``kickoff(session_id=...)``), not a separate Flow field.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from typing import TYPE_CHECKING, Any, Literal, TypedDict, cast
|
||||
from uuid import uuid4
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.flow import Flow
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
|
||||
_EXIT_COMMANDS_DEFAULT: tuple[str, ...] = ("exit", "quit")
|
||||
|
||||
|
||||
class ConversationalInputs(TypedDict, total=False):
|
||||
"""Conventional ``kickoff(inputs=...)`` keys for chat turns."""
|
||||
|
||||
id: str
|
||||
user_message: str | dict[str, Any]
|
||||
last_intent: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConversationalConfig:
|
||||
"""Optional class-level defaults for conversational flows.
|
||||
|
||||
Override per kickoff via ``user_message``, ``session_id``, ``intents``, etc.
|
||||
"""
|
||||
|
||||
default_intents: Sequence[str] | None = None
|
||||
intent_llm: str | None = None
|
||||
interactive_prompt: str = "You: "
|
||||
interactive_timeout: float | None = None
|
||||
exit_commands: Sequence[str] = field(default_factory=lambda: _EXIT_COMMANDS_DEFAULT)
|
||||
defer_trace_finalization: bool = True
|
||||
|
||||
|
||||
class ChatState(BaseModel):
|
||||
"""Recommended persisted state shape for multi-turn flows."""
|
||||
|
||||
id: str = Field(default_factory=lambda: str(uuid4()))
|
||||
messages: list[LLMMessage] = Field(default_factory=list)
|
||||
last_user_message: str | None = None
|
||||
last_intent: str | None = None
|
||||
session_ready: bool = False
|
||||
|
||||
|
||||
def _coerce_user_message_text(user_message: str | dict[str, Any] | Any) -> str:
|
||||
if isinstance(user_message, str):
|
||||
return user_message
|
||||
if isinstance(user_message, dict):
|
||||
content = user_message.get("content")
|
||||
if content is not None:
|
||||
return str(content)
|
||||
return str(user_message)
|
||||
|
||||
|
||||
def normalize_kickoff_inputs(
|
||||
inputs: dict[str, Any] | None,
|
||||
*,
|
||||
user_message: str | dict[str, Any] | None = None,
|
||||
session_id: str | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
"""Merge conversational kickoff kwargs into the inputs dict.
|
||||
|
||||
Returns ``None`` when the caller passed no inputs and no conversational
|
||||
kwargs — so ``FlowStartedEvent.inputs`` stays ``None`` for stateless flows
|
||||
instead of being materialized as an empty dict.
|
||||
"""
|
||||
if inputs is None and user_message is None and session_id is None:
|
||||
return None
|
||||
|
||||
merged: dict[str, Any] = dict(inputs or {})
|
||||
|
||||
if session_id is not None:
|
||||
merged["id"] = session_id
|
||||
|
||||
if user_message is not None:
|
||||
merged["user_message"] = user_message
|
||||
|
||||
return merged
|
||||
|
||||
|
||||
def get_conversation_messages(flow: Flow[Any]) -> list[LLMMessage]:
|
||||
"""Read message history from flow state or the internal fallback buffer."""
|
||||
buffer: list[LLMMessage] = getattr(flow, "_conversation_messages", [])
|
||||
state = getattr(flow, "_state", None)
|
||||
if state is None:
|
||||
return list(buffer)
|
||||
|
||||
if isinstance(state, dict):
|
||||
messages = state.get("messages")
|
||||
if isinstance(messages, list):
|
||||
return cast(list[LLMMessage], messages)
|
||||
elif isinstance(state, BaseModel) and hasattr(state, "messages"):
|
||||
messages = getattr(state, "messages", None)
|
||||
if isinstance(messages, list):
|
||||
return cast(list[LLMMessage], messages)
|
||||
|
||||
return list(buffer)
|
||||
|
||||
|
||||
def append_message(
|
||||
flow: Flow[Any],
|
||||
role: Literal["user", "assistant", "system", "tool"],
|
||||
content: str,
|
||||
**extra: Any,
|
||||
) -> None:
|
||||
"""Append a message to ``state.messages`` or the flow fallback buffer."""
|
||||
message: LLMMessage = {"role": role, "content": content}
|
||||
for key, value in extra.items():
|
||||
if key in ("tool_call_id", "name", "tool_calls", "files"):
|
||||
message[key] = value # type: ignore[literal-required]
|
||||
|
||||
state = getattr(flow, "_state", None)
|
||||
if state is not None:
|
||||
if isinstance(state, dict):
|
||||
messages = state.get("messages")
|
||||
if isinstance(messages, list):
|
||||
messages.append(message)
|
||||
return
|
||||
elif isinstance(state, BaseModel) and hasattr(state, "messages"):
|
||||
messages = getattr(state, "messages", None)
|
||||
if messages is None:
|
||||
object.__setattr__(state, "messages", [])
|
||||
messages = state.messages
|
||||
if isinstance(messages, list):
|
||||
messages.append(message)
|
||||
return
|
||||
|
||||
if not hasattr(flow, "_conversation_messages"):
|
||||
object.__setattr__(flow, "_conversation_messages", [])
|
||||
flow._conversation_messages.append(message)
|
||||
|
||||
|
||||
def set_state_field(flow: Flow[Any], name: str, value: Any) -> None:
|
||||
"""Set a field on structured or dict flow state when present."""
|
||||
state = getattr(flow, "_state", None)
|
||||
if state is None:
|
||||
return
|
||||
if isinstance(state, dict):
|
||||
state[name] = value
|
||||
elif isinstance(state, BaseModel) and hasattr(state, name):
|
||||
object.__setattr__(state, name, value)
|
||||
|
||||
|
||||
def receive_user_message(
|
||||
flow: Flow[Any],
|
||||
text: str,
|
||||
*,
|
||||
outcomes: Sequence[str] | None = None,
|
||||
llm: str | BaseLLM | None = None,
|
||||
) -> str:
|
||||
"""Record a user turn: append message and optionally classify intent."""
|
||||
append_message(flow, "user", text)
|
||||
set_state_field(flow, "last_user_message", text)
|
||||
|
||||
if outcomes and llm is not None:
|
||||
intent = flow.classify_intent(
|
||||
text,
|
||||
outcomes,
|
||||
llm=llm,
|
||||
context=get_conversation_messages(flow),
|
||||
)
|
||||
set_state_field(flow, "last_intent", intent)
|
||||
return intent
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def prepare_conversational_turn(
|
||||
flow: Flow[Any],
|
||||
*,
|
||||
user_message: str | dict[str, Any] | None = None,
|
||||
intents: Sequence[str] | None = None,
|
||||
intent_llm: str | BaseLLM | None = None,
|
||||
config: ConversationalConfig | None = None,
|
||||
) -> None:
|
||||
"""Hydrate conversation state after inputs are merged into flow state."""
|
||||
if user_message is None:
|
||||
state = getattr(flow, "_state", None)
|
||||
if isinstance(state, dict) and "user_message" in state:
|
||||
user_message = state["user_message"]
|
||||
elif isinstance(state, BaseModel) and hasattr(state, "user_message"):
|
||||
user_message = getattr(state, "user_message", None)
|
||||
|
||||
if user_message is None:
|
||||
return
|
||||
|
||||
text = _coerce_user_message_text(user_message)
|
||||
if not text.strip():
|
||||
return
|
||||
|
||||
# Fresh classification each turn (do not reuse prior turn's route label).
|
||||
set_state_field(flow, "last_intent", None)
|
||||
|
||||
resolved_intents = intents
|
||||
if resolved_intents is None and config is not None:
|
||||
resolved_intents = config.default_intents
|
||||
|
||||
resolved_llm = intent_llm
|
||||
if resolved_llm is None and config is not None:
|
||||
resolved_llm = config.intent_llm
|
||||
|
||||
if resolved_intents:
|
||||
if resolved_llm is None:
|
||||
raise ValueError("intent_llm is required when intents are provided")
|
||||
receive_user_message(
|
||||
flow,
|
||||
text,
|
||||
outcomes=resolved_intents,
|
||||
llm=resolved_llm,
|
||||
)
|
||||
else:
|
||||
receive_user_message(flow, text)
|
||||
|
||||
|
||||
def input_history_to_messages(entries: Sequence[Any]) -> list[LLMMessage]:
|
||||
"""Convert ``Flow.input_history`` entries to LLM message format."""
|
||||
messages: list[LLMMessage] = []
|
||||
for entry in entries:
|
||||
prompt = entry.get("message") if isinstance(entry, dict) else None
|
||||
response = entry.get("response") if isinstance(entry, dict) else None
|
||||
if prompt:
|
||||
messages.append({"role": "assistant", "content": str(prompt)})
|
||||
if response:
|
||||
messages.append({"role": "user", "content": str(response)})
|
||||
return messages
|
||||
|
||||
|
||||
def get_conversational_config(flow: Flow[Any]) -> ConversationalConfig | None:
|
||||
"""Return class-level ``conversational_config`` if defined."""
|
||||
return getattr(type(flow), "conversational_config", None)
|
||||
320
lib/crewai/src/crewai/flow/dsl.py
Normal file
320
lib/crewai/src/crewai/flow/dsl.py
Normal file
@@ -0,0 +1,320 @@
|
||||
"""Flow authoring DSL: the ``@start`` / ``@listen`` / ``@router`` decorators
|
||||
plus the ``or_`` / ``and_`` condition combinators.
|
||||
|
||||
These decorators wrap user methods into the typed wrappers defined in
|
||||
``flow_wrappers`` and record their trigger conditions. The structural model
|
||||
those conditions feed is built in ``flow_definition``; execution happens in
|
||||
``runtime``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from typing import Any, ParamSpec, TypeVar
|
||||
|
||||
from crewai.flow.constants import AND_CONDITION, OR_CONDITION
|
||||
from crewai.flow.flow_definition import (
|
||||
_extract_all_methods,
|
||||
is_flow_condition_dict,
|
||||
is_flow_method_callable,
|
||||
is_flow_method_name,
|
||||
)
|
||||
from crewai.flow.flow_wrappers import (
|
||||
FlowCondition,
|
||||
FlowConditions,
|
||||
ListenMethod,
|
||||
RouterMethod,
|
||||
StartMethod,
|
||||
)
|
||||
|
||||
|
||||
P = ParamSpec("P")
|
||||
R = TypeVar("R")
|
||||
|
||||
|
||||
def start(
|
||||
condition: str | FlowCondition | Callable[..., Any] | None = None,
|
||||
) -> Callable[[Callable[P, R]], StartMethod[P, R]]:
|
||||
"""Marks a method as a flow's starting point.
|
||||
|
||||
This decorator designates a method as an entry point for the flow execution.
|
||||
It can optionally specify conditions that trigger the start based on other
|
||||
method executions.
|
||||
|
||||
Args:
|
||||
condition: Defines when the start method should execute. Can be:
|
||||
- str: Name of a method that triggers this start
|
||||
- FlowCondition: Result from or_() or and_(), including nested conditions
|
||||
- Callable[..., Any]: A method reference that triggers this start
|
||||
Default is None, meaning unconditional start.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a flow start point and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @start() # Unconditional start
|
||||
>>> def begin_flow(self):
|
||||
... pass
|
||||
|
||||
>>> @start("method_name") # Start after specific method
|
||||
>>> def conditional_start(self):
|
||||
... pass
|
||||
|
||||
>>> @start(and_("method1", "method2")) # Start after multiple methods
|
||||
>>> def complex_start(self):
|
||||
... pass
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> StartMethod[P, R]:
|
||||
"""Decorator that wraps a function as a start method.
|
||||
|
||||
Args:
|
||||
func: The function to wrap as a start method.
|
||||
|
||||
Returns:
|
||||
A StartMethod wrapper around the function.
|
||||
"""
|
||||
wrapper = StartMethod(func)
|
||||
|
||||
if condition is not None:
|
||||
if is_flow_method_name(condition):
|
||||
wrapper.__trigger_methods__ = [condition]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
elif is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
elif "methods" in condition:
|
||||
wrapper.__trigger_methods__ = condition["methods"]
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition dict must contain 'conditions' or 'methods'"
|
||||
)
|
||||
elif is_flow_method_callable(condition):
|
||||
wrapper.__trigger_methods__ = [condition.__name__]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def listen(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> Callable[[Callable[P, R]], ListenMethod[P, R]]:
|
||||
"""Creates a listener that executes when specified conditions are met.
|
||||
|
||||
This decorator sets up a method to execute in response to other method
|
||||
executions in the flow. It supports both simple and complex triggering
|
||||
conditions.
|
||||
|
||||
Args:
|
||||
condition: Specifies when the listener should execute.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a flow listener and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen("process_data")
|
||||
>>> def handle_processed_data(self):
|
||||
... pass
|
||||
|
||||
>>> @listen("method_name")
|
||||
>>> def handle_completion(self):
|
||||
... pass
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> ListenMethod[P, R]:
|
||||
"""Decorator that wraps a function as a listener method.
|
||||
|
||||
Args:
|
||||
func: The function to wrap as a listener method.
|
||||
|
||||
Returns:
|
||||
A ListenMethod wrapper around the function.
|
||||
"""
|
||||
wrapper = ListenMethod(func)
|
||||
|
||||
if is_flow_method_name(condition):
|
||||
wrapper.__trigger_methods__ = [condition]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
elif is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
elif "methods" in condition:
|
||||
wrapper.__trigger_methods__ = condition["methods"]
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition dict must contain 'conditions' or 'methods'"
|
||||
)
|
||||
elif is_flow_method_callable(condition):
|
||||
wrapper.__trigger_methods__ = [condition.__name__]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def router(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> Callable[[Callable[P, R]], RouterMethod[P, R]]:
|
||||
"""Creates a routing method that directs flow execution based on conditions.
|
||||
|
||||
This decorator marks a method as a router, which can dynamically determine
|
||||
the next steps in the flow based on its return value. Routers are triggered
|
||||
by specified conditions and can return constants that determine which path
|
||||
the flow should take.
|
||||
|
||||
Args:
|
||||
condition: Specifies when the router should execute. Can be:
|
||||
- str: Name of a method that triggers this router
|
||||
- FlowCondition: Result from or_() or and_(), including nested conditions
|
||||
- Callable[..., Any]: A method reference that triggers this router
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a router and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @router("check_status")
|
||||
>>> def route_based_on_status(self):
|
||||
... if self.state.status == "success":
|
||||
... return "SUCCESS"
|
||||
... return "FAILURE"
|
||||
|
||||
>>> @router(and_("validate", "process"))
|
||||
>>> def complex_routing(self):
|
||||
... if all([self.state.valid, self.state.processed]):
|
||||
... return "CONTINUE"
|
||||
... return "STOP"
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> RouterMethod[P, R]:
|
||||
"""Decorator that wraps a function as a router method.
|
||||
|
||||
Args:
|
||||
func: The function to wrap as a router method.
|
||||
|
||||
Returns:
|
||||
A RouterMethod wrapper around the function.
|
||||
"""
|
||||
wrapper = RouterMethod(func)
|
||||
|
||||
if is_flow_method_name(condition):
|
||||
wrapper.__trigger_methods__ = [condition]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
elif is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
elif "methods" in condition:
|
||||
wrapper.__trigger_methods__ = condition["methods"]
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition dict must contain 'conditions' or 'methods'"
|
||||
)
|
||||
elif is_flow_method_callable(condition):
|
||||
wrapper.__trigger_methods__ = [condition.__name__]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
else:
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def or_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
|
||||
"""Combines multiple conditions with OR logic for flow control.
|
||||
|
||||
Creates a condition that is satisfied when any of the specified conditions
|
||||
are met. This is used with @start, @listen, or @router decorators to create
|
||||
complex triggering conditions.
|
||||
|
||||
Args:
|
||||
conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
|
||||
|
||||
Returns:
|
||||
A condition dictionary with format {"type": "OR", "conditions": list_of_conditions} where each condition can be a string (method name) or a nested dict
|
||||
|
||||
Raises:
|
||||
ValueError: If condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen(or_("success", "timeout"))
|
||||
>>> def handle_completion(self):
|
||||
... pass
|
||||
|
||||
>>> @listen(or_(and_("step1", "step2"), "step3"))
|
||||
>>> def handle_nested(self):
|
||||
... pass
|
||||
"""
|
||||
processed_conditions: FlowConditions = []
|
||||
for condition in conditions:
|
||||
if is_flow_condition_dict(condition) or is_flow_method_name(condition):
|
||||
processed_conditions.append(condition)
|
||||
elif is_flow_method_callable(condition):
|
||||
processed_conditions.append(condition.__name__)
|
||||
else:
|
||||
raise ValueError("Invalid condition in or_()")
|
||||
return {"type": OR_CONDITION, "conditions": processed_conditions}
|
||||
|
||||
|
||||
def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
|
||||
"""Combines multiple conditions with AND logic for flow control.
|
||||
|
||||
Creates a condition that is satisfied only when all specified conditions
|
||||
are met. This is used with @start, @listen, or @router decorators to create
|
||||
complex triggering conditions.
|
||||
|
||||
Args:
|
||||
*conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
|
||||
|
||||
Returns:
|
||||
A condition dictionary with format {"type": "AND", "conditions": list_of_conditions}
|
||||
where each condition can be a string (method name) or a nested dict
|
||||
|
||||
Raises:
|
||||
ValueError: If any condition is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen(and_("validated", "processed"))
|
||||
>>> def handle_complete_data(self):
|
||||
... pass
|
||||
|
||||
>>> @listen(and_(or_("step1", "step2"), "step3"))
|
||||
>>> def handle_nested(self):
|
||||
... pass
|
||||
"""
|
||||
processed_conditions: FlowConditions = []
|
||||
for condition in conditions:
|
||||
if is_flow_condition_dict(condition) or is_flow_method_name(condition):
|
||||
processed_conditions.append(condition)
|
||||
elif is_flow_method_callable(condition):
|
||||
processed_conditions.append(condition.__name__)
|
||||
else:
|
||||
raise ValueError("Invalid condition in and_()")
|
||||
return {"type": AND_CONDITION, "conditions": processed_conditions}
|
||||
@@ -1,39 +0,0 @@
|
||||
"""Flow DSL: the Python authoring layer for Flows.
|
||||
|
||||
Provides the ``@start`` / ``@listen`` / ``@router`` decorators and the
|
||||
``or_`` / ``and_`` condition combinators used to write Flow classes in
|
||||
Python. The DSL is one way to produce a Flow Structure: this package
|
||||
extracts a :class:`~crewai.flow.flow_definition.FlowDefinition` from a
|
||||
Python Flow class. Execution is handled by ``runtime``.
|
||||
"""
|
||||
|
||||
from crewai.flow.dsl.human_feedback import (
|
||||
HumanFeedbackResult,
|
||||
human_feedback,
|
||||
)
|
||||
from crewai.flow.dsl.listen import listen
|
||||
from crewai.flow.dsl.router import router
|
||||
from crewai.flow.dsl.start import start
|
||||
from crewai.flow.dsl.utils import (
|
||||
_extract_all_methods as _extract_all_methods,
|
||||
_extract_all_methods_recursive as _extract_all_methods_recursive,
|
||||
_normalize_condition as _normalize_condition,
|
||||
and_,
|
||||
build_flow_definition as build_flow_definition,
|
||||
extract_flow_definition as extract_flow_definition,
|
||||
is_flow_condition_dict as is_flow_condition_dict,
|
||||
is_flow_method as is_flow_method,
|
||||
is_simple_flow_condition as is_simple_flow_condition,
|
||||
or_,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"HumanFeedbackResult",
|
||||
"and_",
|
||||
"human_feedback",
|
||||
"listen",
|
||||
"or_",
|
||||
"router",
|
||||
"start",
|
||||
]
|
||||
@@ -1,98 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable, Sequence
|
||||
from typing import TYPE_CHECKING, Any, TypeVar
|
||||
|
||||
from crewai.flow.flow_definition import FlowMethodDefinition
|
||||
from crewai.flow.human_feedback import (
|
||||
HumanFeedbackConfig,
|
||||
HumanFeedbackResult,
|
||||
_build_human_feedback_runtime_decorator,
|
||||
)
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackProvider
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
|
||||
F = TypeVar("F", bound=Callable[..., Any])
|
||||
|
||||
__all__ = ["HumanFeedbackResult", "human_feedback"]
|
||||
|
||||
|
||||
def _stamp_human_feedback_metadata(
|
||||
wrapper: Any,
|
||||
func: Callable[..., Any],
|
||||
config: HumanFeedbackConfig,
|
||||
) -> None:
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__trigger_condition__",
|
||||
"__is_flow_method__",
|
||||
"__flow_persistence_config__",
|
||||
"__is_router__",
|
||||
"__router_emit__",
|
||||
"__flow_method_definition__",
|
||||
]:
|
||||
if hasattr(func, attr):
|
||||
setattr(wrapper, attr, getattr(func, attr))
|
||||
|
||||
wrapper.__human_feedback_config__ = config
|
||||
wrapper.__is_flow_method__ = True
|
||||
|
||||
if config.emit:
|
||||
wrapper.__is_router__ = True
|
||||
wrapper.__router_emit__ = list(config.emit)
|
||||
fragment = getattr(wrapper, "__flow_method_definition__", None)
|
||||
if isinstance(fragment, FlowMethodDefinition):
|
||||
wrapper.__flow_method_definition__ = fragment.model_copy(
|
||||
update={"router": True, "emit": list(config.emit)}
|
||||
)
|
||||
|
||||
wrapper._human_feedback_llm = config.llm
|
||||
|
||||
|
||||
def human_feedback(
|
||||
message: str,
|
||||
emit: Sequence[str] | None = None,
|
||||
llm: str | BaseLLM | None = "gpt-4o-mini",
|
||||
default_outcome: str | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
provider: HumanFeedbackProvider | None = None,
|
||||
learn: bool = False,
|
||||
learn_source: str = "hitl",
|
||||
learn_strict: bool = False,
|
||||
) -> Callable[[F], F]:
|
||||
"""Decorator for Flow methods that require human feedback."""
|
||||
runtime_decorator = _build_human_feedback_runtime_decorator(
|
||||
message=message,
|
||||
emit=emit,
|
||||
llm=llm,
|
||||
default_outcome=default_outcome,
|
||||
metadata=metadata,
|
||||
provider=provider,
|
||||
learn=learn,
|
||||
learn_source=learn_source,
|
||||
learn_strict=learn_strict,
|
||||
)
|
||||
config = HumanFeedbackConfig(
|
||||
message=message,
|
||||
emit=emit,
|
||||
llm=llm,
|
||||
default_outcome=default_outcome,
|
||||
metadata=metadata,
|
||||
provider=provider,
|
||||
learn=learn,
|
||||
learn_source=learn_source,
|
||||
learn_strict=learn_strict,
|
||||
)
|
||||
|
||||
def decorator(func: F) -> F:
|
||||
wrapper = runtime_decorator(func)
|
||||
_stamp_human_feedback_metadata(wrapper, func, config)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
@@ -1,55 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from typing import Any
|
||||
|
||||
from crewai.flow.dsl.utils import (
|
||||
P,
|
||||
R,
|
||||
_definition_condition_from_runtime,
|
||||
_set_flow_method_definition,
|
||||
_set_trigger_metadata,
|
||||
)
|
||||
from crewai.flow.flow_definition import FlowMethodDefinition
|
||||
from crewai.flow.flow_wrappers import FlowCondition, ListenMethod
|
||||
|
||||
|
||||
def listen(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> Callable[[Callable[P, R]], ListenMethod[P, R]]:
|
||||
"""Creates a listener that executes when specified conditions are met.
|
||||
|
||||
This decorator sets up a method to execute in response to other method
|
||||
executions in the flow. It supports both simple and complex triggering
|
||||
conditions.
|
||||
|
||||
Args:
|
||||
condition: Specifies when the listener should execute.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a flow listener and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen("process_data")
|
||||
>>> def handle_processed_data(self):
|
||||
... pass
|
||||
|
||||
>>> @listen("method_name")
|
||||
>>> def handle_completion(self):
|
||||
... pass
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> ListenMethod[P, R]:
|
||||
wrapper = ListenMethod(func)
|
||||
|
||||
_set_flow_method_definition(
|
||||
wrapper,
|
||||
FlowMethodDefinition(listen=_definition_condition_from_runtime(condition)),
|
||||
)
|
||||
_set_trigger_metadata(wrapper, condition)
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
@@ -1,164 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable, Sequence
|
||||
from enum import Enum
|
||||
import inspect
|
||||
from types import UnionType
|
||||
from typing import (
|
||||
Any,
|
||||
Literal,
|
||||
Union,
|
||||
get_args,
|
||||
get_origin,
|
||||
get_type_hints,
|
||||
)
|
||||
|
||||
from crewai.flow.dsl.utils import (
|
||||
P,
|
||||
R,
|
||||
_definition_condition_from_runtime,
|
||||
_set_flow_method_definition,
|
||||
_set_trigger_metadata,
|
||||
)
|
||||
from crewai.flow.flow_definition import FlowMethodDefinition
|
||||
from crewai.flow.flow_wrappers import FlowCondition, RouterMethod
|
||||
|
||||
|
||||
def _unwrap_function(function: Any) -> Any:
|
||||
if hasattr(function, "__func__"):
|
||||
function = function.__func__
|
||||
|
||||
if hasattr(function, "__wrapped__"):
|
||||
wrapped = function.__wrapped__
|
||||
if hasattr(wrapped, "unwrap"):
|
||||
return wrapped.unwrap()
|
||||
return wrapped
|
||||
|
||||
if hasattr(function, "unwrap"):
|
||||
return function.unwrap()
|
||||
|
||||
return function
|
||||
|
||||
|
||||
def _string_values_from_annotation(annotation: Any) -> list[str]:
|
||||
if annotation is inspect.Signature.empty or isinstance(annotation, str):
|
||||
return []
|
||||
if isinstance(annotation, type) and issubclass(annotation, Enum):
|
||||
return [member.value for member in annotation if isinstance(member.value, str)]
|
||||
|
||||
origin = get_origin(annotation)
|
||||
if origin is None:
|
||||
return []
|
||||
|
||||
args = get_args(annotation)
|
||||
if origin is Literal or getattr(origin, "__name__", "") == "Literal":
|
||||
return [arg for arg in args if isinstance(arg, str)]
|
||||
|
||||
if not (
|
||||
origin is Union
|
||||
or origin is UnionType
|
||||
or getattr(origin, "__name__", "") == "Annotated"
|
||||
):
|
||||
return []
|
||||
|
||||
values: list[str] = []
|
||||
for arg in args:
|
||||
values.extend(_string_values_from_annotation(arg))
|
||||
return values
|
||||
|
||||
|
||||
def _return_annotation(function: Any) -> Any:
|
||||
unwrapped = _unwrap_function(function)
|
||||
|
||||
try:
|
||||
return get_type_hints(unwrapped, include_extras=True).get(
|
||||
"return", inspect.Signature.empty
|
||||
)
|
||||
except (NameError, TypeError, ValueError):
|
||||
try:
|
||||
return inspect.signature(unwrapped).return_annotation
|
||||
except (TypeError, ValueError):
|
||||
return inspect.Signature.empty
|
||||
|
||||
|
||||
def _get_router_return_events(function: Any) -> list[str] | None:
|
||||
values = _string_values_from_annotation(_return_annotation(function))
|
||||
return list(dict.fromkeys(values)) if values else None
|
||||
|
||||
|
||||
def _normalize_router_emit(value: Sequence[Any] | str) -> list[str]:
|
||||
if isinstance(value, str):
|
||||
return [str(value)]
|
||||
return list(dict.fromkeys(str(item) for item in value))
|
||||
|
||||
|
||||
def router(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
*,
|
||||
emit: Sequence[str] | str | None = None,
|
||||
) -> Callable[[Callable[P, R]], RouterMethod[P, R]]:
|
||||
"""Creates a routing method that directs flow execution based on conditions.
|
||||
|
||||
This decorator marks a method as a router, which can dynamically determine
|
||||
the next steps in the flow based on its return value. Routers are triggered
|
||||
by specified conditions and can return constants that emit downstream events.
|
||||
|
||||
Args:
|
||||
condition: Specifies when the router should execute. Can be:
|
||||
- str: Name of a method that triggers this router
|
||||
- FlowCondition: Result from or_() or and_(), including nested conditions
|
||||
- Callable[..., Any]: A method reference that triggers this router
|
||||
emit: Optional explicit router output events for static FlowDefinition
|
||||
and visualization. If omitted, Literal/Enum return annotations are
|
||||
used when available.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a router and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @router("check_status")
|
||||
>>> def route_based_on_status(self):
|
||||
... if self.state.status == "success":
|
||||
... return "SUCCESS"
|
||||
... return "FAILURE"
|
||||
|
||||
>>> @router(and_("validate", "process"))
|
||||
>>> def complex_routing(self):
|
||||
... if all([self.state.valid, self.state.processed]):
|
||||
... return "CONTINUE"
|
||||
... return "STOP"
|
||||
|
||||
>>> @router("check_status", emit=["SUCCESS", "FAILURE"])
|
||||
>>> def explicit_routing(self):
|
||||
... return "SUCCESS"
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> RouterMethod[P, R]:
|
||||
wrapper = RouterMethod(func)
|
||||
|
||||
if emit is not None:
|
||||
router_events = _normalize_router_emit(emit)
|
||||
else:
|
||||
router_events = _get_router_return_events(func) or []
|
||||
|
||||
_set_flow_method_definition(
|
||||
wrapper,
|
||||
FlowMethodDefinition(
|
||||
listen=_definition_condition_from_runtime(condition),
|
||||
router=True,
|
||||
emit=router_events or None,
|
||||
),
|
||||
)
|
||||
|
||||
_set_trigger_metadata(wrapper, condition)
|
||||
|
||||
if emit is not None:
|
||||
wrapper.__router_emit__ = router_events
|
||||
elif router_events:
|
||||
wrapper.__router_emit__ = router_events
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
@@ -1,68 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from typing import Any
|
||||
|
||||
from crewai.flow.dsl.utils import (
|
||||
P,
|
||||
R,
|
||||
_definition_condition_from_runtime,
|
||||
_set_flow_method_definition,
|
||||
_set_trigger_metadata,
|
||||
)
|
||||
from crewai.flow.flow_definition import FlowMethodDefinition
|
||||
from crewai.flow.flow_wrappers import FlowCondition, StartMethod
|
||||
|
||||
|
||||
def start(
|
||||
condition: str | FlowCondition | Callable[..., Any] | None = None,
|
||||
) -> Callable[[Callable[P, R]], StartMethod[P, R]]:
|
||||
"""Marks a method as a flow's starting point.
|
||||
|
||||
This decorator designates a method as an entry point for the flow execution.
|
||||
It can optionally specify conditions that trigger the start based on other
|
||||
method executions.
|
||||
|
||||
Args:
|
||||
condition: Defines when the start method should execute. Can be:
|
||||
- str: Name of a method that triggers this start
|
||||
- FlowCondition: Result from or_() or and_(), including nested conditions
|
||||
- Callable[..., Any]: A method reference that triggers this start
|
||||
Default is None, meaning unconditional start.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method as a flow start point and preserves its signature.
|
||||
|
||||
Raises:
|
||||
ValueError: If the condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @start() # Unconditional start
|
||||
>>> def begin_flow(self):
|
||||
... pass
|
||||
|
||||
>>> @start("method_name") # Start after specific method
|
||||
>>> def conditional_start(self):
|
||||
... pass
|
||||
|
||||
>>> @start(and_("method1", "method2")) # Start after multiple methods
|
||||
>>> def complex_start(self):
|
||||
... pass
|
||||
"""
|
||||
|
||||
def decorator(func: Callable[P, R]) -> StartMethod[P, R]:
|
||||
wrapper = StartMethod(func)
|
||||
|
||||
if condition is not None:
|
||||
_set_flow_method_definition(
|
||||
wrapper,
|
||||
FlowMethodDefinition(
|
||||
start=_definition_condition_from_runtime(condition)
|
||||
),
|
||||
)
|
||||
_set_trigger_metadata(wrapper, condition)
|
||||
else:
|
||||
_set_flow_method_definition(wrapper, FlowMethodDefinition(start=True))
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
@@ -1,775 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable, Sequence
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, ParamSpec, TypeVar
|
||||
|
||||
from pydantic import BaseModel
|
||||
from typing_extensions import TypeIs
|
||||
|
||||
from crewai.flow.constants import AND_CONDITION, OR_CONDITION
|
||||
from crewai.flow.flow_definition import (
|
||||
FlowConfigDefinition,
|
||||
FlowDefinition,
|
||||
FlowDefinitionCondition,
|
||||
FlowDefinitionDiagnostic,
|
||||
FlowHumanFeedbackDefinition,
|
||||
FlowMethodDefinition,
|
||||
FlowPersistenceDefinition,
|
||||
FlowStateDefinition,
|
||||
)
|
||||
from crewai.flow.flow_wrappers import (
|
||||
FlowCondition,
|
||||
FlowConditions,
|
||||
FlowMethod,
|
||||
ListenMethod,
|
||||
RouterMethod,
|
||||
SimpleFlowCondition,
|
||||
StartMethod,
|
||||
)
|
||||
from crewai.flow.types import FlowMethodName
|
||||
|
||||
|
||||
P = ParamSpec("P")
|
||||
R = TypeVar("R")
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_FLOW_METHOD_DEFINITION_ATTR = "__flow_method_definition__"
|
||||
|
||||
|
||||
def is_simple_flow_condition(obj: Any) -> TypeIs[SimpleFlowCondition]:
|
||||
"""Check if the object is a ``(condition_type, methods)`` tuple."""
|
||||
return (
|
||||
isinstance(obj, tuple)
|
||||
and len(obj) == 2
|
||||
and isinstance(obj[0], str)
|
||||
and isinstance(obj[1], list)
|
||||
)
|
||||
|
||||
|
||||
def is_flow_condition_dict(obj: Any) -> TypeIs[FlowCondition]:
|
||||
"""Check if the object matches the FlowCondition structure."""
|
||||
if not isinstance(obj, dict):
|
||||
return False
|
||||
|
||||
type_value = obj.get("type")
|
||||
if type_value not in ("AND", "OR"):
|
||||
return False
|
||||
|
||||
if "conditions" in obj:
|
||||
conditions = obj["conditions"]
|
||||
if not isinstance(conditions, list):
|
||||
return False
|
||||
for cond in conditions:
|
||||
if not (
|
||||
isinstance(cond, str)
|
||||
or (isinstance(cond, dict) and is_flow_condition_dict(cond))
|
||||
):
|
||||
return False
|
||||
|
||||
if "methods" in obj:
|
||||
methods = obj["methods"]
|
||||
if not (isinstance(methods, list) and all(isinstance(m, str) for m in methods)):
|
||||
return False
|
||||
|
||||
allowed_keys = {"type", "conditions", "methods"}
|
||||
if not set(obj).issubset(allowed_keys):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def is_flow_method(obj: Any) -> TypeIs[FlowMethod[Any, Any]]:
|
||||
"""Check if the object carries Flow method wrapper metadata."""
|
||||
return (
|
||||
hasattr(obj, "__is_flow_method__")
|
||||
or hasattr(obj, "__is_start_method__")
|
||||
or hasattr(obj, "__trigger_methods__")
|
||||
or hasattr(obj, "__is_router__")
|
||||
or hasattr(obj, _FLOW_METHOD_DEFINITION_ATTR)
|
||||
)
|
||||
|
||||
|
||||
def _should_include_flow_method(flow_class: type, method: Any) -> bool:
|
||||
if getattr(method, "__conversational_only__", False):
|
||||
return bool(getattr(flow_class, "conversational", False))
|
||||
return True
|
||||
|
||||
|
||||
def _method_reference_name(value: Any) -> FlowMethodName | None:
|
||||
name = getattr(value, "__name__", None)
|
||||
if callable(value) and isinstance(name, str):
|
||||
return FlowMethodName(name)
|
||||
return None
|
||||
|
||||
|
||||
def _flow_method_names(values: Sequence[Any]) -> list[FlowMethodName]:
|
||||
return [FlowMethodName(str(value)) for value in values]
|
||||
|
||||
|
||||
def _extract_all_methods_recursive(
|
||||
condition: str | FlowCondition | dict[str, Any] | list[Any],
|
||||
flow: Any | None = None,
|
||||
) -> list[FlowMethodName]:
|
||||
if isinstance(condition, str):
|
||||
if flow is not None:
|
||||
if condition in flow._methods:
|
||||
return [FlowMethodName(condition)]
|
||||
return []
|
||||
return [FlowMethodName(condition)]
|
||||
if is_flow_condition_dict(condition):
|
||||
normalized = _normalize_condition(condition)
|
||||
methods = []
|
||||
for sub_cond in normalized.get("conditions", []):
|
||||
methods.extend(_extract_all_methods_recursive(sub_cond, flow))
|
||||
return methods
|
||||
if isinstance(condition, list):
|
||||
methods = []
|
||||
for item in condition:
|
||||
methods.extend(_extract_all_methods_recursive(item, flow))
|
||||
return methods
|
||||
return []
|
||||
|
||||
|
||||
def _normalize_condition(
|
||||
condition: FlowConditions | FlowCondition | str,
|
||||
) -> FlowCondition:
|
||||
if isinstance(condition, str):
|
||||
return {"type": OR_CONDITION, "conditions": [FlowMethodName(condition)]}
|
||||
if is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
return condition
|
||||
if "methods" in condition:
|
||||
return {"type": condition["type"], "conditions": condition["methods"]}
|
||||
return condition
|
||||
if isinstance(condition, list) and all(
|
||||
isinstance(item, str) or is_flow_condition_dict(item) for item in condition
|
||||
):
|
||||
return {"type": OR_CONDITION, "conditions": condition}
|
||||
|
||||
raise ValueError(f"Cannot normalize condition: {condition}")
|
||||
|
||||
|
||||
def _extract_all_methods(
|
||||
condition: str | FlowCondition | dict[str, Any] | list[Any],
|
||||
) -> list[FlowMethodName]:
|
||||
if isinstance(condition, str):
|
||||
return [FlowMethodName(condition)]
|
||||
if is_flow_condition_dict(condition):
|
||||
normalized = _normalize_condition(condition)
|
||||
cond_type = normalized.get("type", OR_CONDITION)
|
||||
|
||||
if cond_type == AND_CONDITION:
|
||||
return [
|
||||
FlowMethodName(sub_cond)
|
||||
for sub_cond in normalized.get("conditions", [])
|
||||
if isinstance(sub_cond, str)
|
||||
]
|
||||
return []
|
||||
if isinstance(condition, list):
|
||||
methods = []
|
||||
for item in condition:
|
||||
methods.extend(_extract_all_methods(item))
|
||||
return methods
|
||||
return []
|
||||
|
||||
|
||||
def _condition_trigger(
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> FlowMethodName | FlowCondition:
|
||||
if isinstance(condition, str):
|
||||
return FlowMethodName(condition)
|
||||
if is_flow_condition_dict(condition):
|
||||
return condition
|
||||
method_name = _method_reference_name(condition)
|
||||
if method_name is not None:
|
||||
return method_name
|
||||
raise ValueError("Invalid condition")
|
||||
|
||||
|
||||
def _condition_triggers(
|
||||
conditions: Sequence[str | FlowCondition | Callable[..., Any]],
|
||||
error_message: str,
|
||||
) -> FlowConditions:
|
||||
try:
|
||||
return [_condition_trigger(condition) for condition in conditions]
|
||||
except ValueError as exc:
|
||||
raise ValueError(error_message) from exc
|
||||
|
||||
|
||||
def _definition_condition_from_runtime(condition: Any) -> FlowDefinitionCondition:
|
||||
if isinstance(condition, str):
|
||||
return str(condition)
|
||||
method_name = _method_reference_name(condition)
|
||||
if method_name is not None:
|
||||
return str(method_name)
|
||||
if is_flow_condition_dict(condition):
|
||||
normalized = _normalize_condition(condition)
|
||||
key = "and" if normalized.get("type") == AND_CONDITION else "or"
|
||||
return {
|
||||
key: [
|
||||
_definition_condition_from_runtime(sub_condition)
|
||||
for sub_condition in normalized.get("conditions", [])
|
||||
]
|
||||
}
|
||||
if isinstance(condition, list):
|
||||
return {"or": [_definition_condition_from_runtime(item) for item in condition]}
|
||||
return str(condition)
|
||||
|
||||
|
||||
def _set_trigger_metadata(
|
||||
wrapper: StartMethod[P, R] | ListenMethod[P, R] | RouterMethod[P, R],
|
||||
condition: str | FlowCondition | Callable[..., Any],
|
||||
) -> None:
|
||||
if isinstance(condition, str):
|
||||
wrapper.__trigger_methods__ = [FlowMethodName(condition)]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
return
|
||||
|
||||
if is_flow_condition_dict(condition):
|
||||
if "conditions" in condition:
|
||||
wrapper.__trigger_condition__ = condition
|
||||
wrapper.__trigger_methods__ = _extract_all_methods(condition)
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
return
|
||||
if "methods" in condition:
|
||||
wrapper.__trigger_methods__ = _flow_method_names(condition["methods"])
|
||||
wrapper.__condition_type__ = condition["type"]
|
||||
return
|
||||
raise ValueError("Condition dict must contain 'conditions' or 'methods'")
|
||||
|
||||
method_name = _method_reference_name(condition)
|
||||
if method_name is not None:
|
||||
wrapper.__trigger_methods__ = [method_name]
|
||||
wrapper.__condition_type__ = OR_CONDITION
|
||||
return
|
||||
|
||||
raise ValueError(
|
||||
"Condition must be a method, string, or a result of or_() or and_()"
|
||||
)
|
||||
|
||||
|
||||
def _set_flow_method_definition(
|
||||
wrapper: StartMethod[P, R] | ListenMethod[P, R] | RouterMethod[P, R],
|
||||
definition: FlowMethodDefinition,
|
||||
) -> None:
|
||||
setattr(wrapper, _FLOW_METHOD_DEFINITION_ATTR, definition)
|
||||
|
||||
|
||||
def _get_flow_method_definition(method: Any) -> FlowMethodDefinition | None:
|
||||
definition = getattr(method, _FLOW_METHOD_DEFINITION_ATTR, None)
|
||||
if isinstance(definition, FlowMethodDefinition):
|
||||
return definition
|
||||
if definition is not None:
|
||||
return FlowMethodDefinition.model_validate(definition)
|
||||
return None
|
||||
|
||||
|
||||
def or_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
|
||||
"""Combines multiple conditions with OR logic for flow control.
|
||||
|
||||
Creates a condition that is satisfied when any of the specified conditions
|
||||
are met. This is used with @start, @listen, or @router decorators to create
|
||||
complex triggering conditions.
|
||||
|
||||
Args:
|
||||
conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
|
||||
|
||||
Returns:
|
||||
A condition dictionary with format {"type": "OR", "conditions": list_of_conditions} where each condition can be a string (method name) or a nested dict
|
||||
|
||||
Raises:
|
||||
ValueError: If condition format is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen(or_("success", "timeout"))
|
||||
>>> def handle_completion(self):
|
||||
... pass
|
||||
|
||||
>>> @listen(or_(and_("step1", "step2"), "step3"))
|
||||
>>> def handle_nested(self):
|
||||
... pass
|
||||
"""
|
||||
processed_triggers = _condition_triggers(conditions, "Invalid condition in or_()")
|
||||
return {"type": OR_CONDITION, "conditions": processed_triggers}
|
||||
|
||||
|
||||
def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition:
|
||||
"""Combines multiple conditions with AND logic for flow control.
|
||||
|
||||
Creates a condition that is satisfied only when all specified conditions
|
||||
are met. This is used with @start, @listen, or @router decorators to create
|
||||
complex triggering conditions.
|
||||
|
||||
Args:
|
||||
*conditions: Variable number of conditions that can be method names, existing condition dictionaries, or method references.
|
||||
|
||||
Returns:
|
||||
A condition dictionary with format {"type": "AND", "conditions": list_of_conditions}
|
||||
where each condition can be a string (method name) or a nested dict
|
||||
|
||||
Raises:
|
||||
ValueError: If any condition is invalid.
|
||||
|
||||
Examples:
|
||||
>>> @listen(and_("validated", "processed"))
|
||||
>>> def handle_complete_data(self):
|
||||
... pass
|
||||
|
||||
>>> @listen(and_(or_("step1", "step2"), "step3"))
|
||||
>>> def handle_nested(self):
|
||||
... pass
|
||||
"""
|
||||
processed_triggers = _condition_triggers(conditions, "Invalid condition in and_()")
|
||||
return {"type": AND_CONDITION, "conditions": processed_triggers}
|
||||
|
||||
|
||||
def _object_ref(value: Any) -> str:
|
||||
target = value if isinstance(value, type) else type(value)
|
||||
module = getattr(target, "__module__", "")
|
||||
qualname = getattr(target, "__qualname__", getattr(target, "__name__", ""))
|
||||
return f"{module}:{qualname}" if module and qualname else repr(value)
|
||||
|
||||
|
||||
def _is_json_serializable(value: Any) -> bool:
|
||||
try:
|
||||
json.dumps(value)
|
||||
except (TypeError, ValueError):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _serialize_static_value(
|
||||
value: Any,
|
||||
diagnostics: list[FlowDefinitionDiagnostic],
|
||||
path: str,
|
||||
) -> Any:
|
||||
if value is None or _is_json_serializable(value):
|
||||
return value
|
||||
|
||||
to_config = getattr(value, "to_config_dict", None)
|
||||
if callable(to_config):
|
||||
try:
|
||||
config = to_config()
|
||||
if _is_json_serializable(config):
|
||||
return config
|
||||
except Exception:
|
||||
logger.debug(
|
||||
"Failed to serialize %s via to_config_dict().",
|
||||
path,
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
if isinstance(value, BaseModel):
|
||||
try:
|
||||
data = value.model_dump(mode="json")
|
||||
if _is_json_serializable(data):
|
||||
return data
|
||||
except Exception:
|
||||
logger.debug(
|
||||
"Failed to serialize %s via Pydantic model_dump().",
|
||||
path,
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
ref = _object_ref(value)
|
||||
diagnostics.append(
|
||||
FlowDefinitionDiagnostic(
|
||||
code="non_serializable_value",
|
||||
path=path,
|
||||
message=f"value is not fully serializable; preserved import reference {ref}",
|
||||
)
|
||||
)
|
||||
return {"ref": ref}
|
||||
|
||||
|
||||
def _state_ref(value: Any) -> str | None:
|
||||
if value is None:
|
||||
return None
|
||||
target = value if isinstance(value, type) else type(value)
|
||||
module = getattr(target, "__module__", None)
|
||||
qualname = getattr(target, "__qualname__", None)
|
||||
if module and qualname:
|
||||
return f"{module}:{qualname}"
|
||||
return None
|
||||
|
||||
|
||||
def _build_state_definition(
|
||||
flow_class: type,
|
||||
diagnostics: list[FlowDefinitionDiagnostic],
|
||||
) -> FlowStateDefinition | None:
|
||||
from pydantic import BaseModel as PydanticBaseModel
|
||||
|
||||
state_value = getattr(flow_class, "_initial_state_t", None)
|
||||
initial_state = getattr(flow_class, "initial_state", None)
|
||||
if initial_state is not None:
|
||||
state_value = initial_state
|
||||
|
||||
if state_value is None:
|
||||
return None
|
||||
if state_value is dict or isinstance(state_value, dict):
|
||||
default = None
|
||||
if isinstance(state_value, dict):
|
||||
default = _serialize_static_value(state_value, diagnostics, "state.default")
|
||||
return FlowStateDefinition(type="dict", default=default)
|
||||
if isinstance(state_value, type) and issubclass(state_value, PydanticBaseModel):
|
||||
return FlowStateDefinition(type="pydantic", ref=_state_ref(state_value))
|
||||
if isinstance(state_value, PydanticBaseModel):
|
||||
return FlowStateDefinition(
|
||||
type="pydantic",
|
||||
ref=_state_ref(state_value),
|
||||
default=_serialize_static_value(state_value, diagnostics, "state.default"),
|
||||
)
|
||||
diagnostics.append(
|
||||
FlowDefinitionDiagnostic(
|
||||
code="unknown_state_type",
|
||||
path="state",
|
||||
message=f"could not serialize state type {_object_ref(state_value)}",
|
||||
)
|
||||
)
|
||||
return FlowStateDefinition(type="unknown", ref=_state_ref(state_value))
|
||||
|
||||
|
||||
def _build_config_definition(
|
||||
flow_class: type,
|
||||
diagnostics: list[FlowDefinitionDiagnostic],
|
||||
) -> FlowConfigDefinition:
|
||||
config_field_names = set(FlowConfigDefinition.model_fields)
|
||||
field_defaults = {
|
||||
name: field.default
|
||||
for name, field in getattr(flow_class, "model_fields", {}).items()
|
||||
if name in config_field_names
|
||||
}
|
||||
values: dict[str, Any] = {}
|
||||
for field_name, default in field_defaults.items():
|
||||
value = getattr(flow_class, field_name, default)
|
||||
values[field_name] = _serialize_static_value(
|
||||
value, diagnostics, f"config.{field_name}"
|
||||
)
|
||||
return FlowConfigDefinition(**values)
|
||||
|
||||
|
||||
def _condition_from_method_metadata(method: Any) -> FlowDefinitionCondition | None:
|
||||
trigger_condition = getattr(method, "__trigger_condition__", None)
|
||||
if trigger_condition is not None:
|
||||
return _definition_condition_from_runtime(trigger_condition)
|
||||
|
||||
trigger_methods = getattr(method, "__trigger_methods__", None)
|
||||
if trigger_methods is None:
|
||||
return None
|
||||
condition_type = getattr(method, "__condition_type__", OR_CONDITION)
|
||||
method_names = [str(method_name) for method_name in trigger_methods]
|
||||
if condition_type == AND_CONDITION:
|
||||
return {"and": method_names}
|
||||
if len(method_names) == 1:
|
||||
return method_names[0]
|
||||
return {"or": method_names}
|
||||
|
||||
|
||||
def _flow_method_definition_from_legacy_metadata(method: Any) -> FlowMethodDefinition:
|
||||
is_start = bool(getattr(method, "__is_start_method__", False))
|
||||
is_router = bool(getattr(method, "__is_router__", False))
|
||||
condition = _condition_from_method_metadata(method)
|
||||
|
||||
if not is_start:
|
||||
start_value: bool | FlowDefinitionCondition | None = None
|
||||
elif condition is not None:
|
||||
start_value = condition
|
||||
else:
|
||||
start_value = True
|
||||
|
||||
definition = FlowMethodDefinition(
|
||||
start=start_value,
|
||||
listen=condition if not is_start else None,
|
||||
router=is_router,
|
||||
)
|
||||
|
||||
router_emit = getattr(method, "__router_emit__", None)
|
||||
if router_emit:
|
||||
definition.emit = [str(value) for value in router_emit]
|
||||
return definition
|
||||
|
||||
|
||||
def _definition_trigger_condition(
|
||||
method_definition: FlowMethodDefinition,
|
||||
) -> FlowDefinitionCondition | None:
|
||||
if method_definition.listen is not None:
|
||||
return method_definition.listen
|
||||
if isinstance(method_definition.start, (str, dict)):
|
||||
return method_definition.start
|
||||
return None
|
||||
|
||||
|
||||
def _runtime_condition_from_definition(
|
||||
condition: FlowDefinitionCondition,
|
||||
) -> FlowMethodName | FlowCondition:
|
||||
if isinstance(condition, str):
|
||||
return FlowMethodName(condition)
|
||||
if is_flow_condition_dict(condition):
|
||||
return condition
|
||||
|
||||
if "and" in condition:
|
||||
return {
|
||||
"type": AND_CONDITION,
|
||||
"conditions": [
|
||||
_runtime_condition_from_definition(item)
|
||||
for item in condition.get("and", [])
|
||||
],
|
||||
}
|
||||
return {
|
||||
"type": OR_CONDITION,
|
||||
"conditions": [
|
||||
_runtime_condition_from_definition(item) for item in condition.get("or", [])
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def _runtime_listener_condition_from_definition(
|
||||
condition: FlowDefinitionCondition,
|
||||
) -> SimpleFlowCondition | FlowCondition:
|
||||
runtime_condition = _runtime_condition_from_definition(condition)
|
||||
if isinstance(runtime_condition, str):
|
||||
return (OR_CONDITION, [FlowMethodName(str(runtime_condition))])
|
||||
return runtime_condition
|
||||
|
||||
|
||||
def _build_human_feedback_definition(
|
||||
method: Any,
|
||||
diagnostics: list[FlowDefinitionDiagnostic],
|
||||
path: str,
|
||||
) -> FlowHumanFeedbackDefinition | None:
|
||||
config = getattr(method, "__human_feedback_config__", None)
|
||||
if config is None:
|
||||
return None
|
||||
emit = getattr(config, "emit", None)
|
||||
return FlowHumanFeedbackDefinition(
|
||||
message=str(config.message),
|
||||
emit=[str(value) for value in emit] if emit is not None else None,
|
||||
llm=_serialize_static_value(
|
||||
getattr(config, "llm", None), diagnostics, f"{path}.llm"
|
||||
),
|
||||
default_outcome=getattr(config, "default_outcome", None),
|
||||
metadata=_serialize_static_value(
|
||||
getattr(config, "metadata", None), diagnostics, f"{path}.metadata"
|
||||
),
|
||||
provider=_serialize_static_value(
|
||||
getattr(config, "provider", None), diagnostics, f"{path}.provider"
|
||||
),
|
||||
learn=bool(getattr(config, "learn", False)),
|
||||
learn_source=str(getattr(config, "learn_source", "hitl")),
|
||||
learn_strict=bool(getattr(config, "learn_strict", False)),
|
||||
)
|
||||
|
||||
|
||||
def _build_persistence_definition(
|
||||
value: Any,
|
||||
diagnostics: list[FlowDefinitionDiagnostic],
|
||||
path: str,
|
||||
) -> FlowPersistenceDefinition | None:
|
||||
config = getattr(value, "__flow_persistence_config__", None)
|
||||
if config is None:
|
||||
return None
|
||||
persistence = getattr(config, "persistence", None)
|
||||
verbose = bool(getattr(config, "verbose", False))
|
||||
return FlowPersistenceDefinition(
|
||||
enabled=True,
|
||||
verbose=verbose,
|
||||
persistence=_serialize_static_value(
|
||||
persistence, diagnostics, f"{path}.persistence"
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def _build_method_definition(
|
||||
method: Any,
|
||||
diagnostics: list[FlowDefinitionDiagnostic],
|
||||
path: str,
|
||||
) -> FlowMethodDefinition:
|
||||
fragment = _get_flow_method_definition(method)
|
||||
if fragment is None:
|
||||
method_definition = _flow_method_definition_from_legacy_metadata(method)
|
||||
else:
|
||||
method_definition = fragment.model_copy(deep=True)
|
||||
|
||||
if bool(getattr(method, "__is_router__", False)):
|
||||
method_definition.router = True
|
||||
|
||||
human_feedback = _build_human_feedback_definition(
|
||||
method, diagnostics, f"{path}.human_feedback"
|
||||
)
|
||||
if human_feedback is not None:
|
||||
method_definition.human_feedback = human_feedback
|
||||
if human_feedback.emit:
|
||||
method_definition.router = True
|
||||
method_definition.emit = None
|
||||
|
||||
method_definition.persist = _build_persistence_definition(
|
||||
method, diagnostics, f"{path}.persist"
|
||||
)
|
||||
|
||||
router_emit = getattr(method, "__router_emit__", None)
|
||||
if router_emit and not (human_feedback and human_feedback.emit):
|
||||
if not method_definition.emit:
|
||||
method_definition.emit = [str(value) for value in router_emit]
|
||||
|
||||
return method_definition
|
||||
|
||||
|
||||
def _iter_flow_methods(flow_class: type) -> dict[str, Any]:
|
||||
methods: dict[str, Any] = {}
|
||||
for attr_name in dir(flow_class):
|
||||
if attr_name.startswith("_"):
|
||||
continue
|
||||
try:
|
||||
attr_value = getattr(flow_class, attr_name)
|
||||
except AttributeError:
|
||||
continue
|
||||
if is_flow_method(attr_value) and _should_include_flow_method(
|
||||
flow_class, attr_value
|
||||
):
|
||||
methods[attr_name] = attr_value
|
||||
|
||||
# A wrapped method whose name collides with a base Flow model field
|
||||
# (e.g. ``checkpoint``) is absorbed by Pydantic as a field; the underlying
|
||||
# function is preserved as the field default. Recover those so the
|
||||
# definition still reflects every method once the class is built.
|
||||
for field_name, field in getattr(flow_class, "model_fields", {}).items():
|
||||
if field_name in methods or field_name.startswith("_"):
|
||||
continue
|
||||
default = getattr(field, "default", None)
|
||||
if is_flow_method(default) and _should_include_flow_method(flow_class, default):
|
||||
methods[field_name] = default
|
||||
return methods
|
||||
|
||||
|
||||
def _build_flow_definition_from_class(
|
||||
flow_class: type,
|
||||
namespace: dict[str, Any] | None = None,
|
||||
) -> FlowDefinition:
|
||||
diagnostics: list[FlowDefinitionDiagnostic] = []
|
||||
methods: dict[str, FlowMethodDefinition] = {}
|
||||
flow_methods = _iter_flow_methods(flow_class)
|
||||
if namespace is not None:
|
||||
for attr_name, attr_value in namespace.items():
|
||||
if is_flow_method(attr_value) and _should_include_flow_method(
|
||||
flow_class, attr_value
|
||||
):
|
||||
flow_methods[attr_name] = attr_value
|
||||
|
||||
for method_name, method in flow_methods.items():
|
||||
methods[method_name] = _build_method_definition(
|
||||
method, diagnostics, f"methods.{method_name}"
|
||||
)
|
||||
|
||||
description = None
|
||||
docstring = flow_class.__doc__
|
||||
if docstring:
|
||||
description = docstring.strip()
|
||||
|
||||
definition = FlowDefinition(
|
||||
name=getattr(flow_class, "__name__", "Flow"),
|
||||
description=description,
|
||||
state=_build_state_definition(flow_class, diagnostics),
|
||||
config=_build_config_definition(flow_class, diagnostics),
|
||||
persist=_build_persistence_definition(flow_class, diagnostics, "persist"),
|
||||
methods=methods,
|
||||
diagnostics=diagnostics,
|
||||
)
|
||||
definition.diagnostics.extend(definition.validate_contract())
|
||||
definition.log_diagnostics()
|
||||
return definition
|
||||
|
||||
|
||||
def build_flow_definition(
|
||||
flow_class: type,
|
||||
namespace: dict[str, Any] | None = None,
|
||||
) -> FlowDefinition:
|
||||
"""Build a FlowDefinition from a Python Flow class."""
|
||||
return _build_flow_definition_from_class(flow_class, namespace)
|
||||
|
||||
|
||||
def extract_flow_definition(
|
||||
namespace: dict[str, Any],
|
||||
) -> tuple[list[str], dict[str, Any], set[str], dict[str, Any]]:
|
||||
"""Extract the structural flow registries from a Python class namespace."""
|
||||
start_methods = []
|
||||
listeners = {}
|
||||
router_emit = {}
|
||||
routers = set()
|
||||
|
||||
for attr_name, attr_value in namespace.items():
|
||||
if is_flow_method(attr_value):
|
||||
method_definition = _get_flow_method_definition(attr_value)
|
||||
if method_definition is not None:
|
||||
if method_definition.is_start:
|
||||
start_methods.append(attr_name)
|
||||
|
||||
condition = _definition_trigger_condition(method_definition)
|
||||
if condition is not None:
|
||||
listeners[attr_name] = _runtime_listener_condition_from_definition(
|
||||
condition
|
||||
)
|
||||
|
||||
is_router = method_definition.router or bool(
|
||||
getattr(attr_value, "__is_router__", False)
|
||||
)
|
||||
if is_router:
|
||||
routers.add(attr_name)
|
||||
if method_definition.emit:
|
||||
router_emit[attr_name] = [
|
||||
str(value) for value in method_definition.emit
|
||||
]
|
||||
elif (
|
||||
hasattr(attr_value, "__router_emit__")
|
||||
and attr_value.__router_emit__
|
||||
):
|
||||
router_emit[attr_name] = attr_value.__router_emit__
|
||||
else:
|
||||
router_emit[attr_name] = []
|
||||
continue
|
||||
|
||||
if hasattr(attr_value, "__is_start_method__"):
|
||||
start_methods.append(attr_name)
|
||||
|
||||
if (
|
||||
hasattr(attr_value, "__trigger_methods__")
|
||||
and attr_value.__trigger_methods__ is not None
|
||||
):
|
||||
methods = attr_value.__trigger_methods__
|
||||
condition_type = getattr(attr_value, "__condition_type__", OR_CONDITION)
|
||||
|
||||
if (
|
||||
hasattr(attr_value, "__trigger_condition__")
|
||||
and attr_value.__trigger_condition__ is not None
|
||||
):
|
||||
listeners[attr_name] = attr_value.__trigger_condition__
|
||||
else:
|
||||
listeners[attr_name] = (condition_type, methods)
|
||||
|
||||
if hasattr(attr_value, "__is_router__") and attr_value.__is_router__:
|
||||
routers.add(attr_name)
|
||||
if (
|
||||
hasattr(attr_value, "__router_emit__")
|
||||
and attr_value.__router_emit__
|
||||
):
|
||||
router_emit[attr_name] = attr_value.__router_emit__
|
||||
else:
|
||||
router_emit[attr_name] = []
|
||||
|
||||
if (
|
||||
hasattr(attr_value, "__is_start_method__")
|
||||
and hasattr(attr_value, "__is_router__")
|
||||
and attr_value.__is_router__
|
||||
):
|
||||
routers.add(attr_name)
|
||||
if (
|
||||
hasattr(attr_value, "__router_emit__")
|
||||
and attr_value.__router_emit__
|
||||
):
|
||||
router_emit[attr_name] = attr_value.__router_emit__
|
||||
else:
|
||||
router_emit[attr_name] = []
|
||||
|
||||
return start_methods, listeners, routers, router_emit
|
||||
@@ -3,8 +3,8 @@
|
||||
The implementation now lives in three modules, split by concern:
|
||||
|
||||
- ``crewai.flow.dsl`` -- authoring decorators (``@start`` / ``@listen`` /
|
||||
``@router``, ``or_`` / ``and_``) and Python Flow class projection
|
||||
- ``crewai.flow.flow_definition`` -- the serializable Flow Definition contract
|
||||
``@router``, ``or_`` / ``and_``)
|
||||
- ``crewai.flow.flow_definition`` -- the structural model extracted from the DSL
|
||||
- ``crewai.flow.runtime`` -- the Flow execution engine and state
|
||||
|
||||
Prefer importing from those modules in new code; this module preserves the
|
||||
|
||||
@@ -18,7 +18,3 @@ current_flow_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
|
||||
current_flow_method_name: contextvars.ContextVar[str] = contextvars.ContextVar(
|
||||
"flow_method_name", default="unknown"
|
||||
)
|
||||
|
||||
current_flow_name: contextvars.ContextVar[str | None] = contextvars.ContextVar(
|
||||
"flow_name", default=None
|
||||
)
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
592
lib/crewai/src/crewai/flow/flow_serializer.py
Normal file
592
lib/crewai/src/crewai/flow/flow_serializer.py
Normal file
@@ -0,0 +1,592 @@
|
||||
"""Flow structure serializer for introspecting Flow classes.
|
||||
|
||||
This module provides the flow_structure() function that analyzes a Flow class
|
||||
and returns a JSON-serializable dictionary describing its graph structure.
|
||||
This is used by Studio UI to render a visual flow graph.
|
||||
|
||||
Example:
|
||||
>>> from crewai.flow import Flow, start, listen
|
||||
>>> from crewai.flow.flow_serializer import flow_structure
|
||||
>>>
|
||||
>>> class MyFlow(Flow):
|
||||
... @start()
|
||||
... def begin(self):
|
||||
... return "started"
|
||||
...
|
||||
... @listen(begin)
|
||||
... def process(self):
|
||||
... return "done"
|
||||
>>>
|
||||
>>> structure = flow_structure(MyFlow)
|
||||
>>> print(structure["name"])
|
||||
'MyFlow'
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import inspect
|
||||
import logging
|
||||
import re
|
||||
import textwrap
|
||||
from typing import Any, TypedDict, get_args, get_origin
|
||||
|
||||
from pydantic import BaseModel
|
||||
from pydantic_core import PydanticUndefined
|
||||
|
||||
from crewai.flow.flow_wrappers import (
|
||||
FlowCondition,
|
||||
FlowMethod,
|
||||
ListenMethod,
|
||||
RouterMethod,
|
||||
StartMethod,
|
||||
)
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MethodInfo(TypedDict, total=False):
|
||||
"""Information about a single flow method.
|
||||
|
||||
Attributes:
|
||||
name: The method name.
|
||||
type: Method type - start, listen, router, or start_router.
|
||||
trigger_methods: List of method names that trigger this method.
|
||||
condition_type: 'AND' or 'OR' for composite conditions, null otherwise.
|
||||
router_paths: For routers, the possible route names returned.
|
||||
has_human_feedback: Whether the method has @human_feedback decorator.
|
||||
has_crew: Whether the method body references a Crew.
|
||||
"""
|
||||
|
||||
name: str
|
||||
type: str
|
||||
trigger_methods: list[str]
|
||||
condition_type: str | None
|
||||
router_paths: list[str]
|
||||
has_human_feedback: bool
|
||||
has_crew: bool
|
||||
|
||||
|
||||
class EdgeInfo(TypedDict, total=False):
|
||||
"""Information about an edge between flow methods.
|
||||
|
||||
Attributes:
|
||||
from_method: Source method name.
|
||||
to_method: Target method name.
|
||||
edge_type: Type of edge - 'listen' or 'route'.
|
||||
condition: Route name for router edges, null for listen edges.
|
||||
"""
|
||||
|
||||
from_method: str
|
||||
to_method: str
|
||||
edge_type: str
|
||||
condition: str | None
|
||||
|
||||
|
||||
class StateFieldInfo(TypedDict, total=False):
|
||||
"""Information about a state field.
|
||||
|
||||
Attributes:
|
||||
name: Field name.
|
||||
type: Field type as string.
|
||||
default: Default value if any.
|
||||
"""
|
||||
|
||||
name: str
|
||||
type: str
|
||||
default: Any
|
||||
|
||||
|
||||
class StateSchemaInfo(TypedDict, total=False):
|
||||
"""Information about the flow's state schema.
|
||||
|
||||
Attributes:
|
||||
fields: List of field information.
|
||||
"""
|
||||
|
||||
fields: list[StateFieldInfo]
|
||||
|
||||
|
||||
class FlowStructureInfo(TypedDict, total=False):
|
||||
"""Complete flow structure information.
|
||||
|
||||
Attributes:
|
||||
name: Flow class name.
|
||||
description: Flow docstring if available.
|
||||
methods: List of method information.
|
||||
edges: List of edge information.
|
||||
state_schema: State schema if typed, null otherwise.
|
||||
inputs: Detected flow inputs if available.
|
||||
"""
|
||||
|
||||
name: str
|
||||
description: str | None
|
||||
methods: list[MethodInfo]
|
||||
edges: list[EdgeInfo]
|
||||
state_schema: StateSchemaInfo | None
|
||||
inputs: list[str]
|
||||
|
||||
|
||||
def _get_method_type(
|
||||
method_name: str,
|
||||
method: Any,
|
||||
start_methods: list[str],
|
||||
routers: set[str],
|
||||
) -> str:
|
||||
"""Determine the type of a flow method.
|
||||
|
||||
Args:
|
||||
method_name: Name of the method.
|
||||
method: The method object.
|
||||
start_methods: List of start method names.
|
||||
routers: Set of router method names.
|
||||
|
||||
Returns:
|
||||
One of: 'start', 'listen', 'router', or 'start_router'.
|
||||
"""
|
||||
is_start = method_name in start_methods or getattr(
|
||||
method, "__is_start_method__", False
|
||||
)
|
||||
is_router = method_name in routers or getattr(method, "__is_router__", False)
|
||||
|
||||
if is_start and is_router:
|
||||
return "start_router"
|
||||
if is_start:
|
||||
return "start"
|
||||
if is_router:
|
||||
return "router"
|
||||
return "listen"
|
||||
|
||||
|
||||
def _has_human_feedback(method: Any) -> bool:
|
||||
"""Check if a method has the @human_feedback decorator.
|
||||
|
||||
Args:
|
||||
method: The method object to check.
|
||||
|
||||
Returns:
|
||||
True if the method has __human_feedback_config__ attribute.
|
||||
"""
|
||||
return hasattr(method, "__human_feedback_config__")
|
||||
|
||||
|
||||
def _detect_crew_reference(method: Any) -> bool:
|
||||
"""Detect if a method body references a Crew.
|
||||
|
||||
Checks for patterns like:
|
||||
- .crew() method calls
|
||||
- Crew( instantiation
|
||||
- References to Crew class in type hints
|
||||
|
||||
Note:
|
||||
This is a **best-effort heuristic for UI hints**, not a guarantee.
|
||||
Uses inspect.getsource + regex which can false-positive on comments
|
||||
or string literals, and may fail on dynamically generated methods
|
||||
or lambdas. Do not rely on this for correctness-critical logic.
|
||||
|
||||
Args:
|
||||
method: The method object to inspect.
|
||||
|
||||
Returns:
|
||||
True if crew reference detected, False otherwise.
|
||||
"""
|
||||
try:
|
||||
func = method
|
||||
if hasattr(method, "_meth"):
|
||||
func = method._meth
|
||||
elif hasattr(method, "__wrapped__"):
|
||||
func = method.__wrapped__
|
||||
|
||||
source = inspect.getsource(func)
|
||||
source = textwrap.dedent(source)
|
||||
|
||||
crew_patterns = [
|
||||
r"\.crew\(\)", # .crew() method call
|
||||
r"Crew\s*\(", # Crew( instantiation
|
||||
r":\s*Crew\b", # Type hint with Crew
|
||||
r"->.*Crew", # Return type hint with Crew
|
||||
]
|
||||
|
||||
for pattern in crew_patterns:
|
||||
if re.search(pattern, source):
|
||||
return True
|
||||
|
||||
return False
|
||||
except (OSError, TypeError):
|
||||
return False
|
||||
|
||||
|
||||
def _extract_trigger_methods(method: Any) -> tuple[list[str], str | None]:
|
||||
"""Extract trigger methods and condition type from a method.
|
||||
|
||||
Args:
|
||||
method: The method object to inspect.
|
||||
|
||||
Returns:
|
||||
Tuple of (trigger_methods list, condition_type or None).
|
||||
"""
|
||||
trigger_methods: list[str] = []
|
||||
condition_type: str | None = None
|
||||
|
||||
if hasattr(method, "__trigger_methods__") and method.__trigger_methods__:
|
||||
trigger_methods = [str(m) for m in method.__trigger_methods__]
|
||||
|
||||
# For complex conditions (or_/and_ combinators), extract from __trigger_condition__
|
||||
if (
|
||||
not trigger_methods
|
||||
and hasattr(method, "__trigger_condition__")
|
||||
and method.__trigger_condition__
|
||||
):
|
||||
trigger_condition = method.__trigger_condition__
|
||||
trigger_methods = _extract_all_methods_from_condition(trigger_condition)
|
||||
|
||||
if hasattr(method, "__condition_type__") and method.__condition_type__:
|
||||
condition_type = str(method.__condition_type__)
|
||||
|
||||
return trigger_methods, condition_type
|
||||
|
||||
|
||||
def _extract_router_paths(
|
||||
method: Any, router_paths_registry: dict[str, list[str]]
|
||||
) -> list[str]:
|
||||
"""Extract router paths for a router method.
|
||||
|
||||
Args:
|
||||
method: The method object.
|
||||
router_paths_registry: The class-level _router_paths dict.
|
||||
|
||||
Returns:
|
||||
List of possible route names.
|
||||
"""
|
||||
method_name = getattr(method, "__name__", "")
|
||||
|
||||
if hasattr(method, "__router_paths__") and method.__router_paths__:
|
||||
return [str(p) for p in method.__router_paths__]
|
||||
|
||||
if method_name in router_paths_registry:
|
||||
return [str(p) for p in router_paths_registry[method_name]]
|
||||
|
||||
return []
|
||||
|
||||
|
||||
def _extract_all_methods_from_condition(
|
||||
condition: str | FlowCondition | dict[str, Any] | list[Any],
|
||||
) -> list[str]:
|
||||
"""Extract all method names from a condition tree recursively.
|
||||
|
||||
Args:
|
||||
condition: Can be a string, FlowCondition tuple, dict, or list.
|
||||
|
||||
Returns:
|
||||
List of all method names found in the condition.
|
||||
"""
|
||||
if isinstance(condition, str):
|
||||
return [condition]
|
||||
if isinstance(condition, tuple) and len(condition) == 2:
|
||||
# FlowCondition: (condition_type, methods_list)
|
||||
_, methods = condition
|
||||
if isinstance(methods, list):
|
||||
result: list[str] = []
|
||||
for m in methods:
|
||||
result.extend(_extract_all_methods_from_condition(m))
|
||||
return result
|
||||
return []
|
||||
if isinstance(condition, dict):
|
||||
conditions_list = condition.get("conditions", [])
|
||||
dict_methods: list[str] = []
|
||||
for sub_cond in conditions_list:
|
||||
dict_methods.extend(_extract_all_methods_from_condition(sub_cond))
|
||||
return dict_methods
|
||||
if isinstance(condition, list):
|
||||
list_methods: list[str] = []
|
||||
for item in condition:
|
||||
list_methods.extend(_extract_all_methods_from_condition(item))
|
||||
return list_methods
|
||||
return []
|
||||
|
||||
|
||||
def _generate_edges(
|
||||
listeners: dict[str, tuple[str, list[str]] | FlowCondition],
|
||||
routers: set[str],
|
||||
router_paths: dict[str, list[str]],
|
||||
all_methods: set[str],
|
||||
) -> list[EdgeInfo]:
|
||||
"""Generate edges from listeners and routers.
|
||||
|
||||
Args:
|
||||
listeners: Map of listener_name -> (condition_type, trigger_methods) or FlowCondition.
|
||||
routers: Set of router method names.
|
||||
router_paths: Map of router_name -> possible return values.
|
||||
all_methods: Set of all method names in the flow.
|
||||
|
||||
Returns:
|
||||
List of EdgeInfo dictionaries.
|
||||
"""
|
||||
edges: list[EdgeInfo] = []
|
||||
|
||||
for listener_name, condition_data in listeners.items():
|
||||
trigger_methods: list[str] = []
|
||||
|
||||
if isinstance(condition_data, tuple) and len(condition_data) == 2:
|
||||
_condition_type, methods = condition_data
|
||||
trigger_methods = [str(m) for m in methods]
|
||||
elif isinstance(condition_data, dict):
|
||||
trigger_methods = _extract_all_methods_from_condition(condition_data)
|
||||
|
||||
edges.extend(
|
||||
EdgeInfo(
|
||||
from_method=trigger,
|
||||
to_method=listener_name,
|
||||
edge_type="listen",
|
||||
condition=None,
|
||||
)
|
||||
for trigger in trigger_methods
|
||||
if trigger in all_methods
|
||||
)
|
||||
|
||||
for router_name, paths in router_paths.items():
|
||||
for path in paths:
|
||||
for listener_name, condition_data in listeners.items():
|
||||
path_triggers: list[str] = []
|
||||
|
||||
if isinstance(condition_data, tuple) and len(condition_data) == 2:
|
||||
_, methods = condition_data
|
||||
path_triggers = [str(m) for m in methods]
|
||||
elif isinstance(condition_data, dict):
|
||||
path_triggers = _extract_all_methods_from_condition(condition_data)
|
||||
|
||||
if str(path) in path_triggers:
|
||||
edges.append(
|
||||
EdgeInfo(
|
||||
from_method=router_name,
|
||||
to_method=listener_name,
|
||||
edge_type="route",
|
||||
condition=str(path),
|
||||
)
|
||||
)
|
||||
|
||||
return edges
|
||||
|
||||
|
||||
def _extract_state_schema(flow_class: type) -> StateSchemaInfo | None:
|
||||
"""Extract state schema from a Flow class.
|
||||
|
||||
Checks for:
|
||||
- Generic type parameter (Flow[MyState])
|
||||
- initial_state class attribute
|
||||
|
||||
Args:
|
||||
flow_class: The Flow class to inspect.
|
||||
|
||||
Returns:
|
||||
StateSchemaInfo if a Pydantic model state is detected, None otherwise.
|
||||
"""
|
||||
state_type: type | None = None
|
||||
|
||||
# _initial_state_t is set by Flow.__class_getitem__
|
||||
if hasattr(flow_class, "_initial_state_t"):
|
||||
state_type = flow_class._initial_state_t
|
||||
|
||||
if state_type is None and hasattr(flow_class, "initial_state"):
|
||||
initial_state = flow_class.initial_state
|
||||
if isinstance(initial_state, type) and issubclass(initial_state, BaseModel):
|
||||
state_type = initial_state
|
||||
elif isinstance(initial_state, BaseModel):
|
||||
state_type = type(initial_state)
|
||||
|
||||
if state_type is None and hasattr(flow_class, "__orig_bases__"):
|
||||
for base in flow_class.__orig_bases__:
|
||||
origin = get_origin(base)
|
||||
if origin is not None:
|
||||
args = get_args(base)
|
||||
if args:
|
||||
candidate = args[0]
|
||||
if isinstance(candidate, type) and issubclass(candidate, BaseModel):
|
||||
state_type = candidate
|
||||
break
|
||||
|
||||
if state_type is None or not issubclass(state_type, BaseModel):
|
||||
return None
|
||||
|
||||
fields: list[StateFieldInfo] = []
|
||||
try:
|
||||
model_fields = state_type.model_fields
|
||||
for field_name, field_info in model_fields.items():
|
||||
field_type_str = "Any"
|
||||
if field_info.annotation is not None:
|
||||
field_type_str = str(field_info.annotation)
|
||||
field_type_str = field_type_str.replace("typing.", "")
|
||||
field_type_str = field_type_str.replace("<class '", "").replace(
|
||||
"'>", ""
|
||||
)
|
||||
|
||||
default_value = None
|
||||
if (
|
||||
field_info.default is not PydanticUndefined
|
||||
and field_info.default is not None
|
||||
and not callable(field_info.default)
|
||||
):
|
||||
try:
|
||||
default_value = field_info.default
|
||||
except Exception:
|
||||
default_value = str(field_info.default)
|
||||
|
||||
fields.append(
|
||||
StateFieldInfo(
|
||||
name=field_name,
|
||||
type=field_type_str,
|
||||
default=default_value,
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
logger.debug(
|
||||
"Failed to extract state schema fields for %s", flow_class.__name__
|
||||
)
|
||||
|
||||
return StateSchemaInfo(fields=fields) if fields else None
|
||||
|
||||
|
||||
def _detect_flow_inputs(flow_class: type) -> list[str]:
|
||||
"""Detect flow input parameters.
|
||||
|
||||
Inspects the __init__ signature for custom parameters beyond standard Flow params.
|
||||
|
||||
Args:
|
||||
flow_class: The Flow class to inspect.
|
||||
|
||||
Returns:
|
||||
List of detected input names.
|
||||
"""
|
||||
inputs: list[str] = []
|
||||
|
||||
try:
|
||||
init_method = flow_class.__init__ # type: ignore[misc]
|
||||
init_sig = inspect.signature(init_method)
|
||||
standard_params = {
|
||||
"self",
|
||||
"persistence",
|
||||
"tracing",
|
||||
"suppress_flow_events",
|
||||
"max_method_calls",
|
||||
"kwargs",
|
||||
}
|
||||
inputs.extend(
|
||||
param_name
|
||||
for param_name in init_sig.parameters
|
||||
if param_name not in standard_params and not param_name.startswith("_")
|
||||
)
|
||||
except Exception:
|
||||
logger.debug(
|
||||
"Failed to detect inputs from __init__ for %s", flow_class.__name__
|
||||
)
|
||||
|
||||
return inputs
|
||||
|
||||
|
||||
def flow_structure(flow_class: type) -> FlowStructureInfo:
|
||||
"""Introspect a Flow class and return its structure as a JSON-serializable dict.
|
||||
|
||||
This function analyzes a Flow CLASS (not instance) and returns complete
|
||||
information about its graph structure including methods, edges, and state.
|
||||
|
||||
Args:
|
||||
flow_class: A Flow class (not an instance) to introspect.
|
||||
|
||||
Returns:
|
||||
FlowStructureInfo dictionary containing:
|
||||
- name: Flow class name
|
||||
- description: Docstring if available
|
||||
- methods: List of method info dicts
|
||||
- edges: List of edge info dicts
|
||||
- state_schema: State schema if typed, None otherwise
|
||||
- inputs: Detected input names
|
||||
|
||||
Raises:
|
||||
TypeError: If flow_class is not a class.
|
||||
|
||||
Example:
|
||||
>>> structure = flow_structure(MyFlow)
|
||||
>>> print(structure["name"])
|
||||
'MyFlow'
|
||||
>>> for method in structure["methods"]:
|
||||
... print(method["name"], method["type"])
|
||||
"""
|
||||
if not isinstance(flow_class, type):
|
||||
raise TypeError(
|
||||
f"flow_structure requires a Flow class, not an instance. "
|
||||
f"Got {type(flow_class).__name__}"
|
||||
)
|
||||
|
||||
start_methods: list[str] = getattr(flow_class, "_start_methods", [])
|
||||
listeners: dict[str, Any] = getattr(flow_class, "_listeners", {})
|
||||
routers: set[str] = getattr(flow_class, "_routers", set())
|
||||
router_paths_registry: dict[str, list[str]] = getattr(
|
||||
flow_class, "_router_paths", {}
|
||||
)
|
||||
|
||||
methods: list[MethodInfo] = []
|
||||
all_method_names: set[str] = set()
|
||||
|
||||
for attr_name in dir(flow_class):
|
||||
if attr_name.startswith("_"):
|
||||
continue
|
||||
|
||||
try:
|
||||
attr = getattr(flow_class, attr_name)
|
||||
except AttributeError:
|
||||
continue
|
||||
|
||||
is_flow_method = (
|
||||
isinstance(attr, (FlowMethod, StartMethod, ListenMethod, RouterMethod))
|
||||
or hasattr(attr, "__is_flow_method__")
|
||||
or hasattr(attr, "__is_start_method__")
|
||||
or hasattr(attr, "__trigger_methods__")
|
||||
or hasattr(attr, "__is_router__")
|
||||
)
|
||||
|
||||
if not is_flow_method:
|
||||
continue
|
||||
|
||||
all_method_names.add(attr_name)
|
||||
|
||||
method_type = _get_method_type(attr_name, attr, start_methods, routers)
|
||||
|
||||
trigger_methods, condition_type = _extract_trigger_methods(attr)
|
||||
|
||||
router_paths_list: list[str] = []
|
||||
if method_type in ("router", "start_router"):
|
||||
router_paths_list = _extract_router_paths(attr, router_paths_registry)
|
||||
|
||||
has_hf = _has_human_feedback(attr)
|
||||
|
||||
has_crew = _detect_crew_reference(attr)
|
||||
|
||||
method_info = MethodInfo(
|
||||
name=attr_name,
|
||||
type=method_type,
|
||||
trigger_methods=trigger_methods,
|
||||
condition_type=condition_type,
|
||||
router_paths=router_paths_list,
|
||||
has_human_feedback=has_hf,
|
||||
has_crew=has_crew,
|
||||
)
|
||||
methods.append(method_info)
|
||||
|
||||
edges = _generate_edges(listeners, routers, router_paths_registry, all_method_names)
|
||||
|
||||
state_schema = _extract_state_schema(flow_class)
|
||||
|
||||
inputs = _detect_flow_inputs(flow_class)
|
||||
|
||||
description: str | None = None
|
||||
if flow_class.__doc__:
|
||||
description = flow_class.__doc__.strip()
|
||||
|
||||
return FlowStructureInfo(
|
||||
name=flow_class.__name__,
|
||||
description=description,
|
||||
methods=methods,
|
||||
edges=edges,
|
||||
state_schema=state_schema,
|
||||
inputs=inputs,
|
||||
)
|
||||
@@ -18,17 +18,6 @@ R = TypeVar("R")
|
||||
FlowConditionType: TypeAlias = Literal["OR", "AND"]
|
||||
SimpleFlowCondition: TypeAlias = tuple[FlowConditionType, list[FlowMethodName]]
|
||||
|
||||
__all__ = [
|
||||
"FlowCondition",
|
||||
"FlowConditionType",
|
||||
"FlowConditions",
|
||||
"FlowMethod",
|
||||
"ListenMethod",
|
||||
"RouterMethod",
|
||||
"SimpleFlowCondition",
|
||||
"StartMethod",
|
||||
]
|
||||
|
||||
|
||||
class FlowCondition(TypedDict, total=False):
|
||||
"""Type definition for flow trigger conditions.
|
||||
@@ -84,12 +73,9 @@ class FlowMethod(Generic[P, R]):
|
||||
# Preserve flow-related attributes from wrapped method (e.g., from @human_feedback)
|
||||
for attr in [
|
||||
"__is_router__",
|
||||
"__router_emit__",
|
||||
"__router_paths__",
|
||||
"__human_feedback_config__",
|
||||
"__conversational_only__", # gates registration on Flow.conversational
|
||||
"__flow_persistence_config__",
|
||||
"__flow_method_definition__",
|
||||
"_human_feedback_llm", # Live LLM object for HITL resume
|
||||
"_hf_llm", # Live LLM object for HITL resume
|
||||
]:
|
||||
if hasattr(meth, attr):
|
||||
setattr(self, attr, getattr(meth, attr))
|
||||
@@ -179,4 +165,3 @@ class RouterMethod(FlowMethod[P, R]):
|
||||
__trigger_methods__: list[FlowMethodName] | None = None
|
||||
__condition_type__: FlowConditionType | None = None
|
||||
__trigger_condition__: FlowCondition | None = None
|
||||
__router_emit__: list[str] | None = None
|
||||
|
||||
@@ -78,10 +78,14 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
F = TypeVar("F", bound=Callable[..., Any])
|
||||
|
||||
__all__ = ["HumanFeedbackResult", "human_feedback"]
|
||||
|
||||
|
||||
def _serialize_llm_for_context(llm: Any) -> dict[str, Any] | str | None:
|
||||
"""Serialize a BaseLLM object to a dict preserving full config.
|
||||
|
||||
Delegates to ``llm.to_config_dict()`` when available (BaseLLM and
|
||||
subclasses). Falls back to extracting the model string with provider
|
||||
prefix for unknown LLM types.
|
||||
"""
|
||||
to_config: Callable[[], dict[str, Any]] | None = getattr(
|
||||
llm, "to_config_dict", None
|
||||
)
|
||||
@@ -99,6 +103,13 @@ def _serialize_llm_for_context(llm: Any) -> dict[str, Any] | str | None:
|
||||
def _deserialize_llm_from_context(
|
||||
llm_data: dict[str, Any] | str | None,
|
||||
) -> BaseLLM | None:
|
||||
"""Reconstruct an LLM instance from serialized context data.
|
||||
|
||||
Handles both the new dict format (with full config) and the legacy
|
||||
string format (model name only) for backward compatibility.
|
||||
|
||||
Returns a BaseLLM instance, or None if llm_data is None.
|
||||
"""
|
||||
if llm_data is None:
|
||||
return None
|
||||
|
||||
@@ -191,12 +202,12 @@ class HumanFeedbackMethod(FlowMethod[Any, Any]):
|
||||
|
||||
Attributes:
|
||||
__is_router__: True when emit is specified, enabling router behavior.
|
||||
__router_emit__: List of possible outcomes when acting as a router.
|
||||
__router_paths__: List of possible outcomes when acting as a router.
|
||||
__human_feedback_config__: The HumanFeedbackConfig for this method.
|
||||
"""
|
||||
|
||||
__is_router__: bool = False
|
||||
__router_emit__: list[str] | None = None
|
||||
__router_paths__: list[str] | None = None
|
||||
__human_feedback_config__: HumanFeedbackConfig | None = None
|
||||
|
||||
|
||||
@@ -221,7 +232,7 @@ class DistilledLessons(BaseModel):
|
||||
)
|
||||
|
||||
|
||||
def _build_human_feedback_runtime_decorator(
|
||||
def human_feedback(
|
||||
message: str,
|
||||
emit: Sequence[str] | None = None,
|
||||
llm: str | BaseLLM | None = "gpt-4o-mini",
|
||||
@@ -232,6 +243,102 @@ def _build_human_feedback_runtime_decorator(
|
||||
learn_source: str = "hitl",
|
||||
learn_strict: bool = False,
|
||||
) -> Callable[[F], F]:
|
||||
"""Decorator for Flow methods that require human feedback.
|
||||
|
||||
This decorator wraps a Flow method to:
|
||||
1. Execute the method and capture its output
|
||||
2. Display the output to the human with a feedback request
|
||||
3. Collect the human's free-form feedback
|
||||
4. Optionally collapse the feedback to a predefined outcome using an LLM
|
||||
5. Store the result for access by downstream methods
|
||||
|
||||
When `emit` is specified, the decorator acts as a router, and the
|
||||
collapsed outcome triggers the appropriate @listen decorated method.
|
||||
|
||||
Supports both synchronous (blocking) and asynchronous (non-blocking)
|
||||
feedback collection through the `provider` parameter. If no provider
|
||||
is specified, defaults to synchronous console input.
|
||||
|
||||
Args:
|
||||
message: The message shown to the human when requesting feedback.
|
||||
This should clearly explain what kind of feedback is expected.
|
||||
emit: Optional sequence of outcome strings. When provided, the
|
||||
human's feedback will be collapsed to one of these outcomes
|
||||
using the specified LLM. The outcome then triggers @listen
|
||||
methods that match.
|
||||
llm: The LLM model to use for collapsing feedback to outcomes.
|
||||
Required when emit is specified. Can be a model string
|
||||
like "gpt-4o-mini" or a BaseLLM instance.
|
||||
default_outcome: The outcome to use when the human provides no
|
||||
feedback (empty input). Must be one of the emit values
|
||||
if emit is specified.
|
||||
metadata: Optional metadata for enterprise integrations. This is
|
||||
passed through to the HumanFeedbackResult and can be used
|
||||
by enterprise forks for features like Slack/Teams integration.
|
||||
provider: Optional HumanFeedbackProvider for custom feedback
|
||||
collection. Use this for async workflows that integrate with
|
||||
external systems like Slack, Teams, or webhooks. When the
|
||||
provider raises HumanFeedbackPending, the flow pauses and
|
||||
can be resumed later with Flow.resume().
|
||||
learn: Enable HITL learning. Recall past lessons to pre-review
|
||||
output before the human sees it, and distill new lessons
|
||||
from feedback after.
|
||||
learn_source: Memory source tag for stored/recalled lessons.
|
||||
learn_strict: When True, re-raise exceptions from the pre-review
|
||||
and distillation steps instead of falling back to raw output.
|
||||
Default False preserves graceful degradation; failures are
|
||||
always logged via ``logger.warning`` regardless of this flag.
|
||||
|
||||
Returns:
|
||||
A decorator function that wraps the method with human feedback
|
||||
collection logic.
|
||||
|
||||
Raises:
|
||||
ValueError: If emit is specified but llm is not provided.
|
||||
ValueError: If default_outcome is specified but emit is not.
|
||||
ValueError: If default_outcome is not in the emit list.
|
||||
HumanFeedbackPending: When an async provider pauses execution.
|
||||
|
||||
Example:
|
||||
Basic feedback without routing:
|
||||
```python
|
||||
@start()
|
||||
@human_feedback(message="Please review this output:")
|
||||
def generate_content(self):
|
||||
return "Generated content..."
|
||||
```
|
||||
|
||||
With routing based on feedback:
|
||||
```python
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review and approve or reject:",
|
||||
emit=["approved", "rejected", "needs_revision"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="needs_revision",
|
||||
)
|
||||
def review_document(self):
|
||||
return document_content
|
||||
|
||||
|
||||
@listen("approved")
|
||||
def publish(self):
|
||||
print(f"Publishing: {self.last_human_feedback.output}")
|
||||
```
|
||||
|
||||
Async feedback with custom provider:
|
||||
```python
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review this content:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
provider=SlackProvider(channel="#reviews"),
|
||||
)
|
||||
def generate_content(self):
|
||||
return "Content to review..."
|
||||
```
|
||||
"""
|
||||
if emit is not None:
|
||||
if not llm:
|
||||
raise ValueError(
|
||||
@@ -249,12 +356,20 @@ def _build_human_feedback_runtime_decorator(
|
||||
raise ValueError("default_outcome requires emit to be specified.")
|
||||
|
||||
def decorator(func: F) -> F:
|
||||
"""Inner decorator that wraps the function."""
|
||||
|
||||
def _get_hitl_prompt(key: str) -> str:
|
||||
"""Read a HITL prompt from the i18n translations."""
|
||||
from crewai.utilities.i18n import I18N_DEFAULT
|
||||
|
||||
return I18N_DEFAULT.slice(key)
|
||||
|
||||
def _resolve_llm_instance() -> Any:
|
||||
"""Resolve the ``llm`` parameter to a BaseLLM instance.
|
||||
|
||||
Uses the SAME model specified in the decorator so pre-review,
|
||||
distillation, and outcome collapsing all share one model.
|
||||
"""
|
||||
if llm is None:
|
||||
from crewai.llm import LLM
|
||||
|
||||
@@ -268,6 +383,7 @@ def _build_human_feedback_runtime_decorator(
|
||||
def _pre_review_with_lessons(
|
||||
flow_instance: Flow[Any], method_output: Any
|
||||
) -> Any:
|
||||
"""Recall past HITL lessons and use LLM to pre-review the output."""
|
||||
try:
|
||||
mem = flow_instance.memory
|
||||
if mem is None:
|
||||
@@ -315,6 +431,7 @@ def _build_human_feedback_runtime_decorator(
|
||||
def _distill_and_store_lessons(
|
||||
flow_instance: Flow[Any], method_output: Any, raw_feedback: str
|
||||
) -> None:
|
||||
"""Extract generalizable lessons from output + feedback, store in memory."""
|
||||
try:
|
||||
mem = flow_instance.memory
|
||||
if mem is None:
|
||||
@@ -368,6 +485,7 @@ def _build_human_feedback_runtime_decorator(
|
||||
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
|
||||
|
||||
context = PendingFeedbackContext(
|
||||
@@ -391,6 +509,7 @@ def _build_human_feedback_runtime_decorator(
|
||||
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
|
||||
)
|
||||
@@ -416,6 +535,7 @@ def _build_human_feedback_runtime_decorator(
|
||||
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
|
||||
)
|
||||
@@ -439,6 +559,7 @@ def _build_human_feedback_runtime_decorator(
|
||||
method_output: Any,
|
||||
raw_feedback: str,
|
||||
) -> HumanFeedbackResult | str:
|
||||
"""Process feedback and return result or outcome."""
|
||||
collapsed_outcome: str | None = None
|
||||
|
||||
if not raw_feedback.strip():
|
||||
@@ -534,33 +655,42 @@ def _build_human_feedback_runtime_decorator(
|
||||
|
||||
wrapper = sync_wrapper
|
||||
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__trigger_condition__",
|
||||
"__is_flow_method__",
|
||||
]:
|
||||
if hasattr(func, attr):
|
||||
setattr(wrapper, attr, getattr(func, attr))
|
||||
|
||||
# Create config inline to avoid race conditions
|
||||
wrapper.__human_feedback_config__ = HumanFeedbackConfig(
|
||||
message=message,
|
||||
emit=emit,
|
||||
llm=llm,
|
||||
default_outcome=default_outcome,
|
||||
metadata=metadata,
|
||||
provider=provider,
|
||||
learn=learn,
|
||||
learn_source=learn_source,
|
||||
learn_strict=learn_strict,
|
||||
)
|
||||
wrapper.__is_flow_method__ = True
|
||||
|
||||
if emit:
|
||||
wrapper.__is_router__ = True
|
||||
wrapper.__router_paths__ = list(emit)
|
||||
|
||||
# Stash the live LLM object for HITL resume to retrieve.
|
||||
# When a flow pauses for human feedback and later resumes (possibly in a
|
||||
# different process), the serialized context only contains a model string.
|
||||
# By storing the original LLM on the wrapper, resume_async can retrieve
|
||||
# the fully-configured LLM (with credentials, project, safety_settings, etc.)
|
||||
# instead of creating a bare LLM from just the model string.
|
||||
wrapper._hf_llm = llm
|
||||
|
||||
return wrapper # type: ignore[no-any-return]
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def human_feedback(
|
||||
message: str,
|
||||
emit: Sequence[str] | None = None,
|
||||
llm: str | BaseLLM | None = "gpt-4o-mini",
|
||||
default_outcome: str | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
provider: HumanFeedbackProvider | None = None,
|
||||
learn: bool = False,
|
||||
learn_source: str = "hitl",
|
||||
learn_strict: bool = False,
|
||||
) -> Callable[[F], F]:
|
||||
"""Compatibility import path for the Flow human-feedback DSL decorator."""
|
||||
from crewai.flow.dsl.human_feedback import human_feedback as dsl_human_feedback
|
||||
|
||||
return dsl_human_feedback(
|
||||
message=message,
|
||||
emit=emit,
|
||||
llm=llm,
|
||||
default_outcome=default_outcome,
|
||||
metadata=metadata,
|
||||
provider=provider,
|
||||
learn=learn,
|
||||
learn_source=learn_source,
|
||||
learn_strict=learn_strict,
|
||||
)
|
||||
|
||||
@@ -4,9 +4,16 @@ CrewAI Flow Persistence.
|
||||
This module provides interfaces and implementations for persisting flow states.
|
||||
"""
|
||||
|
||||
from typing import Any, TypeVar
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.flow.persistence.base import FlowPersistence
|
||||
from crewai.flow.persistence.decorators import persist
|
||||
from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
|
||||
|
||||
|
||||
__all__ = ["FlowPersistence", "SQLiteFlowPersistence", "persist"]
|
||||
|
||||
StateType = TypeVar("StateType", bound=dict[str, Any] | BaseModel)
|
||||
DictStateType = dict[str, Any]
|
||||
|
||||
@@ -28,7 +28,6 @@ import asyncio
|
||||
from collections.abc import Callable
|
||||
import functools
|
||||
import logging
|
||||
from types import SimpleNamespace
|
||||
from typing import TYPE_CHECKING, Any, Final, TypeVar, cast
|
||||
|
||||
from crewai_core.printer import PRINTER
|
||||
@@ -45,8 +44,6 @@ if TYPE_CHECKING:
|
||||
logger = logging.getLogger(__name__)
|
||||
T = TypeVar("T")
|
||||
|
||||
__all__ = ["PersistenceDecorator", "persist"]
|
||||
|
||||
LOG_MESSAGES: Final[dict[str, str]] = {
|
||||
"save_state": "Saving flow state to memory for ID: {}",
|
||||
"save_error": "Failed to persist state for method {}: {}",
|
||||
@@ -55,31 +52,6 @@ LOG_MESSAGES: Final[dict[str, str]] = {
|
||||
}
|
||||
|
||||
|
||||
def _stamp_persistence_metadata(
|
||||
target: Any,
|
||||
persistence: FlowPersistence,
|
||||
verbose: bool,
|
||||
) -> None:
|
||||
target.__flow_persistence_config__ = SimpleNamespace(
|
||||
persistence=persistence,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
||||
|
||||
_PRESERVED_FLOW_ATTRS: Final[tuple[str, ...]] = (
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__trigger_condition__",
|
||||
"__is_router__",
|
||||
"__router_emit__",
|
||||
"__human_feedback_config__",
|
||||
"__flow_persistence_config__",
|
||||
"__flow_method_definition__",
|
||||
"_human_feedback_llm",
|
||||
)
|
||||
|
||||
|
||||
class PersistenceDecorator:
|
||||
"""Class to handle flow state persistence with consistent logging."""
|
||||
|
||||
@@ -191,10 +163,10 @@ def persist(
|
||||
"""
|
||||
|
||||
def decorator(target: type | Callable[..., T]) -> type | Callable[..., T]:
|
||||
"""Decorator that handles both class and method decoration."""
|
||||
actual_persistence = persistence or SQLiteFlowPersistence()
|
||||
|
||||
if isinstance(target, type):
|
||||
_stamp_persistence_metadata(target, actual_persistence, verbose)
|
||||
original_init = target.__init__ # type: ignore[misc]
|
||||
|
||||
@functools.wraps(original_init)
|
||||
@@ -239,7 +211,12 @@ def persist(
|
||||
|
||||
wrapped = create_async_wrapper(name, method)
|
||||
|
||||
for attr in _PRESERVED_FLOW_ATTRS:
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__is_router__",
|
||||
]:
|
||||
if hasattr(method, attr):
|
||||
setattr(wrapped, attr, getattr(method, attr))
|
||||
wrapped.__is_flow_method__ = True # type: ignore[attr-defined]
|
||||
@@ -262,7 +239,12 @@ def persist(
|
||||
|
||||
wrapped = create_sync_wrapper(name, method)
|
||||
|
||||
for attr in _PRESERVED_FLOW_ATTRS:
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__is_router__",
|
||||
]:
|
||||
if hasattr(method, attr):
|
||||
setattr(wrapped, attr, getattr(method, attr))
|
||||
wrapped.__is_flow_method__ = True # type: ignore[attr-defined]
|
||||
@@ -272,7 +254,6 @@ def persist(
|
||||
return target
|
||||
method = target
|
||||
method.__is_flow_method__ = True # type: ignore[attr-defined]
|
||||
_stamp_persistence_metadata(method, actual_persistence, verbose)
|
||||
|
||||
if asyncio.iscoroutinefunction(method):
|
||||
|
||||
@@ -290,13 +271,15 @@ def persist(
|
||||
)
|
||||
return cast(T, result)
|
||||
|
||||
for attr in _PRESERVED_FLOW_ATTRS:
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__is_router__",
|
||||
]:
|
||||
if hasattr(method, attr):
|
||||
setattr(method_async_wrapper, attr, getattr(method, attr))
|
||||
method_async_wrapper.__is_flow_method__ = True # type: ignore[attr-defined]
|
||||
_stamp_persistence_metadata(
|
||||
method_async_wrapper, actual_persistence, verbose
|
||||
)
|
||||
return cast(Callable[..., T], method_async_wrapper)
|
||||
|
||||
@functools.wraps(method)
|
||||
@@ -307,11 +290,15 @@ def persist(
|
||||
)
|
||||
return result
|
||||
|
||||
for attr in _PRESERVED_FLOW_ATTRS:
|
||||
for attr in [
|
||||
"__is_start_method__",
|
||||
"__trigger_methods__",
|
||||
"__condition_type__",
|
||||
"__is_router__",
|
||||
]:
|
||||
if hasattr(method, attr):
|
||||
setattr(method_sync_wrapper, attr, getattr(method, attr))
|
||||
method_sync_wrapper.__is_flow_method__ = True # type: ignore[attr-defined]
|
||||
_stamp_persistence_metadata(method_sync_wrapper, actual_persistence, verbose)
|
||||
return cast(Callable[..., T], method_sync_wrapper)
|
||||
|
||||
return decorator
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
"""Flow Runtime: the engine that executes a Flow.
|
||||
"""Flow runtime: the Flow execution engine, its metaclass, and state proxies.
|
||||
|
||||
Provides the ``Flow`` class (kickoff/resume/listener dispatch), the
|
||||
``FlowMeta`` metaclass, and the thread-safe state proxies. Flows
|
||||
authored with the Python DSL (see ``dsl``) are described by a Flow
|
||||
Structure (see ``flow_definition``) and executed here.
|
||||
Holds the Flow class (kickoff/resume/listener dispatch), the FlowMeta
|
||||
metaclass (Pydantic model construction; structural extraction is delegated to
|
||||
``flow_definition.extract_flow_definition``), and the thread-safe state
|
||||
proxies. The authoring decorators live in ``crewai.flow.dsl``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -84,24 +84,18 @@ from crewai.events.types.flow_events import (
|
||||
MethodExecutionPausedEvent,
|
||||
MethodExecutionStartedEvent,
|
||||
)
|
||||
from crewai.experimental.conversational import (
|
||||
ConversationConfig,
|
||||
ConversationState,
|
||||
)
|
||||
from crewai.experimental.conversational_mixin import _ConversationalMixin
|
||||
from crewai.flow.constants import AND_CONDITION, OR_CONDITION
|
||||
from crewai.flow.dsl import (
|
||||
from crewai.flow.flow_context import current_flow_id, current_flow_request_id
|
||||
from crewai.flow.flow_definition import (
|
||||
_extract_all_methods,
|
||||
_extract_all_methods_recursive,
|
||||
_normalize_condition,
|
||||
build_flow_definition,
|
||||
extract_flow_definition,
|
||||
is_flow_condition_dict,
|
||||
is_flow_method,
|
||||
is_flow_method_name,
|
||||
is_simple_flow_condition,
|
||||
)
|
||||
from crewai.flow.flow_context import current_flow_id, current_flow_request_id
|
||||
from crewai.flow.flow_definition import FlowDefinition
|
||||
from crewai.flow.flow_wrappers import (
|
||||
FlowCondition,
|
||||
FlowMethod,
|
||||
@@ -147,16 +141,6 @@ from crewai.utilities.streaming import (
|
||||
signal_end,
|
||||
signal_error,
|
||||
)
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
# Runtime alias so Pydantic can resolve the ``execution_context`` field's
|
||||
# annotation in subclass modules without those modules needing to import
|
||||
# ``crewai.context.ExecutionContext`` themselves. The real class is brought
|
||||
# in under ``TYPE_CHECKING`` above for static analysis. We can't import it at
|
||||
# runtime because ``crewai.context`` is loaded mid-initialization when this
|
||||
# module is imported through ``crewai.__init__`` (circular).
|
||||
ExecutionContext = Any # type: ignore[assignment,misc]
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -601,88 +585,19 @@ class FlowMeta(ModelMetaclass):
|
||||
|
||||
cls = super().__new__(mcs, name, bases, namespace)
|
||||
|
||||
start_methods, listeners, routers, router_emit = extract_flow_definition(
|
||||
start_methods, listeners, routers, router_paths = extract_flow_definition(
|
||||
namespace
|
||||
)
|
||||
|
||||
# === EXPERIMENTAL: conversational gating ===
|
||||
# The built-in conversational graph (``conversation_start``,
|
||||
# ``route_conversation``, ``converse_turn``, ``end_conversation``,
|
||||
# ``answer_from_history_turn``) lives on ``Flow`` itself, decorated
|
||||
# with ``@_conversational_only``. We don't want those methods to
|
||||
# register on non-chat flows. The opt-in is ``conversational = True``
|
||||
# on the subclass; otherwise the methods exist as inert attributes.
|
||||
is_conversational = bool(namespace.get("conversational", False))
|
||||
if not is_conversational:
|
||||
for base in bases:
|
||||
if getattr(base, "conversational", False):
|
||||
is_conversational = True
|
||||
break
|
||||
|
||||
# 1. Strip conversational-only methods that landed in the namespace
|
||||
# extraction when this class isn't conversational. Applies to ``Flow``
|
||||
# itself (its own namespace declares the conversational methods).
|
||||
if not is_conversational:
|
||||
|
||||
def _is_conv_only(attr_name: str) -> bool:
|
||||
attr_value = namespace.get(attr_name)
|
||||
return bool(getattr(attr_value, "__conversational_only__", False))
|
||||
|
||||
start_methods = [m for m in start_methods if not _is_conv_only(m)]
|
||||
listeners = {k: v for k, v in listeners.items() if not _is_conv_only(k)}
|
||||
routers = {r for r in routers if not _is_conv_only(r)}
|
||||
router_emit = {k: v for k, v in router_emit.items() if not _is_conv_only(k)}
|
||||
|
||||
# 2. Harvest conversational-only methods from base classes when this
|
||||
# subclass opts in. (extract_flow_definition only scans the current
|
||||
# namespace; without this step, ``class MyChat(Flow): conversational
|
||||
# = True`` would have an empty graph.)
|
||||
if is_conversational:
|
||||
already_registered: set[str] = set(start_methods) | set(listeners.keys())
|
||||
for base in bases:
|
||||
for attr_name in dir(base):
|
||||
if attr_name.startswith("_") or attr_name in already_registered:
|
||||
continue
|
||||
attr_value = getattr(base, attr_name, None)
|
||||
if not is_flow_method(attr_value):
|
||||
continue
|
||||
if not getattr(attr_value, "__conversational_only__", False):
|
||||
continue
|
||||
already_registered.add(attr_name)
|
||||
|
||||
if hasattr(attr_value, "__is_start_method__"):
|
||||
start_methods.append(attr_name)
|
||||
|
||||
trigger_methods = getattr(attr_value, "__trigger_methods__", None)
|
||||
if trigger_methods is not None:
|
||||
condition_type = getattr(
|
||||
attr_value, "__condition_type__", OR_CONDITION
|
||||
)
|
||||
trigger_condition = getattr(
|
||||
attr_value, "__trigger_condition__", None
|
||||
)
|
||||
if trigger_condition is not None:
|
||||
listeners[attr_name] = trigger_condition
|
||||
else:
|
||||
listeners[attr_name] = (condition_type, trigger_methods)
|
||||
|
||||
if getattr(attr_value, "__is_router__", False):
|
||||
routers.add(attr_name)
|
||||
emit = getattr(attr_value, "__router_emit__", None)
|
||||
router_emit[attr_name] = list(emit) if emit else []
|
||||
|
||||
cls._start_methods = start_methods # type: ignore[attr-defined]
|
||||
cls._listeners = listeners # type: ignore[attr-defined]
|
||||
cls._routers = routers # type: ignore[attr-defined]
|
||||
cls._router_emit = router_emit # type: ignore[attr-defined]
|
||||
# The static FlowDefinition is built lazily (on first access via
|
||||
# ``Flow.flow_definition()`` or visualization), not at class-definition
|
||||
# time, to avoid AST parsing and diagnostic logging on every import.
|
||||
cls._router_paths = router_paths # type: ignore[attr-defined]
|
||||
|
||||
return cls
|
||||
|
||||
|
||||
class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
"""Base class for all flows.
|
||||
|
||||
type parameter T must be either dict[str, Any] or a subclass of BaseModel."""
|
||||
@@ -697,53 +612,10 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
_start_methods: ClassVar[list[FlowMethodName]] = []
|
||||
_listeners: ClassVar[dict[FlowMethodName, SimpleFlowCondition | FlowCondition]] = {}
|
||||
_routers: ClassVar[set[FlowMethodName]] = set()
|
||||
_router_emit: ClassVar[dict[FlowMethodName, list[FlowMethodName]]] = {}
|
||||
_flow_definition: ClassVar[FlowDefinition | None] = None
|
||||
|
||||
# === EXPERIMENTAL: conversational mode ===
|
||||
# When ``conversational = True`` on a subclass, the built-in conversational
|
||||
# graph (``conversation_start`` -> ``route_conversation`` -> ``converse_turn``
|
||||
# / ``end_conversation`` / ``answer_from_history_turn``) registers and
|
||||
# ``handle_turn`` becomes the chat entry point. When ``False`` (default),
|
||||
# the methods exist as inert attributes and never register or fire —
|
||||
# non-chat flows pay no runtime cost.
|
||||
#
|
||||
# ⚠ EXPERIMENTAL FEATURE. The whole conversational surface
|
||||
# (``conversational`` ClassVar, ``handle_turn``, ``ConversationConfig``,
|
||||
# ``RouterConfig``, ``ConversationState``, the built-in graph + helpers)
|
||||
# lives under ``crewai.experimental`` and may change shape before
|
||||
# graduating. Pin your CrewAI version if you depend on specific
|
||||
# behavior, and watch the changelog for breaking updates.
|
||||
conversational: ClassVar[bool] = False
|
||||
conversational_config: ClassVar[ConversationConfig | None] = None
|
||||
builtin_routes: ClassVar[tuple[str, ...]] = ("converse", "end")
|
||||
internal_routes: ClassVar[tuple[str, ...]] = (
|
||||
"answer_from_history",
|
||||
"conversation_start",
|
||||
)
|
||||
builtin_route_descriptions: ClassVar[dict[str, str]] = {
|
||||
"converse": (
|
||||
"Ordinary chat, follow-ups, summaries, clarifications, and "
|
||||
"questions answerable from prior conversation history."
|
||||
),
|
||||
"end": ("User signals the conversation is finished (goodbye, exit, done)."),
|
||||
"answer_from_history": (
|
||||
"Answer directly from prior conversation history without invoking "
|
||||
"tools, agents, or custom routes."
|
||||
),
|
||||
}
|
||||
_router_paths: ClassVar[dict[FlowMethodName, list[FlowMethodName]]] = {}
|
||||
|
||||
entity_type: Literal["flow"] = "flow"
|
||||
|
||||
@classmethod
|
||||
def flow_definition(cls) -> FlowDefinition:
|
||||
"""Return the static Flow Definition built from this Flow class."""
|
||||
flow_definition = cls.__dict__.get("_flow_definition")
|
||||
if flow_definition is None:
|
||||
flow_definition = build_flow_definition(cls)
|
||||
cls._flow_definition = flow_definition
|
||||
return flow_definition
|
||||
|
||||
initial_state: Annotated[ # type: ignore[type-arg]
|
||||
type[BaseModel] | type[dict] | dict[str, Any] | BaseModel | None,
|
||||
BeforeValidator(_deserialize_initial_state),
|
||||
@@ -767,15 +639,6 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
),
|
||||
] = Field(default=None)
|
||||
suppress_flow_events: bool = Field(default=False)
|
||||
defer_trace_finalization: bool = Field(
|
||||
default=False,
|
||||
description=(
|
||||
"When True, skip per-kickoff ``FlowFinishedEvent`` + trace-batch "
|
||||
"finalization. ``finalize_session_traces()`` does the final emit "
|
||||
"+ finalize. Use for multi-turn chat sessions where every "
|
||||
"``handle_turn()`` is a turn within one logical trace."
|
||||
),
|
||||
)
|
||||
human_feedback_history: list[HumanFeedbackResult] = Field(default_factory=list)
|
||||
last_human_feedback: HumanFeedbackResult | None = Field(default=None)
|
||||
|
||||
@@ -906,10 +769,6 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
_human_feedback_method_outputs: dict[str, Any] = PrivateAttr(default_factory=dict)
|
||||
_input_history: list[InputHistoryEntry] = PrivateAttr(default_factory=list)
|
||||
_state: Any = PrivateAttr(default=None)
|
||||
_conversation_messages: list[LLMMessage] = PrivateAttr(default_factory=list)
|
||||
_pending_user_message: str | dict[str, Any] | None = PrivateAttr(default=None)
|
||||
_pending_intents: Sequence[str] | None = PrivateAttr(default=None)
|
||||
_pending_intent_llm: str | "BaseLLM" | None = PrivateAttr(default=None)
|
||||
|
||||
def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]: # type: ignore[override]
|
||||
class _FlowGeneric(cls): # type: ignore[valid-type,misc]
|
||||
@@ -962,48 +821,13 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
flow_name = sanitize_scope_name(self.name or self.__class__.__name__)
|
||||
self.memory = Memory(root_scope=f"/flow/{flow_name}")
|
||||
|
||||
# Build the runtime method lookup. ``_start_methods`` / ``_listeners`` /
|
||||
# ``_routers`` are populated by ``FlowMeta.__new__`` and are the source
|
||||
# of truth for which slots are flow methods — including slots a
|
||||
# subclass overrode without re-decorating. Walk those slots first so
|
||||
# the override (which may be a plain function) still gets bound here.
|
||||
registered_slots: set[str] = set()
|
||||
registered_slots.update(getattr(type(self), "_start_methods", []))
|
||||
registered_slots.update(getattr(type(self), "_listeners", {}).keys())
|
||||
registered_slots.update(getattr(type(self), "_routers", set()))
|
||||
for method_name in registered_slots:
|
||||
method = getattr(self, method_name, None)
|
||||
if method is None:
|
||||
continue
|
||||
if not hasattr(method, "__self__"):
|
||||
method = method.__get__(self, self.__class__)
|
||||
self._methods[FlowMethodName(method_name)] = method
|
||||
|
||||
# Also pick up any leftover flow-decorated attributes that aren't
|
||||
# already registered (defensive — preserves the prior catch-all scan).
|
||||
# We walk the MRO's class ``__dict__`` rather than ``dir(self)`` +
|
||||
# ``getattr`` so we don't trigger ``@property`` descriptors (those
|
||||
# would run user code mid-init, before state is set up — e.g. a
|
||||
# user property accessing ``self.state.messages`` would crash).
|
||||
# Conversational-only methods are skipped on non-chat flows.
|
||||
is_conversational = getattr(type(self), "conversational", False)
|
||||
seen_in_dict: set[str] = set()
|
||||
for klass in type(self).__mro__:
|
||||
for method_name, raw in klass.__dict__.items():
|
||||
if method_name.startswith("_") or method_name in self._methods:
|
||||
continue
|
||||
if method_name in seen_in_dict:
|
||||
continue
|
||||
seen_in_dict.add(method_name)
|
||||
if not is_flow_method(raw):
|
||||
continue
|
||||
if (
|
||||
getattr(raw, "__conversational_only__", False)
|
||||
and not is_conversational
|
||||
):
|
||||
continue
|
||||
bound = raw.__get__(self, self.__class__)
|
||||
self._methods[FlowMethodName(method_name)] = bound
|
||||
for method_name in dir(self):
|
||||
if not method_name.startswith("_"):
|
||||
method = getattr(self, method_name)
|
||||
if is_flow_method(method):
|
||||
if not hasattr(method, "__self__"):
|
||||
method = method.__get__(self, self.__class__)
|
||||
self._methods[method.__name__] = method
|
||||
|
||||
def recall(self, query: str, **kwargs: Any) -> Any:
|
||||
"""Recall relevant memories. Delegates to this flow's memory.
|
||||
@@ -1469,7 +1293,7 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
llm = None
|
||||
method = self._methods.get(FlowMethodName(context.method_name))
|
||||
if method is not None:
|
||||
live_llm = getattr(method, "_human_feedback_llm", None)
|
||||
live_llm = getattr(method, "_hf_llm", None)
|
||||
if live_llm is not None:
|
||||
from crewai.llms.base_llm import BaseLLM as BaseLLMClass
|
||||
|
||||
@@ -1634,18 +1458,6 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
"""
|
||||
init_state = self.initial_state
|
||||
|
||||
# Conversational subclasses default to ``ConversationState`` if the
|
||||
# user didn't supply an explicit type parameter (``Flow[...]``) or an
|
||||
# ``initial_state``. This makes ``class MyChat(Flow): conversational
|
||||
# = True`` work without forcing every user to import and parameterize
|
||||
# ``ConversationState`` themselves.
|
||||
if (
|
||||
init_state is None
|
||||
and getattr(type(self), "conversational", False)
|
||||
and not hasattr(self, "_initial_state_t")
|
||||
):
|
||||
return cast(T, ConversationState())
|
||||
|
||||
if init_state is None and hasattr(self, "_initial_state_t"):
|
||||
state_type = self._initial_state_t
|
||||
if isinstance(state_type, type):
|
||||
@@ -2199,51 +2011,30 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
if filtered_inputs:
|
||||
self._initialize_state(filtered_inputs)
|
||||
|
||||
# Conversational hook: apply the pending user message AFTER state
|
||||
# restore so it survives ``self.persistence.load_state(...)``.
|
||||
# ``handle_turn`` stashes the message on ``self._pending_user_message``
|
||||
# before calling ``kickoff``; this drains it.
|
||||
if (
|
||||
getattr(type(self), "conversational", False)
|
||||
and self._pending_user_message is not None
|
||||
):
|
||||
self._apply_pending_conversational_turn()
|
||||
|
||||
if get_current_parent_id() is None:
|
||||
reset_emission_counter()
|
||||
reset_last_event_id()
|
||||
|
||||
# ``FlowStartedEvent`` always fires — ``suppress_flow_events``
|
||||
# only hides the Rich console panel (and the textual log line
|
||||
# below), it doesn't gate observability events. Tracing /
|
||||
# downstream listeners still need to see flow_started.
|
||||
started_event = FlowStartedEvent(
|
||||
type="flow_started",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
inputs=inputs,
|
||||
)
|
||||
future = crewai_event_bus.emit(self, started_event)
|
||||
if future:
|
||||
try:
|
||||
await asyncio.wrap_future(future)
|
||||
except Exception:
|
||||
logger.warning("FlowStartedEvent handler failed", exc_info=True)
|
||||
# Stash the started event id so a deferred
|
||||
# ``finalize_session_traces()`` can restore the event scope
|
||||
# before emitting ``FlowFinishedEvent`` (otherwise the bus
|
||||
# warns "Ending event 'flow_finished' emitted with empty
|
||||
# scope stack").
|
||||
if self._should_defer_trace_finalization():
|
||||
object.__setattr__(
|
||||
self, "_deferred_flow_started_event_id", started_event.event_id
|
||||
)
|
||||
if not self.suppress_flow_events:
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
FlowStartedEvent(
|
||||
type="flow_started",
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
inputs=inputs,
|
||||
),
|
||||
)
|
||||
if future:
|
||||
try:
|
||||
await asyncio.wrap_future(future)
|
||||
except Exception:
|
||||
logger.warning("FlowStartedEvent handler failed", exc_info=True)
|
||||
self._log_flow_event(
|
||||
f"Flow started with ID: {self.flow_id}", color="bold magenta"
|
||||
)
|
||||
|
||||
# After FlowStarted: env events must not pre-empt trace batch init
|
||||
# with implicit "crew" execution_type.
|
||||
# After FlowStarted (when not suppressed): env events must not pre-empt
|
||||
# trace batch init with implicit "crew" execution_type.
|
||||
get_env_context()
|
||||
|
||||
if inputs is not None and "id" not in inputs:
|
||||
@@ -2270,21 +2061,11 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
if unconditional_starts
|
||||
else self._start_methods
|
||||
)
|
||||
if getattr(type(self), "conversational", False):
|
||||
# Conversational mode: run @start methods sequentially so
|
||||
# user setup (e.g. permission loading) completes before
|
||||
# the router fires. ``_start_methods`` preserves
|
||||
# declaration + harvest order, with ``conversation_start``
|
||||
# at the end — its router decision only runs after every
|
||||
# user start finishes.
|
||||
for start_method in starts_to_execute:
|
||||
await self._execute_start_method(start_method)
|
||||
else:
|
||||
tasks = [
|
||||
self._execute_start_method(start_method)
|
||||
for start_method in starts_to_execute
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
tasks = [
|
||||
self._execute_start_method(start_method)
|
||||
for start_method in starts_to_execute
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
except Exception as e:
|
||||
# Check if flow was paused for human feedback
|
||||
from crewai.flow.async_feedback.types import HumanFeedbackPending
|
||||
@@ -2352,13 +2133,7 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
)
|
||||
self._event_futures.clear()
|
||||
|
||||
# When ``defer_trace_finalization`` is set, skip both per-turn
|
||||
# ``FlowFinishedEvent`` AND trace-batch finalization. The caller
|
||||
# invokes ``finalize_session_traces()`` once at session end to
|
||||
# close out the whole conversation as one trace. The flag is
|
||||
# read from EITHER the instance attribute (set by user code) OR
|
||||
# the class-level ``ConversationConfig.defer_trace_finalization``.
|
||||
if not self._should_defer_trace_finalization():
|
||||
if not self.suppress_flow_events:
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
FlowFinishedEvent(
|
||||
@@ -2376,6 +2151,7 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
"FlowFinishedEvent handler failed", exc_info=True
|
||||
)
|
||||
|
||||
if not self.suppress_flow_events:
|
||||
trace_listener = TraceCollectionListener()
|
||||
if trace_listener.batch_manager.batch_owner_type == "flow":
|
||||
if trace_listener.first_time_handler.is_first_time:
|
||||
@@ -2567,20 +2343,19 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
kwargs or {}
|
||||
)
|
||||
|
||||
# MethodExecution events always fire — ``suppress_flow_events``
|
||||
# only hides the Rich console panel, not observability events.
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
MethodExecutionStartedEvent(
|
||||
type="method_execution_started",
|
||||
method_name=method_name,
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
params=dumped_params,
|
||||
state=self._copy_and_serialize_state(),
|
||||
),
|
||||
)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
if not self.suppress_flow_events:
|
||||
future = crewai_event_bus.emit(
|
||||
self,
|
||||
MethodExecutionStartedEvent(
|
||||
type="method_execution_started",
|
||||
method_name=method_name,
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
params=dumped_params,
|
||||
state=self._copy_and_serialize_state(),
|
||||
),
|
||||
)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
|
||||
# Set method name in context so ask() can read it without
|
||||
# stack inspection. Must happen before copy_context() so the
|
||||
@@ -2622,19 +2397,18 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
self._completed_methods.add(method_name)
|
||||
|
||||
finished_event_id: str | None = None
|
||||
# MethodExecution events always fire even when console panels are
|
||||
# suppressed; tracing depends on them.
|
||||
finished_event = MethodExecutionFinishedEvent(
|
||||
type="method_execution_finished",
|
||||
method_name=method_name,
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
state=self._copy_and_serialize_state(),
|
||||
result=result,
|
||||
)
|
||||
finished_event_id = finished_event.event_id
|
||||
future = crewai_event_bus.emit(self, finished_event)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
if not self.suppress_flow_events:
|
||||
finished_event = MethodExecutionFinishedEvent(
|
||||
type="method_execution_finished",
|
||||
method_name=method_name,
|
||||
flow_name=self.name or self.__class__.__name__,
|
||||
state=self._copy_and_serialize_state(),
|
||||
result=result,
|
||||
)
|
||||
finished_event_id = finished_event.event_id
|
||||
future = crewai_event_bus.emit(self, finished_event)
|
||||
if future:
|
||||
self._event_futures.append(future)
|
||||
|
||||
return result, finished_event_id
|
||||
except Exception as e:
|
||||
@@ -2844,7 +2618,7 @@ class Flow(_ConversationalMixin, BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
Returns:
|
||||
True if the condition is satisfied, False otherwise
|
||||
"""
|
||||
if isinstance(condition, str):
|
||||
if is_flow_method_name(condition):
|
||||
return condition == trigger_method
|
||||
|
||||
if is_flow_condition_dict(condition):
|
||||
|
||||
@@ -22,6 +22,7 @@ P = ParamSpec("P")
|
||||
R = TypeVar("R", covariant=True)
|
||||
|
||||
FlowMethodName = NewType("FlowMethodName", str)
|
||||
FlowRouteName = NewType("FlowRouteName", str)
|
||||
PendingListenerKey = NewType(
|
||||
"PendingListenerKey",
|
||||
Annotated[str, "nested flow conditions use 'listener_name:object_id'"],
|
||||
|
||||
53
lib/crewai/src/crewai/flow/utils.py
Normal file
53
lib/crewai/src/crewai/flow/utils.py
Normal file
@@ -0,0 +1,53 @@
|
||||
"""Backwards-compatible shim. The implementation moved to ``crewai.flow.flow_definition``.
|
||||
|
||||
Import from ``crewai.flow.flow_definition`` directly in new code.
|
||||
"""
|
||||
|
||||
from crewai.flow.flow_definition import (
|
||||
_extract_all_methods,
|
||||
_extract_all_methods_recursive,
|
||||
_extract_string_literals_from_type_annotation,
|
||||
_normalize_condition,
|
||||
_unwrap_function,
|
||||
build_ancestor_dict,
|
||||
build_parent_children_dict,
|
||||
calculate_node_levels,
|
||||
count_outgoing_edges,
|
||||
dfs_ancestors,
|
||||
extract_flow_definition,
|
||||
get_child_index,
|
||||
get_possible_return_constants,
|
||||
is_ancestor,
|
||||
is_flow_condition_dict,
|
||||
is_flow_condition_list,
|
||||
is_flow_method,
|
||||
is_flow_method_callable,
|
||||
is_flow_method_name,
|
||||
is_simple_flow_condition,
|
||||
process_router_paths,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"_extract_all_methods",
|
||||
"_extract_all_methods_recursive",
|
||||
"_extract_string_literals_from_type_annotation",
|
||||
"_normalize_condition",
|
||||
"_unwrap_function",
|
||||
"build_ancestor_dict",
|
||||
"build_parent_children_dict",
|
||||
"calculate_node_levels",
|
||||
"count_outgoing_edges",
|
||||
"dfs_ancestors",
|
||||
"extract_flow_definition",
|
||||
"get_child_index",
|
||||
"get_possible_return_constants",
|
||||
"is_ancestor",
|
||||
"is_flow_condition_dict",
|
||||
"is_flow_condition_list",
|
||||
"is_flow_method",
|
||||
"is_flow_method_callable",
|
||||
"is_flow_method_name",
|
||||
"is_simple_flow_condition",
|
||||
"process_router_paths",
|
||||
]
|
||||
@@ -684,7 +684,7 @@ class TriggeredByHighlighter {
|
||||
});
|
||||
} else {
|
||||
for (const [nodeName, nodeInfo] of Object.entries(nodeData)) {
|
||||
if (nodeInfo.router_events && nodeInfo.router_events.includes(triggerNodeId)) {
|
||||
if (nodeInfo.router_paths && nodeInfo.router_paths.includes(triggerNodeId)) {
|
||||
const routerNode = nodeName;
|
||||
|
||||
const routerEdges = allEdges.filter(
|
||||
@@ -768,7 +768,7 @@ class TriggeredByHighlighter {
|
||||
this.animateEdgeStyles();
|
||||
}
|
||||
|
||||
highlightAllRouterEvents() {
|
||||
highlightAllRouterPaths() {
|
||||
this.clear();
|
||||
|
||||
if (!this.activeDrawerNodeId) {
|
||||
@@ -792,10 +792,10 @@ class TriggeredByHighlighter {
|
||||
routerEdges.forEach(edge => {
|
||||
pathNodes.add(edge.to);
|
||||
});
|
||||
} else if (activeMetadata && activeMetadata.router_events && activeMetadata.router_events.length > 0) {
|
||||
activeMetadata.router_events.forEach(eventName => {
|
||||
} else if (activeMetadata && activeMetadata.router_paths && activeMetadata.router_paths.length > 0) {
|
||||
activeMetadata.router_paths.forEach(pathName => {
|
||||
for (const [nodeName, nodeInfo] of Object.entries(nodeData)) {
|
||||
if (nodeInfo.router_events && nodeInfo.router_events.includes(eventName)) {
|
||||
if (nodeInfo.router_paths && nodeInfo.router_paths.includes(pathName)) {
|
||||
const edgeFromRouter = allEdges.filter(
|
||||
(edge) => edge.from === nodeName && edge.to === this.activeDrawerNodeId && edge.dashes
|
||||
);
|
||||
@@ -821,42 +821,6 @@ class TriggeredByHighlighter {
|
||||
this.animateEdgeStyles();
|
||||
}
|
||||
|
||||
highlightRouterEvent(eventName) {
|
||||
this.clear();
|
||||
|
||||
if (this.activeDrawerEdges && this.activeDrawerEdges.length > 0) {
|
||||
this.resetEdgesToDefault(this.activeDrawerEdges);
|
||||
this.activeDrawerEdges = [];
|
||||
}
|
||||
|
||||
if (!this.activeDrawerNodeId || !eventName) {
|
||||
return;
|
||||
}
|
||||
|
||||
const routerEdges = this.edges.get().filter(
|
||||
(edge) =>
|
||||
edge.from === this.activeDrawerNodeId &&
|
||||
edge.dashes &&
|
||||
edge.label === eventName,
|
||||
);
|
||||
|
||||
if (routerEdges.length === 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
const pathNodes = new Set([this.activeDrawerNodeId]);
|
||||
routerEdges.forEach((edge) => {
|
||||
pathNodes.add(edge.from);
|
||||
pathNodes.add(edge.to);
|
||||
});
|
||||
|
||||
this.highlightedNodes = Array.from(pathNodes);
|
||||
this.highlightedEdges = routerEdges.map((e) => e.id);
|
||||
|
||||
this.animateNodeOpacity();
|
||||
this.animateEdgeStyles();
|
||||
}
|
||||
|
||||
highlightTriggeredBy(triggerNodeId) {
|
||||
this.clear();
|
||||
|
||||
@@ -928,8 +892,8 @@ class TriggeredByHighlighter {
|
||||
) {
|
||||
for (const [nodeName, nodeInfo] of Object.entries(nodeData)) {
|
||||
if (
|
||||
nodeInfo.router_events &&
|
||||
nodeInfo.router_events.includes(triggerNodeId)
|
||||
nodeInfo.router_paths &&
|
||||
nodeInfo.router_paths.includes(triggerNodeId)
|
||||
) {
|
||||
const routerNode = nodeName;
|
||||
|
||||
@@ -1537,7 +1501,7 @@ class DrawerManager {
|
||||
const activeMetadata = nodeData[activeNodeId];
|
||||
if (activeMetadata && activeMetadata.trigger_methods && activeMetadata.trigger_methods.includes(triggerNodeId)) {
|
||||
for (const [nodeName, nodeInfo] of Object.entries(nodeData)) {
|
||||
if (nodeInfo.router_events && nodeInfo.router_events.includes(triggerNodeId)) {
|
||||
if (nodeInfo.router_paths && nodeInfo.router_paths.includes(triggerNodeId)) {
|
||||
const routerEdges = allEdges.filter(
|
||||
(edge) => edge.from === nodeName && edge.dashes
|
||||
);
|
||||
@@ -1696,16 +1660,16 @@ class DrawerManager {
|
||||
`;
|
||||
}
|
||||
|
||||
if (metadata.router_events && metadata.router_events.length > 0) {
|
||||
const uniqueRouterEvents = [...new Set(metadata.router_events)];
|
||||
const routerEventsJson = JSON.stringify(uniqueRouterEvents).replace(/"/g, '"');
|
||||
if (metadata.router_paths && metadata.router_paths.length > 0) {
|
||||
const uniqueRouterPaths = [...new Set(metadata.router_paths)];
|
||||
const routerPathsJson = JSON.stringify(uniqueRouterPaths).replace(/"/g, '"');
|
||||
metadataContent += `
|
||||
<div class="drawer-section">
|
||||
<div class="drawer-section-title router-events-title" data-router-events="${routerEventsJson}" style="cursor: pointer; display: inline-flex; align-items: center; gap: 4px;">
|
||||
Router Events <i data-lucide="chevron-down" style="width: 14px; height: 14px; color: var(--text-primary);"></i>
|
||||
<div class="drawer-section-title router-paths-title" data-router-paths="${routerPathsJson}" style="cursor: pointer; display: inline-flex; align-items: center; gap: 4px;">
|
||||
Router Paths <i data-lucide="chevron-down" style="width: 14px; height: 14px; color: var(--text-primary);"></i>
|
||||
</div>
|
||||
<ul class="drawer-list">
|
||||
${uniqueRouterEvents.map((eventName) => `<li><span class="drawer-code-link" data-router-event="${eventName}" style="color: {{ CREWAI_ORANGE }}; border-color: rgba(255,90,80,0.3);">${eventName}</span></li>`).join("")}
|
||||
${uniqueRouterPaths.map((p) => `<li><span class="drawer-code-link" data-node-id="${p}" style="color: {{ CREWAI_ORANGE }}; border-color: rgba(255,90,80,0.3);">${p}</span></li>`).join("")}
|
||||
</ul>
|
||||
</div>
|
||||
`;
|
||||
@@ -1859,26 +1823,14 @@ class DrawerManager {
|
||||
});
|
||||
});
|
||||
|
||||
const routerEventLinks = this.elements.content.querySelectorAll(
|
||||
".drawer-code-link[data-router-event]",
|
||||
const routerPathsTitle = this.elements.content.querySelector(
|
||||
".router-paths-title[data-router-paths]",
|
||||
);
|
||||
routerEventLinks.forEach((link) => {
|
||||
link.addEventListener("click", (e) => {
|
||||
if (routerPathsTitle) {
|
||||
routerPathsTitle.addEventListener("click", (e) => {
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
const routerEvent = link.getAttribute("data-router-event");
|
||||
this.triggeredByHighlighter.highlightRouterEvent(routerEvent);
|
||||
});
|
||||
});
|
||||
|
||||
const routerEventsTitle = this.elements.content.querySelector(
|
||||
".router-events-title[data-router-events]",
|
||||
);
|
||||
if (routerEventsTitle) {
|
||||
routerEventsTitle.addEventListener("click", (e) => {
|
||||
e.preventDefault();
|
||||
e.stopPropagation();
|
||||
this.triggeredByHighlighter.highlightAllRouterEvents();
|
||||
this.triggeredByHighlighter.highlightAllRouterPaths();
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,118 +1,131 @@
|
||||
"""Flow structure builder for definition-only Flow visualization."""
|
||||
"""Flow structure builder for analyzing Flow execution."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import defaultdict
|
||||
import inspect
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from crewai.flow.constants import AND_CONDITION, OR_CONDITION
|
||||
from crewai.flow.flow_definition import (
|
||||
FlowDefinition,
|
||||
FlowDefinitionCondition,
|
||||
FlowMethodDefinition,
|
||||
from crewai.flow.flow_wrappers import FlowCondition
|
||||
from crewai.flow.types import FlowMethodName
|
||||
from crewai.flow.utils import (
|
||||
is_flow_condition_dict,
|
||||
is_simple_flow_condition,
|
||||
)
|
||||
from crewai.flow.visualization.schema import extract_method_signature
|
||||
from crewai.flow.visualization.types import FlowStructure, NodeMetadata, StructureEdge
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
__all__ = ["build_flow_structure", "calculate_execution_paths"]
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.flow.flow import Flow
|
||||
|
||||
|
||||
def _definition_condition_items(
|
||||
condition: dict[str, Any],
|
||||
key: str,
|
||||
) -> list[FlowDefinitionCondition]:
|
||||
return cast(list[FlowDefinitionCondition], condition.get(key, []))
|
||||
def _extract_direct_or_triggers(
|
||||
condition: str | dict[str, Any] | list[Any] | FlowCondition,
|
||||
) -> list[str]:
|
||||
"""Extract direct OR-level trigger strings from a condition.
|
||||
|
||||
This function extracts strings that would directly trigger a listener,
|
||||
meaning they appear at the top level of an OR condition. Strings nested
|
||||
inside AND conditions are NOT considered direct triggers for router paths.
|
||||
|
||||
def _definition_condition_parts(
|
||||
condition: dict[str, Any],
|
||||
) -> tuple[str, list[FlowDefinitionCondition]]:
|
||||
if "and" in condition:
|
||||
return AND_CONDITION, _definition_condition_items(condition, "and")
|
||||
return OR_CONDITION, _definition_condition_items(condition, "or")
|
||||
For example:
|
||||
- or_("a", "b") -> ["a", "b"] (both are direct triggers)
|
||||
- and_("a", "b") -> [] (neither are direct triggers, both required)
|
||||
- or_(and_("a", "b"), "c") -> ["c"] (only "c" is a direct trigger)
|
||||
|
||||
Args:
|
||||
condition: Can be a string, dict, or list.
|
||||
|
||||
def _condition_type_from_definition(
|
||||
condition: FlowDefinitionCondition | None,
|
||||
) -> str | None:
|
||||
Returns:
|
||||
List of direct OR-level trigger strings.
|
||||
"""
|
||||
if isinstance(condition, str):
|
||||
return [condition]
|
||||
if isinstance(condition, dict):
|
||||
if "and" in condition:
|
||||
return AND_CONDITION
|
||||
if "or" in condition:
|
||||
return OR_CONDITION
|
||||
if isinstance(condition, str):
|
||||
return OR_CONDITION
|
||||
return None
|
||||
cond_type = condition.get("type", OR_CONDITION)
|
||||
conditions_list = condition.get("conditions", [])
|
||||
|
||||
|
||||
def _runtime_condition_from_definition(
|
||||
condition: FlowDefinitionCondition,
|
||||
) -> str | dict[str, Any]:
|
||||
if isinstance(condition, str):
|
||||
return condition
|
||||
condition_type, conditions = _definition_condition_parts(condition)
|
||||
return {
|
||||
"type": condition_type,
|
||||
"conditions": [_runtime_condition_from_definition(item) for item in conditions],
|
||||
}
|
||||
|
||||
|
||||
def _method_trigger_condition(
|
||||
method_definition: FlowMethodDefinition,
|
||||
) -> FlowDefinitionCondition | None:
|
||||
if method_definition.listen is not None:
|
||||
return method_definition.listen
|
||||
if isinstance(method_definition.start, str | dict):
|
||||
return method_definition.start
|
||||
return None
|
||||
|
||||
|
||||
def _method_router_events(method_definition: FlowMethodDefinition) -> list[str]:
|
||||
if method_definition.human_feedback and method_definition.human_feedback.emit:
|
||||
return [str(event) for event in method_definition.human_feedback.emit]
|
||||
if method_definition.emit:
|
||||
return [str(event) for event in method_definition.emit]
|
||||
if cond_type == OR_CONDITION:
|
||||
strings = []
|
||||
for sub_cond in conditions_list:
|
||||
strings.extend(_extract_direct_or_triggers(sub_cond))
|
||||
return strings
|
||||
return []
|
||||
if isinstance(condition, list):
|
||||
strings = []
|
||||
for item in condition:
|
||||
strings.extend(_extract_direct_or_triggers(item))
|
||||
return strings
|
||||
if callable(condition) and hasattr(condition, "__name__"):
|
||||
return [condition.__name__]
|
||||
return []
|
||||
|
||||
|
||||
def _extract_direct_or_triggers(
|
||||
condition: FlowDefinitionCondition,
|
||||
) -> list[str]:
|
||||
if isinstance(condition, str):
|
||||
return [condition]
|
||||
condition_type, conditions = _definition_condition_parts(condition)
|
||||
if condition_type == AND_CONDITION:
|
||||
return []
|
||||
strings: list[str] = []
|
||||
for sub_condition in conditions:
|
||||
strings.extend(_extract_direct_or_triggers(sub_condition))
|
||||
return strings
|
||||
|
||||
|
||||
def _extract_all_trigger_names(
|
||||
condition: FlowDefinitionCondition,
|
||||
condition: str | dict[str, Any] | list[Any] | FlowCondition,
|
||||
) -> list[str]:
|
||||
"""Extract ALL trigger names from a condition for display purposes.
|
||||
|
||||
Unlike _extract_direct_or_triggers, this extracts ALL strings and method
|
||||
names from the entire condition tree, including those nested in AND conditions.
|
||||
This is used for displaying trigger information in the UI.
|
||||
|
||||
For example:
|
||||
- or_("a", "b") -> ["a", "b"]
|
||||
- and_("a", "b") -> ["a", "b"]
|
||||
- or_(and_("a", method_6), method_4) -> ["a", "method_6", "method_4"]
|
||||
|
||||
Args:
|
||||
condition: Can be a string, dict, or list.
|
||||
|
||||
Returns:
|
||||
List of all trigger names found in the condition.
|
||||
"""
|
||||
if isinstance(condition, str):
|
||||
return [condition]
|
||||
_, conditions = _definition_condition_parts(condition)
|
||||
strings: list[str] = []
|
||||
for sub_condition in conditions:
|
||||
strings.extend(_extract_all_trigger_names(sub_condition))
|
||||
return strings
|
||||
if isinstance(condition, dict):
|
||||
conditions_list = condition.get("conditions", [])
|
||||
strings = []
|
||||
for sub_cond in conditions_list:
|
||||
strings.extend(_extract_all_trigger_names(sub_cond))
|
||||
return strings
|
||||
if isinstance(condition, list):
|
||||
strings = []
|
||||
for item in condition:
|
||||
strings.extend(_extract_all_trigger_names(item))
|
||||
return strings
|
||||
if callable(condition) and hasattr(condition, "__name__"):
|
||||
return [condition.__name__]
|
||||
return []
|
||||
|
||||
|
||||
def _create_edges_from_condition(
|
||||
condition: FlowDefinitionCondition,
|
||||
condition: str | dict[str, Any] | list[Any] | FlowCondition,
|
||||
target: str,
|
||||
nodes: dict[str, NodeMetadata],
|
||||
) -> list[StructureEdge]:
|
||||
"""Create edges from a condition tree, preserving AND/OR semantics.
|
||||
|
||||
This function recursively processes the condition tree and creates edges
|
||||
with the appropriate condition_type for each trigger.
|
||||
|
||||
For AND conditions, all triggers get edges with condition_type="AND".
|
||||
For OR conditions, triggers get edges with condition_type="OR".
|
||||
|
||||
Args:
|
||||
condition: The condition tree (string, dict, or list).
|
||||
target: The target node name.
|
||||
nodes: Dictionary of all nodes for validation.
|
||||
|
||||
Returns:
|
||||
List of StructureEdge objects representing the condition.
|
||||
"""
|
||||
edges: list[StructureEdge] = []
|
||||
|
||||
if isinstance(condition, str):
|
||||
@@ -122,11 +135,24 @@ def _create_edges_from_condition(
|
||||
source=condition,
|
||||
target=target,
|
||||
condition_type=OR_CONDITION,
|
||||
is_router_event=False,
|
||||
is_router_path=False,
|
||||
)
|
||||
)
|
||||
elif callable(condition) and hasattr(condition, "__name__"):
|
||||
method_name = condition.__name__
|
||||
if method_name in nodes:
|
||||
edges.append(
|
||||
StructureEdge(
|
||||
source=method_name,
|
||||
target=target,
|
||||
condition_type=OR_CONDITION,
|
||||
is_router_path=False,
|
||||
)
|
||||
)
|
||||
elif isinstance(condition, dict):
|
||||
cond_type, conditions = _definition_condition_parts(condition)
|
||||
cond_type = condition.get("type", OR_CONDITION)
|
||||
conditions_list = condition.get("conditions", [])
|
||||
|
||||
if cond_type == AND_CONDITION:
|
||||
triggers = _extract_all_trigger_names(condition)
|
||||
edges.extend(
|
||||
@@ -134,144 +160,277 @@ def _create_edges_from_condition(
|
||||
source=trigger,
|
||||
target=target,
|
||||
condition_type=AND_CONDITION,
|
||||
is_router_event=False,
|
||||
is_router_path=False,
|
||||
)
|
||||
for trigger in triggers
|
||||
if trigger in nodes
|
||||
)
|
||||
else:
|
||||
for sub_condition in conditions:
|
||||
edges.extend(_create_edges_from_condition(sub_condition, target, nodes))
|
||||
for sub_cond in conditions_list:
|
||||
edges.extend(_create_edges_from_condition(sub_cond, target, nodes))
|
||||
elif isinstance(condition, list):
|
||||
for item in condition:
|
||||
edges.extend(_create_edges_from_condition(item, target, nodes))
|
||||
|
||||
return edges
|
||||
|
||||
|
||||
def _flow_definition_from(
|
||||
flow_or_definition: Flow[Any] | type[Flow[Any]] | FlowDefinition,
|
||||
) -> FlowDefinition:
|
||||
if isinstance(flow_or_definition, FlowDefinition):
|
||||
return flow_or_definition
|
||||
def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
|
||||
"""Build a structure representation of a Flow's execution.
|
||||
|
||||
flow_class = (
|
||||
flow_or_definition
|
||||
if isinstance(flow_or_definition, type)
|
||||
else type(flow_or_definition)
|
||||
)
|
||||
flow_definition = getattr(flow_class, "flow_definition", None)
|
||||
if callable(flow_definition):
|
||||
return cast(FlowDefinition, flow_definition())
|
||||
raise TypeError(
|
||||
"build_flow_structure requires a FlowDefinition or a Flow class/instance "
|
||||
"with flow_definition()."
|
||||
)
|
||||
Args:
|
||||
flow: Flow instance to analyze.
|
||||
|
||||
|
||||
def build_flow_structure(
|
||||
flow_or_definition: Flow[Any] | type[Flow[Any]] | FlowDefinition,
|
||||
) -> FlowStructure:
|
||||
"""Build a visualization structure projection from a FlowDefinition."""
|
||||
definition = _flow_definition_from(flow_or_definition)
|
||||
Returns:
|
||||
Dictionary with nodes, edges, start_methods, and router_methods.
|
||||
"""
|
||||
nodes: dict[str, NodeMetadata] = {}
|
||||
edges: list[StructureEdge] = []
|
||||
start_methods: list[str] = []
|
||||
router_methods: list[str] = []
|
||||
|
||||
for method_name, method_definition in definition.methods.items():
|
||||
node_metadata: NodeMetadata = {"type": "listen", "class_name": definition.name}
|
||||
for method_name, method in flow._methods.items():
|
||||
node_metadata: NodeMetadata = {"type": "listen"}
|
||||
|
||||
if method_definition.is_start:
|
||||
if hasattr(method, "__is_start_method__") and method.__is_start_method__:
|
||||
node_metadata["type"] = "start"
|
||||
start_methods.append(method_name)
|
||||
|
||||
if method_definition.router:
|
||||
if hasattr(method, "__is_router__") and method.__is_router__:
|
||||
node_metadata["is_router"] = True
|
||||
node_metadata["type"] = "router"
|
||||
router_methods.append(method_name)
|
||||
router_events = _method_router_events(method_definition)
|
||||
if router_events:
|
||||
node_metadata["router_events"] = router_events
|
||||
|
||||
trigger_condition = _method_trigger_condition(method_definition)
|
||||
condition_type = _condition_type_from_definition(trigger_condition)
|
||||
if condition_type is not None and trigger_condition is not None:
|
||||
node_metadata["trigger_condition_type"] = condition_type
|
||||
node_metadata["condition_type"] = condition_type
|
||||
extracted = _extract_all_trigger_names(trigger_condition)
|
||||
if extracted:
|
||||
node_metadata["trigger_methods"] = extracted
|
||||
runtime_condition = _runtime_condition_from_definition(trigger_condition)
|
||||
if isinstance(runtime_condition, dict):
|
||||
node_metadata["trigger_condition"] = runtime_condition
|
||||
if method_name in flow._router_paths:
|
||||
node_metadata["router_paths"] = [
|
||||
str(p) for p in flow._router_paths[method_name]
|
||||
]
|
||||
|
||||
if hasattr(method, "__trigger_methods__") and method.__trigger_methods__:
|
||||
node_metadata["trigger_methods"] = [
|
||||
str(m) for m in method.__trigger_methods__
|
||||
]
|
||||
|
||||
if hasattr(method, "__condition_type__") and method.__condition_type__:
|
||||
node_metadata["trigger_condition_type"] = method.__condition_type__
|
||||
if "condition_type" not in node_metadata:
|
||||
node_metadata["condition_type"] = method.__condition_type__
|
||||
|
||||
if node_metadata.get("is_router") and "condition_type" not in node_metadata:
|
||||
node_metadata["condition_type"] = "IF"
|
||||
|
||||
if (
|
||||
hasattr(method, "__trigger_condition__")
|
||||
and method.__trigger_condition__ is not None
|
||||
):
|
||||
node_metadata["trigger_condition"] = method.__trigger_condition__
|
||||
|
||||
if "trigger_methods" not in node_metadata:
|
||||
extracted = _extract_all_trigger_names(method.__trigger_condition__)
|
||||
if extracted:
|
||||
node_metadata["trigger_methods"] = extracted
|
||||
|
||||
node_metadata["method_signature"] = extract_method_signature(
|
||||
method, method_name
|
||||
)
|
||||
|
||||
try:
|
||||
source_code = inspect.getsource(method)
|
||||
node_metadata["source_code"] = source_code
|
||||
|
||||
try:
|
||||
source_lines, start_line = inspect.getsourcelines(method)
|
||||
node_metadata["source_lines"] = source_lines
|
||||
node_metadata["source_start_line"] = start_line
|
||||
except (OSError, TypeError):
|
||||
pass
|
||||
|
||||
try:
|
||||
source_file = inspect.getsourcefile(method)
|
||||
if source_file:
|
||||
node_metadata["source_file"] = source_file
|
||||
except (OSError, TypeError):
|
||||
try:
|
||||
class_file = inspect.getsourcefile(flow.__class__)
|
||||
if class_file:
|
||||
node_metadata["source_file"] = class_file
|
||||
except (OSError, TypeError):
|
||||
pass
|
||||
except (OSError, TypeError):
|
||||
pass
|
||||
|
||||
try:
|
||||
class_obj = flow.__class__
|
||||
|
||||
if class_obj:
|
||||
class_name = class_obj.__name__
|
||||
|
||||
bases = class_obj.__bases__
|
||||
if bases:
|
||||
base_strs = []
|
||||
for base in bases:
|
||||
if hasattr(base, "__name__"):
|
||||
if hasattr(base, "__origin__"):
|
||||
base_strs.append(str(base))
|
||||
else:
|
||||
base_strs.append(base.__name__)
|
||||
else:
|
||||
base_strs.append(str(base))
|
||||
|
||||
try:
|
||||
source_lines = inspect.getsource(class_obj).split("\n")
|
||||
_, class_start_line = inspect.getsourcelines(class_obj)
|
||||
|
||||
for idx, line in enumerate(source_lines):
|
||||
stripped = line.strip()
|
||||
if stripped.startswith("class ") and class_name in stripped:
|
||||
class_signature = stripped.rstrip(":")
|
||||
node_metadata["class_signature"] = class_signature
|
||||
node_metadata["class_line_number"] = (
|
||||
class_start_line + idx
|
||||
)
|
||||
break
|
||||
except (OSError, TypeError):
|
||||
class_signature = f"class {class_name}({', '.join(base_strs)})"
|
||||
node_metadata["class_signature"] = class_signature
|
||||
else:
|
||||
class_signature = f"class {class_name}"
|
||||
node_metadata["class_signature"] = class_signature
|
||||
|
||||
node_metadata["class_name"] = class_name
|
||||
except (OSError, TypeError, AttributeError):
|
||||
pass
|
||||
|
||||
nodes[method_name] = node_metadata
|
||||
|
||||
for method_name, method_definition in definition.methods.items():
|
||||
trigger_condition = _method_trigger_condition(method_definition)
|
||||
if trigger_condition is None:
|
||||
for listener_name, condition_data in flow._listeners.items():
|
||||
if listener_name in router_methods:
|
||||
continue
|
||||
edges.extend(
|
||||
_create_edges_from_condition(trigger_condition, method_name, nodes)
|
||||
)
|
||||
|
||||
all_string_triggers: set[str] = set()
|
||||
for method_definition in definition.methods.values():
|
||||
trigger_condition = _method_trigger_condition(method_definition)
|
||||
if trigger_condition is None:
|
||||
continue
|
||||
for trigger in _extract_direct_or_triggers(trigger_condition):
|
||||
if trigger not in nodes:
|
||||
all_string_triggers.add(trigger)
|
||||
|
||||
all_router_events: set[str] = set()
|
||||
for router_method_name in router_methods:
|
||||
router_events = _method_router_events(definition.methods[router_method_name])
|
||||
if router_events and router_method_name in nodes:
|
||||
nodes[router_method_name]["router_events"] = router_events
|
||||
all_router_events.update(router_events)
|
||||
|
||||
if not router_events:
|
||||
logger.warning(
|
||||
f"Router events for '{router_method_name}' are dynamic or not "
|
||||
f"statically inferable; static visualization may omit event edges."
|
||||
if is_simple_flow_condition(condition_data):
|
||||
cond_type, methods = condition_data
|
||||
edges.extend(
|
||||
StructureEdge(
|
||||
source=str(trigger_method),
|
||||
target=str(listener_name),
|
||||
condition_type=cond_type,
|
||||
is_router_path=False,
|
||||
)
|
||||
for trigger_method in methods
|
||||
if str(trigger_method) in nodes
|
||||
)
|
||||
elif is_flow_condition_dict(condition_data):
|
||||
edges.extend(
|
||||
_create_edges_from_condition(condition_data, str(listener_name), nodes)
|
||||
)
|
||||
|
||||
orphaned_triggers = all_string_triggers - all_router_events
|
||||
for method_name, node_metadata in nodes.items(): # type: ignore[assignment]
|
||||
if node_metadata.get("is_router") and "trigger_methods" in node_metadata:
|
||||
trigger_methods = node_metadata["trigger_methods"]
|
||||
condition_type = node_metadata.get("trigger_condition_type", OR_CONDITION)
|
||||
|
||||
if "trigger_condition" in node_metadata:
|
||||
edges.extend(
|
||||
_create_edges_from_condition(
|
||||
node_metadata["trigger_condition"], # type: ignore[arg-type]
|
||||
method_name,
|
||||
nodes,
|
||||
)
|
||||
)
|
||||
else:
|
||||
edges.extend(
|
||||
StructureEdge(
|
||||
source=trigger_method,
|
||||
target=method_name,
|
||||
condition_type=condition_type,
|
||||
is_router_path=False,
|
||||
)
|
||||
for trigger_method in trigger_methods
|
||||
if trigger_method in nodes
|
||||
)
|
||||
|
||||
all_string_triggers: set[str] = set()
|
||||
for condition_data in flow._listeners.values():
|
||||
if is_simple_flow_condition(condition_data):
|
||||
_, methods = condition_data
|
||||
for m in methods:
|
||||
if str(m) not in nodes: # It's a string trigger, not a method name
|
||||
all_string_triggers.add(str(m))
|
||||
elif is_flow_condition_dict(condition_data):
|
||||
for trigger in _extract_direct_or_triggers(condition_data):
|
||||
if trigger not in nodes:
|
||||
all_string_triggers.add(trigger)
|
||||
|
||||
all_router_outputs: set[str] = set()
|
||||
for router_method_name in router_methods:
|
||||
if router_method_name not in flow._router_paths:
|
||||
flow._router_paths[FlowMethodName(router_method_name)] = []
|
||||
|
||||
current_paths = flow._router_paths.get(FlowMethodName(router_method_name), [])
|
||||
if current_paths and router_method_name in nodes:
|
||||
nodes[router_method_name]["router_paths"] = [str(p) for p in current_paths]
|
||||
all_router_outputs.update(str(p) for p in current_paths)
|
||||
|
||||
if not current_paths:
|
||||
logger.warning(
|
||||
f"Could not determine return paths for router '{router_method_name}'. "
|
||||
f"Add a return type annotation like "
|
||||
f"'-> Literal[\"path1\", \"path2\"]' or '-> YourEnum' "
|
||||
f"to enable proper flow visualization."
|
||||
)
|
||||
|
||||
orphaned_triggers = all_string_triggers - all_router_outputs
|
||||
if orphaned_triggers:
|
||||
logger.warning(
|
||||
f"Static visualization could not match listener triggers "
|
||||
f"{orphaned_triggers} to explicit router events. "
|
||||
f"Dynamic router values may still trigger these listeners at runtime."
|
||||
logger.error(
|
||||
f"Found listeners waiting for triggers {orphaned_triggers} "
|
||||
f"but no router outputs these values explicitly. "
|
||||
f"If your router returns a non-static value, check that your router has proper return type annotations."
|
||||
)
|
||||
|
||||
for router_method_name in router_methods:
|
||||
router_events = _method_router_events(definition.methods[router_method_name])
|
||||
if router_method_name not in flow._router_paths:
|
||||
continue
|
||||
|
||||
for event in router_events:
|
||||
for listener_name, method_definition in definition.methods.items():
|
||||
router_paths = flow._router_paths[FlowMethodName(router_method_name)]
|
||||
|
||||
for path in router_paths:
|
||||
for listener_name, condition_data in flow._listeners.items():
|
||||
if listener_name == router_method_name:
|
||||
continue
|
||||
|
||||
trigger_condition = _method_trigger_condition(method_definition)
|
||||
if trigger_condition is None:
|
||||
continue
|
||||
trigger_strings_from_cond = _extract_direct_or_triggers(
|
||||
trigger_condition
|
||||
)
|
||||
trigger_strings_from_cond: list[str] = []
|
||||
|
||||
if str(event) in trigger_strings_from_cond:
|
||||
if is_simple_flow_condition(condition_data):
|
||||
_, methods = condition_data
|
||||
trigger_strings_from_cond = [str(m) for m in methods]
|
||||
elif is_flow_condition_dict(condition_data):
|
||||
trigger_strings_from_cond = _extract_direct_or_triggers(
|
||||
condition_data
|
||||
)
|
||||
|
||||
if str(path) in trigger_strings_from_cond:
|
||||
edges.append(
|
||||
StructureEdge(
|
||||
source=router_method_name,
|
||||
target=listener_name,
|
||||
target=str(listener_name),
|
||||
condition_type=None,
|
||||
is_router_event=True,
|
||||
router_event=str(event),
|
||||
is_router_path=True,
|
||||
router_path_label=str(path),
|
||||
)
|
||||
)
|
||||
|
||||
for start_method in flow._start_methods:
|
||||
if start_method not in nodes and start_method in flow._methods:
|
||||
method = flow._methods[start_method]
|
||||
nodes[str(start_method)] = NodeMetadata(type="start")
|
||||
|
||||
if hasattr(method, "__trigger_methods__") and method.__trigger_methods__:
|
||||
nodes[str(start_method)]["trigger_methods"] = [
|
||||
str(m) for m in method.__trigger_methods__
|
||||
]
|
||||
if hasattr(method, "__condition_type__") and method.__condition_type__:
|
||||
nodes[str(start_method)]["condition_type"] = method.__condition_type__
|
||||
|
||||
return FlowStructure(
|
||||
nodes=nodes,
|
||||
edges=edges,
|
||||
@@ -294,7 +453,7 @@ def calculate_execution_paths(structure: FlowStructure) -> int:
|
||||
graph[edge["source"]].append(
|
||||
{
|
||||
"target": edge["target"],
|
||||
"is_router": edge["is_router_event"],
|
||||
"is_router": edge["is_router_path"],
|
||||
"condition": edge["condition_type"],
|
||||
}
|
||||
)
|
||||
@@ -307,6 +466,15 @@ def calculate_execution_paths(structure: FlowStructure) -> int:
|
||||
return 0
|
||||
|
||||
def count_paths_from(node: str, visited: set[str]) -> int:
|
||||
"""Recursively count execution paths from a given node.
|
||||
|
||||
Args:
|
||||
node: Node name to start counting from.
|
||||
visited: Set of already visited nodes to prevent cycles.
|
||||
|
||||
Returns:
|
||||
Number of execution paths from this node to terminal nodes.
|
||||
"""
|
||||
if node in terminal_nodes:
|
||||
return 1
|
||||
|
||||
|
||||
@@ -309,18 +309,18 @@ def render_interactive(
|
||||
</div>
|
||||
""")
|
||||
|
||||
if metadata.get("router_events"):
|
||||
router_events = metadata["router_events"]
|
||||
event_items = "".join(
|
||||
if metadata.get("router_paths"):
|
||||
paths = metadata["router_paths"]
|
||||
paths_items = "".join(
|
||||
[
|
||||
f'<li style="margin: 3px 0;"><code style="background: rgba(255,90,80,0.08); padding: 2px 6px; border-radius: 3px; font-size: 10px; color: {CREWAI_ORANGE}; border: 1px solid rgba(255,90,80,0.2); font-weight: 600;">{p}</code></li>'
|
||||
for p in router_events
|
||||
for p in paths
|
||||
]
|
||||
)
|
||||
title_parts.append(f"""
|
||||
<div>
|
||||
<div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;">Router Events</div>
|
||||
<ul style="list-style: none; padding: 0; margin: 0;">{event_items}</ul>
|
||||
<div style="font-size: 10px; text-transform: uppercase; color: {GRAY}; letter-spacing: 0.5px; margin-bottom: 4px; font-weight: 600;">Router Paths</div>
|
||||
<ul style="list-style: none; padding: 0; margin: 0;">{paths_items}</ul>
|
||||
</div>
|
||||
""")
|
||||
|
||||
@@ -364,11 +364,11 @@ def render_interactive(
|
||||
edge_color: str = GRAY
|
||||
edge_dashes: bool | list[int] = False
|
||||
|
||||
if edge["is_router_event"]:
|
||||
if edge["is_router_path"]:
|
||||
edge_color = CREWAI_ORANGE
|
||||
edge_dashes = [15, 10]
|
||||
if "router_event" in edge:
|
||||
edge_label = edge["router_event"] or ""
|
||||
if "router_path_label" in edge:
|
||||
edge_label = edge["router_path_label"]
|
||||
elif edge["condition_type"] == "AND":
|
||||
edge_label = "AND"
|
||||
edge_color = CREWAI_ORANGE
|
||||
|
||||
104
lib/crewai/src/crewai/flow/visualization/schema.py
Normal file
104
lib/crewai/src/crewai/flow/visualization/schema.py
Normal file
@@ -0,0 +1,104 @@
|
||||
"""OpenAPI schema conversion utilities for Flow methods."""
|
||||
|
||||
import inspect
|
||||
from typing import Any, get_args, get_origin
|
||||
|
||||
|
||||
def type_to_openapi_schema(type_hint: Any) -> dict[str, Any]:
|
||||
"""Convert Python type hint to OpenAPI schema.
|
||||
|
||||
Args:
|
||||
type_hint: Python type hint to convert.
|
||||
|
||||
Returns:
|
||||
OpenAPI schema dictionary.
|
||||
"""
|
||||
if type_hint is inspect.Parameter.empty:
|
||||
return {}
|
||||
|
||||
if type_hint is None or type_hint is type(None):
|
||||
return {"type": "null"}
|
||||
|
||||
if hasattr(type_hint, "__module__") and hasattr(type_hint, "__name__"):
|
||||
if type_hint.__module__ == "typing" and type_hint.__name__ == "Any":
|
||||
return {}
|
||||
|
||||
type_str = str(type_hint)
|
||||
if type_str == "typing.Any" or type_str == "<class 'typing.Any'>":
|
||||
return {}
|
||||
|
||||
if isinstance(type_hint, str):
|
||||
return {"type": type_hint}
|
||||
|
||||
origin = get_origin(type_hint)
|
||||
args = get_args(type_hint)
|
||||
|
||||
if type_hint is str:
|
||||
return {"type": "string"}
|
||||
if type_hint is int:
|
||||
return {"type": "integer"}
|
||||
if type_hint is float:
|
||||
return {"type": "number"}
|
||||
if type_hint is bool:
|
||||
return {"type": "boolean"}
|
||||
if type_hint is dict or origin is dict:
|
||||
if args and len(args) > 1:
|
||||
return {
|
||||
"type": "object",
|
||||
"additionalProperties": type_to_openapi_schema(args[1]),
|
||||
}
|
||||
return {"type": "object"}
|
||||
if type_hint is list or origin is list:
|
||||
if args:
|
||||
return {"type": "array", "items": type_to_openapi_schema(args[0])}
|
||||
return {"type": "array"}
|
||||
if hasattr(type_hint, "__name__"):
|
||||
return {"type": "object", "className": type_hint.__name__}
|
||||
|
||||
return {}
|
||||
|
||||
|
||||
def extract_method_signature(method: Any, method_name: str) -> dict[str, Any]:
|
||||
"""Extract method signature as OpenAPI schema with documentation.
|
||||
|
||||
Args:
|
||||
method: Method to analyze.
|
||||
method_name: Method name.
|
||||
|
||||
Returns:
|
||||
Dictionary with operationId, parameters, returns, summary, and description.
|
||||
"""
|
||||
try:
|
||||
sig = inspect.signature(method)
|
||||
|
||||
parameters = {}
|
||||
for param_name, param in sig.parameters.items():
|
||||
if param_name == "self":
|
||||
continue
|
||||
parameters[param_name] = type_to_openapi_schema(param.annotation)
|
||||
|
||||
return_type = type_to_openapi_schema(sig.return_annotation)
|
||||
|
||||
docstring = inspect.getdoc(method)
|
||||
|
||||
result: dict[str, Any] = {
|
||||
"operationId": method_name,
|
||||
"parameters": parameters,
|
||||
"returns": return_type,
|
||||
}
|
||||
|
||||
if docstring:
|
||||
lines = docstring.strip().split("\n")
|
||||
summary = lines[0].strip()
|
||||
|
||||
if summary:
|
||||
result["summary"] = summary
|
||||
|
||||
if len(lines) > 1:
|
||||
description = "\n".join(line.strip() for line in lines[1:]).strip()
|
||||
if description:
|
||||
result["description"] = description
|
||||
|
||||
return result
|
||||
except Exception:
|
||||
return {"operationId": method_name, "parameters": {}, "returns": {}}
|
||||
@@ -1,11 +1,6 @@
|
||||
"""Type definitions for Flow structure visualization."""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from typing_extensions import Required, TypedDict
|
||||
|
||||
|
||||
__all__ = ["FlowStructure", "NodeMetadata", "StructureEdge"]
|
||||
from typing import Any, TypedDict
|
||||
|
||||
|
||||
class NodeMetadata(TypedDict, total=False):
|
||||
@@ -13,12 +8,19 @@ class NodeMetadata(TypedDict, total=False):
|
||||
|
||||
type: str
|
||||
is_router: bool
|
||||
router_events: list[str]
|
||||
router_paths: list[str]
|
||||
condition_type: str | None
|
||||
trigger_condition_type: str | None
|
||||
trigger_methods: list[str]
|
||||
trigger_condition: dict[str, Any] | None
|
||||
method_signature: dict[str, Any]
|
||||
source_code: str
|
||||
source_lines: list[str]
|
||||
source_start_line: int
|
||||
source_file: str
|
||||
class_signature: str
|
||||
class_name: str
|
||||
class_line_number: int
|
||||
|
||||
|
||||
class StructureEdge(TypedDict, total=False):
|
||||
@@ -27,8 +29,8 @@ class StructureEdge(TypedDict, total=False):
|
||||
source: str
|
||||
target: str
|
||||
condition_type: str | None
|
||||
is_router_event: Required[bool]
|
||||
router_event: str | None
|
||||
is_router_path: bool
|
||||
router_path_label: str
|
||||
|
||||
|
||||
class FlowStructure(TypedDict):
|
||||
|
||||
@@ -1925,62 +1925,16 @@ class LLM(BaseLLM):
|
||||
|
||||
@staticmethod
|
||||
def _usage_to_dict(usage: Any) -> dict[str, Any] | None:
|
||||
"""Convert a provider usage object to a plain dict and flatten the
|
||||
cache/reasoning sub-counts that LiteLLM nests under provider-specific
|
||||
shapes into the top-level keys the rest of the pipeline expects.
|
||||
|
||||
LiteLLM hands back provider usage as-is, so cache-read, cache-creation
|
||||
and reasoning tokens may live in nested objects (e.g.
|
||||
``prompt_tokens_details.cached_tokens``) or under Anthropic-style keys
|
||||
(``cache_read_input_tokens``). Downstream span mapping only reads the
|
||||
flat ``cached_prompt_tokens`` / ``reasoning_tokens`` /
|
||||
``cache_creation_tokens`` keys, so we surface them here.
|
||||
|
||||
Only those derived buckets are populated; ``prompt_tokens`` /
|
||||
``completion_tokens`` / ``total_tokens`` are left untouched. Extraction
|
||||
precedence mirrors ``BaseLLM._track_token_usage_internal``.
|
||||
"""
|
||||
if usage is None:
|
||||
return None
|
||||
if isinstance(usage, dict):
|
||||
data: dict[str, Any] = dict(usage)
|
||||
elif isinstance(usage, BaseModel):
|
||||
data = usage.model_dump()
|
||||
elif hasattr(usage, "__dict__"):
|
||||
data = {k: v for k, v in vars(usage).items() if not k.startswith("_")}
|
||||
else:
|
||||
return None
|
||||
|
||||
def _nested(container: Any, key: str) -> Any:
|
||||
if isinstance(container, dict):
|
||||
return container.get(key)
|
||||
return getattr(container, key, None)
|
||||
|
||||
prompt_details = data.get("prompt_tokens_details")
|
||||
completion_details = data.get("completion_tokens_details")
|
||||
|
||||
cached_prompt_tokens = (
|
||||
data.get("cached_tokens")
|
||||
or data.get("cached_prompt_tokens")
|
||||
or data.get("cache_read_input_tokens")
|
||||
or _nested(prompt_details, "cached_tokens")
|
||||
)
|
||||
if cached_prompt_tokens is not None:
|
||||
data["cached_prompt_tokens"] = cached_prompt_tokens
|
||||
|
||||
reasoning_tokens = data.get("reasoning_tokens") or _nested(
|
||||
completion_details, "reasoning_tokens"
|
||||
)
|
||||
if reasoning_tokens is not None:
|
||||
data["reasoning_tokens"] = reasoning_tokens
|
||||
|
||||
cache_creation_tokens = data.get("cache_creation_tokens") or data.get(
|
||||
"cache_creation_input_tokens"
|
||||
)
|
||||
if cache_creation_tokens is not None:
|
||||
data["cache_creation_tokens"] = cache_creation_tokens
|
||||
|
||||
return data
|
||||
return usage
|
||||
if isinstance(usage, BaseModel):
|
||||
result: dict[str, Any] = usage.model_dump()
|
||||
return result
|
||||
if hasattr(usage, "__dict__"):
|
||||
return {k: v for k, v in vars(usage).items() if not k.startswith("_")}
|
||||
return None
|
||||
|
||||
def _handle_emit_call_events(
|
||||
self,
|
||||
|
||||
@@ -35,8 +35,6 @@
|
||||
"knowledge_search_query": "The original query is: {task_prompt}.",
|
||||
"knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions.",
|
||||
"human_feedback_collapse": "Based on the following human feedback, determine which outcome best matches their intent.\n\nFeedback: {feedback}\n\nPossible outcomes: {outcomes}\n\nRespond with ONLY one of the exact outcome values listed above, nothing else.",
|
||||
"conversational_system_prompt": "You are a helpful conversational assistant. Maintain context across turns, answer using the canonical message history when possible, and respond clearly and concisely. Ask for clarification when the user's intent is ambiguous.",
|
||||
"conversational_answer_from_history_prompt": "Given the current user message and the canonical message history, decide whether the assistant can answer from message_history without running additional tools or agents. If so, answer clearly using only that context.",
|
||||
"hitl_pre_review_system": "You are reviewing content before a human sees it. Apply the lessons from past human feedback to improve the output. Preserve the original meaning and structure, but incorporate the corrections and preferences indicated by the lessons.",
|
||||
"hitl_pre_review_user": "Output to review:\n{output}\n\nLessons from past human feedback:\n{lessons}\n\nApply the lessons to improve the output.",
|
||||
"hitl_distill_system": "You extract generalizable lessons from human feedback on system outputs. A lesson should be a reusable rule or preference that applies to future similar outputs -- not a one-time correction specific to this exact content.\n\nExamples of good lessons:\n- Always include source citations when making factual claims\n- Use bullet points instead of long paragraphs for action items\n- Avoid technical jargon when the audience is non-technical\n\nIf the feedback is just approval (e.g. looks good, approved) or contains no generalizable guidance, return an empty list.",
|
||||
|
||||
@@ -17,6 +17,251 @@ interactions:
|
||||
status:
|
||||
code: 200
|
||||
message: OK
|
||||
- request:
|
||||
body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour
|
||||
personal goal is: Test goal\nTo give my best complete final answer to the task
|
||||
respond using the exact following format:\n\nThought: I now can give a great
|
||||
answer\nFinal Answer: Your final answer must be the great and the most complete
|
||||
as possible, it must be outcome described.\n\nI MUST use these formats, my job
|
||||
depends on it!"}, {"role": "user", "content": "\nCurrent Task: Say hello to
|
||||
the world\n\nThis is the expected criteria for your final answer: hello world\nyou
|
||||
MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
|
||||
This is VERY important to you, use the tools available and give your best Final
|
||||
Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop":
|
||||
["\nObservation:"]}'
|
||||
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class Usage(BaseModel):
|
||||
|
||||
@@ -1012,7 +1012,7 @@ class TestLLMObjectPreservedInContext:
|
||||
call_kwargs = mock_collapse.call_args
|
||||
assert call_kwargs.kwargs["feedback"] == "this looks good, proceed!"
|
||||
assert call_kwargs.kwargs["outcomes"] == ["needs_changes", "approved"]
|
||||
# LLM should be a live object (from _human_feedback_llm) or reconstructed, not None
|
||||
# LLM should be a live object (from _hf_llm) or reconstructed, not None
|
||||
assert call_kwargs.kwargs["llm"] is not None
|
||||
assert getattr(call_kwargs.kwargs["llm"], "model", None) == "gemini-2.0-flash"
|
||||
assert flow2.last_human_feedback.outcome == "approved"
|
||||
@@ -1171,8 +1171,8 @@ class TestAsyncHumanFeedbackEdgeCases:
|
||||
class TestLiveLLMPreservationOnResume:
|
||||
"""Tests for preserving the full LLM config across HITL resume."""
|
||||
|
||||
def test_human_feedback_llm_attribute_set_on_wrapper_with_basellm(self) -> None:
|
||||
"""Test that _human_feedback_llm is set on the wrapper when llm is a BaseLLM instance."""
|
||||
def test_hf_llm_attribute_set_on_wrapper_with_basellm(self) -> None:
|
||||
"""Test that _hf_llm is set on the wrapper when llm is a BaseLLM instance."""
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
mock_llm = MagicMock(spec=BaseLLM)
|
||||
@@ -1191,11 +1191,11 @@ class TestLiveLLMPreservationOnResume:
|
||||
flow = TestFlow()
|
||||
method = flow._methods.get("review")
|
||||
assert method is not None
|
||||
assert hasattr(method, "_human_feedback_llm")
|
||||
assert method._human_feedback_llm is mock_llm
|
||||
assert hasattr(method, "_hf_llm")
|
||||
assert method._hf_llm is mock_llm
|
||||
|
||||
def test_human_feedback_llm_attribute_set_on_wrapper_with_string(self) -> None:
|
||||
"""Test that _human_feedback_llm is set on the wrapper even when llm is a string."""
|
||||
def test_hf_llm_attribute_set_on_wrapper_with_string(self) -> None:
|
||||
"""Test that _hf_llm is set on the wrapper even when llm is a string."""
|
||||
|
||||
class TestFlow(Flow):
|
||||
@start()
|
||||
@@ -1210,8 +1210,8 @@ class TestLiveLLMPreservationOnResume:
|
||||
flow = TestFlow()
|
||||
method = flow._methods.get("review")
|
||||
assert method is not None
|
||||
assert hasattr(method, "_human_feedback_llm")
|
||||
assert method._human_feedback_llm == "gpt-4o-mini"
|
||||
assert hasattr(method, "_hf_llm")
|
||||
assert method._hf_llm == "gpt-4o-mini"
|
||||
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_async_uses_live_basellm_over_serialized_string(
|
||||
@@ -1277,20 +1277,20 @@ class TestLiveLLMPreservationOnResume:
|
||||
flow.resume("looks good!")
|
||||
|
||||
# NOT the serialized string. The live_llm was captured at class definition
|
||||
# time and stored on the method wrapper as _human_feedback_llm.
|
||||
# time and stored on the method wrapper as _hf_llm.
|
||||
assert len(captured_llm) == 1
|
||||
# (which is stored on the method's _human_feedback_llm attribute)
|
||||
# (which is stored on the method's _hf_llm attribute)
|
||||
method = flow._methods.get("review")
|
||||
assert method is not None
|
||||
assert captured_llm[0] is method._human_feedback_llm
|
||||
assert captured_llm[0] is method._hf_llm
|
||||
# And verify it's a BaseLLM instance, not a string
|
||||
assert isinstance(captured_llm[0], BaseLLM)
|
||||
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_async_falls_back_to_serialized_string_when_no_human_feedback_llm(
|
||||
def test_resume_async_falls_back_to_serialized_string_when_no_hf_llm(
|
||||
self, mock_emit: MagicMock
|
||||
) -> None:
|
||||
"""Test that resume_async falls back to context.llm when _human_feedback_llm is not available.
|
||||
"""Test that resume_async falls back to context.llm when _hf_llm is not available.
|
||||
|
||||
This ensures backward compatibility with flows that were paused before this fix.
|
||||
"""
|
||||
@@ -1325,10 +1325,10 @@ class TestLiveLLMPreservationOnResume:
|
||||
|
||||
flow = TestFlow.from_pending("fallback-test", persistence)
|
||||
|
||||
# Remove _human_feedback_llm to simulate old decorator without this attribute
|
||||
# Remove _hf_llm to simulate old decorator without this attribute
|
||||
method = flow._methods.get("review")
|
||||
if hasattr(method, "_human_feedback_llm"):
|
||||
delattr(method, "_human_feedback_llm")
|
||||
if hasattr(method, "_hf_llm"):
|
||||
delattr(method, "_hf_llm")
|
||||
|
||||
captured_llm = []
|
||||
|
||||
@@ -1345,10 +1345,10 @@ class TestLiveLLMPreservationOnResume:
|
||||
assert captured_llm[0].model == "gpt-4o-mini"
|
||||
|
||||
@patch("crewai.flow.runtime.crewai_event_bus.emit")
|
||||
def test_resume_async_uses_string_from_context_when_human_feedback_llm_is_string(
|
||||
def test_resume_async_uses_string_from_context_when_hf_llm_is_string(
|
||||
self, mock_emit: MagicMock
|
||||
) -> None:
|
||||
"""Test that when _human_feedback_llm is a string (not BaseLLM), we still use context.llm.
|
||||
"""Test that when _hf_llm is a string (not BaseLLM), we still use context.llm.
|
||||
|
||||
String LLM values offer no benefit over the serialized context.llm,
|
||||
so we don't prefer them.
|
||||
@@ -1385,7 +1385,7 @@ class TestLiveLLMPreservationOnResume:
|
||||
flow = TestFlow.from_pending("string-llm-test", persistence)
|
||||
|
||||
method = flow._methods.get("review")
|
||||
assert method._human_feedback_llm == "gpt-4o-mini"
|
||||
assert method._hf_llm == "gpt-4o-mini"
|
||||
|
||||
captured_llm = []
|
||||
|
||||
@@ -1396,14 +1396,14 @@ class TestLiveLLMPreservationOnResume:
|
||||
with patch.object(flow, "_collapse_to_outcome", side_effect=capture_llm):
|
||||
flow.resume("looks good!")
|
||||
|
||||
# _human_feedback_llm is a string, so resume deserializes context.llm into an LLM instance
|
||||
# _hf_llm is a string, so resume deserializes context.llm into an LLM instance
|
||||
assert len(captured_llm) == 1
|
||||
from crewai.llms.base_llm import BaseLLM as BaseLLMClass
|
||||
assert isinstance(captured_llm[0], BaseLLMClass)
|
||||
assert captured_llm[0].model == "gpt-4o-mini"
|
||||
|
||||
def test_human_feedback_llm_set_for_async_wrapper(self) -> None:
|
||||
"""Test that _human_feedback_llm is set on async wrapper functions."""
|
||||
def test_hf_llm_set_for_async_wrapper(self) -> None:
|
||||
"""Test that _hf_llm is set on async wrapper functions."""
|
||||
import asyncio
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
|
||||
@@ -1423,5 +1423,5 @@ class TestLiveLLMPreservationOnResume:
|
||||
flow = TestFlow()
|
||||
method = flow._methods.get("async_review")
|
||||
assert method is not None
|
||||
assert hasattr(method, "_human_feedback_llm")
|
||||
assert method._human_feedback_llm is mock_llm
|
||||
assert hasattr(method, "_hf_llm")
|
||||
assert method._hf_llm is mock_llm
|
||||
|
||||
@@ -1160,9 +1160,9 @@ def test_router_cascade_chain():
|
||||
@router(process_level_1)
|
||||
def router_level_2(self):
|
||||
execution_order.append("router_level_2")
|
||||
return "level_2_event"
|
||||
return "level_2_path"
|
||||
|
||||
@listen("level_2_event")
|
||||
@listen("level_2_path")
|
||||
def process_level_2(self):
|
||||
execution_order.append("process_level_2")
|
||||
self.state["level"] = 3
|
||||
@@ -1171,9 +1171,9 @@ def test_router_cascade_chain():
|
||||
@router(process_level_2)
|
||||
def router_level_3(self):
|
||||
execution_order.append("router_level_3")
|
||||
return "final_event"
|
||||
return "final_path"
|
||||
|
||||
@listen("final_event")
|
||||
@listen("final_path")
|
||||
def finalize(self):
|
||||
execution_order.append("finalize")
|
||||
return "complete"
|
||||
@@ -1261,14 +1261,14 @@ def test_complex_and_or_branching():
|
||||
assert execution_order.index("final") > execution_order.index("branch_2b")
|
||||
|
||||
|
||||
def test_conditional_router_events_exclusivity():
|
||||
"""Test that only the returned router event activates, not all events."""
|
||||
def test_conditional_router_paths_exclusivity():
|
||||
"""Test that only the returned router path activates, not all paths."""
|
||||
execution_order = []
|
||||
|
||||
class ConditionalRouterFlow(Flow):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.state["condition"] = "take_event_b"
|
||||
self.state["condition"] = "take_path_b"
|
||||
|
||||
@start()
|
||||
def begin(self):
|
||||
@@ -1277,33 +1277,33 @@ def test_conditional_router_events_exclusivity():
|
||||
@router(begin)
|
||||
def decision_point(self):
|
||||
execution_order.append("decision_point")
|
||||
if self.state["condition"] == "take_event_a":
|
||||
return "event_a"
|
||||
elif self.state["condition"] == "take_event_b":
|
||||
return "event_b"
|
||||
if self.state["condition"] == "take_path_a":
|
||||
return "path_a"
|
||||
elif self.state["condition"] == "take_path_b":
|
||||
return "path_b"
|
||||
else:
|
||||
return "event_c"
|
||||
return "path_c"
|
||||
|
||||
@listen("event_a")
|
||||
def handle_event_a(self):
|
||||
execution_order.append("handle_event_a")
|
||||
@listen("path_a")
|
||||
def handle_path_a(self):
|
||||
execution_order.append("handle_path_a")
|
||||
|
||||
@listen("event_b")
|
||||
def handle_event_b(self):
|
||||
execution_order.append("handle_event_b")
|
||||
@listen("path_b")
|
||||
def handle_path_b(self):
|
||||
execution_order.append("handle_path_b")
|
||||
|
||||
@listen("event_c")
|
||||
def handle_event_c(self):
|
||||
execution_order.append("handle_event_c")
|
||||
@listen("path_c")
|
||||
def handle_path_c(self):
|
||||
execution_order.append("handle_path_c")
|
||||
|
||||
flow = ConditionalRouterFlow()
|
||||
flow.kickoff()
|
||||
|
||||
assert "begin" in execution_order
|
||||
assert "decision_point" in execution_order
|
||||
assert "handle_event_b" in execution_order
|
||||
assert "handle_event_a" not in execution_order
|
||||
assert "handle_event_c" not in execution_order
|
||||
assert "handle_path_b" in execution_order
|
||||
assert "handle_path_a" not in execution_order
|
||||
assert "handle_path_c" not in execution_order
|
||||
|
||||
|
||||
def test_state_consistency_across_parallel_branches():
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,847 +0,0 @@
|
||||
"""Tests for the static Flow Definition contract."""
|
||||
|
||||
import ast
|
||||
from enum import Enum
|
||||
import importlib
|
||||
import inspect
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Annotated, Literal
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
import crewai.flow.dsl as flow_dsl
|
||||
import crewai.flow.flow_definition as flow_definition
|
||||
import crewai.flow.visualization.builder as visualization_builder
|
||||
from crewai.flow import Flow, and_, human_feedback, listen, or_, persist, router, start
|
||||
|
||||
|
||||
def test_flow_public_exports_are_explicit():
|
||||
import crewai.flow.visualization as flow_visualization
|
||||
|
||||
flow_package = importlib.import_module("crewai.flow")
|
||||
|
||||
assert "FlowDefinition" not in flow_package.__all__
|
||||
assert "FlowDefinitionDiagnostic" not in flow_package.__all__
|
||||
assert "build_flow_definition" not in flow_package.__all__
|
||||
assert "flow_structure" not in flow_package.__all__
|
||||
assert set(flow_dsl.__all__) == {
|
||||
"HumanFeedbackResult",
|
||||
"and_",
|
||||
"human_feedback",
|
||||
"listen",
|
||||
"or_",
|
||||
"router",
|
||||
"start",
|
||||
}
|
||||
assert set(flow_definition.__all__) == {
|
||||
"FlowConfigDefinition",
|
||||
"FlowDefinition",
|
||||
"FlowDefinitionCondition",
|
||||
"FlowDefinitionDiagnostic",
|
||||
"FlowHumanFeedbackDefinition",
|
||||
"FlowMethodDefinition",
|
||||
"FlowPersistenceDefinition",
|
||||
"FlowStateDefinition",
|
||||
}
|
||||
assert "build_flow_structure" in flow_visualization.__all__
|
||||
assert "calculate_node_levels" not in flow_visualization.__all__
|
||||
|
||||
|
||||
def test_private_flow_helpers_do_not_have_docstrings():
|
||||
import crewai.flow.flow_wrappers as flow_wrappers
|
||||
import crewai.flow.human_feedback as human_feedback
|
||||
import crewai.flow.persistence.decorators as persistence_decorators
|
||||
import crewai.flow.visualization.types as visualization_types
|
||||
|
||||
modules = [
|
||||
flow_dsl,
|
||||
flow_definition,
|
||||
flow_wrappers,
|
||||
human_feedback,
|
||||
persistence_decorators,
|
||||
visualization_builder,
|
||||
visualization_types,
|
||||
]
|
||||
violations: list[str] = []
|
||||
|
||||
for module in modules:
|
||||
source_path = Path(inspect.getsourcefile(module) or "")
|
||||
tree = ast.parse(source_path.read_text())
|
||||
stack: list[ast.AST] = []
|
||||
if getattr(module, "__all__", None) == [] and ast.get_docstring(tree):
|
||||
violations.append(f"{source_path}:1:<module>")
|
||||
|
||||
class PrivateDocstringVisitor(ast.NodeVisitor):
|
||||
def visit_ClassDef(self, node: ast.ClassDef) -> None:
|
||||
self._check_docstring(node)
|
||||
stack.append(node)
|
||||
self.generic_visit(node)
|
||||
stack.pop()
|
||||
|
||||
def visit_FunctionDef(self, node: ast.FunctionDef) -> None:
|
||||
self._check_docstring(node)
|
||||
stack.append(node)
|
||||
self.generic_visit(node)
|
||||
stack.pop()
|
||||
|
||||
def visit_AsyncFunctionDef(self, node: ast.AsyncFunctionDef) -> None:
|
||||
self._check_docstring(node)
|
||||
stack.append(node)
|
||||
self.generic_visit(node)
|
||||
stack.pop()
|
||||
|
||||
def _check_docstring(
|
||||
self,
|
||||
node: ast.ClassDef | ast.FunctionDef | ast.AsyncFunctionDef,
|
||||
) -> None:
|
||||
is_dunder = node.name.startswith("__") and node.name.endswith("__")
|
||||
is_private_name = node.name.startswith("_") and not is_dunder
|
||||
is_nested_function = any(
|
||||
isinstance(parent, (ast.FunctionDef, ast.AsyncFunctionDef))
|
||||
for parent in stack
|
||||
)
|
||||
if (is_private_name or is_nested_function) and ast.get_docstring(node):
|
||||
violations.append(f"{source_path}:{node.lineno}:{node.name}")
|
||||
|
||||
PrivateDocstringVisitor().visit(tree)
|
||||
|
||||
assert violations == []
|
||||
|
||||
|
||||
def test_flow_definition_contract_is_dsl_agnostic():
|
||||
source_path = Path(inspect.getsourcefile(flow_definition) or "")
|
||||
source = source_path.read_text()
|
||||
|
||||
assert "DSL" not in source
|
||||
assert "flow_wrappers" not in source
|
||||
assert "build_flow_definition" not in source
|
||||
assert "extract_flow_definition" not in source
|
||||
|
||||
|
||||
def test_flow_definition_maps_dsl_to_static_contract():
|
||||
class ContractState(BaseModel):
|
||||
topic: str = ""
|
||||
|
||||
class ContractFlow(Flow[ContractState]):
|
||||
"""A flow with every core DSL role."""
|
||||
|
||||
initial_state = ContractState
|
||||
stream = True
|
||||
max_method_calls = 7
|
||||
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@listen(begin)
|
||||
def process(self):
|
||||
return "processed"
|
||||
|
||||
@router(process)
|
||||
def decide(self):
|
||||
return "approved"
|
||||
|
||||
@listen(or_("approved", "revise"))
|
||||
@human_feedback(
|
||||
message="Review this output.",
|
||||
emit=["done", "revise"],
|
||||
llm="gpt-4o-mini",
|
||||
default_outcome="done",
|
||||
metadata={"team": "qa"},
|
||||
learn=True,
|
||||
learn_source="hitl",
|
||||
learn_strict=True,
|
||||
)
|
||||
def review(self):
|
||||
return "review"
|
||||
|
||||
@listen(and_(begin, process))
|
||||
def audit(self):
|
||||
return "audit"
|
||||
|
||||
definition = ContractFlow.flow_definition()
|
||||
|
||||
assert definition.schema_ == "crewai.flow/v1"
|
||||
assert definition.name == "ContractFlow"
|
||||
assert definition.description == "A flow with every core DSL role."
|
||||
assert definition.state is not None
|
||||
assert definition.state.type == "pydantic"
|
||||
assert definition.state.ref and "ContractState" in definition.state.ref
|
||||
assert definition.config.stream is True
|
||||
assert definition.config.max_method_calls == 7
|
||||
|
||||
assert definition.methods["begin"].start is True
|
||||
assert definition.methods["process"].listen == "begin"
|
||||
|
||||
decide = definition.methods["decide"]
|
||||
assert decide.listen == "process"
|
||||
assert decide.router is True
|
||||
assert decide.emit is None
|
||||
|
||||
review = definition.methods["review"]
|
||||
assert review.listen == {"or": ["approved", "revise"]}
|
||||
assert review.router is True
|
||||
assert review.emit is None
|
||||
assert review.human_feedback is not None
|
||||
assert review.human_feedback.emit == ["done", "revise"]
|
||||
assert review.human_feedback.default_outcome == "done"
|
||||
assert review.human_feedback.metadata == {"team": "qa"}
|
||||
assert review.human_feedback.learn is True
|
||||
assert review.human_feedback.learn_strict is True
|
||||
|
||||
assert definition.methods["audit"].listen == {"and": ["begin", "process"]}
|
||||
assert definition.diagnostics == []
|
||||
|
||||
|
||||
def test_flow_definition_excludes_conversational_builtins_for_regular_flows():
|
||||
class RegularFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "begin"
|
||||
|
||||
methods = RegularFlow.flow_definition().methods
|
||||
|
||||
assert set(methods) == {"begin"}
|
||||
assert "conversation_start" not in methods
|
||||
assert "route_conversation" not in methods
|
||||
assert "converse_turn" not in methods
|
||||
|
||||
|
||||
def test_flow_definition_includes_conversational_builtins_when_enabled():
|
||||
class ChatFlow(Flow):
|
||||
conversational = True
|
||||
|
||||
methods = ChatFlow.flow_definition().methods
|
||||
|
||||
assert "conversation_start" in methods
|
||||
assert "route_conversation" in methods
|
||||
assert "converse_turn" in methods
|
||||
assert methods["conversation_start"].start is True
|
||||
|
||||
|
||||
def test_flow_definition_serializes_human_feedback_metadata():
|
||||
marker = object()
|
||||
|
||||
class MetadataFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@listen(begin)
|
||||
@human_feedback(message="Review this output.", metadata={"marker": marker})
|
||||
def review(self):
|
||||
return "review"
|
||||
|
||||
definition = MetadataFlow.flow_definition()
|
||||
review = definition.methods["review"]
|
||||
|
||||
assert review.human_feedback is not None
|
||||
assert review.human_feedback.metadata == {"ref": "builtins:dict"}
|
||||
assert any(
|
||||
diagnostic.code == "non_serializable_value"
|
||||
and diagnostic.path == "methods.review.human_feedback.metadata"
|
||||
for diagnostic in definition.diagnostics
|
||||
)
|
||||
definition.to_json()
|
||||
|
||||
|
||||
def test_flow_definition_fragments_cover_start_listen_and_condition_sugar():
|
||||
class FragmentFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "begin"
|
||||
|
||||
@start("restart_event")
|
||||
def restart(self):
|
||||
return "restart"
|
||||
|
||||
@listen(begin)
|
||||
def by_callable(self):
|
||||
return "callable"
|
||||
|
||||
@listen("manual_event")
|
||||
def by_string(self):
|
||||
return "string"
|
||||
|
||||
@listen(and_(begin, by_callable))
|
||||
def by_and(self):
|
||||
return "and"
|
||||
|
||||
@listen(or_(and_("manual_event", by_string), "fallback_event"))
|
||||
def nested(self):
|
||||
return "nested"
|
||||
|
||||
definition = FragmentFlow.flow_definition()
|
||||
|
||||
assert definition.methods["begin"].start is True
|
||||
assert definition.methods["restart"].start == "restart_event"
|
||||
assert definition.methods["by_callable"].listen == "begin"
|
||||
assert definition.methods["by_string"].listen == "manual_event"
|
||||
assert definition.methods["by_and"].listen == {"and": ["begin", "by_callable"]}
|
||||
assert definition.methods["nested"].listen == {
|
||||
"or": [{"and": ["manual_event", "by_string"]}, "fallback_event"]
|
||||
}
|
||||
|
||||
assert set(FragmentFlow._start_methods) == {"begin", "restart"}
|
||||
assert FragmentFlow._listeners["restart"] == ("OR", ["restart_event"])
|
||||
assert FragmentFlow._listeners["by_callable"] == ("OR", ["begin"])
|
||||
assert FragmentFlow._listeners["by_string"] == ("OR", ["manual_event"])
|
||||
assert FragmentFlow._listeners["by_and"] == {
|
||||
"type": "AND",
|
||||
"conditions": ["begin", "by_callable"],
|
||||
}
|
||||
assert FragmentFlow._listeners["nested"] == {
|
||||
"type": "OR",
|
||||
"conditions": [
|
||||
{"type": "AND", "conditions": ["manual_event", "by_string"]},
|
||||
"fallback_event",
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def test_extract_flow_definition_prefers_fragments_over_legacy_metadata():
|
||||
class RegistryFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "begin"
|
||||
|
||||
@listen(begin)
|
||||
def handle(self):
|
||||
return "handle"
|
||||
|
||||
@router(handle, emit=["done"])
|
||||
def decide(self):
|
||||
return "done"
|
||||
|
||||
handle = RegistryFlow.__dict__["handle"]
|
||||
original_trigger_methods = handle.__trigger_methods__
|
||||
handle.__trigger_methods__ = ["wrong"]
|
||||
try:
|
||||
_, listeners, routers, router_emit = flow_dsl.extract_flow_definition(
|
||||
{
|
||||
"begin": RegistryFlow.__dict__["begin"],
|
||||
"handle": handle,
|
||||
"decide": RegistryFlow.__dict__["decide"],
|
||||
}
|
||||
)
|
||||
finally:
|
||||
handle.__trigger_methods__ = original_trigger_methods
|
||||
|
||||
assert listeners["handle"] == ("OR", ["begin"])
|
||||
assert listeners["decide"] == ("OR", ["handle"])
|
||||
assert routers == {"decide"}
|
||||
assert router_emit == {"decide": ["done"]}
|
||||
|
||||
|
||||
def test_flow_definition_falls_back_to_legacy_metadata_without_fragment():
|
||||
class LegacyMetadataFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "begin"
|
||||
|
||||
@router(begin, emit=["left"])
|
||||
def decide(self):
|
||||
return "left"
|
||||
|
||||
@listen("left")
|
||||
def left(self):
|
||||
return "left"
|
||||
|
||||
for method_name in ("begin", "decide", "left"):
|
||||
method = LegacyMetadataFlow.__dict__[method_name]
|
||||
delattr(method, "__flow_method_definition__")
|
||||
|
||||
definition = flow_dsl.build_flow_definition(LegacyMetadataFlow)
|
||||
|
||||
assert definition.methods["begin"].start is True
|
||||
assert definition.methods["decide"].listen == "begin"
|
||||
assert definition.methods["decide"].router is True
|
||||
assert definition.methods["decide"].emit == ["left"]
|
||||
assert definition.methods["left"].listen == "left"
|
||||
|
||||
|
||||
def test_human_feedback_emit_overrides_inner_router_emit():
|
||||
class FeedbackOverRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "data"
|
||||
|
||||
@human_feedback(
|
||||
message="Review:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
@router(begin, emit=["x", "y"])
|
||||
def route(self):
|
||||
return "approved"
|
||||
|
||||
@listen("approved")
|
||||
def proceed(self):
|
||||
return "ok"
|
||||
|
||||
assert "route" in FeedbackOverRouterFlow._routers
|
||||
assert FeedbackOverRouterFlow._router_emit["route"] == ["approved", "rejected"]
|
||||
|
||||
route = FeedbackOverRouterFlow.flow_definition().methods["route"]
|
||||
assert route.router is True
|
||||
assert route.human_feedback is not None
|
||||
assert route.human_feedback.emit == ["approved", "rejected"]
|
||||
assert route.emit is None
|
||||
|
||||
|
||||
def test_flow_definition_classifies_start_router_from_human_feedback_emit():
|
||||
class StartRouterFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review:",
|
||||
emit=["continue", "stop"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def entry_point(self):
|
||||
return "data"
|
||||
|
||||
@listen("continue")
|
||||
def proceed(self):
|
||||
return "proceeding"
|
||||
|
||||
@listen("stop")
|
||||
def halt(self):
|
||||
return "halted"
|
||||
|
||||
definition = StartRouterFlow.flow_definition()
|
||||
entry_point = definition.methods["entry_point"]
|
||||
|
||||
assert entry_point.is_start is True
|
||||
assert entry_point.router is True
|
||||
assert entry_point.human_feedback is not None
|
||||
assert entry_point.human_feedback.emit == ["continue", "stop"]
|
||||
assert entry_point.emit is None
|
||||
|
||||
|
||||
def test_flow_definition_round_trips_json_and_yaml():
|
||||
class RoundTripFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self):
|
||||
return "left"
|
||||
|
||||
@listen("left")
|
||||
def left(self):
|
||||
return "left"
|
||||
|
||||
definition = RoundTripFlow.flow_definition()
|
||||
|
||||
json_round_trip = flow_definition.FlowDefinition.from_json(definition.to_json())
|
||||
yaml_round_trip = flow_definition.FlowDefinition.from_yaml(definition.to_yaml())
|
||||
|
||||
assert json_round_trip.to_dict() == definition.to_dict()
|
||||
assert yaml_round_trip.to_dict() == definition.to_dict()
|
||||
assert yaml_round_trip.methods["decide"].router is True
|
||||
assert yaml_round_trip.methods["decide"].listen == "begin"
|
||||
|
||||
|
||||
def test_flow_definition_detects_persist_metadata():
|
||||
@persist(verbose=True)
|
||||
class PersistedFlow(Flow[dict]):
|
||||
initial_state = {}
|
||||
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@persist(verbose=False)
|
||||
@listen(begin)
|
||||
def checkpoint(self):
|
||||
return "saved"
|
||||
|
||||
definition = PersistedFlow.flow_definition()
|
||||
|
||||
assert definition.persist is not None
|
||||
assert definition.persist.enabled is True
|
||||
assert definition.persist.verbose is True
|
||||
|
||||
assert definition.methods["begin"].persist is None
|
||||
|
||||
method_persist = definition.methods["checkpoint"].persist
|
||||
assert method_persist is not None
|
||||
assert method_persist.enabled is True
|
||||
assert method_persist.verbose is False
|
||||
|
||||
|
||||
def test_flow_definition_allows_dynamic_router_emit():
|
||||
class DynamicRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self):
|
||||
return self.state["dynamic_event"]
|
||||
|
||||
definition = DynamicRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit is None
|
||||
assert definition.diagnostics == []
|
||||
|
||||
|
||||
def test_flow_definition_infers_literal_router_emit():
|
||||
class LiteralRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self) -> Literal["left", "right"]:
|
||||
return "left"
|
||||
|
||||
@listen("left")
|
||||
def left(self):
|
||||
return "left"
|
||||
|
||||
@listen("right")
|
||||
def right(self):
|
||||
return "right"
|
||||
|
||||
definition = LiteralRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit == ["left", "right"]
|
||||
|
||||
|
||||
def test_flow_definition_infers_enum_router_emit():
|
||||
class Decision(str, Enum):
|
||||
APPROVE = "approve"
|
||||
REJECT = "reject"
|
||||
|
||||
class EnumRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self) -> Decision:
|
||||
return Decision.APPROVE
|
||||
|
||||
@listen("approve")
|
||||
def approve(self):
|
||||
return "approve"
|
||||
|
||||
@listen("reject")
|
||||
def reject(self):
|
||||
return "reject"
|
||||
|
||||
definition = EnumRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit == ["approve", "reject"]
|
||||
|
||||
|
||||
def test_flow_definition_infers_literal_union_router_emit():
|
||||
class LiteralUnionRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self) -> Literal["left"] | Literal["right"]:
|
||||
return "left"
|
||||
|
||||
@listen("left")
|
||||
def left(self):
|
||||
return "left"
|
||||
|
||||
@listen("right")
|
||||
def right(self):
|
||||
return "right"
|
||||
|
||||
definition = LiteralUnionRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit == ["left", "right"]
|
||||
|
||||
|
||||
def test_flow_definition_infers_annotated_literal_router_emit():
|
||||
class AnnotatedRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self) -> Annotated[Literal["left"] | None, "route"]:
|
||||
return "left"
|
||||
|
||||
definition = AnnotatedRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit == ["left"]
|
||||
|
||||
|
||||
def test_flow_definition_does_not_infer_container_literal_router_emit():
|
||||
class ContainerLiteralRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def list_route(self) -> list[Literal["left"]]:
|
||||
return ["left"]
|
||||
|
||||
@router(begin)
|
||||
def dict_route(self) -> dict[str, Literal["right"]]:
|
||||
return {"route": "right"}
|
||||
|
||||
definition = ContainerLiteralRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["list_route"].emit is None
|
||||
assert definition.methods["dict_route"].emit is None
|
||||
|
||||
|
||||
def test_flow_definition_does_not_infer_unannotated_router_body_emit():
|
||||
class UnannotatedRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self):
|
||||
return "left"
|
||||
|
||||
@listen("left")
|
||||
def left(self):
|
||||
return "left"
|
||||
|
||||
definition = UnannotatedRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit is None
|
||||
|
||||
|
||||
def test_flow_definition_accepts_explicit_router_events():
|
||||
class ExplicitRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin, emit=["left", "right", "left"])
|
||||
def decide(self):
|
||||
return self.state["dynamic_event"]
|
||||
|
||||
@listen("left")
|
||||
def left(self):
|
||||
return "left"
|
||||
|
||||
@listen("right")
|
||||
def right(self):
|
||||
return "right"
|
||||
|
||||
definition = ExplicitRouterFlow.flow_definition()
|
||||
|
||||
assert definition.methods["decide"].emit == ["left", "right"]
|
||||
|
||||
|
||||
def test_flow_definition_preserves_diagnostics_loaded_from_contract():
|
||||
definition = flow_definition.FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": "LoadedDiagnosticsFlow",
|
||||
"methods": {
|
||||
"decision": {
|
||||
"router": True,
|
||||
"emit": ["continue"],
|
||||
}
|
||||
},
|
||||
"diagnostics": [
|
||||
{
|
||||
"code": "serialized_warning",
|
||||
"message": "Preserved serialized diagnostic",
|
||||
"severity": "warning",
|
||||
"path": "methods.decision",
|
||||
},
|
||||
{
|
||||
"code": "router_without_trigger",
|
||||
"message": "router: true requires either start or listen",
|
||||
"severity": "error",
|
||||
"path": "methods.decision",
|
||||
},
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
codes = [diagnostic.code for diagnostic in definition.diagnostics]
|
||||
assert "serialized_warning" in codes
|
||||
assert codes.count("router_without_trigger") == 1
|
||||
|
||||
|
||||
def test_router_start_false_without_listen_reports_missing_trigger():
|
||||
definition = flow_definition.FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": "LoadedFlow",
|
||||
"methods": {
|
||||
"decision": {
|
||||
"router": True,
|
||||
"start": False,
|
||||
"emit": ["continue"],
|
||||
}
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
assert any(
|
||||
diagnostic.code == "router_without_trigger"
|
||||
and diagnostic.path == "methods.decision"
|
||||
for diagnostic in definition.diagnostics
|
||||
)
|
||||
|
||||
|
||||
def test_router_human_feedback_preserves_existing_router_metadata():
|
||||
class RouterHumanFeedbackFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@human_feedback(message="Review route:")
|
||||
@router(begin, emit=["approved", "rejected"])
|
||||
def decide(self):
|
||||
return "approved"
|
||||
|
||||
@listen("approved")
|
||||
def approved(self):
|
||||
return "approved"
|
||||
|
||||
definition = RouterHumanFeedbackFlow.flow_definition()
|
||||
method = definition.methods["decide"]
|
||||
|
||||
assert method.router is True
|
||||
assert method.listen == "begin"
|
||||
assert method.emit == ["approved", "rejected"]
|
||||
assert method.human_feedback is not None
|
||||
|
||||
|
||||
def test_dynamic_router_flow_definition_has_no_diagnostics():
|
||||
class LazyDynamicRouterFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self):
|
||||
return self.state["dynamic_event"]
|
||||
|
||||
definition = LazyDynamicRouterFlow.flow_definition()
|
||||
assert definition.diagnostics == []
|
||||
|
||||
|
||||
def test_dynamic_router_string_listener_is_valid_contract():
|
||||
class DynamicRouterListenerFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def decide(self):
|
||||
return self.state["dynamic_event"]
|
||||
|
||||
@listen("dynamic_event")
|
||||
def handle(self):
|
||||
return "handled"
|
||||
|
||||
definition = DynamicRouterListenerFlow.flow_definition()
|
||||
|
||||
assert definition.diagnostics == []
|
||||
|
||||
|
||||
def test_static_string_listener_is_allowed_by_contract():
|
||||
definition = flow_definition.FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": "TypoFlow",
|
||||
"methods": {
|
||||
"begin": {"start": True},
|
||||
"handle": {"listen": "begni"},
|
||||
},
|
||||
}
|
||||
)
|
||||
assert definition.diagnostics == []
|
||||
|
||||
|
||||
def test_start_false_not_classified_as_start_method():
|
||||
definition = flow_definition.FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": "ExplicitNonStartFlow",
|
||||
"methods": {
|
||||
"begin": {"start": True},
|
||||
"handle": {"start": False, "listen": "begin"},
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
assert definition.methods["begin"].is_start is True
|
||||
assert definition.methods["handle"].is_start is False
|
||||
|
||||
class ExplicitNonStartFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@listen(begin)
|
||||
def handle(self):
|
||||
return "handled"
|
||||
|
||||
# Attach the loaded contract (with explicit ``start: false``) so the
|
||||
# projections read from it rather than rebuilding from the DSL.
|
||||
ExplicitNonStartFlow._flow_definition = definition
|
||||
|
||||
flow = ExplicitNonStartFlow()
|
||||
viz_structure = visualization_builder.build_flow_structure(flow)
|
||||
assert "handle" not in viz_structure["start_methods"]
|
||||
assert viz_structure["nodes"]["handle"]["type"] != "start"
|
||||
|
||||
|
||||
def test_flow_definition_cache_is_not_inherited_by_subclasses():
|
||||
class ParentFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "begin"
|
||||
|
||||
parent_definition = ParentFlow.flow_definition()
|
||||
|
||||
class ChildFlow(ParentFlow):
|
||||
@listen(ParentFlow.begin)
|
||||
def child_step(self):
|
||||
return "child"
|
||||
|
||||
child_definition = ChildFlow.flow_definition()
|
||||
|
||||
assert parent_definition.name == "ParentFlow"
|
||||
assert child_definition.name == "ChildFlow"
|
||||
assert child_definition is not parent_definition
|
||||
assert set(child_definition.methods) == {"begin", "child_step"}
|
||||
|
||||
|
||||
def test_flow_definition_logs_diagnostics_when_loaded_from_contract(caplog):
|
||||
caplog.set_level(logging.WARNING, logger="crewai.flow.flow_definition")
|
||||
|
||||
definition = flow_definition.FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": "LoadedFlow",
|
||||
"methods": {
|
||||
"decision": {
|
||||
"router": True,
|
||||
"emit": ["continue"],
|
||||
}
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
assert any(
|
||||
diagnostic.code == "router_without_trigger"
|
||||
for diagnostic in definition.diagnostics
|
||||
)
|
||||
assert any(
|
||||
record.levelno == logging.ERROR
|
||||
and "LoadedFlow" in record.message
|
||||
and "router_without_trigger" in record.message
|
||||
for record in caplog.records
|
||||
)
|
||||
818
lib/crewai/tests/test_flow_serializer.py
Normal file
818
lib/crewai/tests/test_flow_serializer.py
Normal file
@@ -0,0 +1,818 @@
|
||||
"""Tests for flow_serializer.py - Flow structure serialization for Studio UI."""
|
||||
|
||||
from typing import Literal
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.flow.flow import Flow, and_, listen, or_, router, start
|
||||
from crewai.flow.flow_serializer import flow_structure
|
||||
from crewai.flow.human_feedback import human_feedback
|
||||
|
||||
|
||||
class TestSimpleLinearFlow:
|
||||
"""Test simple linear flow (start → listen → listen)."""
|
||||
|
||||
def test_linear_flow_structure(self):
|
||||
"""Test a simple sequential flow structure."""
|
||||
|
||||
class LinearFlow(Flow):
|
||||
"""A simple linear flow for testing."""
|
||||
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@listen(begin)
|
||||
def process(self):
|
||||
return "processed"
|
||||
|
||||
@listen(process)
|
||||
def finalize(self):
|
||||
return "done"
|
||||
|
||||
structure = flow_structure(LinearFlow)
|
||||
|
||||
assert structure["name"] == "LinearFlow"
|
||||
assert structure["description"] == "A simple linear flow for testing."
|
||||
assert len(structure["methods"]) == 3
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["begin"]["type"] == "start"
|
||||
assert method_map["process"]["type"] == "listen"
|
||||
assert method_map["finalize"]["type"] == "listen"
|
||||
|
||||
assert len(structure["edges"]) == 2
|
||||
|
||||
edge_pairs = [(e["from_method"], e["to_method"]) for e in structure["edges"]]
|
||||
assert ("begin", "process") in edge_pairs
|
||||
assert ("process", "finalize") in edge_pairs
|
||||
|
||||
for edge in structure["edges"]:
|
||||
assert edge["edge_type"] == "listen"
|
||||
assert edge["condition"] is None
|
||||
|
||||
|
||||
class TestRouterFlow:
|
||||
"""Test flow with router branching."""
|
||||
|
||||
def test_router_flow_structure(self):
|
||||
"""Test a flow with router that branches to different paths."""
|
||||
|
||||
class BranchingFlow(Flow):
|
||||
@start()
|
||||
def init(self):
|
||||
return "initialized"
|
||||
|
||||
@router(init)
|
||||
def decide(self) -> Literal["path_a", "path_b"]:
|
||||
return "path_a"
|
||||
|
||||
@listen("path_a")
|
||||
def handle_a(self):
|
||||
return "handled_a"
|
||||
|
||||
@listen("path_b")
|
||||
def handle_b(self):
|
||||
return "handled_b"
|
||||
|
||||
structure = flow_structure(BranchingFlow)
|
||||
|
||||
assert structure["name"] == "BranchingFlow"
|
||||
assert len(structure["methods"]) == 4
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["init"]["type"] == "start"
|
||||
assert method_map["decide"]["type"] == "router"
|
||||
assert method_map["handle_a"]["type"] == "listen"
|
||||
assert method_map["handle_b"]["type"] == "listen"
|
||||
|
||||
assert "path_a" in method_map["decide"]["router_paths"]
|
||||
assert "path_b" in method_map["decide"]["router_paths"]
|
||||
|
||||
# Should have: init -> decide (listen), decide -> handle_a (route), decide -> handle_b (route)
|
||||
listen_edges = [e for e in structure["edges"] if e["edge_type"] == "listen"]
|
||||
route_edges = [e for e in structure["edges"] if e["edge_type"] == "route"]
|
||||
|
||||
assert len(listen_edges) == 1
|
||||
assert listen_edges[0]["from_method"] == "init"
|
||||
assert listen_edges[0]["to_method"] == "decide"
|
||||
|
||||
assert len(route_edges) == 2
|
||||
route_targets = {e["to_method"] for e in route_edges}
|
||||
assert "handle_a" in route_targets
|
||||
assert "handle_b" in route_targets
|
||||
|
||||
route_conditions = {e["to_method"]: e["condition"] for e in route_edges}
|
||||
assert route_conditions["handle_a"] == "path_a"
|
||||
assert route_conditions["handle_b"] == "path_b"
|
||||
|
||||
|
||||
class TestAndOrConditions:
|
||||
"""Test flow with AND/OR conditions."""
|
||||
|
||||
def test_and_condition_flow(self):
|
||||
"""Test a flow where a method waits for multiple methods (AND)."""
|
||||
|
||||
class AndConditionFlow(Flow):
|
||||
@start()
|
||||
def step_a(self):
|
||||
return "a"
|
||||
|
||||
@start()
|
||||
def step_b(self):
|
||||
return "b"
|
||||
|
||||
@listen(and_(step_a, step_b))
|
||||
def converge(self):
|
||||
return "converged"
|
||||
|
||||
structure = flow_structure(AndConditionFlow)
|
||||
|
||||
assert len(structure["methods"]) == 3
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["step_a"]["type"] == "start"
|
||||
assert method_map["step_b"]["type"] == "start"
|
||||
assert method_map["converge"]["type"] == "listen"
|
||||
|
||||
assert method_map["converge"]["condition_type"] == "AND"
|
||||
|
||||
triggers = method_map["converge"]["trigger_methods"]
|
||||
assert "step_a" in triggers
|
||||
assert "step_b" in triggers
|
||||
|
||||
converge_edges = [e for e in structure["edges"] if e["to_method"] == "converge"]
|
||||
assert len(converge_edges) == 2
|
||||
|
||||
def test_or_condition_flow(self):
|
||||
"""Test a flow where a method is triggered by any of multiple methods (OR)."""
|
||||
|
||||
class OrConditionFlow(Flow):
|
||||
@start()
|
||||
def path_1(self):
|
||||
return "1"
|
||||
|
||||
@start()
|
||||
def path_2(self):
|
||||
return "2"
|
||||
|
||||
@listen(or_(path_1, path_2))
|
||||
def handle_any(self):
|
||||
return "handled"
|
||||
|
||||
structure = flow_structure(OrConditionFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["handle_any"]["condition_type"] == "OR"
|
||||
|
||||
triggers = method_map["handle_any"]["trigger_methods"]
|
||||
assert "path_1" in triggers
|
||||
assert "path_2" in triggers
|
||||
|
||||
|
||||
class TestHumanFeedbackMethods:
|
||||
"""Test flow with @human_feedback decorated methods."""
|
||||
|
||||
def test_human_feedback_detection(self):
|
||||
"""Test that human feedback methods are correctly identified."""
|
||||
|
||||
class HumanFeedbackFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Please review:",
|
||||
emit=["approved", "rejected"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def review_step(self):
|
||||
return "content to review"
|
||||
|
||||
@listen("approved")
|
||||
def handle_approved(self):
|
||||
return "approved"
|
||||
|
||||
@listen("rejected")
|
||||
def handle_rejected(self):
|
||||
return "rejected"
|
||||
|
||||
structure = flow_structure(HumanFeedbackFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
# review_step should have human feedback
|
||||
assert method_map["review_step"]["has_human_feedback"] is True
|
||||
# It's a start+router (due to emit)
|
||||
assert method_map["review_step"]["type"] == "start_router"
|
||||
assert "approved" in method_map["review_step"]["router_paths"]
|
||||
assert "rejected" in method_map["review_step"]["router_paths"]
|
||||
|
||||
# Other methods should not have human feedback
|
||||
assert method_map["handle_approved"]["has_human_feedback"] is False
|
||||
assert method_map["handle_rejected"]["has_human_feedback"] is False
|
||||
|
||||
def test_listen_plus_human_feedback_router_edges(self):
|
||||
"""Test that @listen + @human_feedback(emit=...) generates router edges.
|
||||
|
||||
This is the pattern used in the whitepaper generator:
|
||||
a listener method that also acts as a router via @human_feedback(emit=[...]).
|
||||
The serializer must generate edges from this method to listeners of its emit paths.
|
||||
"""
|
||||
|
||||
class ReviewFlow(Flow):
|
||||
@start()
|
||||
def generate(self):
|
||||
return "content"
|
||||
|
||||
@listen(generate)
|
||||
@human_feedback(
|
||||
message="Review this:",
|
||||
emit=["approved", "needs_changes", "cancelled"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def review(self):
|
||||
return "review result"
|
||||
|
||||
@listen("approved")
|
||||
def handle_approved(self):
|
||||
return "done"
|
||||
|
||||
@listen("needs_changes")
|
||||
def handle_changes(self):
|
||||
return "regenerating"
|
||||
|
||||
@listen("cancelled")
|
||||
def handle_cancelled(self):
|
||||
return "cancelled"
|
||||
|
||||
structure = flow_structure(ReviewFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
edge_set = {(e["from_method"], e["to_method"], e.get("condition")) for e in structure["edges"]}
|
||||
|
||||
# review should be detected as a router with the emit paths
|
||||
assert method_map["review"]["type"] == "router"
|
||||
assert set(method_map["review"]["router_paths"]) == {"approved", "needs_changes", "cancelled"}
|
||||
assert method_map["review"]["has_human_feedback"] is True
|
||||
|
||||
assert ("generate", "review", None) in edge_set
|
||||
|
||||
assert ("review", "handle_approved", "approved") in edge_set
|
||||
assert ("review", "handle_changes", "needs_changes") in edge_set
|
||||
assert ("review", "handle_cancelled", "cancelled") in edge_set
|
||||
|
||||
|
||||
class TestCrewReferences:
|
||||
"""Test detection of Crew references in method bodies."""
|
||||
|
||||
def test_crew_detection_with_crew_call(self):
|
||||
"""Test that .crew() calls are detected."""
|
||||
|
||||
class FlowWithCrew(Flow):
|
||||
@start()
|
||||
def run_crew(self):
|
||||
return "result"
|
||||
|
||||
@listen(run_crew)
|
||||
def no_crew(self):
|
||||
return "done"
|
||||
|
||||
structure = flow_structure(FlowWithCrew)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
# Note: Since the actual .crew() call is in a comment/string,
|
||||
# We're testing the mechanism exists.
|
||||
assert "has_crew" in method_map["run_crew"]
|
||||
assert "has_crew" in method_map["no_crew"]
|
||||
|
||||
def test_no_crew_when_absent(self):
|
||||
"""Test that methods without Crew refs return has_crew=False."""
|
||||
|
||||
class SimpleNonCrewFlow(Flow):
|
||||
@start()
|
||||
def calculate(self):
|
||||
return 1 + 1
|
||||
|
||||
@listen(calculate)
|
||||
def display(self):
|
||||
return "result"
|
||||
|
||||
structure = flow_structure(SimpleNonCrewFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["calculate"]["has_crew"] is False
|
||||
assert method_map["display"]["has_crew"] is False
|
||||
|
||||
|
||||
class TestTypedStateSchema:
|
||||
"""Test flow with typed Pydantic state."""
|
||||
|
||||
def test_pydantic_state_schema_extraction(self):
|
||||
"""Test extracting state schema from a Flow with Pydantic state."""
|
||||
|
||||
class MyState(BaseModel):
|
||||
counter: int = 0
|
||||
message: str = ""
|
||||
items: list[str] = Field(default_factory=list)
|
||||
|
||||
class TypedStateFlow(Flow[MyState]):
|
||||
initial_state = MyState
|
||||
|
||||
@start()
|
||||
def increment(self):
|
||||
self.state.counter += 1
|
||||
return self.state.counter
|
||||
|
||||
@listen(increment)
|
||||
def display(self):
|
||||
return f"Count: {self.state.counter}"
|
||||
|
||||
structure = flow_structure(TypedStateFlow)
|
||||
|
||||
assert structure["state_schema"] is not None
|
||||
fields = structure["state_schema"]["fields"]
|
||||
|
||||
field_names = {f["name"] for f in fields}
|
||||
assert "counter" in field_names
|
||||
assert "message" in field_names
|
||||
assert "items" in field_names
|
||||
|
||||
field_map = {f["name"]: f for f in fields}
|
||||
assert "int" in field_map["counter"]["type"]
|
||||
assert "str" in field_map["message"]["type"]
|
||||
|
||||
assert field_map["counter"]["default"] == 0
|
||||
assert field_map["message"]["default"] == ""
|
||||
|
||||
def test_dict_state_returns_none(self):
|
||||
"""Test that flows using dict state return None for state_schema."""
|
||||
|
||||
class DictStateFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
self.state["count"] = 1
|
||||
return "started"
|
||||
|
||||
structure = flow_structure(DictStateFlow)
|
||||
|
||||
assert structure["state_schema"] is None
|
||||
|
||||
|
||||
class TestEdgeCases:
|
||||
"""Test edge cases and special scenarios."""
|
||||
|
||||
def test_start_router_combo(self):
|
||||
"""Test a method that is both @start and a router (via human_feedback emit)."""
|
||||
|
||||
class StartRouterFlow(Flow):
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review:",
|
||||
emit=["continue", "stop"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def entry_point(self):
|
||||
return "data"
|
||||
|
||||
@listen("continue")
|
||||
def proceed(self):
|
||||
return "proceeding"
|
||||
|
||||
@listen("stop")
|
||||
def halt(self):
|
||||
return "halted"
|
||||
|
||||
structure = flow_structure(StartRouterFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["entry_point"]["type"] == "start_router"
|
||||
assert method_map["entry_point"]["has_human_feedback"] is True
|
||||
assert "continue" in method_map["entry_point"]["router_paths"]
|
||||
assert "stop" in method_map["entry_point"]["router_paths"]
|
||||
|
||||
def test_multiple_start_methods(self):
|
||||
"""Test a flow with multiple start methods."""
|
||||
|
||||
class MultiStartFlow(Flow):
|
||||
@start()
|
||||
def start_a(self):
|
||||
return "a"
|
||||
|
||||
@start()
|
||||
def start_b(self):
|
||||
return "b"
|
||||
|
||||
@listen(and_(start_a, start_b))
|
||||
def combine(self):
|
||||
return "combined"
|
||||
|
||||
structure = flow_structure(MultiStartFlow)
|
||||
|
||||
start_methods = [m for m in structure["methods"] if m["type"] == "start"]
|
||||
assert len(start_methods) == 2
|
||||
|
||||
start_names = {m["name"] for m in start_methods}
|
||||
assert "start_a" in start_names
|
||||
assert "start_b" in start_names
|
||||
|
||||
def test_orphan_methods(self):
|
||||
"""Test that orphan methods (not connected to flow) are still captured."""
|
||||
|
||||
class FlowWithOrphan(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
@listen(begin)
|
||||
def connected(self):
|
||||
return "connected"
|
||||
|
||||
@listen("never_triggered")
|
||||
def orphan(self):
|
||||
return "orphan"
|
||||
|
||||
structure = flow_structure(FlowWithOrphan)
|
||||
|
||||
method_names = {m["name"] for m in structure["methods"]}
|
||||
assert "orphan" in method_names
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
assert method_map["orphan"]["trigger_methods"] == ["never_triggered"]
|
||||
|
||||
def test_empty_flow(self):
|
||||
"""Test building structure for a flow with no methods."""
|
||||
|
||||
class EmptyFlow(Flow):
|
||||
pass
|
||||
|
||||
structure = flow_structure(EmptyFlow)
|
||||
|
||||
assert structure["name"] == "EmptyFlow"
|
||||
assert structure["methods"] == []
|
||||
assert structure["edges"] == []
|
||||
assert structure["state_schema"] is None
|
||||
|
||||
def test_flow_with_docstring(self):
|
||||
"""Test that flow docstring is captured."""
|
||||
|
||||
class DocumentedFlow(Flow):
|
||||
"""This is a well-documented flow.
|
||||
|
||||
It has multiple lines of documentation.
|
||||
"""
|
||||
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
structure = flow_structure(DocumentedFlow)
|
||||
|
||||
assert structure["description"] is not None
|
||||
assert "well-documented flow" in structure["description"]
|
||||
|
||||
def test_flow_without_docstring(self):
|
||||
"""Test that missing docstring returns None."""
|
||||
|
||||
class UndocumentedFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
structure = flow_structure(UndocumentedFlow)
|
||||
|
||||
assert structure["description"] is None
|
||||
|
||||
def test_nested_conditions(self):
|
||||
"""Test flow with nested AND/OR conditions."""
|
||||
|
||||
class NestedConditionFlow(Flow):
|
||||
@start()
|
||||
def a(self):
|
||||
return "a"
|
||||
|
||||
@start()
|
||||
def b(self):
|
||||
return "b"
|
||||
|
||||
@start()
|
||||
def c(self):
|
||||
return "c"
|
||||
|
||||
@listen(or_(and_(a, b), c))
|
||||
def complex_trigger(self):
|
||||
return "triggered"
|
||||
|
||||
structure = flow_structure(NestedConditionFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
triggers = method_map["complex_trigger"]["trigger_methods"]
|
||||
assert len(triggers) == 3
|
||||
assert "a" in triggers
|
||||
assert "b" in triggers
|
||||
assert "c" in triggers
|
||||
|
||||
|
||||
class TestErrorHandling:
|
||||
"""Test error handling and validation."""
|
||||
|
||||
def test_instance_raises_type_error(self):
|
||||
"""Test that passing an instance raises TypeError."""
|
||||
|
||||
class TestFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
flow_instance = TestFlow()
|
||||
|
||||
with pytest.raises(TypeError) as exc_info:
|
||||
flow_structure(flow_instance)
|
||||
|
||||
assert "requires a Flow class, not an instance" in str(exc_info.value)
|
||||
|
||||
def test_non_class_raises_type_error(self):
|
||||
"""Test that passing non-class raises TypeError."""
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
flow_structure("not a class")
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
flow_structure(123)
|
||||
|
||||
|
||||
class TestEdgeGeneration:
|
||||
"""Test edge generation in various scenarios."""
|
||||
|
||||
def test_all_edges_generated_correctly(self):
|
||||
"""Verify all edges are correctly generated for a complex flow."""
|
||||
|
||||
class ComplexFlow(Flow):
|
||||
@start()
|
||||
def entry(self):
|
||||
return "started"
|
||||
|
||||
@listen(entry)
|
||||
def step_1(self):
|
||||
return "step_1"
|
||||
|
||||
@router(step_1)
|
||||
def branch(self) -> Literal["left", "right"]:
|
||||
return "left"
|
||||
|
||||
@listen("left")
|
||||
def left_path(self):
|
||||
return "left_done"
|
||||
|
||||
@listen("right")
|
||||
def right_path(self):
|
||||
return "right_done"
|
||||
|
||||
@listen(or_(left_path, right_path))
|
||||
def converge(self):
|
||||
return "done"
|
||||
|
||||
structure = flow_structure(ComplexFlow)
|
||||
|
||||
edges = structure["edges"]
|
||||
|
||||
listen_edges = [(e["from_method"], e["to_method"]) for e in edges if e["edge_type"] == "listen"]
|
||||
|
||||
assert ("entry", "step_1") in listen_edges
|
||||
assert ("step_1", "branch") in listen_edges
|
||||
assert ("left_path", "converge") in listen_edges
|
||||
assert ("right_path", "converge") in listen_edges
|
||||
|
||||
route_edges = [(e["from_method"], e["to_method"], e["condition"]) for e in edges if e["edge_type"] == "route"]
|
||||
|
||||
assert ("branch", "left_path", "left") in route_edges
|
||||
assert ("branch", "right_path", "right") in route_edges
|
||||
|
||||
def test_router_edge_conditions(self):
|
||||
"""Test that router edge conditions are properly set."""
|
||||
|
||||
class RouterConditionFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "start"
|
||||
|
||||
@router(begin)
|
||||
def route(self) -> Literal["option_1", "option_2", "option_3"]:
|
||||
return "option_1"
|
||||
|
||||
@listen("option_1")
|
||||
def handle_1(self):
|
||||
return "1"
|
||||
|
||||
@listen("option_2")
|
||||
def handle_2(self):
|
||||
return "2"
|
||||
|
||||
@listen("option_3")
|
||||
def handle_3(self):
|
||||
return "3"
|
||||
|
||||
structure = flow_structure(RouterConditionFlow)
|
||||
|
||||
route_edges = [e for e in structure["edges"] if e["edge_type"] == "route"]
|
||||
|
||||
assert len(route_edges) == 3
|
||||
|
||||
conditions = {e["to_method"]: e["condition"] for e in route_edges}
|
||||
assert conditions["handle_1"] == "option_1"
|
||||
assert conditions["handle_2"] == "option_2"
|
||||
assert conditions["handle_3"] == "option_3"
|
||||
|
||||
|
||||
class TestMethodTypeClassification:
|
||||
"""Test method type classification."""
|
||||
|
||||
def test_all_method_types(self):
|
||||
"""Test classification of all method types."""
|
||||
|
||||
class AllTypesFlow(Flow):
|
||||
@start()
|
||||
def start_only(self):
|
||||
return "start"
|
||||
|
||||
@listen(start_only)
|
||||
def listen_only(self):
|
||||
return "listen"
|
||||
|
||||
@router(listen_only)
|
||||
def router_only(self) -> Literal["path"]:
|
||||
return "path"
|
||||
|
||||
@listen("path")
|
||||
def after_router(self):
|
||||
return "after"
|
||||
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review",
|
||||
emit=["yes", "no"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def start_and_router(self):
|
||||
return "data"
|
||||
|
||||
structure = flow_structure(AllTypesFlow)
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
|
||||
assert method_map["start_only"]["type"] == "start"
|
||||
assert method_map["listen_only"]["type"] == "listen"
|
||||
assert method_map["router_only"]["type"] == "router"
|
||||
assert method_map["after_router"]["type"] == "listen"
|
||||
assert method_map["start_and_router"]["type"] == "start_router"
|
||||
|
||||
|
||||
class TestInputDetection:
|
||||
"""Test flow input detection."""
|
||||
|
||||
def test_inputs_list_exists(self):
|
||||
"""Test that inputs list is always present."""
|
||||
|
||||
class SimpleFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "started"
|
||||
|
||||
structure = flow_structure(SimpleFlow)
|
||||
|
||||
assert "inputs" in structure
|
||||
assert isinstance(structure["inputs"], list)
|
||||
|
||||
|
||||
class TestJsonSerializable:
|
||||
"""Test that output is JSON serializable."""
|
||||
|
||||
def test_structure_is_json_serializable(self):
|
||||
"""Test that the entire structure can be JSON serialized."""
|
||||
import json
|
||||
|
||||
class MyState(BaseModel):
|
||||
value: int = 0
|
||||
|
||||
class SerializableFlow(Flow[MyState]):
|
||||
"""Test flow for JSON serialization."""
|
||||
|
||||
initial_state = MyState
|
||||
|
||||
@start()
|
||||
@human_feedback(
|
||||
message="Review",
|
||||
emit=["ok", "not_ok"],
|
||||
llm="gpt-4o-mini",
|
||||
)
|
||||
def begin(self):
|
||||
return "data"
|
||||
|
||||
@listen("ok")
|
||||
def proceed(self):
|
||||
return "done"
|
||||
|
||||
structure = flow_structure(SerializableFlow)
|
||||
|
||||
json_str = json.dumps(structure)
|
||||
assert json_str is not None
|
||||
|
||||
parsed = json.loads(json_str)
|
||||
assert parsed["name"] == "SerializableFlow"
|
||||
assert len(parsed["methods"]) > 0
|
||||
|
||||
|
||||
class TestFlowInheritance:
|
||||
"""Test flow inheritance scenarios."""
|
||||
|
||||
def test_child_flow_inherits_parent_methods(self):
|
||||
"""Test that FlowB inheriting from FlowA includes methods from both.
|
||||
|
||||
Note: FlowMeta propagates methods but does NOT fully propagate the
|
||||
_listeners registry from parent classes. This means edges defined
|
||||
in the parent class (e.g., parent_start -> parent_process) may not
|
||||
appear in the child's structure. This is a known FlowMeta limitation.
|
||||
"""
|
||||
|
||||
class FlowA(Flow):
|
||||
"""Parent flow with start method."""
|
||||
|
||||
@start()
|
||||
def parent_start(self):
|
||||
return "parent started"
|
||||
|
||||
@listen(parent_start)
|
||||
def parent_process(self):
|
||||
return "parent processed"
|
||||
|
||||
class FlowB(FlowA):
|
||||
"""Child flow with additional methods."""
|
||||
|
||||
@listen(FlowA.parent_process)
|
||||
def child_continue(self):
|
||||
return "child continued"
|
||||
|
||||
@listen(child_continue)
|
||||
def child_finalize(self):
|
||||
return "child finalized"
|
||||
|
||||
structure = flow_structure(FlowB)
|
||||
|
||||
assert structure["name"] == "FlowB"
|
||||
|
||||
method_names = {m["name"] for m in structure["methods"]}
|
||||
assert "parent_start" in method_names
|
||||
assert "parent_process" in method_names
|
||||
assert "child_continue" in method_names
|
||||
assert "child_finalize" in method_names
|
||||
|
||||
method_map = {m["name"]: m for m in structure["methods"]}
|
||||
assert method_map["parent_start"]["type"] == "start"
|
||||
assert method_map["parent_process"]["type"] == "listen"
|
||||
assert method_map["child_continue"]["type"] == "listen"
|
||||
assert method_map["child_finalize"]["type"] == "listen"
|
||||
|
||||
edge_pairs = [(e["from_method"], e["to_method"]) for e in structure["edges"]]
|
||||
assert ("parent_process", "child_continue") in edge_pairs
|
||||
assert ("child_continue", "child_finalize") in edge_pairs
|
||||
|
||||
# KNOWN LIMITATION: Edges defined in parent class (parent_start -> parent_process)
|
||||
# are NOT propagated to child's _listeners registry by FlowMeta.
|
||||
# This is a FlowMeta limitation, not a serializer bug.
|
||||
|
||||
def test_child_flow_can_override_parent_method(self):
|
||||
"""Test that child can override parent methods."""
|
||||
|
||||
class BaseFlow(Flow):
|
||||
@start()
|
||||
def begin(self):
|
||||
return "base begin"
|
||||
|
||||
@listen(begin)
|
||||
def process(self):
|
||||
return "base process"
|
||||
|
||||
class ExtendedFlow(BaseFlow):
|
||||
@listen(BaseFlow.begin)
|
||||
def process(self):
|
||||
return "extended process"
|
||||
|
||||
@listen(process)
|
||||
def finalize(self):
|
||||
return "extended finalize"
|
||||
|
||||
structure = flow_structure(ExtendedFlow)
|
||||
|
||||
method_names = {m["name"] for m in structure["methods"]}
|
||||
assert "begin" in method_names
|
||||
assert "process" in method_names
|
||||
assert "finalize" in method_names
|
||||
|
||||
# Should have 3 methods total (not 4, since process is overridden)
|
||||
assert len(structure["methods"]) == 3
|
||||
@@ -8,7 +8,6 @@ from pathlib import Path
|
||||
import pytest
|
||||
|
||||
from crewai.flow.flow import Flow, and_, listen, or_, router, start
|
||||
from crewai.flow.flow_definition import FlowDefinition
|
||||
from crewai.flow.visualization import (
|
||||
build_flow_structure,
|
||||
visualize_flow_structure,
|
||||
@@ -37,14 +36,14 @@ class RouterFlow(Flow):
|
||||
@router(init)
|
||||
def decide(self):
|
||||
if hasattr(self, "state") and self.state.get("path") == "b":
|
||||
return "event_b"
|
||||
return "event_a"
|
||||
return "path_b"
|
||||
return "path_a"
|
||||
|
||||
@listen("event_a")
|
||||
@listen("path_a")
|
||||
def handle_a(self):
|
||||
return "handled_a"
|
||||
|
||||
@listen("event_b")
|
||||
@listen("path_b")
|
||||
def handle_b(self):
|
||||
return "handled_b"
|
||||
|
||||
@@ -70,23 +69,13 @@ class ComplexFlow(Flow):
|
||||
|
||||
@router(converge_and)
|
||||
def router_decision(self):
|
||||
return "final_event"
|
||||
return "final_path"
|
||||
|
||||
@listen("final_event")
|
||||
@listen("final_path")
|
||||
def finalize(self):
|
||||
return "complete"
|
||||
|
||||
|
||||
def _attach_flow_definition(flow_class: type[Flow], methods: dict[str, object]) -> None:
|
||||
flow_class._flow_definition = FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": flow_class.__name__,
|
||||
"methods": methods,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def test_build_flow_structure_simple():
|
||||
"""Test building structure for a simple sequential flow."""
|
||||
flow = SimpleFlow()
|
||||
@@ -109,47 +98,6 @@ def test_build_flow_structure_simple():
|
||||
assert edge["condition_type"] == "OR"
|
||||
|
||||
|
||||
def test_build_flow_structure_from_flow_class():
|
||||
"""Test building structure from a Flow class via its FlowDefinition."""
|
||||
structure = build_flow_structure(SimpleFlow)
|
||||
|
||||
assert set(structure["nodes"]) == {"begin", "process"}
|
||||
assert structure["start_methods"] == ["begin"]
|
||||
assert structure["nodes"]["begin"]["class_name"] == "SimpleFlow"
|
||||
|
||||
|
||||
def test_build_flow_structure_from_flow_definition():
|
||||
"""Test building visualization directly from a FlowDefinition."""
|
||||
definition = FlowDefinition.from_dict(
|
||||
{
|
||||
"schema": "crewai.flow/v1",
|
||||
"name": "DefinedFlow",
|
||||
"methods": {
|
||||
"begin": {"start": True},
|
||||
"decide": {
|
||||
"listen": "begin",
|
||||
"router": True,
|
||||
"emit": ["done"],
|
||||
},
|
||||
"finish": {"listen": "done"},
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
structure = build_flow_structure(definition)
|
||||
|
||||
assert set(structure["nodes"]) == {"begin", "decide", "finish"}
|
||||
assert structure["start_methods"] == ["begin"]
|
||||
assert structure["router_methods"] == ["decide"]
|
||||
assert structure["nodes"]["begin"]["class_name"] == "DefinedFlow"
|
||||
assert any(
|
||||
edge["source"] == "decide"
|
||||
and edge["target"] == "finish"
|
||||
and edge["router_event"] == "done"
|
||||
for edge in structure["edges"]
|
||||
)
|
||||
|
||||
|
||||
def test_build_flow_structure_with_router():
|
||||
"""Test building structure for a flow with router."""
|
||||
flow = RouterFlow()
|
||||
@@ -163,10 +111,13 @@ def test_build_flow_structure_with_router():
|
||||
|
||||
router_node = structure["nodes"]["decide"]
|
||||
assert router_node["type"] == "router"
|
||||
assert "router_events" not in router_node
|
||||
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_event"]]
|
||||
assert router_edges == []
|
||||
if "router_paths" in router_node:
|
||||
assert len(router_node["router_paths"]) >= 1
|
||||
assert any("path" in path for path in router_node["router_paths"])
|
||||
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_path"]]
|
||||
assert len(router_edges) >= 1
|
||||
|
||||
|
||||
def test_build_flow_structure_with_and_or_conditions():
|
||||
@@ -252,40 +203,49 @@ def test_visualize_flow_structure_json_data():
|
||||
assert "handle_b" in js_content
|
||||
|
||||
assert "router" in js_content.lower()
|
||||
assert "event_a" in js_content
|
||||
assert "event_b" in js_content
|
||||
assert "path_a" in js_content
|
||||
assert "path_b" in js_content
|
||||
|
||||
|
||||
def test_node_metadata_omits_source_info():
|
||||
"""Test that definition-only visualization omits Python source metadata."""
|
||||
def test_node_metadata_includes_source_info():
|
||||
"""Test that nodes include source code and line number information."""
|
||||
flow = SimpleFlow()
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
for node_metadata in structure["nodes"].values():
|
||||
assert "source_code" not in node_metadata
|
||||
assert "source_lines" not in node_metadata
|
||||
assert "source_start_line" not in node_metadata
|
||||
assert "source_file" not in node_metadata
|
||||
for node_name, node_metadata in structure["nodes"].items():
|
||||
assert node_metadata["source_code"] is not None
|
||||
assert len(node_metadata["source_code"]) > 0
|
||||
assert node_metadata["source_start_line"] is not None
|
||||
assert node_metadata["source_start_line"] > 0
|
||||
assert node_metadata["source_file"] is not None
|
||||
assert node_metadata["source_file"].endswith(".py")
|
||||
|
||||
|
||||
def test_node_metadata_omits_method_signature():
|
||||
"""Test that definition-only visualization omits Python method signatures."""
|
||||
def test_node_metadata_includes_method_signature():
|
||||
"""Test that nodes include method signature information."""
|
||||
flow = SimpleFlow()
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
begin_node = structure["nodes"]["begin"]
|
||||
assert "method_signature" not in begin_node
|
||||
assert begin_node["method_signature"] is not None
|
||||
assert "operationId" in begin_node["method_signature"]
|
||||
assert begin_node["method_signature"]["operationId"] == "begin"
|
||||
assert "parameters" in begin_node["method_signature"]
|
||||
assert "returns" in begin_node["method_signature"]
|
||||
|
||||
|
||||
def test_router_node_has_correct_metadata():
|
||||
"""Test that router nodes have correct type and event metadata."""
|
||||
"""Test that router nodes have correct type and paths."""
|
||||
flow = RouterFlow()
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
router_node = structure["nodes"]["decide"]
|
||||
assert router_node["type"] == "router"
|
||||
assert router_node["is_router"] is True
|
||||
assert "router_events" not in router_node
|
||||
assert router_node["router_paths"] is not None
|
||||
assert len(router_node["router_paths"]) == 2
|
||||
assert "path_a" in router_node["router_paths"]
|
||||
assert "path_b" in router_node["router_paths"]
|
||||
|
||||
|
||||
def test_listen_node_has_trigger_methods():
|
||||
@@ -295,7 +255,7 @@ def test_listen_node_has_trigger_methods():
|
||||
|
||||
handle_a_node = structure["nodes"]["handle_a"]
|
||||
assert handle_a_node["trigger_methods"] is not None
|
||||
assert "event_a" in handle_a_node["trigger_methods"]
|
||||
assert "path_a" in handle_a_node["trigger_methods"]
|
||||
|
||||
|
||||
def test_and_condition_node_metadata():
|
||||
@@ -357,15 +317,16 @@ def test_topological_path_counting():
|
||||
assert len(structure["edges"]) > 0
|
||||
|
||||
|
||||
def test_class_metadata_comes_from_definition():
|
||||
"""Test that nodes include only definition-derived class metadata."""
|
||||
def test_class_signature_metadata():
|
||||
"""Test that nodes include class signature information."""
|
||||
flow = SimpleFlow()
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
for node_metadata in structure["nodes"].values():
|
||||
for node_name, node_metadata in structure["nodes"].items():
|
||||
assert node_metadata["class_name"] is not None
|
||||
assert node_metadata["class_name"] == "SimpleFlow"
|
||||
assert "class_signature" not in node_metadata
|
||||
assert node_metadata["class_signature"] is not None
|
||||
assert "SimpleFlow" in node_metadata["class_signature"]
|
||||
|
||||
|
||||
def test_visualization_plot_method():
|
||||
@@ -377,8 +338,8 @@ def test_visualization_plot_method():
|
||||
assert os.path.exists(html_file)
|
||||
|
||||
|
||||
def test_router_events_to_string_conditions():
|
||||
"""Test that router events correctly connect to listeners with string conditions."""
|
||||
def test_router_paths_to_string_conditions():
|
||||
"""Test that router paths correctly connect to listeners with string conditions."""
|
||||
|
||||
class RouterToStringFlow(Flow):
|
||||
@start()
|
||||
@@ -388,34 +349,25 @@ def test_router_events_to_string_conditions():
|
||||
@router(init)
|
||||
def decide(self):
|
||||
if hasattr(self, "state") and self.state.get("path") == "b":
|
||||
return "event_b"
|
||||
return "event_a"
|
||||
return "path_b"
|
||||
return "path_a"
|
||||
|
||||
@listen(or_("event_a", "event_b"))
|
||||
@listen(or_("path_a", "path_b"))
|
||||
def handle_either(self):
|
||||
return "handled"
|
||||
|
||||
@listen("event_b")
|
||||
@listen("path_b")
|
||||
def handle_b_only(self):
|
||||
return "handled_b"
|
||||
|
||||
flow = RouterToStringFlow()
|
||||
_attach_flow_definition(
|
||||
RouterToStringFlow,
|
||||
{
|
||||
"init": {"start": True},
|
||||
"decide": {"listen": "init", "router": True, "emit": ["event_a", "event_b"]},
|
||||
"handle_either": {"listen": {"or": ["event_a", "event_b"]}},
|
||||
"handle_b_only": {"listen": "event_b"},
|
||||
},
|
||||
)
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
decide_node = structure["nodes"]["decide"]
|
||||
assert "event_a" in decide_node["router_events"]
|
||||
assert "event_b" in decide_node["router_events"]
|
||||
assert "path_a" in decide_node["router_paths"]
|
||||
assert "path_b" in decide_node["router_paths"]
|
||||
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_event"]]
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_path"]]
|
||||
|
||||
assert len(router_edges) == 3
|
||||
|
||||
@@ -430,8 +382,8 @@ def test_router_events_to_string_conditions():
|
||||
assert len(edges_to_handle_b_only) == 1
|
||||
|
||||
|
||||
def test_router_events_not_in_and_conditions():
|
||||
"""Test that router events don't create edges to AND-nested conditions."""
|
||||
def test_router_paths_not_in_and_conditions():
|
||||
"""Test that router paths don't create edges to AND-nested conditions."""
|
||||
|
||||
class RouterAndConditionFlow(Flow):
|
||||
@start()
|
||||
@@ -440,34 +392,24 @@ def test_router_events_not_in_and_conditions():
|
||||
|
||||
@router(init)
|
||||
def decide(self):
|
||||
return "event_a"
|
||||
return "path_a"
|
||||
|
||||
@listen("event_a")
|
||||
@listen("path_a")
|
||||
def step_1(self):
|
||||
return "step_1_done"
|
||||
|
||||
@listen(and_("event_a", step_1))
|
||||
@listen(and_("path_a", step_1))
|
||||
def step_2_and(self):
|
||||
return "step_2_done"
|
||||
|
||||
@listen(or_(and_("event_a", step_1), "event_a"))
|
||||
@listen(or_(and_("path_a", step_1), "path_a"))
|
||||
def step_3_or(self):
|
||||
return "step_3_done"
|
||||
|
||||
flow = RouterAndConditionFlow()
|
||||
_attach_flow_definition(
|
||||
RouterAndConditionFlow,
|
||||
{
|
||||
"init": {"start": True},
|
||||
"decide": {"listen": "init", "router": True, "emit": ["event_a"]},
|
||||
"step_1": {"listen": "event_a"},
|
||||
"step_2_and": {"listen": {"and": ["event_a", "step_1"]}},
|
||||
"step_3_or": {"listen": {"or": [{"and": ["event_a", "step_1"]}, "event_a"]}},
|
||||
},
|
||||
)
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_event"]]
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_path"]]
|
||||
|
||||
targets = [edge["target"] for edge in router_edges]
|
||||
|
||||
@@ -512,17 +454,6 @@ def test_chained_routers_no_self_loops():
|
||||
return "need_auth"
|
||||
|
||||
flow = ChainedRouterFlow()
|
||||
_attach_flow_definition(
|
||||
ChainedRouterFlow,
|
||||
{
|
||||
"entrance": {"start": True},
|
||||
"session_in_cache": {"listen": "entrance", "router": True, "emit": ["exp"]},
|
||||
"check_exp": {"listen": "exp", "router": True, "emit": ["auth"]},
|
||||
"call_ai_auth": {"listen": "auth", "router": True, "emit": ["action"]},
|
||||
"forward_to_action": {"listen": "action"},
|
||||
"forward_to_authenticate": {"listen": "authenticate"},
|
||||
},
|
||||
)
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
for edge in structure["edges"]:
|
||||
@@ -530,13 +461,13 @@ def test_chained_routers_no_self_loops():
|
||||
f"Self-loop detected: {edge['source']} -> {edge['target']}"
|
||||
)
|
||||
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_event"]]
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_path"]]
|
||||
|
||||
# session_in_cache -> check_exp (via 'exp')
|
||||
exp_edges = [
|
||||
edge
|
||||
for edge in router_edges
|
||||
if edge["router_event"] == "exp" and edge["source"] == "session_in_cache"
|
||||
if edge["router_path_label"] == "exp" and edge["source"] == "session_in_cache"
|
||||
]
|
||||
assert len(exp_edges) == 1
|
||||
assert exp_edges[0]["target"] == "check_exp"
|
||||
@@ -545,7 +476,7 @@ def test_chained_routers_no_self_loops():
|
||||
auth_edges = [
|
||||
edge
|
||||
for edge in router_edges
|
||||
if edge["router_event"] == "auth" and edge["source"] == "check_exp"
|
||||
if edge["router_path_label"] == "auth" and edge["source"] == "check_exp"
|
||||
]
|
||||
assert len(auth_edges) == 1
|
||||
assert auth_edges[0]["target"] == "call_ai_auth"
|
||||
@@ -554,7 +485,7 @@ def test_chained_routers_no_self_loops():
|
||||
action_edges = [
|
||||
edge
|
||||
for edge in router_edges
|
||||
if edge["router_event"] == "action" and edge["source"] == "call_ai_auth"
|
||||
if edge["router_path_label"] == "action" and edge["source"] == "call_ai_auth"
|
||||
]
|
||||
assert len(action_edges) == 1
|
||||
assert action_edges[0]["target"] == "forward_to_action"
|
||||
@@ -592,16 +523,6 @@ def test_routers_with_shared_output_strings():
|
||||
return "skipped"
|
||||
|
||||
flow = SharedOutputRouterFlow()
|
||||
_attach_flow_definition(
|
||||
SharedOutputRouterFlow,
|
||||
{
|
||||
"start": {"start": True},
|
||||
"router_a": {"listen": "start", "router": True, "emit": ["auth"]},
|
||||
"router_b": {"listen": "auth", "router": True, "emit": ["done"]},
|
||||
"finalize": {"listen": "done"},
|
||||
"handle_skip": {"listen": "skip"},
|
||||
},
|
||||
)
|
||||
structure = build_flow_structure(flow)
|
||||
|
||||
for edge in structure["edges"]:
|
||||
@@ -610,11 +531,11 @@ def test_routers_with_shared_output_strings():
|
||||
)
|
||||
|
||||
# router_a should connect to router_b via 'auth'
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_event"]]
|
||||
router_edges = [edge for edge in structure["edges"] if edge["is_router_path"]]
|
||||
auth_from_a = [
|
||||
edge
|
||||
for edge in router_edges
|
||||
if edge["source"] == "router_a" and edge["router_event"] == "auth"
|
||||
if edge["source"] == "router_a" and edge["router_path_label"] == "auth"
|
||||
]
|
||||
assert len(auth_from_a) == 1
|
||||
assert auth_from_a[0]["target"] == "router_b"
|
||||
@@ -623,17 +544,17 @@ def test_routers_with_shared_output_strings():
|
||||
done_from_b = [
|
||||
edge
|
||||
for edge in router_edges
|
||||
if edge["source"] == "router_b" and edge["router_event"] == "done"
|
||||
if edge["source"] == "router_b" and edge["router_path_label"] == "done"
|
||||
]
|
||||
assert len(done_from_b) == 1
|
||||
assert done_from_b[0]["target"] == "finalize"
|
||||
|
||||
|
||||
def test_warning_for_router_without_events(caplog):
|
||||
"""Test that a warning is logged when a router has no determinable events."""
|
||||
def test_warning_for_router_without_paths(caplog):
|
||||
"""Test that a warning is logged when a router has no determinable paths."""
|
||||
import logging
|
||||
|
||||
class RouterWithoutEventsFlow(Flow):
|
||||
class RouterWithoutPathsFlow(Flow):
|
||||
"""Flow with a router that returns a dynamic value."""
|
||||
|
||||
@start()
|
||||
@@ -643,35 +564,34 @@ def test_warning_for_router_without_events(caplog):
|
||||
@router(begin)
|
||||
def dynamic_router(self):
|
||||
import random
|
||||
return random.choice(["event_a", "event_b"])
|
||||
return random.choice(["path_a", "path_b"])
|
||||
|
||||
@listen("event_a")
|
||||
@listen("path_a")
|
||||
def handle_a(self):
|
||||
return "a"
|
||||
|
||||
@listen("event_b")
|
||||
@listen("path_b")
|
||||
def handle_b(self):
|
||||
return "b"
|
||||
|
||||
flow = RouterWithoutEventsFlow()
|
||||
flow = RouterWithoutPathsFlow()
|
||||
|
||||
with caplog.at_level(logging.WARNING):
|
||||
build_flow_structure(flow)
|
||||
|
||||
assert any(
|
||||
"Router events for 'dynamic_router' are dynamic" in record.message
|
||||
"Could not determine return paths for router 'dynamic_router'" in record.message
|
||||
for record in caplog.records
|
||||
)
|
||||
|
||||
assert any(
|
||||
"Static visualization could not match listener triggers" in record.message
|
||||
"Found listeners waiting for triggers" in record.message
|
||||
for record in caplog.records
|
||||
)
|
||||
assert not any(record.levelno >= logging.ERROR for record in caplog.records)
|
||||
|
||||
|
||||
def test_warning_for_orphaned_listeners(caplog):
|
||||
"""Test that a warning is logged when a trigger has no explicit router output."""
|
||||
"""Test that an error is logged when listeners wait for triggers no router outputs."""
|
||||
import logging
|
||||
from typing import Literal
|
||||
|
||||
@@ -695,33 +615,19 @@ def test_warning_for_orphaned_listeners(caplog):
|
||||
return "orphan"
|
||||
|
||||
flow = OrphanedListenerFlow()
|
||||
_attach_flow_definition(
|
||||
OrphanedListenerFlow,
|
||||
{
|
||||
"begin": {"start": True},
|
||||
"my_router": {
|
||||
"listen": "begin",
|
||||
"router": True,
|
||||
"emit": ["option_a", "option_b"],
|
||||
},
|
||||
"handle_a": {"listen": "option_a"},
|
||||
"handle_orphan": {"listen": "option_c"},
|
||||
},
|
||||
)
|
||||
|
||||
with caplog.at_level(logging.WARNING):
|
||||
with caplog.at_level(logging.ERROR):
|
||||
build_flow_structure(flow)
|
||||
|
||||
assert any(
|
||||
"Static visualization could not match listener triggers" in record.message
|
||||
"Found listeners waiting for triggers" in record.message
|
||||
and "option_c" in record.message
|
||||
for record in caplog.records
|
||||
)
|
||||
assert not any(record.levelno >= logging.ERROR for record in caplog.records)
|
||||
|
||||
|
||||
def test_no_warning_for_explicit_contract_router_events(caplog):
|
||||
"""Test no warning is logged when router events are declared in the contract."""
|
||||
def test_no_warning_for_properly_typed_router(caplog):
|
||||
"""Test that no warning is logged when router has proper type annotations."""
|
||||
import logging
|
||||
from typing import Literal
|
||||
|
||||
@@ -733,39 +639,23 @@ def test_no_warning_for_explicit_contract_router_events(caplog):
|
||||
return "started"
|
||||
|
||||
@router(begin)
|
||||
def typed_router(self) -> Literal["event_a", "event_b"]:
|
||||
return "event_a"
|
||||
def typed_router(self) -> Literal["path_a", "path_b"]:
|
||||
return "path_a"
|
||||
|
||||
@listen("event_a")
|
||||
@listen("path_a")
|
||||
def handle_a(self):
|
||||
return "a"
|
||||
|
||||
@listen("event_b")
|
||||
@listen("path_b")
|
||||
def handle_b(self):
|
||||
return "b"
|
||||
|
||||
flow = ProperlyTypedRouterFlow()
|
||||
_attach_flow_definition(
|
||||
ProperlyTypedRouterFlow,
|
||||
{
|
||||
"begin": {"start": True},
|
||||
"typed_router": {
|
||||
"listen": "begin",
|
||||
"router": True,
|
||||
"emit": ["event_a", "event_b"],
|
||||
},
|
||||
"handle_a": {"listen": "event_a"},
|
||||
"handle_b": {"listen": "event_b"},
|
||||
},
|
||||
)
|
||||
|
||||
with caplog.at_level(logging.WARNING):
|
||||
build_flow_structure(flow)
|
||||
|
||||
# No warnings should be logged
|
||||
warning_messages = [r.message for r in caplog.records if r.levelno >= logging.WARNING]
|
||||
assert not any("Router events for" in msg for msg in warning_messages)
|
||||
assert not any(
|
||||
"Static visualization could not match listener triggers" in msg
|
||||
for msg in warning_messages
|
||||
)
|
||||
assert not any("Could not determine return paths" in msg for msg in warning_messages)
|
||||
assert not any("Found listeners waiting for triggers" in msg for msg in warning_messages)
|
||||
@@ -13,7 +13,7 @@ from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai.flow import Flow, human_feedback, listen, persist, start
|
||||
from crewai.flow import Flow, human_feedback, listen, start
|
||||
from crewai.flow.human_feedback import (
|
||||
HumanFeedbackConfig,
|
||||
HumanFeedbackResult,
|
||||
@@ -79,7 +79,7 @@ class TestHumanFeedbackValidation:
|
||||
|
||||
assert hasattr(test_method, "__human_feedback_config__")
|
||||
assert test_method.__is_router__ is True
|
||||
assert test_method.__router_emit__ == ["approve", "reject"]
|
||||
assert test_method.__router_paths__ == ["approve", "reject"]
|
||||
|
||||
def test_valid_configuration_without_routing(self):
|
||||
"""Test that valid configuration without routing doesn't raise."""
|
||||
@@ -91,22 +91,6 @@ class TestHumanFeedbackValidation:
|
||||
assert hasattr(test_method, "__human_feedback_config__")
|
||||
assert not hasattr(test_method, "__is_router__") or not test_method.__is_router__
|
||||
|
||||
def test_persist_preserves_human_feedback_llm_attribute(self):
|
||||
"""Test @persist preserves the live LLM stashed by @human_feedback."""
|
||||
llm = object()
|
||||
|
||||
@persist()
|
||||
@human_feedback(
|
||||
message="Review this:",
|
||||
emit=["approve", "reject"],
|
||||
llm=llm,
|
||||
)
|
||||
def test_method(self):
|
||||
return "output"
|
||||
|
||||
assert hasattr(test_method, "_human_feedback_llm")
|
||||
assert test_method._human_feedback_llm is llm
|
||||
|
||||
|
||||
class TestHumanFeedbackConfig:
|
||||
"""Tests for HumanFeedbackConfig dataclass."""
|
||||
@@ -205,7 +189,7 @@ class TestDecoratorAttributePreservation:
|
||||
return "output"
|
||||
|
||||
assert review_method.__is_router__ is True
|
||||
assert review_method.__router_emit__ == ["approved", "rejected"]
|
||||
assert review_method.__router_paths__ == ["approved", "rejected"]
|
||||
|
||||
|
||||
class TestAsyncSupport:
|
||||
|
||||
@@ -778,14 +778,14 @@ class TestEdgeCases:
|
||||
class TestLLMConfigPreservation:
|
||||
"""Tests that LLM config is preserved through @human_feedback serialization.
|
||||
|
||||
PR #4970 introduced _human_feedback_llm stashing so the live LLM object survives
|
||||
PR #4970 introduced _hf_llm stashing so the live LLM object survives
|
||||
decorator wrapping for same-process resume. The serialization path
|
||||
(_serialize_llm_for_context / _deserialize_llm_from_context) preserves
|
||||
config for cross-process resume.
|
||||
"""
|
||||
|
||||
def test_human_feedback_llm_stashed_on_wrapper_with_llm_instance(self):
|
||||
"""Test that passing an LLM instance stashes it on the wrapper as _human_feedback_llm."""
|
||||
def test_hf_llm_stashed_on_wrapper_with_llm_instance(self):
|
||||
"""Test that passing an LLM instance stashes it on the wrapper as _hf_llm."""
|
||||
from crewai.llm import LLM
|
||||
|
||||
llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)
|
||||
@@ -801,11 +801,11 @@ class TestLLMConfigPreservation:
|
||||
return "content"
|
||||
|
||||
method = ConfigFlow.review
|
||||
assert hasattr(method, "_human_feedback_llm"), "_human_feedback_llm not found on wrapper"
|
||||
assert method._human_feedback_llm is llm_instance, "_human_feedback_llm is not the same object"
|
||||
assert hasattr(method, "_hf_llm"), "_hf_llm not found on wrapper"
|
||||
assert method._hf_llm is llm_instance, "_hf_llm is not the same object"
|
||||
|
||||
def test_human_feedback_llm_preserved_on_listen_method(self):
|
||||
"""Test that _human_feedback_llm is preserved when @human_feedback is on a @listen method."""
|
||||
def test_hf_llm_preserved_on_listen_method(self):
|
||||
"""Test that _hf_llm is preserved when @human_feedback is on a @listen method."""
|
||||
from crewai.llm import LLM
|
||||
|
||||
llm_instance = LLM(model="gpt-4o-mini", temperature=0.7)
|
||||
@@ -825,11 +825,11 @@ class TestLLMConfigPreservation:
|
||||
return "content"
|
||||
|
||||
method = ListenConfigFlow.review
|
||||
assert hasattr(method, "_human_feedback_llm")
|
||||
assert method._human_feedback_llm is llm_instance
|
||||
assert hasattr(method, "_hf_llm")
|
||||
assert method._hf_llm is llm_instance
|
||||
|
||||
def test_human_feedback_llm_accessible_on_instance(self):
|
||||
"""Test that _human_feedback_llm survives Flow instantiation (bound method access)."""
|
||||
def test_hf_llm_accessible_on_instance(self):
|
||||
"""Test that _hf_llm survives Flow instantiation (bound method access)."""
|
||||
from crewai.llm import LLM
|
||||
|
||||
llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)
|
||||
@@ -846,8 +846,8 @@ class TestLLMConfigPreservation:
|
||||
|
||||
flow = InstanceFlow()
|
||||
instance_method = flow.review
|
||||
assert hasattr(instance_method, "_human_feedback_llm")
|
||||
assert instance_method._human_feedback_llm is llm_instance
|
||||
assert hasattr(instance_method, "_hf_llm")
|
||||
assert instance_method._hf_llm is llm_instance
|
||||
|
||||
def test_serialize_llm_preserves_config_fields(self):
|
||||
"""Test that _serialize_llm_for_context captures temperature, base_url, etc."""
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
import os
|
||||
from threading import Thread
|
||||
from unittest.mock import MagicMock, Mock, patch
|
||||
|
||||
import pytest
|
||||
@@ -868,122 +867,6 @@ class TestTraceListenerSetup:
|
||||
mock_mark_failed.assert_called_once_with(
|
||||
"test_batch_id_12345", "Internal Server Error"
|
||||
)
|
||||
assert batch_manager.current_batch is not None
|
||||
assert batch_manager.trace_batch_id == "test_batch_id_12345"
|
||||
assert batch_manager._batch_finalized is False
|
||||
|
||||
def test_finalize_batch_clears_buffer_after_successful_send(self) -> None:
|
||||
"""Successful send must not restore a stale event buffer (duplicate events)."""
|
||||
from crewai.events.listeners.tracing.types import TraceEvent
|
||||
|
||||
with patch(
|
||||
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
|
||||
return_value=True,
|
||||
):
|
||||
batch_manager = TraceBatchManager()
|
||||
batch_manager.current_batch = batch_manager.initialize_batch(
|
||||
user_context={"privacy_level": "standard"},
|
||||
execution_metadata={
|
||||
"execution_type": "flow",
|
||||
"flow_name": "TestFlow",
|
||||
},
|
||||
)
|
||||
batch_manager.trace_batch_id = "batch-clear-test"
|
||||
batch_manager.backend_initialized = True
|
||||
batch_manager.event_buffer = [
|
||||
TraceEvent(
|
||||
type="llm_call_started",
|
||||
timestamp="2026-01-01T00:00:00",
|
||||
event_id="evt-1",
|
||||
emission_sequence=1,
|
||||
)
|
||||
]
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
batch_manager.plus_api,
|
||||
"send_trace_events",
|
||||
return_value=MagicMock(status_code=200),
|
||||
),
|
||||
patch.object(
|
||||
batch_manager.plus_api,
|
||||
"finalize_trace_batch",
|
||||
return_value=MagicMock(status_code=200, json=MagicMock(return_value={})),
|
||||
),
|
||||
):
|
||||
batch_manager.finalize_batch()
|
||||
|
||||
assert batch_manager.event_buffer == []
|
||||
|
||||
def test_finalize_backend_batch_uses_captured_batch_id_for_ephemeral_panel(
|
||||
self,
|
||||
) -> None:
|
||||
"""Finalization output must not render None if manager state is reset."""
|
||||
batch_manager = TraceBatchManager()
|
||||
batch_manager.trace_batch_id = "ephemeral-batch-id"
|
||||
batch_manager.is_current_batch_ephemeral = True
|
||||
|
||||
def clear_batch_id_during_response() -> dict[str, str]:
|
||||
batch_manager.trace_batch_id = None
|
||||
return {"access_code": "TRACE-test"}
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
batch_manager.plus_api,
|
||||
"finalize_ephemeral_trace_batch",
|
||||
return_value=MagicMock(
|
||||
status_code=200,
|
||||
json=clear_batch_id_during_response,
|
||||
),
|
||||
),
|
||||
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.Console.print"
|
||||
) as mock_print,
|
||||
):
|
||||
assert batch_manager._finalize_backend_batch() is True
|
||||
|
||||
panel = mock_print.call_args.args[0]
|
||||
panel_text = str(panel.renderable)
|
||||
assert "session ID: ephemeral-batch-id" in panel_text
|
||||
assert "ephemeral_trace_batches/ephemeral-batch-id" in panel_text
|
||||
assert "session ID: None" not in panel_text
|
||||
assert "ephemeral_trace_batches/None" not in panel_text
|
||||
|
||||
def test_finalize_backend_batch_is_serialized(self) -> None:
|
||||
"""Concurrent finalizers must only call the backend once."""
|
||||
batch_manager = TraceBatchManager()
|
||||
batch_manager.trace_batch_id = "ephemeral-batch-id"
|
||||
batch_manager.is_current_batch_ephemeral = True
|
||||
response = MagicMock(status_code=200, json=MagicMock(return_value={}))
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
batch_manager.plus_api,
|
||||
"finalize_ephemeral_trace_batch",
|
||||
return_value=response,
|
||||
) as mock_finalize,
|
||||
patch(
|
||||
"crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
|
||||
return_value=True,
|
||||
),
|
||||
):
|
||||
results: list[bool] = []
|
||||
|
||||
def finalize() -> None:
|
||||
results.append(batch_manager._finalize_backend_batch())
|
||||
|
||||
threads = [Thread(target=finalize), Thread(target=finalize)]
|
||||
for thread in threads:
|
||||
thread.start()
|
||||
for thread in threads:
|
||||
thread.join()
|
||||
|
||||
assert results == [True, True]
|
||||
mock_finalize.assert_called_once()
|
||||
|
||||
def test_ephemeral_batch_includes_anon_id(self):
|
||||
"""Test that ephemeral batch initialization sends anon_id from get_user_id()"""
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""CrewAI development tools."""
|
||||
|
||||
__version__ = "1.14.7a1"
|
||||
__version__ = "1.14.6"
|
||||
|
||||
@@ -186,7 +186,7 @@ exclude-newer = "3 days"
|
||||
# gitpython <3.1.50 has GHSA-mv93-w799-cj2w (config_writer newline injection bypassing the 3.1.49 patch -> RCE via core.hooksPath).
|
||||
# urllib3 <2.7.0 has GHSA-qccp-gfcp-xxvc (ProxyManager cross-origin redirect leaks Authorization/Cookie) and GHSA-mf9v-mfxr-j63j (streaming decompression-bomb bypass); force 2.7.0+.
|
||||
# langsmith <0.8.0 has GHSA-3644-q5cj-c5c7 (public prompt manifest deserialization, SSRF/secret disclosure); force 0.8.0+.
|
||||
# authlib <1.6.12 has GHSA-jj8c-mmj3-mmgv (CSRF bypass in cache-based state storage) and PYSEC-2026-188.
|
||||
# authlib <1.6.11 has GHSA-jj8c-mmj3-mmgv (CSRF bypass in cache-based state storage).
|
||||
# pip <26.1.1 has GHSA-58qw-9mgm-455v (archive handling); OSV considers 26.1.1 unaffected.
|
||||
# paramiko <5.0.0 has GHSA-r374-rxx8-8654 (SHA-1 in rsakey.py); OSV considers 5.0.0 unaffected. Transitive via composio-core.
|
||||
# starlette <1.0.1 has PYSEC-2026-161 (missing Host header validation poisons request.url.path, bypassing path-based auth). Transitive via fastapi.
|
||||
@@ -207,7 +207,7 @@ override-dependencies = [
|
||||
"python-multipart>=0.0.27,<1",
|
||||
"gitpython>=3.1.50,<4",
|
||||
"langsmith>=0.8.0,<1",
|
||||
"authlib>=1.6.12",
|
||||
"authlib>=1.6.11",
|
||||
"pip>=26.1.1",
|
||||
"paramiko>=5.0.0",
|
||||
"starlette>=1.0.1",
|
||||
|
||||
23
uv.lock
generated
23
uv.lock
generated
@@ -13,7 +13,7 @@ resolution-markers = [
|
||||
]
|
||||
|
||||
[options]
|
||||
exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
|
||||
exclude-newer = "2026-05-30T15:40:20.821639605Z"
|
||||
exclude-newer-span = "P3D"
|
||||
|
||||
[manifest]
|
||||
@@ -26,7 +26,7 @@ members = [
|
||||
"crewai-tools",
|
||||
]
|
||||
overrides = [
|
||||
{ name = "authlib", specifier = ">=1.6.12" },
|
||||
{ name = "authlib", specifier = ">=1.6.11" },
|
||||
{ name = "cryptography", specifier = ">=46.0.7" },
|
||||
{ name = "gitpython", specifier = ">=3.1.50,<4" },
|
||||
{ name = "langchain-core", specifier = ">=1.3.3,<2" },
|
||||
@@ -444,15 +444,14 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "authlib"
|
||||
version = "1.7.2"
|
||||
version = "1.6.11"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "cryptography" },
|
||||
{ name = "joserfc" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/36/98/7d93f30d029643c0275dbc0bd6d5a6f670661ee6c9a94d93af7ab4887600/authlib-1.7.2.tar.gz", hash = "sha256:2cea25fefcd4e7173bdf1372c0afc265c8034b23a8cd5dcb6a9164b826c64231", size = 176511, upload-time = "2026-05-06T08:10:23.116Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/28/10/b325d58ffe86815b399334a101e63bc6fa4e1953921cb23703b48a0a0220/authlib-1.6.11.tar.gz", hash = "sha256:64db35b9b01aeccb4715a6c9a6613a06f2bd7be2ab9d2eb89edd1dfc7580a38f", size = 165359, upload-time = "2026-04-16T07:22:50.279Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fb/95/adcb68e20c34162e9135f370d6e31737719c2b6f94bc953fe7ed1f10fe21/authlib-1.7.2-py2.py3-none-any.whl", hash = "sha256:3e1faedc9d87e7d56a164eca3ccb6ace0d61b94abe83e92242f8dc8bba9b4a9f", size = 259548, upload-time = "2026-05-06T08:10:21.436Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/57/2f/55fca558f925a51db046e5b929deb317ddb05afed74b22d89f4eca578980/authlib-1.6.11-py2.py3-none-any.whl", hash = "sha256:c8687a9a26451c51a34a06fa17bb97cb15bba46a6a626755e2d7f50da8bff3e3", size = 244469, upload-time = "2026-04-16T07:22:48.413Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -3571,18 +3570,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "joserfc"
|
||||
version = "1.6.8"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "cryptography" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5d/ac/d4fd5b30f82900eac60d765f179f0ba005825ac462cc8ced6e13ec685ab3/joserfc-1.6.8.tar.gz", hash = "sha256:878620c553a6ebdd76ccdc356782fee3f735f21a356d079a546b42a4670ace5f", size = 232930, upload-time = "2026-05-27T03:22:37.819Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/98/8c/5cdce2cf3ce8155849baf9a5e2ce77e89dc87ec3bdb38259e5d85fbc45bd/joserfc-1.6.8-py3-none-any.whl", hash = "sha256:22fb31a69094a5e6f44632002a9df2c30c941fc6c8ce1b037e92c03de954cf9f", size = 70927, upload-time = "2026-05-27T03:22:35.796Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "json-repair"
|
||||
version = "0.25.3"
|
||||
|
||||
Reference in New Issue
Block a user