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18 Commits

Author SHA1 Message Date
Lucas Gomide
f3d6009519 Merge branch 'main' into luzk/propagate-is-litellm 2026-04-10 10:33:39 -03:00
Lucas Gomide
ea610af201 feat: propagate is_litellm flag through LLMCallCompletedEvent
Add is_litellm field to LLMCallCompletedEvent so downstream consumers
2026-04-10 10:32:21 -03:00
Greyson LaLonde
1293dee241 feat: checkpoint TUI with tree view, fork support, editable inputs/outputs
- Rewrite TUI with Tree widget showing branch/fork lineage
- Add Resume and Fork buttons in detail panel with Collapsible entities
- Show branch and parent_id in detail panel and CLI info output
- Auto-detect .checkpoints.db when default dir missing
- Append .db to location for SqliteProvider when no extension set
- Fix RuntimeState.from_checkpoint not setting provider/location
- Fork now writes initial checkpoint on new branch
- Add from_checkpoint, fork, and CLI docs to checkpointing.mdx
2026-04-10 21:24:49 +08:00
Greyson LaLonde
6efa142e22 fix: forward strict mode to Anthropic and Bedrock providers
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The OpenAI-format tool schema sets strict: true but this was dropped
during conversion to Anthropic/Bedrock formats, so neither provider
used constrained decoding. Without it, the model can return string
"None" instead of JSON null for nullable fields, causing Pydantic
validation failures.
2026-04-10 15:32:54 +08:00
Lucas Gomide
fc6792d067 feat: enrich LLM token tracking with reasoning tokens, cache creation tokens (#5389)
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2026-04-10 00:22:27 -04:00
Greyson LaLonde
84b1b0a0b0 feat: add from_checkpoint parameter to kickoff methods
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Accept CheckpointConfig on Crew and Flow kickoff/kickoff_async/akickoff.
When restore_from is set, the entity resumes from that checkpoint.
When only config fields are set, checkpointing is enabled for the run.
Adds restore_from field (Path | str | None) to CheckpointConfig.
2026-04-10 03:47:23 +08:00
Greyson LaLonde
56cf8a4384 feat: embed crewai_version in checkpoints with migration framework
Write the crewAI package version into every checkpoint blob. On restore,
run version-based migrations so older checkpoints can be transformed
forward to the current format. Adds crewai.utilities.version module.
2026-04-10 01:13:30 +08:00
Greyson LaLonde
68c754883d feat: add checkpoint forking with lineage tracking 2026-04-10 00:03:28 +08:00
alex-clawd
ce56472fc3 fix: harden NL2SQLTool — read-only default, query validation, parameterized queries (#5311)
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* fix: harden NL2SQLTool — read-only by default, parameterized queries, query validation

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: address CI lint failures and remove unused import

- Remove unused `sessionmaker` import from test_nl2sql_security.py
- Use `Self` return type on `_apply_env_override` (fixes UP037/F821)
- Fix ruff errors auto-fixed in lib/crewai (UP007, etc.)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: expand _WRITE_COMMANDS and block multi-statement semicolon injection

- Add missing write commands: UPSERT, LOAD, COPY, VACUUM, ANALYZE,
  ANALYSE, REINDEX, CLUSTER, REFRESH, COMMENT, SET, RESET
- _validate_query() now splits on ';' and validates each statement
  independently; multi-statement queries are rejected outright in
  read-only mode to prevent 'SELECT 1; DROP TABLE users' bypass
- Extract single-statement logic into _validate_statement() helper
- Add TestSemicolonInjection and TestExtendedWriteCommands test classes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* ci: retrigger

* fix: use typing_extensions.Self for Python 3.10 compat

* chore: update tool specifications

* docs: document NL2SQLTool read-only default and DML configuration

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: close three NL2SQLTool security gaps (writable CTEs, EXPLAIN ANALYZE, multi-stmt commit)

- Remove WITH from _READ_ONLY_COMMANDS; scan CTE body for write keywords so
  writable CTEs like `WITH d AS (DELETE …) SELECT …` are blocked in read-only mode.
- EXPLAIN ANALYZE/ANALYSE now resolves the underlying command; EXPLAIN ANALYZE DELETE
  is treated as a write and blocked in read-only mode.
- execute_sql commit decision now checks ALL semicolon-separated statements so
  a SELECT-first batch like `SELECT 1; DROP TABLE t` still triggers a commit
  when allow_dml=True.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: handle parenthesized EXPLAIN options syntax; remove unused _seed_db

_validate_statement now strips parenthesized options from EXPLAIN (e.g.
EXPLAIN (ANALYZE) DELETE, EXPLAIN (ANALYZE, VERBOSE) DELETE) before
checking whether ANALYZE/ANALYSE is present — closing the bypass where
the options-list form was silently allowed in read-only mode.

Adds three new tests:
  - EXPLAIN (ANALYZE) DELETE  → blocked
  - EXPLAIN (ANALYZE, VERBOSE) DELETE  → blocked
  - EXPLAIN (VERBOSE) SELECT  → allowed

Also removes the unused _seed_db helper from test_nl2sql_security.py.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* chore: update tool specifications

* fix: smarter CTE write detection, fix commit logic for writable CTEs

- Replace naive token-set matching with positional AS() body inspection
  to avoid false positives on column names like 'comment', 'set', 'reset'
- Fix execute_sql commit logic to detect writable CTEs (WITH + DELETE/INSERT)
  not just top-level write commands
- Add tests for false positive cases and writable CTE commit behavior
- Format nl2sql_tool.py to pass ruff format check

* fix: catch write commands in CTE main query + handle whitespace in AS()

- WITH cte AS (SELECT 1) DELETE FROM users now correctly blocked
- AS followed by newline/tab/multi-space before ( now detected
- execute_sql commit logic updated for both cases
- 4 new tests

* fix: EXPLAIN ANALYZE VERBOSE handling, string literal paren bypass, commit logic for EXPLAIN ANALYZE

- EXPLAIN handler now consumes all known options (ANALYZE, ANALYSE, VERBOSE) before
  extracting the real command, fixing 'EXPLAIN ANALYZE VERBOSE SELECT' being blocked
- Paren walker in _extract_main_query_after_cte now skips string literals, preventing
  'WITH cte AS (SELECT '\''('\'' FROM t) DELETE FROM users' from bypassing detection
- _is_write_stmt in execute_sql now resolves EXPLAIN ANALYZE to underlying command
  via _resolve_explain_command, ensuring session.commit() fires for write operations
- 10 new tests covering all three fixes

* fix: deduplicate EXPLAIN parsing, fix AS( regex in strings, block unknown CTE commands, bump langchain-core

- Refactor _validate_statement to use _resolve_explain_command (single source of truth)
- _iter_as_paren_matches skips string literals so 'AS (' in data doesn't confuse CTE detection
- Unknown commands after CTE definitions now blocked in read-only mode
- Bump langchain-core override to >=1.2.28 (GHSA-926x-3r5x-gfhw)

* fix: add return type annotation to _iter_as_paren_matches

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2026-04-09 03:21:38 -03:00
Greyson LaLonde
06fe163611 docs: update changelog and version for v1.14.2a1
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2026-04-09 07:26:22 +08:00
Greyson LaLonde
3b52b1a800 feat: bump versions to 1.14.2a1 2026-04-09 07:21:39 +08:00
Greyson LaLonde
9ab67552a7 fix: emit flow_finished event after HITL resume
resume_async() was missing trace infrastructure that kickoff_async()
sets up, causing flow_finished to never reach the platform after HITL
feedback. Add FlowStartedEvent emission to initialize the trace batch,
await event futures, finalize the trace batch, and guard with
suppress_flow_events.
2026-04-09 05:31:31 +08:00
Greyson LaLonde
8cdde16ac8 fix: bump cryptography to 46.0.7 for CVE-2026-39892 2026-04-09 05:17:31 +08:00
Greyson LaLonde
0e590ff669 refactor: use shared I18N_DEFAULT singleton 2026-04-09 04:29:53 +08:00
Greyson LaLonde
15f5bff043 docs: update changelog and version for v1.14.1
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2026-04-09 01:56:51 +08:00
Greyson LaLonde
a0578bb6c3 feat: bump versions to 1.14.1 2026-04-09 01:45:40 +08:00
Greyson LaLonde
00400a9f31 ci: skip python tests, lint, and type checks on docs-only PRs 2026-04-09 01:34:47 +08:00
Lorenze Jay
5c08e566b5 dedicate skills page (#5331) 2026-04-08 10:10:18 -07:00
105 changed files with 6540 additions and 1270 deletions

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

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

View File

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

View File

@@ -4,6 +4,62 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
icon: "clock"
mode: "wide"
---
<Update label="9 أبريل 2026">
## v1.14.2a1
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
## ما الذي تغير
### إصلاحات الأخطاء
- إصلاح إصدار حدث flow_finished بعد استئناف HITL
- إصلاح إصدار التشفير إلى 46.0.7 لمعالجة CVE-2026-39892
### إعادة هيكلة
- إعادة هيكلة لاستخدام I18N_DEFAULT المشترك
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.14.1
## المساهمون
@greysonlalonde
</Update>
<Update label="9 أبريل 2026">
## v1.14.1
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
## ما الذي تغير
### الميزات
- إضافة متصفح TUI لنقاط التفتيش غير المتزامنة
- إضافة دالة aclose()/close() ومدير سياق غير متزامن لمخرجات البث
### إصلاحات الأخطاء
- إصلاح التعبير النمطي لزيادة إصدار pyproject.toml
- تنظيف أسماء الأدوات في مرشحات زخرفة الخطاف
- إصلاح تسجيل معالجات نقاط التفتيش عند إنشاء CheckpointConfig
- رفع إصدار transformers إلى 5.5.0 لحل CVE-2026-1839
- إزالة غلاف FilteredStream لـ stdout/stderr
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.14.1rc1
### إعادة الهيكلة
- استبدال القائمة المحظورة الثابتة باستبعاد حقل BaseTool الديناميكي في توليد المواصفات
- استبدال التعبير النمطي بـ tomlkit في واجهة سطر أوامر أدوات التطوير
- استخدام كائن PRINTER المشترك
- جعل BaseProvider نموذجاً أساسياً مع مميز نوع المزود
## المساهمون
@greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
</Update>
<Update label="9 أبريل 2026">
## v1.14.1rc1

50
docs/ar/skills.mdx Normal file
View File

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

View File

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

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@@ -4,6 +4,62 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="Apr 09, 2026">
## v1.14.2a1
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
## What's Changed
### Bug Fixes
- Fix emission of flow_finished event after HITL resume
- Fix cryptography version to 46.0.7 to address CVE-2026-39892
### Refactoring
- Refactor to use shared I18N_DEFAULT singleton
### Documentation
- Update changelog and version for v1.14.1
## Contributors
@greysonlalonde
</Update>
<Update label="Apr 09, 2026">
## v1.14.1
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
## What's Changed
### Features
- Add async checkpoint TUI browser
- Add aclose()/close() and async context manager to streaming outputs
### Bug Fixes
- Fix regex for template pyproject.toml version bumps
- Sanitize tool names in hook decorator filters
- Fix checkpoint handlers registration when CheckpointConfig is created
- Bump transformers to 5.5.0 to resolve CVE-2026-1839
- Remove FilteredStream stdout/stderr wrapper
### Documentation
- Update changelog and version for v1.14.1rc1
### Refactoring
- Replace hardcoded denylist with dynamic BaseTool field exclusion in spec gen
- Replace regex with tomlkit in devtools CLI
- Use shared PRINTER singleton
- Make BaseProvider a BaseModel with provider_type discriminator
## Contributors
@greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
</Update>
<Update label="Apr 09, 2026">
## v1.14.1rc1

View File

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

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@@ -1,163 +0,0 @@
---
title: "Organization Management"
description: "Learn how to manage organizations via the CrewAI CLI — list, switch, and verify your active org before publishing or deploying."
icon: "building"
mode: "wide"
---
## Overview
When you sign up for [CrewAI AMP](https://app.crewai.com), you may belong to multiple organizations — for example, a personal org and one or more team/company orgs. The **active organization** determines where your tools are published, where deployments are created, and which resources you can access.
Managing your active organization is important because actions like `crewai tool publish` and `crewai deploy create` target whatever org is currently active in your CLI session.
## Authentication and Organizations
When you run `crewai login`, the CLI:
1. Authenticates you via a secure device code flow (OAuth2)
2. Logs you in to the Tool Repository
3. Sets your **active organization** to the default organization returned by the server
<Warning>
If you belong to multiple organizations, the default org set after login may not be the one you intend to use. Always verify your active organization before publishing tools or creating deployments.
</Warning>
### Logging In
```shell Terminal
crewai login
```
After running this command:
- A verification URL and code are displayed in your terminal
- Your browser opens for you to confirm authentication
- On success, your active org is automatically set
## Checking Your Current Organization
To see which organization is currently active:
```shell Terminal
crewai org current
```
Example output:
```
Currently logged in to organization My Company (a1b2c3d4-e5f6-7890-abcd-ef1234567890)
```
If no organization is set:
```
You're not currently logged in to any organization.
Use 'crewai org list' to see available organizations.
Use 'crewai org switch <id>' to switch to an organization.
```
## Listing Your Organizations
To see all organizations you belong to:
```shell Terminal
crewai org list
```
Example output:
```
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Name ┃ ID ┃
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Personal │ 11111111-1111-1111-1111-111111111111 │
│ My Company │ 22222222-2222-2222-2222-222222222222 │
└────────────────────┴──────────────────────────────────────┘
```
## Switching Organizations
To switch your active organization:
```shell Terminal
crewai org switch <organization_id>
```
For example, to switch to "My Company":
```shell Terminal
crewai org switch 22222222-2222-2222-2222-222222222222
```
Output:
```
Successfully switched to My Company (22222222-2222-2222-2222-222222222222)
```
The organization ID is the UUID shown in `crewai org list`.
<Note>
You must be authenticated (`crewai login`) before using any `crewai org` commands.
</Note>
## Practical Example: Publishing Tools to the Right Organization
A common scenario is when you belong to both a personal org and a team org. If you run `crewai login` and immediately publish a tool, the tool may end up in your personal org rather than your team org.
To avoid this:
<Steps>
<Step title="Log in to CrewAI AMP">
```shell Terminal
crewai login
```
</Step>
<Step title="Check your current organization">
```shell Terminal
crewai org current
```
</Step>
<Step title="List available organizations (if needed)">
```shell Terminal
crewai org list
```
</Step>
<Step title="Switch to the correct organization">
```shell Terminal
crewai org switch <your-team-org-id>
```
</Step>
<Step title="Publish your tool">
```shell Terminal
crewai tool publish
```
</Step>
</Steps>
<Tip>
Make it a habit to run `crewai org current` before publishing tools or creating deployments, especially if you belong to multiple organizations.
</Tip>
## How Settings Are Stored
Organization settings are stored in `~/.config/crewai/settings.json`. The relevant fields are:
- `org_name`: Name of the currently active organization
- `org_uuid`: UUID of the currently active organization
These fields are managed automatically by `crewai login` and `crewai org switch` — you should not edit them manually.
<Note>
Running `crewai login` clears your current organization settings before re-authenticating. After login, the active org is set based on the server's default for your account.
</Note>
## Command Reference
| Command | Description |
| :--- | :--- |
| `crewai login` | Authenticate to CrewAI AMP (sets active org to default) |
| `crewai org current` | Show the currently active organization |
| `crewai org list` | List all organizations you belong to |
| `crewai org switch <id>` | Switch to a specific organization by UUID |
| `crewai logout` | Log out and clear organization settings |

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@@ -82,10 +82,6 @@ crewai uv remove requests
This will add the package to your project and update `pyproject.toml` accordingly.
<Tip>
If you belong to multiple organizations, make sure you are publishing to the correct one. Run `crewai org current` to verify your active organization, and `crewai org switch <org_id>` to change it if needed. See the [Organization Management](/en/enterprise/guides/organization-management) guide for details.
</Tip>
## Creating and Publishing Tools
To create a new tool project:

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

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

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@@ -4,6 +4,62 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
icon: "clock"
mode: "wide"
---
<Update label="2026년 4월 9일">
## v1.14.2a1
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
## 변경 사항
### 버그 수정
- HITL 재개 후 flow_finished 이벤트 방출 수정
- CVE-2026-39892 문제를 해결하기 위해 암호화 버전을 46.0.7로 수정
### 리팩토링
- 공유 I18N_DEFAULT 싱글톤을 사용하도록 리팩토링
### 문서
- v1.14.1에 대한 변경 로그 및 버전 업데이트
## 기여자
@greysonlalonde
</Update>
<Update label="2026년 4월 9일">
## v1.14.1
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
## 변경 사항
### 기능
- 비동기 체크포인트 TUI 브라우저 추가
- 스트리밍 출력에 aclose()/close() 및 비동기 컨텍스트 관리자 추가
### 버그 수정
- 템플릿 pyproject.toml 버전 증가를 위한 정규 표현식 수정
- 훅 데코레이터 필터에서 도구 이름 정리
- CheckpointConfig 생성 시 체크포인트 핸들러 등록 수정
- CVE-2026-1839 해결을 위해 transformers를 5.5.0으로 업데이트
- FilteredStream stdout/stderr 래퍼 제거
### 문서
- v1.14.1rc1에 대한 변경 로그 및 버전 업데이트
### 리팩토링
- 하드코딩된 거부 목록을 동적 BaseTool 필드 제외로 교체
- devtools CLI에서 정규 표현식을 tomlkit으로 교체
- 공유 PRINTER 싱글톤 사용
- BaseProvider를 provider_type 식별자가 있는 BaseModel로 변경
## 기여자
@greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
</Update>
<Update label="2026년 4월 9일">
## v1.14.1rc1

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

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

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@@ -4,6 +4,62 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="09 abr 2026">
## v1.14.2a1
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
## O que Mudou
### Correções de Bugs
- Corrigir a emissão do evento flow_finished após a retomada do HITL
- Corrigir a versão da criptografia para 46.0.7 para resolver o CVE-2026-39892
### Refatoração
- Refatorar para usar o singleton I18N_DEFAULT compartilhado
### Documentação
- Atualizar o changelog e a versão para v1.14.1
## Contribuidores
@greysonlalonde
</Update>
<Update label="09 abr 2026">
## v1.14.1
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
## O que Mudou
### Funcionalidades
- Adicionar navegador TUI de ponto de verificação assíncrono
- Adicionar aclose()/close() e gerenciador de contexto assíncrono para saídas de streaming
### Correções de Bugs
- Corrigir regex para aumentos de versão do template pyproject.toml
- Sanitizar nomes de ferramentas nos filtros do decorador de hook
- Corrigir registro de manipuladores de ponto de verificação quando CheckpointConfig é criado
- Atualizar transformers para 5.5.0 para resolver CVE-2026-1839
- Remover wrapper stdout/stderr de FilteredStream
### Documentação
- Atualizar changelog e versão para v1.14.1rc1
### Refatoração
- Substituir lista de negação codificada por exclusão dinâmica de campo BaseTool na geração de especificações
- Substituir regex por tomlkit na CLI do devtools
- Usar singleton PRINTER compartilhado
- Fazer BaseProvider um BaseModel com discriminador provider_type
## Contribuidores
@greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
</Update>
<Update label="09 abr 2026">
## v1.14.1rc1

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

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

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@@ -152,4 +152,4 @@ __all__ = [
"wrap_file_source",
]
__version__ = "1.14.1rc1"
__version__ = "1.14.2a1"

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@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
dependencies = [
"pytube~=15.0.0",
"requests~=2.32.5",
"crewai==1.14.1rc1",
"crewai==1.14.2a1",
"tiktoken~=0.8.0",
"beautifulsoup4~=4.13.4",
"python-docx~=1.2.0",

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@@ -305,4 +305,4 @@ __all__ = [
"ZapierActionTools",
]
__version__ = "1.14.1rc1"
__version__ = "1.14.2a1"

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

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

View File

@@ -14051,7 +14051,7 @@
}
},
{
"description": "Converts natural language to SQL queries and executes them.",
"description": "Converts natural language to SQL queries and executes them against a database. Read-only by default \u2014 only SELECT/SHOW/DESCRIBE/EXPLAIN queries (and read-only CTEs) are allowed unless configured with allow_dml=True.",
"env_vars": [],
"humanized_name": "NL2SQLTool",
"init_params_schema": {
@@ -14092,7 +14092,14 @@
"type": "object"
}
},
"description": "Tool that converts natural language to SQL and executes it against a database.\n\nBy default the tool operates in **read-only mode**: only SELECT, SHOW,\nDESCRIBE, EXPLAIN, and read-only CTEs (WITH \u2026 SELECT) are permitted. Write\noperations (INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, TRUNCATE, \u2026) are\nblocked unless ``allow_dml=True`` is set explicitly or the environment\nvariable ``CREWAI_NL2SQL_ALLOW_DML=true`` is present.\n\nWritable CTEs (``WITH d AS (DELETE \u2026) SELECT \u2026``) and\n``EXPLAIN ANALYZE <write-stmt>`` are treated as write operations and are\nblocked in read-only mode.\n\nThe ``_fetch_all_available_columns`` helper uses parameterised queries so\nthat table names coming from the database catalogue cannot be used as an\ninjection vector.",
"properties": {
"allow_dml": {
"default": false,
"description": "When False (default) only read statements are permitted. Set to True to allow INSERT/UPDATE/DELETE/DROP and other write operations.",
"title": "Allow DML",
"type": "boolean"
},
"columns": {
"additionalProperties": {
"anyOf": [

View File

@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
[project.optional-dependencies]
tools = [
"crewai-tools==1.14.1rc1",
"crewai-tools==1.14.2a1",
]
embeddings = [
"tiktoken~=0.8.0"
@@ -74,8 +74,8 @@ qdrant = [
"qdrant-client[fastembed]~=1.14.3",
]
aws = [
"boto3~=1.40.38",
"aiobotocore~=2.25.2",
"boto3~=1.42.79",
"aiobotocore~=3.4.0",
]
watson = [
"ibm-watsonx-ai~=1.3.39",
@@ -87,7 +87,7 @@ litellm = [
"litellm~=1.83.0",
]
bedrock = [
"boto3~=1.40.45",
"boto3~=1.42.79",
]
google-genai = [
"google-genai~=1.65.0",

View File

@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
_suppress_pydantic_deprecation_warnings()
__version__ = "1.14.1rc1"
__version__ = "1.14.2a1"
_telemetry_submitted = False

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

@@ -2,17 +2,23 @@
from __future__ import annotations
from collections import defaultdict
from typing import Any, ClassVar
from textual.app import App, ComposeResult
from textual.binding import Binding
from textual.containers import Horizontal, Vertical
from textual.screen import ModalScreen
from textual.widgets import Button, Footer, Header, OptionList, Static
from textual.widgets.option_list import Option
from textual.containers import Horizontal, Vertical, VerticalScroll
from textual.widgets import (
Button,
Footer,
Header,
Input,
Static,
TextArea,
Tree,
)
from crewai.cli.checkpoint_cli import (
_entity_summary,
_format_size,
_is_sqlite,
_list_json,
@@ -34,151 +40,54 @@ def _load_entries(location: str) -> list[dict[str, Any]]:
return _list_json(location)
def _format_list_label(entry: dict[str, Any]) -> str:
"""Format a checkpoint entry for the list panel."""
name = entry.get("name", "")
ts = entry.get("ts") or ""
trigger = entry.get("trigger") or ""
summary = _entity_summary(entry.get("entities", []))
line1 = f"[bold]{name}[/]"
parts = []
if ts:
parts.append(f"[dim]{ts}[/]")
if "size" in entry:
parts.append(f"[dim]{_format_size(entry['size'])}[/]")
if trigger:
parts.append(f"[{_PRIMARY}]{trigger}[/]")
line2 = " ".join(parts)
line3 = f" [{_DIM}]{summary}[/]"
return f"{line1}\n{line2}\n{line3}"
def _short_id(name: str) -> str:
"""Shorten a checkpoint name for tree display."""
if len(name) > 30:
return name[:27] + "..."
return name
def _format_detail(entry: dict[str, Any]) -> str:
"""Format checkpoint details for the right panel."""
def _entry_id(entry: dict[str, Any]) -> str:
"""Normalize an entry's name into its checkpoint ID.
JSON filenames are ``{ts}_{uuid}_p-{parent}.json``; SQLite IDs
are already ``{ts}_{uuid}``. This strips the JSON suffix so
fork-parent lookups work in both providers.
"""
name = str(entry.get("name", ""))
if name.endswith(".json"):
name = name[: -len(".json")]
idx = name.find("_p-")
if idx != -1:
name = name[:idx]
return name
def _build_entity_header(ent: dict[str, Any]) -> str:
"""Build rich text header for an entity (progress bar only)."""
lines: list[str] = []
# Header
name = entry.get("name", "")
lines.append(f"[bold {_PRIMARY}]{name}[/]")
lines.append(f"[{_DIM}]{'' * 50}[/]")
lines.append("")
# Metadata table
ts = entry.get("ts") or "unknown"
trigger = entry.get("trigger") or ""
lines.append(f" [bold]Time[/] {ts}")
if "size" in entry:
lines.append(f" [bold]Size[/] {_format_size(entry['size'])}")
lines.append(f" [bold]Events[/] {entry.get('event_count', 0)}")
if trigger:
lines.append(f" [bold]Trigger[/] [{_PRIMARY}]{trigger}[/]")
if "path" in entry:
lines.append(f" [bold]Path[/] [{_DIM}]{entry['path']}[/]")
if "db" in entry:
lines.append(f" [bold]Database[/] [{_DIM}]{entry['db']}[/]")
# Entities
for ent in entry.get("entities", []):
eid = str(ent.get("id", ""))[:8]
etype = ent.get("type", "unknown")
ename = ent.get("name", "unnamed")
lines.append("")
lines.append(f" [{_DIM}]{'' * 50}[/]")
lines.append(f" [bold {_SECONDARY}]{etype}[/]: {ename} [{_DIM}]{eid}[/]")
tasks = ent.get("tasks")
if isinstance(tasks, list):
completed = ent.get("tasks_completed", 0)
total = ent.get("tasks_total", 0)
pct = int(completed / total * 100) if total else 0
bar_len = 20
filled = int(bar_len * completed / total) if total else 0
bar = f"[{_PRIMARY}]{'' * filled}[/][{_DIM}]{'' * (bar_len - filled)}[/]"
lines.append(f" {bar} {completed}/{total} tasks ({pct}%)")
lines.append("")
for i, task in enumerate(tasks):
if task.get("completed"):
icon = "[green]✓[/]"
else:
icon = "[yellow]○[/]"
desc = str(task.get("description", ""))
if len(desc) > 55:
desc = desc[:52] + "..."
lines.append(f" {icon} {i + 1}. {desc}")
tasks = ent.get("tasks")
if isinstance(tasks, list):
completed = ent.get("tasks_completed", 0)
total = ent.get("tasks_total", 0)
pct = int(completed / total * 100) if total else 0
bar_len = 20
filled = int(bar_len * completed / total) if total else 0
bar = f"[{_PRIMARY}]{'' * filled}[/][{_DIM}]{'' * (bar_len - filled)}[/]"
lines.append(f"{bar} {completed}/{total} tasks ({pct}%)")
return "\n".join(lines)
class ConfirmResumeScreen(ModalScreen[bool]):
"""Modal confirmation before resuming from a checkpoint."""
CSS = f"""
ConfirmResumeScreen {{
align: center middle;
}}
#confirm-dialog {{
width: 60;
height: auto;
padding: 1 2;
background: {_BG_PANEL};
border: round {_PRIMARY};
}}
#confirm-label {{
width: 100%;
content-align: center middle;
margin-bottom: 1;
}}
#confirm-name {{
width: 100%;
content-align: center middle;
color: {_PRIMARY};
text-style: bold;
margin-bottom: 1;
}}
#confirm-buttons {{
width: 100%;
height: 3;
layout: horizontal;
align: center middle;
}}
Button {{
margin: 0 2;
min-width: 12;
}}
"""
def __init__(self, checkpoint_name: str) -> None:
super().__init__()
self._checkpoint_name = checkpoint_name
def compose(self) -> ComposeResult:
with Vertical(id="confirm-dialog"):
yield Static("Resume from this checkpoint?", id="confirm-label")
yield Static(self._checkpoint_name, id="confirm-name")
with Horizontal(id="confirm-buttons"):
yield Button("Resume", variant="success", id="btn-yes")
yield Button("Cancel", variant="default", id="btn-no")
def on_button_pressed(self, event: Button.Pressed) -> None:
self.dismiss(event.button.id == "btn-yes")
def on_key(self, event: Any) -> None:
if event.key == "y":
self.dismiss(True)
elif event.key in ("n", "escape"):
self.dismiss(False)
# Return type: (location, action, inputs, task_output_overrides)
_TuiResult = tuple[str, str, dict[str, Any] | None, dict[int, str] | None] | None
class CheckpointTUI(App[str | None]):
class CheckpointTUI(App[_TuiResult]):
"""TUI to browse and inspect checkpoints.
Returns the checkpoint location string to resume from, or None if
the user quit without selecting.
Returns ``(location, action, inputs)`` where action is ``"resume"`` or
``"fork"`` and inputs is a parsed dict or ``None``,
or ``None`` if the user quit without selecting.
"""
TITLE = "CrewAI Checkpoints"
@@ -199,145 +108,431 @@ class CheckpointTUI(App[str | None]):
background: {_PRIMARY};
color: {_TERTIARY};
}}
Horizontal {{
#main-layout {{
height: 1fr;
}}
#cp-list {{
width: 38%;
#tree-panel {{
width: 45%;
background: {_BG_PANEL};
border: round {_SECONDARY};
padding: 0 1;
scrollbar-color: {_PRIMARY};
}}
#cp-list:focus {{
#tree-panel:focus-within {{
border: round {_PRIMARY};
}}
#cp-list > .option-list--option-highlighted {{
background: {_SECONDARY};
color: {_TERTIARY};
text-style: none;
}}
#cp-list > .option-list--option-highlighted * {{
color: {_TERTIARY};
}}
#detail-container {{
width: 62%;
padding: 0 1;
width: 55%;
height: 1fr;
}}
#detail {{
#detail-scroll {{
height: 1fr;
background: {_BG_PANEL};
border: round {_SECONDARY};
padding: 1 2;
overflow-y: auto;
scrollbar-color: {_PRIMARY};
}}
#detail:focus {{
#detail-scroll:focus-within {{
border: round {_PRIMARY};
}}
#detail-header {{
margin-bottom: 1;
}}
#status {{
height: 1;
padding: 0 2;
color: {_DIM};
}}
#inputs-section {{
display: none;
height: auto;
max-height: 8;
padding: 0 1;
}}
#inputs-section.visible {{
display: block;
}}
#inputs-label {{
height: 1;
color: {_DIM};
padding: 0 1;
}}
.input-row {{
height: 3;
padding: 0 1;
}}
.input-row Static {{
width: auto;
min-width: 12;
padding: 1 1 0 0;
color: {_TERTIARY};
}}
.input-row Input {{
width: 1fr;
}}
#no-inputs-label {{
height: 1;
color: {_DIM};
padding: 0 1;
}}
#action-buttons {{
height: 3;
align: right middle;
padding: 0 1;
display: none;
}}
#action-buttons.visible {{
display: block;
}}
#action-buttons Button {{
margin: 0 0 0 1;
min-width: 10;
}}
#btn-resume {{
background: {_SECONDARY};
color: {_TERTIARY};
}}
#btn-resume:hover {{
background: {_PRIMARY};
}}
#btn-fork {{
background: {_PRIMARY};
color: {_TERTIARY};
}}
#btn-fork:hover {{
background: {_SECONDARY};
}}
.entity-title {{
padding: 1 1 0 1;
}}
.entity-detail {{
padding: 0 1;
}}
.task-output-editor {{
height: auto;
max-height: 10;
margin: 0 1 1 1;
border: round {_DIM};
}}
.task-output-editor:focus {{
border: round {_PRIMARY};
}}
.task-label {{
padding: 0 1;
}}
Tree {{
background: {_BG_PANEL};
}}
Tree > .tree--cursor {{
background: {_SECONDARY};
color: {_TERTIARY};
}}
"""
BINDINGS: ClassVar[list[Binding | tuple[str, str] | tuple[str, str, str]]] = [
("q", "quit", "Quit"),
("r", "refresh", "Refresh"),
("j", "cursor_down", "Down"),
("k", "cursor_up", "Up"),
]
def __init__(self, location: str = "./.checkpoints") -> None:
super().__init__()
self._location = location
self._entries: list[dict[str, Any]] = []
self._selected_idx: int = 0
self._pending_location: str = ""
self._selected_entry: dict[str, Any] | None = None
self._input_keys: list[str] = []
self._task_output_ids: list[tuple[int, str, str]] = []
def compose(self) -> ComposeResult:
yield Header(show_clock=False)
with Horizontal():
yield OptionList(id="cp-list")
with Horizontal(id="main-layout"):
tree: Tree[dict[str, Any]] = Tree("Checkpoints", id="tree-panel")
tree.show_root = True
tree.guide_depth = 3
yield tree
with Vertical(id="detail-container"):
yield Static("", id="status")
yield Static(
f"\n [{_DIM}]Select a checkpoint from the list[/]", # noqa: S608
id="detail",
)
with VerticalScroll(id="detail-scroll"):
yield Static(
f"[{_DIM}]Select a checkpoint from the tree[/]", # noqa: S608
id="detail-header",
)
with Vertical(id="inputs-section"):
yield Static("Inputs", id="inputs-label")
with Horizontal(id="action-buttons"):
yield Button("Resume", id="btn-resume")
yield Button("Fork", id="btn-fork")
yield Footer()
async def on_mount(self) -> None:
self.query_one("#cp-list", OptionList).border_title = "Checkpoints"
self.query_one("#detail", Static).border_title = "Detail"
self._refresh_list()
self._refresh_tree()
self.query_one("#tree-panel", Tree).root.expand()
def _refresh_list(self) -> None:
def _refresh_tree(self) -> None:
self._entries = _load_entries(self._location)
option_list = self.query_one("#cp-list", OptionList)
option_list.clear_options()
self._selected_entry = None
tree = self.query_one("#tree-panel", Tree)
tree.clear()
if not self._entries:
self.query_one("#detail", Static).update(
f"\n [{_DIM}]No checkpoints in {self._location}[/]"
self.query_one("#detail-header", Static).update(
f"[{_DIM}]No checkpoints in {self._location}[/]"
)
self.query_one("#status", Static).update("")
self.sub_title = self._location
return
# Group by branch
branches: dict[str, list[dict[str, Any]]] = defaultdict(list)
for entry in self._entries:
option_list.add_option(Option(_format_list_label(entry)))
branch = entry.get("branch", "main")
branches[branch].append(entry)
# Index checkpoint names to tree nodes so forks can attach
node_by_name: dict[str, Any] = {}
def _make_label(e: dict[str, Any]) -> str:
name = e.get("name", "")
ts = e.get("ts") or ""
trigger = e.get("trigger") or ""
parts = [f"[bold]{_short_id(name)}[/]"]
if ts:
time_part = ts.split(" ")[-1] if " " in ts else ts
parts.append(f"[{_DIM}]{time_part}[/]")
if trigger:
parts.append(f"[{_PRIMARY}]{trigger}[/]")
return " ".join(parts)
fork_parents: set[str] = set()
for branch_name, entries in branches.items():
if branch_name == "main" or not entries:
continue
oldest = min(entries, key=lambda e: str(e.get("name", "")))
first_parent = oldest.get("parent_id")
if first_parent:
fork_parents.add(str(first_parent))
def _add_checkpoint(parent_node: Any, e: dict[str, Any]) -> None:
"""Add a checkpoint node — expandable only if a fork attaches to it."""
cp_id = _entry_id(e)
if cp_id in fork_parents:
node = parent_node.add(
_make_label(e), data=e, expand=False, allow_expand=True
)
else:
node = parent_node.add_leaf(_make_label(e), data=e)
node_by_name[cp_id] = node
if "main" in branches:
for entry in reversed(branches["main"]):
_add_checkpoint(tree.root, entry)
fork_branches = [
(name, sorted(entries, key=lambda e: str(e.get("name", ""))))
for name, entries in branches.items()
if name != "main"
]
remaining = fork_branches
max_passes = len(remaining) + 1
while remaining and max_passes > 0:
max_passes -= 1
deferred = []
made_progress = False
for branch_name, entries in remaining:
first_parent = entries[0].get("parent_id") if entries else None
if first_parent and str(first_parent) not in node_by_name:
deferred.append((branch_name, entries))
continue
attach_to: Any = tree.root
if first_parent:
attach_to = node_by_name.get(str(first_parent), tree.root)
branch_label = (
f"[bold {_SECONDARY}]{branch_name}[/] [{_DIM}]({len(entries)})[/]"
)
branch_node = attach_to.add(branch_label, expand=False)
for entry in entries:
_add_checkpoint(branch_node, entry)
made_progress = True
remaining = deferred
if not made_progress:
break
for branch_name, entries in remaining:
branch_label = (
f"[bold {_SECONDARY}]{branch_name}[/] "
f"[{_DIM}]({len(entries)})[/] [{_DIM}](orphaned)[/]"
)
branch_node = tree.root.add(branch_label, expand=False)
for entry in entries:
_add_checkpoint(branch_node, entry)
count = len(self._entries)
storage = "SQLite" if _is_sqlite(self._location) else "JSON"
self.sub_title = f"{self._location}"
self.sub_title = self._location
self.query_one("#status", Static).update(f" {count} checkpoint(s) | {storage}")
async def on_option_list_option_highlighted(
self,
event: OptionList.OptionHighlighted,
) -> None:
idx = event.option_index
if idx is None:
return
if idx < len(self._entries):
self._selected_idx = idx
entry = self._entries[idx]
self.query_one("#detail", Static).update(_format_detail(entry))
async def _show_detail(self, entry: dict[str, Any]) -> None:
"""Update the detail panel for a checkpoint entry."""
self._selected_entry = entry
self.query_one("#action-buttons").add_class("visible")
def action_cursor_down(self) -> None:
self.query_one("#cp-list", OptionList).action_cursor_down()
detail_scroll = self.query_one("#detail-scroll", VerticalScroll)
def action_cursor_up(self) -> None:
self.query_one("#cp-list", OptionList).action_cursor_up()
# Remove all dynamic children except the header — await so IDs are freed
to_remove = [c for c in detail_scroll.children if c.id != "detail-header"]
for child in to_remove:
await child.remove()
async def on_option_list_option_selected(
self,
event: OptionList.OptionSelected,
) -> None:
idx = event.option_index
if idx is None or idx >= len(self._entries):
return
entry = self._entries[idx]
# Header
name = entry.get("name", "")
ts = entry.get("ts") or "unknown"
trigger = entry.get("trigger") or ""
branch = entry.get("branch", "main")
parent_id = entry.get("parent_id")
header_lines = [
f"[bold {_PRIMARY}]{name}[/]",
f"[{_DIM}]{'' * 50}[/]",
"",
f" [bold]Time[/] {ts}",
]
if "size" in entry:
header_lines.append(f" [bold]Size[/] {_format_size(entry['size'])}")
header_lines.append(f" [bold]Events[/] {entry.get('event_count', 0)}")
if trigger:
header_lines.append(f" [bold]Trigger[/] [{_PRIMARY}]{trigger}[/]")
header_lines.append(f" [bold]Branch[/] [{_SECONDARY}]{branch}[/]")
if parent_id:
header_lines.append(f" [bold]Parent[/] [{_DIM}]{parent_id}[/]")
if "path" in entry:
loc = entry["path"]
elif _is_sqlite(self._location):
loc = f"{self._location}#{entry['name']}"
else:
loc = entry.get("name", "")
self._pending_location = loc
name = entry.get("name", loc)
self.push_screen(ConfirmResumeScreen(name), self._on_confirm)
header_lines.append(f" [bold]Path[/] [{_DIM}]{entry['path']}[/]")
if "db" in entry:
header_lines.append(f" [bold]Database[/] [{_DIM}]{entry['db']}[/]")
def _on_confirm(self, confirmed: bool | None) -> None:
if confirmed:
self.exit(self._pending_location)
else:
self._pending_location = ""
self.query_one("#detail-header", Static).update("\n".join(header_lines))
# Entity details and editable task outputs — mounted flat for scrolling
self._task_output_ids = []
flat_task_idx = 0
for ent_idx, ent in enumerate(entry.get("entities", [])):
etype = ent.get("type", "unknown")
ename = ent.get("name", "unnamed")
completed = ent.get("tasks_completed")
total = ent.get("tasks_total")
entity_title = f"[bold {_SECONDARY}]{etype}: {ename}[/]"
if completed is not None and total is not None:
entity_title += f" [{_DIM}]{completed}/{total} tasks[/]"
await detail_scroll.mount(Static(entity_title, classes="entity-title"))
await detail_scroll.mount(
Static(_build_entity_header(ent), classes="entity-detail")
)
tasks = ent.get("tasks", [])
for i, task in enumerate(tasks):
desc = str(task.get("description", ""))
if len(desc) > 55:
desc = desc[:52] + "..."
if task.get("completed"):
icon = "[green]✓[/]"
await detail_scroll.mount(
Static(f" {icon} {i + 1}. {desc}", classes="task-label")
)
output_text = task.get("output", "")
editor_id = f"task-output-{ent_idx}-{i}"
await detail_scroll.mount(
TextArea(
str(output_text),
classes="task-output-editor",
id=editor_id,
)
)
self._task_output_ids.append(
(flat_task_idx, editor_id, str(output_text))
)
else:
icon = "[yellow]○[/]"
await detail_scroll.mount(
Static(f" {icon} {i + 1}. {desc}", classes="task-label")
)
flat_task_idx += 1
# Build input fields
await self._build_input_fields(entry.get("inputs", {}))
async def _build_input_fields(self, inputs: dict[str, Any]) -> None:
"""Rebuild the inputs section with one field per input key."""
section = self.query_one("#inputs-section")
# Remove old dynamic children — await so IDs are freed
for widget in list(section.query(".input-row, .no-inputs")):
await widget.remove()
self._input_keys = []
if not inputs:
await section.mount(Static(f"[{_DIM}]No inputs[/]", classes="no-inputs"))
section.add_class("visible")
return
for key, value in inputs.items():
self._input_keys.append(key)
row = Horizontal(classes="input-row")
row.compose_add_child(Static(f"[bold]{key}[/]"))
row.compose_add_child(
Input(value=str(value), placeholder=key, id=f"input-{key}")
)
await section.mount(row)
section.add_class("visible")
def _collect_inputs(self) -> dict[str, Any] | None:
"""Collect current values from input fields."""
if not self._input_keys:
return None
result: dict[str, Any] = {}
for key in self._input_keys:
widget = self.query_one(f"#input-{key}", Input)
result[key] = widget.value
return result
def _collect_task_overrides(self) -> dict[int, str] | None:
"""Collect edited task outputs. Returns only changed values."""
if not self._task_output_ids or self._selected_entry is None:
return None
overrides: dict[int, str] = {}
for task_idx, editor_id, original in self._task_output_ids:
editor = self.query_one(f"#{editor_id}", TextArea)
if editor.text != original:
overrides[task_idx] = editor.text
return overrides or None
def _resolve_location(self, entry: dict[str, Any]) -> str:
"""Get the restore location string for a checkpoint entry."""
if "path" in entry:
return str(entry["path"])
if _is_sqlite(self._location):
return f"{self._location}#{entry['name']}"
return str(entry.get("name", ""))
async def on_tree_node_highlighted(
self, event: Tree.NodeHighlighted[dict[str, Any]]
) -> None:
if event.node.data is not None:
await self._show_detail(event.node.data)
def on_button_pressed(self, event: Button.Pressed) -> None:
if self._selected_entry is None:
return
inputs = self._collect_inputs()
overrides = self._collect_task_overrides()
loc = self._resolve_location(self._selected_entry)
if event.button.id == "btn-resume":
self.exit((loc, "resume", inputs, overrides))
elif event.button.id == "btn-fork":
self.exit((loc, "fork", inputs, overrides))
def action_refresh(self) -> None:
self._refresh_list()
self._refresh_tree()
async def _run_checkpoint_tui_async(location: str) -> None:
@@ -345,17 +540,78 @@ async def _run_checkpoint_tui_async(location: str) -> None:
import click
app = CheckpointTUI(location=location)
selected = await app.run_async()
selection = await app.run_async()
if selected is None:
if selection is None:
return
click.echo(f"\nResuming from: {selected}\n")
selected, action, inputs, task_overrides = selection
from crewai.crew import Crew
from crewai.state.checkpoint_config import CheckpointConfig
crew = Crew.from_checkpoint(selected)
result = await crew.akickoff()
config = CheckpointConfig(restore_from=selected)
if action == "fork":
click.echo(f"\nForking from: {selected}\n")
crew = Crew.fork(config)
else:
click.echo(f"\nResuming from: {selected}\n")
crew = Crew.from_checkpoint(config)
if task_overrides:
click.echo("Modifications:")
overridden_agents: set[int] = set()
for task_idx, new_output in task_overrides.items():
if task_idx < len(crew.tasks) and crew.tasks[task_idx].output is not None:
desc = crew.tasks[task_idx].description or f"Task {task_idx + 1}"
if len(desc) > 60:
desc = desc[:57] + "..."
crew.tasks[task_idx].output.raw = new_output # type: ignore[union-attr]
preview = new_output.replace("\n", " ")
if len(preview) > 80:
preview = preview[:77] + "..."
click.echo(f" Task {task_idx + 1}: {desc}")
click.echo(f" -> {preview}")
agent = crew.tasks[task_idx].agent
if agent and agent.agent_executor:
nth = sum(1 for t in crew.tasks[:task_idx] if t.agent is agent)
messages = agent.agent_executor.messages
system_positions = [
i for i, m in enumerate(messages) if m.get("role") == "system"
]
if nth < len(system_positions):
seg_start = system_positions[nth]
seg_end = (
system_positions[nth + 1]
if nth + 1 < len(system_positions)
else len(messages)
)
for j in range(seg_end - 1, seg_start, -1):
if messages[j].get("role") == "assistant":
messages[j]["content"] = new_output
break
overridden_agents.add(id(agent))
earliest = min(task_overrides)
for offset, subsequent in enumerate(
crew.tasks[earliest + 1 :], start=earliest + 1
):
if subsequent.output and offset not in task_overrides:
subsequent.output = None
if subsequent.agent and subsequent.agent.agent_executor:
subsequent.agent.agent_executor._resuming = False
if id(subsequent.agent) not in overridden_agents:
subsequent.agent.agent_executor.messages = []
click.echo()
if inputs:
click.echo("Inputs:")
for k, v in inputs.items():
click.echo(f" {k}: {v}")
click.echo()
result = await crew.akickoff(inputs=inputs)
click.echo(f"\nResult: {getattr(result, 'raw', result)}")

View File

@@ -793,6 +793,9 @@ def traces_status() -> None:
@click.pass_context
def checkpoint(ctx: click.Context, location: str) -> None:
"""Browse and inspect checkpoints. Launches a TUI when called without a subcommand."""
from crewai.cli.checkpoint_cli import _detect_location
location = _detect_location(location)
ctx.ensure_object(dict)
ctx.obj["location"] = location
if ctx.invoked_subcommand is None:
@@ -805,18 +808,18 @@ def checkpoint(ctx: click.Context, location: str) -> None:
@click.argument("location", default="./.checkpoints")
def checkpoint_list(location: str) -> None:
"""List checkpoints in a directory."""
from crewai.cli.checkpoint_cli import list_checkpoints
from crewai.cli.checkpoint_cli import _detect_location, list_checkpoints
list_checkpoints(location)
list_checkpoints(_detect_location(location))
@checkpoint.command("info")
@click.argument("path", default="./.checkpoints")
def checkpoint_info(path: str) -> None:
"""Show details of a checkpoint. Pass a file or directory for latest."""
from crewai.cli.checkpoint_cli import info_checkpoint
from crewai.cli.checkpoint_cli import _detect_location, info_checkpoint
info_checkpoint(path)
info_checkpoint(_detect_location(path))
if __name__ == "__main__":

View File

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

View File

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

View File

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

View File

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

View File

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.14.1rc1"
"crewai[tools]==1.14.2a1"
]
[project.scripts]

View File

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.14.1rc1"
"crewai[tools]==1.14.2a1"
]
[project.scripts]

View File

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
readme = "README.md"
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.14.1rc1"
"crewai[tools]==1.14.2a1"
]
[tool.crewai]

View File

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

View File

@@ -42,7 +42,6 @@ if TYPE_CHECKING:
from opentelemetry.trace import Span
from crewai.context import ExecutionContext
from crewai.state.provider.core import BaseProvider
try:
from crewai_files import get_supported_content_types
@@ -104,7 +103,11 @@ from crewai.rag.types import SearchResult
from crewai.security.fingerprint import Fingerprint
from crewai.security.security_config import SecurityConfig
from crewai.skills.models import Skill
from crewai.state.checkpoint_config import CheckpointConfig, _coerce_checkpoint
from crewai.state.checkpoint_config import (
CheckpointConfig,
_coerce_checkpoint,
apply_checkpoint,
)
from crewai.task import Task
from crewai.tasks.conditional_task import ConditionalTask
from crewai.tasks.task_output import TaskOutput
@@ -365,32 +368,21 @@ class Crew(FlowTrackable, BaseModel):
checkpoint_kickoff_event_id: str | None = Field(default=None)
@classmethod
def from_checkpoint(
cls, path: str, *, provider: BaseProvider | None = None
) -> Crew:
"""Restore a Crew from a checkpoint file, ready to resume via kickoff().
def from_checkpoint(cls, config: CheckpointConfig) -> Crew:
"""Restore a Crew from a checkpoint, ready to resume via kickoff().
Args:
path: Path to a checkpoint JSON file.
provider: Storage backend to read from. Defaults to JsonProvider.
config: Checkpoint configuration with ``restore_from`` set to
the path of the checkpoint to load.
Returns:
A Crew instance. Call kickoff() to resume from the last completed task.
"""
from crewai.context import apply_execution_context
from crewai.events.event_bus import crewai_event_bus
from crewai.state.provider.json_provider import JsonProvider
from crewai.state.provider.utils import detect_provider
from crewai.state.runtime import RuntimeState
if provider is None:
provider = detect_provider(path)
state = RuntimeState.from_checkpoint(
path,
provider=provider or JsonProvider(),
context={"from_checkpoint": True},
)
state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
crewai_event_bus.set_runtime_state(state)
for entity in state.root:
if isinstance(entity, cls):
@@ -398,7 +390,32 @@ class Crew(FlowTrackable, BaseModel):
apply_execution_context(entity.execution_context)
entity._restore_runtime()
return entity
raise ValueError(f"No Crew found in checkpoint: {path}")
raise ValueError(f"No Crew found in checkpoint: {config.restore_from}")
@classmethod
def fork(
cls,
config: CheckpointConfig,
branch: str | None = None,
) -> Crew:
"""Fork a Crew from a checkpoint, creating a new execution branch.
Args:
config: Checkpoint configuration with ``restore_from`` set.
branch: Branch label for the fork. Auto-generated if not provided.
Returns:
A Crew instance on the new branch. Call kickoff() to run.
"""
crew = cls.from_checkpoint(config)
state = crewai_event_bus._runtime_state
if state is None:
raise RuntimeError(
"Cannot fork: no runtime state on the event bus. "
"Ensure from_checkpoint() succeeded before calling fork()."
)
state.fork(branch)
return crew
def _restore_runtime(self) -> None:
"""Re-create runtime objects after restoring from a checkpoint."""
@@ -419,6 +436,13 @@ class Crew(FlowTrackable, BaseModel):
if agent.agent_executor is not None and task.output is None:
agent.agent_executor.task = task
break
for task in self.tasks:
if task.checkpoint_original_description is not None:
task._original_description = task.checkpoint_original_description
if task.checkpoint_original_expected_output is not None:
task._original_expected_output = (
task.checkpoint_original_expected_output
)
if self.checkpoint_inputs is not None:
self._inputs = self.checkpoint_inputs
if self.checkpoint_kickoff_event_id is not None:
@@ -854,16 +878,23 @@ class Crew(FlowTrackable, BaseModel):
self,
inputs: dict[str, Any] | None = None,
input_files: dict[str, FileInput] | None = None,
from_checkpoint: CheckpointConfig | None = None,
) -> CrewOutput | CrewStreamingOutput:
"""Execute the crew's workflow.
Args:
inputs: Optional input dictionary for task interpolation.
input_files: Optional dict of named file inputs for the crew.
from_checkpoint: Optional checkpoint config. If ``restore_from``
is set, the crew resumes from that checkpoint. Remaining
config fields enable checkpointing for the run.
Returns:
CrewOutput or CrewStreamingOutput if streaming is enabled.
"""
restored = apply_checkpoint(self, from_checkpoint)
if restored is not None:
return restored.kickoff(inputs=inputs, input_files=input_files) # type: ignore[no-any-return]
get_env_context()
if self.stream:
enable_agent_streaming(self.agents)
@@ -976,12 +1007,15 @@ class Crew(FlowTrackable, BaseModel):
self,
inputs: dict[str, Any] | None = None,
input_files: dict[str, FileInput] | None = None,
from_checkpoint: CheckpointConfig | None = None,
) -> CrewOutput | CrewStreamingOutput:
"""Asynchronous kickoff method to start the crew execution.
Args:
inputs: Optional input dictionary for task interpolation.
input_files: Optional dict of named file inputs for the crew.
from_checkpoint: Optional checkpoint config. If ``restore_from``
is set, the crew resumes from that checkpoint.
Returns:
CrewOutput or CrewStreamingOutput if streaming is enabled.
@@ -990,6 +1024,9 @@ class Crew(FlowTrackable, BaseModel):
to get stream chunks. After iteration completes, access the final result
via .result.
"""
restored = apply_checkpoint(self, from_checkpoint)
if restored is not None:
return await restored.kickoff_async(inputs=inputs, input_files=input_files) # type: ignore[no-any-return]
inputs = inputs or {}
if self.stream:
@@ -1050,6 +1087,7 @@ class Crew(FlowTrackable, BaseModel):
self,
inputs: dict[str, Any] | None = None,
input_files: dict[str, FileInput] | None = None,
from_checkpoint: CheckpointConfig | None = None,
) -> CrewOutput | CrewStreamingOutput:
"""Native async kickoff method using async task execution throughout.
@@ -1060,10 +1098,15 @@ class Crew(FlowTrackable, BaseModel):
Args:
inputs: Optional input dictionary for task interpolation.
input_files: Optional dict of named file inputs for the crew.
from_checkpoint: Optional checkpoint config. If ``restore_from``
is set, the crew resumes from that checkpoint.
Returns:
CrewOutput or CrewStreamingOutput if streaming is enabled.
"""
restored = apply_checkpoint(self, from_checkpoint)
if restored is not None:
return await restored.akickoff(inputs=inputs, input_files=input_files) # type: ignore[no-any-return]
if self.stream:
enable_agent_streaming(self.agents)
ctx = StreamingContext(use_async=True)

View File

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

View File

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

View File

@@ -58,6 +58,7 @@ class LLMCallCompletedEvent(LLMEventBase):
response: Any
call_type: LLMCallType
usage: dict[str, Any] | None = None
is_litellm: bool = False
class LLMCallFailedEvent(LLMEventBase):

View File

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

View File

@@ -113,7 +113,11 @@ from crewai.flow.utils import (
)
from crewai.memory.memory_scope import MemoryScope, MemorySlice
from crewai.memory.unified_memory import Memory
from crewai.state.checkpoint_config import CheckpointConfig, _coerce_checkpoint
from crewai.state.checkpoint_config import (
CheckpointConfig,
_coerce_checkpoint,
apply_checkpoint,
)
if TYPE_CHECKING:
@@ -122,7 +126,6 @@ if TYPE_CHECKING:
from crewai.context import ExecutionContext
from crewai.flow.async_feedback.types import PendingFeedbackContext
from crewai.llms.base_llm import BaseLLM
from crewai.state.provider.core import BaseProvider
from crewai.flow.visualization import build_flow_structure, render_interactive
from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
@@ -928,20 +931,21 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
] = Field(default=None)
@classmethod
def from_checkpoint(
cls, path: str, *, provider: BaseProvider | None = None
) -> Flow: # type: ignore[type-arg]
"""Restore a Flow from a checkpoint file."""
def from_checkpoint(cls, config: CheckpointConfig) -> Flow: # type: ignore[type-arg]
"""Restore a Flow from a checkpoint.
Args:
config: Checkpoint configuration with ``restore_from`` set to
the path of the checkpoint to load.
Returns:
A Flow instance ready to resume.
"""
from crewai.context import apply_execution_context
from crewai.events.event_bus import crewai_event_bus
from crewai.state.provider.json_provider import JsonProvider
from crewai.state.runtime import RuntimeState
state = RuntimeState.from_checkpoint(
path,
provider=provider or JsonProvider(),
context={"from_checkpoint": True},
)
state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
crewai_event_bus.set_runtime_state(state)
for entity in state.root:
if not isinstance(entity, Flow):
@@ -958,7 +962,32 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
instance.checkpoint_state = entity.checkpoint_state
instance._restore_from_checkpoint()
return instance
raise ValueError(f"No Flow found in checkpoint: {path}")
raise ValueError(f"No Flow found in checkpoint: {config.restore_from}")
@classmethod
def fork(
cls,
config: CheckpointConfig,
branch: str | None = None,
) -> Flow: # type: ignore[type-arg]
"""Fork a Flow from a checkpoint, creating a new execution branch.
Args:
config: Checkpoint configuration with ``restore_from`` set.
branch: Branch label for the fork. Auto-generated if not provided.
Returns:
A Flow instance on the new branch. Call kickoff() to run.
"""
flow = cls.from_checkpoint(config)
state = crewai_event_bus._runtime_state
if state is None:
raise RuntimeError(
"Cannot fork: no runtime state on the event bus. "
"Ensure from_checkpoint() succeeded before calling fork()."
)
state.fork(branch)
return flow
checkpoint_completed_methods: set[str] | None = Field(default=None)
checkpoint_method_outputs: list[Any] | None = Field(default=None)
@@ -1455,6 +1484,25 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
"No pending feedback context. Use from_pending() to restore a paused flow."
)
if get_current_parent_id() is None:
reset_emission_counter()
reset_last_event_id()
if not self.suppress_flow_events:
future = crewai_event_bus.emit(
self,
FlowStartedEvent(
type="flow_started",
flow_name=self.name or self.__class__.__name__,
inputs=None,
),
)
if future and isinstance(future, Future):
try:
await asyncio.wrap_future(future)
except Exception:
logger.warning("FlowStartedEvent handler failed", exc_info=True)
context = self._pending_feedback_context
emit = context.emit
default_outcome = context.default_outcome
@@ -1594,16 +1642,39 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
final_result = self._method_outputs[-1] if self._method_outputs else result
# Emit flow finished
crewai_event_bus.emit(
self,
FlowFinishedEvent(
type="flow_finished",
flow_name=self.name or self.__class__.__name__,
result=final_result,
state=self._state,
),
)
if self._event_futures:
await asyncio.gather(
*[
asyncio.wrap_future(f)
for f in self._event_futures
if isinstance(f, Future)
]
)
self._event_futures.clear()
if not self.suppress_flow_events:
future = crewai_event_bus.emit(
self,
FlowFinishedEvent(
type="flow_finished",
flow_name=self.name or self.__class__.__name__,
result=final_result,
state=self._copy_and_serialize_state(),
),
)
if future and isinstance(future, Future):
try:
await asyncio.wrap_future(future)
except Exception:
logger.warning("FlowFinishedEvent handler failed", exc_info=True)
trace_listener = TraceCollectionListener()
if trace_listener.batch_manager.batch_owner_type == "flow":
if trace_listener.first_time_handler.is_first_time:
trace_listener.first_time_handler.mark_events_collected()
trace_listener.first_time_handler.handle_execution_completion()
else:
trace_listener.batch_manager.finalize_batch()
return final_result
@@ -1914,6 +1985,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
self,
inputs: dict[str, Any] | None = None,
input_files: dict[str, FileInput] | None = None,
from_checkpoint: CheckpointConfig | None = None,
) -> Any | FlowStreamingOutput:
"""Start the flow execution in a synchronous context.
@@ -1923,10 +1995,15 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
Args:
inputs: Optional dictionary containing input values and/or a state ID.
input_files: Optional dict of named file inputs for the flow.
from_checkpoint: Optional checkpoint config. If ``restore_from``
is set, the flow resumes from that checkpoint.
Returns:
The final output from the flow or FlowStreamingOutput if streaming.
"""
restored = apply_checkpoint(self, from_checkpoint)
if restored is not None:
return restored.kickoff(inputs=inputs, input_files=input_files)
get_env_context()
if self.stream:
result_holder: list[Any] = []
@@ -1983,6 +2060,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
self,
inputs: dict[str, Any] | None = None,
input_files: dict[str, FileInput] | None = None,
from_checkpoint: CheckpointConfig | None = None,
) -> Any | FlowStreamingOutput:
"""Start the flow execution asynchronously.
@@ -1994,10 +2072,15 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
Args:
inputs: Optional dictionary containing input values and/or a state ID for restoration.
input_files: Optional dict of named file inputs for the flow.
from_checkpoint: Optional checkpoint config. If ``restore_from``
is set, the flow resumes from that checkpoint.
Returns:
The final output from the flow, which is the result of the last executed method.
"""
restored = apply_checkpoint(self, from_checkpoint)
if restored is not None:
return await restored.kickoff_async(inputs=inputs, input_files=input_files)
if self.stream:
result_holder: list[Any] = []
current_task_info: TaskInfo = {
@@ -2256,17 +2339,20 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
self,
inputs: dict[str, Any] | None = None,
input_files: dict[str, FileInput] | None = None,
from_checkpoint: CheckpointConfig | None = None,
) -> Any | FlowStreamingOutput:
"""Native async method to start the flow execution. Alias for kickoff_async.
Args:
inputs: Optional dictionary containing input values and/or a state ID for restoration.
input_files: Optional dict of named file inputs for the flow.
from_checkpoint: Optional checkpoint config. If ``restore_from``
is set, the flow resumes from that checkpoint.
Returns:
The final output from the flow, which is the result of the last executed method.
"""
return await self.kickoff_async(inputs, input_files)
return await self.kickoff_async(inputs, input_files, from_checkpoint)
async def _execute_start_method(self, start_method_name: FlowMethodName) -> None:
"""Executes a flow's start method and its triggered listeners.
@@ -3194,7 +3280,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
from crewai.llm import LLM
from crewai.llms.base_llm import BaseLLM as BaseLLMClass
from crewai.utilities.i18n import get_i18n
from crewai.utilities.i18n import I18N_DEFAULT
llm_instance: BaseLLMClass
if isinstance(llm, str):
@@ -3214,9 +3300,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
description=f"The outcome that best matches the feedback. Must be one of: {', '.join(outcomes)}"
)
# Load prompt from translations (using cached instance)
i18n = get_i18n()
prompt_template = i18n.slice("human_feedback_collapse")
prompt_template = I18N_DEFAULT.slice("human_feedback_collapse")
prompt = prompt_template.format(
feedback=feedback,

View File

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

View File

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

View File

@@ -1957,6 +1957,7 @@ class LLM(BaseLLM):
model=self.model,
call_id=get_current_call_id(),
usage=usage,
is_litellm=self.is_litellm,
),
)

View File

@@ -172,6 +172,8 @@ class BaseLLM(BaseModel, ABC):
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
"reasoning_tokens": 0,
"cache_creation_tokens": 0,
}
)
@@ -477,6 +479,7 @@ class BaseLLM(BaseModel, ABC):
model=self.model,
call_id=get_current_call_id(),
usage=usage,
is_litellm=self.is_litellm,
),
)
@@ -808,14 +811,24 @@ class BaseLLM(BaseModel, ABC):
cached_tokens = (
usage_data.get("cached_tokens")
or usage_data.get("cached_prompt_tokens")
or usage_data.get("cache_read_input_tokens")
or 0
)
if not cached_tokens:
prompt_details = usage_data.get("prompt_tokens_details")
if isinstance(prompt_details, dict):
cached_tokens = prompt_details.get("cached_tokens", 0) or 0
reasoning_tokens = usage_data.get("reasoning_tokens", 0) or 0
cache_creation_tokens = usage_data.get("cache_creation_tokens", 0) or 0
self._token_usage["prompt_tokens"] += prompt_tokens
self._token_usage["completion_tokens"] += completion_tokens
self._token_usage["total_tokens"] += prompt_tokens + completion_tokens
self._token_usage["successful_requests"] += 1
self._token_usage["cached_prompt_tokens"] += cached_tokens
self._token_usage["reasoning_tokens"] += reasoning_tokens
self._token_usage["cache_creation_tokens"] += cache_creation_tokens
def get_token_usage_summary(self) -> UsageMetrics:
"""Get summary of token usage for this LLM instance.

View File

@@ -494,6 +494,10 @@ class AnthropicCompletion(BaseLLM):
"required": [],
}
func_info = tool.get("function", {})
if func_info.get("strict"):
anthropic_tool["strict"] = True
anthropic_tools.append(anthropic_tool)
return anthropic_tools
@@ -1704,18 +1708,23 @@ class AnthropicCompletion(BaseLLM):
def _extract_anthropic_token_usage(
response: Message | BetaMessage,
) -> dict[str, Any]:
"""Extract token usage from Anthropic response."""
"""Extract token usage and response metadata from Anthropic response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage
input_tokens = getattr(usage, "input_tokens", 0)
output_tokens = getattr(usage, "output_tokens", 0)
cache_read_tokens = getattr(usage, "cache_read_input_tokens", 0) or 0
return {
cache_creation_tokens = (
getattr(usage, "cache_creation_input_tokens", 0) or 0
)
result: dict[str, Any] = {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens,
"cached_prompt_tokens": cache_read_tokens,
"cache_creation_tokens": cache_creation_tokens,
}
return result
return {"total_tokens": 0}
def supports_multimodal(self) -> bool:

View File

@@ -1076,19 +1076,27 @@ class AzureCompletion(BaseLLM):
@staticmethod
def _extract_azure_token_usage(response: ChatCompletions) -> dict[str, Any]:
"""Extract token usage from Azure response."""
"""Extract token usage and response metadata from Azure response."""
if hasattr(response, "usage") and response.usage:
usage = response.usage
cached_tokens = 0
prompt_details = getattr(usage, "prompt_tokens_details", None)
if prompt_details:
cached_tokens = getattr(prompt_details, "cached_tokens", 0) or 0
return {
reasoning_tokens = 0
completion_details = getattr(usage, "completion_tokens_details", None)
if completion_details:
reasoning_tokens = (
getattr(completion_details, "reasoning_tokens", 0) or 0
)
result: dict[str, Any] = {
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
"completion_tokens": getattr(usage, "completion_tokens", 0),
"total_tokens": getattr(usage, "total_tokens", 0),
"cached_prompt_tokens": cached_tokens,
"reasoning_tokens": reasoning_tokens,
}
return result
return {"total_tokens": 0}
async def aclose(self) -> None:

View File

@@ -169,6 +169,7 @@ class ToolSpec(TypedDict, total=False):
name: Required[str]
description: Required[str]
inputSchema: ToolInputSchema
strict: bool
class ConverseToolTypeDef(TypedDict):
@@ -1965,6 +1966,10 @@ class BedrockCompletion(BaseLLM):
input_schema: ToolInputSchema = {"json": parameters}
tool_spec["inputSchema"] = input_schema
func_info = tool.get("function", {})
if func_info.get("strict"):
tool_spec["strict"] = True
converse_tool: ConverseToolTypeDef = {"toolSpec": tool_spec}
converse_tools.append(converse_tool)
@@ -2025,11 +2030,18 @@ class BedrockCompletion(BaseLLM):
input_tokens = usage.get("inputTokens", 0)
output_tokens = usage.get("outputTokens", 0)
total_tokens = usage.get("totalTokens", input_tokens + output_tokens)
raw_cached = (
usage.get("cacheReadInputTokenCount")
or usage.get("cacheReadInputTokens")
or 0
)
cached_tokens = raw_cached if isinstance(raw_cached, int) else 0
self._token_usage["prompt_tokens"] += input_tokens
self._token_usage["completion_tokens"] += output_tokens
self._token_usage["total_tokens"] += total_tokens
self._token_usage["successful_requests"] += 1
self._token_usage["cached_prompt_tokens"] += cached_tokens
def supports_function_calling(self) -> bool:
"""Check if the model supports function calling."""

View File

@@ -1306,17 +1306,20 @@ class GeminiCompletion(BaseLLM):
@staticmethod
def _extract_token_usage(response: GenerateContentResponse) -> dict[str, Any]:
"""Extract token usage from Gemini response."""
"""Extract token usage and response metadata from Gemini response."""
if response.usage_metadata:
usage = response.usage_metadata
cached_tokens = getattr(usage, "cached_content_token_count", 0) or 0
return {
thinking_tokens = getattr(usage, "thoughts_token_count", 0) or 0
result: dict[str, Any] = {
"prompt_token_count": getattr(usage, "prompt_token_count", 0),
"candidates_token_count": getattr(usage, "candidates_token_count", 0),
"total_token_count": getattr(usage, "total_token_count", 0),
"total_tokens": getattr(usage, "total_token_count", 0),
"cached_prompt_tokens": cached_tokens,
"reasoning_tokens": thinking_tokens,
}
return result
return {"total_tokens": 0}
@staticmethod

View File

@@ -1324,19 +1324,23 @@ class OpenAICompletion(BaseLLM):
]
def _extract_responses_token_usage(self, response: Response) -> dict[str, Any]:
"""Extract token usage from Responses API response."""
"""Extract token usage and response metadata from Responses API response."""
if response.usage:
result = {
result: dict[str, Any] = {
"prompt_tokens": response.usage.input_tokens,
"completion_tokens": response.usage.output_tokens,
"total_tokens": response.usage.total_tokens,
}
# Extract cached prompt tokens from input_tokens_details
input_details = getattr(response.usage, "input_tokens_details", None)
if input_details:
result["cached_prompt_tokens"] = (
getattr(input_details, "cached_tokens", 0) or 0
)
output_details = getattr(response.usage, "output_tokens_details", None)
if output_details:
result["reasoning_tokens"] = (
getattr(output_details, "reasoning_tokens", 0) or 0
)
return result
return {"total_tokens": 0}
@@ -2307,20 +2311,24 @@ class OpenAICompletion(BaseLLM):
def _extract_openai_token_usage(
self, response: ChatCompletion | ChatCompletionChunk
) -> dict[str, Any]:
"""Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
"""Extract token usage and response metadata from OpenAI ChatCompletion."""
if hasattr(response, "usage") and response.usage:
usage = response.usage
result = {
result: dict[str, Any] = {
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
"completion_tokens": getattr(usage, "completion_tokens", 0),
"total_tokens": getattr(usage, "total_tokens", 0),
}
# Extract cached prompt tokens from prompt_tokens_details
prompt_details = getattr(usage, "prompt_tokens_details", None)
if prompt_details:
result["cached_prompt_tokens"] = (
getattr(prompt_details, "cached_tokens", 0) or 0
)
completion_details = getattr(usage, "completion_tokens_details", None)
if completion_details:
result["reasoning_tokens"] = (
getattr(completion_details, "reasoning_tokens", 0) or 0
)
return result
return {"total_tokens": 0}

View File

@@ -9,7 +9,7 @@ from typing import Any
from pydantic import BaseModel, ConfigDict, Field
from crewai.memory.types import MemoryRecord, ScopeInfo
from crewai.utilities.i18n import get_i18n
from crewai.utilities.i18n import I18N_DEFAULT
_logger = logging.getLogger(__name__)
@@ -149,7 +149,7 @@ def _get_prompt(key: str) -> str:
Returns:
The prompt string.
"""
return get_i18n().memory(key)
return I18N_DEFAULT.memory(key)
def extract_memories_from_content(content: str, llm: Any) -> list[str]:

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

@@ -52,6 +52,7 @@ from crewai.telemetry.utils import (
add_crew_attributes,
close_span,
)
from crewai.utilities.i18n import I18N_DEFAULT
from crewai.utilities.logger_utils import suppress_warnings
from crewai.utilities.string_utils import sanitize_tool_name
@@ -314,7 +315,7 @@ class Telemetry:
"verbose?": agent.verbose,
"max_iter": agent.max_iter,
"max_rpm": agent.max_rpm,
"i18n": agent.i18n.prompt_file,
"i18n": I18N_DEFAULT.prompt_file,
"function_calling_llm": (
getattr(
getattr(agent, "function_calling_llm", None),
@@ -844,7 +845,7 @@ class Telemetry:
"verbose?": agent.verbose,
"max_iter": agent.max_iter,
"max_rpm": agent.max_rpm,
"i18n": agent.i18n.prompt_file,
"i18n": I18N_DEFAULT.prompt_file,
"llm": agent.llm.model
if isinstance(agent.llm, BaseLLM)
else str(agent.llm),

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

@@ -142,3 +142,6 @@ def get_i18n(prompt_file: str | None = None) -> I18N:
Cached I18N instance.
"""
return I18N(prompt_file=prompt_file)
I18N_DEFAULT: I18N = get_i18n()

View File

@@ -6,7 +6,7 @@ from typing import Any, Literal
from pydantic import BaseModel, Field
from crewai.utilities.i18n import I18N, get_i18n
from crewai.utilities.i18n import I18N_DEFAULT
class StandardPromptResult(BaseModel):
@@ -49,7 +49,6 @@ class Prompts(BaseModel):
- Need to refactor so that prompt is not tightly coupled to agent.
"""
i18n: I18N = Field(default_factory=get_i18n)
has_tools: bool = Field(
default=False, description="Indicates if the agent has access to tools"
)
@@ -140,13 +139,13 @@ class Prompts(BaseModel):
if not system_template or not prompt_template:
# If any of the required templates are missing, fall back to the default format
prompt_parts: list[str] = [
self.i18n.slice(component) for component in components
I18N_DEFAULT.slice(component) for component in components
]
prompt = "".join(prompt_parts)
else:
# All templates are provided, use them
template_parts: list[str] = [
self.i18n.slice(component)
I18N_DEFAULT.slice(component)
for component in components
if component != "task"
]
@@ -154,7 +153,7 @@ class Prompts(BaseModel):
"{{ .System }}", "".join(template_parts)
)
prompt = prompt_template.replace(
"{{ .Prompt }}", "".join(self.i18n.slice("task"))
"{{ .Prompt }}", "".join(I18N_DEFAULT.slice("task"))
)
# Handle missing response_template
if response_template:

View File

@@ -15,6 +15,7 @@ from crewai.events.types.reasoning_events import (
AgentReasoningStartedEvent,
)
from crewai.llm import LLM
from crewai.utilities.i18n import I18N_DEFAULT
from crewai.utilities.llm_utils import create_llm
from crewai.utilities.planning_types import PlanStep
from crewai.utilities.string_utils import sanitize_tool_name
@@ -481,17 +482,17 @@ class AgentReasoning:
"""Get the system prompt for planning.
Returns:
The system prompt, either custom or from i18n.
The system prompt, either custom or from I18N_DEFAULT.
"""
if self.config.system_prompt is not None:
return self.config.system_prompt
# Try new "planning" section first, fall back to "reasoning" for compatibility
try:
return self.agent.i18n.retrieve("planning", "system_prompt")
return I18N_DEFAULT.retrieve("planning", "system_prompt")
except (KeyError, AttributeError):
# Fallback to reasoning section for backward compatibility
return self.agent.i18n.retrieve("reasoning", "initial_plan").format(
return I18N_DEFAULT.retrieve("reasoning", "initial_plan").format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self._get_agent_backstory(),
@@ -527,7 +528,7 @@ class AgentReasoning:
# Try new "planning" section first
try:
return self.agent.i18n.retrieve("planning", "create_plan_prompt").format(
return I18N_DEFAULT.retrieve("planning", "create_plan_prompt").format(
description=self.description,
expected_output=self.expected_output,
tools=available_tools,
@@ -535,7 +536,7 @@ class AgentReasoning:
)
except (KeyError, AttributeError):
# Fallback to reasoning section for backward compatibility
return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
return I18N_DEFAULT.retrieve("reasoning", "create_plan_prompt").format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self._get_agent_backstory(),
@@ -584,12 +585,12 @@ class AgentReasoning:
# Try new "planning" section first
try:
return self.agent.i18n.retrieve("planning", "refine_plan_prompt").format(
return I18N_DEFAULT.retrieve("planning", "refine_plan_prompt").format(
current_plan=current_plan,
)
except (KeyError, AttributeError):
# Fallback to reasoning section for backward compatibility
return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
return I18N_DEFAULT.retrieve("reasoning", "refine_plan_prompt").format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self._get_agent_backstory(),
@@ -642,7 +643,7 @@ def _call_llm_with_reasoning_prompt(
Returns:
The LLM response.
"""
system_prompt = reasoning_agent.i18n.retrieve("reasoning", plan_type).format(
system_prompt = I18N_DEFAULT.retrieve("reasoning", plan_type).format(
role=reasoning_agent.role,
goal=reasoning_agent.goal,
backstory=backstory,

View File

@@ -13,7 +13,7 @@ from crewai.security.fingerprint import Fingerprint
from crewai.tools.structured_tool import CrewStructuredTool
from crewai.tools.tool_types import ToolResult
from crewai.tools.tool_usage import ToolUsage, ToolUsageError
from crewai.utilities.i18n import I18N
from crewai.utilities.i18n import I18N_DEFAULT
from crewai.utilities.logger import Logger
from crewai.utilities.string_utils import sanitize_tool_name
@@ -30,7 +30,6 @@ if TYPE_CHECKING:
async def aexecute_tool_and_check_finality(
agent_action: AgentAction,
tools: list[CrewStructuredTool],
i18n: I18N,
agent_key: str | None = None,
agent_role: str | None = None,
tools_handler: ToolsHandler | None = None,
@@ -49,7 +48,6 @@ async def aexecute_tool_and_check_finality(
Args:
agent_action: The action containing the tool to execute.
tools: List of available tools.
i18n: Internationalization settings.
agent_key: Optional key for event emission.
agent_role: Optional role for event emission.
tools_handler: Optional tools handler for tool execution.
@@ -142,7 +140,7 @@ async def aexecute_tool_and_check_finality(
return ToolResult(modified_result, tool.result_as_answer)
tool_result = i18n.errors("wrong_tool_name").format(
tool_result = I18N_DEFAULT.errors("wrong_tool_name").format(
tool=sanitized_tool_name,
tools=", ".join(tool_name_to_tool_map.keys()),
)
@@ -152,7 +150,6 @@ async def aexecute_tool_and_check_finality(
def execute_tool_and_check_finality(
agent_action: AgentAction,
tools: list[CrewStructuredTool],
i18n: I18N,
agent_key: str | None = None,
agent_role: str | None = None,
tools_handler: ToolsHandler | None = None,
@@ -170,7 +167,6 @@ def execute_tool_and_check_finality(
Args:
agent_action: The action containing the tool to execute
tools: List of available tools
i18n: Internationalization settings
agent_key: Optional key for event emission
agent_role: Optional role for event emission
tools_handler: Optional tools handler for tool execution
@@ -263,7 +259,7 @@ def execute_tool_and_check_finality(
return ToolResult(modified_result, tool.result_as_answer)
tool_result = i18n.errors("wrong_tool_name").format(
tool_result = I18N_DEFAULT.errors("wrong_tool_name").format(
tool=sanitized_tool_name,
tools=", ".join(tool_name_to_tool_map.keys()),
)

View File

@@ -0,0 +1,12 @@
"""Version utilities for crewAI."""
from __future__ import annotations
from functools import cache
import importlib.metadata
@cache
def get_crewai_version() -> str:
"""Get the installed crewAI version string."""
return importlib.metadata.version("crewai")

View File

@@ -1208,12 +1208,10 @@ def test_llm_call_with_error():
def test_handle_context_length_exceeds_limit():
# Import necessary modules
from crewai.utilities.agent_utils import handle_context_length
from crewai.utilities.i18n import I18N
from crewai.utilities.printer import Printer
# Create mocks for dependencies
printer = Printer()
i18n = I18N()
# Create an agent just for its LLM
agent = Agent(
@@ -1249,7 +1247,6 @@ def test_handle_context_length_exceeds_limit():
messages=messages,
llm=llm,
callbacks=callbacks,
i18n=i18n,
)
# Verify our patch was called and raised the correct error
@@ -1994,7 +1991,7 @@ def test_litellm_anthropic_error_handling():
@pytest.mark.vcr()
def test_get_knowledge_search_query():
"""Test that _get_knowledge_search_query calls the LLM with the correct prompts."""
from crewai.utilities.i18n import I18N
from crewai.utilities.i18n import I18N_DEFAULT
content = "The capital of France is Paris."
string_source = StringKnowledgeSource(content=content)
@@ -2013,7 +2010,6 @@ def test_get_knowledge_search_query():
agent=agent,
)
i18n = I18N()
task_prompt = task.prompt()
with (
@@ -2050,13 +2046,13 @@ def test_get_knowledge_search_query():
[
{
"role": "system",
"content": i18n.slice(
"content": I18N_DEFAULT.slice(
"knowledge_search_query_system_prompt"
).format(task_prompt=task.description),
},
{
"role": "user",
"content": i18n.slice("knowledge_search_query").format(
"content": I18N_DEFAULT.slice("knowledge_search_query").format(
task_prompt=task_prompt
),
},

View File

@@ -48,8 +48,6 @@ def _build_executor(**kwargs: Any) -> AgentExecutor:
executor._last_context_error = None
executor._step_executor = None
executor._planner_observer = None
from crewai.utilities.i18n import get_i18n
executor._i18n = kwargs.get("i18n") or get_i18n()
return executor
from crewai.agents.planner_observer import PlannerObserver
from crewai.experimental.agent_executor import (

View File

@@ -174,3 +174,72 @@ class TestEmitCallCompletedEventPassesUsage:
event = mock_emit.call_args[1]["event"]
assert isinstance(event, LLMCallCompletedEvent)
assert event.usage is None
def test_is_litellm_is_passed_to_event(self, mock_emit, llm):
llm.is_litellm = True
llm._emit_call_completed_event(
response="hello",
call_type=LLMCallType.LLM_CALL,
messages="test prompt",
)
event = mock_emit.call_args[1]["event"]
assert event.is_litellm is True
def test_is_litellm_defaults_to_false(self, mock_emit, llm):
llm._emit_call_completed_event(
response="hello",
call_type=LLMCallType.LLM_CALL,
messages="test prompt",
)
event = mock_emit.call_args[1]["event"]
assert event.is_litellm is False
class TestUsageMetricsNewFields:
def test_add_usage_metrics_aggregates_reasoning_and_cache_creation(self):
from crewai.types.usage_metrics import UsageMetrics
metrics1 = UsageMetrics(
total_tokens=100,
prompt_tokens=60,
completion_tokens=40,
cached_prompt_tokens=10,
reasoning_tokens=15,
cache_creation_tokens=5,
successful_requests=1,
)
metrics2 = UsageMetrics(
total_tokens=200,
prompt_tokens=120,
completion_tokens=80,
cached_prompt_tokens=20,
reasoning_tokens=25,
cache_creation_tokens=10,
successful_requests=1,
)
metrics1.add_usage_metrics(metrics2)
assert metrics1.total_tokens == 300
assert metrics1.prompt_tokens == 180
assert metrics1.completion_tokens == 120
assert metrics1.cached_prompt_tokens == 30
assert metrics1.reasoning_tokens == 40
assert metrics1.cache_creation_tokens == 15
assert metrics1.successful_requests == 2
def test_new_fields_default_to_zero(self):
from crewai.types.usage_metrics import UsageMetrics
metrics = UsageMetrics()
assert metrics.reasoning_tokens == 0
assert metrics.cache_creation_tokens == 0
def test_model_dump_includes_new_fields(self):
from crewai.types.usage_metrics import UsageMetrics
metrics = UsageMetrics(reasoning_tokens=10, cache_creation_tokens=5)
dumped = metrics.model_dump()
assert dumped["reasoning_tokens"] == 10
assert dumped["cache_creation_tokens"] == 5

View File

@@ -1463,3 +1463,45 @@ def test_tool_search_saves_input_tokens():
f"Expected tool_search ({usage_search.prompt_tokens}) to use fewer input tokens "
f"than no search ({usage_no_search.prompt_tokens})"
)
def test_anthropic_cache_creation_tokens_extraction():
"""Test that cache_creation_input_tokens are extracted from Anthropic responses."""
llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
mock_response = MagicMock()
mock_response.content = [MagicMock(text="test response")]
mock_response.usage = MagicMock(
input_tokens=100,
output_tokens=50,
cache_read_input_tokens=30,
cache_creation_input_tokens=20,
)
mock_response.stop_reason = None
mock_response.model = None
usage = llm._extract_anthropic_token_usage(mock_response)
assert usage["input_tokens"] == 100
assert usage["output_tokens"] == 50
assert usage["total_tokens"] == 150
assert usage["cached_prompt_tokens"] == 30
assert usage["cache_creation_tokens"] == 20
def test_anthropic_missing_cache_fields_default_to_zero():
"""Test that missing cache fields default to zero."""
llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
mock_response = MagicMock()
mock_response.content = [MagicMock(text="test response")]
mock_response.usage = MagicMock(
input_tokens=40,
output_tokens=20,
spec=["input_tokens", "output_tokens"],
)
mock_response.usage.cache_read_input_tokens = None
mock_response.usage.cache_creation_input_tokens = None
usage = llm._extract_anthropic_token_usage(mock_response)
assert usage["cached_prompt_tokens"] == 0
assert usage["cache_creation_tokens"] == 0

View File

@@ -1403,3 +1403,44 @@ def test_azure_stop_words_still_applied_to_regular_responses():
assert "Observation:" not in result
assert "Found results" not in result
assert "I need to search for more information" in result
def test_azure_reasoning_tokens_and_cached_tokens():
"""Test that reasoning_tokens and cached_tokens are extracted from Azure responses."""
llm = LLM(model="azure/gpt-4")
mock_response = MagicMock()
mock_response.usage = MagicMock(
prompt_tokens=100,
completion_tokens=200,
total_tokens=300,
)
mock_response.usage.prompt_tokens_details = MagicMock(cached_tokens=40)
mock_response.usage.completion_tokens_details = MagicMock(reasoning_tokens=60)
usage = llm._extract_azure_token_usage(mock_response)
assert usage["prompt_tokens"] == 100
assert usage["completion_tokens"] == 200
assert usage["total_tokens"] == 300
assert usage["cached_prompt_tokens"] == 40
assert usage["reasoning_tokens"] == 60
def test_azure_no_detail_fields():
"""Test Azure extraction without detail fields."""
llm = LLM(model="azure/gpt-4")
mock_response = MagicMock()
mock_response.usage = MagicMock(
prompt_tokens=50,
completion_tokens=30,
total_tokens=80,
)
mock_response.usage.prompt_tokens_details = None
mock_response.usage.completion_tokens_details = None
usage = llm._extract_azure_token_usage(mock_response)
assert usage["prompt_tokens"] == 50
assert usage["completion_tokens"] == 30
assert usage["cached_prompt_tokens"] == 0
assert usage["reasoning_tokens"] == 0

View File

@@ -1175,3 +1175,81 @@ def test_bedrock_tool_results_not_merged_across_assistant_messages():
)
assert tool_result_messages[0]["content"][0]["toolResult"]["toolUseId"] == "call_a"
assert tool_result_messages[1]["content"][0]["toolResult"]["toolUseId"] == "call_b"
def test_bedrock_cached_token_tracking():
"""Test that cached tokens (cacheReadInputTokenCount) are tracked for Bedrock."""
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
with patch.object(llm._client, 'converse') as mock_converse:
mock_response = {
'output': {
'message': {
'role': 'assistant',
'content': [{'text': 'test response'}]
}
},
'usage': {
'inputTokens': 100,
'outputTokens': 50,
'totalTokens': 150,
'cacheReadInputTokenCount': 30,
}
}
mock_converse.return_value = mock_response
result = llm.call("Hello")
assert result == "test response"
assert llm._token_usage['prompt_tokens'] == 100
assert llm._token_usage['completion_tokens'] == 50
assert llm._token_usage['total_tokens'] == 150
assert llm._token_usage['cached_prompt_tokens'] == 30
def test_bedrock_cached_token_alternate_key():
"""Test that the alternate key cacheReadInputTokens also works."""
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
with patch.object(llm._client, 'converse') as mock_converse:
mock_response = {
'output': {
'message': {
'role': 'assistant',
'content': [{'text': 'test response'}]
}
},
'usage': {
'inputTokens': 80,
'outputTokens': 40,
'totalTokens': 120,
'cacheReadInputTokens': 25,
}
}
mock_converse.return_value = mock_response
llm.call("Hello")
assert llm._token_usage['cached_prompt_tokens'] == 25
def test_bedrock_no_cache_tokens_defaults_to_zero():
"""Test that missing cache token keys default to zero."""
llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
with patch.object(llm._client, 'converse') as mock_converse:
mock_response = {
'output': {
'message': {
'role': 'assistant',
'content': [{'text': 'test response'}]
}
},
'usage': {
'inputTokens': 60,
'outputTokens': 30,
'totalTokens': 90,
}
}
mock_converse.return_value = mock_response
llm.call("Hello")
assert llm._token_usage['cached_prompt_tokens'] == 0

View File

@@ -1190,3 +1190,42 @@ def test_gemini_cached_prompt_tokens_with_tools():
# cached_prompt_tokens should be populated (may be 0 if Gemini
# doesn't cache for this particular request, but the field should exist)
assert usage.cached_prompt_tokens >= 0
def test_gemini_reasoning_tokens_extraction():
"""Test that thoughts_token_count is extracted as reasoning_tokens from Gemini."""
llm = LLM(model="google/gemini-2.0-flash-001")
mock_response = MagicMock()
mock_response.usage_metadata = MagicMock(
prompt_token_count=100,
candidates_token_count=50,
total_token_count=150,
cached_content_token_count=10,
thoughts_token_count=30,
)
usage = llm._extract_token_usage(mock_response)
assert usage["prompt_token_count"] == 100
assert usage["candidates_token_count"] == 50
assert usage["total_tokens"] == 150
assert usage["cached_prompt_tokens"] == 10
assert usage["reasoning_tokens"] == 30
def test_gemini_no_thinking_tokens_defaults_to_zero():
"""Test that missing thoughts_token_count defaults to zero."""
llm = LLM(model="google/gemini-2.0-flash-001")
mock_response = MagicMock()
mock_response.usage_metadata = MagicMock(
prompt_token_count=80,
candidates_token_count=40,
total_token_count=120,
cached_content_token_count=0,
thoughts_token_count=None,
)
mock_response.candidates = []
usage = llm._extract_token_usage(mock_response)
assert usage["reasoning_tokens"] == 0
assert usage["cached_prompt_tokens"] == 0

View File

@@ -1929,6 +1929,47 @@ def test_openai_streaming_returns_tool_calls_without_available_functions():
assert result[0]["type"] == "function"
def test_openai_responses_api_reasoning_tokens_extraction():
"""Test that reasoning_tokens are extracted from Responses API responses."""
llm = LLM(model="openai/gpt-4o")
mock_response = MagicMock()
mock_response.usage = MagicMock(
input_tokens=100,
output_tokens=200,
total_tokens=300,
)
mock_response.usage.input_tokens_details = MagicMock(cached_tokens=25)
mock_response.usage.output_tokens_details = MagicMock(reasoning_tokens=80)
usage = llm._extract_responses_token_usage(mock_response)
assert usage["prompt_tokens"] == 100
assert usage["completion_tokens"] == 200
assert usage["total_tokens"] == 300
assert usage["cached_prompt_tokens"] == 25
assert usage["reasoning_tokens"] == 80
def test_openai_responses_api_no_detail_fields_omitted():
"""Test that reasoning/cached fields are omitted when Responses API details are absent."""
llm = LLM(model="openai/gpt-4o")
mock_response = MagicMock()
mock_response.usage = MagicMock(
input_tokens=50,
output_tokens=30,
total_tokens=80,
)
mock_response.usage.input_tokens_details = None
mock_response.usage.output_tokens_details = None
usage = llm._extract_responses_token_usage(mock_response)
assert usage["prompt_tokens"] == 50
assert usage["completion_tokens"] == 30
assert "cached_prompt_tokens" not in usage
assert "reasoning_tokens" not in usage
@pytest.mark.asyncio
async def test_openai_async_streaming_returns_tool_calls_without_available_functions():
"""Test that async streaming returns tool calls list when available_functions is None.
@@ -2018,3 +2059,44 @@ async def test_openai_async_streaming_returns_tool_calls_without_available_funct
assert result[0]["function"]["arguments"] == '{"expression": "1+1"}'
assert result[0]["id"] == "call_abc123"
assert result[0]["type"] == "function"
def test_openai_reasoning_tokens_extraction():
"""Test that reasoning_tokens are extracted from OpenAI o-series responses."""
llm = LLM(model="openai/gpt-4o")
mock_response = MagicMock()
mock_response.usage = MagicMock(
prompt_tokens=100,
completion_tokens=200,
total_tokens=300,
)
mock_response.usage.prompt_tokens_details = MagicMock(cached_tokens=25)
mock_response.usage.completion_tokens_details = MagicMock(reasoning_tokens=80)
usage = llm._extract_openai_token_usage(mock_response)
assert usage["prompt_tokens"] == 100
assert usage["completion_tokens"] == 200
assert usage["total_tokens"] == 300
assert usage["cached_prompt_tokens"] == 25
assert usage["reasoning_tokens"] == 80
def test_openai_no_detail_fields_omitted():
"""Test that reasoning/cached fields are omitted when details are absent."""
llm = LLM(model="openai/gpt-4o")
mock_response = MagicMock()
mock_response.usage = MagicMock(
prompt_tokens=50,
completion_tokens=30,
total_tokens=80,
)
mock_response.usage.prompt_tokens_details = None
mock_response.usage.completion_tokens_details = None
usage = llm._extract_openai_token_usage(mock_response)
assert usage["prompt_tokens"] == 50
assert usage["completion_tokens"] == 30
assert "cached_prompt_tokens" not in usage
assert "reasoning_tokens" not in usage

View File

@@ -1,8 +1,10 @@
"""Tests for CheckpointConfig, checkpoint listener, and pruning."""
"""Tests for CheckpointConfig, checkpoint listener, pruning, and forking."""
from __future__ import annotations
import json
import os
import sqlite3
import tempfile
import time
from typing import Any
@@ -21,6 +23,8 @@ from crewai.state.checkpoint_listener import (
_SENTINEL,
)
from crewai.state.provider.json_provider import JsonProvider
from crewai.state.provider.sqlite_provider import SqliteProvider
from crewai.state.runtime import RuntimeState
from crewai.task import Task
@@ -116,35 +120,41 @@ class TestFindCheckpoint:
class TestPrune:
def test_prune_keeps_newest(self) -> None:
with tempfile.TemporaryDirectory() as d:
branch_dir = os.path.join(d, "main")
os.makedirs(branch_dir)
for i in range(5):
path = os.path.join(d, f"cp_{i}.json")
path = os.path.join(branch_dir, f"cp_{i}.json")
with open(path, "w") as f:
f.write("{}")
# Ensure distinct mtime
time.sleep(0.01)
JsonProvider().prune(d, max_keep=2)
remaining = os.listdir(d)
JsonProvider().prune(d, max_keep=2, branch="main")
remaining = os.listdir(branch_dir)
assert len(remaining) == 2
assert "cp_3.json" in remaining
assert "cp_4.json" in remaining
def test_prune_zero_removes_all(self) -> None:
with tempfile.TemporaryDirectory() as d:
branch_dir = os.path.join(d, "main")
os.makedirs(branch_dir)
for i in range(3):
with open(os.path.join(d, f"cp_{i}.json"), "w") as f:
with open(os.path.join(branch_dir, f"cp_{i}.json"), "w") as f:
f.write("{}")
JsonProvider().prune(d, max_keep=0)
assert os.listdir(d) == []
JsonProvider().prune(d, max_keep=0, branch="main")
assert os.listdir(branch_dir) == []
def test_prune_more_than_existing(self) -> None:
with tempfile.TemporaryDirectory() as d:
with open(os.path.join(d, "cp.json"), "w") as f:
branch_dir = os.path.join(d, "main")
os.makedirs(branch_dir)
with open(os.path.join(branch_dir, "cp.json"), "w") as f:
f.write("{}")
JsonProvider().prune(d, max_keep=10)
assert len(os.listdir(d)) == 1
JsonProvider().prune(d, max_keep=10, branch="main")
assert len(os.listdir(branch_dir)) == 1
# ---------- CheckpointConfig ----------
@@ -162,8 +172,368 @@ class TestCheckpointConfig:
cfg = CheckpointConfig(on_events=["*"])
assert cfg.trigger_all
def test_restore_from_field(self) -> None:
cfg = CheckpointConfig(restore_from="/path/to/checkpoint.json")
assert cfg.restore_from == "/path/to/checkpoint.json"
def test_restore_from_default_none(self) -> None:
cfg = CheckpointConfig()
assert cfg.restore_from is None
def test_trigger_events(self) -> None:
cfg = CheckpointConfig(
on_events=["task_completed", "crew_kickoff_completed"]
)
assert cfg.trigger_events == {"task_completed", "crew_kickoff_completed"}
# ---------- RuntimeState lineage ----------
class TestRuntimeStateLineage:
def _make_state(self) -> RuntimeState:
from crewai import Agent, Crew
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
crew = Crew(agents=[agent], tasks=[], verbose=False)
return RuntimeState(root=[crew])
def test_default_lineage_fields(self) -> None:
state = self._make_state()
assert state._checkpoint_id is None
assert state._parent_id is None
assert state._branch == "main"
def test_serialize_includes_version(self) -> None:
from crewai.utilities.version import get_crewai_version
state = self._make_state()
dumped = json.loads(state.model_dump_json())
assert dumped["crewai_version"] == get_crewai_version()
def test_deserialize_migrates_on_version_mismatch(self, caplog: Any) -> None:
import logging
state = self._make_state()
raw = state.model_dump_json()
data = json.loads(raw)
data["crewai_version"] = "0.1.0"
with caplog.at_level(logging.DEBUG):
RuntimeState.model_validate_json(
json.dumps(data), context={"from_checkpoint": True}
)
assert "Migrating checkpoint from crewAI 0.1.0" in caplog.text
def test_deserialize_warns_on_missing_version(self, caplog: Any) -> None:
import logging
state = self._make_state()
raw = state.model_dump_json()
data = json.loads(raw)
data.pop("crewai_version", None)
with caplog.at_level(logging.WARNING):
RuntimeState.model_validate_json(
json.dumps(data), context={"from_checkpoint": True}
)
assert "treating as 0.0.0" in caplog.text
def test_serialize_includes_lineage(self) -> None:
state = self._make_state()
state._parent_id = "parent456"
state._branch = "experiment"
dumped = json.loads(state.model_dump_json())
assert dumped["parent_id"] == "parent456"
assert dumped["branch"] == "experiment"
assert "checkpoint_id" not in dumped
def test_deserialize_restores_lineage(self) -> None:
state = self._make_state()
state._parent_id = "parent456"
state._branch = "experiment"
raw = state.model_dump_json()
restored = RuntimeState.model_validate_json(
raw, context={"from_checkpoint": True}
)
assert restored._parent_id == "parent456"
assert restored._branch == "experiment"
def test_deserialize_defaults_missing_lineage(self) -> None:
state = self._make_state()
raw = state.model_dump_json()
data = json.loads(raw)
data.pop("parent_id", None)
data.pop("branch", None)
restored = RuntimeState.model_validate_json(
json.dumps(data), context={"from_checkpoint": True}
)
assert restored._parent_id is None
assert restored._branch == "main"
def test_from_checkpoint_sets_checkpoint_id(self) -> None:
"""from_checkpoint sets _checkpoint_id from the location, not the blob."""
state = self._make_state()
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
loc = state.checkpoint(d)
written_id = state._checkpoint_id
cfg = CheckpointConfig(restore_from=loc)
restored = RuntimeState.from_checkpoint(
cfg, context={"from_checkpoint": True}
)
assert restored._checkpoint_id == written_id
assert restored._parent_id == written_id
def test_fork_sets_branch(self) -> None:
state = self._make_state()
state._checkpoint_id = "abc12345"
state._parent_id = "abc12345"
state.fork("my-experiment")
assert state._branch == "my-experiment"
assert state._parent_id == "abc12345"
def test_fork_auto_branch(self) -> None:
state = self._make_state()
state._checkpoint_id = "20260409T120000_abc12345"
state.fork()
assert state._branch.startswith("fork/20260409T120000_abc12345_")
assert len(state._branch) == len("fork/20260409T120000_abc12345_") + 6
def test_fork_no_checkpoint_id_unique(self) -> None:
state = self._make_state()
state.fork()
assert state._branch.startswith("fork/")
assert len(state._branch) == len("fork/") + 8
# Two forks without checkpoint_id produce different branches
first = state._branch
state.fork()
assert state._branch != first
# ---------- JsonProvider forking ----------
class TestJsonProviderFork:
def test_checkpoint_writes_to_branch_subdir(self) -> None:
provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
path = provider.checkpoint("{}", d, branch="main")
assert "/main/" in path
assert path.endswith(".json")
assert os.path.isfile(path)
def test_checkpoint_fork_branch_subdir(self) -> None:
provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
path = provider.checkpoint("{}", d, branch="fork/exp1")
assert "/fork/exp1/" in path
assert os.path.isfile(path)
def test_prune_branch_aware(self) -> None:
provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
# Write 3 checkpoints on main, 2 on fork
for _ in range(3):
provider.checkpoint("{}", d, branch="main")
time.sleep(0.01)
for _ in range(2):
provider.checkpoint("{}", d, branch="fork/a")
time.sleep(0.01)
# Prune main to 1
provider.prune(d, max_keep=1, branch="main")
main_dir = os.path.join(d, "main")
fork_dir = os.path.join(d, "fork", "a")
assert len(os.listdir(main_dir)) == 1
assert len(os.listdir(fork_dir)) == 2 # untouched
def test_extract_id(self) -> None:
provider = JsonProvider()
assert provider.extract_id("/dir/main/20260409T120000_abc12345_p-none.json") == "20260409T120000_abc12345"
assert provider.extract_id("/dir/main/20260409T120000_abc12345_p-20260409T115900_def67890.json") == "20260409T120000_abc12345"
def test_branch_traversal_rejected(self) -> None:
provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
with pytest.raises(ValueError, match="escapes checkpoint directory"):
provider.checkpoint("{}", d, branch="../../etc")
with pytest.raises(ValueError, match="escapes checkpoint directory"):
provider.prune(d, max_keep=1, branch="../../etc")
def test_filename_encodes_parent_id(self) -> None:
provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
# First checkpoint — no parent
path1 = provider.checkpoint("{}", d, branch="main")
assert "_p-none.json" in path1
# Second checkpoint — with parent
id1 = provider.extract_id(path1)
path2 = provider.checkpoint("{}", d, parent_id=id1, branch="main")
assert f"_p-{id1}.json" in path2
def test_checkpoint_chaining(self) -> None:
"""RuntimeState.checkpoint() chains parent_id after each write."""
state = self._make_state()
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
state.checkpoint(d)
id1 = state._checkpoint_id
assert id1 is not None
assert state._parent_id == id1
loc2 = state.checkpoint(d)
id2 = state._checkpoint_id
assert id2 is not None
assert id2 != id1
assert state._parent_id == id2
# Verify the second checkpoint blob has parent_id == id1
with open(loc2) as f:
data2 = json.loads(f.read())
assert data2["parent_id"] == id1
@pytest.mark.asyncio
async def test_acheckpoint_chaining(self) -> None:
"""Async checkpoint path chains lineage identically to sync."""
state = self._make_state()
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
await state.acheckpoint(d)
id1 = state._checkpoint_id
assert id1 is not None
loc2 = await state.acheckpoint(d)
id2 = state._checkpoint_id
assert id2 != id1
assert state._parent_id == id2
with open(loc2) as f:
data2 = json.loads(f.read())
assert data2["parent_id"] == id1
def _make_state(self) -> RuntimeState:
from crewai import Agent, Crew
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
crew = Crew(agents=[agent], tasks=[], verbose=False)
return RuntimeState(root=[crew])
# ---------- SqliteProvider forking ----------
class TestSqliteProviderFork:
def test_checkpoint_stores_branch_and_parent(self) -> None:
provider = SqliteProvider()
with tempfile.TemporaryDirectory() as d:
db = os.path.join(d, "cp.db")
loc = provider.checkpoint("{}", db, parent_id="p1", branch="exp")
cid = provider.extract_id(loc)
with sqlite3.connect(db) as conn:
row = conn.execute(
"SELECT parent_id, branch FROM checkpoints WHERE id = ?",
(cid,),
).fetchone()
assert row == ("p1", "exp")
def test_prune_branch_aware(self) -> None:
provider = SqliteProvider()
with tempfile.TemporaryDirectory() as d:
db = os.path.join(d, "cp.db")
for _ in range(3):
provider.checkpoint("{}", db, branch="main")
for _ in range(2):
provider.checkpoint("{}", db, branch="fork/a")
provider.prune(db, max_keep=1, branch="main")
with sqlite3.connect(db) as conn:
main_count = conn.execute(
"SELECT COUNT(*) FROM checkpoints WHERE branch = 'main'"
).fetchone()[0]
fork_count = conn.execute(
"SELECT COUNT(*) FROM checkpoints WHERE branch = 'fork/a'"
).fetchone()[0]
assert main_count == 1
assert fork_count == 2
def test_extract_id(self) -> None:
provider = SqliteProvider()
assert provider.extract_id("/path/to/db#abc123") == "abc123"
def test_checkpoint_chaining_sqlite(self) -> None:
state = self._make_state()
state._provider = SqliteProvider()
with tempfile.TemporaryDirectory() as d:
db = os.path.join(d, "cp.db")
state.checkpoint(db)
id1 = state._checkpoint_id
state.checkpoint(db)
id2 = state._checkpoint_id
assert id2 != id1
# Second row should have parent_id == id1
with sqlite3.connect(db) as conn:
row = conn.execute(
"SELECT parent_id FROM checkpoints WHERE id = ?", (id2,)
).fetchone()
assert row[0] == id1
def _make_state(self) -> RuntimeState:
from crewai import Agent, Crew
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
crew = Crew(agents=[agent], tasks=[], verbose=False)
return RuntimeState(root=[crew])
# ---------- Kickoff from_checkpoint parameter ----------
class TestKickoffFromCheckpoint:
def test_crew_kickoff_delegates_to_from_checkpoint(self) -> None:
mock_restored = MagicMock(spec=Crew)
mock_restored.kickoff.return_value = "result"
cfg = CheckpointConfig(restore_from="/path/to/cp.json")
with patch.object(Crew, "from_checkpoint", return_value=mock_restored):
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
crew = Crew(agents=[agent], tasks=[], verbose=False)
result = crew.kickoff(inputs={"k": "v"}, from_checkpoint=cfg)
mock_restored.kickoff.assert_called_once_with(
inputs={"k": "v"}, input_files=None
)
assert mock_restored.checkpoint.restore_from is None
assert result == "result"
def test_crew_kickoff_config_only_sets_checkpoint(self) -> None:
cfg = CheckpointConfig(on_events=["task_completed"])
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
crew = Crew(agents=[agent], tasks=[], verbose=False)
assert crew.checkpoint is None
with patch("crewai.crew.get_env_context"), \
patch("crewai.crew.prepare_kickoff", side_effect=RuntimeError("stop")):
with pytest.raises(RuntimeError, match="stop"):
crew.kickoff(from_checkpoint=cfg)
assert isinstance(crew.checkpoint, CheckpointConfig)
assert crew.checkpoint.on_events == ["task_completed"]
def test_flow_kickoff_delegates_to_from_checkpoint(self) -> None:
mock_restored = MagicMock(spec=Flow)
mock_restored.kickoff.return_value = "flow_result"
cfg = CheckpointConfig(restore_from="/path/to/flow_cp.json")
with patch.object(Flow, "from_checkpoint", return_value=mock_restored):
flow = Flow()
result = flow.kickoff(from_checkpoint=cfg)
mock_restored.kickoff.assert_called_once_with(
inputs=None, input_files=None
)
assert mock_restored.checkpoint.restore_from is None
assert result == "flow_result"

View File

@@ -1001,6 +1001,8 @@ def test_usage_info_non_streaming_with_call():
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
"reasoning_tokens": 0,
"cache_creation_tokens": 0,
}
assert llm.stream is False
@@ -1025,6 +1027,8 @@ def test_usage_info_streaming_with_call():
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
"reasoning_tokens": 0,
"cache_creation_tokens": 0,
}
assert llm.stream is True
@@ -1056,6 +1060,8 @@ async def test_usage_info_non_streaming_with_acall():
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
"reasoning_tokens": 0,
"cache_creation_tokens": 0,
}
with patch.object(
@@ -1089,6 +1095,8 @@ async def test_usage_info_non_streaming_with_acall_and_stop():
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
"reasoning_tokens": 0,
"cache_creation_tokens": 0,
}
with patch.object(
@@ -1121,6 +1129,8 @@ async def test_usage_info_streaming_with_acall():
"completion_tokens": 0,
"successful_requests": 0,
"cached_prompt_tokens": 0,
"reasoning_tokens": 0,
"cache_creation_tokens": 0,
}
with patch.object(

View File

@@ -308,7 +308,6 @@ def test_validate_tool_input_invalid_input():
mock_agent.key = "test_agent_key" # Must be a string
mock_agent.role = "test_agent_role" # Must be a string
mock_agent._original_role = "test_agent_role" # Must be a string
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock action with proper string value
@@ -443,7 +442,6 @@ def test_tool_selection_error_event_direct():
mock_agent = MagicMock()
mock_agent.key = "test_key"
mock_agent.role = "test_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
mock_task = MagicMock()
@@ -518,13 +516,6 @@ def test_tool_validate_input_error_event():
mock_agent.verbose = False
mock_agent._original_role = "test_role"
# Mock i18n with error message
mock_i18n = MagicMock()
mock_i18n.errors.return_value = (
"Tool input must be a valid dictionary in JSON or Python literal format"
)
mock_agent.i18n = mock_i18n
# Mock task and tools handler
mock_task = MagicMock()
mock_tools_handler = MagicMock()
@@ -590,7 +581,6 @@ def test_tool_usage_finished_event_with_result():
mock_agent.key = "test_agent_key"
mock_agent.role = "test_agent_role"
mock_agent._original_role = "test_agent_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock task
@@ -670,7 +660,6 @@ def test_tool_usage_finished_event_with_cached_result():
mock_agent.key = "test_agent_key"
mock_agent.role = "test_agent_role"
mock_agent._original_role = "test_agent_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock task
@@ -761,9 +750,6 @@ def test_tool_error_does_not_emit_finished_event():
mock_agent._original_role = "test_agent_role"
mock_agent.verbose = False
mock_agent.fingerprint = None
mock_agent.i18n.tools.return_value = {"name": "Add Image"}
mock_agent.i18n.errors.return_value = "Error: {error}"
mock_agent.i18n.slice.return_value = "Available tools: {tool_names}"
mock_task = MagicMock()
mock_task.delegations = 0

View File

@@ -225,16 +225,6 @@ class TestConvertToolsToOpenaiSchema:
assert max_results_prop["default"] == 10
def _make_mock_i18n() -> MagicMock:
"""Create a mock i18n with the new structured prompt keys."""
mock_i18n = MagicMock()
mock_i18n.slice.side_effect = lambda key: {
"summarizer_system_message": "You are a precise assistant that creates structured summaries.",
"summarize_instruction": "Summarize the conversation:\n{conversation}",
"summary": "<summary>\n{merged_summary}\n</summary>\nContinue the task.",
}.get(key, "")
return mock_i18n
class MCPStyleInput(BaseModel):
"""Input schema mimicking an MCP tool with optional fields."""
@@ -330,7 +320,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
# System message preserved + summary message = 2
@@ -361,7 +351,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
assert len(messages) == 1
@@ -387,7 +377,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
assert len(messages) == 1
@@ -410,7 +400,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
assert id(messages) == original_list_id
@@ -432,7 +422,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
assert len(messages) == 2
@@ -456,7 +446,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
# Check what was passed to llm.call
@@ -482,7 +472,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
assert "The extracted summary content." in messages[0]["content"]
@@ -506,7 +496,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
# Verify the conversation text sent to LLM contains tool labels
@@ -528,7 +518,7 @@ class TestSummarizeMessages:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
# No LLM call should have been made
@@ -733,7 +723,7 @@ class TestParallelSummarization:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
# acall should have been awaited once per chunk
@@ -757,7 +747,7 @@ class TestParallelSummarization:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
mock_llm.call.assert_called_once()
@@ -788,7 +778,7 @@ class TestParallelSummarization:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
# The final summary message should have A, B, C in order
@@ -816,7 +806,7 @@ class TestParallelSummarization:
chunks=[chunk_a, chunk_b],
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
)
@@ -843,7 +833,7 @@ class TestParallelSummarization:
messages=messages,
llm=mock_llm,
callbacks=[],
i18n=_make_mock_i18n(),
)
assert mock_llm.acall.await_count == 2
@@ -940,10 +930,8 @@ class TestParallelSummarizationVCR:
def test_parallel_summarize_openai(self) -> None:
"""Test that parallel summarization with gpt-4o-mini produces a valid summary."""
from crewai.llm import LLM
from crewai.utilities.i18n import I18N
llm = LLM(model="gpt-4o-mini", temperature=0)
i18n = I18N()
messages = _build_long_conversation()
original_system = messages[0]["content"]
@@ -959,7 +947,6 @@ class TestParallelSummarizationVCR:
messages=messages,
llm=llm,
callbacks=[],
i18n=i18n,
)
# System message preserved
@@ -975,10 +962,8 @@ class TestParallelSummarizationVCR:
def test_parallel_summarize_preserves_files(self) -> None:
"""Test that file references survive parallel summarization."""
from crewai.llm import LLM
from crewai.utilities.i18n import I18N
llm = LLM(model="gpt-4o-mini", temperature=0)
i18n = I18N()
messages = _build_long_conversation()
mock_file = MagicMock()
@@ -989,7 +974,6 @@ class TestParallelSummarizationVCR:
messages=messages,
llm=llm,
callbacks=[],
i18n=i18n,
)
summary_msg = messages[-1]

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