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fix/trace-
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devin/1773
| Author | SHA1 | Date | |
|---|---|---|---|
|
d79c4a62a1 | ||
|
|
79013a6dc2 |
@@ -24,14 +24,6 @@ repos:
|
||||
rev: 0.11.3
|
||||
hooks:
|
||||
- id: uv-lock
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: pip-audit
|
||||
name: pip-audit
|
||||
entry: bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable --ignore-vuln CVE-2025-69872 --ignore-vuln CVE-2026-25645 --ignore-vuln CVE-2026-27448 --ignore-vuln CVE-2026-27459 --ignore-vuln PYSEC-2023-235' --
|
||||
language: system
|
||||
pass_filenames: false
|
||||
stages: [pre-push, manual]
|
||||
- repo: https://github.com/commitizen-tools/commitizen
|
||||
rev: v4.10.1
|
||||
hooks:
|
||||
|
||||
@@ -4,87 +4,6 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="15 أبريل 2026">
|
||||
## v1.14.2a4
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الميزات
|
||||
- إضافة تلميحات استئناف إلى إصدار أدوات المطورين عند الفشل
|
||||
|
||||
### إصلاحات الأخطاء
|
||||
- إصلاح توجيه وضع الصرامة إلى واجهة برمجة تطبيقات Bedrock Converse
|
||||
- إصلاح إصدار pytest إلى 9.0.3 لثغرة الأمان GHSA-6w46-j5rx-g56g
|
||||
- رفع الحد الأدنى لـ OpenAI إلى >=2.0.0
|
||||
|
||||
### الوثائق
|
||||
- تحديث سجل التغييرات والإصدار لـ v1.14.2a3
|
||||
|
||||
## المساهمون
|
||||
|
||||
@greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="13 أبريل 2026">
|
||||
## v1.14.2a3
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a3)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الميزات
|
||||
- إضافة واجهة سطر الأوامر للتحقق من النشر
|
||||
- تحسين سهولة استخدام تهيئة LLM
|
||||
|
||||
### إصلاحات الأخطاء
|
||||
- تجاوز pypdf و uv إلى إصدارات مصححة لـ CVE-2026-40260 و GHSA-pjjw-68hj-v9mw
|
||||
- ترقية requests إلى >=2.33.0 لمعالجة ثغرة ملف مؤقت CVE
|
||||
- الحفاظ على معلمات استدعاء أداة Bedrock من خلال إزالة القيمة الافتراضية الصحيحة
|
||||
- تنظيف مخططات الأدوات لوضع صارم
|
||||
- إصلاح اختبار تسلسل تضمين MemoryRecord
|
||||
|
||||
### الوثائق
|
||||
- تنظيف لغة A2A الخاصة بالمؤسسات
|
||||
- إضافة وثائق ميزات A2A الخاصة بالمؤسسات
|
||||
- تحديث وثائق A2A الخاصة بالمصادر المفتوحة
|
||||
- تحديث سجل التغييرات والإصدار لـ v1.14.2a2
|
||||
|
||||
## المساهمون
|
||||
|
||||
@Yanhu007, @greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="10 أبريل 2026">
|
||||
## v1.14.2a2
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الميزات
|
||||
- إضافة واجهة مستخدم نصية لنقطة التحقق مع عرض شجري، ودعم التفرع، ومدخلات/مخرجات قابلة للتعديل
|
||||
- إثراء تتبع رموز LLM مع رموز الاستدلال ورموز إنشاء التخزين المؤقت
|
||||
- إضافة معلمة `from_checkpoint` إلى طرق الانطلاق
|
||||
- تضمين `crewai_version` في نقاط التحقق مع إطار عمل الهجرة
|
||||
- إضافة تفرع نقاط التحقق مع تتبع السلالة
|
||||
|
||||
### إصلاحات الأخطاء
|
||||
- إصلاح توجيه الوضع الصارم إلى مزودي Anthropic وBedrock
|
||||
- تعزيز NL2SQLTool مع وضع القراءة فقط الافتراضي، والتحقق من الاستعلامات، والاستعلامات المعلمة
|
||||
|
||||
### الوثائق
|
||||
- تحديث سجل التغييرات والإصدار لـ v1.14.2a1
|
||||
|
||||
## المساهمون
|
||||
|
||||
@alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="9 أبريل 2026">
|
||||
## v1.14.2a1
|
||||
|
||||
|
||||
@@ -392,8 +392,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -866,8 +865,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1340,8 +1338,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1814,8 +1811,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -2287,8 +2283,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -2759,8 +2754,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -3231,8 +3225,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -3705,8 +3698,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -4177,8 +4169,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -4652,8 +4643,7 @@
|
||||
"en/enterprise/features/marketplace",
|
||||
"en/enterprise/features/agent-repositories",
|
||||
"en/enterprise/features/tools-and-integrations",
|
||||
"en/enterprise/features/pii-trace-redactions",
|
||||
"en/enterprise/features/a2a"
|
||||
"en/enterprise/features/pii-trace-redactions"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -4,87 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="Apr 15, 2026">
|
||||
## v1.14.2a4
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add resume hints to devtools release on failure
|
||||
|
||||
### Bug Fixes
|
||||
- Fix strict mode forwarding to Bedrock Converse API
|
||||
- Fix pytest version to 9.0.3 for security vulnerability GHSA-6w46-j5rx-g56g
|
||||
- Bump OpenAI lower bound to >=2.0.0
|
||||
|
||||
### Documentation
|
||||
- Update changelog and version for v1.14.2a3
|
||||
|
||||
## Contributors
|
||||
|
||||
@greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 13, 2026">
|
||||
## v1.14.2a3
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a3)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add deploy validation CLI
|
||||
- Improve LLM initialization ergonomics
|
||||
|
||||
### Bug Fixes
|
||||
- Override pypdf and uv to patched versions for CVE-2026-40260 and GHSA-pjjw-68hj-v9mw
|
||||
- Upgrade requests to >=2.33.0 for CVE temp file vulnerability
|
||||
- Preserve Bedrock tool call arguments by removing truthy default
|
||||
- Sanitize tool schemas for strict mode
|
||||
- Deflake MemoryRecord embedding serialization test
|
||||
|
||||
### Documentation
|
||||
- Clean up enterprise A2A language
|
||||
- Add enterprise A2A feature documentation
|
||||
- Update OSS A2A documentation
|
||||
- Update changelog and version for v1.14.2a2
|
||||
|
||||
## Contributors
|
||||
|
||||
@Yanhu007, @greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 10, 2026">
|
||||
## v1.14.2a2
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add checkpoint TUI with tree view, fork support, and editable inputs/outputs
|
||||
- Enrich LLM token tracking with reasoning tokens and cache creation tokens
|
||||
- Add `from_checkpoint` parameter to kickoff methods
|
||||
- Embed `crewai_version` in checkpoints with migration framework
|
||||
- Add checkpoint forking with lineage tracking
|
||||
|
||||
### Bug Fixes
|
||||
- Fix strict mode forwarding to Anthropic and Bedrock providers
|
||||
- Harden NL2SQLTool with read-only default, query validation, and parameterized queries
|
||||
|
||||
### Documentation
|
||||
- Update changelog and version for v1.14.2a1
|
||||
|
||||
## Contributors
|
||||
|
||||
@alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 09, 2026">
|
||||
## v1.14.2a1
|
||||
|
||||
|
||||
@@ -54,7 +54,6 @@ 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
|
||||
|
||||
@@ -80,42 +79,13 @@ 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
|
||||
from crewai import Crew, CheckpointConfig
|
||||
|
||||
crew = Crew(agents=[...], tasks=[...])
|
||||
result = crew.kickoff(
|
||||
from_checkpoint=CheckpointConfig(
|
||||
restore_from="./my_checkpoints/20260407T120000_abc123.json",
|
||||
),
|
||||
)
|
||||
# Restore and resume
|
||||
crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
|
||||
result = crew.kickoff() # picks up from last completed task
|
||||
```
|
||||
|
||||
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.
|
||||
The restored crew skips already-completed tasks and resumes from the first incomplete one.
|
||||
|
||||
## Works on Crew, Flow, and Agent
|
||||
|
||||
@@ -155,8 +125,7 @@ flow = MyFlow(
|
||||
result = flow.kickoff()
|
||||
|
||||
# Resume
|
||||
config = CheckpointConfig(restore_from="./flow_cp/20260407T120000_abc123.json")
|
||||
flow = MyFlow.from_checkpoint(config)
|
||||
flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
|
||||
result = flow.kickoff()
|
||||
```
|
||||
|
||||
@@ -262,44 +231,3 @@ 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
|
||||
```
|
||||
|
||||
@@ -1,227 +0,0 @@
|
||||
---
|
||||
title: A2A on AMP
|
||||
description: Production-grade Agent-to-Agent communication with distributed state and multi-scheme authentication
|
||||
icon: "network-wired"
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
<Warning>
|
||||
A2A server agents on AMP are in early release. APIs may change in future versions.
|
||||
</Warning>
|
||||
|
||||
## Overview
|
||||
|
||||
CrewAI AMP extends the open-source [A2A protocol implementation](/en/learn/a2a-agent-delegation) with production infrastructure for deploying distributed agents at scale. AMP supports A2A protocol versions 0.2 and 0.3. When you deploy a crew or agent with A2A server configuration to AMP, the platform automatically provisions distributed state management, authentication, multi-transport endpoints, and lifecycle management.
|
||||
|
||||
<Note>
|
||||
For A2A protocol fundamentals, client/server configuration, and authentication schemes, see the [A2A Agent Delegation](/en/learn/a2a-agent-delegation) documentation. This page covers what AMP adds on top of the open-source implementation.
|
||||
</Note>
|
||||
|
||||
### Usage
|
||||
|
||||
Add `A2AServerConfig` to any agent in your crew and deploy to AMP. The platform detects agents with server configuration and automatically registers A2A endpoints, generates agent cards, and provisions the infrastructure described below.
|
||||
|
||||
```python
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.a2a import A2AServerConfig
|
||||
from crewai.a2a.auth import EnterpriseTokenAuth
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze datasets and provide insights",
|
||||
backstory="Expert data scientist with statistical analysis skills",
|
||||
llm="gpt-4o",
|
||||
a2a=A2AServerConfig(
|
||||
auth=EnterpriseTokenAuth()
|
||||
)
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Analyze the provided dataset",
|
||||
expected_output="Statistical summary with key insights",
|
||||
agent=agent
|
||||
)
|
||||
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
```
|
||||
|
||||
After [deploying to AMP](/en/enterprise/guides/deploy-to-amp), the platform registers two levels of A2A endpoints:
|
||||
|
||||
- **Crew-level**: an aggregate agent card at `/.well-known/agent-card.json` where each agent with `A2AServerConfig` is listed as a skill, with a JSON-RPC endpoint at `/a2a`
|
||||
- **Per-agent**: isolated agent cards and JSON-RPC endpoints mounted at `/a2a/agents/{role}/`, each with its own tenancy
|
||||
|
||||
Clients can interact with the crew as a whole or target a specific agent directly. To route a request to a specific agent through the crew-level endpoint, include `"target_agent"` in the message metadata with the agent's slugified role name (e.g., `"data-analyst"` for an agent with role `"Data Analyst"`). If no `target_agent` is provided, the request is handled by the first agent in the crew.
|
||||
|
||||
See [A2A Agent Delegation](/en/learn/a2a-agent-delegation#server-configuration-options) for the full list of `A2AServerConfig` options.
|
||||
|
||||
<Warning>
|
||||
Per the A2A protocol, agent cards are publicly accessible to enable discovery. This includes both the crew-level card at `/.well-known/agent-card.json` and per-agent cards at `/a2a/agents/{role}/.well-known/agent-card.json`. Do not include sensitive information in agent names, descriptions, or skill definitions.
|
||||
</Warning>
|
||||
|
||||
### File Inputs and Structured Output
|
||||
|
||||
A2A on AMP supports passing files and requesting structured output in both directions. Clients can send files as `FilePart`s and request structured responses by embedding a JSON schema in the message. Server agents receive files as `input_files` on the task, and return structured data as `DataPart`s when a schema is provided. See [File Inputs and Structured Output](/en/learn/a2a-agent-delegation#file-inputs-and-structured-output) for details.
|
||||
|
||||
### What AMP Adds
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Distributed State" icon="database">
|
||||
Persistent task, context, and result storage
|
||||
</Card>
|
||||
<Card title="Enterprise Authentication" icon="shield-halved">
|
||||
OIDC, OAuth2, mTLS, and Enterprise token validation beyond simple bearer tokens
|
||||
</Card>
|
||||
<Card title="gRPC Transport" icon="bolt">
|
||||
Full gRPC server with TLS and authentication
|
||||
</Card>
|
||||
<Card title="Context Lifecycle" icon="clock-rotate-left">
|
||||
Automatic idle detection, expiration, and cleanup of long-running conversations
|
||||
</Card>
|
||||
<Card title="Signed Webhooks" icon="signature">
|
||||
HMAC-SHA256 signed push notifications with replay protection
|
||||
</Card>
|
||||
<Card title="Multi-Transport" icon="arrows-split-up-and-left">
|
||||
REST, JSON-RPC, and gRPC endpoints served simultaneously from a single deployment
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## Distributed State Management
|
||||
|
||||
In the open-source implementation, task and context state lives in memory on a single process. AMP replaces this with persistent, distributed stores.
|
||||
|
||||
### Storage Layers
|
||||
|
||||
| Store | Purpose |
|
||||
|---|---|
|
||||
| **Task Store** | Persists A2A task state and metadata |
|
||||
| **Context Store** | Tracks conversation context, creation time, last activity, and associated tasks |
|
||||
| **Result Store** | Caches task results for retrieval |
|
||||
| **Push Config Store** | Manages webhook subscriptions per task |
|
||||
|
||||
Multiple A2A deployments are automatically isolated from each other, preventing data collisions when sharing infrastructure.
|
||||
|
||||
---
|
||||
|
||||
## Enterprise Authentication
|
||||
|
||||
AMP supports six authentication schemes for incoming A2A requests, configurable per deployment. Authentication works across both HTTP and gRPC transports.
|
||||
|
||||
| Scheme | Description | Use Case |
|
||||
|---|---|---|
|
||||
| **SimpleTokenAuth** | Static bearer token from `AUTH_TOKEN` env var | Development, simple deployments |
|
||||
| **EnterpriseTokenAuth** | Token verification via CrewAI PlusAPI with integration token claims | AMP-to-AMP agent communication |
|
||||
| **OIDCAuth** | OpenID Connect JWT validation with JWKS endpoint caching | Enterprise SSO integration |
|
||||
| **OAuth2ServerAuth** | OAuth2 with configurable scopes | Fine-grained access control |
|
||||
| **APIKeyServerAuth** | API key validation via header or query parameter | Third-party integrations |
|
||||
| **MTLSServerAuth** | Mutual TLS certificate-based authentication | Zero-trust environments |
|
||||
|
||||
The configured auth scheme automatically populates the agent card's `securitySchemes` and `security` fields. Clients discover authentication requirements by fetching the agent card before making requests.
|
||||
|
||||
---
|
||||
|
||||
## Extended Agent Cards
|
||||
|
||||
AMP supports role-based skill visibility through extended agent cards. Unauthenticated users see the standard agent card with public skills. Authenticated users receive an extended card with additional capabilities.
|
||||
|
||||
This enables patterns like:
|
||||
- Public agents that expose basic skills to anyone, with advanced skills available to authenticated clients
|
||||
- Internal agents that advertise different capabilities based on the caller's identity
|
||||
|
||||
---
|
||||
|
||||
## gRPC Transport
|
||||
|
||||
If enabled, AMP provides full gRPC support alongside the default JSON-RPC transport.
|
||||
|
||||
- **TLS termination** with configurable certificate and key paths
|
||||
- **gRPC reflection** for debugging with tools like `grpcurl`
|
||||
- **Authentication** using the same schemes available for HTTP
|
||||
- **Extension validation** ensuring clients support required protocol extensions
|
||||
- **Version negotiation** across A2A protocol versions 0.2 and 0.3
|
||||
|
||||
For deployments exposing multiple agents, AMP automatically allocates per-agent gRPC ports and coordinates TLS, startup, and shutdown across all servers.
|
||||
|
||||
---
|
||||
|
||||
## Context Lifecycle Management
|
||||
|
||||
AMP tracks the lifecycle of A2A conversation contexts and automatically manages cleanup.
|
||||
|
||||
### Lifecycle States
|
||||
|
||||
| State | Condition | Action |
|
||||
|---|---|---|
|
||||
| **Active** | Context has recent activity | None |
|
||||
| **Idle** | No activity for a configured period | Marked idle, event emitted |
|
||||
| **Expired** | Context exceeds its maximum lifetime | Marked expired, associated tasks cleaned up, event emitted |
|
||||
|
||||
A background cleanup task runs hourly to scan for idle and expired contexts. All state transitions emit CrewAI events that integrate with the platform's observability features.
|
||||
|
||||
---
|
||||
|
||||
## Signed Push Notifications
|
||||
|
||||
When an A2A agent sends push notifications to a client webhook, AMP signs each request with HMAC-SHA256 to ensure integrity and prevent tampering.
|
||||
|
||||
### Signature Headers
|
||||
|
||||
| Header | Purpose |
|
||||
|---|---|
|
||||
| `X-A2A-Signature` | HMAC-SHA256 signature in `sha256={hex_digest}` format |
|
||||
| `X-A2A-Signature-Timestamp` | Unix timestamp bound to the signature |
|
||||
| `X-A2A-Notification-Token` | Optional notification auth token |
|
||||
|
||||
### Security Properties
|
||||
|
||||
- **Integrity**: payload cannot be modified without invalidating the signature
|
||||
- **Replay protection**: signatures are timestamp-bound with a configurable tolerance window
|
||||
- **Retry with backoff**: failed deliveries retry with exponential backoff
|
||||
|
||||
---
|
||||
|
||||
## Distributed Event Streaming
|
||||
|
||||
In the open-source implementation, SSE streaming works within a single process. AMP propagates SSE events across instances so that clients receive updates even when the instance holding the streaming connection differs from the instance executing the task.
|
||||
|
||||
---
|
||||
|
||||
## Multi-Transport Endpoints
|
||||
|
||||
AMP serves REST and JSON-RPC by default. gRPC is available as an additional transport if enabled.
|
||||
|
||||
| Transport | Path Convention | Description |
|
||||
|---|---|---|
|
||||
| **REST** | `/v1/message:send`, `/v1/message:stream`, `/v1/tasks` | Google API conventions |
|
||||
| **JSON-RPC** | Standard A2A JSON-RPC endpoint | Default A2A protocol transport |
|
||||
| **gRPC** | Per-agent port allocation | Optional, high-performance binary protocol |
|
||||
|
||||
All active transports share the same authentication, version negotiation, and extension validation. Agent cards are generated from agent and crew metadata — roles, goals, and tools become skills and descriptions — and automatically include interfaces for each active transport. They can also be manually configured via `A2AServerConfig`.
|
||||
|
||||
---
|
||||
|
||||
## Version and Extension Negotiation
|
||||
|
||||
AMP validates A2A protocol versions and extensions at the transport layer.
|
||||
|
||||
### Version Negotiation
|
||||
|
||||
- Clients send the `A2A-Version` header with their preferred version
|
||||
- AMP validates against supported versions (0.2, 0.3) and falls back to 0.3 if unspecified
|
||||
- The negotiated version is returned in the response headers
|
||||
|
||||
### Extension Validation
|
||||
|
||||
- Clients declare supported extensions via the `X-A2A-Extensions` header
|
||||
- AMP validates that clients support all extensions the agent requires
|
||||
- Requests from clients missing required extensions receive an `UnsupportedExtensionError`
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [A2A Agent Delegation](/en/learn/a2a-agent-delegation) — A2A protocol fundamentals and configuration
|
||||
- [A2UI](/en/learn/a2ui) — Interactive UI rendering over A2A
|
||||
- [Deploy to AMP](/en/enterprise/guides/deploy-to-amp) — General deployment guide
|
||||
- [Webhook Streaming](/en/enterprise/features/webhook-streaming) — Event streaming for deployed automations
|
||||
@@ -7,10 +7,6 @@ mode: "wide"
|
||||
|
||||
## A2A Agent Delegation
|
||||
|
||||
<Info>
|
||||
Deploying A2A agents to production? See [A2A on AMP](/en/enterprise/features/a2a) for distributed state, enterprise authentication, gRPC transport, and horizontal scaling.
|
||||
</Info>
|
||||
|
||||
CrewAI treats [A2A protocol](https://a2a-protocol.org/latest/) as a first-class delegation primitive, enabling agents to delegate tasks, request information, and collaborate with remote agents, as well as act as A2A-compliant server agents.
|
||||
In client mode, agents autonomously choose between local execution and remote delegation based on task requirements.
|
||||
|
||||
@@ -100,28 +96,24 @@ The `A2AClientConfig` class accepts the following parameters:
|
||||
Update mechanism for receiving task status. Options: `StreamingConfig`, `PollingConfig`, or `PushNotificationConfig`.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="transport_protocol" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="JSONRPC">
|
||||
Transport protocol for A2A communication. Options: `JSONRPC` (default), `GRPC`, or `HTTP+JSON`.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="accepted_output_modes" type="list[str]" default='["application/json"]'>
|
||||
Media types the client can accept in responses.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="supported_transports" type="list[str]" default='["JSONRPC"]'>
|
||||
Ordered list of transport protocols the client supports.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="use_client_preference" type="bool" default="False">
|
||||
Whether to prioritize client transport preferences over server.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="extensions" type="list[str]" default="[]">
|
||||
A2A protocol extension URIs the client supports.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="client_extensions" type="list[A2AExtension]" default="[]">
|
||||
Client-side processing hooks for tool injection, prompt augmentation, and response modification.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="transport" type="ClientTransportConfig" default="ClientTransportConfig()">
|
||||
Transport configuration including preferred transport, supported transports for negotiation, and protocol-specific settings (gRPC message sizes, keepalive, etc.).
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="transport_protocol" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="None">
|
||||
**Deprecated**: Use `transport=ClientTransportConfig(preferred=...)` instead.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="supported_transports" type="list[str]" default="None">
|
||||
**Deprecated**: Use `transport=ClientTransportConfig(supported=...)` instead.
|
||||
Extension URIs the client supports.
|
||||
</ParamField>
|
||||
|
||||
## Authentication
|
||||
@@ -413,7 +405,11 @@ agent = Agent(
|
||||
Preferred endpoint URL. If set, overrides the URL passed to `to_agent_card()`.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="protocol_version" type="str" default="0.3.0">
|
||||
<ParamField path="preferred_transport" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="JSONRPC">
|
||||
Transport protocol for the preferred endpoint.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="protocol_version" type="str" default="0.3">
|
||||
A2A protocol version this agent supports.
|
||||
</ParamField>
|
||||
|
||||
@@ -445,36 +441,8 @@ agent = Agent(
|
||||
Whether agent provides extended card to authenticated users.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="extended_skills" type="list[AgentSkill]" default="[]">
|
||||
Additional skills visible only to authenticated users in the extended agent card.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="signing_config" type="AgentCardSigningConfig" default="None">
|
||||
Configuration for signing the AgentCard with JWS. Supports RS256, ES256, PS256, and related algorithms.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="server_extensions" type="list[ServerExtension]" default="[]">
|
||||
Server-side A2A protocol extensions with `on_request`/`on_response` hooks that modify agent behavior.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="push_notifications" type="ServerPushNotificationConfig" default="None">
|
||||
Configuration for outgoing push notifications, including HMAC-SHA256 signing secret.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="transport" type="ServerTransportConfig" default="ServerTransportConfig()">
|
||||
Transport configuration including preferred transport, gRPC server settings, JSON-RPC paths, and HTTP+JSON settings.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="auth" type="ServerAuthScheme" default="None">
|
||||
Authentication scheme for incoming A2A requests. Defaults to `SimpleTokenAuth` using the `AUTH_TOKEN` environment variable.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="preferred_transport" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="None">
|
||||
**Deprecated**: Use `transport=ServerTransportConfig(preferred=...)` instead.
|
||||
</ParamField>
|
||||
|
||||
<ParamField path="signatures" type="list[AgentCardSignature]" default="None">
|
||||
**Deprecated**: Use `signing_config=AgentCardSigningConfig(...)` instead.
|
||||
<ParamField path="signatures" type="list[AgentCardSignature]" default="[]">
|
||||
JSON Web Signatures for the AgentCard.
|
||||
</ParamField>
|
||||
|
||||
### Combined Client and Server
|
||||
@@ -500,14 +468,6 @@ agent = Agent(
|
||||
)
|
||||
```
|
||||
|
||||
### File Inputs and Structured Output
|
||||
|
||||
A2A supports passing files and requesting structured output in both directions.
|
||||
|
||||
**Client side**: When delegating to a remote A2A agent, files from the task's `input_files` are sent as `FilePart`s in the outgoing message. If `response_model` is set on the `A2AClientConfig`, the Pydantic model's JSON schema is embedded in the message metadata, requesting structured output from the remote agent.
|
||||
|
||||
**Server side**: Incoming `FilePart`s are extracted and passed to the agent's task as `input_files`. If the client included a JSON schema, the server creates a response model from it and applies it to the task. When the agent returns structured data, the response is sent back as a `DataPart` rather than plain text.
|
||||
|
||||
## Best Practices
|
||||
|
||||
<CardGroup cols={2}>
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 315 KiB |
@@ -4,87 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="2026년 4월 15일">
|
||||
## v1.14.2a4
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- 실패 시 devtools 릴리스에 이력서 힌트 추가
|
||||
|
||||
### 버그 수정
|
||||
- Bedrock Converse API로의 엄격 모드 포워딩 수정
|
||||
- 보안 취약점 GHSA-6w46-j5rx-g56g에 대해 pytest 버전을 9.0.3으로 수정
|
||||
- OpenAI 하한을 >=2.0.0으로 상향 조정
|
||||
|
||||
### 문서
|
||||
- v1.14.2a3에 대한 변경 로그 및 버전 업데이트
|
||||
|
||||
## 기여자
|
||||
|
||||
@greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 13일">
|
||||
## v1.14.2a3
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a3)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- 배포 검증 CLI 추가
|
||||
- LLM 초기화 사용성 개선
|
||||
|
||||
### 버그 수정
|
||||
- CVE-2026-40260 및 GHSA-pjjw-68hj-v9mw에 대한 패치된 버전으로 pypdf 및 uv 재정의
|
||||
- CVE 임시 파일 취약점에 대해 requests를 >=2.33.0으로 업그레이드
|
||||
- 진리값 기본값을 제거하여 Bedrock 도구 호출 인수 보존
|
||||
- 엄격 모드를 위한 도구 스키마 정리
|
||||
- MemoryRecord 임베딩 직렬화 테스트의 불안정성 제거
|
||||
|
||||
### 문서
|
||||
- 기업 A2A 언어 정리
|
||||
- 기업 A2A 기능 문서 추가
|
||||
- OSS A2A 문서 업데이트
|
||||
- v1.14.2a2에 대한 변경 로그 및 버전 업데이트
|
||||
|
||||
## 기여자
|
||||
|
||||
@Yanhu007, @greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 10일">
|
||||
## v1.14.2a2
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- 트리 뷰, 포크 지원 및 편집 가능한 입력/출력을 갖춘 체크포인트 TUI 추가
|
||||
- 추론 토큰 및 캐시 생성 토큰으로 LLM 토큰 추적 강화
|
||||
- 킥오프 메서드에 `from_checkpoint` 매개변수 추가
|
||||
- 마이그레이션 프레임워크와 함께 체크포인트에 `crewai_version` 포함
|
||||
- 계보 추적이 가능한 체크포인트 포킹 추가
|
||||
|
||||
### 버그 수정
|
||||
- Anthropic 및 Bedrock 공급자로의 엄격 모드 포워딩 수정
|
||||
- 읽기 전용 기본값, 쿼리 검증 및 매개변수화된 쿼리로 NL2SQLTool 강화
|
||||
|
||||
### 문서
|
||||
- v1.14.2a1에 대한 변경 로그 및 버전 업데이트
|
||||
|
||||
## 기여자
|
||||
|
||||
@alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 9일">
|
||||
## v1.14.2a1
|
||||
|
||||
|
||||
@@ -4,87 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="15 abr 2026">
|
||||
## v1.14.2a4
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
|
||||
|
||||
## O que Mudou
|
||||
|
||||
### Recursos
|
||||
- Adicionar dicas de retomar ao release do devtools em caso de falha
|
||||
|
||||
### Correções de Bugs
|
||||
- Corrigir o encaminhamento do modo estrito para a API Bedrock Converse
|
||||
- Corrigir a versão do pytest para 9.0.3 devido à vulnerabilidade de segurança GHSA-6w46-j5rx-g56g
|
||||
- Aumentar o limite inferior do OpenAI para >=2.0.0
|
||||
|
||||
### Documentação
|
||||
- Atualizar o changelog e a versão para v1.14.2a3
|
||||
|
||||
## Contribuidores
|
||||
|
||||
@greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="13 abr 2026">
|
||||
## v1.14.2a3
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a3)
|
||||
|
||||
## O que Mudou
|
||||
|
||||
### Recursos
|
||||
- Adicionar CLI de validação de deploy
|
||||
- Melhorar a ergonomia de inicialização do LLM
|
||||
|
||||
### Correções de Bugs
|
||||
- Substituir pypdf e uv por versões corrigidas para CVE-2026-40260 e GHSA-pjjw-68hj-v9mw
|
||||
- Atualizar requests para >=2.33.0 devido à vulnerabilidade de arquivo temporário CVE
|
||||
- Preservar os argumentos de chamada da ferramenta Bedrock removendo o padrão truthy
|
||||
- Sanitizar esquemas de ferramentas para modo estrito
|
||||
- Remover flakiness do teste de serialização de embedding MemoryRecord
|
||||
|
||||
### Documentação
|
||||
- Limpar a linguagem do A2A empresarial
|
||||
- Adicionar documentação de recursos do A2A empresarial
|
||||
- Atualizar documentação do A2A OSS
|
||||
- Atualizar changelog e versão para v1.14.2a2
|
||||
|
||||
## Contribuidores
|
||||
|
||||
@Yanhu007, @greysonlalonde
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="10 abr 2026">
|
||||
## v1.14.2a2
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a2)
|
||||
|
||||
## O que Mudou
|
||||
|
||||
### Funcionalidades
|
||||
- Adicionar TUI de ponto de verificação com visualização em árvore, suporte a bifurcações e entradas/saídas editáveis
|
||||
- Enriquecer o rastreamento de tokens LLM com tokens de raciocínio e tokens de criação de cache
|
||||
- Adicionar parâmetro `from_checkpoint` aos métodos de inicialização
|
||||
- Incorporar `crewai_version` em pontos de verificação com o framework de migração
|
||||
- Adicionar bifurcação de ponto de verificação com rastreamento de linhagem
|
||||
|
||||
### Correções de Bugs
|
||||
- Corrigir o encaminhamento em modo estrito para os provedores Anthropic e Bedrock
|
||||
- Fortalecer NL2SQLTool com padrão somente leitura, validação de consultas e consultas parametrizadas
|
||||
|
||||
### Documentação
|
||||
- Atualizar changelog e versão para v1.14.2a1
|
||||
|
||||
## Contributors
|
||||
|
||||
@alex-clawd, @github-actions[bot], @greysonlalonde, @lucasgomide
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="09 abr 2026">
|
||||
## v1.14.2a1
|
||||
|
||||
|
||||
@@ -9,7 +9,7 @@ authors = [
|
||||
requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
"Pillow~=12.1.1",
|
||||
"pypdf~=6.10.0",
|
||||
"pypdf~=6.9.1",
|
||||
"python-magic>=0.4.27",
|
||||
"aiocache~=0.12.3",
|
||||
"aiofiles~=24.1.0",
|
||||
|
||||
@@ -152,4 +152,4 @@ __all__ = [
|
||||
"wrap_file_source",
|
||||
]
|
||||
|
||||
__version__ = "1.14.2a4"
|
||||
__version__ = "1.14.2a1"
|
||||
|
||||
@@ -9,8 +9,8 @@ authors = [
|
||||
requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
"pytube~=15.0.0",
|
||||
"requests>=2.33.0,<3",
|
||||
"crewai==1.14.2a4",
|
||||
"requests~=2.32.5",
|
||||
"crewai==1.14.2a1",
|
||||
"tiktoken~=0.8.0",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
"python-docx~=1.2.0",
|
||||
|
||||
@@ -305,4 +305,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.14.2a4"
|
||||
__version__ = "1.14.2a1"
|
||||
|
||||
@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
# Core Dependencies
|
||||
"pydantic~=2.11.9",
|
||||
"openai>=2.0.0,<3",
|
||||
"openai>=1.83.0,<3",
|
||||
"instructor>=1.3.3",
|
||||
# Text Processing
|
||||
"pdfplumber~=0.11.4",
|
||||
@@ -40,7 +40,7 @@ dependencies = [
|
||||
"pydantic-settings~=2.10.1",
|
||||
"httpx~=0.28.1",
|
||||
"mcp~=1.26.0",
|
||||
"uv~=0.11.6",
|
||||
"uv~=0.9.13",
|
||||
"aiosqlite~=0.21.0",
|
||||
"pyyaml~=6.0",
|
||||
"aiofiles~=24.1.0",
|
||||
@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.14.2a4",
|
||||
"crewai-tools==1.14.2a1",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken~=0.8.0"
|
||||
@@ -74,8 +74,8 @@ qdrant = [
|
||||
"qdrant-client[fastembed]~=1.14.3",
|
||||
]
|
||||
aws = [
|
||||
"boto3~=1.42.79",
|
||||
"aiobotocore~=3.4.0",
|
||||
"boto3~=1.40.38",
|
||||
"aiobotocore~=2.25.2",
|
||||
]
|
||||
watson = [
|
||||
"ibm-watsonx-ai~=1.3.39",
|
||||
@@ -87,7 +87,7 @@ litellm = [
|
||||
"litellm~=1.83.0",
|
||||
]
|
||||
bedrock = [
|
||||
"boto3~=1.42.79",
|
||||
"boto3~=1.40.45",
|
||||
]
|
||||
google-genai = [
|
||||
"google-genai~=1.65.0",
|
||||
|
||||
@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.14.2a4"
|
||||
__version__ = "1.14.2a1"
|
||||
_telemetry_submitted = False
|
||||
|
||||
|
||||
|
||||
@@ -98,6 +98,7 @@ class A2AErrorCode(IntEnum):
|
||||
"""The specified artifact was not found."""
|
||||
|
||||
|
||||
# Error code to default message mapping
|
||||
ERROR_MESSAGES: dict[int, str] = {
|
||||
A2AErrorCode.JSON_PARSE_ERROR: "Parse error",
|
||||
A2AErrorCode.INVALID_REQUEST: "Invalid Request",
|
||||
|
||||
@@ -63,21 +63,25 @@ class A2AExtension(Protocol):
|
||||
Example:
|
||||
class MyExtension:
|
||||
def inject_tools(self, agent: Agent) -> None:
|
||||
# Add custom tools to the agent
|
||||
pass
|
||||
|
||||
def extract_state_from_history(
|
||||
self, conversation_history: Sequence[Message]
|
||||
) -> ConversationState | None:
|
||||
# Extract state from conversation
|
||||
return None
|
||||
|
||||
def augment_prompt(
|
||||
self, base_prompt: str, conversation_state: ConversationState | None
|
||||
) -> str:
|
||||
# Add custom instructions
|
||||
return base_prompt
|
||||
|
||||
def process_response(
|
||||
self, agent_response: Any, conversation_state: ConversationState | None
|
||||
) -> Any:
|
||||
# Modify response if needed
|
||||
return agent_response
|
||||
"""
|
||||
|
||||
|
||||
@@ -77,6 +77,7 @@ def extract_a2a_agent_ids_from_config(
|
||||
else:
|
||||
configs = a2a_config
|
||||
|
||||
# Filter to only client configs (those with endpoint)
|
||||
client_configs: list[A2AClientConfigTypes] = [
|
||||
config for config in configs if isinstance(config, (A2AConfig, A2AClientConfig))
|
||||
]
|
||||
|
||||
@@ -1161,9 +1161,19 @@ class Agent(BaseAgent):
|
||||
|
||||
return task_prompt
|
||||
|
||||
def _use_trained_data(self, task_prompt: str) -> str:
|
||||
"""Use trained data for the agent task prompt to improve output."""
|
||||
if data := CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).load():
|
||||
def _use_trained_data(
|
||||
self, task_prompt: str, trained_agents_data_file: str | None = None
|
||||
) -> str:
|
||||
"""Use trained data for the agent task prompt to improve output.
|
||||
|
||||
Args:
|
||||
task_prompt: The task prompt to augment.
|
||||
trained_agents_data_file: Optional path to the trained agents data
|
||||
file. Falls back to the default ``TRAINED_AGENTS_DATA_FILE``
|
||||
when not provided.
|
||||
"""
|
||||
filename = trained_agents_data_file or TRAINED_AGENTS_DATA_FILE
|
||||
if data := CrewTrainingHandler(filename).load():
|
||||
if trained_data_output := data.get(self.role):
|
||||
task_prompt += (
|
||||
"\n\nYou MUST follow these instructions: \n - "
|
||||
@@ -1341,6 +1351,7 @@ class Agent(BaseAgent):
|
||||
|
||||
raw_tools: list[BaseTool] = self.tools or []
|
||||
|
||||
# Inject memory tools for standalone kickoff (crew path handles its own)
|
||||
agent_memory = getattr(self, "memory", None)
|
||||
if agent_memory is not None:
|
||||
from crewai.tools.memory_tools import create_memory_tools
|
||||
@@ -1398,6 +1409,7 @@ class Agent(BaseAgent):
|
||||
if input_files:
|
||||
all_files.update(input_files)
|
||||
|
||||
# Inject memory context for standalone kickoff (recall before execution)
|
||||
if agent_memory is not None:
|
||||
try:
|
||||
crewai_event_bus.emit(
|
||||
@@ -1483,6 +1495,8 @@ class Agent(BaseAgent):
|
||||
Note:
|
||||
For explicit async usage outside of Flow, use kickoff_async() directly.
|
||||
"""
|
||||
# Magic auto-async: if inside event loop (e.g., inside a Flow),
|
||||
# return coroutine for Flow to await
|
||||
if is_inside_event_loop():
|
||||
return self.kickoff_async(messages, response_format, input_files)
|
||||
|
||||
@@ -1633,7 +1647,7 @@ class Agent(BaseAgent):
|
||||
if isinstance(conversion_result, BaseModel):
|
||||
formatted_result = conversion_result
|
||||
except ConverterError:
|
||||
pass
|
||||
pass # Keep raw output if conversion fails
|
||||
else:
|
||||
raw_output = str(output) if not isinstance(output, str) else output
|
||||
|
||||
@@ -1715,6 +1729,7 @@ class Agent(BaseAgent):
|
||||
elif callable(self.guardrail):
|
||||
guardrail_callable = self.guardrail
|
||||
else:
|
||||
# Should not happen if called from kickoff with guardrail check
|
||||
return output
|
||||
|
||||
guardrail_result = process_guardrail(
|
||||
|
||||
@@ -41,6 +41,7 @@ class PlanningConfig(BaseModel):
|
||||
from crewai import Agent
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
|
||||
# Simple usage — fast, linear execution (default)
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
@@ -48,6 +49,7 @@ class PlanningConfig(BaseModel):
|
||||
planning_config=PlanningConfig(),
|
||||
)
|
||||
|
||||
# Balanced — replan only when steps fail
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
@@ -57,6 +59,7 @@ class PlanningConfig(BaseModel):
|
||||
),
|
||||
)
|
||||
|
||||
# Full adaptive planning with refinement and replanning
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
@@ -66,7 +69,7 @@ class PlanningConfig(BaseModel):
|
||||
max_attempts=3,
|
||||
max_steps=10,
|
||||
plan_prompt="Create a focused plan for: {description}",
|
||||
llm="gpt-4o-mini",
|
||||
llm="gpt-4o-mini", # Use cheaper model for planning
|
||||
),
|
||||
)
|
||||
```
|
||||
|
||||
@@ -39,6 +39,7 @@ def handle_reasoning(agent: Agent, task: Task) -> None:
|
||||
agent: The agent performing the task.
|
||||
task: The task to execute.
|
||||
"""
|
||||
# Check if planning is enabled using the planning_enabled property
|
||||
if not getattr(agent, "planning_enabled", False):
|
||||
return
|
||||
|
||||
@@ -249,7 +250,13 @@ def apply_training_data(agent: Agent, task_prompt: str) -> str:
|
||||
"""
|
||||
if agent.crew and not isinstance(agent.crew, str) and agent.crew._train:
|
||||
return agent._training_handler(task_prompt=task_prompt)
|
||||
return agent._use_trained_data(task_prompt=task_prompt)
|
||||
trained_agents_data_file = (
|
||||
agent.crew.trained_agents_data_file if agent.crew else None
|
||||
)
|
||||
return agent._use_trained_data(
|
||||
task_prompt=task_prompt,
|
||||
trained_agents_data_file=trained_agents_data_file,
|
||||
)
|
||||
|
||||
|
||||
def process_tool_results(agent: Agent, result: Any) -> Any:
|
||||
|
||||
@@ -99,10 +99,12 @@ class OpenAIAgentToolAdapter(BaseToolAdapter):
|
||||
Returns:
|
||||
Tool execution result.
|
||||
"""
|
||||
# Get the parameter name from the schema
|
||||
param_name: str = next(
|
||||
iter(tool.args_schema.model_json_schema()["properties"].keys())
|
||||
)
|
||||
|
||||
# Handle different argument types
|
||||
args_dict: dict[str, Any]
|
||||
if isinstance(arguments, dict):
|
||||
args_dict = arguments
|
||||
@@ -114,13 +116,16 @@ class OpenAIAgentToolAdapter(BaseToolAdapter):
|
||||
else:
|
||||
args_dict = {param_name: str(arguments)}
|
||||
|
||||
# Run the tool with the processed arguments
|
||||
output: Any | Awaitable[Any] = tool._run(**args_dict)
|
||||
|
||||
# Await if the tool returned a coroutine
|
||||
if inspect.isawaitable(output):
|
||||
result: Any = await output
|
||||
else:
|
||||
result = output
|
||||
|
||||
# Ensure the result is JSON serializable
|
||||
if isinstance(result, (dict, list, str, int, float, bool, type(None))):
|
||||
return result
|
||||
return str(result)
|
||||
|
||||
@@ -383,6 +383,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
|
||||
if isinstance(tool, BaseTool):
|
||||
processed_tools.append(tool)
|
||||
elif all(hasattr(tool, attr) for attr in required_attrs):
|
||||
# Tool has the required attributes, create a Tool instance
|
||||
processed_tools.append(Tool.from_langchain(tool))
|
||||
else:
|
||||
raise ValueError(
|
||||
@@ -447,12 +448,14 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def validate_and_set_attributes(self) -> Self:
|
||||
# Validate required fields
|
||||
for field in ["role", "goal", "backstory"]:
|
||||
if getattr(self, field) is None:
|
||||
raise ValueError(
|
||||
f"{field} must be provided either directly or through config"
|
||||
)
|
||||
|
||||
# Set private attributes
|
||||
self._logger = Logger(verbose=self.verbose)
|
||||
if self.max_rpm and not self._rpm_controller:
|
||||
self._rpm_controller = RPMController(
|
||||
@@ -461,6 +464,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
|
||||
if not self._token_process:
|
||||
self._token_process = TokenProcess()
|
||||
|
||||
# Initialize security_config if not provided
|
||||
if self.security_config is None:
|
||||
self.security_config = SecurityConfig()
|
||||
|
||||
@@ -562,11 +566,14 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
|
||||
"actions",
|
||||
}
|
||||
|
||||
# Copy llm
|
||||
existing_llm = shallow_copy(self.llm)
|
||||
copied_knowledge = shallow_copy(self.knowledge)
|
||||
copied_knowledge_storage = shallow_copy(self.knowledge_storage)
|
||||
# Properly copy knowledge sources if they exist
|
||||
existing_knowledge_sources = None
|
||||
if self.knowledge_sources:
|
||||
# Create a shared storage instance for all knowledge sources
|
||||
shared_storage = (
|
||||
self.knowledge_sources[0].storage if self.knowledge_sources else None
|
||||
)
|
||||
@@ -578,6 +585,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
|
||||
if hasattr(source, "model_copy")
|
||||
else shallow_copy(source)
|
||||
)
|
||||
# Ensure all copied sources use the same storage instance
|
||||
copied_source.storage = shared_storage
|
||||
existing_knowledge_sources.append(copied_source)
|
||||
|
||||
|
||||
@@ -4,6 +4,8 @@ import re
|
||||
from typing import Final
|
||||
|
||||
|
||||
# crewai.agents.parser constants
|
||||
|
||||
FINAL_ANSWER_ACTION: Final[str] = "Final Answer:"
|
||||
MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE: Final[str] = (
|
||||
"I did it wrong. Invalid Format: I missed the 'Action:' after 'Thought:'. I will do right next, and don't use a tool I have already used.\n"
|
||||
|
||||
@@ -296,6 +296,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
Returns:
|
||||
Final answer from the agent.
|
||||
"""
|
||||
# Check if model supports native function calling
|
||||
use_native_tools = (
|
||||
hasattr(self.llm, "supports_function_calling")
|
||||
and callable(getattr(self.llm, "supports_function_calling", None))
|
||||
@@ -306,6 +307,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
if use_native_tools:
|
||||
return self._invoke_loop_native_tools()
|
||||
|
||||
# Fall back to ReAct text-based pattern
|
||||
return self._invoke_loop_react()
|
||||
|
||||
def _invoke_loop_react(self) -> AgentFinish:
|
||||
@@ -345,6 +347,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
# breakpoint()
|
||||
if self.response_model is not None:
|
||||
try:
|
||||
if isinstance(answer, BaseModel):
|
||||
@@ -362,6 +365,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
text=answer,
|
||||
)
|
||||
except ValidationError:
|
||||
# If validation fails, convert BaseModel to JSON string for parsing
|
||||
answer_str = (
|
||||
answer.model_dump_json()
|
||||
if isinstance(answer, BaseModel)
|
||||
@@ -371,12 +375,14 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
answer_str, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
else:
|
||||
# When no response_model, answer should be a string
|
||||
answer_str = str(answer) if not isinstance(answer, str) else answer
|
||||
formatted_answer = process_llm_response(
|
||||
answer_str, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
|
||||
if isinstance(formatted_answer, AgentAction):
|
||||
# Extract agent fingerprint if available
|
||||
fingerprint_context = {}
|
||||
if (
|
||||
self.agent
|
||||
@@ -420,6 +426,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
|
||||
except Exception as e:
|
||||
if e.__class__.__module__.startswith("litellm"):
|
||||
# Do not retry on litellm errors
|
||||
raise e
|
||||
if is_context_length_exceeded(e):
|
||||
handle_context_length(
|
||||
@@ -436,6 +443,10 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
finally:
|
||||
self.iterations += 1
|
||||
|
||||
# During the invoke loop, formatted_answer alternates between AgentAction
|
||||
# (when the agent is using tools) and eventually becomes AgentFinish
|
||||
# (when the agent reaches a final answer). This check confirms we've
|
||||
# reached a final answer and helps type checking understand this transition.
|
||||
if not isinstance(formatted_answer, AgentFinish):
|
||||
raise RuntimeError(
|
||||
"Agent execution ended without reaching a final answer. "
|
||||
@@ -454,7 +465,9 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
Returns:
|
||||
Final answer from the agent.
|
||||
"""
|
||||
# Convert tools to OpenAI schema format
|
||||
if not self.original_tools:
|
||||
# No tools available, fall back to simple LLM call
|
||||
return self._invoke_loop_native_no_tools()
|
||||
|
||||
openai_tools, available_functions, self._tool_name_mapping = (
|
||||
@@ -477,6 +490,10 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
|
||||
enforce_rpm_limit(self.request_within_rpm_limit)
|
||||
|
||||
# Call LLM with native tools
|
||||
# Pass available_functions=None so the LLM returns tool_calls
|
||||
# without executing them. The executor handles tool execution
|
||||
# via _handle_native_tool_calls to properly manage message history.
|
||||
answer = get_llm_response(
|
||||
llm=cast("BaseLLM", self.llm),
|
||||
messages=self.messages,
|
||||
@@ -491,26 +508,32 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
|
||||
# Check if the response is a list of tool calls
|
||||
if (
|
||||
isinstance(answer, list)
|
||||
and answer
|
||||
and self._is_tool_call_list(answer)
|
||||
):
|
||||
# Handle tool calls - execute tools and add results to messages
|
||||
tool_finish = self._handle_native_tool_calls(
|
||||
answer, available_functions
|
||||
)
|
||||
# If tool has result_as_answer=True, return immediately
|
||||
if tool_finish is not None:
|
||||
return tool_finish
|
||||
# Continue loop to let LLM analyze results and decide next steps
|
||||
continue
|
||||
|
||||
# Text or other response - handle as potential final answer
|
||||
if isinstance(answer, str):
|
||||
# Text response - this is the final answer
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=answer,
|
||||
)
|
||||
self._invoke_step_callback(formatted_answer)
|
||||
self._append_message(answer)
|
||||
self._append_message(answer) # Save final answer to messages
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
@@ -526,13 +549,14 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
# Unexpected response type, treat as final answer
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=str(answer),
|
||||
text=str(answer),
|
||||
)
|
||||
self._invoke_step_callback(formatted_answer)
|
||||
self._append_message(str(answer))
|
||||
self._append_message(str(answer)) # Save final answer to messages
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
@@ -603,10 +627,12 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
if not response:
|
||||
return False
|
||||
first_item = response[0]
|
||||
# OpenAI-style
|
||||
if hasattr(first_item, "function") or (
|
||||
isinstance(first_item, dict) and "function" in first_item
|
||||
):
|
||||
return True
|
||||
# Anthropic-style (object with attributes)
|
||||
if (
|
||||
hasattr(first_item, "type")
|
||||
and getattr(first_item, "type", None) == "tool_use"
|
||||
@@ -614,12 +640,14 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
return True
|
||||
if hasattr(first_item, "name") and hasattr(first_item, "input"):
|
||||
return True
|
||||
# Bedrock-style (dict with name and input keys)
|
||||
if (
|
||||
isinstance(first_item, dict)
|
||||
and "name" in first_item
|
||||
and "input" in first_item
|
||||
):
|
||||
return True
|
||||
# Gemini-style
|
||||
if hasattr(first_item, "function_call") and first_item.function_call:
|
||||
return True
|
||||
return False
|
||||
@@ -678,6 +706,8 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
for _, func_name, _ in parsed_calls
|
||||
)
|
||||
|
||||
# Preserve historical sequential behavior for result_as_answer batches.
|
||||
# Also avoid threading around usage counters for max_usage_count tools.
|
||||
if has_result_as_answer_in_batch or has_max_usage_count_in_batch:
|
||||
logger.debug(
|
||||
"Skipping parallel native execution because batch includes result_as_answer or max_usage_count tool"
|
||||
@@ -743,6 +773,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
self.messages.append(reasoning_message)
|
||||
return None
|
||||
|
||||
# Sequential behavior: process only first tool call, then force reflection.
|
||||
call_id, func_name, func_args = parsed_calls[0]
|
||||
self._append_assistant_tool_calls_message([(call_id, func_name, func_args)])
|
||||
|
||||
@@ -796,7 +827,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
func_name = sanitize_tool_name(
|
||||
func_info.get("name", "") or tool_call.get("name", "")
|
||||
)
|
||||
func_args = func_info.get("arguments") or tool_call.get("input", {})
|
||||
func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
|
||||
return call_id, func_name, func_args
|
||||
return None
|
||||
|
||||
@@ -1171,6 +1202,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
text=answer,
|
||||
)
|
||||
except ValidationError:
|
||||
# If validation fails, convert BaseModel to JSON string for parsing
|
||||
answer_str = (
|
||||
answer.model_dump_json()
|
||||
if isinstance(answer, BaseModel)
|
||||
@@ -1180,6 +1212,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
answer_str, self.use_stop_words
|
||||
) # type: ignore[assignment]
|
||||
else:
|
||||
# When no response_model, answer should be a string
|
||||
answer_str = str(answer) if not isinstance(answer, str) else answer
|
||||
formatted_answer = process_llm_response(
|
||||
answer_str, self.use_stop_words
|
||||
@@ -1286,6 +1319,10 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
|
||||
enforce_rpm_limit(self.request_within_rpm_limit)
|
||||
|
||||
# Call LLM with native tools
|
||||
# Pass available_functions=None so the LLM returns tool_calls
|
||||
# without executing them. The executor handles tool execution
|
||||
# via _handle_native_tool_calls to properly manage message history.
|
||||
answer = await aget_llm_response(
|
||||
llm=cast("BaseLLM", self.llm),
|
||||
messages=self.messages,
|
||||
@@ -1299,26 +1336,32 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
executor_context=self,
|
||||
verbose=self.agent.verbose,
|
||||
)
|
||||
# Check if the response is a list of tool calls
|
||||
if (
|
||||
isinstance(answer, list)
|
||||
and answer
|
||||
and self._is_tool_call_list(answer)
|
||||
):
|
||||
# Handle tool calls - execute tools and add results to messages
|
||||
tool_finish = self._handle_native_tool_calls(
|
||||
answer, available_functions
|
||||
)
|
||||
# If tool has result_as_answer=True, return immediately
|
||||
if tool_finish is not None:
|
||||
return tool_finish
|
||||
# Continue loop to let LLM analyze results and decide next steps
|
||||
continue
|
||||
|
||||
# Text or other response - handle as potential final answer
|
||||
if isinstance(answer, str):
|
||||
# Text response - this is the final answer
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=answer,
|
||||
text=answer,
|
||||
)
|
||||
await self._ainvoke_step_callback(formatted_answer)
|
||||
self._append_message(answer)
|
||||
self._append_message(answer) # Save final answer to messages
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
@@ -1334,13 +1377,14 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
# Unexpected response type, treat as final answer
|
||||
formatted_answer = AgentFinish(
|
||||
thought="",
|
||||
output=str(answer),
|
||||
text=str(answer),
|
||||
)
|
||||
await self._ainvoke_step_callback(formatted_answer)
|
||||
self._append_message(str(answer))
|
||||
self._append_message(str(answer)) # Save final answer to messages
|
||||
self._show_logs(formatted_answer)
|
||||
return formatted_answer
|
||||
|
||||
@@ -1411,6 +1455,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
Returns:
|
||||
Updated action or final answer.
|
||||
"""
|
||||
# Special case for add_image_tool
|
||||
add_image_tool = I18N_DEFAULT.tools("add_image")
|
||||
if (
|
||||
isinstance(add_image_tool, dict)
|
||||
@@ -1530,14 +1575,17 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
|
||||
training_data = training_handler.load() or {}
|
||||
|
||||
# Initialize or retrieve agent's training data
|
||||
agent_training_data = training_data.get(agent_id, {})
|
||||
|
||||
if human_feedback is not None:
|
||||
# Save initial output and human feedback
|
||||
agent_training_data[train_iteration] = {
|
||||
"initial_output": result.output,
|
||||
"human_feedback": human_feedback,
|
||||
}
|
||||
else:
|
||||
# Save improved output
|
||||
if train_iteration in agent_training_data:
|
||||
agent_training_data[train_iteration]["improved_output"] = result.output
|
||||
else:
|
||||
@@ -1551,6 +1599,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
|
||||
)
|
||||
return
|
||||
|
||||
# Update the training data and save
|
||||
training_data[agent_id] = agent_training_data
|
||||
training_handler.save(training_data)
|
||||
|
||||
|
||||
@@ -94,8 +94,11 @@ def parse(text: str) -> AgentAction | AgentFinish:
|
||||
|
||||
if includes_answer:
|
||||
final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
|
||||
# Check whether the final answer ends with triple backticks.
|
||||
if final_answer.endswith("```"):
|
||||
# Count occurrences of triple backticks in the final answer.
|
||||
count = final_answer.count("```")
|
||||
# If count is odd then it's an unmatched trailing set; remove it.
|
||||
if count % 2 != 0:
|
||||
final_answer = final_answer[:-3].rstrip()
|
||||
return AgentFinish(thought=thought, output=final_answer, text=text)
|
||||
@@ -143,6 +146,7 @@ def _extract_thought(text: str) -> str:
|
||||
if thought_index == -1:
|
||||
return ""
|
||||
thought = text[:thought_index].strip()
|
||||
# Remove any triple backticks from the thought string
|
||||
return thought.replace("```", "").strip()
|
||||
|
||||
|
||||
@@ -167,9 +171,18 @@ def _safe_repair_json(tool_input: str) -> str:
|
||||
Returns:
|
||||
The repaired JSON string or original if repair fails.
|
||||
"""
|
||||
# Skip repair if the input starts and ends with square brackets
|
||||
# Explanation: The JSON parser has issues handling inputs that are enclosed in square brackets ('[]').
|
||||
# These are typically valid JSON arrays or strings that do not require repair. Attempting to repair such inputs
|
||||
# might lead to unintended alterations, such as wrapping the entire input in additional layers or modifying
|
||||
# the structure in a way that changes its meaning. By skipping the repair for inputs that start and end with
|
||||
# square brackets, we preserve the integrity of these valid JSON structures and avoid unnecessary modifications.
|
||||
if tool_input.startswith("[") and tool_input.endswith("]"):
|
||||
return tool_input
|
||||
|
||||
# Before repair, handle common LLM issues:
|
||||
# 1. Replace """ with " to avoid JSON parser errors
|
||||
|
||||
tool_input = tool_input.replace('"""', '"')
|
||||
|
||||
result = repair_json(tool_input)
|
||||
|
||||
@@ -83,6 +83,10 @@ class PlannerObserver:
|
||||
return create_llm(config.llm)
|
||||
return self.agent.llm
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public API
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def observe(
|
||||
self,
|
||||
completed_step: TodoItem,
|
||||
@@ -178,6 +182,9 @@ class PlannerObserver:
|
||||
),
|
||||
)
|
||||
|
||||
# Don't force a full replan — the step may have succeeded even if the
|
||||
# observer LLM failed to parse the result. Defaulting to "continue" is
|
||||
# far less disruptive than wiping the entire plan on every observer error.
|
||||
return StepObservation(
|
||||
step_completed_successfully=True,
|
||||
key_information_learned="",
|
||||
@@ -214,6 +221,10 @@ class PlannerObserver:
|
||||
|
||||
return remaining_todos
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Message building
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_observation_messages(
|
||||
self,
|
||||
completed_step: TodoItem,
|
||||
@@ -228,11 +239,15 @@ class PlannerObserver:
|
||||
task_desc = self.task.description or ""
|
||||
task_goal = self.task.expected_output or ""
|
||||
elif self.kickoff_input:
|
||||
# Standalone kickoff path — no Task object, but we have the raw input.
|
||||
# Extract just the ## Task section so the observer sees the actual goal,
|
||||
# not the full enriched instruction with env/tools/verification noise.
|
||||
task_desc = extract_task_section(self.kickoff_input)
|
||||
task_goal = "Complete the task successfully"
|
||||
|
||||
system_prompt = I18N_DEFAULT.retrieve("planning", "observation_system_prompt")
|
||||
|
||||
# Build context of what's been done
|
||||
completed_summary = ""
|
||||
if all_completed:
|
||||
completed_lines = []
|
||||
@@ -246,6 +261,7 @@ class PlannerObserver:
|
||||
completed_lines
|
||||
)
|
||||
|
||||
# Build remaining plan
|
||||
remaining_summary = ""
|
||||
if remaining_todos:
|
||||
remaining_lines = [
|
||||
@@ -290,14 +306,17 @@ class PlannerObserver:
|
||||
if isinstance(response, StepObservation):
|
||||
return response
|
||||
|
||||
# JSON string path — most common miss before this fix
|
||||
if isinstance(response, str):
|
||||
text = response.strip()
|
||||
try:
|
||||
return StepObservation.model_validate_json(text)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
# Some LLMs wrap the JSON in markdown fences
|
||||
if text.startswith("```"):
|
||||
lines = text.split("\n")
|
||||
# Strip first and last lines (``` markers)
|
||||
inner = "\n".join(
|
||||
lines[1:-1] if lines[-1].strip() == "```" else lines[1:]
|
||||
)
|
||||
@@ -306,12 +325,14 @@ class PlannerObserver:
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
# Dict path
|
||||
if isinstance(response, dict):
|
||||
try:
|
||||
return StepObservation.model_validate(response)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
# Last resort — log what we got so it's diagnosable
|
||||
logger.warning(
|
||||
"Could not parse observation response (type=%s). "
|
||||
"Falling back to default failure observation. Preview: %.200s",
|
||||
|
||||
@@ -108,6 +108,7 @@ class StepExecutor:
|
||||
self.request_within_rpm_limit = request_within_rpm_limit
|
||||
self.callbacks = callbacks or []
|
||||
|
||||
# Native tool support — set up once
|
||||
self._use_native_tools = check_native_tool_support(
|
||||
self.llm, self.original_tools
|
||||
)
|
||||
@@ -120,6 +121,10 @@ class StepExecutor:
|
||||
_,
|
||||
) = setup_native_tools(self.original_tools)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public API
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def execute(
|
||||
self,
|
||||
todo: TodoItem,
|
||||
@@ -185,6 +190,10 @@ class StepExecutor:
|
||||
execution_time=elapsed,
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Message building
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_isolated_messages(
|
||||
self, todo: TodoItem, context: StepExecutionContext
|
||||
) -> list[LLMMessage]:
|
||||
@@ -228,6 +237,10 @@ class StepExecutor:
|
||||
"""Build the user prompt for this specific step."""
|
||||
parts: list[str] = []
|
||||
|
||||
# Include overall task context so the executor knows the full goal and
|
||||
# required output format/location — critical for knowing WHAT to produce.
|
||||
# We extract only the task body (not tool instructions or verification
|
||||
# sections) to avoid duplicating directives already in the system prompt.
|
||||
if context.task_description:
|
||||
task_section = extract_task_section(context.task_description)
|
||||
if task_section:
|
||||
@@ -254,6 +267,7 @@ class StepExecutor:
|
||||
)
|
||||
)
|
||||
|
||||
# Include dependency results (final results only, no traces)
|
||||
if context.dependency_results:
|
||||
parts.append(
|
||||
I18N_DEFAULT.retrieve("planning", "step_executor_context_header")
|
||||
@@ -269,6 +283,10 @@ class StepExecutor:
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Multi-turn execution loop
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _execute_text_parsed(
|
||||
self,
|
||||
messages: list[LLMMessage],
|
||||
@@ -288,6 +306,7 @@ class StepExecutor:
|
||||
last_tool_result = ""
|
||||
|
||||
for _ in range(max_step_iterations):
|
||||
# Check step timeout
|
||||
if step_timeout and start_time:
|
||||
elapsed = time.monotonic() - start_time
|
||||
if elapsed >= step_timeout:
|
||||
@@ -312,12 +331,17 @@ class StepExecutor:
|
||||
tool_calls_made.append(formatted.tool)
|
||||
tool_result = self._execute_text_tool_with_events(formatted)
|
||||
last_tool_result = tool_result
|
||||
# Append the assistant's reasoning + action, then the observation.
|
||||
# _build_observation_message handles vision sentinels so the LLM
|
||||
# receives an image content block instead of raw base64 text.
|
||||
messages.append({"role": "assistant", "content": answer_str})
|
||||
messages.append(self._build_observation_message(tool_result))
|
||||
continue
|
||||
|
||||
# Raw text response with no Final Answer marker — treat as done
|
||||
return answer_str
|
||||
|
||||
# Max iterations reached — return the last tool result we accumulated
|
||||
return last_tool_result
|
||||
|
||||
def _execute_text_tool_with_events(self, formatted: AgentAction) -> str:
|
||||
@@ -405,6 +429,10 @@ class StepExecutor:
|
||||
return {"input": stripped_input}
|
||||
return {"input": str(tool_input)}
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Vision support
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _parse_vision_sentinel(raw: str) -> tuple[str, str] | None:
|
||||
"""Parse a VISION_IMAGE sentinel into (media_type, base64_data), or None."""
|
||||
@@ -489,6 +517,7 @@ class StepExecutor:
|
||||
accumulated_results: list[str] = []
|
||||
|
||||
for _ in range(max_step_iterations):
|
||||
# Check step timeout
|
||||
if step_timeout and start_time:
|
||||
elapsed = time.monotonic() - start_time
|
||||
if elapsed >= step_timeout:
|
||||
@@ -512,14 +541,19 @@ class StepExecutor:
|
||||
return answer.model_dump_json()
|
||||
|
||||
if isinstance(answer, list) and answer and is_tool_call_list(answer):
|
||||
# _execute_native_tool_calls appends assistant + tool messages
|
||||
# to `messages` as a side-effect, so the next LLM call will
|
||||
# see the full conversation history including tool outputs.
|
||||
result = self._execute_native_tool_calls(
|
||||
answer, messages, tool_calls_made
|
||||
)
|
||||
accumulated_results.append(result)
|
||||
continue
|
||||
|
||||
# Text answer → LLM decided the step is done
|
||||
return str(answer)
|
||||
|
||||
# Max iterations reached — return everything we accumulated
|
||||
return "\n".join(filter(None, accumulated_results))
|
||||
|
||||
def _execute_native_tool_calls(
|
||||
@@ -565,6 +599,9 @@ class StepExecutor:
|
||||
parsed = self._parse_vision_sentinel(raw_content)
|
||||
if parsed:
|
||||
media_type, b64_data = parsed
|
||||
# Replace the sentinel with a standard image_url content block.
|
||||
# Each provider's _format_messages handles conversion to
|
||||
# its native format (e.g. Anthropic image blocks).
|
||||
modified: LLMMessage = cast(
|
||||
LLMMessage, dict(call_result.tool_message)
|
||||
)
|
||||
|
||||
@@ -6,16 +6,12 @@ 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 = """
|
||||
@@ -38,25 +34,6 @@ 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):
|
||||
@@ -75,7 +52,13 @@ def _parse_checkpoint_json(raw: str, source: str) -> dict[str, Any]:
|
||||
nodes = data.get("event_record", {}).get("nodes", {})
|
||||
event_count = len(nodes)
|
||||
|
||||
trigger_event = data.get("trigger")
|
||||
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")
|
||||
|
||||
parsed_entities: list[dict[str, Any]] = []
|
||||
for entity in entities:
|
||||
@@ -93,47 +76,16 @@ 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,
|
||||
}
|
||||
|
||||
|
||||
@@ -237,7 +189,6 @@ def _list_sqlite(db_path: str) -> list[dict[str, Any]]:
|
||||
"entities": [],
|
||||
"source": checkpoint_id,
|
||||
}
|
||||
meta["db"] = db_path
|
||||
results.append(meta)
|
||||
return results
|
||||
|
||||
@@ -360,10 +311,6 @@ 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]
|
||||
|
||||
@@ -2,23 +2,17 @@
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import defaultdict
|
||||
from typing import Any, ClassVar
|
||||
|
||||
from textual.app import App, ComposeResult
|
||||
from textual.binding import Binding
|
||||
from textual.containers import Horizontal, Vertical, VerticalScroll
|
||||
from textual.widgets import (
|
||||
Button,
|
||||
Footer,
|
||||
Header,
|
||||
Input,
|
||||
Static,
|
||||
TextArea,
|
||||
Tree,
|
||||
)
|
||||
from 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 crewai.cli.checkpoint_cli import (
|
||||
_entity_summary,
|
||||
_format_size,
|
||||
_is_sqlite,
|
||||
_list_json,
|
||||
@@ -40,54 +34,151 @@ def _load_entries(location: str) -> list[dict[str, Any]]:
|
||||
return _list_json(location)
|
||||
|
||||
|
||||
def _short_id(name: str) -> str:
|
||||
"""Shorten a checkpoint name for tree display."""
|
||||
if len(name) > 30:
|
||||
return name[:27] + "..."
|
||||
return name
|
||||
def _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 _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)."""
|
||||
def _format_detail(entry: dict[str, Any]) -> str:
|
||||
"""Format checkpoint details for the right panel."""
|
||||
lines: list[str] = []
|
||||
tasks = ent.get("tasks")
|
||||
if isinstance(tasks, list):
|
||||
completed = ent.get("tasks_completed", 0)
|
||||
total = ent.get("tasks_total", 0)
|
||||
pct = int(completed / total * 100) if total else 0
|
||||
bar_len = 20
|
||||
filled = int(bar_len * completed / total) if total else 0
|
||||
bar = f"[{_PRIMARY}]{'█' * filled}[/][{_DIM}]{'░' * (bar_len - filled)}[/]"
|
||||
lines.append(f"{bar} {completed}/{total} tasks ({pct}%)")
|
||||
|
||||
# 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}")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
# Return type: (location, action, inputs, task_output_overrides)
|
||||
_TuiResult = tuple[str, str, dict[str, Any] | None, dict[int, str] | None] | None
|
||||
class 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)
|
||||
|
||||
|
||||
class CheckpointTUI(App[_TuiResult]):
|
||||
class CheckpointTUI(App[str | None]):
|
||||
"""TUI to browse and inspect checkpoints.
|
||||
|
||||
Returns ``(location, action, inputs)`` where action is ``"resume"`` or
|
||||
``"fork"`` and inputs is a parsed dict or ``None``,
|
||||
or ``None`` if the user quit without selecting.
|
||||
Returns the checkpoint location string to resume from, or None if
|
||||
the user quit without selecting.
|
||||
"""
|
||||
|
||||
TITLE = "CrewAI Checkpoints"
|
||||
@@ -108,431 +199,145 @@ class CheckpointTUI(App[_TuiResult]):
|
||||
background: {_PRIMARY};
|
||||
color: {_TERTIARY};
|
||||
}}
|
||||
#main-layout {{
|
||||
Horizontal {{
|
||||
height: 1fr;
|
||||
}}
|
||||
#tree-panel {{
|
||||
width: 45%;
|
||||
#cp-list {{
|
||||
width: 38%;
|
||||
background: {_BG_PANEL};
|
||||
border: round {_SECONDARY};
|
||||
padding: 0 1;
|
||||
scrollbar-color: {_PRIMARY};
|
||||
}}
|
||||
#tree-panel:focus-within {{
|
||||
#cp-list:focus {{
|
||||
border: round {_PRIMARY};
|
||||
}}
|
||||
#detail-container {{
|
||||
width: 55%;
|
||||
height: 1fr;
|
||||
#cp-list > .option-list--option-highlighted {{
|
||||
background: {_SECONDARY};
|
||||
color: {_TERTIARY};
|
||||
text-style: none;
|
||||
}}
|
||||
#detail-scroll {{
|
||||
#cp-list > .option-list--option-highlighted * {{
|
||||
color: {_TERTIARY};
|
||||
}}
|
||||
#detail-container {{
|
||||
width: 62%;
|
||||
padding: 0 1;
|
||||
}}
|
||||
#detail {{
|
||||
height: 1fr;
|
||||
background: {_BG_PANEL};
|
||||
border: round {_SECONDARY};
|
||||
padding: 1 2;
|
||||
overflow-y: auto;
|
||||
scrollbar-color: {_PRIMARY};
|
||||
}}
|
||||
#detail-scroll:focus-within {{
|
||||
#detail:focus {{
|
||||
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_entry: dict[str, Any] | None = None
|
||||
self._input_keys: list[str] = []
|
||||
self._task_output_ids: list[tuple[int, str, str]] = []
|
||||
self._selected_idx: int = 0
|
||||
self._pending_location: str = ""
|
||||
|
||||
def compose(self) -> ComposeResult:
|
||||
yield Header(show_clock=False)
|
||||
with Horizontal(id="main-layout"):
|
||||
tree: Tree[dict[str, Any]] = Tree("Checkpoints", id="tree-panel")
|
||||
tree.show_root = True
|
||||
tree.guide_depth = 3
|
||||
yield tree
|
||||
with Horizontal():
|
||||
yield OptionList(id="cp-list")
|
||||
with Vertical(id="detail-container"):
|
||||
yield Static("", id="status")
|
||||
with VerticalScroll(id="detail-scroll"):
|
||||
yield Static(
|
||||
f"[{_DIM}]Select a checkpoint from the tree[/]", # noqa: S608
|
||||
id="detail-header",
|
||||
)
|
||||
with Vertical(id="inputs-section"):
|
||||
yield Static("Inputs", id="inputs-label")
|
||||
with Horizontal(id="action-buttons"):
|
||||
yield Button("Resume", id="btn-resume")
|
||||
yield Button("Fork", id="btn-fork")
|
||||
yield Static(
|
||||
f"\n [{_DIM}]Select a checkpoint from the list[/]", # noqa: S608
|
||||
id="detail",
|
||||
)
|
||||
yield Footer()
|
||||
|
||||
async def on_mount(self) -> None:
|
||||
self._refresh_tree()
|
||||
self.query_one("#tree-panel", Tree).root.expand()
|
||||
self.query_one("#cp-list", OptionList).border_title = "Checkpoints"
|
||||
self.query_one("#detail", Static).border_title = "Detail"
|
||||
self._refresh_list()
|
||||
|
||||
def _refresh_tree(self) -> None:
|
||||
def _refresh_list(self) -> None:
|
||||
self._entries = _load_entries(self._location)
|
||||
self._selected_entry = None
|
||||
|
||||
tree = self.query_one("#tree-panel", Tree)
|
||||
tree.clear()
|
||||
option_list = self.query_one("#cp-list", OptionList)
|
||||
option_list.clear_options()
|
||||
|
||||
if not self._entries:
|
||||
self.query_one("#detail-header", Static).update(
|
||||
f"[{_DIM}]No checkpoints in {self._location}[/]"
|
||||
self.query_one("#detail", Static).update(
|
||||
f"\n [{_DIM}]No checkpoints in {self._location}[/]"
|
||||
)
|
||||
self.query_one("#status", Static).update("")
|
||||
self.sub_title = self._location
|
||||
return
|
||||
|
||||
# Group by branch
|
||||
branches: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||||
for entry in self._entries:
|
||||
branch = entry.get("branch", "main")
|
||||
branches[branch].append(entry)
|
||||
|
||||
# Index checkpoint names to tree nodes so forks can attach
|
||||
node_by_name: dict[str, Any] = {}
|
||||
|
||||
def _make_label(e: dict[str, Any]) -> str:
|
||||
name = e.get("name", "")
|
||||
ts = e.get("ts") or ""
|
||||
trigger = e.get("trigger") or ""
|
||||
parts = [f"[bold]{_short_id(name)}[/]"]
|
||||
if ts:
|
||||
time_part = ts.split(" ")[-1] if " " in ts else ts
|
||||
parts.append(f"[{_DIM}]{time_part}[/]")
|
||||
if trigger:
|
||||
parts.append(f"[{_PRIMARY}]{trigger}[/]")
|
||||
return " ".join(parts)
|
||||
|
||||
fork_parents: set[str] = set()
|
||||
for branch_name, entries in branches.items():
|
||||
if branch_name == "main" or not entries:
|
||||
continue
|
||||
oldest = min(entries, key=lambda e: str(e.get("name", "")))
|
||||
first_parent = oldest.get("parent_id")
|
||||
if first_parent:
|
||||
fork_parents.add(str(first_parent))
|
||||
|
||||
def _add_checkpoint(parent_node: Any, e: dict[str, Any]) -> None:
|
||||
"""Add a checkpoint node — expandable only if a fork attaches to it."""
|
||||
cp_id = _entry_id(e)
|
||||
if cp_id in fork_parents:
|
||||
node = parent_node.add(
|
||||
_make_label(e), data=e, expand=False, allow_expand=True
|
||||
)
|
||||
else:
|
||||
node = parent_node.add_leaf(_make_label(e), data=e)
|
||||
node_by_name[cp_id] = node
|
||||
|
||||
if "main" in branches:
|
||||
for entry in reversed(branches["main"]):
|
||||
_add_checkpoint(tree.root, entry)
|
||||
|
||||
fork_branches = [
|
||||
(name, sorted(entries, key=lambda e: str(e.get("name", ""))))
|
||||
for name, entries in branches.items()
|
||||
if name != "main"
|
||||
]
|
||||
remaining = fork_branches
|
||||
max_passes = len(remaining) + 1
|
||||
while remaining and max_passes > 0:
|
||||
max_passes -= 1
|
||||
deferred = []
|
||||
made_progress = False
|
||||
for branch_name, entries in remaining:
|
||||
first_parent = entries[0].get("parent_id") if entries else None
|
||||
if first_parent and str(first_parent) not in node_by_name:
|
||||
deferred.append((branch_name, entries))
|
||||
continue
|
||||
attach_to: Any = tree.root
|
||||
if first_parent:
|
||||
attach_to = node_by_name.get(str(first_parent), tree.root)
|
||||
branch_label = (
|
||||
f"[bold {_SECONDARY}]{branch_name}[/] [{_DIM}]({len(entries)})[/]"
|
||||
)
|
||||
branch_node = attach_to.add(branch_label, expand=False)
|
||||
for entry in entries:
|
||||
_add_checkpoint(branch_node, entry)
|
||||
made_progress = True
|
||||
remaining = deferred
|
||||
if not made_progress:
|
||||
break
|
||||
|
||||
for branch_name, entries in remaining:
|
||||
branch_label = (
|
||||
f"[bold {_SECONDARY}]{branch_name}[/] "
|
||||
f"[{_DIM}]({len(entries)})[/] [{_DIM}](orphaned)[/]"
|
||||
)
|
||||
branch_node = tree.root.add(branch_label, expand=False)
|
||||
for entry in entries:
|
||||
_add_checkpoint(branch_node, entry)
|
||||
option_list.add_option(Option(_format_list_label(entry)))
|
||||
|
||||
count = len(self._entries)
|
||||
storage = "SQLite" if _is_sqlite(self._location) else "JSON"
|
||||
self.sub_title = self._location
|
||||
self.sub_title = f"{self._location}"
|
||||
self.query_one("#status", Static).update(f" {count} checkpoint(s) | {storage}")
|
||||
|
||||
async def _show_detail(self, entry: dict[str, Any]) -> None:
|
||||
"""Update the detail panel for a checkpoint entry."""
|
||||
self._selected_entry = entry
|
||||
self.query_one("#action-buttons").add_class("visible")
|
||||
|
||||
detail_scroll = self.query_one("#detail-scroll", VerticalScroll)
|
||||
|
||||
# Remove all dynamic children except the header — await so IDs are freed
|
||||
to_remove = [c for c in detail_scroll.children if c.id != "detail-header"]
|
||||
for child in to_remove:
|
||||
await child.remove()
|
||||
|
||||
# Header
|
||||
name = entry.get("name", "")
|
||||
ts = entry.get("ts") or "unknown"
|
||||
trigger = entry.get("trigger") or ""
|
||||
branch = entry.get("branch", "main")
|
||||
parent_id = entry.get("parent_id")
|
||||
|
||||
header_lines = [
|
||||
f"[bold {_PRIMARY}]{name}[/]",
|
||||
f"[{_DIM}]{'─' * 50}[/]",
|
||||
"",
|
||||
f" [bold]Time[/] {ts}",
|
||||
]
|
||||
if "size" in entry:
|
||||
header_lines.append(f" [bold]Size[/] {_format_size(entry['size'])}")
|
||||
header_lines.append(f" [bold]Events[/] {entry.get('event_count', 0)}")
|
||||
if trigger:
|
||||
header_lines.append(f" [bold]Trigger[/] [{_PRIMARY}]{trigger}[/]")
|
||||
header_lines.append(f" [bold]Branch[/] [{_SECONDARY}]{branch}[/]")
|
||||
if parent_id:
|
||||
header_lines.append(f" [bold]Parent[/] [{_DIM}]{parent_id}[/]")
|
||||
if "path" in entry:
|
||||
header_lines.append(f" [bold]Path[/] [{_DIM}]{entry['path']}[/]")
|
||||
if "db" in entry:
|
||||
header_lines.append(f" [bold]Database[/] [{_DIM}]{entry['db']}[/]")
|
||||
|
||||
self.query_one("#detail-header", Static).update("\n".join(header_lines))
|
||||
|
||||
# Entity details and editable task outputs — mounted flat for scrolling
|
||||
self._task_output_ids = []
|
||||
flat_task_idx = 0
|
||||
for ent_idx, ent in enumerate(entry.get("entities", [])):
|
||||
etype = ent.get("type", "unknown")
|
||||
ename = ent.get("name", "unnamed")
|
||||
completed = ent.get("tasks_completed")
|
||||
total = ent.get("tasks_total")
|
||||
entity_title = f"[bold {_SECONDARY}]{etype}: {ename}[/]"
|
||||
if completed is not None and total is not None:
|
||||
entity_title += f" [{_DIM}]{completed}/{total} tasks[/]"
|
||||
await detail_scroll.mount(Static(entity_title, classes="entity-title"))
|
||||
await detail_scroll.mount(
|
||||
Static(_build_entity_header(ent), classes="entity-detail")
|
||||
)
|
||||
|
||||
tasks = ent.get("tasks", [])
|
||||
for i, task in enumerate(tasks):
|
||||
desc = str(task.get("description", ""))
|
||||
if len(desc) > 55:
|
||||
desc = desc[:52] + "..."
|
||||
if task.get("completed"):
|
||||
icon = "[green]✓[/]"
|
||||
await detail_scroll.mount(
|
||||
Static(f" {icon} {i + 1}. {desc}", classes="task-label")
|
||||
)
|
||||
output_text = task.get("output", "")
|
||||
editor_id = f"task-output-{ent_idx}-{i}"
|
||||
await detail_scroll.mount(
|
||||
TextArea(
|
||||
str(output_text),
|
||||
classes="task-output-editor",
|
||||
id=editor_id,
|
||||
)
|
||||
)
|
||||
self._task_output_ids.append(
|
||||
(flat_task_idx, editor_id, str(output_text))
|
||||
)
|
||||
else:
|
||||
icon = "[yellow]○[/]"
|
||||
await detail_scroll.mount(
|
||||
Static(f" {icon} {i + 1}. {desc}", classes="task-label")
|
||||
)
|
||||
flat_task_idx += 1
|
||||
|
||||
# Build input fields
|
||||
await self._build_input_fields(entry.get("inputs", {}))
|
||||
|
||||
async def _build_input_fields(self, inputs: dict[str, Any]) -> None:
|
||||
"""Rebuild the inputs section with one field per input key."""
|
||||
section = self.query_one("#inputs-section")
|
||||
|
||||
# Remove old dynamic children — await so IDs are freed
|
||||
for widget in list(section.query(".input-row, .no-inputs")):
|
||||
await widget.remove()
|
||||
|
||||
self._input_keys = []
|
||||
|
||||
if not inputs:
|
||||
await section.mount(Static(f"[{_DIM}]No inputs[/]", classes="no-inputs"))
|
||||
section.add_class("visible")
|
||||
return
|
||||
|
||||
for key, value in inputs.items():
|
||||
self._input_keys.append(key)
|
||||
row = Horizontal(classes="input-row")
|
||||
row.compose_add_child(Static(f"[bold]{key}[/]"))
|
||||
row.compose_add_child(
|
||||
Input(value=str(value), placeholder=key, id=f"input-{key}")
|
||||
)
|
||||
await section.mount(row)
|
||||
|
||||
section.add_class("visible")
|
||||
|
||||
def _collect_inputs(self) -> dict[str, Any] | None:
|
||||
"""Collect current values from input fields."""
|
||||
if not self._input_keys:
|
||||
return None
|
||||
result: dict[str, Any] = {}
|
||||
for key in self._input_keys:
|
||||
widget = self.query_one(f"#input-{key}", Input)
|
||||
result[key] = widget.value
|
||||
return result
|
||||
|
||||
def _collect_task_overrides(self) -> dict[int, str] | None:
|
||||
"""Collect edited task outputs. Returns only changed values."""
|
||||
if not self._task_output_ids or self._selected_entry is None:
|
||||
return None
|
||||
overrides: dict[int, str] = {}
|
||||
for task_idx, editor_id, original in self._task_output_ids:
|
||||
editor = self.query_one(f"#{editor_id}", TextArea)
|
||||
if editor.text != original:
|
||||
overrides[task_idx] = editor.text
|
||||
return overrides or None
|
||||
|
||||
def _resolve_location(self, entry: dict[str, Any]) -> str:
|
||||
"""Get the restore location string for a checkpoint entry."""
|
||||
if "path" in entry:
|
||||
return str(entry["path"])
|
||||
if _is_sqlite(self._location):
|
||||
return f"{self._location}#{entry['name']}"
|
||||
return str(entry.get("name", ""))
|
||||
|
||||
async def on_tree_node_highlighted(
|
||||
self, event: Tree.NodeHighlighted[dict[str, Any]]
|
||||
async def on_option_list_option_highlighted(
|
||||
self,
|
||||
event: OptionList.OptionHighlighted,
|
||||
) -> 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:
|
||||
idx = event.option_index
|
||||
if idx 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))
|
||||
if idx < len(self._entries):
|
||||
self._selected_idx = idx
|
||||
entry = self._entries[idx]
|
||||
self.query_one("#detail", Static).update(_format_detail(entry))
|
||||
|
||||
def action_cursor_down(self) -> None:
|
||||
self.query_one("#cp-list", OptionList).action_cursor_down()
|
||||
|
||||
def action_cursor_up(self) -> None:
|
||||
self.query_one("#cp-list", OptionList).action_cursor_up()
|
||||
|
||||
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]
|
||||
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)
|
||||
|
||||
def _on_confirm(self, confirmed: bool | None) -> None:
|
||||
if confirmed:
|
||||
self.exit(self._pending_location)
|
||||
else:
|
||||
self._pending_location = ""
|
||||
|
||||
def action_refresh(self) -> None:
|
||||
self._refresh_tree()
|
||||
self._refresh_list()
|
||||
|
||||
|
||||
async def _run_checkpoint_tui_async(location: str) -> None:
|
||||
@@ -540,78 +345,18 @@ async def _run_checkpoint_tui_async(location: str) -> None:
|
||||
import click
|
||||
|
||||
app = CheckpointTUI(location=location)
|
||||
selection = await app.run_async()
|
||||
selected = await app.run_async()
|
||||
|
||||
if selection is None:
|
||||
if selected is None:
|
||||
return
|
||||
|
||||
selected, action, inputs, task_overrides = selection
|
||||
click.echo(f"\nResuming from: {selected}\n")
|
||||
|
||||
from crewai.crew import Crew
|
||||
from crewai.state.checkpoint_config import CheckpointConfig
|
||||
|
||||
config = CheckpointConfig(restore_from=selected)
|
||||
|
||||
if action == "fork":
|
||||
click.echo(f"\nForking from: {selected}\n")
|
||||
crew = Crew.fork(config)
|
||||
else:
|
||||
click.echo(f"\nResuming from: {selected}\n")
|
||||
crew = Crew.from_checkpoint(config)
|
||||
|
||||
if task_overrides:
|
||||
click.echo("Modifications:")
|
||||
overridden_agents: set[int] = set()
|
||||
for task_idx, new_output in task_overrides.items():
|
||||
if task_idx < len(crew.tasks) and crew.tasks[task_idx].output is not None:
|
||||
desc = crew.tasks[task_idx].description or f"Task {task_idx + 1}"
|
||||
if len(desc) > 60:
|
||||
desc = desc[:57] + "..."
|
||||
crew.tasks[task_idx].output.raw = new_output # type: ignore[union-attr]
|
||||
preview = new_output.replace("\n", " ")
|
||||
if len(preview) > 80:
|
||||
preview = preview[:77] + "..."
|
||||
click.echo(f" Task {task_idx + 1}: {desc}")
|
||||
click.echo(f" -> {preview}")
|
||||
agent = crew.tasks[task_idx].agent
|
||||
if agent and agent.agent_executor:
|
||||
nth = sum(1 for t in crew.tasks[:task_idx] if t.agent is agent)
|
||||
messages = agent.agent_executor.messages
|
||||
system_positions = [
|
||||
i for i, m in enumerate(messages) if m.get("role") == "system"
|
||||
]
|
||||
if nth < len(system_positions):
|
||||
seg_start = system_positions[nth]
|
||||
seg_end = (
|
||||
system_positions[nth + 1]
|
||||
if nth + 1 < len(system_positions)
|
||||
else len(messages)
|
||||
)
|
||||
for j in range(seg_end - 1, seg_start, -1):
|
||||
if messages[j].get("role") == "assistant":
|
||||
messages[j]["content"] = new_output
|
||||
break
|
||||
overridden_agents.add(id(agent))
|
||||
|
||||
earliest = min(task_overrides)
|
||||
for offset, subsequent in enumerate(
|
||||
crew.tasks[earliest + 1 :], start=earliest + 1
|
||||
):
|
||||
if subsequent.output and offset not in task_overrides:
|
||||
subsequent.output = None
|
||||
if subsequent.agent and subsequent.agent.agent_executor:
|
||||
subsequent.agent.agent_executor._resuming = False
|
||||
if id(subsequent.agent) not in overridden_agents:
|
||||
subsequent.agent.agent_executor.messages = []
|
||||
click.echo()
|
||||
|
||||
if inputs:
|
||||
click.echo("Inputs:")
|
||||
for k, v in inputs.items():
|
||||
click.echo(f" {k}: {v}")
|
||||
click.echo()
|
||||
|
||||
result = await crew.akickoff(inputs=inputs)
|
||||
crew = Crew.from_checkpoint(CheckpointConfig(restore_from=selected))
|
||||
result = await crew.akickoff()
|
||||
click.echo(f"\nResult: {getattr(result, 'raw', result)}")
|
||||
|
||||
|
||||
|
||||
@@ -392,15 +392,10 @@ def deploy() -> None:
|
||||
|
||||
@deploy.command(name="create")
|
||||
@click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
|
||||
@click.option(
|
||||
"--skip-validate",
|
||||
is_flag=True,
|
||||
help="Skip the pre-deploy validation checks.",
|
||||
)
|
||||
def deploy_create(yes: bool, skip_validate: bool) -> None:
|
||||
def deploy_create(yes: bool) -> None:
|
||||
"""Create a Crew deployment."""
|
||||
deploy_cmd = DeployCommand()
|
||||
deploy_cmd.create_crew(yes, skip_validate=skip_validate)
|
||||
deploy_cmd.create_crew(yes)
|
||||
|
||||
|
||||
@deploy.command(name="list")
|
||||
@@ -412,28 +407,10 @@ def deploy_list() -> None:
|
||||
|
||||
@deploy.command(name="push")
|
||||
@click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
|
||||
@click.option(
|
||||
"--skip-validate",
|
||||
is_flag=True,
|
||||
help="Skip the pre-deploy validation checks.",
|
||||
)
|
||||
def deploy_push(uuid: str | None, skip_validate: bool) -> None:
|
||||
def deploy_push(uuid: str | None) -> None:
|
||||
"""Deploy the Crew."""
|
||||
deploy_cmd = DeployCommand()
|
||||
deploy_cmd.deploy(uuid=uuid, skip_validate=skip_validate)
|
||||
|
||||
|
||||
@deploy.command(name="validate")
|
||||
def deploy_validate() -> None:
|
||||
"""Validate the current project against common deployment failures.
|
||||
|
||||
Runs the same pre-deploy checks that `crewai deploy create` and
|
||||
`crewai deploy push` run automatically, without contacting the platform.
|
||||
Exits non-zero if any blocking issues are found.
|
||||
"""
|
||||
from crewai.cli.deploy.validate import run_validate_command
|
||||
|
||||
run_validate_command()
|
||||
deploy_cmd.deploy(uuid=uuid)
|
||||
|
||||
|
||||
@deploy.command(name="status")
|
||||
@@ -816,9 +793,6 @@ 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:
|
||||
@@ -831,18 +805,18 @@ 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 _detect_location, list_checkpoints
|
||||
from crewai.cli.checkpoint_cli import list_checkpoints
|
||||
|
||||
list_checkpoints(_detect_location(location))
|
||||
list_checkpoints(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 _detect_location, info_checkpoint
|
||||
from crewai.cli.checkpoint_cli import info_checkpoint
|
||||
|
||||
info_checkpoint(_detect_location(path))
|
||||
info_checkpoint(path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -4,35 +4,12 @@ from rich.console import Console
|
||||
|
||||
from crewai.cli import git
|
||||
from crewai.cli.command import BaseCommand, PlusAPIMixin
|
||||
from crewai.cli.deploy.validate import validate_project
|
||||
from crewai.cli.utils import fetch_and_json_env_file, get_project_name
|
||||
|
||||
|
||||
console = Console()
|
||||
|
||||
|
||||
def _run_predeploy_validation(skip_validate: bool) -> bool:
|
||||
"""Run pre-deploy validation unless skipped.
|
||||
|
||||
Returns True if deployment should proceed, False if it should abort.
|
||||
"""
|
||||
if skip_validate:
|
||||
console.print(
|
||||
"[yellow]Skipping pre-deploy validation (--skip-validate).[/yellow]"
|
||||
)
|
||||
return True
|
||||
|
||||
console.print("Running pre-deploy validation...", style="bold blue")
|
||||
validator = validate_project()
|
||||
if not validator.ok:
|
||||
console.print(
|
||||
"\n[bold red]Pre-deploy validation failed. "
|
||||
"Fix the issues above or re-run with --skip-validate.[/bold red]"
|
||||
)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
class DeployCommand(BaseCommand, PlusAPIMixin):
|
||||
"""
|
||||
A class to handle deployment-related operations for CrewAI projects.
|
||||
@@ -83,16 +60,13 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
|
||||
f"{log_message['timestamp']} - {log_message['level']}: {log_message['message']}"
|
||||
)
|
||||
|
||||
def deploy(self, uuid: str | None = None, skip_validate: bool = False) -> None:
|
||||
def deploy(self, uuid: str | None = None) -> None:
|
||||
"""
|
||||
Deploy a crew using either UUID or project name.
|
||||
|
||||
Args:
|
||||
uuid (Optional[str]): The UUID of the crew to deploy.
|
||||
skip_validate (bool): Skip pre-deploy validation checks.
|
||||
"""
|
||||
if not _run_predeploy_validation(skip_validate):
|
||||
return
|
||||
self._telemetry.start_deployment_span(uuid)
|
||||
console.print("Starting deployment...", style="bold blue")
|
||||
if uuid:
|
||||
@@ -106,16 +80,10 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
|
||||
self._validate_response(response)
|
||||
self._display_deployment_info(response.json())
|
||||
|
||||
def create_crew(self, confirm: bool = False, skip_validate: bool = False) -> None:
|
||||
def create_crew(self, confirm: bool = False) -> None:
|
||||
"""
|
||||
Create a new crew deployment.
|
||||
|
||||
Args:
|
||||
confirm (bool): Whether to skip the interactive confirmation prompt.
|
||||
skip_validate (bool): Skip pre-deploy validation checks.
|
||||
"""
|
||||
if not _run_predeploy_validation(skip_validate):
|
||||
return
|
||||
self._telemetry.create_crew_deployment_span()
|
||||
console.print("Creating deployment...", style="bold blue")
|
||||
env_vars = fetch_and_json_env_file()
|
||||
|
||||
@@ -1,845 +0,0 @@
|
||||
"""Pre-deploy validation for CrewAI projects.
|
||||
|
||||
Catches locally what a deploy would reject at build or runtime so users
|
||||
don't burn deployment attempts on fixable project-structure problems.
|
||||
|
||||
Each check is grouped into one of:
|
||||
- ERROR: will block a deployment; validator exits non-zero.
|
||||
- WARNING: may still deploy but is almost always a deployment bug; printed
|
||||
but does not block.
|
||||
|
||||
The individual checks mirror the categories observed in production
|
||||
deployment-failure logs:
|
||||
|
||||
1. pyproject.toml present with ``[project].name``
|
||||
2. lockfile (``uv.lock`` or ``poetry.lock``) present and not stale
|
||||
3. package directory at ``src/<package>/`` exists (no empty name, no egg-info)
|
||||
4. standard crew files: ``crew.py``, ``config/agents.yaml``, ``config/tasks.yaml``
|
||||
5. flow entrypoint: ``main.py`` with a Flow subclass
|
||||
6. hatch wheel target resolves (packages = [...] or default dir matches name)
|
||||
7. crew/flow module imports cleanly (catches ``@CrewBase not found``,
|
||||
``No Flow subclass found``, provider import errors)
|
||||
8. environment variables referenced in code vs ``.env`` / deployment env
|
||||
9. installed crewai vs lockfile pin (catches missing-attribute failures from
|
||||
stale pins)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
from typing import Any
|
||||
|
||||
from rich.console import Console
|
||||
|
||||
from crewai.cli.utils import parse_toml
|
||||
|
||||
|
||||
console = Console()
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Severity(str, Enum):
|
||||
"""Severity of a validation finding."""
|
||||
|
||||
ERROR = "error"
|
||||
WARNING = "warning"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationResult:
|
||||
"""A single finding from a validation check.
|
||||
|
||||
Attributes:
|
||||
severity: whether this blocks deploy or is advisory.
|
||||
code: stable short identifier, used in tests and docs
|
||||
(e.g. ``missing_pyproject``, ``stale_lockfile``).
|
||||
title: one-line summary shown to the user.
|
||||
detail: optional multi-line explanation.
|
||||
hint: optional remediation suggestion.
|
||||
"""
|
||||
|
||||
severity: Severity
|
||||
code: str
|
||||
title: str
|
||||
detail: str = ""
|
||||
hint: str = ""
|
||||
|
||||
|
||||
# Maps known provider env var names → label used in hint messages.
|
||||
_KNOWN_API_KEY_HINTS: dict[str, str] = {
|
||||
"OPENAI_API_KEY": "OpenAI",
|
||||
"ANTHROPIC_API_KEY": "Anthropic",
|
||||
"GOOGLE_API_KEY": "Google",
|
||||
"GEMINI_API_KEY": "Gemini",
|
||||
"AZURE_OPENAI_API_KEY": "Azure OpenAI",
|
||||
"AZURE_API_KEY": "Azure",
|
||||
"AWS_ACCESS_KEY_ID": "AWS",
|
||||
"AWS_SECRET_ACCESS_KEY": "AWS",
|
||||
"COHERE_API_KEY": "Cohere",
|
||||
"GROQ_API_KEY": "Groq",
|
||||
"MISTRAL_API_KEY": "Mistral",
|
||||
"TAVILY_API_KEY": "Tavily",
|
||||
"SERPER_API_KEY": "Serper",
|
||||
"SERPLY_API_KEY": "Serply",
|
||||
"PERPLEXITY_API_KEY": "Perplexity",
|
||||
"DEEPSEEK_API_KEY": "DeepSeek",
|
||||
"OPENROUTER_API_KEY": "OpenRouter",
|
||||
"FIRECRAWL_API_KEY": "Firecrawl",
|
||||
"EXA_API_KEY": "Exa",
|
||||
"BROWSERBASE_API_KEY": "Browserbase",
|
||||
}
|
||||
|
||||
|
||||
def normalize_package_name(project_name: str) -> str:
|
||||
"""Normalize a pyproject project.name into a Python package directory name.
|
||||
|
||||
Mirrors the rules in ``crewai.cli.create_crew.create_crew`` so the
|
||||
validator agrees with the scaffolder about where ``src/<pkg>/`` should
|
||||
live.
|
||||
"""
|
||||
folder = project_name.replace(" ", "_").replace("-", "_").lower()
|
||||
return re.sub(r"[^a-zA-Z0-9_]", "", folder)
|
||||
|
||||
|
||||
class DeployValidator:
|
||||
"""Runs the full pre-deploy validation suite against a project directory."""
|
||||
|
||||
def __init__(self, project_root: Path | None = None) -> None:
|
||||
self.project_root: Path = (project_root or Path.cwd()).resolve()
|
||||
self.results: list[ValidationResult] = []
|
||||
self._pyproject: dict[str, Any] | None = None
|
||||
self._project_name: str | None = None
|
||||
self._package_name: str | None = None
|
||||
self._package_dir: Path | None = None
|
||||
self._is_flow: bool = False
|
||||
|
||||
def _add(
|
||||
self,
|
||||
severity: Severity,
|
||||
code: str,
|
||||
title: str,
|
||||
detail: str = "",
|
||||
hint: str = "",
|
||||
) -> None:
|
||||
self.results.append(
|
||||
ValidationResult(
|
||||
severity=severity,
|
||||
code=code,
|
||||
title=title,
|
||||
detail=detail,
|
||||
hint=hint,
|
||||
)
|
||||
)
|
||||
|
||||
@property
|
||||
def errors(self) -> list[ValidationResult]:
|
||||
return [r for r in self.results if r.severity is Severity.ERROR]
|
||||
|
||||
@property
|
||||
def warnings(self) -> list[ValidationResult]:
|
||||
return [r for r in self.results if r.severity is Severity.WARNING]
|
||||
|
||||
@property
|
||||
def ok(self) -> bool:
|
||||
return not self.errors
|
||||
|
||||
def run(self) -> list[ValidationResult]:
|
||||
"""Run all checks. Later checks are skipped when earlier ones make
|
||||
them impossible (e.g. no pyproject.toml → no lockfile check)."""
|
||||
if not self._check_pyproject():
|
||||
return self.results
|
||||
|
||||
self._check_lockfile()
|
||||
|
||||
if not self._check_package_dir():
|
||||
self._check_hatch_wheel_target()
|
||||
return self.results
|
||||
|
||||
if self._is_flow:
|
||||
self._check_flow_entrypoint()
|
||||
else:
|
||||
self._check_crew_entrypoint()
|
||||
self._check_config_yamls()
|
||||
|
||||
self._check_hatch_wheel_target()
|
||||
self._check_module_imports()
|
||||
self._check_env_vars()
|
||||
self._check_version_vs_lockfile()
|
||||
|
||||
return self.results
|
||||
|
||||
def _check_pyproject(self) -> bool:
|
||||
pyproject_path = self.project_root / "pyproject.toml"
|
||||
if not pyproject_path.exists():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_pyproject",
|
||||
"Cannot find pyproject.toml",
|
||||
detail=(
|
||||
f"Expected pyproject.toml at {pyproject_path}. "
|
||||
"CrewAI projects must be installable Python packages."
|
||||
),
|
||||
hint="Run `crewai create crew <name>` to scaffold a valid project layout.",
|
||||
)
|
||||
return False
|
||||
|
||||
try:
|
||||
self._pyproject = parse_toml(pyproject_path.read_text())
|
||||
except Exception as e:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"invalid_pyproject",
|
||||
"pyproject.toml is not valid TOML",
|
||||
detail=str(e),
|
||||
)
|
||||
return False
|
||||
|
||||
project = self._pyproject.get("project") or {}
|
||||
name = project.get("name")
|
||||
if not isinstance(name, str) or not name.strip():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_project_name",
|
||||
"pyproject.toml is missing [project].name",
|
||||
detail=(
|
||||
"Without a project name the platform cannot resolve your "
|
||||
"package directory (this produces errors like "
|
||||
"'Cannot find src//crew.py')."
|
||||
),
|
||||
hint='Set a `name = "..."` field under `[project]` in pyproject.toml.',
|
||||
)
|
||||
return False
|
||||
|
||||
self._project_name = name
|
||||
self._package_name = normalize_package_name(name)
|
||||
self._is_flow = (self._pyproject.get("tool") or {}).get("crewai", {}).get(
|
||||
"type"
|
||||
) == "flow"
|
||||
return True
|
||||
|
||||
def _check_lockfile(self) -> None:
|
||||
uv_lock = self.project_root / "uv.lock"
|
||||
poetry_lock = self.project_root / "poetry.lock"
|
||||
pyproject = self.project_root / "pyproject.toml"
|
||||
|
||||
if not uv_lock.exists() and not poetry_lock.exists():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_lockfile",
|
||||
"Expected to find at least one of these files: uv.lock or poetry.lock",
|
||||
hint=(
|
||||
"Run `uv lock` (recommended) or `poetry lock` in your project "
|
||||
"directory, commit the lockfile, then redeploy."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
lockfile = uv_lock if uv_lock.exists() else poetry_lock
|
||||
try:
|
||||
if lockfile.stat().st_mtime < pyproject.stat().st_mtime:
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"stale_lockfile",
|
||||
f"{lockfile.name} is older than pyproject.toml",
|
||||
detail=(
|
||||
"Your lockfile may not reflect recent dependency changes. "
|
||||
"The platform resolves from the lockfile, so deployed "
|
||||
"dependencies may differ from local."
|
||||
),
|
||||
hint="Run `uv lock` (or `poetry lock`) and commit the result.",
|
||||
)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
def _check_package_dir(self) -> bool:
|
||||
if self._package_name is None:
|
||||
return False
|
||||
|
||||
src_dir = self.project_root / "src"
|
||||
if not src_dir.is_dir():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_src_dir",
|
||||
"Missing src/ directory",
|
||||
detail=(
|
||||
"CrewAI deployments expect a src-layout project: "
|
||||
f"src/{self._package_name}/crew.py (or main.py for flows)."
|
||||
),
|
||||
hint="Run `crewai create crew <name>` to see the expected layout.",
|
||||
)
|
||||
return False
|
||||
|
||||
package_dir = src_dir / self._package_name
|
||||
if not package_dir.is_dir():
|
||||
siblings = [
|
||||
p.name
|
||||
for p in src_dir.iterdir()
|
||||
if p.is_dir() and not p.name.endswith(".egg-info")
|
||||
]
|
||||
egg_info = [
|
||||
p.name for p in src_dir.iterdir() if p.name.endswith(".egg-info")
|
||||
]
|
||||
|
||||
hint_parts = [
|
||||
f'Create src/{self._package_name}/ to match [project].name = "{self._project_name}".'
|
||||
]
|
||||
if siblings:
|
||||
hint_parts.append(
|
||||
f"Found other package directories: {', '.join(siblings)}. "
|
||||
f"Either rename one to '{self._package_name}' or update [project].name."
|
||||
)
|
||||
if egg_info:
|
||||
hint_parts.append(
|
||||
f"Delete stale build artifacts: {', '.join(egg_info)} "
|
||||
"(these confuse the platform's package discovery)."
|
||||
)
|
||||
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_package_dir",
|
||||
f"Cannot find src/{self._package_name}/",
|
||||
detail=(
|
||||
"The platform looks for your crew source under "
|
||||
"src/<package_name>/, derived from [project].name."
|
||||
),
|
||||
hint=" ".join(hint_parts),
|
||||
)
|
||||
return False
|
||||
|
||||
for p in src_dir.iterdir():
|
||||
if p.name.endswith(".egg-info"):
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"stale_egg_info",
|
||||
f"Stale build artifact in src/: {p.name}",
|
||||
detail=(
|
||||
".egg-info directories can be mistaken for your package "
|
||||
"and cause 'Cannot find src/<name>.egg-info/crew.py' errors."
|
||||
),
|
||||
hint=f"Delete {p} and add `*.egg-info/` to .gitignore.",
|
||||
)
|
||||
|
||||
self._package_dir = package_dir
|
||||
return True
|
||||
|
||||
def _check_crew_entrypoint(self) -> None:
|
||||
if self._package_dir is None:
|
||||
return
|
||||
crew_py = self._package_dir / "crew.py"
|
||||
if not crew_py.is_file():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_crew_py",
|
||||
f"Cannot find {crew_py.relative_to(self.project_root)}",
|
||||
detail=(
|
||||
"Standard crew projects must define a Crew class decorated "
|
||||
"with @CrewBase inside crew.py."
|
||||
),
|
||||
hint=(
|
||||
"Create crew.py with an @CrewBase-annotated class, or set "
|
||||
'`[tool.crewai] type = "flow"` in pyproject.toml if this is a flow.'
|
||||
),
|
||||
)
|
||||
|
||||
def _check_config_yamls(self) -> None:
|
||||
if self._package_dir is None:
|
||||
return
|
||||
config_dir = self._package_dir / "config"
|
||||
if not config_dir.is_dir():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_config_dir",
|
||||
f"Cannot find {config_dir.relative_to(self.project_root)}",
|
||||
hint="Create a config/ directory with agents.yaml and tasks.yaml.",
|
||||
)
|
||||
return
|
||||
|
||||
for yaml_name in ("agents.yaml", "tasks.yaml"):
|
||||
yaml_path = config_dir / yaml_name
|
||||
if not yaml_path.is_file():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
f"missing_{yaml_name.replace('.', '_')}",
|
||||
f"Cannot find {yaml_path.relative_to(self.project_root)}",
|
||||
detail=(
|
||||
"CrewAI loads agent and task config from these files; "
|
||||
"missing them causes empty-config warnings and runtime crashes."
|
||||
),
|
||||
)
|
||||
|
||||
def _check_flow_entrypoint(self) -> None:
|
||||
if self._package_dir is None:
|
||||
return
|
||||
main_py = self._package_dir / "main.py"
|
||||
if not main_py.is_file():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_flow_main",
|
||||
f"Cannot find {main_py.relative_to(self.project_root)}",
|
||||
detail=(
|
||||
"Flow projects must define a Flow subclass in main.py. "
|
||||
'This project has `[tool.crewai] type = "flow"` set.'
|
||||
),
|
||||
hint="Create main.py with a `class MyFlow(Flow[...])`.",
|
||||
)
|
||||
|
||||
def _check_hatch_wheel_target(self) -> None:
|
||||
if not self._pyproject:
|
||||
return
|
||||
|
||||
build_system = self._pyproject.get("build-system") or {}
|
||||
backend = build_system.get("build-backend", "")
|
||||
if "hatchling" not in backend:
|
||||
return
|
||||
|
||||
hatch_wheel = (
|
||||
(self._pyproject.get("tool") or {})
|
||||
.get("hatch", {})
|
||||
.get("build", {})
|
||||
.get("targets", {})
|
||||
.get("wheel", {})
|
||||
)
|
||||
if hatch_wheel.get("packages") or hatch_wheel.get("only-include"):
|
||||
return
|
||||
|
||||
if self._package_dir and self._package_dir.is_dir():
|
||||
return
|
||||
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"hatch_wheel_target_missing",
|
||||
"Hatchling cannot determine which files to ship",
|
||||
detail=(
|
||||
"Your pyproject uses hatchling but has no "
|
||||
"[tool.hatch.build.targets.wheel] configuration and no "
|
||||
"directory matching your project name."
|
||||
),
|
||||
hint=(
|
||||
"Add:\n"
|
||||
" [tool.hatch.build.targets.wheel]\n"
|
||||
f' packages = ["src/{self._package_name}"]'
|
||||
),
|
||||
)
|
||||
|
||||
def _check_module_imports(self) -> None:
|
||||
"""Import the user's crew/flow via `uv run` so the check sees the same
|
||||
package versions as `crewai run` would. Result is reported as JSON on
|
||||
the subprocess's stdout."""
|
||||
script = (
|
||||
"import json, sys, traceback, os\n"
|
||||
"os.chdir(sys.argv[1])\n"
|
||||
"try:\n"
|
||||
" from crewai.cli.utils import get_crews, get_flows\n"
|
||||
" is_flow = sys.argv[2] == 'flow'\n"
|
||||
" if is_flow:\n"
|
||||
" instances = get_flows()\n"
|
||||
" kind = 'flow'\n"
|
||||
" else:\n"
|
||||
" instances = get_crews()\n"
|
||||
" kind = 'crew'\n"
|
||||
" print(json.dumps({'ok': True, 'kind': kind, 'count': len(instances)}))\n"
|
||||
"except BaseException as e:\n"
|
||||
" print(json.dumps({\n"
|
||||
" 'ok': False,\n"
|
||||
" 'error_type': type(e).__name__,\n"
|
||||
" 'error': str(e),\n"
|
||||
" 'traceback': traceback.format_exc(),\n"
|
||||
" }))\n"
|
||||
)
|
||||
|
||||
uv_path = shutil.which("uv")
|
||||
if uv_path is None:
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"uv_not_found",
|
||||
"Skipping import check: `uv` not installed",
|
||||
hint="Install uv: https://docs.astral.sh/uv/",
|
||||
)
|
||||
return
|
||||
|
||||
try:
|
||||
proc = subprocess.run( # noqa: S603 - args constructed from trusted inputs
|
||||
[
|
||||
uv_path,
|
||||
"run",
|
||||
"python",
|
||||
"-c",
|
||||
script,
|
||||
str(self.project_root),
|
||||
"flow" if self._is_flow else "crew",
|
||||
],
|
||||
cwd=self.project_root,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=120,
|
||||
check=False,
|
||||
)
|
||||
except subprocess.TimeoutExpired:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"import_timeout",
|
||||
"Importing your crew/flow module timed out after 120s",
|
||||
detail=(
|
||||
"User code may be making network calls or doing heavy work "
|
||||
"at import time. Move that work into agent methods."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
# The payload is the last JSON object on stdout; user code may print
|
||||
# other lines before it.
|
||||
payload: dict[str, Any] | None = None
|
||||
for line in reversed(proc.stdout.splitlines()):
|
||||
line = line.strip()
|
||||
if line.startswith("{") and line.endswith("}"):
|
||||
try:
|
||||
payload = json.loads(line)
|
||||
break
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
if payload is None:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"import_failed",
|
||||
"Could not import your crew/flow module",
|
||||
detail=(proc.stderr or proc.stdout or "").strip()[:1500],
|
||||
hint="Run `crewai run` locally first to reproduce the error.",
|
||||
)
|
||||
return
|
||||
|
||||
if payload.get("ok"):
|
||||
if payload.get("count", 0) == 0:
|
||||
kind = payload.get("kind", "crew")
|
||||
if kind == "flow":
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"no_flow_subclass",
|
||||
"No Flow subclass found in the module",
|
||||
hint=(
|
||||
"main.py must define a class extending "
|
||||
"`crewai.flow.Flow`, instantiable with no arguments."
|
||||
),
|
||||
)
|
||||
else:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"no_crewbase_class",
|
||||
"Crew class annotated with @CrewBase not found",
|
||||
hint=(
|
||||
"Decorate your crew class with @CrewBase from "
|
||||
"crewai.project (see `crewai create crew` template)."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
err_msg = str(payload.get("error", ""))
|
||||
err_type = str(payload.get("error_type", "Exception"))
|
||||
tb = str(payload.get("traceback", ""))
|
||||
self._classify_import_error(err_type, err_msg, tb)
|
||||
|
||||
def _classify_import_error(self, err_type: str, err_msg: str, tb: str) -> None:
|
||||
"""Turn a raw import-time exception into a user-actionable finding."""
|
||||
# Must be checked before the generic "native provider" branch below:
|
||||
# the extras-missing message contains the same phrase. Providers
|
||||
# format the install command as plain text (`to install: uv add
|
||||
# "crewai[extra]"`); also tolerate backtick-delimited variants.
|
||||
m = re.search(
|
||||
r"(?P<pkg>[A-Za-z0-9_ -]+?)\s+native provider not available"
|
||||
r".*?to install:\s*`?(?P<cmd>uv add [\"']crewai\[[^\]]+\][\"'])`?",
|
||||
err_msg,
|
||||
)
|
||||
if m:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"missing_provider_extra",
|
||||
f"{m.group('pkg').strip()} provider extra not installed",
|
||||
hint=f"Run: {m.group('cmd')}",
|
||||
)
|
||||
return
|
||||
|
||||
# crewai.llm.LLM.__new__ wraps provider init errors as
|
||||
# ImportError("Error importing native provider: ...").
|
||||
if "Error importing native provider" in err_msg or "native provider" in err_msg:
|
||||
missing_key = self._extract_missing_api_key(err_msg)
|
||||
if missing_key:
|
||||
provider = _KNOWN_API_KEY_HINTS.get(missing_key, missing_key)
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"llm_init_missing_key",
|
||||
f"LLM is constructed at import time but {missing_key} is not set",
|
||||
detail=(
|
||||
f"Your crew instantiates a {provider} LLM during module "
|
||||
"load (e.g. in a class field default or @crew method). "
|
||||
f"The {provider} provider currently requires {missing_key} "
|
||||
"at construction time, so this will fail on the platform "
|
||||
"unless the key is set in your deployment environment."
|
||||
),
|
||||
hint=(
|
||||
f"Add {missing_key} to your deployment's Environment "
|
||||
"Variables before deploying, or move LLM construction "
|
||||
"inside agent methods so it runs lazily."
|
||||
),
|
||||
)
|
||||
return
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"llm_provider_init_failed",
|
||||
"LLM native provider failed to initialize",
|
||||
detail=err_msg,
|
||||
hint=(
|
||||
"Check your LLM(model=...) configuration and provider-specific "
|
||||
"extras (e.g. `uv add 'crewai[azure-ai-inference]'` for Azure)."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
if err_type == "KeyError":
|
||||
key = err_msg.strip("'\"")
|
||||
if key in _KNOWN_API_KEY_HINTS or key.endswith("_API_KEY"):
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"env_var_read_at_import",
|
||||
f"{key} is read at import time via os.environ[...]",
|
||||
detail=(
|
||||
"Using os.environ[...] (rather than os.getenv(...)) "
|
||||
"at module scope crashes the build if the key isn't set."
|
||||
),
|
||||
hint=(
|
||||
f"Either add {key} as a deployment env var, or switch "
|
||||
"to os.getenv() and move the access inside agent methods."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
if "Crew class annotated with @CrewBase not found" in err_msg:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"no_crewbase_class",
|
||||
"Crew class annotated with @CrewBase not found",
|
||||
detail=err_msg,
|
||||
)
|
||||
return
|
||||
if "No Flow subclass found" in err_msg:
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"no_flow_subclass",
|
||||
"No Flow subclass found in the module",
|
||||
detail=err_msg,
|
||||
)
|
||||
return
|
||||
|
||||
if (
|
||||
err_type == "AttributeError"
|
||||
and "has no attribute '_load_response_format'" in err_msg
|
||||
):
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"stale_crewai_pin",
|
||||
"Your lockfile pins a crewai version missing `_load_response_format`",
|
||||
detail=err_msg,
|
||||
hint=(
|
||||
"Run `uv lock --upgrade-package crewai` (or `poetry update crewai`) "
|
||||
"to pin a newer release."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
if "pydantic" in tb.lower() or "validation error" in err_msg.lower():
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"pydantic_validation_error",
|
||||
"Pydantic validation failed while loading your crew",
|
||||
detail=err_msg[:800],
|
||||
hint=(
|
||||
"Check agent/task configuration fields. `crewai run` locally "
|
||||
"will show the full traceback."
|
||||
),
|
||||
)
|
||||
return
|
||||
|
||||
self._add(
|
||||
Severity.ERROR,
|
||||
"import_failed",
|
||||
f"Importing your crew failed: {err_type}",
|
||||
detail=err_msg[:800],
|
||||
hint="Run `crewai run` locally to see the full traceback.",
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _extract_missing_api_key(err_msg: str) -> str | None:
|
||||
"""Pull 'FOO_API_KEY' out of '... FOO_API_KEY is required ...'."""
|
||||
m = re.search(r"([A-Z][A-Z0-9_]*_API_KEY)\s+is required", err_msg)
|
||||
if m:
|
||||
return m.group(1)
|
||||
m = re.search(r"['\"]([A-Z][A-Z0-9_]*_API_KEY)['\"]", err_msg)
|
||||
if m:
|
||||
return m.group(1)
|
||||
return None
|
||||
|
||||
def _check_env_vars(self) -> None:
|
||||
"""Warn about env vars referenced in user code but missing locally.
|
||||
Best-effort only — the platform sets vars server-side, so we never error.
|
||||
"""
|
||||
if not self._package_dir:
|
||||
return
|
||||
|
||||
referenced: set[str] = set()
|
||||
pattern = re.compile(
|
||||
r"""(?x)
|
||||
(?:os\.environ\s*(?:\[\s*|\.get\s*\(\s*)
|
||||
|os\.getenv\s*\(\s*
|
||||
|getenv\s*\(\s*)
|
||||
['"]([A-Z][A-Z0-9_]*)['"]
|
||||
"""
|
||||
)
|
||||
|
||||
for path in self._package_dir.rglob("*.py"):
|
||||
try:
|
||||
text = path.read_text(encoding="utf-8", errors="ignore")
|
||||
except OSError:
|
||||
continue
|
||||
referenced.update(pattern.findall(text))
|
||||
|
||||
for path in self._package_dir.rglob("*.yaml"):
|
||||
try:
|
||||
text = path.read_text(encoding="utf-8", errors="ignore")
|
||||
except OSError:
|
||||
continue
|
||||
referenced.update(re.findall(r"\$\{?([A-Z][A-Z0-9_]+)\}?", text))
|
||||
|
||||
env_file = self.project_root / ".env"
|
||||
env_keys: set[str] = set()
|
||||
if env_file.exists():
|
||||
for line in env_file.read_text(errors="ignore").splitlines():
|
||||
line = line.strip()
|
||||
if not line or line.startswith("#") or "=" not in line:
|
||||
continue
|
||||
env_keys.add(line.split("=", 1)[0].strip())
|
||||
|
||||
missing_known: list[str] = sorted(
|
||||
var
|
||||
for var in referenced
|
||||
if var in _KNOWN_API_KEY_HINTS
|
||||
and var not in env_keys
|
||||
and var not in os.environ
|
||||
)
|
||||
if missing_known:
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"env_vars_not_in_dotenv",
|
||||
f"{len(missing_known)} referenced API key(s) not in .env",
|
||||
detail=(
|
||||
"These env vars are referenced in your source but not set "
|
||||
f"locally: {', '.join(missing_known)}. Deploys will fail "
|
||||
"unless they are added to the deployment's Environment "
|
||||
"Variables in the CrewAI dashboard."
|
||||
),
|
||||
)
|
||||
|
||||
def _check_version_vs_lockfile(self) -> None:
|
||||
"""Warn when the lockfile pins a crewai release older than 1.13.0,
|
||||
which is where ``_load_response_format`` was introduced.
|
||||
"""
|
||||
uv_lock = self.project_root / "uv.lock"
|
||||
poetry_lock = self.project_root / "poetry.lock"
|
||||
lockfile = (
|
||||
uv_lock
|
||||
if uv_lock.exists()
|
||||
else poetry_lock
|
||||
if poetry_lock.exists()
|
||||
else None
|
||||
)
|
||||
if lockfile is None:
|
||||
return
|
||||
|
||||
try:
|
||||
text = lockfile.read_text(errors="ignore")
|
||||
except OSError:
|
||||
return
|
||||
|
||||
m = re.search(
|
||||
r'name\s*=\s*"crewai"\s*\nversion\s*=\s*"([^"]+)"',
|
||||
text,
|
||||
)
|
||||
if not m:
|
||||
return
|
||||
locked = m.group(1)
|
||||
|
||||
try:
|
||||
from packaging.version import Version
|
||||
|
||||
if Version(locked) < Version("1.13.0"):
|
||||
self._add(
|
||||
Severity.WARNING,
|
||||
"old_crewai_pin",
|
||||
f"Lockfile pins crewai=={locked} (older than 1.13.0)",
|
||||
detail=(
|
||||
"Older pinned versions are missing API surface the "
|
||||
"platform builder expects (e.g. `_load_response_format`)."
|
||||
),
|
||||
hint="Run `uv lock --upgrade-package crewai` and redeploy.",
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug("Could not parse crewai pin from lockfile: %s", e)
|
||||
|
||||
|
||||
def render_report(results: list[ValidationResult]) -> None:
|
||||
"""Pretty-print results to the shared rich console."""
|
||||
if not results:
|
||||
console.print("[bold green]Pre-deploy validation passed.[/bold green]")
|
||||
return
|
||||
|
||||
errors = [r for r in results if r.severity is Severity.ERROR]
|
||||
warnings = [r for r in results if r.severity is Severity.WARNING]
|
||||
|
||||
for result in errors:
|
||||
console.print(f"[bold red]ERROR[/bold red] [{result.code}] {result.title}")
|
||||
if result.detail:
|
||||
console.print(f" {result.detail}")
|
||||
if result.hint:
|
||||
console.print(f" [dim]hint:[/dim] {result.hint}")
|
||||
|
||||
for result in warnings:
|
||||
console.print(
|
||||
f"[bold yellow]WARNING[/bold yellow] [{result.code}] {result.title}"
|
||||
)
|
||||
if result.detail:
|
||||
console.print(f" {result.detail}")
|
||||
if result.hint:
|
||||
console.print(f" [dim]hint:[/dim] {result.hint}")
|
||||
|
||||
summary_parts: list[str] = []
|
||||
if errors:
|
||||
summary_parts.append(f"[bold red]{len(errors)} error(s)[/bold red]")
|
||||
if warnings:
|
||||
summary_parts.append(f"[bold yellow]{len(warnings)} warning(s)[/bold yellow]")
|
||||
console.print(f"\n{' / '.join(summary_parts)}")
|
||||
|
||||
|
||||
def validate_project(project_root: Path | None = None) -> DeployValidator:
|
||||
"""Entrypoint: run validation, render results, return the validator.
|
||||
|
||||
The caller inspects ``validator.ok`` to decide whether to proceed with a
|
||||
deploy.
|
||||
"""
|
||||
validator = DeployValidator(project_root=project_root)
|
||||
validator.run()
|
||||
render_report(validator.results)
|
||||
return validator
|
||||
|
||||
|
||||
def run_validate_command() -> None:
|
||||
"""Implementation of `crewai deploy validate`."""
|
||||
validator = validate_project()
|
||||
if not validator.ok:
|
||||
sys.exit(1)
|
||||
@@ -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]==1.14.2a4"
|
||||
"crewai[tools]==1.14.2a1"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.2a4"
|
||||
"crewai[tools]==1.14.2a1"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.2a4"
|
||||
"crewai[tools]==1.14.2a1"
|
||||
]
|
||||
|
||||
[tool.crewai]
|
||||
|
||||
@@ -117,7 +117,11 @@ from crewai.tools.base_tool import BaseTool
|
||||
from crewai.types.callback import SerializableCallable
|
||||
from crewai.types.streaming import CrewStreamingOutput
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
|
||||
from crewai.utilities.constants import (
|
||||
NOT_SPECIFIED,
|
||||
TRAINED_AGENTS_DATA_FILE,
|
||||
TRAINING_DATA_FILE,
|
||||
)
|
||||
from crewai.utilities.crew.models import CrewContext
|
||||
from crewai.utilities.env import get_env_context
|
||||
from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
|
||||
@@ -361,6 +365,14 @@ class Crew(FlowTrackable, BaseModel):
|
||||
default=None,
|
||||
description="Whether to enable tracing for the crew. True=always enable, False=always disable, None=check environment/user settings.",
|
||||
)
|
||||
trained_agents_data_file: str = Field(
|
||||
default=TRAINED_AGENTS_DATA_FILE,
|
||||
description=(
|
||||
"Path to the file containing trained agent suggestions. "
|
||||
"Defaults to 'trained_agents_data.pkl'. Set this to match the "
|
||||
"custom filename used during training (e.g., via `crewai train -f`)."
|
||||
),
|
||||
)
|
||||
|
||||
execution_context: ExecutionContext | None = Field(default=None)
|
||||
checkpoint_inputs: dict[str, Any] | None = Field(default=None)
|
||||
@@ -436,13 +448,6 @@ 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:
|
||||
|
||||
@@ -2,56 +2,14 @@ from collections.abc import Iterator
|
||||
import contextvars
|
||||
from datetime import datetime, timezone
|
||||
import itertools
|
||||
from typing import Any, TypedDict
|
||||
from typing import Any
|
||||
import uuid
|
||||
|
||||
from pydantic import BaseModel, Field, SerializationInfo
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.utilities.serialization import Serializable, to_serializable
|
||||
|
||||
|
||||
def _is_trace_context(info: SerializationInfo) -> bool:
|
||||
"""Check if serialization is happening in trace context."""
|
||||
return bool(info.context and info.context.get("trace"))
|
||||
|
||||
|
||||
class AgentRef(TypedDict):
|
||||
id: str
|
||||
role: str
|
||||
|
||||
|
||||
class TaskRef(TypedDict):
|
||||
id: str
|
||||
name: str
|
||||
|
||||
|
||||
def _trace_agent_ref(agent: Any) -> AgentRef | None:
|
||||
"""Return a lightweight agent reference for trace serialization."""
|
||||
if agent is None:
|
||||
return None
|
||||
return AgentRef(
|
||||
id=str(getattr(agent, "id", "")),
|
||||
role=getattr(agent, "role", ""),
|
||||
)
|
||||
|
||||
|
||||
def _trace_task_ref(task: Any) -> TaskRef | None:
|
||||
"""Return a lightweight task reference for trace serialization."""
|
||||
if task is None:
|
||||
return None
|
||||
return TaskRef(
|
||||
id=str(getattr(task, "id", "")),
|
||||
name=str(getattr(task, "name", None) or getattr(task, "description", "")),
|
||||
)
|
||||
|
||||
|
||||
def _trace_tool_names(tools: Any) -> list[str] | None:
|
||||
"""Return a list of tool names for trace serialization."""
|
||||
if not tools:
|
||||
return None
|
||||
return [getattr(t, "name", str(t)) for t in tools]
|
||||
|
||||
|
||||
_emission_counter: contextvars.ContextVar[Iterator[int]] = contextvars.ContextVar(
|
||||
"_emission_counter"
|
||||
)
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
"""Trace collection listener for orchestrating trace collection."""
|
||||
|
||||
import os
|
||||
from typing import Any
|
||||
from typing import Any, ClassVar
|
||||
import uuid
|
||||
|
||||
from typing_extensions import Self
|
||||
@@ -129,13 +129,18 @@ from crewai.events.utils.console_formatter import ConsoleFormatter
|
||||
from crewai.utilities.version import get_crewai_version
|
||||
|
||||
|
||||
_TRACE_CONTEXT: dict[str, bool] = {"trace": True}
|
||||
"""Serialization context that triggers lightweight field serializers on event models."""
|
||||
|
||||
|
||||
class TraceCollectionListener(BaseEventListener):
|
||||
"""Trace collection listener that orchestrates trace collection."""
|
||||
|
||||
complex_events: ClassVar[list[str]] = [
|
||||
"task_started",
|
||||
"task_completed",
|
||||
"llm_call_started",
|
||||
"llm_call_completed",
|
||||
"agent_execution_started",
|
||||
"agent_execution_completed",
|
||||
]
|
||||
|
||||
_instance: Self | None = None
|
||||
_initialized: bool = False
|
||||
_listeners_setup: bool = False
|
||||
@@ -819,19 +824,9 @@ class TraceCollectionListener(BaseEventListener):
|
||||
def _build_event_data(
|
||||
self, event_type: str, event: Any, source: Any
|
||||
) -> dict[str, Any]:
|
||||
"""Build event data with context-based serialization to reduce trace bloat.
|
||||
|
||||
Field serializers on event models check for context={"trace": True} and
|
||||
return lightweight references instead of full nested objects. This replaces
|
||||
the old denylist approach with Pydantic v2's native context mechanism.
|
||||
|
||||
Only crew_kickoff_started gets a full crew structure (built separately).
|
||||
Complex events (task_started, etc.) use custom projections for specific shapes.
|
||||
All other events get context-aware serialization automatically.
|
||||
"""
|
||||
if event_type == "crew_kickoff_started":
|
||||
return self._build_crew_started_data(event)
|
||||
|
||||
"""Build event data"""
|
||||
if event_type not in self.complex_events:
|
||||
return safe_serialize_to_dict(event)
|
||||
if event_type == "task_started":
|
||||
task_name = event.task.name or event.task.description
|
||||
task_display_name = (
|
||||
@@ -872,77 +867,19 @@ class TraceCollectionListener(BaseEventListener):
|
||||
"agent_backstory": event.agent.backstory,
|
||||
}
|
||||
if event_type == "llm_call_started":
|
||||
event_data = safe_serialize_to_dict(event, context=_TRACE_CONTEXT)
|
||||
event_data = safe_serialize_to_dict(event)
|
||||
event_data["task_name"] = event.task_name or getattr(
|
||||
event, "task_description", None
|
||||
)
|
||||
return event_data
|
||||
if event_type == "llm_call_completed":
|
||||
return safe_serialize_to_dict(event, context=_TRACE_CONTEXT)
|
||||
return safe_serialize_to_dict(event)
|
||||
|
||||
return safe_serialize_to_dict(event, context=_TRACE_CONTEXT)
|
||||
|
||||
def _build_crew_started_data(self, event: Any) -> dict[str, Any]:
|
||||
"""Build comprehensive crew structure for crew_kickoff_started event.
|
||||
|
||||
This is the ONE place where we serialize the full crew structure.
|
||||
Subsequent events use lightweight references via field serializers.
|
||||
"""
|
||||
event_data = safe_serialize_to_dict(event, context=_TRACE_CONTEXT)
|
||||
|
||||
crew = getattr(event, "crew", None)
|
||||
if crew is not None:
|
||||
agents_data = []
|
||||
for agent in getattr(crew, "agents", []) or []:
|
||||
agent_data = {
|
||||
"id": str(getattr(agent, "id", "")),
|
||||
"role": getattr(agent, "role", ""),
|
||||
"goal": getattr(agent, "goal", ""),
|
||||
"backstory": getattr(agent, "backstory", ""),
|
||||
"verbose": getattr(agent, "verbose", False),
|
||||
"allow_delegation": getattr(agent, "allow_delegation", False),
|
||||
"max_iter": getattr(agent, "max_iter", None),
|
||||
"max_rpm": getattr(agent, "max_rpm", None),
|
||||
}
|
||||
tools = getattr(agent, "tools", None)
|
||||
if tools:
|
||||
agent_data["tool_names"] = [
|
||||
getattr(t, "name", str(t)) for t in tools
|
||||
]
|
||||
agents_data.append(agent_data)
|
||||
|
||||
tasks_data = []
|
||||
for task in getattr(crew, "tasks", []) or []:
|
||||
task_data = {
|
||||
"id": str(getattr(task, "id", "")),
|
||||
"name": getattr(task, "name", None),
|
||||
"description": getattr(task, "description", ""),
|
||||
"expected_output": getattr(task, "expected_output", ""),
|
||||
"async_execution": getattr(task, "async_execution", False),
|
||||
"human_input": getattr(task, "human_input", False),
|
||||
}
|
||||
task_agent = getattr(task, "agent", None)
|
||||
if task_agent:
|
||||
task_data["agent_ref"] = {
|
||||
"id": str(getattr(task_agent, "id", "")),
|
||||
"role": getattr(task_agent, "role", ""),
|
||||
}
|
||||
context_tasks = getattr(task, "context", None)
|
||||
if context_tasks:
|
||||
task_data["context_task_ids"] = [
|
||||
str(getattr(ct, "id", "")) for ct in context_tasks
|
||||
]
|
||||
tasks_data.append(task_data)
|
||||
|
||||
event_data["crew_structure"] = {
|
||||
"agents": agents_data,
|
||||
"tasks": tasks_data,
|
||||
"process": str(getattr(crew, "process", "")),
|
||||
"verbose": getattr(crew, "verbose", False),
|
||||
"memory": getattr(crew, "memory", False),
|
||||
}
|
||||
|
||||
return event_data
|
||||
return {
|
||||
"event_type": event_type,
|
||||
"event": safe_serialize_to_dict(event),
|
||||
"source": source,
|
||||
}
|
||||
|
||||
def _show_tracing_disabled_message(self) -> None:
|
||||
"""Show a message when tracing is disabled."""
|
||||
|
||||
@@ -429,22 +429,10 @@ def mark_first_execution_done(user_consented: bool = False) -> None:
|
||||
p.write_text(json.dumps(data, indent=2))
|
||||
|
||||
|
||||
def safe_serialize_to_dict(
|
||||
obj: Any,
|
||||
exclude: set[str] | None = None,
|
||||
context: dict[str, Any] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Safely serialize an object to a dictionary for event data.
|
||||
|
||||
Args:
|
||||
obj: Object to serialize.
|
||||
exclude: Set of keys to exclude from the result.
|
||||
context: Optional context dict passed through to Pydantic's model_dump().
|
||||
Field serializers can inspect this to customize output
|
||||
(e.g. context={"trace": True} for lightweight trace serialization).
|
||||
"""
|
||||
def safe_serialize_to_dict(obj: Any, exclude: set[str] | None = None) -> dict[str, Any]:
|
||||
"""Safely serialize an object to a dictionary for event data."""
|
||||
try:
|
||||
serialized = to_serializable(obj, exclude, context=context)
|
||||
serialized = to_serializable(obj, exclude)
|
||||
if isinstance(serialized, dict):
|
||||
return serialized
|
||||
return {"serialized_data": serialized}
|
||||
|
||||
@@ -5,17 +5,11 @@ from __future__ import annotations
|
||||
from collections.abc import Sequence
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import ConfigDict, SerializationInfo, field_serializer, model_validator
|
||||
from pydantic import ConfigDict, model_validator
|
||||
from typing_extensions import Self
|
||||
|
||||
from crewai.agents.agent_builder.base_agent import BaseAgent
|
||||
from crewai.events.base_events import (
|
||||
BaseEvent,
|
||||
_is_trace_context,
|
||||
_trace_agent_ref,
|
||||
_trace_task_ref,
|
||||
_trace_tool_names,
|
||||
)
|
||||
from crewai.events.base_events import BaseEvent
|
||||
from crewai.tools.base_tool import BaseTool
|
||||
from crewai.tools.structured_tool import CrewStructuredTool
|
||||
|
||||
@@ -37,21 +31,6 @@ class AgentExecutionStartedEvent(BaseEvent):
|
||||
_set_agent_fingerprint(self, self.agent)
|
||||
return self
|
||||
|
||||
@field_serializer("agent")
|
||||
@classmethod
|
||||
def _serialize_agent(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_agent_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
@field_serializer("tools")
|
||||
@classmethod
|
||||
def _serialize_tools(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_tool_names(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class AgentExecutionCompletedEvent(BaseEvent):
|
||||
"""Event emitted when an agent completes executing a task"""
|
||||
@@ -69,16 +48,6 @@ class AgentExecutionCompletedEvent(BaseEvent):
|
||||
_set_agent_fingerprint(self, self.agent)
|
||||
return self
|
||||
|
||||
@field_serializer("agent")
|
||||
@classmethod
|
||||
def _serialize_agent(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_agent_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class AgentExecutionErrorEvent(BaseEvent):
|
||||
"""Event emitted when an agent encounters an error during execution"""
|
||||
@@ -96,16 +65,6 @@ class AgentExecutionErrorEvent(BaseEvent):
|
||||
_set_agent_fingerprint(self, self.agent)
|
||||
return self
|
||||
|
||||
@field_serializer("agent")
|
||||
@classmethod
|
||||
def _serialize_agent(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_agent_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
# New event classes for LiteAgent
|
||||
class LiteAgentExecutionStartedEvent(BaseEvent):
|
||||
@@ -118,11 +77,6 @@ class LiteAgentExecutionStartedEvent(BaseEvent):
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
@field_serializer("tools")
|
||||
@classmethod
|
||||
def _serialize_tools(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_tool_names(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class LiteAgentExecutionCompletedEvent(BaseEvent):
|
||||
"""Event emitted when a LiteAgent completes execution"""
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
from typing import TYPE_CHECKING, Any, Literal
|
||||
|
||||
from pydantic import SerializationInfo, field_serializer
|
||||
|
||||
from crewai.events.base_events import BaseEvent, _is_trace_context
|
||||
from crewai.events.base_events import BaseEvent
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -28,14 +26,6 @@ class CrewBaseEvent(BaseEvent):
|
||||
if self.crew.fingerprint.metadata:
|
||||
self.fingerprint_metadata = self.crew.fingerprint.metadata
|
||||
|
||||
@field_serializer("crew")
|
||||
@classmethod
|
||||
def _serialize_crew(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
"""Exclude crew in trace context — crew_kickoff_started builds structure separately."""
|
||||
if _is_trace_context(info):
|
||||
return None
|
||||
return v
|
||||
|
||||
def to_json(self, exclude: set[str] | None = None) -> Any:
|
||||
if exclude is None:
|
||||
exclude = set()
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
from enum import Enum
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import BaseModel, SerializationInfo, field_serializer
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.events.base_events import BaseEvent, _is_trace_context
|
||||
from crewai.events.base_events import BaseEvent
|
||||
|
||||
|
||||
class LLMEventBase(BaseEvent):
|
||||
@@ -49,16 +49,6 @@ class LLMCallStartedEvent(LLMEventBase):
|
||||
callbacks: list[Any] | None = None
|
||||
available_functions: dict[str, Any] | None = None
|
||||
|
||||
@field_serializer("callbacks")
|
||||
@classmethod
|
||||
def _serialize_callbacks(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return None if _is_trace_context(info) else v
|
||||
|
||||
@field_serializer("available_functions")
|
||||
@classmethod
|
||||
def _serialize_available_functions(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return None if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class LLMCallCompletedEvent(LLMEventBase):
|
||||
"""Event emitted when a LLM call completes"""
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import SerializationInfo, field_serializer
|
||||
|
||||
from crewai.events.base_events import BaseEvent, _is_trace_context, _trace_task_ref
|
||||
from crewai.events.base_events import BaseEvent
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
|
||||
|
||||
@@ -34,11 +32,6 @@ class TaskStartedEvent(BaseEvent):
|
||||
super().__init__(**data)
|
||||
_set_task_fingerprint(self, self.task)
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class TaskCompletedEvent(BaseEvent):
|
||||
"""Event emitted when a task completes"""
|
||||
@@ -51,11 +44,6 @@ class TaskCompletedEvent(BaseEvent):
|
||||
super().__init__(**data)
|
||||
_set_task_fingerprint(self, self.task)
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class TaskFailedEvent(BaseEvent):
|
||||
"""Event emitted when a task fails"""
|
||||
@@ -68,11 +56,6 @@ class TaskFailedEvent(BaseEvent):
|
||||
super().__init__(**data)
|
||||
_set_task_fingerprint(self, self.task)
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
|
||||
class TaskEvaluationEvent(BaseEvent):
|
||||
"""Event emitted when a task evaluation is completed"""
|
||||
@@ -84,8 +67,3 @@ class TaskEvaluationEvent(BaseEvent):
|
||||
def __init__(self, **data: Any) -> None:
|
||||
super().__init__(**data)
|
||||
_set_task_fingerprint(self, self.task)
|
||||
|
||||
@field_serializer("task")
|
||||
@classmethod
|
||||
def _serialize_task(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_task_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
@@ -2,9 +2,9 @@ from collections.abc import Callable
|
||||
from datetime import datetime
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import ConfigDict, SerializationInfo, field_serializer
|
||||
from pydantic import ConfigDict
|
||||
|
||||
from crewai.events.base_events import BaseEvent, _is_trace_context, _trace_agent_ref
|
||||
from crewai.events.base_events import BaseEvent
|
||||
|
||||
|
||||
class ToolUsageEvent(BaseEvent):
|
||||
@@ -26,11 +26,6 @@ class ToolUsageEvent(BaseEvent):
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
@field_serializer("agent")
|
||||
@classmethod
|
||||
def _serialize_agent(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_agent_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
def __init__(self, **data: Any) -> None:
|
||||
if data.get("from_task"):
|
||||
task = data["from_task"]
|
||||
@@ -104,11 +99,6 @@ class ToolExecutionErrorEvent(BaseEvent):
|
||||
tool_class: Callable[..., Any]
|
||||
agent: Any | None = None
|
||||
|
||||
@field_serializer("agent")
|
||||
@classmethod
|
||||
def _serialize_agent(cls, v: Any, info: SerializationInfo) -> Any:
|
||||
return _trace_agent_ref(v) if _is_trace_context(info) else v
|
||||
|
||||
def __init__(self, **data: Any) -> None:
|
||||
super().__init__(**data)
|
||||
# Set fingerprint data from the agent
|
||||
|
||||
@@ -16,6 +16,7 @@ from typing import (
|
||||
get_origin,
|
||||
)
|
||||
import uuid
|
||||
import warnings
|
||||
|
||||
from pydantic import (
|
||||
UUID4,
|
||||
@@ -25,7 +26,7 @@ from pydantic import (
|
||||
field_validator,
|
||||
model_validator,
|
||||
)
|
||||
from typing_extensions import Self, deprecated
|
||||
from typing_extensions import Self
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -172,12 +173,9 @@ def _kickoff_with_a2a_support(
|
||||
)
|
||||
|
||||
|
||||
@deprecated(
|
||||
"LiteAgent is deprecated and will be removed in v2.0.0.",
|
||||
category=FutureWarning,
|
||||
)
|
||||
class LiteAgent(FlowTrackable, BaseModel):
|
||||
"""A lightweight agent that can process messages and use tools.
|
||||
"""
|
||||
A lightweight agent that can process messages and use tools.
|
||||
|
||||
.. deprecated::
|
||||
LiteAgent is deprecated and will be removed in a future version.
|
||||
@@ -280,6 +278,18 @@ class LiteAgent(FlowTrackable, BaseModel):
|
||||
)
|
||||
_memory: Any = PrivateAttr(default=None)
|
||||
|
||||
@model_validator(mode="after")
|
||||
def emit_deprecation_warning(self) -> Self:
|
||||
"""Emit deprecation warning for LiteAgent usage."""
|
||||
warnings.warn(
|
||||
"LiteAgent is deprecated and will be removed in a future version. "
|
||||
"Use Agent().kickoff(messages) instead, which provides the same "
|
||||
"functionality with additional features like memory and knowledge support.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
return self
|
||||
|
||||
@model_validator(mode="after")
|
||||
def setup_llm(self) -> Self:
|
||||
"""Set up the LLM and other components after initialization."""
|
||||
|
||||
@@ -51,7 +51,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
)
|
||||
from crewai.utilities.logger_utils import suppress_warnings
|
||||
from crewai.utilities.string_utils import sanitize_tool_name
|
||||
from crewai.utilities.token_counter_callback import TokenCalcHandler
|
||||
|
||||
|
||||
try:
|
||||
@@ -76,13 +75,8 @@ try:
|
||||
from litellm.types.utils import (
|
||||
ChatCompletionDeltaToolCall,
|
||||
Choices,
|
||||
Delta as LiteLLMDelta,
|
||||
Function,
|
||||
Message,
|
||||
ModelResponse,
|
||||
ModelResponseBase,
|
||||
ModelResponseStream,
|
||||
StreamingChoices as LiteLLMStreamingChoices,
|
||||
)
|
||||
from litellm.utils import supports_response_schema
|
||||
|
||||
@@ -91,11 +85,6 @@ except ImportError:
|
||||
LITELLM_AVAILABLE = False
|
||||
litellm = None # type: ignore[assignment]
|
||||
Choices = None # type: ignore[assignment, misc]
|
||||
LiteLLMDelta = None # type: ignore[assignment, misc]
|
||||
Message = None # type: ignore[assignment, misc]
|
||||
ModelResponseBase = None # type: ignore[assignment, misc]
|
||||
ModelResponseStream = None # type: ignore[assignment, misc]
|
||||
LiteLLMStreamingChoices = None # type: ignore[assignment, misc]
|
||||
get_supported_openai_params = None # type: ignore[assignment]
|
||||
ChatCompletionDeltaToolCall = None # type: ignore[assignment, misc]
|
||||
Function = None # type: ignore[assignment, misc]
|
||||
@@ -720,7 +709,7 @@ class LLM(BaseLLM):
|
||||
chunk_content = None
|
||||
response_id = None
|
||||
|
||||
if isinstance(chunk, ModelResponseBase):
|
||||
if hasattr(chunk, "id"):
|
||||
response_id = chunk.id
|
||||
|
||||
# Safely extract content from various chunk formats
|
||||
@@ -729,16 +718,18 @@ class LLM(BaseLLM):
|
||||
choices = None
|
||||
if isinstance(chunk, dict) and "choices" in chunk:
|
||||
choices = chunk["choices"]
|
||||
elif isinstance(chunk, ModelResponseStream):
|
||||
choices = chunk.choices
|
||||
elif hasattr(chunk, "choices"):
|
||||
# Check if choices is not a type but an actual attribute with value
|
||||
if not isinstance(chunk.choices, type):
|
||||
choices = chunk.choices
|
||||
|
||||
# Try to extract usage information if available
|
||||
# NOTE: usage is a pydantic extra field on ModelResponseBase,
|
||||
# so it must be accessed via model_extra.
|
||||
if isinstance(chunk, dict) and "usage" in chunk:
|
||||
usage_info = chunk["usage"]
|
||||
elif isinstance(chunk, ModelResponseBase) and chunk.model_extra:
|
||||
usage_info = chunk.model_extra.get("usage") or usage_info
|
||||
elif hasattr(chunk, "usage"):
|
||||
# Check if usage is not a type but an actual attribute with value
|
||||
if not isinstance(chunk.usage, type):
|
||||
usage_info = chunk.usage
|
||||
|
||||
if choices and len(choices) > 0:
|
||||
choice = choices[0]
|
||||
@@ -747,7 +738,7 @@ class LLM(BaseLLM):
|
||||
delta = None
|
||||
if isinstance(choice, dict) and "delta" in choice:
|
||||
delta = choice["delta"]
|
||||
elif isinstance(choice, LiteLLMStreamingChoices):
|
||||
elif hasattr(choice, "delta"):
|
||||
delta = choice.delta
|
||||
|
||||
# Extract content from delta
|
||||
@@ -757,7 +748,7 @@ class LLM(BaseLLM):
|
||||
if "content" in delta and delta["content"] is not None:
|
||||
chunk_content = delta["content"]
|
||||
# Handle object format
|
||||
elif isinstance(delta, LiteLLMDelta):
|
||||
elif hasattr(delta, "content"):
|
||||
chunk_content = delta.content
|
||||
|
||||
# Handle case where content might be None or empty
|
||||
@@ -830,8 +821,9 @@ class LLM(BaseLLM):
|
||||
choices = None
|
||||
if isinstance(last_chunk, dict) and "choices" in last_chunk:
|
||||
choices = last_chunk["choices"]
|
||||
elif isinstance(last_chunk, ModelResponseStream):
|
||||
choices = last_chunk.choices
|
||||
elif hasattr(last_chunk, "choices"):
|
||||
if not isinstance(last_chunk.choices, type):
|
||||
choices = last_chunk.choices
|
||||
|
||||
if choices and len(choices) > 0:
|
||||
choice = choices[0]
|
||||
@@ -840,14 +832,14 @@ class LLM(BaseLLM):
|
||||
message = None
|
||||
if isinstance(choice, dict) and "message" in choice:
|
||||
message = choice["message"]
|
||||
elif isinstance(choice, Choices):
|
||||
elif hasattr(choice, "message"):
|
||||
message = choice.message
|
||||
|
||||
if message:
|
||||
content = None
|
||||
if isinstance(message, dict) and "content" in message:
|
||||
content = message["content"]
|
||||
elif isinstance(message, Message):
|
||||
elif hasattr(message, "content"):
|
||||
content = message.content
|
||||
|
||||
if content:
|
||||
@@ -874,23 +866,24 @@ class LLM(BaseLLM):
|
||||
choices = None
|
||||
if isinstance(last_chunk, dict) and "choices" in last_chunk:
|
||||
choices = last_chunk["choices"]
|
||||
elif isinstance(last_chunk, ModelResponseStream):
|
||||
choices = last_chunk.choices
|
||||
elif hasattr(last_chunk, "choices"):
|
||||
if not isinstance(last_chunk.choices, type):
|
||||
choices = last_chunk.choices
|
||||
|
||||
if choices and len(choices) > 0:
|
||||
choice = choices[0]
|
||||
|
||||
delta = None
|
||||
if isinstance(choice, dict) and "delta" in choice:
|
||||
delta = choice["delta"]
|
||||
elif isinstance(choice, LiteLLMStreamingChoices):
|
||||
delta = choice.delta
|
||||
message = None
|
||||
if isinstance(choice, dict) and "message" in choice:
|
||||
message = choice["message"]
|
||||
elif hasattr(choice, "message"):
|
||||
message = choice.message
|
||||
|
||||
if delta:
|
||||
if isinstance(delta, dict) and "tool_calls" in delta:
|
||||
tool_calls = delta["tool_calls"]
|
||||
elif isinstance(delta, LiteLLMDelta):
|
||||
tool_calls = delta.tool_calls
|
||||
if message:
|
||||
if isinstance(message, dict) and "tool_calls" in message:
|
||||
tool_calls = message["tool_calls"]
|
||||
elif hasattr(message, "tool_calls"):
|
||||
tool_calls = message.tool_calls
|
||||
except Exception as e:
|
||||
logging.debug(f"Error checking for tool calls: {e}")
|
||||
|
||||
@@ -1044,7 +1037,7 @@ class LLM(BaseLLM):
|
||||
"""
|
||||
if callbacks and len(callbacks) > 0:
|
||||
for callback in callbacks:
|
||||
if isinstance(callback, TokenCalcHandler):
|
||||
if hasattr(callback, "log_success_event"):
|
||||
# Use the usage_info we've been tracking
|
||||
if not usage_info:
|
||||
# Try to get usage from the last chunk if we haven't already
|
||||
@@ -1055,14 +1048,9 @@ class LLM(BaseLLM):
|
||||
and "usage" in last_chunk
|
||||
):
|
||||
usage_info = last_chunk["usage"]
|
||||
elif (
|
||||
isinstance(last_chunk, ModelResponseBase)
|
||||
and last_chunk.model_extra
|
||||
):
|
||||
usage_info = (
|
||||
last_chunk.model_extra.get("usage")
|
||||
or usage_info
|
||||
)
|
||||
elif hasattr(last_chunk, "usage"):
|
||||
if not isinstance(last_chunk.usage, type):
|
||||
usage_info = last_chunk.usage
|
||||
except Exception as e:
|
||||
logging.debug(f"Error extracting usage info: {e}")
|
||||
|
||||
@@ -1135,10 +1123,13 @@ class LLM(BaseLLM):
|
||||
params["response_model"] = response_model
|
||||
response = litellm.completion(**params)
|
||||
|
||||
if isinstance(response, ModelResponseBase) and response.model_extra:
|
||||
usage_info = response.model_extra.get("usage")
|
||||
if usage_info:
|
||||
self._track_token_usage_internal(usage_info)
|
||||
if (
|
||||
hasattr(response, "usage")
|
||||
and not isinstance(response.usage, type)
|
||||
and response.usage
|
||||
):
|
||||
usage_info = response.usage
|
||||
self._track_token_usage_internal(usage_info)
|
||||
|
||||
except LLMContextLengthExceededError:
|
||||
# Re-raise our own context length error
|
||||
@@ -1150,11 +1141,7 @@ class LLM(BaseLLM):
|
||||
raise LLMContextLengthExceededError(error_msg) from e
|
||||
raise
|
||||
|
||||
response_usage = self._usage_to_dict(
|
||||
response.model_extra.get("usage")
|
||||
if isinstance(response, ModelResponseBase) and response.model_extra
|
||||
else None
|
||||
)
|
||||
response_usage = self._usage_to_dict(getattr(response, "usage", None))
|
||||
|
||||
# --- 2) Handle structured output response (when response_model is provided)
|
||||
if response_model is not None:
|
||||
@@ -1179,13 +1166,8 @@ class LLM(BaseLLM):
|
||||
# --- 3) Handle callbacks with usage info
|
||||
if callbacks and len(callbacks) > 0:
|
||||
for callback in callbacks:
|
||||
if isinstance(callback, TokenCalcHandler):
|
||||
usage_info = (
|
||||
response.model_extra.get("usage")
|
||||
if isinstance(response, ModelResponseBase)
|
||||
and response.model_extra
|
||||
else None
|
||||
)
|
||||
if hasattr(callback, "log_success_event"):
|
||||
usage_info = getattr(response, "usage", None)
|
||||
if usage_info:
|
||||
callback.log_success_event(
|
||||
kwargs=params,
|
||||
@@ -1194,7 +1176,7 @@ class LLM(BaseLLM):
|
||||
end_time=0,
|
||||
)
|
||||
# --- 4) Check for tool calls
|
||||
tool_calls = response_message.tool_calls or []
|
||||
tool_calls = getattr(response_message, "tool_calls", [])
|
||||
|
||||
# --- 5) If no tool calls or no available functions, return the text response directly as long as there is a text response
|
||||
if (not tool_calls or not available_functions) and text_response:
|
||||
@@ -1287,10 +1269,13 @@ class LLM(BaseLLM):
|
||||
params["response_model"] = response_model
|
||||
response = await litellm.acompletion(**params)
|
||||
|
||||
if isinstance(response, ModelResponseBase) and response.model_extra:
|
||||
usage_info = response.model_extra.get("usage")
|
||||
if usage_info:
|
||||
self._track_token_usage_internal(usage_info)
|
||||
if (
|
||||
hasattr(response, "usage")
|
||||
and not isinstance(response.usage, type)
|
||||
and response.usage
|
||||
):
|
||||
usage_info = response.usage
|
||||
self._track_token_usage_internal(usage_info)
|
||||
|
||||
except LLMContextLengthExceededError:
|
||||
# Re-raise our own context length error
|
||||
@@ -1302,11 +1287,7 @@ class LLM(BaseLLM):
|
||||
raise LLMContextLengthExceededError(error_msg) from e
|
||||
raise
|
||||
|
||||
response_usage = self._usage_to_dict(
|
||||
response.model_extra.get("usage")
|
||||
if isinstance(response, ModelResponseBase) and response.model_extra
|
||||
else None
|
||||
)
|
||||
response_usage = self._usage_to_dict(getattr(response, "usage", None))
|
||||
|
||||
if response_model is not None:
|
||||
if isinstance(response, BaseModel):
|
||||
@@ -1328,13 +1309,8 @@ class LLM(BaseLLM):
|
||||
|
||||
if callbacks and len(callbacks) > 0:
|
||||
for callback in callbacks:
|
||||
if isinstance(callback, TokenCalcHandler):
|
||||
usage_info = (
|
||||
response.model_extra.get("usage")
|
||||
if isinstance(response, ModelResponseBase)
|
||||
and response.model_extra
|
||||
else None
|
||||
)
|
||||
if hasattr(callback, "log_success_event"):
|
||||
usage_info = getattr(response, "usage", None)
|
||||
if usage_info:
|
||||
callback.log_success_event(
|
||||
kwargs=params,
|
||||
@@ -1343,7 +1319,7 @@ class LLM(BaseLLM):
|
||||
end_time=0,
|
||||
)
|
||||
|
||||
tool_calls = response_message.tool_calls or []
|
||||
tool_calls = getattr(response_message, "tool_calls", [])
|
||||
|
||||
if (not tool_calls or not available_functions) and text_response:
|
||||
self._handle_emit_call_events(
|
||||
@@ -1418,19 +1394,18 @@ class LLM(BaseLLM):
|
||||
async for chunk in await litellm.acompletion(**params):
|
||||
chunk_count += 1
|
||||
chunk_content = None
|
||||
response_id = chunk.id if isinstance(chunk, ModelResponseBase) else None
|
||||
response_id = chunk.id if hasattr(chunk, "id") else None
|
||||
|
||||
try:
|
||||
choices = None
|
||||
if isinstance(chunk, dict) and "choices" in chunk:
|
||||
choices = chunk["choices"]
|
||||
elif isinstance(chunk, ModelResponseStream):
|
||||
choices = chunk.choices
|
||||
elif hasattr(chunk, "choices"):
|
||||
if not isinstance(chunk.choices, type):
|
||||
choices = chunk.choices
|
||||
|
||||
if isinstance(chunk, ModelResponseBase) and chunk.model_extra:
|
||||
chunk_usage = chunk.model_extra.get("usage")
|
||||
if chunk_usage is not None:
|
||||
usage_info = chunk_usage
|
||||
if hasattr(chunk, "usage") and chunk.usage is not None:
|
||||
usage_info = chunk.usage
|
||||
|
||||
if choices and len(choices) > 0:
|
||||
first_choice = choices[0]
|
||||
@@ -1438,19 +1413,19 @@ class LLM(BaseLLM):
|
||||
|
||||
if isinstance(first_choice, dict):
|
||||
delta = first_choice.get("delta", {})
|
||||
elif isinstance(first_choice, LiteLLMStreamingChoices):
|
||||
elif hasattr(first_choice, "delta"):
|
||||
delta = first_choice.delta
|
||||
|
||||
if delta:
|
||||
if isinstance(delta, dict):
|
||||
chunk_content = delta.get("content")
|
||||
elif isinstance(delta, LiteLLMDelta):
|
||||
elif hasattr(delta, "content"):
|
||||
chunk_content = delta.content
|
||||
|
||||
tool_calls: list[ChatCompletionDeltaToolCall] | None = None
|
||||
if isinstance(delta, dict):
|
||||
tool_calls = delta.get("tool_calls")
|
||||
elif isinstance(delta, LiteLLMDelta):
|
||||
elif hasattr(delta, "tool_calls"):
|
||||
tool_calls = delta.tool_calls
|
||||
|
||||
if tool_calls:
|
||||
@@ -1486,7 +1461,7 @@ class LLM(BaseLLM):
|
||||
|
||||
if callbacks and len(callbacks) > 0 and usage_info:
|
||||
for callback in callbacks:
|
||||
if isinstance(callback, TokenCalcHandler):
|
||||
if hasattr(callback, "log_success_event"):
|
||||
callback.log_success_event(
|
||||
kwargs=params,
|
||||
response_obj={"usage": usage_info},
|
||||
@@ -1945,7 +1920,7 @@ class LLM(BaseLLM):
|
||||
return None
|
||||
if isinstance(usage, dict):
|
||||
return usage
|
||||
if isinstance(usage, BaseModel):
|
||||
if hasattr(usage, "model_dump"):
|
||||
result: dict[str, Any] = usage.model_dump()
|
||||
return result
|
||||
if hasattr(usage, "__dict__"):
|
||||
@@ -2009,7 +1984,7 @@ class LLM(BaseLLM):
|
||||
)
|
||||
return messages
|
||||
|
||||
provider = self.provider or self.model
|
||||
provider = getattr(self, "provider", None) or self.model
|
||||
|
||||
for msg in messages:
|
||||
files = msg.get("files")
|
||||
@@ -2060,7 +2035,7 @@ class LLM(BaseLLM):
|
||||
)
|
||||
return messages
|
||||
|
||||
provider = self.provider or self.model
|
||||
provider = getattr(self, "provider", None) or self.model
|
||||
|
||||
for msg in messages:
|
||||
files = msg.get("files")
|
||||
|
||||
@@ -172,8 +172,6 @@ class BaseLLM(BaseModel, ABC):
|
||||
"completion_tokens": 0,
|
||||
"successful_requests": 0,
|
||||
"cached_prompt_tokens": 0,
|
||||
"reasoning_tokens": 0,
|
||||
"cache_creation_tokens": 0,
|
||||
}
|
||||
)
|
||||
|
||||
@@ -810,24 +808,14 @@ 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.
|
||||
|
||||
@@ -11,14 +11,10 @@ from crewai.events.types.llm_events import LLMCallType
|
||||
from crewai.llms.base_llm import BaseLLM, JsonResponseFormat, llm_call_context
|
||||
from crewai.llms.hooks.base import BaseInterceptor
|
||||
from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import (
|
||||
sanitize_tool_params_for_anthropic_strict,
|
||||
)
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
@@ -193,41 +189,16 @@ class AnthropicCompletion(BaseLLM):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _init_clients(self) -> AnthropicCompletion:
|
||||
"""Eagerly build clients when the API key is available, otherwise
|
||||
defer so ``LLM(model="anthropic/...")`` can be constructed at module
|
||||
import time even before deployment env vars are set.
|
||||
"""
|
||||
try:
|
||||
self._client = self._build_sync_client()
|
||||
self._async_client = self._build_async_client()
|
||||
except ValueError:
|
||||
pass
|
||||
return self
|
||||
self._client = Anthropic(**self._get_client_params())
|
||||
|
||||
def _build_sync_client(self) -> Any:
|
||||
return Anthropic(**self._get_client_params())
|
||||
|
||||
def _build_async_client(self) -> Any:
|
||||
# Skip the sync httpx.Client that `_get_client_params` would
|
||||
# otherwise construct under `interceptor`; we attach an async one
|
||||
# below and would leak the sync one if both were built.
|
||||
async_client_params = self._get_client_params(include_http_client=False)
|
||||
async_client_params = self._get_client_params()
|
||||
if self.interceptor:
|
||||
async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
|
||||
async_client_params["http_client"] = httpx.AsyncClient(
|
||||
transport=async_transport
|
||||
)
|
||||
return AsyncAnthropic(**async_client_params)
|
||||
async_http_client = httpx.AsyncClient(transport=async_transport)
|
||||
async_client_params["http_client"] = async_http_client
|
||||
|
||||
def _get_sync_client(self) -> Any:
|
||||
if self._client is None:
|
||||
self._client = self._build_sync_client()
|
||||
return self._client
|
||||
|
||||
def _get_async_client(self) -> Any:
|
||||
if self._async_client is None:
|
||||
self._async_client = self._build_async_client()
|
||||
return self._async_client
|
||||
self._async_client = AsyncAnthropic(**async_client_params)
|
||||
return self
|
||||
|
||||
def to_config_dict(self) -> dict[str, Any]:
|
||||
"""Extend base config with Anthropic-specific fields."""
|
||||
@@ -242,15 +213,8 @@ class AnthropicCompletion(BaseLLM):
|
||||
config["timeout"] = self.timeout
|
||||
return config
|
||||
|
||||
def _get_client_params(self, include_http_client: bool = True) -> dict[str, Any]:
|
||||
"""Get client parameters.
|
||||
|
||||
Args:
|
||||
include_http_client: When True (default) and an interceptor is
|
||||
set, attach a sync ``httpx.Client``. The async builder
|
||||
passes ``False`` so it can attach its own async client
|
||||
without leaking a sync one.
|
||||
"""
|
||||
def _get_client_params(self) -> dict[str, Any]:
|
||||
"""Get client parameters."""
|
||||
|
||||
if self.api_key is None:
|
||||
self.api_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
@@ -264,7 +228,7 @@ class AnthropicCompletion(BaseLLM):
|
||||
"max_retries": self.max_retries,
|
||||
}
|
||||
|
||||
if include_http_client and self.interceptor:
|
||||
if self.interceptor:
|
||||
transport = HTTPTransport(interceptor=self.interceptor)
|
||||
http_client = httpx.Client(transport=transport)
|
||||
client_params["http_client"] = http_client # type: ignore[assignment]
|
||||
@@ -509,8 +473,10 @@ class AnthropicCompletion(BaseLLM):
|
||||
continue
|
||||
|
||||
try:
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
|
||||
name, description, parameters = safe_tool_conversion(tool, "Anthropic")
|
||||
except (KeyError, ValueError) as e:
|
||||
except (ImportError, KeyError, ValueError) as e:
|
||||
logging.error(f"Error converting tool to Anthropic format: {e}")
|
||||
raise e
|
||||
|
||||
@@ -519,15 +485,8 @@ class AnthropicCompletion(BaseLLM):
|
||||
"description": description,
|
||||
}
|
||||
|
||||
func_info = tool.get("function", {})
|
||||
strict_enabled = bool(func_info.get("strict"))
|
||||
|
||||
if parameters and isinstance(parameters, dict):
|
||||
anthropic_tool["input_schema"] = (
|
||||
sanitize_tool_params_for_anthropic_strict(parameters)
|
||||
if strict_enabled
|
||||
else parameters
|
||||
)
|
||||
anthropic_tool["input_schema"] = parameters
|
||||
else:
|
||||
anthropic_tool["input_schema"] = {
|
||||
"type": "object",
|
||||
@@ -535,9 +494,6 @@ class AnthropicCompletion(BaseLLM):
|
||||
"required": [],
|
||||
}
|
||||
|
||||
if strict_enabled:
|
||||
anthropic_tool["strict"] = True
|
||||
|
||||
anthropic_tools.append(anthropic_tool)
|
||||
|
||||
return anthropic_tools
|
||||
@@ -830,11 +786,11 @@ class AnthropicCompletion(BaseLLM):
|
||||
try:
|
||||
if betas:
|
||||
params["betas"] = betas
|
||||
response = self._get_sync_client().beta.messages.create(
|
||||
response = self._client.beta.messages.create(
|
||||
**params, extra_body=extra_body
|
||||
)
|
||||
else:
|
||||
response = self._get_sync_client().messages.create(**params)
|
||||
response = self._client.messages.create(**params)
|
||||
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
@@ -982,11 +938,9 @@ class AnthropicCompletion(BaseLLM):
|
||||
current_tool_calls: dict[int, dict[str, Any]] = {}
|
||||
|
||||
stream_context = (
|
||||
self._get_sync_client().beta.messages.stream(
|
||||
**stream_params, extra_body=extra_body
|
||||
)
|
||||
self._client.beta.messages.stream(**stream_params, extra_body=extra_body)
|
||||
if betas
|
||||
else self._get_sync_client().messages.stream(**stream_params)
|
||||
else self._client.messages.stream(**stream_params)
|
||||
)
|
||||
with stream_context as stream:
|
||||
response_id = None
|
||||
@@ -1265,9 +1219,7 @@ class AnthropicCompletion(BaseLLM):
|
||||
|
||||
try:
|
||||
# Send tool results back to Claude for final response
|
||||
final_response: Message = self._get_sync_client().messages.create(
|
||||
**follow_up_params
|
||||
)
|
||||
final_response: Message = self._client.messages.create(**follow_up_params)
|
||||
|
||||
# Track token usage for follow-up call
|
||||
follow_up_usage = self._extract_anthropic_token_usage(final_response)
|
||||
@@ -1363,11 +1315,11 @@ class AnthropicCompletion(BaseLLM):
|
||||
try:
|
||||
if betas:
|
||||
params["betas"] = betas
|
||||
response = await self._get_async_client().beta.messages.create(
|
||||
response = await self._async_client.beta.messages.create(
|
||||
**params, extra_body=extra_body
|
||||
)
|
||||
else:
|
||||
response = await self._get_async_client().messages.create(**params)
|
||||
response = await self._async_client.messages.create(**params)
|
||||
|
||||
except Exception as e:
|
||||
if is_context_length_exceeded(e):
|
||||
@@ -1501,11 +1453,11 @@ class AnthropicCompletion(BaseLLM):
|
||||
current_tool_calls: dict[int, dict[str, Any]] = {}
|
||||
|
||||
stream_context = (
|
||||
self._get_async_client().beta.messages.stream(
|
||||
self._async_client.beta.messages.stream(
|
||||
**stream_params, extra_body=extra_body
|
||||
)
|
||||
if betas
|
||||
else self._get_async_client().messages.stream(**stream_params)
|
||||
else self._async_client.messages.stream(**stream_params)
|
||||
)
|
||||
async with stream_context as stream:
|
||||
response_id = None
|
||||
@@ -1670,7 +1622,7 @@ class AnthropicCompletion(BaseLLM):
|
||||
]
|
||||
|
||||
try:
|
||||
final_response: Message = await self._get_async_client().messages.create(
|
||||
final_response: Message = await self._async_client.messages.create(
|
||||
**follow_up_params
|
||||
)
|
||||
|
||||
@@ -1752,23 +1704,18 @@ class AnthropicCompletion(BaseLLM):
|
||||
def _extract_anthropic_token_usage(
|
||||
response: Message | BetaMessage,
|
||||
) -> dict[str, Any]:
|
||||
"""Extract token usage and response metadata from Anthropic response."""
|
||||
"""Extract token usage 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
|
||||
cache_creation_tokens = (
|
||||
getattr(usage, "cache_creation_input_tokens", 0) or 0
|
||||
)
|
||||
result: dict[str, Any] = {
|
||||
return {
|
||||
"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:
|
||||
@@ -1798,8 +1745,8 @@ class AnthropicCompletion(BaseLLM):
|
||||
from crewai_files.uploaders.anthropic import AnthropicFileUploader
|
||||
|
||||
return AnthropicFileUploader(
|
||||
client=self._get_sync_client(),
|
||||
async_client=self._get_async_client(),
|
||||
client=self._client,
|
||||
async_client=self._async_client,
|
||||
)
|
||||
except ImportError:
|
||||
return None
|
||||
|
||||
@@ -116,100 +116,43 @@ class AzureCompletion(BaseLLM):
|
||||
data.get("api_version") or os.getenv("AZURE_API_VERSION") or "2024-06-01"
|
||||
)
|
||||
|
||||
# Credentials and endpoint are validated lazily in `_init_clients`
|
||||
# so the LLM can be constructed before deployment env vars are set.
|
||||
model = data.get("model", "")
|
||||
if data["endpoint"]:
|
||||
data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
|
||||
data["endpoint"], model
|
||||
if not data["api_key"]:
|
||||
raise ValueError(
|
||||
"Azure API key is required. Set AZURE_API_KEY environment variable or pass api_key parameter."
|
||||
)
|
||||
data["is_azure_openai_endpoint"] = AzureCompletion._is_azure_openai_endpoint(
|
||||
data["endpoint"]
|
||||
if not data["endpoint"]:
|
||||
raise ValueError(
|
||||
"Azure endpoint is required. Set AZURE_ENDPOINT environment variable or pass endpoint parameter."
|
||||
)
|
||||
|
||||
model = data.get("model", "")
|
||||
data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
|
||||
data["endpoint"], model
|
||||
)
|
||||
data["is_openai_model"] = any(
|
||||
prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
|
||||
)
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def _is_azure_openai_endpoint(endpoint: str | None) -> bool:
|
||||
if not endpoint:
|
||||
return False
|
||||
hostname = urlparse(endpoint).hostname or ""
|
||||
return (
|
||||
parsed = urlparse(data["endpoint"])
|
||||
hostname = parsed.hostname or ""
|
||||
data["is_azure_openai_endpoint"] = (
|
||||
hostname == "openai.azure.com" or hostname.endswith(".openai.azure.com")
|
||||
) and "/openai/deployments/" in endpoint
|
||||
) and "/openai/deployments/" in data["endpoint"]
|
||||
return data
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _init_clients(self) -> AzureCompletion:
|
||||
"""Eagerly build clients when credentials are available, otherwise
|
||||
defer so ``LLM(model="azure/...")`` can be constructed at module
|
||||
import time even before deployment env vars are set.
|
||||
"""
|
||||
try:
|
||||
self._client = self._build_sync_client()
|
||||
self._async_client = self._build_async_client()
|
||||
except ValueError:
|
||||
pass
|
||||
return self
|
||||
|
||||
def _build_sync_client(self) -> Any:
|
||||
return ChatCompletionsClient(**self._make_client_kwargs())
|
||||
|
||||
def _build_async_client(self) -> Any:
|
||||
return AsyncChatCompletionsClient(**self._make_client_kwargs())
|
||||
|
||||
def _make_client_kwargs(self) -> dict[str, Any]:
|
||||
# Re-read env vars so that a deferred build can pick up credentials
|
||||
# that weren't set at instantiation time (e.g. LLM constructed at
|
||||
# module import before deployment env vars were injected).
|
||||
if not self.api_key:
|
||||
self.api_key = os.getenv("AZURE_API_KEY")
|
||||
if not self.endpoint:
|
||||
endpoint = (
|
||||
os.getenv("AZURE_ENDPOINT")
|
||||
or os.getenv("AZURE_OPENAI_ENDPOINT")
|
||||
or os.getenv("AZURE_API_BASE")
|
||||
)
|
||||
if endpoint:
|
||||
self.endpoint = AzureCompletion._validate_and_fix_endpoint(
|
||||
endpoint, self.model
|
||||
)
|
||||
# Recompute the routing flag now that the endpoint is known —
|
||||
# _prepare_completion_params uses it to decide whether to
|
||||
# include `model` in the request body (Azure OpenAI endpoints
|
||||
# embed the deployment name in the URL and reject it).
|
||||
self.is_azure_openai_endpoint = (
|
||||
AzureCompletion._is_azure_openai_endpoint(self.endpoint)
|
||||
)
|
||||
|
||||
if not self.api_key:
|
||||
raise ValueError(
|
||||
"Azure API key is required. Set AZURE_API_KEY environment "
|
||||
"variable or pass api_key parameter."
|
||||
)
|
||||
if not self.endpoint:
|
||||
raise ValueError(
|
||||
"Azure endpoint is required. Set AZURE_ENDPOINT environment "
|
||||
"variable or pass endpoint parameter."
|
||||
)
|
||||
raise ValueError("Azure API key is required.")
|
||||
client_kwargs: dict[str, Any] = {
|
||||
"endpoint": self.endpoint,
|
||||
"credential": AzureKeyCredential(self.api_key),
|
||||
}
|
||||
if self.api_version:
|
||||
client_kwargs["api_version"] = self.api_version
|
||||
return client_kwargs
|
||||
|
||||
def _get_sync_client(self) -> Any:
|
||||
if self._client is None:
|
||||
self._client = self._build_sync_client()
|
||||
return self._client
|
||||
|
||||
def _get_async_client(self) -> Any:
|
||||
if self._async_client is None:
|
||||
self._async_client = self._build_async_client()
|
||||
return self._async_client
|
||||
self._client = ChatCompletionsClient(**client_kwargs)
|
||||
self._async_client = AsyncChatCompletionsClient(**client_kwargs)
|
||||
return self
|
||||
|
||||
def to_config_dict(self) -> dict[str, Any]:
|
||||
"""Extend base config with Azure-specific fields."""
|
||||
@@ -770,7 +713,8 @@ class AzureCompletion(BaseLLM):
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming chat completion."""
|
||||
try:
|
||||
response: ChatCompletions = self._get_sync_client().complete(**params)
|
||||
# Cast params to Any to avoid type checking issues with TypedDict unpacking
|
||||
response: ChatCompletions = self._client.complete(**params)
|
||||
return self._process_completion_response(
|
||||
response=response,
|
||||
params=params,
|
||||
@@ -969,7 +913,7 @@ class AzureCompletion(BaseLLM):
|
||||
tool_calls: dict[int, dict[str, Any]] = {}
|
||||
|
||||
usage_data: dict[str, Any] | None = None
|
||||
for update in self._get_sync_client().complete(**params):
|
||||
for update in self._client.complete(**params):
|
||||
if isinstance(update, StreamingChatCompletionsUpdate):
|
||||
if update.usage:
|
||||
usage = update.usage
|
||||
@@ -1009,9 +953,8 @@ class AzureCompletion(BaseLLM):
|
||||
) -> str | Any:
|
||||
"""Handle non-streaming chat completion asynchronously."""
|
||||
try:
|
||||
response: ChatCompletions = await self._get_async_client().complete(
|
||||
**params
|
||||
)
|
||||
# Cast params to Any to avoid type checking issues with TypedDict unpacking
|
||||
response: ChatCompletions = await self._async_client.complete(**params)
|
||||
return self._process_completion_response(
|
||||
response=response,
|
||||
params=params,
|
||||
@@ -1037,7 +980,7 @@ class AzureCompletion(BaseLLM):
|
||||
|
||||
usage_data: dict[str, Any] | None = None
|
||||
|
||||
stream = await self._get_async_client().complete(**params)
|
||||
stream = await self._async_client.complete(**params)
|
||||
async for update in stream:
|
||||
if isinstance(update, StreamingChatCompletionsUpdate):
|
||||
if hasattr(update, "usage") and update.usage:
|
||||
@@ -1133,39 +1076,28 @@ class AzureCompletion(BaseLLM):
|
||||
|
||||
@staticmethod
|
||||
def _extract_azure_token_usage(response: ChatCompletions) -> dict[str, Any]:
|
||||
"""Extract token usage and response metadata from Azure response."""
|
||||
"""Extract token usage 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
|
||||
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] = {
|
||||
return {
|
||||
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
|
||||
"completion_tokens": getattr(usage, "completion_tokens", 0),
|
||||
"total_tokens": getattr(usage, "total_tokens", 0),
|
||||
"cached_prompt_tokens": cached_tokens,
|
||||
"reasoning_tokens": reasoning_tokens,
|
||||
}
|
||||
return result
|
||||
return {"total_tokens": 0}
|
||||
|
||||
async def aclose(self) -> None:
|
||||
"""Close the async client and clean up resources.
|
||||
|
||||
This ensures proper cleanup of the underlying aiohttp session
|
||||
to avoid unclosed connector warnings. Accesses the cached client
|
||||
directly rather than going through `_get_async_client` so a
|
||||
cleanup on an uninitialized LLM is a harmless no-op rather than
|
||||
a credential-required error.
|
||||
to avoid unclosed connector warnings.
|
||||
"""
|
||||
if self._async_client is not None and hasattr(self._async_client, "close"):
|
||||
if hasattr(self._async_client, "close"):
|
||||
await self._async_client.close()
|
||||
|
||||
async def __aenter__(self) -> Self:
|
||||
|
||||
@@ -12,7 +12,6 @@ from typing_extensions import Required
|
||||
|
||||
from crewai.events.types.llm_events import LLMCallType
|
||||
from crewai.llms.base_llm import BaseLLM, llm_call_context
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
@@ -303,22 +302,6 @@ class BedrockCompletion(BaseLLM):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _init_clients(self) -> BedrockCompletion:
|
||||
"""Eagerly build the sync client when AWS credentials resolve,
|
||||
otherwise defer so ``LLM(model="bedrock/...")`` can be constructed
|
||||
at module import time even before deployment env vars are set.
|
||||
|
||||
Only credential/SDK errors are caught — programming errors like
|
||||
``TypeError`` or ``AttributeError`` propagate so real bugs aren't
|
||||
silently swallowed.
|
||||
"""
|
||||
try:
|
||||
self._client = self._build_sync_client()
|
||||
except (BotoCoreError, ClientError, ValueError) as e:
|
||||
logging.debug("Deferring Bedrock client construction: %s", e)
|
||||
self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
|
||||
return self
|
||||
|
||||
def _build_sync_client(self) -> Any:
|
||||
config = Config(
|
||||
read_timeout=300,
|
||||
retries={"max_attempts": 3, "mode": "adaptive"},
|
||||
@@ -330,17 +313,9 @@ class BedrockCompletion(BaseLLM):
|
||||
aws_session_token=self.aws_session_token,
|
||||
region_name=self.region_name,
|
||||
)
|
||||
return session.client("bedrock-runtime", config=config)
|
||||
|
||||
def _get_sync_client(self) -> Any:
|
||||
if self._client is None:
|
||||
self._client = self._build_sync_client()
|
||||
return self._client
|
||||
|
||||
def _get_async_client(self) -> Any:
|
||||
"""Async client is set up separately by ``_ensure_async_client``
|
||||
using ``aiobotocore`` inside an exit stack."""
|
||||
return self._async_client
|
||||
self._client = session.client("bedrock-runtime", config=config)
|
||||
self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
|
||||
return self
|
||||
|
||||
def to_config_dict(self) -> dict[str, Any]:
|
||||
"""Extend base config with Bedrock-specific fields."""
|
||||
@@ -680,7 +655,7 @@ class BedrockCompletion(BaseLLM):
|
||||
raise ValueError(f"Invalid message format at index {i}")
|
||||
|
||||
# Call Bedrock Converse API with proper error handling
|
||||
response = self._get_sync_client().converse(
|
||||
response = self._client.converse(
|
||||
modelId=self.model_id,
|
||||
messages=cast(
|
||||
"Sequence[MessageTypeDef | MessageOutputTypeDef]",
|
||||
@@ -969,7 +944,7 @@ class BedrockCompletion(BaseLLM):
|
||||
usage_data: dict[str, Any] | None = None
|
||||
|
||||
try:
|
||||
response = self._get_sync_client().converse_stream(
|
||||
response = self._client.converse_stream(
|
||||
modelId=self.model_id,
|
||||
messages=cast(
|
||||
"Sequence[MessageTypeDef | MessageOutputTypeDef]",
|
||||
@@ -1973,6 +1948,8 @@ class BedrockCompletion(BaseLLM):
|
||||
tools: list[dict[str, Any]],
|
||||
) -> list[ConverseToolTypeDef]:
|
||||
"""Convert CrewAI tools to Converse API format following AWS specification."""
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
|
||||
converse_tools: list[ConverseToolTypeDef] = []
|
||||
|
||||
for tool in tools:
|
||||
@@ -2048,18 +2025,11 @@ 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."""
|
||||
|
||||
@@ -118,33 +118,9 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _init_client(self) -> GeminiCompletion:
|
||||
"""Eagerly build the client when credentials resolve, otherwise defer
|
||||
so ``LLM(model="gemini/...")`` can be constructed at module import time
|
||||
even before deployment env vars are set.
|
||||
"""
|
||||
try:
|
||||
self._client = self._initialize_client(self.use_vertexai)
|
||||
except ValueError:
|
||||
pass
|
||||
self._client = self._initialize_client(self.use_vertexai)
|
||||
return self
|
||||
|
||||
def _get_sync_client(self) -> Any:
|
||||
if self._client is None:
|
||||
# Re-read env vars so a deferred build can pick up credentials
|
||||
# that weren't set at instantiation time.
|
||||
if not self.api_key:
|
||||
self.api_key = os.getenv("GOOGLE_API_KEY") or os.getenv(
|
||||
"GEMINI_API_KEY"
|
||||
)
|
||||
if not self.project:
|
||||
self.project = os.getenv("GOOGLE_CLOUD_PROJECT")
|
||||
self._client = self._initialize_client(self.use_vertexai)
|
||||
return self._client
|
||||
|
||||
def _get_async_client(self) -> Any:
|
||||
"""Gemini uses a single client for both sync and async calls."""
|
||||
return self._get_sync_client()
|
||||
|
||||
def to_config_dict(self) -> dict[str, Any]:
|
||||
"""Extend base config with Gemini/Vertex-specific fields."""
|
||||
config = super().to_config_dict()
|
||||
@@ -252,7 +228,6 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
if (
|
||||
hasattr(self, "client")
|
||||
and self._client is not None
|
||||
and hasattr(self._client, "vertexai")
|
||||
and self._client.vertexai
|
||||
):
|
||||
@@ -1137,7 +1112,7 @@ class GeminiCompletion(BaseLLM):
|
||||
try:
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
response = self._get_sync_client().models.generate_content(
|
||||
response = self._client.models.generate_content(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
@@ -1178,7 +1153,7 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
for chunk in self._get_sync_client().models.generate_content_stream(
|
||||
for chunk in self._client.models.generate_content_stream(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
@@ -1216,7 +1191,7 @@ class GeminiCompletion(BaseLLM):
|
||||
try:
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
response = await self._get_async_client().aio.models.generate_content(
|
||||
response = await self._client.aio.models.generate_content(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
@@ -1257,7 +1232,7 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
# The API accepts list[Content] but mypy is overly strict about variance
|
||||
contents_for_api: Any = contents
|
||||
stream = await self._get_async_client().aio.models.generate_content_stream(
|
||||
stream = await self._client.aio.models.generate_content_stream(
|
||||
model=self.model,
|
||||
contents=contents_for_api,
|
||||
config=config,
|
||||
@@ -1331,20 +1306,17 @@ class GeminiCompletion(BaseLLM):
|
||||
|
||||
@staticmethod
|
||||
def _extract_token_usage(response: GenerateContentResponse) -> dict[str, Any]:
|
||||
"""Extract token usage and response metadata from Gemini response."""
|
||||
"""Extract token usage from Gemini response."""
|
||||
if response.usage_metadata:
|
||||
usage = response.usage_metadata
|
||||
cached_tokens = getattr(usage, "cached_content_token_count", 0) or 0
|
||||
thinking_tokens = getattr(usage, "thoughts_token_count", 0) or 0
|
||||
result: dict[str, Any] = {
|
||||
return {
|
||||
"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
|
||||
@@ -1464,6 +1436,6 @@ class GeminiCompletion(BaseLLM):
|
||||
try:
|
||||
from crewai_files.uploaders.gemini import GeminiFileUploader
|
||||
|
||||
return GeminiFileUploader(client=self._get_sync_client())
|
||||
return GeminiFileUploader(client=self._client)
|
||||
except ImportError:
|
||||
return None
|
||||
|
||||
@@ -32,15 +32,11 @@ from crewai.events.types.llm_events import LLMCallType
|
||||
from crewai.llms.base_llm import BaseLLM, JsonResponseFormat, llm_call_context
|
||||
from crewai.llms.hooks.base import BaseInterceptor
|
||||
from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
from crewai.utilities.agent_utils import is_context_length_exceeded
|
||||
from crewai.utilities.exceptions.context_window_exceeding_exception import (
|
||||
LLMContextLengthExceededError,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import (
|
||||
generate_model_description,
|
||||
sanitize_tool_params_for_openai_strict,
|
||||
)
|
||||
from crewai.utilities.pydantic_schema_utils import generate_model_description
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
@@ -257,40 +253,22 @@ class OpenAICompletion(BaseLLM):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _init_clients(self) -> OpenAICompletion:
|
||||
"""Eagerly build clients when the API key is available, otherwise
|
||||
defer so ``LLM(model="openai/...")`` can be constructed at module
|
||||
import time even before deployment env vars are set.
|
||||
"""
|
||||
try:
|
||||
self._client = self._build_sync_client()
|
||||
self._async_client = self._build_async_client()
|
||||
except ValueError:
|
||||
pass
|
||||
return self
|
||||
|
||||
def _build_sync_client(self) -> Any:
|
||||
client_config = self._get_client_params()
|
||||
if self.interceptor:
|
||||
transport = HTTPTransport(interceptor=self.interceptor)
|
||||
client_config["http_client"] = httpx.Client(transport=transport)
|
||||
return OpenAI(**client_config)
|
||||
http_client = httpx.Client(transport=transport)
|
||||
client_config["http_client"] = http_client
|
||||
|
||||
def _build_async_client(self) -> Any:
|
||||
client_config = self._get_client_params()
|
||||
self._client = OpenAI(**client_config)
|
||||
|
||||
async_client_config = self._get_client_params()
|
||||
if self.interceptor:
|
||||
transport = AsyncHTTPTransport(interceptor=self.interceptor)
|
||||
client_config["http_client"] = httpx.AsyncClient(transport=transport)
|
||||
return AsyncOpenAI(**client_config)
|
||||
async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
|
||||
async_http_client = httpx.AsyncClient(transport=async_transport)
|
||||
async_client_config["http_client"] = async_http_client
|
||||
|
||||
def _get_sync_client(self) -> Any:
|
||||
if self._client is None:
|
||||
self._client = self._build_sync_client()
|
||||
return self._client
|
||||
|
||||
def _get_async_client(self) -> Any:
|
||||
if self._async_client is None:
|
||||
self._async_client = self._build_async_client()
|
||||
return self._async_client
|
||||
self._async_client = AsyncOpenAI(**async_client_config)
|
||||
return self
|
||||
|
||||
@property
|
||||
def last_response_id(self) -> str | None:
|
||||
@@ -786,6 +764,8 @@ class OpenAICompletion(BaseLLM):
|
||||
"function": {"name": "...", "description": "...", "parameters": {...}}
|
||||
}
|
||||
"""
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
|
||||
responses_tools = []
|
||||
|
||||
for tool in tools:
|
||||
@@ -817,7 +797,7 @@ class OpenAICompletion(BaseLLM):
|
||||
) -> str | ResponsesAPIResult | Any:
|
||||
"""Handle non-streaming Responses API call."""
|
||||
try:
|
||||
response: Response = self._get_sync_client().responses.create(**params)
|
||||
response: Response = self._client.responses.create(**params)
|
||||
|
||||
# Track response ID for auto-chaining
|
||||
if self.auto_chain and response.id:
|
||||
@@ -953,9 +933,7 @@ class OpenAICompletion(BaseLLM):
|
||||
) -> str | ResponsesAPIResult | Any:
|
||||
"""Handle async non-streaming Responses API call."""
|
||||
try:
|
||||
response: Response = await self._get_async_client().responses.create(
|
||||
**params
|
||||
)
|
||||
response: Response = await self._async_client.responses.create(**params)
|
||||
|
||||
# Track response ID for auto-chaining
|
||||
if self.auto_chain and response.id:
|
||||
@@ -1091,7 +1069,7 @@ class OpenAICompletion(BaseLLM):
|
||||
final_response: Response | None = None
|
||||
usage: dict[str, Any] | None = None
|
||||
|
||||
stream = self._get_sync_client().responses.create(**params)
|
||||
stream = self._client.responses.create(**params)
|
||||
response_id_stream = None
|
||||
|
||||
for event in stream:
|
||||
@@ -1219,7 +1197,7 @@ class OpenAICompletion(BaseLLM):
|
||||
final_response: Response | None = None
|
||||
usage: dict[str, Any] | None = None
|
||||
|
||||
stream = await self._get_async_client().responses.create(**params)
|
||||
stream = await self._async_client.responses.create(**params)
|
||||
response_id_stream = None
|
||||
|
||||
async for event in stream:
|
||||
@@ -1346,23 +1324,19 @@ class OpenAICompletion(BaseLLM):
|
||||
]
|
||||
|
||||
def _extract_responses_token_usage(self, response: Response) -> dict[str, Any]:
|
||||
"""Extract token usage and response metadata from Responses API response."""
|
||||
"""Extract token usage from Responses API response."""
|
||||
if response.usage:
|
||||
result: dict[str, Any] = {
|
||||
result = {
|
||||
"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}
|
||||
|
||||
@@ -1570,6 +1544,11 @@ class OpenAICompletion(BaseLLM):
|
||||
self, tools: list[dict[str, BaseTool]]
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Convert CrewAI tool format to OpenAI function calling format."""
|
||||
from crewai.llms.providers.utils.common import safe_tool_conversion
|
||||
from crewai.utilities.pydantic_schema_utils import (
|
||||
force_additional_properties_false,
|
||||
)
|
||||
|
||||
openai_tools = []
|
||||
|
||||
for tool in tools:
|
||||
@@ -1588,9 +1567,8 @@ class OpenAICompletion(BaseLLM):
|
||||
params_dict = (
|
||||
parameters if isinstance(parameters, dict) else dict(parameters)
|
||||
)
|
||||
openai_tool["function"]["parameters"] = (
|
||||
sanitize_tool_params_for_openai_strict(params_dict)
|
||||
)
|
||||
params_dict = force_additional_properties_false(params_dict)
|
||||
openai_tool["function"]["parameters"] = params_dict
|
||||
|
||||
openai_tools.append(openai_tool)
|
||||
return openai_tools
|
||||
@@ -1609,7 +1587,7 @@ class OpenAICompletion(BaseLLM):
|
||||
parse_params = {
|
||||
k: v for k, v in params.items() if k != "response_format"
|
||||
}
|
||||
parsed_response = self._get_sync_client().beta.chat.completions.parse(
|
||||
parsed_response = self._client.beta.chat.completions.parse(
|
||||
**parse_params,
|
||||
response_format=response_model,
|
||||
)
|
||||
@@ -1633,9 +1611,7 @@ class OpenAICompletion(BaseLLM):
|
||||
)
|
||||
return parsed_object
|
||||
|
||||
response: ChatCompletion = self._get_sync_client().chat.completions.create(
|
||||
**params
|
||||
)
|
||||
response: ChatCompletion = self._client.chat.completions.create(**params)
|
||||
|
||||
usage = self._extract_openai_token_usage(response)
|
||||
|
||||
@@ -1862,7 +1838,7 @@ class OpenAICompletion(BaseLLM):
|
||||
}
|
||||
|
||||
stream: ChatCompletionStream[BaseModel]
|
||||
with self._get_sync_client().beta.chat.completions.stream(
|
||||
with self._client.beta.chat.completions.stream(
|
||||
**parse_params, response_format=response_model
|
||||
) as stream:
|
||||
for chunk in stream:
|
||||
@@ -1899,7 +1875,7 @@ class OpenAICompletion(BaseLLM):
|
||||
return ""
|
||||
|
||||
completion_stream: Stream[ChatCompletionChunk] = (
|
||||
self._get_sync_client().chat.completions.create(**params)
|
||||
self._client.chat.completions.create(**params)
|
||||
)
|
||||
|
||||
usage_data: dict[str, Any] | None = None
|
||||
@@ -1996,11 +1972,9 @@ class OpenAICompletion(BaseLLM):
|
||||
parse_params = {
|
||||
k: v for k, v in params.items() if k != "response_format"
|
||||
}
|
||||
parsed_response = (
|
||||
await self._get_async_client().beta.chat.completions.parse(
|
||||
**parse_params,
|
||||
response_format=response_model,
|
||||
)
|
||||
parsed_response = await self._async_client.beta.chat.completions.parse(
|
||||
**parse_params,
|
||||
response_format=response_model,
|
||||
)
|
||||
math_reasoning = parsed_response.choices[0].message
|
||||
|
||||
@@ -2022,8 +1996,8 @@ class OpenAICompletion(BaseLLM):
|
||||
)
|
||||
return parsed_object
|
||||
|
||||
response: ChatCompletion = (
|
||||
await self._get_async_client().chat.completions.create(**params)
|
||||
response: ChatCompletion = await self._async_client.chat.completions.create(
|
||||
**params
|
||||
)
|
||||
|
||||
usage = self._extract_openai_token_usage(response)
|
||||
@@ -2149,7 +2123,7 @@ class OpenAICompletion(BaseLLM):
|
||||
if response_model:
|
||||
completion_stream: AsyncIterator[
|
||||
ChatCompletionChunk
|
||||
] = await self._get_async_client().chat.completions.create(**params)
|
||||
] = await self._async_client.chat.completions.create(**params)
|
||||
|
||||
accumulated_content = ""
|
||||
usage_data: dict[str, Any] | None = None
|
||||
@@ -2205,7 +2179,7 @@ class OpenAICompletion(BaseLLM):
|
||||
|
||||
stream: AsyncIterator[
|
||||
ChatCompletionChunk
|
||||
] = await self._get_async_client().chat.completions.create(**params)
|
||||
] = await self._async_client.chat.completions.create(**params)
|
||||
|
||||
usage_data = None
|
||||
|
||||
@@ -2333,24 +2307,20 @@ class OpenAICompletion(BaseLLM):
|
||||
def _extract_openai_token_usage(
|
||||
self, response: ChatCompletion | ChatCompletionChunk
|
||||
) -> dict[str, Any]:
|
||||
"""Extract token usage and response metadata from OpenAI ChatCompletion."""
|
||||
"""Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
|
||||
if hasattr(response, "usage") and response.usage:
|
||||
usage = response.usage
|
||||
result: dict[str, Any] = {
|
||||
result = {
|
||||
"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}
|
||||
|
||||
@@ -2401,8 +2371,8 @@ class OpenAICompletion(BaseLLM):
|
||||
from crewai_files.uploaders.openai import OpenAIFileUploader
|
||||
|
||||
return OpenAIFileUploader(
|
||||
client=self._get_sync_client(),
|
||||
async_client=self._get_async_client(),
|
||||
client=self._client,
|
||||
async_client=self._async_client,
|
||||
)
|
||||
except ImportError:
|
||||
return None
|
||||
|
||||
@@ -213,9 +213,6 @@ class CheckpointConfig(BaseModel):
|
||||
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
|
||||
|
||||
|
||||
@@ -7,7 +7,6 @@ avoids per-event overhead when no entity uses checkpointing.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
@@ -103,15 +102,10 @@ def _find_checkpoint(source: Any) -> CheckpointConfig | None:
|
||||
return None
|
||||
|
||||
|
||||
def _do_checkpoint(
|
||||
state: RuntimeState, cfg: CheckpointConfig, event: BaseEvent | None = None
|
||||
) -> None:
|
||||
def _do_checkpoint(state: RuntimeState, cfg: CheckpointConfig) -> None:
|
||||
"""Write a checkpoint and prune old ones if configured."""
|
||||
_prepare_entities(state.root)
|
||||
payload = state.model_dump(mode="json")
|
||||
if event is not None:
|
||||
payload["trigger"] = event.type
|
||||
data = json.dumps(payload)
|
||||
data = state.model_dump_json()
|
||||
location = cfg.provider.checkpoint(
|
||||
data,
|
||||
cfg.location,
|
||||
@@ -140,7 +134,7 @@ def _on_any_event(source: Any, event: BaseEvent, state: Any) -> None:
|
||||
if cfg is None:
|
||||
return
|
||||
try:
|
||||
_do_checkpoint(state, cfg, event)
|
||||
_do_checkpoint(state, cfg)
|
||||
except Exception:
|
||||
logger.warning("Auto-checkpoint failed for event %s", event.type, exc_info=True)
|
||||
|
||||
|
||||
@@ -66,9 +66,6 @@ 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]:
|
||||
@@ -124,7 +121,7 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
|
||||
"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(mode="json"),
|
||||
"event_record": self._event_record.model_dump(),
|
||||
}
|
||||
|
||||
@model_validator(mode="wrap")
|
||||
@@ -197,10 +194,7 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
|
||||
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.
|
||||
"""Mark this state as a fork for subsequent checkpoints.
|
||||
|
||||
Args:
|
||||
branch: Branch label. Auto-generated from the current checkpoint
|
||||
@@ -210,7 +204,7 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
|
||||
if branch:
|
||||
self._branch = branch
|
||||
elif self._checkpoint_id:
|
||||
self._branch = f"fork/{self._checkpoint_id}_{uuid.uuid4().hex[:6]}"
|
||||
self._branch = f"fork/{self._checkpoint_id}"
|
||||
else:
|
||||
self._branch = f"fork/{uuid.uuid4().hex[:8]}"
|
||||
|
||||
@@ -233,7 +227,6 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
|
||||
provider = detect_provider(location)
|
||||
raw = provider.from_checkpoint(location)
|
||||
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
|
||||
@@ -260,7 +253,6 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
|
||||
provider = detect_provider(location)
|
||||
raw = await provider.afrom_checkpoint(location)
|
||||
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
|
||||
|
||||
@@ -45,7 +45,6 @@ from crewai.events.types.task_events import (
|
||||
TaskStartedEvent,
|
||||
)
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
from crewai.llms.providers.openai.completion import OpenAICompletion
|
||||
from crewai.security import Fingerprint, SecurityConfig
|
||||
from crewai.tasks.output_format import OutputFormat
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
@@ -231,8 +230,6 @@ 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}
|
||||
|
||||
@@ -302,14 +299,12 @@ class Task(BaseModel):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def validate_required_fields(self) -> Self:
|
||||
if self.description is None:
|
||||
raise ValueError(
|
||||
"description must be provided either directly or through config"
|
||||
)
|
||||
if self.expected_output is None:
|
||||
raise ValueError(
|
||||
"expected_output must be provided either directly or through config"
|
||||
)
|
||||
required_fields = ["description", "expected_output"]
|
||||
for field in required_fields:
|
||||
if getattr(self, field) is None:
|
||||
raise ValueError(
|
||||
f"{field} must be provided either directly or through config"
|
||||
)
|
||||
return self
|
||||
|
||||
@model_validator(mode="after")
|
||||
@@ -841,8 +836,8 @@ class Task(BaseModel):
|
||||
should_inject = self.allow_crewai_trigger_context
|
||||
|
||||
if should_inject and self.agent:
|
||||
crew = self.agent.crew
|
||||
if crew and not isinstance(crew, str) and crew._inputs:
|
||||
crew = getattr(self.agent, "crew", None)
|
||||
if crew and hasattr(crew, "_inputs") and crew._inputs:
|
||||
trigger_payload = crew._inputs.get("crewai_trigger_payload")
|
||||
if trigger_payload is not None:
|
||||
description += f"\n\nTrigger Payload: {trigger_payload}"
|
||||
@@ -855,12 +850,11 @@ class Task(BaseModel):
|
||||
isinstance(self.agent.llm, BaseLLM)
|
||||
and self.agent.llm.supports_multimodal()
|
||||
):
|
||||
provider: str = self.agent.llm.provider or self.agent.llm.model
|
||||
api: str | None = (
|
||||
self.agent.llm.api
|
||||
if isinstance(self.agent.llm, OpenAICompletion)
|
||||
else None
|
||||
provider: str = str(
|
||||
getattr(self.agent.llm, "provider", None)
|
||||
or getattr(self.agent.llm, "model", "openai")
|
||||
)
|
||||
api: str | None = getattr(self.agent.llm, "api", None)
|
||||
supported_types = get_supported_content_types(provider, api)
|
||||
|
||||
def is_auto_injected(content_type: str) -> bool:
|
||||
|
||||
@@ -29,14 +29,6 @@ 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."
|
||||
)
|
||||
@@ -51,6 +43,4 @@ 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
|
||||
|
||||
@@ -19,7 +19,7 @@ from collections.abc import Callable
|
||||
from copy import deepcopy
|
||||
import datetime
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Annotated, Any, Final, Literal, TypedDict, Union, cast
|
||||
from typing import TYPE_CHECKING, Annotated, Any, Final, Literal, TypedDict, Union
|
||||
import uuid
|
||||
|
||||
import jsonref # type: ignore[import-untyped]
|
||||
@@ -417,119 +417,6 @@ def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
return schema
|
||||
|
||||
|
||||
_STRICT_METADATA_KEYS: Final[tuple[str, ...]] = (
|
||||
"title",
|
||||
"default",
|
||||
"examples",
|
||||
"example",
|
||||
"$comment",
|
||||
"readOnly",
|
||||
"writeOnly",
|
||||
"deprecated",
|
||||
)
|
||||
|
||||
_CLAUDE_STRICT_UNSUPPORTED: Final[tuple[str, ...]] = (
|
||||
"minimum",
|
||||
"maximum",
|
||||
"exclusiveMinimum",
|
||||
"exclusiveMaximum",
|
||||
"multipleOf",
|
||||
"minLength",
|
||||
"maxLength",
|
||||
"pattern",
|
||||
"minItems",
|
||||
"maxItems",
|
||||
"uniqueItems",
|
||||
"minContains",
|
||||
"maxContains",
|
||||
"minProperties",
|
||||
"maxProperties",
|
||||
"patternProperties",
|
||||
"propertyNames",
|
||||
"dependentRequired",
|
||||
"dependentSchemas",
|
||||
)
|
||||
|
||||
|
||||
def _strip_keys_recursive(d: Any, keys: tuple[str, ...]) -> Any:
|
||||
"""Recursively delete a fixed set of keys from a schema."""
|
||||
if isinstance(d, dict):
|
||||
for key in keys:
|
||||
d.pop(key, None)
|
||||
for v in d.values():
|
||||
_strip_keys_recursive(v, keys)
|
||||
elif isinstance(d, list):
|
||||
for i in d:
|
||||
_strip_keys_recursive(i, keys)
|
||||
return d
|
||||
|
||||
|
||||
def lift_top_level_anyof(schema: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Unwrap a top-level anyOf/oneOf/allOf wrapping a single object variant.
|
||||
|
||||
Anthropic's strict ``input_schema`` rejects top-level union keywords. When
|
||||
exactly one variant is an object schema, lift it so the root is a plain
|
||||
object; otherwise leave the schema alone.
|
||||
"""
|
||||
for key in ("anyOf", "oneOf", "allOf"):
|
||||
variants = schema.get(key)
|
||||
if not isinstance(variants, list):
|
||||
continue
|
||||
object_variants = [
|
||||
v for v in variants if isinstance(v, dict) and v.get("type") == "object"
|
||||
]
|
||||
if len(object_variants) == 1:
|
||||
lifted = deepcopy(object_variants[0])
|
||||
schema.pop(key)
|
||||
schema.update(lifted)
|
||||
break
|
||||
return schema
|
||||
|
||||
|
||||
def _common_strict_pipeline(params: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Shared strict sanitization: inline refs, close objects, require all properties."""
|
||||
sanitized = resolve_refs(deepcopy(params))
|
||||
sanitized.pop("$defs", None)
|
||||
sanitized = convert_oneof_to_anyof(sanitized)
|
||||
sanitized = ensure_type_in_schemas(sanitized)
|
||||
sanitized = force_additional_properties_false(sanitized)
|
||||
sanitized = ensure_all_properties_required(sanitized)
|
||||
return cast(dict[str, Any], _strip_keys_recursive(sanitized, _STRICT_METADATA_KEYS))
|
||||
|
||||
|
||||
def sanitize_tool_params_for_openai_strict(
|
||||
params: dict[str, Any],
|
||||
) -> dict[str, Any]:
|
||||
"""Sanitize a JSON schema for OpenAI strict function calling."""
|
||||
if not isinstance(params, dict):
|
||||
return params
|
||||
return cast(
|
||||
dict[str, Any], strip_unsupported_formats(_common_strict_pipeline(params))
|
||||
)
|
||||
|
||||
|
||||
def sanitize_tool_params_for_anthropic_strict(
|
||||
params: dict[str, Any],
|
||||
) -> dict[str, Any]:
|
||||
"""Sanitize a JSON schema for Anthropic strict tool use."""
|
||||
if not isinstance(params, dict):
|
||||
return params
|
||||
sanitized = lift_top_level_anyof(_common_strict_pipeline(params))
|
||||
sanitized = _strip_keys_recursive(sanitized, _CLAUDE_STRICT_UNSUPPORTED)
|
||||
return cast(dict[str, Any], strip_unsupported_formats(sanitized))
|
||||
|
||||
|
||||
def sanitize_tool_params_for_bedrock_strict(
|
||||
params: dict[str, Any],
|
||||
) -> dict[str, Any]:
|
||||
"""Sanitize a JSON schema for Bedrock Converse strict tool use.
|
||||
|
||||
Bedrock Converse uses the same grammar compiler as the underlying Claude
|
||||
model, so the constraints match Anthropic's.
|
||||
"""
|
||||
return sanitize_tool_params_for_anthropic_strict(params)
|
||||
|
||||
|
||||
def generate_model_description(
|
||||
model: type[BaseModel],
|
||||
*,
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import date, datetime
|
||||
from enum import Enum
|
||||
import json
|
||||
from typing import Any, TypeAlias
|
||||
import uuid
|
||||
@@ -21,7 +20,6 @@ def to_serializable(
|
||||
max_depth: int = 5,
|
||||
_current_depth: int = 0,
|
||||
_ancestors: set[int] | None = None,
|
||||
context: dict[str, Any] | None = None,
|
||||
) -> Serializable:
|
||||
"""Converts a Python object into a JSON-compatible representation.
|
||||
|
||||
@@ -35,9 +33,6 @@ def to_serializable(
|
||||
max_depth: Maximum recursion depth. Defaults to 5.
|
||||
_current_depth: Current recursion depth (for internal use).
|
||||
_ancestors: Set of ancestor object ids for cycle detection (for internal use).
|
||||
context: Optional context dict passed to Pydantic's model_dump(context=...).
|
||||
Field serializers on the model can inspect this to customize output
|
||||
(e.g. context={"trace": True} for lightweight trace serialization).
|
||||
|
||||
Returns:
|
||||
Serializable: A JSON-compatible structure.
|
||||
@@ -53,15 +48,6 @@ def to_serializable(
|
||||
|
||||
if isinstance(obj, (str, int, float, bool, type(None))):
|
||||
return obj
|
||||
if isinstance(obj, Enum):
|
||||
return to_serializable(
|
||||
obj.value,
|
||||
exclude=exclude,
|
||||
max_depth=max_depth,
|
||||
_current_depth=_current_depth,
|
||||
_ancestors=_ancestors,
|
||||
context=context,
|
||||
)
|
||||
if isinstance(obj, uuid.UUID):
|
||||
return str(obj)
|
||||
if isinstance(obj, (date, datetime)):
|
||||
@@ -80,7 +66,6 @@ def to_serializable(
|
||||
max_depth=max_depth,
|
||||
_current_depth=_current_depth + 1,
|
||||
_ancestors=new_ancestors,
|
||||
context=context,
|
||||
)
|
||||
for item in obj
|
||||
]
|
||||
@@ -92,24 +77,17 @@ def to_serializable(
|
||||
max_depth=max_depth,
|
||||
_current_depth=_current_depth + 1,
|
||||
_ancestors=new_ancestors,
|
||||
context=context,
|
||||
)
|
||||
for key, value in obj.items()
|
||||
if key not in exclude
|
||||
}
|
||||
if isinstance(obj, BaseModel):
|
||||
try:
|
||||
dump_kwargs: dict[str, Any] = {}
|
||||
if exclude:
|
||||
dump_kwargs["exclude"] = exclude
|
||||
if context is not None:
|
||||
dump_kwargs["context"] = context
|
||||
return to_serializable(
|
||||
obj=obj.model_dump(**dump_kwargs),
|
||||
obj=obj.model_dump(exclude=exclude),
|
||||
max_depth=max_depth,
|
||||
_current_depth=_current_depth + 1,
|
||||
_ancestors=new_ancestors,
|
||||
context=context,
|
||||
)
|
||||
except Exception:
|
||||
try:
|
||||
@@ -119,30 +97,12 @@ def to_serializable(
|
||||
max_depth=max_depth,
|
||||
_current_depth=_current_depth + 1,
|
||||
_ancestors=new_ancestors,
|
||||
context=context,
|
||||
)
|
||||
for k, v in obj.__dict__.items()
|
||||
if k not in (exclude or set())
|
||||
}
|
||||
except Exception:
|
||||
return repr(obj)
|
||||
if callable(obj):
|
||||
return repr(obj)
|
||||
if hasattr(obj, "__dict__"):
|
||||
try:
|
||||
return {
|
||||
_to_serializable_key(k): to_serializable(
|
||||
v,
|
||||
max_depth=max_depth,
|
||||
_current_depth=_current_depth + 1,
|
||||
_ancestors=new_ancestors,
|
||||
context=context,
|
||||
)
|
||||
for k, v in obj.__dict__.items()
|
||||
if not k.startswith("_")
|
||||
}
|
||||
except Exception:
|
||||
return repr(obj)
|
||||
return repr(obj)
|
||||
|
||||
|
||||
|
||||
@@ -1064,6 +1064,59 @@ def test_agent_use_trained_data(crew_training_handler):
|
||||
)
|
||||
|
||||
|
||||
@patch("crewai.agent.core.CrewTrainingHandler")
|
||||
def test_agent_use_trained_data_with_custom_filename(crew_training_handler):
|
||||
"""Test that _use_trained_data respects a custom filename when provided."""
|
||||
task_prompt = "What is 1 + 1?"
|
||||
agent = Agent(
|
||||
role="researcher",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
verbose=True,
|
||||
)
|
||||
crew_training_handler.return_value.load.return_value = {
|
||||
agent.role: {
|
||||
"suggestions": [
|
||||
"The result of the math operation must be right.",
|
||||
"Result must be better than 1.",
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
custom_filename = "my_custom_trained.pkl"
|
||||
result = agent._use_trained_data(
|
||||
task_prompt=task_prompt, trained_agents_data_file=custom_filename
|
||||
)
|
||||
|
||||
assert (
|
||||
result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n"
|
||||
" - The result of the math operation must be right.\n - Result must be better than 1."
|
||||
)
|
||||
crew_training_handler.assert_has_calls(
|
||||
[mock.call(custom_filename), mock.call().load()]
|
||||
)
|
||||
|
||||
|
||||
@patch("crewai.agent.core.CrewTrainingHandler")
|
||||
def test_agent_use_trained_data_defaults_without_custom_filename(crew_training_handler):
|
||||
"""Test that _use_trained_data falls back to the default file when no custom filename is given."""
|
||||
task_prompt = "What is 1 + 1?"
|
||||
agent = Agent(
|
||||
role="researcher",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
verbose=True,
|
||||
)
|
||||
crew_training_handler.return_value.load.return_value = {}
|
||||
|
||||
result = agent._use_trained_data(task_prompt=task_prompt)
|
||||
|
||||
assert result == task_prompt
|
||||
crew_training_handler.assert_has_calls(
|
||||
[mock.call("trained_agents_data.pkl"), mock.call().load()]
|
||||
)
|
||||
|
||||
|
||||
def test_agent_max_retry_limit():
|
||||
agent = Agent(
|
||||
role="test role",
|
||||
|
||||
@@ -1051,7 +1051,7 @@ def test_lite_agent_verbose_false_suppresses_printer_output():
|
||||
successful_requests=1,
|
||||
)
|
||||
|
||||
with pytest.warns(FutureWarning):
|
||||
with pytest.warns(DeprecationWarning):
|
||||
agent = LiteAgent(
|
||||
role="Test Agent",
|
||||
goal="Test goal",
|
||||
|
||||
@@ -55,7 +55,7 @@ interactions:
|
||||
x-stainless-os:
|
||||
- X-STAINLESS-OS-XXX
|
||||
x-stainless-package-version:
|
||||
- 2.31.0
|
||||
- 1.83.0
|
||||
x-stainless-read-timeout:
|
||||
- X-STAINLESS-READ-TIMEOUT-XXX
|
||||
x-stainless-retry-count:
|
||||
@@ -63,51 +63,50 @@ interactions:
|
||||
x-stainless-runtime:
|
||||
- CPython
|
||||
x-stainless-runtime-version:
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||||
- 3.13.12
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||||
- 3.13.3
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||||
method: POST
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uri: https://api.openai.com/v1/chat/completions
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response:
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body:
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have potential for in-depth exploration and appeal to a broad audience, possibly
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topics or ideas in various fields that could be explored for an article?\\\",
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\\\"context\\\": \\\"We need to generate a list of 5 interesting ideas to
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explore for an article. These ideas should be engaging and relevant to current
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Server:
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openai-processing-ms:
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- '1981'
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openai-project:
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openai-version:
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Come up with a list of 5 interesting ideas to explore for an article.\n\nThis
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asking you this, accounting for the context shared.\nyou MUST return the actual
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|
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What are some trending topics or ideas in various fields that could be explored
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for an article?\n\nThis is the expected criteria for your final answer: Your
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headers:
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|
||||
in the #crewai-deployment-failures Slack channel that motivated this work.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from textwrap import dedent
|
||||
from typing import Iterable
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai.cli.deploy.validate import (
|
||||
DeployValidator,
|
||||
Severity,
|
||||
normalize_package_name,
|
||||
)
|
||||
|
||||
|
||||
def _make_pyproject(
|
||||
name: str = "my_crew",
|
||||
dependencies: Iterable[str] = ("crewai>=1.14.0",),
|
||||
*,
|
||||
hatchling: bool = False,
|
||||
flow: bool = False,
|
||||
extra: str = "",
|
||||
) -> str:
|
||||
deps = ", ".join(f'"{d}"' for d in dependencies)
|
||||
lines = [
|
||||
"[project]",
|
||||
f'name = "{name}"',
|
||||
'version = "0.1.0"',
|
||||
f"dependencies = [{deps}]",
|
||||
]
|
||||
if hatchling:
|
||||
lines += [
|
||||
"",
|
||||
"[build-system]",
|
||||
'requires = ["hatchling"]',
|
||||
'build-backend = "hatchling.build"',
|
||||
]
|
||||
if flow:
|
||||
lines += ["", "[tool.crewai]", 'type = "flow"']
|
||||
if extra:
|
||||
lines += ["", extra]
|
||||
return "\n".join(lines) + "\n"
|
||||
|
||||
|
||||
def _scaffold_standard_crew(
|
||||
root: Path,
|
||||
*,
|
||||
name: str = "my_crew",
|
||||
include_crew_py: bool = True,
|
||||
include_agents_yaml: bool = True,
|
||||
include_tasks_yaml: bool = True,
|
||||
include_lockfile: bool = True,
|
||||
pyproject: str | None = None,
|
||||
) -> Path:
|
||||
(root / "pyproject.toml").write_text(pyproject or _make_pyproject(name=name))
|
||||
if include_lockfile:
|
||||
(root / "uv.lock").write_text("# dummy uv lockfile\n")
|
||||
|
||||
pkg_dir = root / "src" / normalize_package_name(name)
|
||||
pkg_dir.mkdir(parents=True)
|
||||
(pkg_dir / "__init__.py").write_text("")
|
||||
|
||||
if include_crew_py:
|
||||
(pkg_dir / "crew.py").write_text(
|
||||
dedent(
|
||||
"""
|
||||
from crewai.project import CrewBase, crew
|
||||
|
||||
@CrewBase
|
||||
class MyCrew:
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
@crew
|
||||
def crew(self):
|
||||
from crewai import Crew
|
||||
return Crew(agents=[], tasks=[])
|
||||
"""
|
||||
).strip()
|
||||
+ "\n"
|
||||
)
|
||||
|
||||
config_dir = pkg_dir / "config"
|
||||
config_dir.mkdir()
|
||||
if include_agents_yaml:
|
||||
(config_dir / "agents.yaml").write_text("{}\n")
|
||||
if include_tasks_yaml:
|
||||
(config_dir / "tasks.yaml").write_text("{}\n")
|
||||
|
||||
return pkg_dir
|
||||
|
||||
|
||||
def _codes(validator: DeployValidator) -> set[str]:
|
||||
return {r.code for r in validator.results}
|
||||
|
||||
|
||||
def _run_without_import_check(root: Path) -> DeployValidator:
|
||||
"""Run validation with the subprocess-based import check stubbed out;
|
||||
the classifier is exercised directly in its own tests below."""
|
||||
with patch.object(DeployValidator, "_check_module_imports", lambda self: None):
|
||||
v = DeployValidator(project_root=root)
|
||||
v.run()
|
||||
return v
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"project_name, expected",
|
||||
[
|
||||
("my-crew", "my_crew"),
|
||||
("My Cool-Project", "my_cool_project"),
|
||||
("crew123", "crew123"),
|
||||
("crew.name!with$chars", "crewnamewithchars"),
|
||||
],
|
||||
)
|
||||
def test_normalize_package_name(project_name: str, expected: str) -> None:
|
||||
assert normalize_package_name(project_name) == expected
|
||||
|
||||
|
||||
def test_valid_standard_crew_project_passes(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert v.ok, f"expected clean run, got {v.results}"
|
||||
|
||||
|
||||
def test_missing_pyproject_errors(tmp_path: Path) -> None:
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_pyproject" in _codes(v)
|
||||
assert not v.ok
|
||||
|
||||
|
||||
def test_invalid_pyproject_errors(tmp_path: Path) -> None:
|
||||
(tmp_path / "pyproject.toml").write_text("this is not valid toml ====\n")
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "invalid_pyproject" in _codes(v)
|
||||
|
||||
|
||||
def test_missing_project_name_errors(tmp_path: Path) -> None:
|
||||
(tmp_path / "pyproject.toml").write_text(
|
||||
'[project]\nversion = "0.1.0"\ndependencies = ["crewai>=1.14.0"]\n'
|
||||
)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_project_name" in _codes(v)
|
||||
|
||||
|
||||
def test_missing_lockfile_errors(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path, include_lockfile=False)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_lockfile" in _codes(v)
|
||||
|
||||
|
||||
def test_poetry_lock_is_accepted(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path, include_lockfile=False)
|
||||
(tmp_path / "poetry.lock").write_text("# poetry lockfile\n")
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_lockfile" not in _codes(v)
|
||||
|
||||
|
||||
def test_stale_lockfile_warns(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path)
|
||||
# Make lockfile older than pyproject.
|
||||
lock = tmp_path / "uv.lock"
|
||||
pyproject = tmp_path / "pyproject.toml"
|
||||
old_time = pyproject.stat().st_mtime - 60
|
||||
import os
|
||||
|
||||
os.utime(lock, (old_time, old_time))
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "stale_lockfile" in _codes(v)
|
||||
# Stale is a warning, so the run can still be ok (no errors).
|
||||
assert v.ok
|
||||
|
||||
|
||||
def test_missing_package_dir_errors(tmp_path: Path) -> None:
|
||||
# pyproject says name=my_crew but we only create src/other_pkg/
|
||||
(tmp_path / "pyproject.toml").write_text(_make_pyproject(name="my_crew"))
|
||||
(tmp_path / "uv.lock").write_text("")
|
||||
(tmp_path / "src" / "other_pkg").mkdir(parents=True)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
codes = _codes(v)
|
||||
assert "missing_package_dir" in codes
|
||||
finding = next(r for r in v.results if r.code == "missing_package_dir")
|
||||
assert "other_pkg" in finding.hint
|
||||
|
||||
|
||||
def test_egg_info_only_errors_with_targeted_hint(tmp_path: Path) -> None:
|
||||
"""Regression for the case where only src/<name>.egg-info/ exists."""
|
||||
(tmp_path / "pyproject.toml").write_text(_make_pyproject(name="odoo_pm_agents"))
|
||||
(tmp_path / "uv.lock").write_text("")
|
||||
(tmp_path / "src" / "odoo_pm_agents.egg-info").mkdir(parents=True)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
finding = next(r for r in v.results if r.code == "missing_package_dir")
|
||||
assert "egg-info" in finding.hint
|
||||
|
||||
|
||||
def test_stale_egg_info_sibling_warns(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path)
|
||||
(tmp_path / "src" / "my_crew.egg-info").mkdir()
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "stale_egg_info" in _codes(v)
|
||||
|
||||
|
||||
def test_missing_crew_py_errors(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path, include_crew_py=False)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_crew_py" in _codes(v)
|
||||
|
||||
|
||||
def test_missing_agents_yaml_errors(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path, include_agents_yaml=False)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_agents_yaml" in _codes(v)
|
||||
|
||||
|
||||
def test_missing_tasks_yaml_errors(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path, include_tasks_yaml=False)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_tasks_yaml" in _codes(v)
|
||||
|
||||
|
||||
def test_flow_project_requires_main_py(tmp_path: Path) -> None:
|
||||
(tmp_path / "pyproject.toml").write_text(
|
||||
_make_pyproject(name="my_flow", flow=True)
|
||||
)
|
||||
(tmp_path / "uv.lock").write_text("")
|
||||
(tmp_path / "src" / "my_flow").mkdir(parents=True)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_flow_main" in _codes(v)
|
||||
|
||||
|
||||
def test_flow_project_with_main_py_passes(tmp_path: Path) -> None:
|
||||
(tmp_path / "pyproject.toml").write_text(
|
||||
_make_pyproject(name="my_flow", flow=True)
|
||||
)
|
||||
(tmp_path / "uv.lock").write_text("")
|
||||
pkg = tmp_path / "src" / "my_flow"
|
||||
pkg.mkdir(parents=True)
|
||||
(pkg / "main.py").write_text("# flow entrypoint\n")
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "missing_flow_main" not in _codes(v)
|
||||
|
||||
|
||||
def test_hatchling_without_wheel_config_passes_when_pkg_dir_matches(
|
||||
tmp_path: Path,
|
||||
) -> None:
|
||||
_scaffold_standard_crew(
|
||||
tmp_path, pyproject=_make_pyproject(name="my_crew", hatchling=True)
|
||||
)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
# src/my_crew/ exists, so hatch default should find it — no wheel error.
|
||||
assert "hatch_wheel_target_missing" not in _codes(v)
|
||||
|
||||
|
||||
def test_hatchling_with_explicit_wheel_config_passes(tmp_path: Path) -> None:
|
||||
extra = (
|
||||
"[tool.hatch.build.targets.wheel]\n"
|
||||
'packages = ["src/my_crew"]'
|
||||
)
|
||||
_scaffold_standard_crew(
|
||||
tmp_path,
|
||||
pyproject=_make_pyproject(name="my_crew", hatchling=True, extra=extra),
|
||||
)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "hatch_wheel_target_missing" not in _codes(v)
|
||||
|
||||
|
||||
def test_classify_missing_openai_key_is_warning(tmp_path: Path) -> None:
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error(
|
||||
"ImportError",
|
||||
"Error importing native provider: 1 validation error for OpenAICompletion\n"
|
||||
" Value error, OPENAI_API_KEY is required",
|
||||
tb="",
|
||||
)
|
||||
assert len(v.results) == 1
|
||||
result = v.results[0]
|
||||
assert result.code == "llm_init_missing_key"
|
||||
assert result.severity is Severity.WARNING
|
||||
assert "OPENAI_API_KEY" in result.title
|
||||
|
||||
|
||||
def test_classify_azure_extra_missing_is_error(tmp_path: Path) -> None:
|
||||
"""The real message raised by the Azure provider module uses plain
|
||||
double quotes around the install command (no backticks). Match the
|
||||
exact string that ships in the provider source so this test actually
|
||||
guards the regex used in production."""
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error(
|
||||
"ImportError",
|
||||
'Azure AI Inference native provider not available, to install: uv add "crewai[azure-ai-inference]"',
|
||||
tb="",
|
||||
)
|
||||
assert "missing_provider_extra" in _codes(v)
|
||||
finding = next(r for r in v.results if r.code == "missing_provider_extra")
|
||||
assert finding.title.startswith("Azure AI Inference")
|
||||
assert 'uv add "crewai[azure-ai-inference]"' in finding.hint
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"pkg_label, install_cmd",
|
||||
[
|
||||
("Anthropic", 'uv add "crewai[anthropic]"'),
|
||||
("AWS Bedrock", 'uv add "crewai[bedrock]"'),
|
||||
("Google Gen AI", 'uv add "crewai[google-genai]"'),
|
||||
],
|
||||
)
|
||||
def test_classify_missing_provider_extra_matches_real_messages(
|
||||
tmp_path: Path, pkg_label: str, install_cmd: str
|
||||
) -> None:
|
||||
"""Regression for the four provider error strings verbatim."""
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error(
|
||||
"ImportError",
|
||||
f"{pkg_label} native provider not available, to install: {install_cmd}",
|
||||
tb="",
|
||||
)
|
||||
assert "missing_provider_extra" in _codes(v)
|
||||
finding = next(r for r in v.results if r.code == "missing_provider_extra")
|
||||
assert install_cmd in finding.hint
|
||||
|
||||
|
||||
def test_classify_keyerror_at_import_is_warning(tmp_path: Path) -> None:
|
||||
"""Regression for `KeyError: 'SERPLY_API_KEY'` raised at import time."""
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error("KeyError", "'SERPLY_API_KEY'", tb="")
|
||||
codes = _codes(v)
|
||||
assert "env_var_read_at_import" in codes
|
||||
|
||||
|
||||
def test_classify_no_crewbase_class_is_error(tmp_path: Path) -> None:
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error(
|
||||
"ValueError",
|
||||
"Crew class annotated with @CrewBase not found.",
|
||||
tb="",
|
||||
)
|
||||
assert "no_crewbase_class" in _codes(v)
|
||||
|
||||
|
||||
def test_classify_no_flow_subclass_is_error(tmp_path: Path) -> None:
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error("ValueError", "No Flow subclass found in the module.", tb="")
|
||||
assert "no_flow_subclass" in _codes(v)
|
||||
|
||||
|
||||
def test_classify_stale_crewai_pin_attribute_error(tmp_path: Path) -> None:
|
||||
"""Regression for a stale crewai pin missing `_load_response_format`."""
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error(
|
||||
"AttributeError",
|
||||
"'EmploymentServiceDecisionSupportSystemCrew' object has no attribute '_load_response_format'",
|
||||
tb="",
|
||||
)
|
||||
assert "stale_crewai_pin" in _codes(v)
|
||||
|
||||
|
||||
def test_classify_unknown_error_is_fallback(tmp_path: Path) -> None:
|
||||
v = DeployValidator(project_root=tmp_path)
|
||||
v._classify_import_error("RuntimeError", "something weird happened", tb="")
|
||||
assert "import_failed" in _codes(v)
|
||||
|
||||
|
||||
def test_env_var_referenced_but_missing_warns(tmp_path: Path) -> None:
|
||||
pkg = _scaffold_standard_crew(tmp_path)
|
||||
(pkg / "tools.py").write_text(
|
||||
'import os\nkey = os.getenv("TAVILY_API_KEY")\n'
|
||||
)
|
||||
import os
|
||||
|
||||
# Make sure the test doesn't inherit the key from the host environment.
|
||||
with patch.dict(os.environ, {}, clear=False):
|
||||
os.environ.pop("TAVILY_API_KEY", None)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
codes = _codes(v)
|
||||
assert "env_vars_not_in_dotenv" in codes
|
||||
|
||||
|
||||
def test_env_var_in_dotenv_does_not_warn(tmp_path: Path) -> None:
|
||||
pkg = _scaffold_standard_crew(tmp_path)
|
||||
(pkg / "tools.py").write_text(
|
||||
'import os\nkey = os.getenv("TAVILY_API_KEY")\n'
|
||||
)
|
||||
(tmp_path / ".env").write_text("TAVILY_API_KEY=abc\n")
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "env_vars_not_in_dotenv" not in _codes(v)
|
||||
|
||||
|
||||
def test_old_crewai_pin_in_uv_lock_warns(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path)
|
||||
(tmp_path / "uv.lock").write_text(
|
||||
'name = "crewai"\nversion = "1.10.0"\nsource = { registry = "..." }\n'
|
||||
)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "old_crewai_pin" in _codes(v)
|
||||
|
||||
|
||||
def test_modern_crewai_pin_does_not_warn(tmp_path: Path) -> None:
|
||||
_scaffold_standard_crew(tmp_path)
|
||||
(tmp_path / "uv.lock").write_text(
|
||||
'name = "crewai"\nversion = "1.14.1"\nsource = { registry = "..." }\n'
|
||||
)
|
||||
v = _run_without_import_check(tmp_path)
|
||||
assert "old_crewai_pin" not in _codes(v)
|
||||
|
||||
|
||||
def test_create_crew_aborts_on_validation_error(tmp_path: Path) -> None:
|
||||
"""`crewai deploy create` must not contact the API when validation fails."""
|
||||
from unittest.mock import MagicMock, patch as mock_patch
|
||||
|
||||
from crewai.cli.deploy.main import DeployCommand
|
||||
|
||||
with (
|
||||
mock_patch("crewai.cli.command.get_auth_token", return_value="tok"),
|
||||
mock_patch("crewai.cli.deploy.main.get_project_name", return_value="p"),
|
||||
mock_patch("crewai.cli.command.PlusAPI") as mock_api,
|
||||
mock_patch(
|
||||
"crewai.cli.deploy.main.validate_project"
|
||||
) as mock_validate,
|
||||
):
|
||||
mock_validate.return_value = MagicMock(ok=False)
|
||||
cmd = DeployCommand()
|
||||
cmd.create_crew()
|
||||
assert not cmd.plus_api_client.create_crew.called
|
||||
del mock_api # silence unused-var lint
|
||||
@@ -367,7 +367,7 @@ def test_deploy_push(command, runner):
|
||||
result = runner.invoke(deploy_push, ["-u", uuid])
|
||||
|
||||
assert result.exit_code == 0
|
||||
mock_deploy.deploy.assert_called_once_with(uuid=uuid, skip_validate=False)
|
||||
mock_deploy.deploy.assert_called_once_with(uuid=uuid)
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.cli.DeployCommand")
|
||||
@@ -376,7 +376,7 @@ def test_deploy_push_no_uuid(command, runner):
|
||||
result = runner.invoke(deploy_push)
|
||||
|
||||
assert result.exit_code == 0
|
||||
mock_deploy.deploy.assert_called_once_with(uuid=None, skip_validate=False)
|
||||
mock_deploy.deploy.assert_called_once_with(uuid=None)
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.cli.DeployCommand")
|
||||
|
||||
@@ -174,51 +174,3 @@ class TestEmitCallCompletedEventPassesUsage:
|
||||
event = mock_emit.call_args[1]["event"]
|
||||
assert isinstance(event, LLMCallCompletedEvent)
|
||||
assert event.usage is None
|
||||
|
||||
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
|
||||
|
||||
@@ -1463,45 +1463,3 @@ 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
|
||||
|
||||
@@ -3,9 +3,13 @@ import json
|
||||
import logging
|
||||
|
||||
import pytest
|
||||
import tiktoken
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.llm import LLM
|
||||
|
||||
# Pre-cache tiktoken encoding so VCR doesn't intercept the download request
|
||||
tiktoken.get_encoding("cl100k_base")
|
||||
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
||||
|
||||
|
||||
@@ -44,7 +48,9 @@ async def test_anthropic_async_with_max_tokens():
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, str)
|
||||
assert len(result.split()) <= 10
|
||||
encoder = tiktoken.get_encoding("cl100k_base")
|
||||
token_count = len(encoder.encode(result))
|
||||
assert token_count <= 10
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
@@ -2,7 +2,6 @@ import os
|
||||
import sys
|
||||
import types
|
||||
from unittest.mock import patch, MagicMock, Mock
|
||||
from urllib.parse import urlparse
|
||||
import pytest
|
||||
|
||||
from crewai.llm import LLM
|
||||
@@ -379,72 +378,23 @@ def test_azure_completion_with_tools():
|
||||
|
||||
|
||||
def test_azure_raises_error_when_endpoint_missing():
|
||||
"""Credentials are validated lazily: construction succeeds, first
|
||||
client build raises the descriptive error."""
|
||||
"""Test that AzureCompletion raises ValueError when endpoint is missing"""
|
||||
from crewai.llms.providers.azure.completion import AzureCompletion
|
||||
|
||||
# Clear environment variables
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
llm = AzureCompletion(model="gpt-4", api_key="test-key")
|
||||
with pytest.raises(ValueError, match="Azure endpoint is required"):
|
||||
llm._get_sync_client()
|
||||
AzureCompletion(model="gpt-4", api_key="test-key")
|
||||
|
||||
|
||||
def test_azure_raises_error_when_api_key_missing():
|
||||
"""Credentials are validated lazily: construction succeeds, first
|
||||
client build raises the descriptive error."""
|
||||
"""Test that AzureCompletion raises ValueError when API key is missing"""
|
||||
from crewai.llms.providers.azure.completion import AzureCompletion
|
||||
|
||||
# Clear environment variables
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
llm = AzureCompletion(
|
||||
model="gpt-4", endpoint="https://test.openai.azure.com"
|
||||
)
|
||||
with pytest.raises(ValueError, match="Azure API key is required"):
|
||||
llm._get_sync_client()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_azure_aclose_is_noop_when_uninitialized():
|
||||
"""`aclose` (and `async with`) on an uninstantiated-client LLM must be
|
||||
a harmless no-op, not force lazy construction that then raises for
|
||||
missing credentials."""
|
||||
from crewai.llms.providers.azure.completion import AzureCompletion
|
||||
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
llm = AzureCompletion(model="gpt-4")
|
||||
assert llm._async_client is None
|
||||
await llm.aclose()
|
||||
async with llm:
|
||||
pass
|
||||
|
||||
|
||||
def test_azure_lazy_build_reads_env_vars_set_after_construction():
|
||||
"""When `LLM(model="azure/...")` is constructed before env vars are set,
|
||||
the lazy client builder must re-read `AZURE_API_KEY` / `AZURE_ENDPOINT`
|
||||
so the LLM actually works once credentials become available, and the
|
||||
`is_azure_openai_endpoint` routing flag must be recomputed off the
|
||||
newly-resolved endpoint."""
|
||||
from crewai.llms.providers.azure.completion import AzureCompletion
|
||||
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
llm = AzureCompletion(model="gpt-4")
|
||||
assert llm.api_key is None
|
||||
assert llm.endpoint is None
|
||||
assert llm.is_azure_openai_endpoint is False
|
||||
|
||||
with patch.dict(
|
||||
os.environ,
|
||||
{
|
||||
"AZURE_API_KEY": "late-key",
|
||||
"AZURE_ENDPOINT": "https://test.openai.azure.com/openai/deployments/gpt-4",
|
||||
},
|
||||
clear=True,
|
||||
):
|
||||
client = llm._get_sync_client()
|
||||
assert client is not None
|
||||
assert llm.api_key == "late-key"
|
||||
assert llm.endpoint is not None
|
||||
assert urlparse(llm.endpoint).hostname == "test.openai.azure.com"
|
||||
assert llm.is_azure_openai_endpoint is True
|
||||
AzureCompletion(model="gpt-4", endpoint="https://test.openai.azure.com")
|
||||
|
||||
|
||||
def test_azure_endpoint_configuration():
|
||||
@@ -1453,44 +1403,3 @@ 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
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
"""Tests for Azure async completion functionality."""
|
||||
|
||||
import pytest
|
||||
import tiktoken
|
||||
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai.llm import LLM
|
||||
@@ -56,7 +57,9 @@ async def test_azure_async_with_max_tokens():
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, str)
|
||||
assert len(result.split()) <= 10
|
||||
encoder = tiktoken.get_encoding("cl100k_base")
|
||||
token_count = len(encoder.encode(result))
|
||||
assert token_count <= 10
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
@@ -1175,81 +1175,3 @@ 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
|
||||
|
||||
@@ -6,6 +6,7 @@ cannot be played back properly in CI.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import tiktoken
|
||||
|
||||
from crewai.llm import LLM
|
||||
|
||||
@@ -50,7 +51,9 @@ async def test_bedrock_async_with_max_tokens():
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, str)
|
||||
assert len(result.split()) <= 10
|
||||
encoder = tiktoken.get_encoding("cl100k_base")
|
||||
token_count = len(encoder.encode(result))
|
||||
assert token_count <= 10
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
@@ -64,23 +64,6 @@ def test_gemini_completion_module_is_imported():
|
||||
assert hasattr(completion_mod, 'GeminiCompletion')
|
||||
|
||||
|
||||
def test_gemini_lazy_build_reads_env_vars_set_after_construction():
|
||||
"""When `LLM(model="gemini/...")` is constructed before env vars are set,
|
||||
the lazy client builder must re-read `GOOGLE_API_KEY` / `GEMINI_API_KEY`
|
||||
so the LLM works once credentials become available."""
|
||||
from crewai.llms.providers.gemini.completion import GeminiCompletion
|
||||
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
llm = GeminiCompletion(model="gemini-1.5-pro")
|
||||
assert llm.api_key is None
|
||||
assert llm._client is None
|
||||
|
||||
with patch.dict(os.environ, {"GEMINI_API_KEY": "late-key"}, clear=True):
|
||||
client = llm._get_sync_client()
|
||||
assert client is not None
|
||||
assert llm.api_key == "late-key"
|
||||
|
||||
|
||||
def test_native_gemini_raises_error_when_initialization_fails():
|
||||
"""
|
||||
Test that LLM raises ImportError when native Gemini completion fails.
|
||||
@@ -1207,42 +1190,3 @@ 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
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
"""Tests for Google (Gemini) async completion functionality."""
|
||||
|
||||
import pytest
|
||||
import tiktoken
|
||||
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai.llm import LLM
|
||||
@@ -42,7 +43,9 @@ async def test_gemini_async_with_max_tokens():
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, str)
|
||||
assert len(result.split()) <= 1000
|
||||
encoder = tiktoken.get_encoding("cl100k_base")
|
||||
token_count = len(encoder.encode(result))
|
||||
assert token_count <= 1000
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
"""Tests for LiteLLM fallback async completion functionality."""
|
||||
|
||||
import pytest
|
||||
import tiktoken
|
||||
|
||||
from crewai.llm import LLM
|
||||
|
||||
@@ -43,7 +44,9 @@ async def test_litellm_async_with_max_tokens():
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, str)
|
||||
assert len(result.split()) <= 10
|
||||
encoder = tiktoken.get_encoding("cl100k_base")
|
||||
token_count = len(encoder.encode(result))
|
||||
assert token_count <= 10
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
||||
@@ -1929,47 +1929,6 @@ 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.
|
||||
@@ -2059,44 +2018,3 @@ 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
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
"""Tests for OpenAI async completion functionality."""
|
||||
|
||||
import pytest
|
||||
import tiktoken
|
||||
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai.llm import LLM
|
||||
@@ -41,7 +42,9 @@ async def test_openai_async_with_max_tokens():
|
||||
|
||||
assert result is not None
|
||||
assert isinstance(result, str)
|
||||
assert len(result.split()) <= 10
|
||||
encoder = tiktoken.get_encoding("cl100k_base")
|
||||
token_count = len(encoder.encode(result))
|
||||
assert token_count <= 10
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
@@ -51,13 +51,14 @@ def test_memory_record_embedding_excluded_from_serialization() -> None:
|
||||
dumped = r.model_dump()
|
||||
assert "embedding" not in dumped
|
||||
assert dumped["content"] == "hello"
|
||||
|
||||
# model_dump_json excludes embedding
|
||||
json_str = r.model_dump_json()
|
||||
assert "0.1" not in json_str
|
||||
assert "embedding" not in json_str
|
||||
rehydrated = MemoryRecord.model_validate_json(json_str)
|
||||
assert rehydrated.embedding is None
|
||||
|
||||
# repr excludes embedding
|
||||
assert "embedding=" not in repr(r)
|
||||
assert "0.1" not in repr(r)
|
||||
|
||||
# Direct attribute access still works for storage layer
|
||||
assert r.embedding is not None
|
||||
|
||||
@@ -296,8 +296,7 @@ class TestRuntimeStateLineage:
|
||||
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
|
||||
assert state._branch == "fork/20260409T120000_abc12345"
|
||||
|
||||
def test_fork_no_checkpoint_id_unique(self) -> None:
|
||||
state = self._make_state()
|
||||
|
||||
@@ -2971,6 +2971,75 @@ def test__setup_for_training(researcher, writer):
|
||||
assert agent.allow_delegation is False
|
||||
|
||||
|
||||
def test_crew_trained_agents_data_file_defaults(researcher, writer):
|
||||
"""Test that Crew.trained_agents_data_file defaults to 'trained_agents_data.pkl'."""
|
||||
task = Task(
|
||||
description="Test task",
|
||||
expected_output="Test output",
|
||||
agent=researcher,
|
||||
)
|
||||
crew = Crew(agents=[researcher, writer], tasks=[task])
|
||||
assert crew.trained_agents_data_file == "trained_agents_data.pkl"
|
||||
|
||||
|
||||
def test_crew_trained_agents_data_file_custom(researcher, writer):
|
||||
"""Test that Crew.trained_agents_data_file can be set to a custom value."""
|
||||
task = Task(
|
||||
description="Test task",
|
||||
expected_output="Test output",
|
||||
agent=researcher,
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=[task],
|
||||
trained_agents_data_file="my_custom_trained.pkl",
|
||||
)
|
||||
assert crew.trained_agents_data_file == "my_custom_trained.pkl"
|
||||
|
||||
|
||||
@patch("crewai.agent.core.CrewTrainingHandler")
|
||||
def test_apply_training_data_uses_crew_custom_filename(mock_handler, researcher):
|
||||
"""Test that apply_training_data propagates the crew's trained_agents_data_file."""
|
||||
from crewai.agent.utils import apply_training_data
|
||||
|
||||
task = Task(
|
||||
description="Test task",
|
||||
expected_output="Test output",
|
||||
agent=researcher,
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
trained_agents_data_file="my_custom_trained.pkl",
|
||||
)
|
||||
researcher.crew = crew
|
||||
|
||||
mock_handler.return_value.load.return_value = {
|
||||
researcher.role: {
|
||||
"suggestions": ["Be concise."]
|
||||
}
|
||||
}
|
||||
|
||||
result = apply_training_data(researcher, "Do the task")
|
||||
|
||||
mock_handler.assert_called_with("my_custom_trained.pkl")
|
||||
assert "Be concise." in result
|
||||
|
||||
|
||||
@patch("crewai.agent.core.CrewTrainingHandler")
|
||||
def test_apply_training_data_uses_default_when_no_crew(mock_handler, researcher):
|
||||
"""Test that apply_training_data falls back to the default file when agent has no crew."""
|
||||
from crewai.agent.utils import apply_training_data
|
||||
|
||||
researcher.crew = None
|
||||
mock_handler.return_value.load.return_value = {}
|
||||
|
||||
result = apply_training_data(researcher, "Do the task")
|
||||
|
||||
mock_handler.assert_called_with("trained_agents_data.pkl")
|
||||
assert result == "Do the task"
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_replay_feature(researcher, writer):
|
||||
list_ideas = Task(
|
||||
|
||||
@@ -1001,8 +1001,6 @@ 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
|
||||
|
||||
@@ -1027,8 +1025,6 @@ 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
|
||||
|
||||
@@ -1060,8 +1056,6 @@ 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(
|
||||
@@ -1095,8 +1089,6 @@ 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(
|
||||
@@ -1129,8 +1121,6 @@ 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(
|
||||
|
||||
@@ -1,612 +0,0 @@
|
||||
"""Tests for trace serialization optimization using Pydantic v2 context-based serialization.
|
||||
|
||||
These tests verify that trace events use @field_serializer with SerializationInfo.context
|
||||
to produce lightweight representations, reducing event sizes from 50-100KB to a few KB.
|
||||
"""
|
||||
|
||||
import json
|
||||
import uuid
|
||||
from typing import Any
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
from pydantic import ConfigDict
|
||||
|
||||
from crewai.agents.agent_builder.base_agent import BaseAgent
|
||||
from crewai.events.base_events import _trace_agent_ref, _trace_task_ref, _trace_tool_names
|
||||
from crewai.events.listeners.tracing.utils import safe_serialize_to_dict
|
||||
from crewai.utilities.serialization import to_serializable
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Lightweight BaseAgent subclass for tests (avoids heavy dependencies)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class _StubAgent(BaseAgent):
|
||||
"""Minimal BaseAgent subclass that satisfies validation without heavy deps."""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
def execute_task(self, *a: Any, **kw: Any) -> str:
|
||||
return ""
|
||||
|
||||
def create_agent_executor(self, *a: Any, **kw: Any) -> None:
|
||||
pass
|
||||
|
||||
def _parse_tools(self, *a: Any, **kw: Any) -> list:
|
||||
return []
|
||||
|
||||
def get_delegation_tools(self, *a: Any, **kw: Any) -> list:
|
||||
return []
|
||||
|
||||
def get_output_converter(self, *a: Any, **kw: Any) -> Any:
|
||||
return None
|
||||
|
||||
def get_multimodal_tools(self, *a: Any, **kw: Any) -> list:
|
||||
return []
|
||||
|
||||
async def aexecute_task(self, *a: Any, **kw: Any) -> str:
|
||||
return ""
|
||||
|
||||
def get_mcp_tools(self, *a: Any, **kw: Any) -> list:
|
||||
return []
|
||||
|
||||
def get_platform_tools(self, *a: Any, **kw: Any) -> list:
|
||||
return []
|
||||
|
||||
|
||||
def _make_stub_agent(**overrides) -> _StubAgent:
|
||||
"""Create a minimal BaseAgent instance for testing."""
|
||||
defaults = {
|
||||
"role": "Researcher",
|
||||
"goal": "Research things",
|
||||
"backstory": "Expert researcher",
|
||||
"tools": [],
|
||||
}
|
||||
defaults.update(overrides)
|
||||
return _StubAgent(**defaults)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers to build realistic mock objects for event fields
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _make_mock_task(**overrides):
|
||||
task = MagicMock()
|
||||
task.id = overrides.get("id", uuid.uuid4())
|
||||
task.name = overrides.get("name", "Research Task")
|
||||
task.description = overrides.get("description", "Do research")
|
||||
task.expected_output = overrides.get("expected_output", "Research results")
|
||||
task.async_execution = overrides.get("async_execution", False)
|
||||
task.human_input = overrides.get("human_input", False)
|
||||
task.agent = overrides.get("agent", _make_stub_agent())
|
||||
task.context = overrides.get("context", None)
|
||||
task.crew = MagicMock()
|
||||
task.tools = overrides.get("tools", [MagicMock(), MagicMock()])
|
||||
|
||||
fp = MagicMock()
|
||||
fp.uuid_str = str(uuid.uuid4())
|
||||
fp.metadata = {"name": task.name}
|
||||
task.fingerprint = fp
|
||||
|
||||
return task
|
||||
|
||||
|
||||
def _make_stub_tool(tool_name="web_search") -> Any:
|
||||
"""Create a minimal BaseTool instance for testing."""
|
||||
from crewai.tools.base_tool import BaseTool
|
||||
|
||||
class _StubTool(BaseTool):
|
||||
name: str = "stub"
|
||||
description: str = "stub tool"
|
||||
|
||||
def _run(self, *a: Any, **kw: Any) -> str:
|
||||
return ""
|
||||
|
||||
return _StubTool(name=tool_name, description=f"{tool_name} tool")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Unit tests: trace ref helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestTraceRefHelpers:
|
||||
def test_trace_agent_ref(self):
|
||||
agent = _make_stub_agent(role="Analyst")
|
||||
ref = _trace_agent_ref(agent)
|
||||
assert ref["role"] == "Analyst"
|
||||
assert "id" in ref
|
||||
assert len(ref) == 2 # only id and role
|
||||
|
||||
def test_trace_agent_ref_none(self):
|
||||
assert _trace_agent_ref(None) is None
|
||||
|
||||
def test_trace_task_ref(self):
|
||||
task = _make_mock_task(name="Write Report")
|
||||
ref = _trace_task_ref(task)
|
||||
assert ref["name"] == "Write Report"
|
||||
assert "id" in ref
|
||||
assert len(ref) == 2
|
||||
|
||||
def test_trace_task_ref_falls_back_to_description(self):
|
||||
task = _make_mock_task(name=None, description="Describe the report")
|
||||
ref = _trace_task_ref(task)
|
||||
assert ref["name"] == "Describe the report"
|
||||
|
||||
def test_trace_task_ref_none(self):
|
||||
assert _trace_task_ref(None) is None
|
||||
|
||||
def test_trace_tool_names(self):
|
||||
tools = [_make_stub_tool("search"), _make_stub_tool("read")]
|
||||
names = _trace_tool_names(tools)
|
||||
assert names == ["search", "read"]
|
||||
|
||||
def test_trace_tool_names_empty(self):
|
||||
assert _trace_tool_names([]) is None
|
||||
assert _trace_tool_names(None) is None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Integration tests: field serializers on real event classes
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestAgentEventFieldSerializers:
|
||||
"""Test that agent event field serializers respond to trace context."""
|
||||
|
||||
def test_agent_execution_started_trace_context(self):
|
||||
from crewai.events.types.agent_events import AgentExecutionStartedEvent
|
||||
|
||||
agent = _make_stub_agent(role="Researcher")
|
||||
task = _make_mock_task(name="Research Task")
|
||||
tools = [_make_stub_tool("search"), _make_stub_tool("read")]
|
||||
|
||||
event = AgentExecutionStartedEvent(
|
||||
agent=agent, task=task, tools=tools, task_prompt="Do research"
|
||||
)
|
||||
|
||||
# With trace context: lightweight refs
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
assert trace_dump["agent"] == {"id": str(agent.id), "role": "Researcher"}
|
||||
assert trace_dump["task"] == {"id": str(task.id), "name": "Research Task"}
|
||||
assert trace_dump["tools"] == ["search", "read"]
|
||||
|
||||
def test_agent_execution_started_no_context(self):
|
||||
from crewai.events.types.agent_events import AgentExecutionStartedEvent
|
||||
|
||||
agent = _make_stub_agent(role="SpecificRole")
|
||||
task = _make_mock_task()
|
||||
|
||||
event = AgentExecutionStartedEvent(
|
||||
agent=agent, task=task, tools=None, task_prompt="Do research"
|
||||
)
|
||||
|
||||
# Without context: full agent dict (Pydantic model_dump expands it)
|
||||
normal_dump = event.model_dump()
|
||||
assert isinstance(normal_dump["agent"], dict)
|
||||
assert normal_dump["agent"]["role"] == "SpecificRole"
|
||||
# Should have ALL agent fields, not just the lightweight ref
|
||||
assert "goal" in normal_dump["agent"]
|
||||
assert "backstory" in normal_dump["agent"]
|
||||
assert "max_iter" in normal_dump["agent"]
|
||||
|
||||
def test_agent_execution_error_preserves_identification(self):
|
||||
from crewai.events.types.agent_events import AgentExecutionErrorEvent
|
||||
|
||||
agent = _make_stub_agent(role="Analyst")
|
||||
task = _make_mock_task(name="Analysis Task")
|
||||
|
||||
event = AgentExecutionErrorEvent(
|
||||
agent=agent, task=task, error="Something went wrong"
|
||||
)
|
||||
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
# Error events should still have agent/task identification as refs
|
||||
assert trace_dump["agent"]["role"] == "Analyst"
|
||||
assert trace_dump["task"]["name"] == "Analysis Task"
|
||||
assert trace_dump["error"] == "Something went wrong"
|
||||
|
||||
def test_agent_execution_completed_trace_context(self):
|
||||
from crewai.events.types.agent_events import AgentExecutionCompletedEvent
|
||||
|
||||
agent = _make_stub_agent(role="Writer")
|
||||
task = _make_mock_task(name="Writing Task")
|
||||
|
||||
event = AgentExecutionCompletedEvent(
|
||||
agent=agent, task=task, output="Final output"
|
||||
)
|
||||
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
assert trace_dump["agent"]["role"] == "Writer"
|
||||
assert trace_dump["task"]["name"] == "Writing Task"
|
||||
assert trace_dump["output"] == "Final output"
|
||||
|
||||
|
||||
class TestTaskEventFieldSerializers:
|
||||
"""Test that task event field serializers respond to trace context."""
|
||||
|
||||
def test_task_started_trace_context(self):
|
||||
from crewai.events.types.task_events import TaskStartedEvent
|
||||
|
||||
task = _make_mock_task(name="Test Task")
|
||||
event = TaskStartedEvent(task=task, context="some context")
|
||||
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
assert trace_dump["task"] == {"id": str(task.id), "name": "Test Task"}
|
||||
assert trace_dump["context"] == "some context"
|
||||
|
||||
def test_task_failed_trace_context(self):
|
||||
from crewai.events.types.task_events import TaskFailedEvent
|
||||
|
||||
task = _make_mock_task(name="Failing Task")
|
||||
event = TaskFailedEvent(task=task, error="Task failed")
|
||||
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
assert trace_dump["task"]["name"] == "Failing Task"
|
||||
assert trace_dump["error"] == "Task failed"
|
||||
|
||||
|
||||
class TestCrewEventFieldSerializers:
|
||||
"""Test that crew event field serializers respond to trace context."""
|
||||
|
||||
def test_crew_kickoff_started_excludes_crew_in_trace(self):
|
||||
from crewai.events.types.crew_events import CrewKickoffStartedEvent
|
||||
|
||||
crew = MagicMock()
|
||||
crew.fingerprint = MagicMock()
|
||||
crew.fingerprint.uuid_str = str(uuid.uuid4())
|
||||
crew.fingerprint.metadata = {}
|
||||
|
||||
event = CrewKickoffStartedEvent(
|
||||
crew=crew, crew_name="TestCrew", inputs={"key": "value"}
|
||||
)
|
||||
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
# crew field should be None in trace context
|
||||
assert trace_dump["crew"] is None
|
||||
# scalar fields preserved
|
||||
assert trace_dump["crew_name"] == "TestCrew"
|
||||
assert trace_dump["inputs"] == {"key": "value"}
|
||||
|
||||
def test_crew_event_no_context_preserves_crew(self):
|
||||
from crewai.events.types.crew_events import CrewKickoffStartedEvent
|
||||
|
||||
crew = MagicMock()
|
||||
crew.fingerprint = MagicMock()
|
||||
crew.fingerprint.uuid_str = str(uuid.uuid4())
|
||||
crew.fingerprint.metadata = {}
|
||||
|
||||
event = CrewKickoffStartedEvent(
|
||||
crew=crew, crew_name="TestCrew", inputs=None
|
||||
)
|
||||
|
||||
normal_dump = event.model_dump()
|
||||
# Without trace context, crew should NOT be None (field serializer didn't fire)
|
||||
assert normal_dump["crew"] is not None
|
||||
|
||||
|
||||
class TestLLMEventFieldSerializers:
|
||||
"""Test that LLM event field serializers respond to trace context."""
|
||||
|
||||
def test_llm_call_started_excludes_callbacks_in_trace(self):
|
||||
from crewai.events.types.llm_events import LLMCallStartedEvent
|
||||
|
||||
event = LLMCallStartedEvent(
|
||||
call_id="test-call",
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
tools=[{"name": "search", "description": "Search tool"}],
|
||||
callbacks=[MagicMock(), MagicMock()],
|
||||
available_functions={"search": MagicMock()},
|
||||
)
|
||||
|
||||
trace_dump = event.model_dump(context={"trace": True})
|
||||
# callbacks and available_functions excluded
|
||||
assert trace_dump["callbacks"] is None
|
||||
assert trace_dump["available_functions"] is None
|
||||
# tools preserved (lightweight list of dicts)
|
||||
assert trace_dump["tools"] == [{"name": "search", "description": "Search tool"}]
|
||||
# messages preserved
|
||||
assert trace_dump["messages"] == [{"role": "user", "content": "Hello"}]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Integration tests: safe_serialize_to_dict with context
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestSafeSerializeWithContext:
|
||||
"""Test that safe_serialize_to_dict properly passes context through."""
|
||||
|
||||
def test_context_flows_through_to_field_serializers(self):
|
||||
from crewai.events.types.agent_events import AgentExecutionErrorEvent
|
||||
|
||||
agent = _make_stub_agent(role="Worker")
|
||||
task = _make_mock_task(name="Work Task")
|
||||
|
||||
event = AgentExecutionErrorEvent(
|
||||
agent=agent, task=task, error="error msg"
|
||||
)
|
||||
|
||||
result = safe_serialize_to_dict(event, context={"trace": True})
|
||||
# Field serializers should have fired
|
||||
assert result["agent"] == {"id": str(agent.id), "role": "Worker"}
|
||||
assert result["task"] == {"id": str(task.id), "name": "Work Task"}
|
||||
assert result["error"] == "error msg"
|
||||
|
||||
def test_no_context_preserves_full_serialization(self):
|
||||
from crewai.events.types.task_events import TaskFailedEvent
|
||||
|
||||
task = _make_mock_task(name="Test")
|
||||
event = TaskFailedEvent(task=task, error="fail")
|
||||
|
||||
result = safe_serialize_to_dict(event)
|
||||
# Without context, task should not be a lightweight ref
|
||||
assert result.get("task") is not None
|
||||
# It should be the raw object (model_dump returns it as-is for Any fields)
|
||||
# to_serializable will then repr() or process it further
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Integration tests: TraceCollectionListener._build_event_data
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestBuildEventData:
|
||||
@pytest.fixture
|
||||
def listener(self):
|
||||
from crewai.events.listeners.tracing.trace_listener import (
|
||||
TraceCollectionListener,
|
||||
)
|
||||
TraceCollectionListener._instance = None
|
||||
TraceCollectionListener._initialized = False
|
||||
TraceCollectionListener._listeners_setup = False
|
||||
return TraceCollectionListener()
|
||||
|
||||
def test_crew_kickoff_started_has_crew_structure(self, listener):
|
||||
agent = _make_stub_agent(role="Researcher")
|
||||
agent.tools = [_make_stub_tool("search"), _make_stub_tool("read")]
|
||||
|
||||
task = _make_mock_task(name="Research Task", agent=agent)
|
||||
task.context = None
|
||||
|
||||
crew = MagicMock()
|
||||
crew.agents = [agent]
|
||||
crew.tasks = [task]
|
||||
crew.process = "sequential"
|
||||
crew.verbose = True
|
||||
crew.memory = False
|
||||
crew.fingerprint = MagicMock()
|
||||
crew.fingerprint.uuid_str = str(uuid.uuid4())
|
||||
crew.fingerprint.metadata = {}
|
||||
|
||||
from crewai.events.types.crew_events import CrewKickoffStartedEvent
|
||||
event = CrewKickoffStartedEvent(
|
||||
crew=crew, crew_name="TestCrew", inputs={"key": "value"}
|
||||
)
|
||||
|
||||
result = listener._build_event_data("crew_kickoff_started", event, None)
|
||||
|
||||
assert "crew_structure" in result
|
||||
cs = result["crew_structure"]
|
||||
assert len(cs["agents"]) == 1
|
||||
assert cs["agents"][0]["role"] == "Researcher"
|
||||
assert cs["agents"][0]["tool_names"] == ["search", "read"]
|
||||
assert len(cs["tasks"]) == 1
|
||||
assert cs["tasks"][0]["name"] == "Research Task"
|
||||
assert "agent_ref" in cs["tasks"][0]
|
||||
assert cs["tasks"][0]["agent_ref"]["role"] == "Researcher"
|
||||
|
||||
def test_crew_kickoff_started_context_task_ids(self, listener):
|
||||
agent = _make_stub_agent()
|
||||
task1 = _make_mock_task(name="Task 1", agent=agent)
|
||||
task1.context = None
|
||||
task2 = _make_mock_task(name="Task 2", agent=agent)
|
||||
task2.context = [task1]
|
||||
|
||||
crew = MagicMock()
|
||||
crew.agents = [agent]
|
||||
crew.tasks = [task1, task2]
|
||||
crew.process = "sequential"
|
||||
crew.verbose = False
|
||||
crew.memory = False
|
||||
crew.fingerprint = MagicMock()
|
||||
crew.fingerprint.uuid_str = str(uuid.uuid4())
|
||||
crew.fingerprint.metadata = {}
|
||||
|
||||
from crewai.events.types.crew_events import CrewKickoffStartedEvent
|
||||
event = CrewKickoffStartedEvent(
|
||||
crew=crew, crew_name="TestCrew", inputs=None
|
||||
)
|
||||
|
||||
result = listener._build_event_data("crew_kickoff_started", event, None)
|
||||
task2_data = result["crew_structure"]["tasks"][1]
|
||||
assert "context_task_ids" in task2_data
|
||||
assert str(task1.id) in task2_data["context_task_ids"]
|
||||
|
||||
def test_generic_event_uses_trace_context(self, listener):
|
||||
"""Non-complex events should use context-based serialization."""
|
||||
from crewai.events.types.crew_events import CrewKickoffCompletedEvent
|
||||
|
||||
crew = MagicMock()
|
||||
crew.fingerprint = MagicMock()
|
||||
crew.fingerprint.uuid_str = str(uuid.uuid4())
|
||||
crew.fingerprint.metadata = {}
|
||||
|
||||
event = CrewKickoffCompletedEvent(
|
||||
crew=crew, crew_name="TestCrew", output="Final result", total_tokens=5000
|
||||
)
|
||||
|
||||
result = listener._build_event_data("crew_kickoff_completed", event, None)
|
||||
|
||||
# Scalar fields preserved
|
||||
assert result.get("crew_name") == "TestCrew"
|
||||
assert result.get("total_tokens") == 5000
|
||||
# crew excluded by field serializer
|
||||
assert result.get("crew") is None
|
||||
# No crew_structure (that's only for kickoff_started)
|
||||
assert "crew_structure" not in result
|
||||
|
||||
def test_task_started_custom_projection(self, listener):
|
||||
task = _make_mock_task(name="Test Task")
|
||||
from crewai.events.types.task_events import TaskStartedEvent
|
||||
event = TaskStartedEvent(task=task, context="test context")
|
||||
source = MagicMock()
|
||||
source.agent = _make_stub_agent(role="Worker")
|
||||
|
||||
result = listener._build_event_data("task_started", event, source)
|
||||
|
||||
assert result["task_name"] == "Test Task"
|
||||
assert result["agent_role"] == "Worker"
|
||||
assert result["task_id"] == str(task.id)
|
||||
assert result["context"] == "test context"
|
||||
|
||||
def test_llm_call_started_uses_trace_context(self, listener):
|
||||
from crewai.events.types.llm_events import LLMCallStartedEvent
|
||||
|
||||
event = LLMCallStartedEvent(
|
||||
call_id="test",
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
tools=[{"name": "search"}],
|
||||
callbacks=[MagicMock()],
|
||||
available_functions={"fn": MagicMock()},
|
||||
)
|
||||
|
||||
result = listener._build_event_data("llm_call_started", event, None)
|
||||
|
||||
# callbacks and available_functions excluded via field serializer
|
||||
assert result.get("callbacks") is None
|
||||
assert result.get("available_functions") is None
|
||||
# tools preserved (lightweight schemas)
|
||||
assert result.get("tools") == [{"name": "search"}]
|
||||
|
||||
def test_agent_execution_error_preserves_identification(self, listener):
|
||||
"""Error events should preserve agent/task identification via field serializers."""
|
||||
from crewai.events.types.agent_events import AgentExecutionErrorEvent
|
||||
|
||||
agent = _make_stub_agent(role="Analyst")
|
||||
task = _make_mock_task(name="Analysis")
|
||||
|
||||
event = AgentExecutionErrorEvent(
|
||||
agent=agent, task=task, error="Something broke"
|
||||
)
|
||||
|
||||
result = listener._build_event_data("agent_execution_error", event, None)
|
||||
|
||||
# Field serializers return lightweight refs, not None
|
||||
assert result["agent"] == {"id": str(agent.id), "role": "Analyst"}
|
||||
assert result["task"] == {"id": str(task.id), "name": "Analysis"}
|
||||
assert result["error"] == "Something broke"
|
||||
|
||||
def test_task_failed_preserves_identification(self, listener):
|
||||
from crewai.events.types.task_events import TaskFailedEvent
|
||||
|
||||
task = _make_mock_task(name="Failed Task")
|
||||
event = TaskFailedEvent(task=task, error="Task failed")
|
||||
|
||||
result = listener._build_event_data("task_failed", event, None)
|
||||
|
||||
assert result["task"] == {"id": str(task.id), "name": "Failed Task"}
|
||||
assert result["error"] == "Task failed"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Size reduction verification
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestSizeReduction:
|
||||
@pytest.fixture
|
||||
def listener(self):
|
||||
from crewai.events.listeners.tracing.trace_listener import (
|
||||
TraceCollectionListener,
|
||||
)
|
||||
TraceCollectionListener._instance = None
|
||||
TraceCollectionListener._initialized = False
|
||||
TraceCollectionListener._listeners_setup = False
|
||||
return TraceCollectionListener()
|
||||
|
||||
def test_task_started_event_size(self, listener):
|
||||
"""task_started event data should be well under 2KB."""
|
||||
agent = _make_stub_agent(
|
||||
role="Researcher",
|
||||
goal="Research" * 50,
|
||||
backstory="Expert" * 100,
|
||||
)
|
||||
agent.tools = [_make_stub_tool(f"tool_{i}") for i in range(5)]
|
||||
|
||||
task = _make_mock_task(
|
||||
name="Research Task",
|
||||
description="Detailed description" * 20,
|
||||
expected_output="Expected" * 10,
|
||||
agent=agent,
|
||||
)
|
||||
task.context = [_make_mock_task() for _ in range(3)]
|
||||
task.tools = [_make_stub_tool(f"t_{i}") for i in range(3)]
|
||||
|
||||
from crewai.events.types.task_events import TaskStartedEvent
|
||||
event = TaskStartedEvent(task=task, context="test context")
|
||||
source = MagicMock()
|
||||
source.agent = agent
|
||||
|
||||
result = listener._build_event_data("task_started", event, source)
|
||||
serialized = json.dumps(result, default=str)
|
||||
|
||||
assert len(serialized) < 2000, f"task_started too large: {len(serialized)} bytes"
|
||||
assert "task_name" in result
|
||||
assert "agent_role" in result
|
||||
|
||||
def test_error_event_size(self, listener):
|
||||
"""Error events should be small despite having agent/task refs."""
|
||||
from crewai.events.types.agent_events import AgentExecutionErrorEvent
|
||||
|
||||
agent = _make_stub_agent(
|
||||
goal="Very long goal " * 100,
|
||||
backstory="Very long backstory " * 100,
|
||||
)
|
||||
task = _make_mock_task(description="Very long description " * 100)
|
||||
|
||||
event = AgentExecutionErrorEvent(
|
||||
agent=agent, task=task, error="error"
|
||||
)
|
||||
|
||||
result = listener._build_event_data("agent_execution_error", event, None)
|
||||
serialized = json.dumps(result, default=str)
|
||||
|
||||
# Should be small - agent/task are just {id, role/name} refs
|
||||
assert len(serialized) < 5000, f"error event too large: {len(serialized)} bytes"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# to_serializable context threading
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestToSerializableContext:
|
||||
"""Test that context parameter flows through to_serializable correctly."""
|
||||
|
||||
def test_context_passed_to_model_dump(self):
|
||||
from crewai.events.types.agent_events import AgentExecutionErrorEvent
|
||||
|
||||
agent = _make_stub_agent(role="Tester")
|
||||
task = _make_mock_task(name="Test Task")
|
||||
|
||||
event = AgentExecutionErrorEvent(
|
||||
agent=agent, task=task, error="test error"
|
||||
)
|
||||
|
||||
# Directly use to_serializable with context
|
||||
result = to_serializable(event, context={"trace": True})
|
||||
assert isinstance(result, dict)
|
||||
assert result["agent"] == {"id": str(agent.id), "role": "Tester"}
|
||||
assert result["task"] == {"id": str(task.id), "name": "Test Task"}
|
||||
|
||||
def test_no_context_does_not_trigger_serializers(self):
|
||||
from crewai.events.types.crew_events import CrewKickoffStartedEvent
|
||||
|
||||
crew = MagicMock()
|
||||
crew.fingerprint = MagicMock()
|
||||
crew.fingerprint.uuid_str = str(uuid.uuid4())
|
||||
crew.fingerprint.metadata = {}
|
||||
|
||||
event = CrewKickoffStartedEvent(
|
||||
crew=crew, crew_name="Test", inputs=None
|
||||
)
|
||||
|
||||
# Without context, crew should NOT be None
|
||||
result = event.model_dump()
|
||||
assert result["crew"] is not None
|
||||
@@ -119,12 +119,10 @@ def test_create_llm_with_invalid_type() -> None:
|
||||
|
||||
|
||||
def test_create_llm_openai_missing_api_key() -> None:
|
||||
"""Credentials are validated lazily: `create_llm` succeeds, and the
|
||||
descriptive error only surfaces when the client is actually built."""
|
||||
"""Test that create_llm raises error when OpenAI API key is missing"""
|
||||
with patch.dict(os.environ, {}, clear=True):
|
||||
llm = create_llm(llm_value="gpt-4o")
|
||||
with pytest.raises((ValueError, ImportError)) as exc_info:
|
||||
llm._get_sync_client()
|
||||
create_llm(llm_value="gpt-4o")
|
||||
|
||||
error_message = str(exc_info.value).lower()
|
||||
assert "openai_api_key" in error_message or "api_key" in error_message
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""CrewAI development tools."""
|
||||
|
||||
__version__ = "1.14.2a4"
|
||||
__version__ = "1.14.2a1"
|
||||
|
||||
@@ -29,33 +29,6 @@ load_dotenv()
|
||||
console = Console()
|
||||
|
||||
|
||||
def _resume_hint(message: str) -> None:
|
||||
"""Print a boxed resume hint after a failure."""
|
||||
console.print()
|
||||
console.print(
|
||||
Panel(
|
||||
message,
|
||||
title="[bold yellow]How to resume[/bold yellow]",
|
||||
border_style="yellow",
|
||||
padding=(1, 2),
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _print_release_error(e: BaseException) -> None:
|
||||
"""Print a release error with stderr if available."""
|
||||
if isinstance(e, KeyboardInterrupt):
|
||||
raise
|
||||
if isinstance(e, SystemExit):
|
||||
return
|
||||
if isinstance(e, subprocess.CalledProcessError):
|
||||
console.print(f"[red]Error running command:[/red] {e}")
|
||||
if e.stderr:
|
||||
console.print(e.stderr)
|
||||
else:
|
||||
console.print(f"[red]Error:[/red] {e}")
|
||||
|
||||
|
||||
def run_command(cmd: list[str], cwd: Path | None = None) -> str:
|
||||
"""Run a shell command and return output.
|
||||
|
||||
@@ -291,9 +264,11 @@ def add_docs_version(docs_json_path: Path, version: str) -> bool:
|
||||
if not versions:
|
||||
continue
|
||||
|
||||
# Skip if this version already exists for this language
|
||||
if any(v.get("version") == version_label for v in versions):
|
||||
continue
|
||||
|
||||
# Find the current default and copy its tabs
|
||||
default_version = next(
|
||||
(v for v in versions if v.get("default")),
|
||||
versions[0],
|
||||
@@ -305,7 +280,10 @@ def add_docs_version(docs_json_path: Path, version: str) -> bool:
|
||||
"tabs": default_version.get("tabs", []),
|
||||
}
|
||||
|
||||
# Remove default flag from old default
|
||||
default_version.pop("default", None)
|
||||
|
||||
# Insert new version at the beginning
|
||||
versions.insert(0, new_version)
|
||||
updated = True
|
||||
|
||||
@@ -499,7 +477,7 @@ def _is_crewai_dep(spec: str) -> bool:
|
||||
"""Return True if *spec* is a ``crewai`` or ``crewai[...]`` dependency."""
|
||||
if not spec.startswith("crewai"):
|
||||
return False
|
||||
rest = spec[6:]
|
||||
rest = spec[6:] # after "crewai"
|
||||
return len(rest) > 0 and rest[0] in ("[", "=", ">", "<", "~", "!")
|
||||
|
||||
|
||||
@@ -521,6 +499,7 @@ def _pin_crewai_deps(content: str, version: str) -> str:
|
||||
deps = doc.get("project", {}).get(key)
|
||||
if deps is None:
|
||||
continue
|
||||
# optional-dependencies is a table of lists; dependencies is a list
|
||||
dep_lists = deps.values() if isinstance(deps, Mapping) else [deps]
|
||||
for dep_list in dep_lists:
|
||||
for i, dep in enumerate(dep_list):
|
||||
@@ -659,6 +638,7 @@ def get_github_contributors(commit_range: str) -> list[str]:
|
||||
List of GitHub usernames sorted alphabetically.
|
||||
"""
|
||||
try:
|
||||
# Get GitHub token from gh CLI
|
||||
try:
|
||||
gh_token = run_command(["gh", "auth", "token"])
|
||||
except subprocess.CalledProcessError:
|
||||
@@ -700,6 +680,11 @@ def get_github_contributors(commit_range: str) -> list[str]:
|
||||
return []
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Shared workflow helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _poll_pr_until_merged(
|
||||
branch_name: str, label: str, repo: str | None = None
|
||||
) -> None:
|
||||
@@ -779,6 +764,7 @@ def _update_all_versions(
|
||||
"[yellow]Warning:[/yellow] No __version__ attributes found to update"
|
||||
)
|
||||
|
||||
# Update CLI template pyproject.toml files
|
||||
templates_dir = lib_dir / "crewai" / "src" / "crewai" / "cli" / "templates"
|
||||
if templates_dir.exists():
|
||||
if dry_run:
|
||||
@@ -1177,11 +1163,13 @@ def _repin_crewai_install(run_value: str, version: str) -> str:
|
||||
while marker in remainder:
|
||||
before, _, after = remainder.partition(marker)
|
||||
result.append(before)
|
||||
# after looks like: a2a]==1.14.0" ...
|
||||
bracket_end = after.index("]")
|
||||
extras = after[:bracket_end]
|
||||
rest = after[bracket_end + 1 :]
|
||||
if rest.startswith("=="):
|
||||
ver_start = 2
|
||||
# Find end of version — next quote or whitespace
|
||||
ver_start = 2 # len("==")
|
||||
ver_end = ver_start
|
||||
while ver_end < len(rest) and rest[ver_end] not in ('"', "'", " ", "\n"):
|
||||
ver_end += 1
|
||||
@@ -1343,6 +1331,7 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
|
||||
run_command(["gh", "repo", "clone", enterprise_repo, str(repo_dir)])
|
||||
console.print(f"[green]✓[/green] Cloned {enterprise_repo}")
|
||||
|
||||
# --- bump versions ---
|
||||
for rel_dir in _ENTERPRISE_VERSION_DIRS:
|
||||
pkg_dir = repo_dir / rel_dir
|
||||
if not pkg_dir.exists():
|
||||
@@ -1372,12 +1361,14 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
|
||||
f"{pyproject.relative_to(repo_dir)}"
|
||||
)
|
||||
|
||||
# --- update crewai[tools] pin ---
|
||||
enterprise_pyproject = repo_dir / enterprise_dep_path
|
||||
if _update_enterprise_crewai_dep(enterprise_pyproject, version):
|
||||
console.print(
|
||||
f"[green]✓[/green] Updated crewai[tools] dep in {enterprise_dep_path}"
|
||||
)
|
||||
|
||||
# --- update crewai pins in CI workflows ---
|
||||
for wf in _update_enterprise_workflows(repo_dir, version):
|
||||
console.print(
|
||||
f"[green]✓[/green] Updated crewai pin in {wf.relative_to(repo_dir)}"
|
||||
@@ -1417,6 +1408,7 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
|
||||
time.sleep(_PYPI_POLL_INTERVAL)
|
||||
console.print("[green]✓[/green] Workspace synced")
|
||||
|
||||
# --- branch, commit, push, PR ---
|
||||
branch_name = f"feat/bump-version-{version}"
|
||||
run_command(["git", "checkout", "-b", branch_name], cwd=repo_dir)
|
||||
run_command(["git", "add", "."], cwd=repo_dir)
|
||||
@@ -1450,6 +1442,7 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
|
||||
|
||||
_poll_pr_until_merged(branch_name, "enterprise bump PR", repo=enterprise_repo)
|
||||
|
||||
# --- tag and release ---
|
||||
run_command(["git", "checkout", "main"], cwd=repo_dir)
|
||||
run_command(["git", "pull"], cwd=repo_dir)
|
||||
|
||||
@@ -1491,6 +1484,7 @@ def _trigger_pypi_publish(tag_name: str, wait: bool = False) -> None:
|
||||
tag_name: The release tag to publish.
|
||||
wait: Block until the workflow run completes.
|
||||
"""
|
||||
# Capture the latest run ID before triggering so we can detect the new one
|
||||
prev_run_id = ""
|
||||
if wait:
|
||||
try:
|
||||
@@ -1565,6 +1559,11 @@ def _trigger_pypi_publish(tag_name: str, wait: bool = False) -> None:
|
||||
console.print("[green]✓[/green] PyPI publish workflow completed")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CLI commands
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@click.group()
|
||||
def cli() -> None:
|
||||
"""Development tools for version bumping and git automation."""
|
||||
@@ -1832,80 +1831,62 @@ def release(
|
||||
skip_enterprise: Skip the enterprise release phase.
|
||||
skip_to_enterprise: Skip phases 1 & 2, run only the enterprise release phase.
|
||||
"""
|
||||
flags: list[str] = []
|
||||
if no_edit:
|
||||
flags.append("--no-edit")
|
||||
if skip_enterprise:
|
||||
flags.append("--skip-enterprise")
|
||||
flag_suffix = (" " + " ".join(flags)) if flags else ""
|
||||
enterprise_hint = (
|
||||
""
|
||||
if skip_enterprise
|
||||
else f"\n\nThen release enterprise:\n\n"
|
||||
f" devtools release {version} --skip-to-enterprise"
|
||||
)
|
||||
|
||||
check_gh_installed()
|
||||
|
||||
if skip_enterprise and skip_to_enterprise:
|
||||
console.print(
|
||||
"[red]Error:[/red] Cannot use both --skip-enterprise "
|
||||
"and --skip-to-enterprise"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
if not skip_enterprise or skip_to_enterprise:
|
||||
missing: list[str] = []
|
||||
if not _ENTERPRISE_REPO:
|
||||
missing.append("ENTERPRISE_REPO")
|
||||
if not _ENTERPRISE_VERSION_DIRS:
|
||||
missing.append("ENTERPRISE_VERSION_DIRS")
|
||||
if not _ENTERPRISE_CREWAI_DEP_PATH:
|
||||
missing.append("ENTERPRISE_CREWAI_DEP_PATH")
|
||||
if missing:
|
||||
console.print(
|
||||
f"[red]Error:[/red] Missing required environment variable(s): "
|
||||
f"{', '.join(missing)}\n"
|
||||
f"Set them or pass --skip-enterprise to skip the enterprise release."
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
cwd = Path.cwd()
|
||||
lib_dir = cwd / "lib"
|
||||
|
||||
is_prerelease = _is_prerelease(version)
|
||||
|
||||
if skip_to_enterprise:
|
||||
try:
|
||||
_release_enterprise(version, is_prerelease, dry_run)
|
||||
except BaseException as e:
|
||||
_print_release_error(e)
|
||||
_resume_hint(
|
||||
f"Fix the issue, then re-run:\n\n"
|
||||
f" devtools release {version} --skip-to-enterprise"
|
||||
)
|
||||
sys.exit(1)
|
||||
console.print(
|
||||
f"\n[green]✓[/green] Enterprise release [bold]{version}[/bold] complete!"
|
||||
)
|
||||
return
|
||||
|
||||
if not dry_run:
|
||||
console.print("Checking git status...")
|
||||
check_git_clean()
|
||||
console.print("[green]✓[/green] Working directory is clean")
|
||||
else:
|
||||
console.print("[dim][DRY RUN][/dim] Would check git status")
|
||||
|
||||
packages = get_packages(lib_dir)
|
||||
|
||||
console.print(f"\nFound {len(packages)} package(s) to update:")
|
||||
for pkg in packages:
|
||||
console.print(f" - {pkg.name}")
|
||||
|
||||
console.print(f"\n[bold cyan]Phase 1: Bumping versions to {version}[/bold cyan]")
|
||||
|
||||
try:
|
||||
check_gh_installed()
|
||||
|
||||
if skip_enterprise and skip_to_enterprise:
|
||||
console.print(
|
||||
"[red]Error:[/red] Cannot use both --skip-enterprise "
|
||||
"and --skip-to-enterprise"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
if not skip_enterprise or skip_to_enterprise:
|
||||
missing: list[str] = []
|
||||
if not _ENTERPRISE_REPO:
|
||||
missing.append("ENTERPRISE_REPO")
|
||||
if not _ENTERPRISE_VERSION_DIRS:
|
||||
missing.append("ENTERPRISE_VERSION_DIRS")
|
||||
if not _ENTERPRISE_CREWAI_DEP_PATH:
|
||||
missing.append("ENTERPRISE_CREWAI_DEP_PATH")
|
||||
if missing:
|
||||
console.print(
|
||||
f"[red]Error:[/red] Missing required environment variable(s): "
|
||||
f"{', '.join(missing)}\n"
|
||||
f"Set them or pass --skip-enterprise to skip the enterprise release."
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
cwd = Path.cwd()
|
||||
lib_dir = cwd / "lib"
|
||||
|
||||
is_prerelease = _is_prerelease(version)
|
||||
|
||||
if skip_to_enterprise:
|
||||
_release_enterprise(version, is_prerelease, dry_run)
|
||||
console.print(
|
||||
f"\n[green]✓[/green] Enterprise release [bold]{version}[/bold] complete!"
|
||||
)
|
||||
return
|
||||
|
||||
if not dry_run:
|
||||
console.print("Checking git status...")
|
||||
check_git_clean()
|
||||
console.print("[green]✓[/green] Working directory is clean")
|
||||
else:
|
||||
console.print("[dim][DRY RUN][/dim] Would check git status")
|
||||
|
||||
packages = get_packages(lib_dir)
|
||||
|
||||
console.print(f"\nFound {len(packages)} package(s) to update:")
|
||||
for pkg in packages:
|
||||
console.print(f" - {pkg.name}")
|
||||
|
||||
# --- Phase 1: Bump versions ---
|
||||
console.print(
|
||||
f"\n[bold cyan]Phase 1: Bumping versions to {version}[/bold cyan]"
|
||||
)
|
||||
|
||||
_update_all_versions(cwd, lib_dir, version, packages, dry_run)
|
||||
|
||||
branch_name = f"feat/bump-version-{version}"
|
||||
@@ -1949,17 +1930,12 @@ def release(
|
||||
console.print(
|
||||
"[dim][DRY RUN][/dim] Would push branch, create PR, and wait for merge"
|
||||
)
|
||||
except BaseException as e:
|
||||
_print_release_error(e)
|
||||
_resume_hint(
|
||||
f"Phase 1 failed. Fix the issue, then re-run:\n\n"
|
||||
f" devtools release {version}{flag_suffix}"
|
||||
|
||||
# --- Phase 2: Tag and release ---
|
||||
console.print(
|
||||
f"\n[bold cyan]Phase 2: Tagging and releasing {version}[/bold cyan]"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
console.print(f"\n[bold cyan]Phase 2: Tagging and releasing {version}[/bold cyan]")
|
||||
|
||||
try:
|
||||
tag_name = version
|
||||
|
||||
if not dry_run:
|
||||
@@ -1986,57 +1962,22 @@ def release(
|
||||
|
||||
if not dry_run:
|
||||
_create_tag_and_release(tag_name, release_notes, is_prerelease)
|
||||
except BaseException as e:
|
||||
_print_release_error(e)
|
||||
_resume_hint(
|
||||
"Phase 2 failed before PyPI publish. The bump PR is already merged.\n"
|
||||
"Fix the issue, then resume with:\n\n"
|
||||
" devtools tag"
|
||||
f"\n\nAfter tagging, publish to PyPI and update deployment test:\n\n"
|
||||
f" gh workflow run publish.yml -f release_tag={version}"
|
||||
f"{enterprise_hint}"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
if not dry_run:
|
||||
_trigger_pypi_publish(tag_name, wait=True)
|
||||
except BaseException as e:
|
||||
_print_release_error(e)
|
||||
_resume_hint(
|
||||
f"Phase 2 failed at PyPI publish. Tag and GitHub release already exist.\n"
|
||||
f"Retry PyPI publish manually:\n\n"
|
||||
f" gh workflow run publish.yml -f release_tag={version}"
|
||||
f"{enterprise_hint}"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
if not dry_run:
|
||||
_update_deployment_test_repo(version, is_prerelease)
|
||||
except BaseException as e:
|
||||
_print_release_error(e)
|
||||
_resume_hint(
|
||||
f"Phase 2 failed updating deployment test repo. "
|
||||
f"Tag, release, and PyPI are done.\n"
|
||||
f"Fix the issue and update {_DEPLOYMENT_TEST_REPO} manually."
|
||||
f"{enterprise_hint}"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
if not skip_enterprise:
|
||||
try:
|
||||
if not skip_enterprise:
|
||||
_release_enterprise(version, is_prerelease, dry_run)
|
||||
except BaseException as e:
|
||||
_print_release_error(e)
|
||||
_resume_hint(
|
||||
f"Phase 3 (enterprise) failed. Phases 1 & 2 completed successfully.\n"
|
||||
f"Fix the issue, then resume:\n\n"
|
||||
f" devtools release {version} --skip-to-enterprise"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
console.print(f"\n[green]✓[/green] Release [bold]{version}[/bold] complete!")
|
||||
console.print(f"\n[green]✓[/green] Release [bold]{version}[/bold] complete!")
|
||||
|
||||
except subprocess.CalledProcessError as e:
|
||||
console.print(f"[red]Error running command:[/red] {e}")
|
||||
if e.stderr:
|
||||
console.print(e.stderr)
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
console.print(f"[red]Error:[/red] {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
cli.add_command(bump)
|
||||
|
||||
@@ -12,7 +12,7 @@ dev = [
|
||||
"mypy==1.19.1",
|
||||
"pre-commit==4.5.1",
|
||||
"bandit==1.9.2",
|
||||
"pytest==9.0.3",
|
||||
"pytest==8.4.2",
|
||||
"pytest-asyncio==1.3.0",
|
||||
"pytest-subprocess==1.5.3",
|
||||
"vcrpy==7.0.0", # pinned, less versions break pytest-recording
|
||||
@@ -20,7 +20,7 @@ dev = [
|
||||
"pytest-randomly==4.0.1",
|
||||
"pytest-timeout==2.4.0",
|
||||
"pytest-xdist==3.8.0",
|
||||
"pytest-split==0.11.0",
|
||||
"pytest-split==0.10.0",
|
||||
"types-requests~=2.31.0.6",
|
||||
"types-pyyaml==6.0.*",
|
||||
"types-regex==2026.1.15.*",
|
||||
@@ -30,7 +30,6 @@ dev = [
|
||||
"types-pymysql==1.1.0.20250916",
|
||||
"types-aiofiles~=25.1.0",
|
||||
"commitizen>=4.13.9",
|
||||
"pip-audit==2.9.0",
|
||||
]
|
||||
|
||||
|
||||
@@ -162,7 +161,7 @@ info = "Commits must follow Conventional Commits 1.0.0."
|
||||
|
||||
|
||||
[tool.uv]
|
||||
exclude-newer = "3 days"
|
||||
exclude-newer = "2026-04-10" # pinned for CVE-2026-39892; restore to "3 days" after 2026-04-11
|
||||
|
||||
# composio-core pins rich<14 but textual requires rich>=14.
|
||||
# onnxruntime 1.24+ dropped Python 3.10 wheels; cap it so qdrant[fastembed] resolves on 3.10.
|
||||
@@ -170,8 +169,6 @@ exclude-newer = "3 days"
|
||||
# langchain-core <1.2.28 has GHSA-926x-3r5x-gfhw (incomplete f-string validation).
|
||||
# transformers 4.57.6 has CVE-2026-1839; force 5.4+ (docling 2.84 allows huggingface-hub>=1).
|
||||
# cryptography 46.0.6 has CVE-2026-39892; force 46.0.7+.
|
||||
# pypdf <6.10.0 has CVE-2026-40260; force 6.10.0+.
|
||||
# uv <0.11.6 has GHSA-pjjw-68hj-v9mw; force 0.11.6+.
|
||||
override-dependencies = [
|
||||
"rich>=13.7.1",
|
||||
"onnxruntime<1.24; python_version < '3.11'",
|
||||
@@ -180,8 +177,6 @@ override-dependencies = [
|
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"urllib3>=2.6.3",
|
||||
"transformers>=5.4.0; python_version >= '3.10'",
|
||||
"cryptography>=46.0.7",
|
||||
"pypdf>=6.10.0,<7",
|
||||
"uv>=0.11.6,<1",
|
||||
]
|
||||
|
||||
[tool.uv.workspace]
|
||||
|
||||
538
uv.lock
generated
538
uv.lock
generated
@@ -13,8 +13,7 @@ resolution-markers = [
|
||||
]
|
||||
|
||||
[options]
|
||||
exclude-newer = "2026-04-10T18:30:59.748668Z"
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exclude-newer-span = "P3D"
|
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exclude-newer = "2026-04-11T07:00:00Z"
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|
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[manifest]
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||||
members = [
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@@ -28,11 +27,9 @@ overrides = [
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{ name = "langchain-core", specifier = ">=1.2.28,<2" },
|
||||
{ name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.24" },
|
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{ name = "pillow", specifier = ">=12.1.1" },
|
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{ name = "pypdf", specifier = ">=6.10.0,<7" },
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{ name = "rich", specifier = ">=13.7.1" },
|
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{ name = "transformers", marker = "python_full_version >= '3.10'", specifier = ">=5.4.0" },
|
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{ name = "urllib3", specifier = ">=2.6.3" },
|
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{ name = "uv", specifier = ">=0.11.6,<1" },
|
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]
|
||||
|
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[manifest.dependency-groups]
|
||||
@@ -41,13 +38,12 @@ dev = [
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{ name = "boto3-stubs", extras = ["bedrock-runtime"], specifier = "==1.42.40" },
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{ name = "commitizen", specifier = ">=4.13.9" },
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{ name = "mypy", specifier = "==1.19.1" },
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{ name = "pip-audit", specifier = "==2.9.0" },
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{ name = "pre-commit", specifier = "==4.5.1" },
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{ name = "pytest", specifier = "==9.0.3" },
|
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{ name = "pytest", specifier = "==8.4.2" },
|
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{ name = "pytest-asyncio", specifier = "==1.3.0" },
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{ name = "pytest-randomly", specifier = "==4.0.1" },
|
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{ name = "pytest-recording", specifier = "==0.13.4" },
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{ name = "pytest-split", specifier = "==0.11.0" },
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{ name = "pytest-split", specifier = "==0.10.0" },
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{ name = "pytest-subprocess", specifier = "==1.5.3" },
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{ name = "pytest-timeout", specifier = "==2.4.0" },
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{ name = "pytest-xdist", specifier = "==3.8.0" },
|
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@@ -64,7 +60,7 @@ dev = [
|
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|
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[[package]]
|
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|
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|
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{ name = "google-api-core" },
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@@ -73,9 +69,9 @@ dependencies = [
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{ name = "pydantic" },
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/dd/d5/51f4ee1bf3b736add42a542d3c8a3fd3fa85f3d36c17972127defc46c26f/a2a_sdk-0.3.26-py3-none-any.whl", hash = "sha256:754e0573f6d33b225c1d8d51f640efa69cbbed7bdfb06ce9c3540ea9f58d4a91", size = 151016, upload-time = "2026-04-09T15:21:12.35Z" },
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{ url = "https://files.pythonhosted.org/packages/bd/f9/6a62520b7ecb945188a6e1192275f4732ff9341cd4629bc975a6c146aeab/a2a_sdk-0.3.25-py3-none-any.whl", hash = "sha256:2fce38faea82eb0b6f9f9c2bcf761b0d78612c80ef0e599b50d566db1b2654b5", size = 149609, upload-time = "2026-03-10T13:08:44.7Z" },
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]
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|
||||
[[package]]
|
||||
@@ -99,7 +95,7 @@ wheels = [
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|
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[[package]]
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name = "aiobotocore"
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{ name = "aiohttp" },
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@@ -108,12 +104,11 @@ dependencies = [
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{ name = "jmespath" },
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{ name = "multidict" },
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{ name = "python-dateutil" },
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{ name = "typing-extensions", marker = "python_full_version < '3.11'" },
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{ name = "wrapt" },
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sdist = { url = "https://files.pythonhosted.org/packages/52/48/cf3c88c5e3fecdeed824f97a8a98a9fc0d7ef33e603f8f22c2fd32b9ef09/aiobotocore-2.25.2.tar.gz", hash = "sha256:ae0a512b34127097910b7af60752956254099ae54402a84c2021830768f92cda", size = 120585, upload-time = "2025-11-11T18:51:28.056Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/df/d8/ce9386e6d76ea79e61dee15e62aa48cff6be69e89246b0ac4a11857cb02c/aiobotocore-3.4.0-py3-none-any.whl", hash = "sha256:26290eb6830ea92d8a6f5f90b56e9f5cedd6d126074d5db63b195e281d982465", size = 88018, upload-time = "2026-04-07T06:12:22.684Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8e/ad/a2f3964aa37da5a4c94c1e5f3934d6ac1333f991f675fcf08a618397a413/aiobotocore-2.25.2-py3-none-any.whl", hash = "sha256:0cec45c6ba7627dd5e5460337291c86ac38c3b512ec4054ce76407d0f7f2a48f", size = 86048, upload-time = "2025-11-11T18:51:26.139Z" },
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|
||||
[[package]]
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@@ -617,27 +612,18 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/36/b7/a5cc566901af27314408b95701f8e1d9c286b0aecfa50fc76c53d73efa6f/bedrock_agentcore-1.3.2-py3-none-any.whl", hash = "sha256:3a4e7122f777916f8bd74b42f29eb881415e37fda784a5ff8fab3c813b921706", size = 121703, upload-time = "2026-02-23T20:52:55.038Z" },
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||||
|
||||
[[package]]
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||||
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/e5/ca/78d423b324b8d77900030fa59c4aa9054261ef0925631cd2501dd015b7b7/boolean_py-5.0-py3-none-any.whl", hash = "sha256:ef28a70bd43115208441b53a045d1549e2f0ec6e3d08a9d142cbc41c1938e8d9", size = 26577, upload-time = "2025-04-03T10:39:48.449Z" },
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||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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{ name = "jmespath" },
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{ name = "s3transfer" },
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/f3/cf/e24d08b37cd318754a8e94906c8b34b88676899aad1907ff6942311f13c4/boto3-1.40.70-py3-none-any.whl", hash = "sha256:e8c2f4f4cb36297270f1023ebe5b100333e0e88ab6457a9687d80143d2e15bf9", size = 139358, upload-time = "2025-11-10T20:29:13.512Z" },
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]
|
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|
||||
[[package]]
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@@ -661,16 +647,16 @@ bedrock-runtime = [
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||||
|
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[[package]]
|
||||
name = "botocore"
|
||||
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "jmespath" },
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{ name = "python-dateutil" },
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{ name = "urllib3" },
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/e3/37/0c0c90361c8a1b9e6c75222ca24ae12996a298c0e18822a72ab229c37207/botocore-1.42.84-py3-none-any.whl", hash = "sha256:15f3fe07dfa6545e46a60c4b049fe2bdf63803c595ae4a4eec90e8f8172764f3", size = 14827061, upload-time = "2026-04-06T19:38:53.613Z" },
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{ url = "https://files.pythonhosted.org/packages/55/d2/507fd0ee4dd574d2bdbdeac5df83f39d2cae1ffe97d4622cca6f6bab39f1/botocore-1.40.70-py3-none-any.whl", hash = "sha256:4a394ad25f5d9f1ef0bed610365744523eeb5c22de6862ab25d8c93f9f6d295c", size = 14106829, upload-time = "2025-11-10T20:29:01.101Z" },
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[[package]]
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@@ -718,24 +704,6 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/4a/57/3b7d4dd193ade4641c865bc2b93aeeb71162e81fc348b8dad020215601ed/build-1.4.2-py3-none-any.whl", hash = "sha256:7a4d8651ea877cb2a89458b1b198f2e69f536c95e89129dbf5d448045d60db88", size = 24643, upload-time = "2026-03-25T14:20:26.568Z" },
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[[package]]
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/ef/79/c45f2d53efe6ada1110cf6f9fca095e4ff47a0454444aefdde6ac4789179/cachecontrol-0.14.4-py3-none-any.whl", hash = "sha256:b7ac014ff72ee199b5f8af1de29d60239954f223e948196fa3d84adaffc71d2b", size = 22247, upload-time = "2025-11-14T04:32:11.733Z" },
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[package.optional-dependencies]
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[[package]]
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@@ -1328,15 +1296,15 @@ watson = [
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@@ -1355,7 +1323,7 @@ requires-dist = [
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@@ -1423,7 +1391,7 @@ requires-dist = [
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@@ -1739,21 +1707,6 @@ wheels = [
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[[package]]
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Reference in New Issue
Block a user