fix: use HubSpot form link for enterprise options card

Updated per team feedback — the enterprise card should link to the
HubSpot demo form instead of crewai.com/amp.

.pre-commit-config.yaml

@@ -24,6 +24,14 @@ 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:

docs/ar/changelog.mdx

@@ -4,6 +4,36 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<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
 

docs/ar/installation.mdx

@@ -196,7 +196,7 @@ python3 --version
 - يدعم أي مزود سحابي بما في ذلك النشر المحلي
 - تكامل مع أنظمة الأمان الحالية
 
-<Card title="استكشف خيارات المؤسسات" icon="building" href="https://crewai.com/enterprise">
+<Card title="استكشف خيارات المؤسسات" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   تعرّف على عروض CrewAI للمؤسسات وجدول عرضًا توضيحيًا
 </Card>
 </Note>

docs/docs.json

@@ -392,7 +392,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -865,7 +866,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -1338,7 +1340,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -1811,7 +1814,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -2283,7 +2287,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -2754,7 +2759,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -3225,7 +3231,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -3698,7 +3705,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -4169,7 +4177,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {
@@ -4643,7 +4652,8 @@
                       "en/enterprise/features/marketplace",
                       "en/enterprise/features/agent-repositories",
                       "en/enterprise/features/tools-and-integrations",
-                      "en/enterprise/features/pii-trace-redactions"
+                      "en/enterprise/features/pii-trace-redactions",
+                      "en/enterprise/features/a2a"
                     ]
                   },
                   {

docs/en/changelog.mdx

@@ -4,6 +4,36 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<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
 

docs/en/enterprise/features/a2a.mdx

@@ -0,0 +1,227 @@
+---
+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

docs/en/installation.mdx

@@ -199,7 +199,7 @@ For teams and organizations, CrewAI offers enterprise deployment options that el
 - Supports any hyperscaler including on prem deployments
 - Integration with your existing security systems
 
-<Card title="Explore Enterprise Options" icon="building" href="https://crewai.com/enterprise">
+<Card title="Explore Enterprise Options" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   Learn about CrewAI's enterprise offerings and schedule a demo
 </Card>
 </Note>

docs/en/learn/a2a-agent-delegation.mdx

@@ -7,6 +7,10 @@ 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.
 
@@ -96,24 +100,28 @@ 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="[]">
-  Extension URIs the client supports.
+  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.
 </ParamField>
 
 ## Authentication
@@ -405,11 +413,7 @@ agent = Agent(
   Preferred endpoint URL. If set, overrides the URL passed to `to_agent_card()`.
 </ParamField>
 
-<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">
+<ParamField path="protocol_version" type="str" default="0.3.0">
   A2A protocol version this agent supports.
 </ParamField>
 
@@ -441,8 +445,36 @@ agent = Agent(
   Whether agent provides extended card to authenticated users.
 </ParamField>
 
-<ParamField path="signatures" type="list[AgentCardSignature]" default="[]">
-  JSON Web Signatures for the AgentCard.
+<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>
 
 ### Combined Client and Server
@@ -468,6 +500,14 @@ 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}>

docs/ko/changelog.mdx

@@ -4,6 +4,36 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<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
 

docs/ko/installation.mdx

@@ -189,7 +189,7 @@ CrewAI는 의존성 관리와 패키지 처리를 위해 `uv`를 사용합니다
 - 온프레미스 배포를 포함하여 모든 하이퍼스케일러 지원
 - 기존 보안 시스템과의 통합
 
-<Card title="엔터프라이즈 옵션 살펴보기" icon="building" href="https://crewai.com/enterprise">
+<Card title="엔터프라이즈 옵션 살펴보기" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   CrewAI의 엔터프라이즈 서비스에 대해 알아보고 데모를 예약하세요
 </Card>
 </Note>

docs/pt-BR/changelog.mdx

@@ -4,6 +4,36 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<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
 

docs/pt-BR/installation.mdx

@@ -191,7 +191,7 @@ Para equipes e organizações, o CrewAI oferece opções de implantação corpor
 - Compatível com qualquer hyperscaler, incluindo ambientes on-premises
 - Integração com seus sistemas de segurança existentes
 
-<Card title="Explore as Opções Enterprise" icon="building" href="https://crewai.com/enterprise">
+<Card title="Explore as Opções Enterprise" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   Saiba mais sobre as soluções enterprise do CrewAI e agende uma demonstração
 </Card>
 </Note>

lib/crewai-files/pyproject.toml

@@ -9,7 +9,7 @@ authors = [
 requires-python = ">=3.10, <3.14"
 dependencies = [
     "Pillow~=12.1.1",
-    "pypdf~=6.9.1",
+    "pypdf~=6.10.0",
     "python-magic>=0.4.27",
     "aiocache~=0.12.3",
     "aiofiles~=24.1.0",

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.14.2a2"
+__version__ = "1.14.2a3"

lib/crewai-tools/pyproject.toml

@@ -9,8 +9,8 @@ authors = [
 requires-python = ">=3.10, <3.14"
 dependencies = [
     "pytube~=15.0.0",
-    "requests~=2.32.5",
-    "crewai==1.14.2a2",
+    "requests>=2.33.0,<3",
+    "crewai==1.14.2a3",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -305,4 +305,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.14.2a2"
+__version__ = "1.14.2a3"

lib/crewai/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     # Core Dependencies
     "pydantic~=2.11.9",
-    "openai>=1.83.0,<3",
+    "openai>=2.0.0,<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~=0.9.13",
+    "uv~=0.11.6",
     "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==1.14.2a2",
+    "crewai-tools==1.14.2a3",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.14.2a2"
+__version__ = "1.14.2a3"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/a2a/errors.py

@@ -98,7 +98,6 @@ 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",

lib/crewai/src/crewai/a2a/extensions/base.py

@@ -63,25 +63,21 @@ 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
     """
 

lib/crewai/src/crewai/a2a/utils/response_model.py

@@ -77,7 +77,6 @@ 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))
     ]

lib/crewai/src/crewai/agent/core.py

@@ -1341,7 +1341,6 @@ 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
@@ -1399,7 +1398,6 @@ 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(
@@ -1485,8 +1483,6 @@ 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)
 
@@ -1637,7 +1633,7 @@ class Agent(BaseAgent):
                 if isinstance(conversion_result, BaseModel):
                     formatted_result = conversion_result
             except ConverterError:
-                pass  # Keep raw output if conversion fails
+                pass
         else:
             raw_output = str(output) if not isinstance(output, str) else output
 
@@ -1719,7 +1715,6 @@ 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(

lib/crewai/src/crewai/agent/planning_config.py

@@ -41,7 +41,6 @@ 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",
@@ -49,7 +48,6 @@ class PlanningConfig(BaseModel):
             planning_config=PlanningConfig(),
         )
 
-        # Balanced — replan only when steps fail
         agent = Agent(
             role="Researcher",
             goal="Research topics",
@@ -59,7 +57,6 @@ class PlanningConfig(BaseModel):
             ),
         )
 
-        # Full adaptive planning with refinement and replanning
         agent = Agent(
             role="Researcher",
             goal="Research topics",
@@ -69,7 +66,7 @@ class PlanningConfig(BaseModel):
                 max_attempts=3,
                 max_steps=10,
                 plan_prompt="Create a focused plan for: {description}",
-                llm="gpt-4o-mini",  # Use cheaper model for planning
+                llm="gpt-4o-mini",
             ),
         )
         ```

lib/crewai/src/crewai/agent/utils.py

@@ -39,7 +39,6 @@ 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
 

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py

@@ -99,12 +99,10 @@ 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
@@ -116,16 +114,13 @@ 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)

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -383,7 +383,6 @@ 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(
@@ -448,14 +447,12 @@ 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(
@@ -464,7 +461,6 @@ 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()
 
@@ -566,14 +562,11 @@ 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
             )
@@ -585,7 +578,6 @@ 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)
 

lib/crewai/src/crewai/agents/constants.py

@@ -4,8 +4,6 @@ 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"

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -296,7 +296,6 @@ 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))
@@ -307,7 +306,6 @@ 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:
@@ -347,7 +345,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     executor_context=self,
                     verbose=self.agent.verbose,
                 )
-                # breakpoint()
                 if self.response_model is not None:
                     try:
                         if isinstance(answer, BaseModel):
@@ -365,7 +362,6 @@ 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)
@@ -375,14 +371,12 @@ 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
@@ -426,7 +420,6 @@ 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(
@@ -443,10 +436,6 @@ 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. "
@@ -465,9 +454,7 @@ 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 = (
@@ -490,10 +477,6 @@ 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,
@@ -508,32 +491,26 @@ 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)  # Save final answer to messages
+                    self._append_message(answer)
                     self._show_logs(formatted_answer)
                     return formatted_answer
 
@@ -549,14 +526,13 @@ 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))  # Save final answer to messages
+                self._append_message(str(answer))
                 self._show_logs(formatted_answer)
                 return formatted_answer
 
@@ -627,12 +603,10 @@ 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"
@@ -640,14 +614,12 @@ 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
@@ -706,8 +678,6 @@ 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"
@@ -773,7 +743,6 @@ 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)])
 
@@ -827,7 +796,7 @@ 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
 
@@ -1202,7 +1171,6 @@ 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)
@@ -1212,7 +1180,6 @@ 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
@@ -1319,10 +1286,6 @@ 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,
@@ -1336,32 +1299,26 @@ 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)  # Save final answer to messages
+                    self._append_message(answer)
                     self._show_logs(formatted_answer)
                     return formatted_answer
 
@@ -1377,14 +1334,13 @@ 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))  # Save final answer to messages
+                self._append_message(str(answer))
                 self._show_logs(formatted_answer)
                 return formatted_answer
 
@@ -1455,7 +1411,6 @@ 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)
@@ -1575,17 +1530,14 @@ 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:
@@ -1599,7 +1551,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
                     )
                 return
 
-        # Update the training data and save
         training_data[agent_id] = agent_training_data
         training_handler.save(training_data)
 

lib/crewai/src/crewai/agents/parser.py

@@ -94,11 +94,8 @@ 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)
@@ -146,7 +143,6 @@ 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()
 
 
@@ -171,18 +167,9 @@ 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)

lib/crewai/src/crewai/agents/planner_observer.py

@@ -83,10 +83,6 @@ class PlannerObserver:
             return create_llm(config.llm)
         return self.agent.llm
 
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
     def observe(
         self,
         completed_step: TodoItem,
@@ -182,9 +178,6 @@ 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="",
@@ -221,10 +214,6 @@ class PlannerObserver:
 
         return remaining_todos
 
-    # ------------------------------------------------------------------
-    # Internal: Message building
-    # ------------------------------------------------------------------
-
     def _build_observation_messages(
         self,
         completed_step: TodoItem,
@@ -239,15 +228,11 @@ 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 = []
@@ -261,7 +246,6 @@ class PlannerObserver:
                 completed_lines
             )
 
-        # Build remaining plan
         remaining_summary = ""
         if remaining_todos:
             remaining_lines = [
@@ -306,17 +290,14 @@ 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:]
                 )
@@ -325,14 +306,12 @@ 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",

lib/crewai/src/crewai/agents/step_executor.py

@@ -108,7 +108,6 @@ 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
         )
@@ -121,10 +120,6 @@ class StepExecutor:
                 _,
             ) = setup_native_tools(self.original_tools)
 
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
     def execute(
         self,
         todo: TodoItem,
@@ -190,10 +185,6 @@ class StepExecutor:
                 execution_time=elapsed,
             )
 
-    # ------------------------------------------------------------------
-    # Internal: Message building
-    # ------------------------------------------------------------------
-
     def _build_isolated_messages(
         self, todo: TodoItem, context: StepExecutionContext
     ) -> list[LLMMessage]:
@@ -237,10 +228,6 @@ 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:
@@ -267,7 +254,6 @@ class StepExecutor:
                 )
             )
 
-        # Include dependency results (final results only, no traces)
         if context.dependency_results:
             parts.append(
                 I18N_DEFAULT.retrieve("planning", "step_executor_context_header")
@@ -283,10 +269,6 @@ class StepExecutor:
 
         return "\n".join(parts)
 
-    # ------------------------------------------------------------------
-    # Internal: Multi-turn execution loop
-    # ------------------------------------------------------------------
-
     def _execute_text_parsed(
         self,
         messages: list[LLMMessage],
@@ -306,7 +288,6 @@ 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:
@@ -331,17 +312,12 @@ 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:
@@ -429,10 +405,6 @@ 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."""
@@ -517,7 +489,6 @@ 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:
@@ -541,19 +512,14 @@ 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(
@@ -599,9 +565,6 @@ 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)
                         )

lib/crewai/src/crewai/cli/cli.py

@@ -392,10 +392,15 @@ def deploy() -> None:
 
 @deploy.command(name="create")
 @click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
-def deploy_create(yes: bool) -> None:
+@click.option(
+    "--skip-validate",
+    is_flag=True,
+    help="Skip the pre-deploy validation checks.",
+)
+def deploy_create(yes: bool, skip_validate: bool) -> None:
     """Create a Crew deployment."""
     deploy_cmd = DeployCommand()
-    deploy_cmd.create_crew(yes)
+    deploy_cmd.create_crew(yes, skip_validate=skip_validate)
 
 
 @deploy.command(name="list")
@@ -407,10 +412,28 @@ def deploy_list() -> None:
 
 @deploy.command(name="push")
 @click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_push(uuid: str | None) -> None:
+@click.option(
+    "--skip-validate",
+    is_flag=True,
+    help="Skip the pre-deploy validation checks.",
+)
+def deploy_push(uuid: str | None, skip_validate: bool) -> None:
     """Deploy the Crew."""
     deploy_cmd = DeployCommand()
-    deploy_cmd.deploy(uuid=uuid)
+    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.command(name="status")

lib/crewai/src/crewai/cli/deploy/main.py

@@ -4,12 +4,35 @@ 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.
@@ -60,13 +83,16 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
                 f"{log_message['timestamp']} - {log_message['level']}: {log_message['message']}"
             )
 
-    def deploy(self, uuid: str | None = None) -> None:
+    def deploy(self, uuid: str | None = None, skip_validate: bool = False) -> 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:
@@ -80,10 +106,16 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         self._validate_response(response)
         self._display_deployment_info(response.json())
 
-    def create_crew(self, confirm: bool = False) -> None:
+    def create_crew(self, confirm: bool = False, skip_validate: 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()

lib/crewai/src/crewai/cli/deploy/validate.py

@@ -0,0 +1,845 @@
+"""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)

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -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]==1.14.2a2"
+    "crewai[tools]==1.14.2a3"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -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]==1.14.2a2"
+    "crewai[tools]==1.14.2a3"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.2a2"
+    "crewai[tools]==1.14.2a3"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/lite_agent.py

@@ -16,7 +16,6 @@ from typing import (
     get_origin,
 )
 import uuid
-import warnings
 
 from pydantic import (
     UUID4,
@@ -26,7 +25,7 @@ from pydantic import (
     field_validator,
     model_validator,
 )
-from typing_extensions import Self
+from typing_extensions import Self, deprecated
 
 
 if TYPE_CHECKING:
@@ -173,9 +172,12 @@ 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.
@@ -278,18 +280,6 @@ 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."""

lib/crewai/src/crewai/llm.py

@@ -51,6 +51,7 @@ 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:
@@ -75,8 +76,13 @@ 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
 
@@ -85,6 +91,11 @@ 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]
@@ -709,7 +720,7 @@ class LLM(BaseLLM):
                 chunk_content = None
                 response_id = None
 
-                if hasattr(chunk, "id"):
+                if isinstance(chunk, ModelResponseBase):
                     response_id = chunk.id
 
                 # Safely extract content from various chunk formats
@@ -718,18 +729,16 @@ class LLM(BaseLLM):
                     choices = None
                     if isinstance(chunk, dict) and "choices" in chunk:
                         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
+                    elif isinstance(chunk, ModelResponseStream):
+                        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 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
+                    elif isinstance(chunk, ModelResponseBase) and chunk.model_extra:
+                        usage_info = chunk.model_extra.get("usage") or usage_info
 
                     if choices and len(choices) > 0:
                         choice = choices[0]
@@ -738,7 +747,7 @@ class LLM(BaseLLM):
                         delta = None
                         if isinstance(choice, dict) and "delta" in choice:
                             delta = choice["delta"]
-                        elif hasattr(choice, "delta"):
+                        elif isinstance(choice, LiteLLMStreamingChoices):
                             delta = choice.delta
 
                         # Extract content from delta
@@ -748,7 +757,7 @@ class LLM(BaseLLM):
                                 if "content" in delta and delta["content"] is not None:
                                     chunk_content = delta["content"]
                             # Handle object format
-                            elif hasattr(delta, "content"):
+                            elif isinstance(delta, LiteLLMDelta):
                                 chunk_content = delta.content
 
                             # Handle case where content might be None or empty
@@ -821,9 +830,8 @@ class LLM(BaseLLM):
                         choices = None
                         if isinstance(last_chunk, dict) and "choices" in last_chunk:
                             choices = last_chunk["choices"]
-                        elif hasattr(last_chunk, "choices"):
-                            if not isinstance(last_chunk.choices, type):
-                                choices = last_chunk.choices
+                        elif isinstance(last_chunk, ModelResponseStream):
+                            choices = last_chunk.choices
 
                         if choices and len(choices) > 0:
                             choice = choices[0]
@@ -832,14 +840,14 @@ class LLM(BaseLLM):
                             message = None
                             if isinstance(choice, dict) and "message" in choice:
                                 message = choice["message"]
-                            elif hasattr(choice, "message"):
+                            elif isinstance(choice, Choices):
                                 message = choice.message
 
                             if message:
                                 content = None
                                 if isinstance(message, dict) and "content" in message:
                                     content = message["content"]
-                                elif hasattr(message, "content"):
+                                elif isinstance(message, Message):
                                     content = message.content
 
                                 if content:
@@ -866,24 +874,23 @@ class LLM(BaseLLM):
                     choices = None
                     if isinstance(last_chunk, dict) and "choices" in last_chunk:
                         choices = last_chunk["choices"]
-                    elif hasattr(last_chunk, "choices"):
-                        if not isinstance(last_chunk.choices, type):
-                            choices = last_chunk.choices
+                    elif isinstance(last_chunk, ModelResponseStream):
+                        choices = last_chunk.choices
 
                     if choices and len(choices) > 0:
                         choice = choices[0]
 
-                        message = None
-                        if isinstance(choice, dict) and "message" in choice:
-                            message = choice["message"]
-                        elif hasattr(choice, "message"):
-                            message = choice.message
+                        delta = None
+                        if isinstance(choice, dict) and "delta" in choice:
+                            delta = choice["delta"]
+                        elif isinstance(choice, LiteLLMStreamingChoices):
+                            delta = choice.delta
 
-                        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
+                        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
             except Exception as e:
                 logging.debug(f"Error checking for tool calls: {e}")
 
@@ -1037,7 +1044,7 @@ class LLM(BaseLLM):
         """
         if callbacks and len(callbacks) > 0:
             for callback in callbacks:
-                if hasattr(callback, "log_success_event"):
+                if isinstance(callback, TokenCalcHandler):
                     # 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
@@ -1048,9 +1055,14 @@ class LLM(BaseLLM):
                                     and "usage" in last_chunk
                                 ):
                                     usage_info = last_chunk["usage"]
-                                elif hasattr(last_chunk, "usage"):
-                                    if not isinstance(last_chunk.usage, type):
-                                        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
+                                    )
                         except Exception as e:
                             logging.debug(f"Error extracting usage info: {e}")
 
@@ -1123,13 +1135,10 @@ class LLM(BaseLLM):
                 params["response_model"] = response_model
             response = litellm.completion(**params)
 
-            if (
-                hasattr(response, "usage")
-                and not isinstance(response.usage, type)
-                and response.usage
-            ):
-                usage_info = response.usage
-                self._track_token_usage_internal(usage_info)
+            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)
 
         except LLMContextLengthExceededError:
             # Re-raise our own context length error
@@ -1141,7 +1150,11 @@ class LLM(BaseLLM):
                 raise LLMContextLengthExceededError(error_msg) from e
             raise
 
-        response_usage = self._usage_to_dict(getattr(response, "usage", None))
+        response_usage = self._usage_to_dict(
+            response.model_extra.get("usage")
+            if isinstance(response, ModelResponseBase) and response.model_extra
+            else None
+        )
 
         # --- 2) Handle structured output response (when response_model is provided)
         if response_model is not None:
@@ -1166,8 +1179,13 @@ class LLM(BaseLLM):
         # --- 3) Handle callbacks with usage info
         if callbacks and len(callbacks) > 0:
             for callback in callbacks:
-                if hasattr(callback, "log_success_event"):
-                    usage_info = getattr(response, "usage", None)
+                if isinstance(callback, TokenCalcHandler):
+                    usage_info = (
+                        response.model_extra.get("usage")
+                        if isinstance(response, ModelResponseBase)
+                        and response.model_extra
+                        else None
+                    )
                     if usage_info:
                         callback.log_success_event(
                             kwargs=params,
@@ -1176,7 +1194,7 @@ class LLM(BaseLLM):
                             end_time=0,
                         )
         # --- 4) Check for tool calls
-        tool_calls = getattr(response_message, "tool_calls", [])
+        tool_calls = response_message.tool_calls or []
 
         # --- 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:
@@ -1269,13 +1287,10 @@ class LLM(BaseLLM):
                 params["response_model"] = response_model
             response = await litellm.acompletion(**params)
 
-            if (
-                hasattr(response, "usage")
-                and not isinstance(response.usage, type)
-                and response.usage
-            ):
-                usage_info = response.usage
-                self._track_token_usage_internal(usage_info)
+            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)
 
         except LLMContextLengthExceededError:
             # Re-raise our own context length error
@@ -1287,7 +1302,11 @@ class LLM(BaseLLM):
                 raise LLMContextLengthExceededError(error_msg) from e
             raise
 
-        response_usage = self._usage_to_dict(getattr(response, "usage", None))
+        response_usage = self._usage_to_dict(
+            response.model_extra.get("usage")
+            if isinstance(response, ModelResponseBase) and response.model_extra
+            else None
+        )
 
         if response_model is not None:
             if isinstance(response, BaseModel):
@@ -1309,8 +1328,13 @@ class LLM(BaseLLM):
 
         if callbacks and len(callbacks) > 0:
             for callback in callbacks:
-                if hasattr(callback, "log_success_event"):
-                    usage_info = getattr(response, "usage", None)
+                if isinstance(callback, TokenCalcHandler):
+                    usage_info = (
+                        response.model_extra.get("usage")
+                        if isinstance(response, ModelResponseBase)
+                        and response.model_extra
+                        else None
+                    )
                     if usage_info:
                         callback.log_success_event(
                             kwargs=params,
@@ -1319,7 +1343,7 @@ class LLM(BaseLLM):
                             end_time=0,
                         )
 
-        tool_calls = getattr(response_message, "tool_calls", [])
+        tool_calls = response_message.tool_calls or []
 
         if (not tool_calls or not available_functions) and text_response:
             self._handle_emit_call_events(
@@ -1394,18 +1418,19 @@ class LLM(BaseLLM):
             async for chunk in await litellm.acompletion(**params):
                 chunk_count += 1
                 chunk_content = None
-                response_id = chunk.id if hasattr(chunk, "id") else None
+                response_id = chunk.id if isinstance(chunk, ModelResponseBase) else None
 
                 try:
                     choices = None
                     if isinstance(chunk, dict) and "choices" in chunk:
                         choices = chunk["choices"]
-                    elif hasattr(chunk, "choices"):
-                        if not isinstance(chunk.choices, type):
-                            choices = chunk.choices
+                    elif isinstance(chunk, ModelResponseStream):
+                        choices = chunk.choices
 
-                    if hasattr(chunk, "usage") and chunk.usage is not None:
-                        usage_info = chunk.usage
+                    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 choices and len(choices) > 0:
                         first_choice = choices[0]
@@ -1413,19 +1438,19 @@ class LLM(BaseLLM):
 
                         if isinstance(first_choice, dict):
                             delta = first_choice.get("delta", {})
-                        elif hasattr(first_choice, "delta"):
+                        elif isinstance(first_choice, LiteLLMStreamingChoices):
                             delta = first_choice.delta
 
                         if delta:
                             if isinstance(delta, dict):
                                 chunk_content = delta.get("content")
-                            elif hasattr(delta, "content"):
+                            elif isinstance(delta, LiteLLMDelta):
                                 chunk_content = delta.content
 
                             tool_calls: list[ChatCompletionDeltaToolCall] | None = None
                             if isinstance(delta, dict):
                                 tool_calls = delta.get("tool_calls")
-                            elif hasattr(delta, "tool_calls"):
+                            elif isinstance(delta, LiteLLMDelta):
                                 tool_calls = delta.tool_calls
 
                             if tool_calls:
@@ -1461,7 +1486,7 @@ class LLM(BaseLLM):
 
             if callbacks and len(callbacks) > 0 and usage_info:
                 for callback in callbacks:
-                    if hasattr(callback, "log_success_event"):
+                    if isinstance(callback, TokenCalcHandler):
                         callback.log_success_event(
                             kwargs=params,
                             response_obj={"usage": usage_info},
@@ -1920,7 +1945,7 @@ class LLM(BaseLLM):
             return None
         if isinstance(usage, dict):
             return usage
-        if hasattr(usage, "model_dump"):
+        if isinstance(usage, BaseModel):
             result: dict[str, Any] = usage.model_dump()
             return result
         if hasattr(usage, "__dict__"):
@@ -1984,7 +2009,7 @@ class LLM(BaseLLM):
                 )
             return messages
 
-        provider = getattr(self, "provider", None) or self.model
+        provider = self.provider or self.model
 
         for msg in messages:
             files = msg.get("files")
@@ -2035,7 +2060,7 @@ class LLM(BaseLLM):
                 )
             return messages
 
-        provider = getattr(self, "provider", None) or self.model
+        provider = self.provider or self.model
 
         for msg in messages:
             files = msg.get("files")

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -11,10 +11,14 @@ 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
 
 
@@ -189,16 +193,41 @@ class AnthropicCompletion(BaseLLM):
 
     @model_validator(mode="after")
     def _init_clients(self) -> AnthropicCompletion:
-        self._client = Anthropic(**self._get_client_params())
+        """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
 
-        async_client_params = 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)
         if self.interceptor:
             async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
-            async_http_client = httpx.AsyncClient(transport=async_transport)
-            async_client_params["http_client"] = async_http_client
+            async_client_params["http_client"] = httpx.AsyncClient(
+                transport=async_transport
+            )
+        return AsyncAnthropic(**async_client_params)
 
-        self._async_client = AsyncAnthropic(**async_client_params)
-        return self
+    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
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Anthropic-specific fields."""
@@ -213,8 +242,15 @@ class AnthropicCompletion(BaseLLM):
             config["timeout"] = self.timeout
         return config
 
-    def _get_client_params(self) -> dict[str, Any]:
-        """Get client parameters."""
+    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.
+        """
 
         if self.api_key is None:
             self.api_key = os.getenv("ANTHROPIC_API_KEY")
@@ -228,7 +264,7 @@ class AnthropicCompletion(BaseLLM):
             "max_retries": self.max_retries,
         }
 
-        if self.interceptor:
+        if include_http_client and self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
             http_client = httpx.Client(transport=transport)
             client_params["http_client"] = http_client  # type: ignore[assignment]
@@ -473,10 +509,8 @@ class AnthropicCompletion(BaseLLM):
                 continue
 
             try:
-                from crewai.llms.providers.utils.common import safe_tool_conversion
-
                 name, description, parameters = safe_tool_conversion(tool, "Anthropic")
-            except (ImportError, KeyError, ValueError) as e:
+            except (KeyError, ValueError) as e:
                 logging.error(f"Error converting tool to Anthropic format: {e}")
                 raise e
 
@@ -485,8 +519,15 @@ 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"] = parameters
+                anthropic_tool["input_schema"] = (
+                    sanitize_tool_params_for_anthropic_strict(parameters)
+                    if strict_enabled
+                    else parameters
+                )
             else:
                 anthropic_tool["input_schema"] = {
                     "type": "object",
@@ -494,8 +535,7 @@ class AnthropicCompletion(BaseLLM):
                     "required": [],
                 }
 
-            func_info = tool.get("function", {})
-            if func_info.get("strict"):
+            if strict_enabled:
                 anthropic_tool["strict"] = True
 
             anthropic_tools.append(anthropic_tool)
@@ -790,11 +830,11 @@ class AnthropicCompletion(BaseLLM):
         try:
             if betas:
                 params["betas"] = betas
-                response = self._client.beta.messages.create(
+                response = self._get_sync_client().beta.messages.create(
                     **params, extra_body=extra_body
                 )
             else:
-                response = self._client.messages.create(**params)
+                response = self._get_sync_client().messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -942,9 +982,11 @@ class AnthropicCompletion(BaseLLM):
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
         stream_context = (
-            self._client.beta.messages.stream(**stream_params, extra_body=extra_body)
+            self._get_sync_client().beta.messages.stream(
+                **stream_params, extra_body=extra_body
+            )
             if betas
-            else self._client.messages.stream(**stream_params)
+            else self._get_sync_client().messages.stream(**stream_params)
         )
         with stream_context as stream:
             response_id = None
@@ -1223,7 +1265,9 @@ class AnthropicCompletion(BaseLLM):
 
         try:
             # Send tool results back to Claude for final response
-            final_response: Message = self._client.messages.create(**follow_up_params)
+            final_response: Message = self._get_sync_client().messages.create(
+                **follow_up_params
+            )
 
             # Track token usage for follow-up call
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
@@ -1319,11 +1363,11 @@ class AnthropicCompletion(BaseLLM):
         try:
             if betas:
                 params["betas"] = betas
-                response = await self._async_client.beta.messages.create(
+                response = await self._get_async_client().beta.messages.create(
                     **params, extra_body=extra_body
                 )
             else:
-                response = await self._async_client.messages.create(**params)
+                response = await self._get_async_client().messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -1457,11 +1501,11 @@ class AnthropicCompletion(BaseLLM):
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
         stream_context = (
-            self._async_client.beta.messages.stream(
+            self._get_async_client().beta.messages.stream(
                 **stream_params, extra_body=extra_body
             )
             if betas
-            else self._async_client.messages.stream(**stream_params)
+            else self._get_async_client().messages.stream(**stream_params)
         )
         async with stream_context as stream:
             response_id = None
@@ -1626,7 +1670,7 @@ class AnthropicCompletion(BaseLLM):
         ]
 
         try:
-            final_response: Message = await self._async_client.messages.create(
+            final_response: Message = await self._get_async_client().messages.create(
                 **follow_up_params
             )
 
@@ -1754,8 +1798,8 @@ class AnthropicCompletion(BaseLLM):
             from crewai_files.uploaders.anthropic import AnthropicFileUploader
 
             return AnthropicFileUploader(
-                client=self._client,
-                async_client=self._async_client,
+                client=self._get_sync_client(),
+                async_client=self._get_async_client(),
             )
         except ImportError:
             return None

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -116,43 +116,100 @@ class AzureCompletion(BaseLLM):
             data.get("api_version") or os.getenv("AZURE_API_VERSION") or "2024-06-01"
         )
 
-        if not data["api_key"]:
-            raise ValueError(
-                "Azure API key is required. Set AZURE_API_KEY environment variable or pass api_key parameter."
-            )
-        if not data["endpoint"]:
-            raise ValueError(
-                "Azure endpoint is required. Set AZURE_ENDPOINT environment variable or pass endpoint parameter."
-            )
-
+        # 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", "")
-        data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
-            data["endpoint"], model
+        if data["endpoint"]:
+            data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
+                data["endpoint"], model
+            )
+        data["is_azure_openai_endpoint"] = AzureCompletion._is_azure_openai_endpoint(
+            data["endpoint"]
         )
         data["is_openai_model"] = any(
             prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
         )
-        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 data["endpoint"]
         return data
 
+    @staticmethod
+    def _is_azure_openai_endpoint(endpoint: str | None) -> bool:
+        if not endpoint:
+            return False
+        hostname = urlparse(endpoint).hostname or ""
+        return (
+            hostname == "openai.azure.com" or hostname.endswith(".openai.azure.com")
+        ) and "/openai/deployments/" in endpoint
+
     @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:
-            raise ValueError("Azure API key is required.")
+            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."
+            )
         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
 
-        self._client = ChatCompletionsClient(**client_kwargs)
-        self._async_client = AsyncChatCompletionsClient(**client_kwargs)
-        return self
+    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
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Azure-specific fields."""
@@ -713,8 +770,7 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle non-streaming chat completion."""
         try:
-            # Cast params to Any to avoid type checking issues with TypedDict unpacking
-            response: ChatCompletions = self._client.complete(**params)
+            response: ChatCompletions = self._get_sync_client().complete(**params)
             return self._process_completion_response(
                 response=response,
                 params=params,
@@ -913,7 +969,7 @@ class AzureCompletion(BaseLLM):
         tool_calls: dict[int, dict[str, Any]] = {}
 
         usage_data: dict[str, Any] | None = None
-        for update in self._client.complete(**params):
+        for update in self._get_sync_client().complete(**params):
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if update.usage:
                     usage = update.usage
@@ -953,8 +1009,9 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle non-streaming chat completion asynchronously."""
         try:
-            # Cast params to Any to avoid type checking issues with TypedDict unpacking
-            response: ChatCompletions = await self._async_client.complete(**params)
+            response: ChatCompletions = await self._get_async_client().complete(
+                **params
+            )
             return self._process_completion_response(
                 response=response,
                 params=params,
@@ -980,7 +1037,7 @@ class AzureCompletion(BaseLLM):
 
         usage_data: dict[str, Any] | None = None
 
-        stream = await self._async_client.complete(**params)
+        stream = await self._get_async_client().complete(**params)
         async for update in stream:
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if hasattr(update, "usage") and update.usage:
@@ -1103,9 +1160,12 @@ class AzureCompletion(BaseLLM):
         """Close the async client and clean up resources.
 
         This ensures proper cleanup of the underlying aiohttp session
-        to avoid unclosed connector warnings.
+        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.
         """
-        if hasattr(self._async_client, "close"):
+        if self._async_client is not None and hasattr(self._async_client, "close"):
             await self._async_client.close()
 
     async def __aenter__(self) -> Self:

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -12,11 +12,15 @@ 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,
 )
-from crewai.utilities.pydantic_schema_utils import generate_model_description
+from crewai.utilities.pydantic_schema_utils import (
+    generate_model_description,
+    sanitize_tool_params_for_bedrock_strict,
+)
 from crewai.utilities.types import LLMMessage
 
 
@@ -303,6 +307,22 @@ 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"},
@@ -314,9 +334,17 @@ class BedrockCompletion(BaseLLM):
             aws_session_token=self.aws_session_token,
             region_name=self.region_name,
         )
-        self._client = session.client("bedrock-runtime", config=config)
-        self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
-        return self
+        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
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Bedrock-specific fields."""
@@ -656,7 +684,7 @@ class BedrockCompletion(BaseLLM):
                     raise ValueError(f"Invalid message format at index {i}")
 
             # Call Bedrock Converse API with proper error handling
-            response = self._client.converse(
+            response = self._get_sync_client().converse(
                 modelId=self.model_id,
                 messages=cast(
                     "Sequence[MessageTypeDef | MessageOutputTypeDef]",
@@ -945,7 +973,7 @@ class BedrockCompletion(BaseLLM):
         usage_data: dict[str, Any] | None = None
 
         try:
-            response = self._client.converse_stream(
+            response = self._get_sync_client().converse_stream(
                 modelId=self.model_id,
                 messages=cast(
                     "Sequence[MessageTypeDef | MessageOutputTypeDef]",
@@ -1949,8 +1977,6 @@ 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:
@@ -1962,12 +1988,19 @@ class BedrockCompletion(BaseLLM):
                     "description": description,
                 }
 
+                func_info = tool.get("function", {})
+                strict_enabled = bool(func_info.get("strict"))
+
                 if parameters and isinstance(parameters, dict):
-                    input_schema: ToolInputSchema = {"json": parameters}
+                    schema_params = (
+                        sanitize_tool_params_for_bedrock_strict(parameters)
+                        if strict_enabled
+                        else parameters
+                    )
+                    input_schema: ToolInputSchema = {"json": schema_params}
                     tool_spec["inputSchema"] = input_schema
 
-                func_info = tool.get("function", {})
-                if func_info.get("strict"):
+                if strict_enabled:
                     tool_spec["strict"] = True
 
                 converse_tool: ConverseToolTypeDef = {"toolSpec": tool_spec}

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -118,9 +118,33 @@ class GeminiCompletion(BaseLLM):
 
     @model_validator(mode="after")
     def _init_client(self) -> GeminiCompletion:
-        self._client = self._initialize_client(self.use_vertexai)
+        """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
         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()
@@ -228,6 +252,7 @@ class GeminiCompletion(BaseLLM):
 
         if (
             hasattr(self, "client")
+            and self._client is not None
             and hasattr(self._client, "vertexai")
             and self._client.vertexai
         ):
@@ -1112,7 +1137,7 @@ class GeminiCompletion(BaseLLM):
         try:
             # The API accepts list[Content] but mypy is overly strict about variance
             contents_for_api: Any = contents
-            response = self._client.models.generate_content(
+            response = self._get_sync_client().models.generate_content(
                 model=self.model,
                 contents=contents_for_api,
                 config=config,
@@ -1153,7 +1178,7 @@ class GeminiCompletion(BaseLLM):
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
-        for chunk in self._client.models.generate_content_stream(
+        for chunk in self._get_sync_client().models.generate_content_stream(
             model=self.model,
             contents=contents_for_api,
             config=config,
@@ -1191,7 +1216,7 @@ class GeminiCompletion(BaseLLM):
         try:
             # The API accepts list[Content] but mypy is overly strict about variance
             contents_for_api: Any = contents
-            response = await self._client.aio.models.generate_content(
+            response = await self._get_async_client().aio.models.generate_content(
                 model=self.model,
                 contents=contents_for_api,
                 config=config,
@@ -1232,7 +1257,7 @@ class GeminiCompletion(BaseLLM):
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
-        stream = await self._client.aio.models.generate_content_stream(
+        stream = await self._get_async_client().aio.models.generate_content_stream(
             model=self.model,
             contents=contents_for_api,
             config=config,
@@ -1439,6 +1464,6 @@ class GeminiCompletion(BaseLLM):
         try:
             from crewai_files.uploaders.gemini import GeminiFileUploader
 
-            return GeminiFileUploader(client=self._client)
+            return GeminiFileUploader(client=self._get_sync_client())
         except ImportError:
             return None

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -32,11 +32,15 @@ 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
+from crewai.utilities.pydantic_schema_utils import (
+    generate_model_description,
+    sanitize_tool_params_for_openai_strict,
+)
 from crewai.utilities.types import LLMMessage
 
 
@@ -253,22 +257,40 @@ 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)
-            http_client = httpx.Client(transport=transport)
-            client_config["http_client"] = http_client
+            client_config["http_client"] = httpx.Client(transport=transport)
+        return OpenAI(**client_config)
 
-        self._client = OpenAI(**client_config)
-
-        async_client_config = self._get_client_params()
+    def _build_async_client(self) -> Any:
+        client_config = self._get_client_params()
         if self.interceptor:
-            async_transport = AsyncHTTPTransport(interceptor=self.interceptor)
-            async_http_client = httpx.AsyncClient(transport=async_transport)
-            async_client_config["http_client"] = async_http_client
+            transport = AsyncHTTPTransport(interceptor=self.interceptor)
+            client_config["http_client"] = httpx.AsyncClient(transport=transport)
+        return AsyncOpenAI(**client_config)
 
-        self._async_client = AsyncOpenAI(**async_client_config)
-        return self
+    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
 
     @property
     def last_response_id(self) -> str | None:
@@ -764,8 +786,6 @@ class OpenAICompletion(BaseLLM):
             "function": {"name": "...", "description": "...", "parameters": {...}}
         }
         """
-        from crewai.llms.providers.utils.common import safe_tool_conversion
-
         responses_tools = []
 
         for tool in tools:
@@ -797,7 +817,7 @@ class OpenAICompletion(BaseLLM):
     ) -> str | ResponsesAPIResult | Any:
         """Handle non-streaming Responses API call."""
         try:
-            response: Response = self._client.responses.create(**params)
+            response: Response = self._get_sync_client().responses.create(**params)
 
             # Track response ID for auto-chaining
             if self.auto_chain and response.id:
@@ -933,7 +953,9 @@ class OpenAICompletion(BaseLLM):
     ) -> str | ResponsesAPIResult | Any:
         """Handle async non-streaming Responses API call."""
         try:
-            response: Response = await self._async_client.responses.create(**params)
+            response: Response = await self._get_async_client().responses.create(
+                **params
+            )
 
             # Track response ID for auto-chaining
             if self.auto_chain and response.id:
@@ -1069,7 +1091,7 @@ class OpenAICompletion(BaseLLM):
         final_response: Response | None = None
         usage: dict[str, Any] | None = None
 
-        stream = self._client.responses.create(**params)
+        stream = self._get_sync_client().responses.create(**params)
         response_id_stream = None
 
         for event in stream:
@@ -1197,7 +1219,7 @@ class OpenAICompletion(BaseLLM):
         final_response: Response | None = None
         usage: dict[str, Any] | None = None
 
-        stream = await self._async_client.responses.create(**params)
+        stream = await self._get_async_client().responses.create(**params)
         response_id_stream = None
 
         async for event in stream:
@@ -1548,11 +1570,6 @@ 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:
@@ -1571,8 +1588,9 @@ class OpenAICompletion(BaseLLM):
                 params_dict = (
                     parameters if isinstance(parameters, dict) else dict(parameters)
                 )
-                params_dict = force_additional_properties_false(params_dict)
-                openai_tool["function"]["parameters"] = params_dict
+                openai_tool["function"]["parameters"] = (
+                    sanitize_tool_params_for_openai_strict(params_dict)
+                )
 
             openai_tools.append(openai_tool)
         return openai_tools
@@ -1591,7 +1609,7 @@ class OpenAICompletion(BaseLLM):
                 parse_params = {
                     k: v for k, v in params.items() if k != "response_format"
                 }
-                parsed_response = self._client.beta.chat.completions.parse(
+                parsed_response = self._get_sync_client().beta.chat.completions.parse(
                     **parse_params,
                     response_format=response_model,
                 )
@@ -1615,7 +1633,9 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
-            response: ChatCompletion = self._client.chat.completions.create(**params)
+            response: ChatCompletion = self._get_sync_client().chat.completions.create(
+                **params
+            )
 
             usage = self._extract_openai_token_usage(response)
 
@@ -1842,7 +1862,7 @@ class OpenAICompletion(BaseLLM):
             }
 
             stream: ChatCompletionStream[BaseModel]
-            with self._client.beta.chat.completions.stream(
+            with self._get_sync_client().beta.chat.completions.stream(
                 **parse_params, response_format=response_model
             ) as stream:
                 for chunk in stream:
@@ -1879,7 +1899,7 @@ class OpenAICompletion(BaseLLM):
             return ""
 
         completion_stream: Stream[ChatCompletionChunk] = (
-            self._client.chat.completions.create(**params)
+            self._get_sync_client().chat.completions.create(**params)
         )
 
         usage_data: dict[str, Any] | None = None
@@ -1976,9 +1996,11 @@ class OpenAICompletion(BaseLLM):
                 parse_params = {
                     k: v for k, v in params.items() if k != "response_format"
                 }
-                parsed_response = await self._async_client.beta.chat.completions.parse(
-                    **parse_params,
-                    response_format=response_model,
+                parsed_response = (
+                    await self._get_async_client().beta.chat.completions.parse(
+                        **parse_params,
+                        response_format=response_model,
+                    )
                 )
                 math_reasoning = parsed_response.choices[0].message
 
@@ -2000,8 +2022,8 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
-            response: ChatCompletion = await self._async_client.chat.completions.create(
-                **params
+            response: ChatCompletion = (
+                await self._get_async_client().chat.completions.create(**params)
             )
 
             usage = self._extract_openai_token_usage(response)
@@ -2127,7 +2149,7 @@ class OpenAICompletion(BaseLLM):
         if response_model:
             completion_stream: AsyncIterator[
                 ChatCompletionChunk
-            ] = await self._async_client.chat.completions.create(**params)
+            ] = await self._get_async_client().chat.completions.create(**params)
 
             accumulated_content = ""
             usage_data: dict[str, Any] | None = None
@@ -2183,7 +2205,7 @@ class OpenAICompletion(BaseLLM):
 
         stream: AsyncIterator[
             ChatCompletionChunk
-        ] = await self._async_client.chat.completions.create(**params)
+        ] = await self._get_async_client().chat.completions.create(**params)
 
         usage_data = None
 
@@ -2379,8 +2401,8 @@ class OpenAICompletion(BaseLLM):
             from crewai_files.uploaders.openai import OpenAIFileUploader
 
             return OpenAIFileUploader(
-                client=self._client,
-                async_client=self._async_client,
+                client=self._get_sync_client(),
+                async_client=self._get_async_client(),
             )
         except ImportError:
             return None

lib/crewai/src/crewai/task.py

@@ -45,6 +45,7 @@ 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
@@ -301,12 +302,14 @@ class Task(BaseModel):
 
     @model_validator(mode="after")
     def validate_required_fields(self) -> Self:
-        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"
-                )
+        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"
+            )
         return self
 
     @model_validator(mode="after")
@@ -838,8 +841,8 @@ class Task(BaseModel):
         should_inject = self.allow_crewai_trigger_context
 
         if should_inject and self.agent:
-            crew = getattr(self.agent, "crew", None)
-            if crew and hasattr(crew, "_inputs") and crew._inputs:
+            crew = self.agent.crew
+            if crew and not isinstance(crew, str) and crew._inputs:
                 trigger_payload = crew._inputs.get("crewai_trigger_payload")
                 if trigger_payload is not None:
                     description += f"\n\nTrigger Payload: {trigger_payload}"
@@ -852,11 +855,12 @@ class Task(BaseModel):
                     isinstance(self.agent.llm, BaseLLM)
                     and self.agent.llm.supports_multimodal()
                 ):
-                    provider: str = str(
-                        getattr(self.agent.llm, "provider", None)
-                        or getattr(self.agent.llm, "model", "openai")
+                    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
                     )
-                    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:

lib/crewai/src/crewai/utilities/pydantic_schema_utils.py

@@ -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
+from typing import TYPE_CHECKING, Annotated, Any, Final, Literal, TypedDict, Union, cast
 import uuid
 
 import jsonref  # type: ignore[import-untyped]
@@ -417,6 +417,119 @@ 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],
     *,

lib/crewai/tests/agents/test_lite_agent.py

@@ -1051,7 +1051,7 @@ def test_lite_agent_verbose_false_suppresses_printer_output():
         successful_requests=1,
     )
 
-    with pytest.warns(DeprecationWarning):
+    with pytest.warns(FutureWarning):
         agent = LiteAgent(
             role="Test Agent",
             goal="Test goal",

lib/crewai/tests/cassettes/test_hierarchical_verbose_false_manager_agent.yaml

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+        and autonomous vehicles, highlighting both opportunities and ethical concerns.\\n\\n2.
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-        uncovers how DAOs powered by AI agents might spontaneously evolve new forms
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+        decision-making with minimal human intervention. An article exploring the
+        rise of autonomous AI agents would captivate readers by diving into how advancements
+        in machine learning and natural language processing have matured these agents
+        from simple chatbots to intelligent collaborators. Themes could include practical
+        applications in industries like healthcare, finance, and personal productivity,
+        the challenges of trust and transparency, and a glimpse into the ethical questions
+        surrounding AI autonomy. This topic not only showcases cutting-edge technology
+        but also invites readers to envision the near future of human-AI synergy.\\n\\n2.
+        **Building Ethical AI Agents: Balancing Innovation with Responsibility**  \\nAs
+        AI agents become more powerful and independent, the imperative to embed ethical
+        frameworks within their design comes sharply into focus. An insightful article
+        on this theme would engage readers by unpacking the complexities of programming
+        morality, fairness, and accountability into AI systems that influence critical
+        decisions\u2014whether in hiring processes, law enforcement, or digital content
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+        technological innovation and societal values, offering a nuanced discussion
+        that appeals to technologists, ethicists, and everyday users alike.\\n\\n3.
+        **AI Agents in Startups: Accelerating Growth and Disrupting Markets**  \\nStartups
+        are uniquely positioned to leverage AI agents as game-changers that turbocharge
+        growth, optimize workflows, and unlock new business models. This article could
+        enthrall readers by detailing how nimble companies integrate AI-driven agents
+        for customer engagement, market analysis, and personalized product recommendations\u2014outpacing
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       good an article about this topic could be. Return the list of ideas with their
       paragraph and your notes.\\n\\nThis is the expected criteria for your final
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-      \\\"coworker\\\": \\\"Senior Writer\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_G2i9RJGNXKVfnd8ZTaBG8Fwi\",\"name\":\"ask_question_to_coworker\",\"content\":\"Here
-      are five trending and engaging topics across various fields that could be explored
-      for an article:\\n\\n1. **The Rise of Autonomous AI Agents and Their Impact
-      on the Future of Work**  \\nExplore how autonomous AI agents\u2014systems capable
-      of performing complex tasks independently\u2014are transforming industries such
-      as customer service, software development, and logistics. Discuss implications
-      for job automation, human-AI collaboration, and ethical considerations surrounding
-      decision-making autonomy.\\n\\n2. **Generative AI Beyond Text: Innovations in
-      Audio, Video, and 3D Content Creation**  \\nDelve into advancements in generative
-      AI models that create not only text but also realistic audio, video content,
-      virtual environments, and 3D models. Highlight applications in gaming, entertainment,
-      education, and digital marketing, as well as challenges like misinformation
-      and deepfake detection.\\n\\n3. **AI-Driven Climate Modeling: Enhancing Predictive
-      Accuracy to Combat Climate Change**  \\nExamine how AI and machine learning
-      are improving climate models by analyzing vast datasets, uncovering patterns,
-      and simulating environmental scenarios. Discuss how these advances are aiding
-      policymakers in making informed decisions to address climate risks and sustainability
-      goals.\\n\\n4. **The Ethical Frontiers of AI in Healthcare: Balancing Innovation
-      with Patient Privacy**  \\nInvestigate ethical challenges posed by AI applications
-      in healthcare, including diagnosis, personalized treatment, and patient data
-      management. Focus on balancing rapid technological innovation with privacy,
-      bias mitigation, and regulatory frameworks to ensure equitable access and trust.\\n\\n5.
-      **Quantum Computing Meets AI: Exploring the Next Leap in Computational Power**
-      \ \\nCover the intersection of quantum computing and artificial intelligence,
-      exploring how quantum algorithms could accelerate AI training processes and
-      solve problems beyond the reach of classical computers. Outline current research,
-      potential breakthroughs, and the timeline for real-world applications.\\n\\nEach
-      of these topics is timely, relevant, and has the potential to engage readers
-      interested in cutting-edge technology, societal impact, and future trends. Let
-      me know if you want me to help develop an outline or deeper research into any
-      of these areas!\"},{\"role\":\"tool\",\"tool_call_id\":\"call_j4KH2SGZvNeioql0HcRQ9NTp\",\"name\":\"ask_question_to_coworker\",\"content\":\"Absolutely!
-      To create compelling and engaging AI articles that stand out, we need to go
-      beyond surface-level discussions and deliver fresh perspectives that challenge
-      assumptions and spark curiosity. Here are five unique angles with their highlight
-      paragraphs that could really captivate our readers:\\n\\n1. **The Hidden Psychology
-      of AI Agents: How They Learn Human Biases and What That Means for Our Future**
-      \ \\n*Highlight:* AI agents don\u2019t just process data\u2014they absorb the
-      subtle nuances and biases embedded in human language, behavior, and culture.
-      This article dives deep into the psychological parallels between AI learning
-      mechanisms and human cognitive biases, revealing surprising ways AI can both
-      mirror and amplify our prejudices. Understanding these dynamics is crucial for
-      building trustworthy AI systems and reshaping the future relationship between
-      humans and machines.\\n\\n2. **From Assistants to Autonomous Creators: The Rise
-      of AI Agents as Artists, Writers, and Innovators**  \\n*Highlight:* What do
-      we lose and gain when AI agents start producing original art, literature, and
-      innovations? This piece explores groundbreaking examples where AI isn\u2019t
-      just a tool but a creative partner that challenges our definition of authorship
-      and genius. We\u2019ll examine ethical dilemmas, collaborative workflows, and
-      the exciting frontier where human intuition meets algorithmic originality.\\n\\n3.
-      **AI Agents in the Wild: How Decentralized Autonomous Organizations Could Redefine
-      Economy and Governance**  \\n*Highlight:* Imagine AI agents operating autonomously
-      in decentralized networks, making real-time decisions that affect finances,
-      resource management, and governance without human intervention. This article
-      uncovers how DAOs powered by AI agents might spontaneously evolve new forms
-      of organization\u2014transparent, efficient, and resistant to traditional corruption.
-      We\u2019ll investigate early case studies and speculate on how this might disrupt
-      centuries-old societal structures.\\n\\n4. **Beyond Chatbots: The Next Generation
-      of AI Agents as Empathetic Digital Companions**  \\n*Highlight:* Moving past
-      scripted conversations, emerging AI agents simulate empathy and emotional intelligence
-      in ways that can transform mental health care, education, and companionship.
-      This article provides an insider look at the complex algorithms and biofeedback
-      mechanisms enabling AI to recognize, respond to, and foster human emotions\u2014potentially
-      filling gaps in underserved populations while raising profound questions about
-      authenticity and connection.\\n\\n5. **The Environmental Toll of AI Agents:
-      Unmasking the Ecological Cost of Intelligent Automation**  \\n*Highlight:* While
-      AI promises efficiency and innovation, the environmental footprint of training
-      and deploying millions of AI agents is rarely discussed. This eye-opening article
-      quantifies the energy demands of current models, challenges the narrative of
-      AI as an unequivocal green solution, and explores emerging approaches pathing
-      toward sustainable intelligent automation\u2014an urgent conversation for an
-      increasingly eco-conscious tech landscape.\\n\\nEach of these angles opens a
-      door to rich storytelling that blends technical depth, ethical inquiry, and
-      visionary implications\u2014perfect for readers hungry for insight that\u2019s
-      both sophisticated and accessible. Let me know which ones resonate most, or
-      if you want me to refine any into full article outlines!\"},{\"role\":\"user\",\"content\":\"Analyze
+      actual complete content as the final answer, not a summary.\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_BCh6lXsBTdixRuRh6OTBPoIJ\",\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"arguments\":\"{\\\"task\\\":
+      \\\"Come up with a list of 5 interesting ideas to explore for an article.\\\",
+      \\\"context\\\": \\\"We need five intriguing ideas worth exploring for an article.
+      Each idea should have potential for in-depth exploration and appeal to a broad
+      audience, possibly touching on current trends, historical insights, future possibilities,
+      or human interest stories.\\\", \\\"coworker\\\": \\\"Researcher\\\"}\"}},{\"id\":\"call_rAQFeCrS4ogsqvIWRGAYFHGI\",\"type\":\"function\",\"function\":{\"name\":\"delegate_work_to_coworker\",\"arguments\":\"{\\\"task\\\":
+      \\\"Write one amazing paragraph highlight for each of 5 ideas that showcases
+      how good an article about this topic could be.\\\", \\\"context\\\": \\\"Upon
+      receiving five intriguing ideas from the Researcher, create a compelling paragraph
+      for each idea that highlights its potential as a fascinating article. These
+      paragraphs must capture the essence of the topic and explain why it would captivate
+      readers, incorporating possible themes and insights.\\\", \\\"coworker\\\":
+      \\\"Senior Writer\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_BCh6lXsBTdixRuRh6OTBPoIJ\",\"name\":\"delegate_work_to_coworker\",\"content\":\"Certainly!
+      Here are five intriguing article ideas that offer rich potential for deep exploration
+      and broad audience appeal, especially aligned with current trends and human
+      interest in AI and technology:\\n\\n1. **The Evolution of AI Agents: From Rule-Based
+      Bots to Autonomous Decision Makers**  \\n   Explore the historical development
+      of AI agents, tracing the journey from simple scripted chatbots to advanced
+      autonomous systems capable of complex decision-making and learning. Dive into
+      key technological milestones, breakthroughs in machine learning, and current
+      state-of-the-art AI agents. Discuss implications for industries such as customer
+      service, healthcare, and autonomous vehicles, highlighting both opportunities
+      and ethical concerns.\\n\\n2. **AI in Daily Life: How Intelligent Agents Are
+      Reshaping Human Routines**  \\n   Investigate the integration of AI agents in
+      everyday life\u2014from virtual assistants like Siri and Alexa to personalized
+      recommendation systems and smart home devices. Analyze how these AI tools influence
+      productivity, privacy, and social behavior. Include human interest elements
+      through stories of individuals or communities who have embraced or resisted
+      these technologies.\\n\\n3. **The Future of Work: AI Agents as Collaborative
+      Colleagues**  \\n   Examine how AI agents are transforming workplaces by acting
+      as collaborators rather than just tools. Cover applications in creative fields,
+      data analysis, and decision support, while addressing potential challenges such
+      as job displacement, new skill requirements, and the evolving definition of
+      teamwork. Use expert opinions and case studies to paint a nuanced future outlook.\\n\\n4.
+      **Ethics and Accountability in AI Agent Development**  \\n   Delve into the
+      ethical dilemmas posed by increasingly autonomous AI agents\u2014topics like
+      bias in algorithms, data privacy, and accountability for AI-driven decisions.
+      Explore measures being taken globally to regulate AI, frameworks for responsible
+      AI development, and the role of public awareness. Include historical context
+      about technology ethics to provide depth.\\n\\n5. **Human-AI Symbiosis: Stories
+      of Innovative Partnerships Shaping Our World**  \\n   Tell compelling human
+      interest stories about individuals or organizations pioneering collaborative
+      projects with AI agents that lead to breakthroughs in science, art, or social
+      good. Highlight how these partnerships transcend traditional human-machine interaction
+      and open new creative and problem-solving possibilities, inspiring readers about
+      the potential of human-AI synergy.\\n\\nThese ideas are designed to be both
+      engaging and informative, offering multiple angles\u2014technical, historical,
+      ethical, and personal\u2014to keep readers captivated while providing substantial
+      content for in-depth analysis.\"},{\"role\":\"tool\",\"tool_call_id\":\"call_rAQFeCrS4ogsqvIWRGAYFHGI\",\"name\":\"delegate_work_to_coworker\",\"content\":\"1.
+      **The Rise of Autonomous AI Agents: Revolutionizing Everyday Tasks**  \\nImagine
+      a world where AI agents autonomously manage your daily schedule, optimize your
+      work routines, and even handle complex decision-making with minimal human intervention.
+      An article exploring the rise of autonomous AI agents would captivate readers
+      by diving into how advancements in machine learning and natural language processing
+      have matured these agents from simple chatbots to intelligent collaborators.
+      Themes could include practical applications in industries like healthcare, finance,
+      and personal productivity, the challenges of trust and transparency, and a glimpse
+      into the ethical questions surrounding AI autonomy. This topic not only showcases
+      cutting-edge technology but also invites readers to envision the near future
+      of human-AI synergy.\\n\\n2. **Building Ethical AI Agents: Balancing Innovation
+      with Responsibility**  \\nAs AI agents become more powerful and independent,
+      the imperative to embed ethical frameworks within their design comes sharply
+      into focus. An insightful article on this theme would engage readers by unpacking
+      the complexities of programming morality, fairness, and accountability into
+      AI systems that influence critical decisions\u2014whether in hiring processes,
+      law enforcement, or digital content moderation. Exploring real-world case studies
+      alongside philosophical and regulatory perspectives, the piece could illuminate
+      the delicate balance between technological innovation and societal values, offering
+      a nuanced discussion that appeals to technologists, ethicists, and everyday
+      users alike.\\n\\n3. **AI Agents in Startups: Accelerating Growth and Disrupting
+      Markets**  \\nStartups are uniquely positioned to leverage AI agents as game-changers
+      that turbocharge growth, optimize workflows, and unlock new business models.
+      This article could enthrall readers by detailing how nimble companies integrate
+      AI-driven agents for customer engagement, market analysis, and personalized
+      product recommendations\u2014outpacing larger incumbents. It would also examine
+      hurdles such as data privacy, scaling complexities, and the human-AI collaboration
+      dynamic, providing actionable insights for entrepreneurs and investors. The
+      story of AI agents fueling startup innovation not only inspires but also outlines
+      the practical pathways and pitfalls on the frontier of modern entrepreneurship.\\n\\n4.
+      **The Future of Work with AI Agents: Redefining Roles and Skills**  \\nAI agents
+      are redefining professional landscapes by automating routine tasks and augmenting
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+      offer a forward-thinking analysis that resonates deeply with employees, managers,
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+      How Next-Gen AI Agents Anticipate Needs**  \\nThe leap from reactive AI assistants
+      to truly proactive AI agents signifies one of the most thrilling advances in
+      artificial intelligence. An article centered on this evolution would captivate
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+      understanding, and continuous learning to anticipate user needs before they
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+      management, smart homes, and adaptive learning platforms, the article would
+      highlight the profound shift toward intuitive, anticipatory technology. This
+      theme not only excites with futuristic promise but also probes the technical
+      and privacy challenges that come with increased agency and foresight.\"},{\"role\":\"user\",\"content\":\"Analyze
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-        content, virtual environments, and 3D models. Highlight applications in gaming,
-        entertainment, education, and digital marketing, as well as challenges like
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+        development of AI agents, tracing the journey from simple scripted chatbots
+        to advanced autonomous systems capable of complex decision-making and learning.
+        Dive into key technological milestones, breakthroughs in machine learning,
+        and current state-of-the-art AI agents. Discuss implications for industries
+        such as customer service, healthcare, and autonomous vehicles, highlighting
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+        AI agents in everyday life\u2014from virtual assistants like Siri and Alexa
+        to personalized recommendation systems and smart home devices. Analyze how
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+        human interest elements through stories of individuals or communities who
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+        AI Agents as Collaborative Colleagues**  \\n   Examine how AI agents are transforming
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lib/crewai/tests/cli/deploy/test_deploy_main.py

@@ -125,7 +125,7 @@ class TestDeployCommand(unittest.TestCase):
         mock_response.json.return_value = {"uuid": "test-uuid"}
         self.mock_client.deploy_by_uuid.return_value = mock_response
 
-        self.deploy_command.deploy(uuid="test-uuid")
+        self.deploy_command.deploy(uuid="test-uuid", skip_validate=True)
 
         self.mock_client.deploy_by_uuid.assert_called_once_with("test-uuid")
         mock_display.assert_called_once_with({"uuid": "test-uuid"})
@@ -137,7 +137,7 @@ class TestDeployCommand(unittest.TestCase):
         mock_response.json.return_value = {"uuid": "test-uuid"}
         self.mock_client.deploy_by_name.return_value = mock_response
 
-        self.deploy_command.deploy()
+        self.deploy_command.deploy(skip_validate=True)
 
         self.mock_client.deploy_by_name.assert_called_once_with("test_project")
         mock_display.assert_called_once_with({"uuid": "test-uuid"})
@@ -156,7 +156,7 @@ class TestDeployCommand(unittest.TestCase):
         self.mock_client.create_crew.return_value = mock_response
 
         with patch("sys.stdout", new=StringIO()) as fake_out:
-            self.deploy_command.create_crew()
+            self.deploy_command.create_crew(skip_validate=True)
             self.assertIn("Deployment created successfully!", fake_out.getvalue())
             self.assertIn("new-uuid", fake_out.getvalue())
 

lib/crewai/tests/cli/deploy/test_validate.py

@@ -0,0 +1,430 @@
+"""Tests for `crewai.cli.deploy.validate`.
+
+The fixtures here correspond 1:1 to the deployment-failure patterns observed
+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

lib/crewai/tests/cli/test_cli.py

@@ -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)
+    mock_deploy.deploy.assert_called_once_with(uuid=uuid, skip_validate=False)
 
 
 @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)
+    mock_deploy.deploy.assert_called_once_with(uuid=None, skip_validate=False)
 
 
 @mock.patch("crewai.cli.cli.DeployCommand")

lib/crewai/tests/llms/anthropic/test_anthropic_async.py

@@ -3,13 +3,9 @@ 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
 
 
@@ -48,9 +44,7 @@ async def test_anthropic_async_with_max_tokens():
 
     assert result is not None
     assert isinstance(result, str)
-    encoder = tiktoken.get_encoding("cl100k_base")
-    token_count = len(encoder.encode(result))
-    assert token_count <= 10
+    assert len(result.split()) <= 10
 
 
 @pytest.mark.vcr()

lib/crewai/tests/llms/azure/test_azure.py

@@ -2,6 +2,7 @@ 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
@@ -378,23 +379,72 @@ def test_azure_completion_with_tools():
 
 
 def test_azure_raises_error_when_endpoint_missing():
-    """Test that AzureCompletion raises ValueError when endpoint is missing"""
+    """Credentials are validated lazily: construction succeeds, first
+    client build raises the descriptive error."""
     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"):
-            AzureCompletion(model="gpt-4", api_key="test-key")
+            llm._get_sync_client()
 
 
 def test_azure_raises_error_when_api_key_missing():
-    """Test that AzureCompletion raises ValueError when API key is missing"""
+    """Credentials are validated lazily: construction succeeds, first
+    client build raises the descriptive error."""
     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"):
-            AzureCompletion(model="gpt-4", endpoint="https://test.openai.azure.com")
+            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
 
 
 def test_azure_endpoint_configuration():

lib/crewai/tests/llms/azure/test_azure_async.py

@@ -1,7 +1,6 @@
 """Tests for Azure async completion functionality."""
 
 import pytest
-import tiktoken
 
 from crewai import Agent, Task, Crew
 from crewai.llm import LLM
@@ -57,9 +56,7 @@ async def test_azure_async_with_max_tokens():
 
     assert result is not None
     assert isinstance(result, str)
-    encoder = tiktoken.get_encoding("cl100k_base")
-    token_count = len(encoder.encode(result))
-    assert token_count <= 10
+    assert len(result.split()) <= 10
 
 
 @pytest.mark.vcr()

lib/crewai/tests/llms/bedrock/test_bedrock_async.py

@@ -6,7 +6,6 @@ cannot be played back properly in CI.
 """
 
 import pytest
-import tiktoken
 
 from crewai.llm import LLM
 
@@ -51,9 +50,7 @@ async def test_bedrock_async_with_max_tokens():
 
     assert result is not None
     assert isinstance(result, str)
-    encoder = tiktoken.get_encoding("cl100k_base")
-    token_count = len(encoder.encode(result))
-    assert token_count <= 10
+    assert len(result.split()) <= 10
 
 
 @pytest.mark.vcr()

lib/crewai/tests/llms/google/test_google.py

@@ -64,6 +64,23 @@ 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.

lib/crewai/tests/llms/google/test_google_async.py

@@ -1,7 +1,6 @@
 """Tests for Google (Gemini) async completion functionality."""
 
 import pytest
-import tiktoken
 
 from crewai import Agent, Task, Crew
 from crewai.llm import LLM
@@ -43,9 +42,7 @@ async def test_gemini_async_with_max_tokens():
 
     assert result is not None
     assert isinstance(result, str)
-    encoder = tiktoken.get_encoding("cl100k_base")
-    token_count = len(encoder.encode(result))
-    assert token_count <= 1000
+    assert len(result.split()) <= 1000
 
 
 @pytest.mark.vcr()

lib/crewai/tests/llms/litellm/test_litellm_async.py

@@ -1,7 +1,6 @@
 """Tests for LiteLLM fallback async completion functionality."""
 
 import pytest
-import tiktoken
 
 from crewai.llm import LLM
 
@@ -44,9 +43,7 @@ async def test_litellm_async_with_max_tokens():
 
     assert result is not None
     assert isinstance(result, str)
-    encoder = tiktoken.get_encoding("cl100k_base")
-    token_count = len(encoder.encode(result))
-    assert token_count <= 10
+    assert len(result.split()) <= 10
 
 
 @pytest.mark.asyncio

lib/crewai/tests/llms/openai/test_openai_async.py

@@ -1,7 +1,6 @@
 """Tests for OpenAI async completion functionality."""
 
 import pytest
-import tiktoken
 
 from crewai import Agent, Task, Crew
 from crewai.llm import LLM
@@ -42,9 +41,7 @@ async def test_openai_async_with_max_tokens():
 
     assert result is not None
     assert isinstance(result, str)
-    encoder = tiktoken.get_encoding("cl100k_base")
-    token_count = len(encoder.encode(result))
-    assert token_count <= 10
+    assert len(result.split()) <= 10
 
 
 @pytest.mark.vcr()

lib/crewai/tests/memory/test_unified_memory.py

@@ -51,14 +51,13 @@ 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 "0.1" not in repr(r)
+    assert "embedding=" not in repr(r)
 
     # Direct attribute access still works for storage layer
     assert r.embedding is not None

lib/crewai/tests/utilities/test_llm_utils.py

@@ -119,10 +119,12 @@ def test_create_llm_with_invalid_type() -> None:
 
 
 def test_create_llm_openai_missing_api_key() -> None:
-    """Test that create_llm raises error when OpenAI API key is missing"""
+    """Credentials are validated lazily: `create_llm` succeeds, and the
+    descriptive error only surfaces when the client is actually built."""
     with patch.dict(os.environ, {}, clear=True):
+        llm = create_llm(llm_value="gpt-4o")
         with pytest.raises((ValueError, ImportError)) as exc_info:
-            create_llm(llm_value="gpt-4o")
+            llm._get_sync_client()
 
         error_message = str(exc_info.value).lower()
         assert "openai_api_key" in error_message or "api_key" in error_message

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.14.2a2"
+__version__ = "1.14.2a3"

lib/devtools/src/crewai_devtools/cli.py

@@ -29,6 +29,33 @@ 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.
 
@@ -264,11 +291,9 @@ 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],
@@ -280,10 +305,7 @@ 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
 
@@ -477,7 +499,7 @@ 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:]  # after "crewai"
+    rest = spec[6:]
     return len(rest) > 0 and rest[0] in ("[", "=", ">", "<", "~", "!")
 
 
@@ -499,7 +521,6 @@ 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):
@@ -638,7 +659,6 @@ 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:
@@ -680,11 +700,6 @@ 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:
@@ -764,7 +779,6 @@ 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:
@@ -1163,13 +1177,11 @@ 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("=="):
-            # Find end of version — next quote or whitespace
-            ver_start = 2  # len("==")
+            ver_start = 2
             ver_end = ver_start
             while ver_end < len(rest) and rest[ver_end] not in ('"', "'", " ", "\n"):
                 ver_end += 1
@@ -1331,7 +1343,6 @@ 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():
@@ -1361,14 +1372,12 @@ 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)}"
@@ -1408,7 +1417,6 @@ 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)
@@ -1442,7 +1450,6 @@ 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)
 
@@ -1484,7 +1491,6 @@ 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:
@@ -1559,11 +1565,6 @@ 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."""
@@ -1831,62 +1832,80 @@ def release(
         skip_enterprise: Skip the enterprise release phase.
         skip_to_enterprise: Skip phases 1 & 2, run only the enterprise release phase.
     """
-    try:
-        check_gh_installed()
+    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"
+    )
 
-        if skip_enterprise and 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(
-                "[red]Error:[/red] Cannot use both --skip-enterprise "
-                "and --skip-to-enterprise"
+                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)
 
-        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"
 
-        cwd = Path.cwd()
-        lib_dir = cwd / "lib"
+    is_prerelease = _is_prerelease(version)
 
-        is_prerelease = _is_prerelease(version)
-
-        if skip_to_enterprise:
+    if skip_to_enterprise:
+        try:
             _release_enterprise(version, is_prerelease, dry_run)
-            console.print(
-                f"\n[green]✓[/green] Enterprise release [bold]{version}[/bold] complete!"
+        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"
             )
-            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 ---
+            sys.exit(1)
         console.print(
-            f"\n[bold cyan]Phase 1: Bumping versions to {version}[/bold cyan]"
+            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:
         _update_all_versions(cwd, lib_dir, version, packages, dry_run)
 
         branch_name = f"feat/bump-version-{version}"
@@ -1930,12 +1949,17 @@ def release(
             console.print(
                 "[dim][DRY RUN][/dim] Would push branch, create PR, and wait for merge"
             )
-
-        # --- Phase 2: Tag and release ---
-        console.print(
-            f"\n[bold cyan]Phase 2: Tagging and releasing {version}[/bold cyan]"
+    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}"
         )
+        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:
@@ -1962,22 +1986,57 @@ 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:
+    if not skip_enterprise:
+        try:
             _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!")
-
-    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)
+    console.print(f"\n[green]✓[/green] Release [bold]{version}[/bold] complete!")
 
 
 cli.add_command(bump)

pyproject.toml

@@ -12,7 +12,7 @@ dev = [
     "mypy==1.19.1",
     "pre-commit==4.5.1",
     "bandit==1.9.2",
-    "pytest==8.4.2",
+    "pytest==9.0.3",
     "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==0.10.0",
+    "pytest-split==0.11.0",
     "types-requests~=2.31.0.6",
     "types-pyyaml==6.0.*",
     "types-regex==2026.1.15.*",
@@ -30,6 +30,7 @@ dev = [
     "types-pymysql==1.1.0.20250916",
     "types-aiofiles~=25.1.0",
     "commitizen>=4.13.9",
+    "pip-audit==2.9.0",
 ]
 
 
@@ -161,7 +162,7 @@ info = "Commits must follow Conventional Commits 1.0.0."
 
 
 [tool.uv]
-exclude-newer = "2026-04-10"  # pinned for CVE-2026-39892; restore to "3 days" after 2026-04-11
+exclude-newer = "3 days"
 
 # 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.
@@ -169,6 +170,8 @@ exclude-newer = "2026-04-10"  # pinned for CVE-2026-39892; restore to "3 days" a
 # 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'",
@@ -177,6 +180,8 @@ override-dependencies = [
     "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]

uv.lock

@@ -13,7 +13,8 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-04-10T16:00:00Z"
+exclude-newer = "2026-04-10T18:30:59.748668Z"
+exclude-newer-span = "P3D"
 
 [manifest]
 members = [
@@ -27,9 +28,11 @@ overrides = [
     { name = "langchain-core", specifier = ">=1.2.28,<2" },
     { name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.24" },
     { name = "pillow", specifier = ">=12.1.1" },
+    { name = "pypdf", specifier = ">=6.10.0,<7" },
     { name = "rich", specifier = ">=13.7.1" },
     { name = "transformers", marker = "python_full_version >= '3.10'", specifier = ">=5.4.0" },
     { name = "urllib3", specifier = ">=2.6.3" },
+    { name = "uv", specifier = ">=0.11.6,<1" },
 ]
 
 [manifest.dependency-groups]
@@ -38,12 +41,13 @@ dev = [
     { name = "boto3-stubs", extras = ["bedrock-runtime"], specifier = "==1.42.40" },
     { name = "commitizen", specifier = ">=4.13.9" },
     { name = "mypy", specifier = "==1.19.1" },
+    { name = "pip-audit", specifier = "==2.9.0" },
     { name = "pre-commit", specifier = "==4.5.1" },
-    { name = "pytest", specifier = "==8.4.2" },
+    { name = "pytest", specifier = "==9.0.3" },
     { name = "pytest-asyncio", specifier = "==1.3.0" },
     { name = "pytest-randomly", specifier = "==4.0.1" },
     { name = "pytest-recording", specifier = "==0.13.4" },
-    { name = "pytest-split", specifier = "==0.10.0" },
+    { name = "pytest-split", specifier = "==0.11.0" },
     { name = "pytest-subprocess", specifier = "==1.5.3" },
     { name = "pytest-timeout", specifier = "==2.4.0" },
     { name = "pytest-xdist", specifier = "==3.8.0" },
@@ -613,6 +617,15 @@ wheels = [
     { 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" },
 ]
 
+[[package]]
+name = "boolean-py"
+version = "5.0"
+source = { registry = "https://pypi.org/simple" }
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