refactor: decouple internal plumbing from litellm (token counting, callbacks, feature detection, errors)

- Token counting: Make TokenCalcHandler standalone class that conditionally
  inherits from litellm.CustomLogger when litellm is available, works as
  plain object when not installed

- Callbacks: Guard set_callbacks() and set_env_callbacks() behind
  LITELLM_AVAILABLE checks - these only affect the litellm fallback path,
  native providers emit events via base_llm.py

- Feature detection: Guard supports_function_calling(), supports_stop_words(),
  and _validate_call_params() behind LITELLM_AVAILABLE checks with sensible
  defaults (True for function calling/stop words since all modern models
  support them)

- Error types: Replace litellm.exceptions.ContextWindowExceededError catches
  with pattern-based detection using LLMContextLengthExceededError._is_context_limit_error()

This decouples crewAI's internal infrastructure from litellm, allowing the
native providers (OpenAI, Anthropic, Azure, Bedrock, Gemini) to work without
litellm installed. The litellm fallback for niche providers still works when
litellm IS installed.

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

.github/workflows/pr-size.yml

@@ -0,0 +1,32 @@
+name: PR Size Check
+
+on:
+  pull_request:
+    types: [opened, synchronize, reopened]
+
+jobs:
+  pr-size:
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    steps:
+      - uses: codelytv/pr-size-labeler@v1
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          xs_label: "size/XS"
+          xs_max_size: 25
+          s_label: "size/S"
+          s_max_size: 100
+          m_label: "size/M"
+          m_max_size: 250
+          l_label: "size/L"
+          l_max_size: 500
+          xl_label: "size/XL"
+          fail_if_xl: false
+          files_to_ignore: |
+            uv.lock
+            *.lock
+            lib/crewai/src/crewai/cli/templates/**
+            **/*.json
+            **/test_durations/**
+            **/cassettes/**

.github/workflows/pr-title.yml

@@ -0,0 +1,41 @@
+name: PR Title Check
+
+on:
+  pull_request:
+    types: [opened, edited, synchronize, reopened]
+
+permissions:
+  contents: read
+  pull-requests: read
+
+jobs:
+  pr-title:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: amannn/action-semantic-pull-request@v5
+        continue-on-error: true
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          types: |
+            feat
+            fix
+            refactor
+            perf
+            test
+            docs
+            chore
+            ci
+            style
+            revert
+          requireScope: false
+          subjectPattern: ^[a-z].+[^.]$
+          subjectPatternError: >
+            The PR title "{title}" does not follow conventional commit format.
+
+            Expected: <type>(<scope>): <lowercase description without trailing period>
+
+            Examples:
+              feat(memory): add lancedb storage backend
+              fix(agents): resolve deadlock in concurrent execution
+              chore(deps): bump pydantic to 2.11.9

docs/en/changelog.mdx

@@ -4,6 +4,38 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Mar 23, 2026">
+  ## v1.11.1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.11.1)
+
+  ## What's Changed
+
+  ### Features
+  - Add flow_structure() serializer for Flow class introspection.
+
+  ### Bug Fixes
+  - Fix security vulnerabilities by bumping pypdf, tinytag, and langchain-core.
+  - Preserve full LLM config across HITL resume for non-OpenAI providers.
+  - Prevent path traversal in FileWriterTool.
+  - Fix lock_store crash when redis package is not installed.
+  - Pass cache_function from BaseTool to CrewStructuredTool.
+
+  ### Documentation
+  - Add publish custom tools guide with translations.
+  - Update changelog and version for v1.11.0.
+  - Add missing event listeners documentation.
+
+  ### Refactoring
+  - Replace urllib with requests in pdf loader.
+  - Replace Any-typed callback and model fields with serializable types.
+
+  ## Contributors
+
+  @alex-clawd, @danielfsbarreto, @dependabot[bot], @greysonlalonde, @lorenzejay, @lucasgomide, @mattatcha, @theCyberTech, @vinibrsl
+
+</Update>
+
 <Update label="Mar 18, 2026">
   ## v1.11.0
 

docs/en/concepts/skills.mdx

@@ -0,0 +1,115 @@
+---
+title: Skills
+description: Filesystem-based skill packages that inject context into agent prompts.
+icon: bolt
+mode: "wide"
+---
+
+## Overview
+
+Skills are self-contained directories that provide agents with domain-specific instructions, references, and assets. Each skill is defined by a `SKILL.md` file with YAML frontmatter and a markdown body.
+
+Skills use **progressive disclosure** — metadata is loaded first, full instructions only when activated, and resource catalogs only when needed.
+
+## Directory Structure
+
+```
+my-skill/
+├── SKILL.md            # Required — frontmatter + instructions
+├── scripts/            # Optional — executable scripts
+├── references/         # Optional — reference documents
+└── assets/             # Optional — static files (configs, data)
+```
+
+The directory name must match the `name` field in `SKILL.md`.
+
+## SKILL.md Format
+
+```markdown
+---
+name: my-skill
+description: Short description of what this skill does and when to use it.
+license: Apache-2.0                    # optional
+compatibility: crewai>=0.1.0          # optional
+metadata:                              # optional
+  author: your-name
+  version: "1.0"
+allowed-tools: web-search file-read   # optional, space-delimited
+---
+
+Instructions for the agent go here. This markdown body is injected
+into the agent's prompt when the skill is activated.
+```
+
+### Frontmatter Fields
+
+| Field           | Required | Constraints                                                              |
+| :-------------- | :------- | :----------------------------------------------------------------------- |
+| `name`          | Yes      | 1–64 chars. Lowercase alphanumeric and hyphens. No leading/trailing/consecutive hyphens. Must match directory name. |
+| `description`   | Yes      | 1–1024 chars. Describes what the skill does and when to use it.          |
+| `license`       | No       | License name or reference to a bundled license file.                     |
+| `compatibility` | No       | Max 500 chars. Environment requirements (products, packages, network).   |
+| `metadata`      | No       | Arbitrary string key-value mapping.                                      |
+| `allowed-tools` | No       | Space-delimited list of pre-approved tools. Experimental.                |
+
+## Usage
+
+### Agent-level Skills
+
+Pass skill directory paths to an agent:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # discovers all skills in this directory
+)
+```
+
+### Crew-level Skills
+
+Skill paths on a crew are merged into every agent:
+
+```python
+from crewai import Crew
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### Pre-loaded Skills
+
+You can also pass `Skill` objects directly:
+
+```python
+from pathlib import Path
+from crewai.skills import discover_skills, activate_skill
+
+skills = discover_skills(Path("./skills"))
+activated = [activate_skill(s) for s in skills]
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=activated,
+)
+```
+
+## How Skills Are Loaded
+
+Skills load progressively — only the data needed at each stage is read:
+
+| Stage            | What's loaded                                     | When               |
+| :--------------- | :------------------------------------------------ | :----------------- |
+| Discovery        | Name, description, frontmatter fields             | `discover_skills()` |
+| Activation       | Full SKILL.md body text                           | `activate_skill()` |
+
+During normal agent execution, skills are automatically discovered and activated. The `scripts/`, `references/`, and `assets/` directories are available on the skill's `path` for agents that need to reference files directly.
+

docs/ko/changelog.mdx

@@ -4,6 +4,38 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 3월 23일">
+  ## v1.11.1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.11.1)
+
+  ## 변경 사항
+
+  ### 기능
+  - Flow 클래스 내성 검사를 위한 flow_structure() 직렬 변환기 추가.
+
+  ### 버그 수정
+  - pypdf, tinytag 및 langchain-core의 버전을 업데이트하여 보안 취약점 수정.
+  - 비-OpenAI 제공자의 HITL 재개 시 전체 LLM 구성 유지.
+  - FileWriterTool에서 경로 탐색 방지.
+  - redis 패키지가 설치되지 않았을 때 lock_store 충돌 수정.
+  - BaseTool에서 CrewStructuredTool로 cache_function 전달.
+
+  ### 문서화
+  - 번역이 포함된 사용자 정의 도구 게시 가이드 추가.
+  - v1.11.0에 대한 변경 로그 및 버전 업데이트.
+  - 누락된 이벤트 리스너 문서 추가.
+
+  ### 리팩토링
+  - pdf 로더에서 urllib를 requests로 교체.
+  - Any 유형의 콜백 및 모델 필드를 직렬화 가능한 유형으로 교체.
+
+  ## 기여자
+
+  @alex-clawd, @danielfsbarreto, @dependabot[bot], @greysonlalonde, @lorenzejay, @lucasgomide, @mattatcha, @theCyberTech, @vinibrsl
+
+</Update>
+
 <Update label="2026년 3월 18일">
   ## v1.11.0
 

docs/ko/concepts/skills.mdx

@@ -0,0 +1,114 @@
+---
+title: 스킬
+description: 에이전트 프롬프트에 컨텍스트를 주입하는 파일 시스템 기반 스킬 패키지.
+icon: bolt
+mode: "wide"
+---
+
+## 개요
+
+스킬은 에이전트에게 도메인별 지침, 참조 자료, 에셋을 제공하는 자체 포함 디렉터리입니다. 각 스킬은 YAML 프론트매터와 마크다운 본문이 포함된 `SKILL.md` 파일로 정의됩니다.
+
+스킬은 **점진적 공개**를 사용합니다 — 메타데이터가 먼저 로드되고, 활성화 시에만 전체 지침이 로드되며, 필요할 때만 리소스 카탈로그가 로드됩니다.
+
+## 디렉터리 구조
+
+```
+my-skill/
+├── SKILL.md            # 필수 — 프론트매터 + 지침
+├── scripts/            # 선택 — 실행 가능한 스크립트
+├── references/         # 선택 — 참조 문서
+└── assets/             # 선택 — 정적 파일 (설정, 데이터)
+```
+
+디렉터리 이름은 `SKILL.md`의 `name` 필드와 일치해야 합니다.
+
+## SKILL.md 형식
+
+```markdown
+---
+name: my-skill
+description: 이 스킬이 무엇을 하고 언제 사용하는지에 대한 간단한 설명.
+license: Apache-2.0                    # 선택
+compatibility: crewai>=0.1.0          # 선택
+metadata:                              # 선택
+  author: your-name
+  version: "1.0"
+allowed-tools: web-search file-read   # 선택, 공백으로 구분
+---
+
+에이전트를 위한 지침이 여기에 들어갑니다. 이 마크다운 본문은
+스킬이 활성화되면 에이전트의 프롬프트에 주입됩니다.
+```
+
+### 프론트매터 필드
+
+| 필드             | 필수   | 제약 조건                                                                |
+| :-------------- | :----- | :----------------------------------------------------------------------- |
+| `name`          | 예     | 1–64자. 소문자 영숫자와 하이픈. 선행/후행/연속 하이픈 불가. 디렉터리 이름과 일치 필수. |
+| `description`   | 예     | 1–1024자. 스킬이 무엇을 하고 언제 사용하는지 설명.                          |
+| `license`       | 아니오  | 라이선스 이름 또는 번들된 라이선스 파일 참조.                                |
+| `compatibility` | 아니오  | 최대 500자. 환경 요구 사항 (제품, 패키지, 네트워크).                         |
+| `metadata`      | 아니오  | 임의의 문자열 키-값 매핑.                                                  |
+| `allowed-tools` | 아니오  | 공백으로 구분된 사전 승인 도구 목록. 실험적.                                 |
+
+## 사용법
+
+### 에이전트 레벨 스킬
+
+에이전트에 스킬 디렉터리 경로를 전달합니다:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # 이 디렉터리의 모든 스킬을 검색
+)
+```
+
+### 크루 레벨 스킬
+
+크루의 스킬 경로는 모든 에이전트에 병합됩니다:
+
+```python
+from crewai import Crew
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### 사전 로드된 스킬
+
+`Skill` 객체를 직접 전달할 수도 있습니다:
+
+```python
+from pathlib import Path
+from crewai.skills import discover_skills, activate_skill
+
+skills = discover_skills(Path("./skills"))
+activated = [activate_skill(s) for s in skills]
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=activated,
+)
+```
+
+## 스킬 로드 방식
+
+스킬은 점진적으로 로드됩니다 — 각 단계에서 필요한 데이터만 읽습니다:
+
+| 단계              | 로드되는 내용                                      | 시점                |
+| :--------------- | :------------------------------------------------ | :----------------- |
+| 검색              | 이름, 설명, 프론트매터 필드                          | `discover_skills()` |
+| 활성화            | 전체 SKILL.md 본문 텍스트                           | `activate_skill()` |
+
+일반적인 에이전트 실행 중에 스킬은 자동으로 검색되고 활성화됩니다. `scripts/`, `references/`, `assets/` 디렉터리는 파일을 직접 참조해야 하는 에이전트를 위해 스킬의 `path`에서 사용할 수 있습니다.

docs/pt-BR/changelog.mdx

@@ -4,6 +4,38 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="23 mar 2026">
+  ## v1.11.1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.11.1)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar o serializer flow_structure() para introspecção da classe Flow.
+
+  ### Correções de Bugs
+  - Corrigir vulnerabilidades de segurança atualizando pypdf, tinytag e langchain-core.
+  - Preservar a configuração completa do LLM durante a retomada do HITL para provedores que não são da OpenAI.
+  - Prevenir a travessia de caminho no FileWriterTool.
+  - Corrigir a falha do lock_store quando o pacote redis não está instalado.
+  - Passar cache_function de BaseTool para CrewStructuredTool.
+
+  ### Documentação
+  - Adicionar guia de publicação de ferramentas personalizadas com traduções.
+  - Atualizar changelog e versão para v1.11.0.
+  - Adicionar documentação de ouvintes de eventos ausentes.
+
+  ### Refatoração
+  - Substituir urllib por requests no carregador de pdf.
+  - Substituir campos de callback e modelo do tipo Any por tipos serializáveis.
+
+  ## Contribuidores
+
+  @alex-clawd, @danielfsbarreto, @dependabot[bot], @greysonlalonde, @lorenzejay, @lucasgomide, @mattatcha, @theCyberTech, @vinibrsl
+
+</Update>
+
 <Update label="18 mar 2026">
   ## v1.11.0
 

docs/pt-BR/concepts/skills.mdx

@@ -0,0 +1,114 @@
+---
+title: Skills
+description: Pacotes de skills baseados em sistema de arquivos que injetam contexto nos prompts dos agentes.
+icon: bolt
+mode: "wide"
+---
+
+## Visão Geral
+
+Skills são diretórios autocontidos que fornecem aos agentes instruções, referências e assets específicos de domínio. Cada skill é definida por um arquivo `SKILL.md` com frontmatter YAML e um corpo em markdown.
+
+Skills usam **divulgação progressiva** — metadados são carregados primeiro, instruções completas apenas quando ativadas, e catálogos de recursos apenas quando necessário.
+
+## Estrutura de Diretório
+
+```
+my-skill/
+├── SKILL.md            # Obrigatório — frontmatter + instruções
+├── scripts/            # Opcional — scripts executáveis
+├── references/         # Opcional — documentos de referência
+└── assets/             # Opcional — arquivos estáticos (configs, dados)
+```
+
+O nome do diretório deve corresponder ao campo `name` no `SKILL.md`.
+
+## Formato do SKILL.md
+
+```markdown
+---
+name: my-skill
+description: Descrição curta do que esta skill faz e quando usá-la.
+license: Apache-2.0                    # opcional
+compatibility: crewai>=0.1.0          # opcional
+metadata:                              # opcional
+  author: your-name
+  version: "1.0"
+allowed-tools: web-search file-read   # opcional, delimitado por espaços
+---
+
+Instruções para o agente vão aqui. Este corpo em markdown é injetado
+no prompt do agente quando a skill é ativada.
+```
+
+### Campos do Frontmatter
+
+| Campo           | Obrigatório | Restrições                                                               |
+| :-------------- | :---------- | :----------------------------------------------------------------------- |
+| `name`          | Sim         | 1–64 chars. Alfanumérico minúsculo e hifens. Sem hifens iniciais/finais/consecutivos. Deve corresponder ao nome do diretório. |
+| `description`   | Sim         | 1–1024 chars. Descreve o que a skill faz e quando usá-la.                |
+| `license`       | Não         | Nome da licença ou referência a um arquivo de licença incluído.           |
+| `compatibility` | Não         | Máx 500 chars. Requisitos de ambiente (produtos, pacotes, rede).          |
+| `metadata`      | Não         | Mapeamento arbitrário de chave-valor string.                              |
+| `allowed-tools` | Não         | Lista de ferramentas pré-aprovadas delimitada por espaços. Experimental.  |
+
+## Uso
+
+### Skills no Nível do Agente
+
+Passe caminhos de diretório de skills para um agente:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # descobre todas as skills neste diretório
+)
+```
+
+### Skills no Nível do Crew
+
+Caminhos de skills no crew são mesclados em todos os agentes:
+
+```python
+from crewai import Crew
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### Skills Pré-carregadas
+
+Você também pode passar objetos `Skill` diretamente:
+
+```python
+from pathlib import Path
+from crewai.skills import discover_skills, activate_skill
+
+skills = discover_skills(Path("./skills"))
+activated = [activate_skill(s) for s in skills]
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=activated,
+)
+```
+
+## Como as Skills São Carregadas
+
+Skills carregam progressivamente — apenas os dados necessários em cada etapa são lidos:
+
+| Etapa            | O que é carregado                                  | Quando              |
+| :--------------- | :------------------------------------------------ | :------------------ |
+| Descoberta       | Nome, descrição, campos do frontmatter             | `discover_skills()` |
+| Ativação         | Texto completo do corpo do SKILL.md                | `activate_skill()` |
+
+Durante a execução normal do agente, skills são automaticamente descobertas e ativadas. Os diretórios `scripts/`, `references/` e `assets/` estão disponíveis no `path` da skill para agentes que precisam referenciar arquivos diretamente.

lib/crewai-files/pyproject.toml

@@ -9,11 +9,11 @@ authors = [
 requires-python = ">=3.10, <3.14"
 dependencies = [
     "Pillow~=12.1.1",
-    "pypdf~=6.7.5",
+    "pypdf~=6.9.1",
     "python-magic>=0.4.27",
     "aiocache~=0.12.3",
     "aiofiles~=24.1.0",
-    "tinytag~=1.10.0",
+    "tinytag~=2.2.1",
     "av~=13.0.0",
 ]
 

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.11.0"
+__version__ = "1.11.1"

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.11.0",
+    "crewai==1.11.1",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

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

@@ -309,4 +309,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.11.0"
+__version__ = "1.11.1"

lib/crewai-tools/src/crewai_tools/rag/loaders/pdf_loader.py

@@ -1,10 +1,12 @@
 """PDF loader for extracting text from PDF files."""
 
 import os
+import tempfile
 from pathlib import Path
-from typing import Any, cast
+from typing import Any
 from urllib.parse import urlparse
-import urllib.request
+
+import requests
 
 from crewai_tools.rag.base_loader import BaseLoader, LoaderResult
 from crewai_tools.rag.source_content import SourceContent
@@ -23,22 +25,34 @@ class PDFLoader(BaseLoader):
             return False
 
     @staticmethod
-    def _download_pdf(url: str) -> bytes:
-        """Download PDF content from a URL.
+    def _download_from_url(url: str, kwargs: dict) -> str:
+        """Download PDF from a URL to a temporary file and return its path.
 
         Args:
             url: The URL to download from.
+            kwargs: Optional dict that may contain custom headers.
 
         Returns:
-            The PDF content as bytes.
+            Path to the temporary file containing the PDF.
 
         Raises:
             ValueError: If the download fails.
         """
+        headers = kwargs.get(
+            "headers",
+            {
+                "Accept": "application/pdf",
+                "User-Agent": "Mozilla/5.0 (compatible; crewai-tools PDFLoader)",
+            },
+        )
 
         try:
-            with urllib.request.urlopen(url, timeout=30) as response:  # noqa: S310
-                return cast(bytes, response.read())
+            response = requests.get(url, headers=headers, timeout=30)
+            response.raise_for_status()
+
+            with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as temp_file:
+                temp_file.write(response.content)
+                return temp_file.name
         except Exception as e:
             raise ValueError(f"Failed to download PDF from {url}: {e!s}") from e
 
@@ -80,8 +94,8 @@ class PDFLoader(BaseLoader):
 
         try:
             if is_url:
-                pdf_bytes = self._download_pdf(file_path)
-                doc = pymupdf.open(stream=pdf_bytes, filetype="pdf")
+                local_path = self._download_from_url(file_path, kwargs)
+                doc = pymupdf.open(local_path)
             else:
                 if not os.path.isfile(file_path):
                     raise FileNotFoundError(f"PDF file not found: {file_path}")

lib/crewai/pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "mcp~=1.26.0",
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
+    "pyyaml~=6.0",
     "lancedb>=0.29.2",
 ]
 
@@ -53,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.11.0",
+    "crewai-tools==1.11.1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -42,7 +42,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.11.0"
+__version__ = "1.11.1"
 _telemetry_submitted = False
 
 

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

@@ -3,6 +3,7 @@ from __future__ import annotations
 import asyncio
 from collections.abc import Callable, Coroutine, Sequence
 import contextvars
+from pathlib import Path
 import shutil
 import subprocess
 import time
@@ -26,6 +27,7 @@ from typing_extensions import Self
 from crewai.agent.planning_config import PlanningConfig
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
+    append_skill_context,
     apply_training_data,
     build_task_prompt_with_schema,
     format_task_with_context,
@@ -65,6 +67,8 @@ from crewai.mcp import MCPServerConfig
 from crewai.mcp.tool_resolver import MCPToolResolver
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.fingerprint import Fingerprint
+from crewai.skills.loader import activate_skill, discover_skills
+from crewai.skills.models import INSTRUCTIONS, Skill as SkillModel
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.types.callback import SerializableCallable
 from crewai.utilities.agent_utils import (
@@ -278,6 +282,8 @@ class Agent(BaseAgent):
         if self.allow_code_execution:
             self._validate_docker_installation()
 
+        self.set_skills()
+
         # Handle backward compatibility: convert reasoning=True to planning_config
         if self.reasoning and self.planning_config is None:
             import warnings
@@ -321,6 +327,76 @@ class Agent(BaseAgent):
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {e!s}") from e
 
+    def set_skills(
+        self,
+        resolved_crew_skills: list[SkillModel] | None = None,
+    ) -> None:
+        """Resolve skill paths and activate skills to INSTRUCTIONS level.
+
+        Path entries trigger discovery and activation. Pre-loaded Skill objects
+        below INSTRUCTIONS level are activated. Crew-level skills are merged in
+        with event emission so observability is consistent regardless of origin.
+
+        Args:
+            resolved_crew_skills: Pre-resolved crew skills (already discovered
+                and activated). When provided, avoids redundant discovery per agent.
+        """
+        from crewai.crew import Crew
+        from crewai.events.event_bus import crewai_event_bus
+        from crewai.events.types.skill_events import SkillActivatedEvent
+
+        if resolved_crew_skills is None:
+            crew_skills: list[Path | SkillModel] | None = (
+                self.crew.skills
+                if isinstance(self.crew, Crew) and isinstance(self.crew.skills, list)
+                else None
+            )
+        else:
+            crew_skills = list(resolved_crew_skills)
+
+        if not self.skills and not crew_skills:
+            return
+
+        needs_work = self.skills and any(
+            isinstance(s, Path)
+            or (isinstance(s, SkillModel) and s.disclosure_level < INSTRUCTIONS)
+            for s in self.skills
+        )
+        if not needs_work and not crew_skills:
+            return
+
+        seen: set[str] = set()
+        resolved: list[Path | SkillModel] = []
+        items: list[Path | SkillModel] = list(self.skills) if self.skills else []
+
+        if crew_skills:
+            items.extend(crew_skills)
+
+        for item in items:
+            if isinstance(item, Path):
+                discovered = discover_skills(item, source=self)
+                for skill in discovered:
+                    if skill.name not in seen:
+                        seen.add(skill.name)
+                        resolved.append(activate_skill(skill, source=self))
+            elif isinstance(item, SkillModel):
+                if item.name not in seen:
+                    seen.add(item.name)
+                    activated = activate_skill(item, source=self)
+                    if activated is item and item.disclosure_level >= INSTRUCTIONS:
+                        crewai_event_bus.emit(
+                            self,
+                            event=SkillActivatedEvent(
+                                from_agent=self,
+                                skill_name=item.name,
+                                skill_path=item.path,
+                                disclosure_level=item.disclosure_level,
+                            ),
+                        )
+                    resolved.append(activated)
+
+        self.skills = resolved if resolved else None
+
     def _is_any_available_memory(self) -> bool:
         """Check if unified memory is available (agent or crew)."""
         if getattr(self, "memory", None):
@@ -442,6 +518,8 @@ class Agent(BaseAgent):
             self.crew.query_knowledge if self.crew else lambda *a, **k: None,
         )
 
+        task_prompt = append_skill_context(self, task_prompt)
+
         prepare_tools(self, tools, task)
         task_prompt = apply_training_data(self, task_prompt)
 
@@ -682,6 +760,8 @@ class Agent(BaseAgent):
             self, task, task_prompt, knowledge_config
         )
 
+        task_prompt = append_skill_context(self, task_prompt)
+
         prepare_tools(self, tools, task)
         task_prompt = apply_training_data(self, task_prompt)
 
@@ -1343,6 +1423,8 @@ class Agent(BaseAgent):
                     ),
                 )
 
+        formatted_messages = append_skill_context(self, formatted_messages)
+
         # Build the input dict for the executor
         inputs: dict[str, Any] = {
             "input": formatted_messages,

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

@@ -210,6 +210,30 @@ def _combine_knowledge_context(agent: Agent) -> str:
     return agent_ctx + separator + crew_ctx
 
 
+def append_skill_context(agent: Agent, task_prompt: str) -> str:
+    """Append activated skill context sections to the task prompt.
+
+    Args:
+        agent: The agent with optional skills.
+        task_prompt: The current task prompt.
+
+    Returns:
+        The task prompt with skill context appended.
+    """
+    if not agent.skills:
+        return task_prompt
+
+    from crewai.skills.loader import format_skill_context
+    from crewai.skills.models import Skill
+
+    skill_sections = [
+        format_skill_context(s) for s in agent.skills if isinstance(s, Skill)
+    ]
+    if skill_sections:
+        task_prompt += "\n\n" + "\n\n".join(skill_sections)
+    return task_prompt
+
+
 def apply_training_data(agent: Agent, task_prompt: str) -> str:
     """Apply training data to the task prompt.
 

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

@@ -3,6 +3,7 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from copy import copy as shallow_copy
 from hashlib import md5
+from pathlib import Path
 import re
 from typing import Any, Final, Literal
 import uuid
@@ -32,6 +33,7 @@ from crewai.memory.memory_scope import MemoryScope, MemorySlice
 from crewai.memory.unified_memory import Memory
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.security_config import SecurityConfig
+from crewai.skills.models import Skill
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.types.callback import SerializableCallable
 from crewai.utilities.config import process_config
@@ -217,6 +219,11 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             "If not set, falls back to crew memory."
         ),
     )
+    skills: list[Path | Skill] | None = Field(
+        default=None,
+        description="Agent Skills. Accepts paths for discovery or pre-loaded Skill objects.",
+        min_length=1,
+    )
 
     @model_validator(mode="before")
     @classmethod
@@ -500,3 +507,6 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
 
     def set_knowledge(self, crew_embedder: EmbedderConfig | None = None) -> None:
         pass
+
+    def set_skills(self, resolved_crew_skills: list[Any] | None = None) -> None:
+        pass

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

@@ -3,6 +3,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from crewai.agents.parser import AgentFinish
+from crewai.memory.utils import sanitize_scope_name
 from crewai.utilities.printer import Printer
 from crewai.utilities.string_utils import sanitize_tool_name
 
@@ -26,7 +27,12 @@ class CrewAgentExecutorMixin:
     _printer: Printer = Printer()
 
     def _save_to_memory(self, output: AgentFinish) -> None:
-        """Save task result to unified memory (memory or crew._memory)."""
+        """Save task result to unified memory (memory or crew._memory).
+
+        Extends the memory's root_scope with agent-specific path segment
+        (e.g., '/crew/research-crew/agent/researcher') so that agent memories
+        are scoped hierarchically under their crew.
+        """
         memory = getattr(self.agent, "memory", None) or (
             getattr(self.crew, "_memory", None) if self.crew else None
         )
@@ -43,6 +49,21 @@ class CrewAgentExecutorMixin:
             )
             extracted = memory.extract_memories(raw)
             if extracted:
-                memory.remember_many(extracted, agent_role=self.agent.role)
+                # Get the memory's existing root_scope
+                base_root = getattr(memory, "root_scope", None)
+
+                if isinstance(base_root, str) and base_root:
+                    # Memory has a root_scope — extend it with agent info
+                    agent_role = self.agent.role or "unknown"
+                    sanitized_role = sanitize_scope_name(agent_role)
+                    agent_root = f"{base_root.rstrip('/')}/agent/{sanitized_role}"
+                    if not agent_root.startswith("/"):
+                        agent_root = "/" + agent_root
+                    memory.remember_many(
+                        extracted, agent_role=self.agent.role, root_scope=agent_root
+                    )
+                else:
+                    # No base root_scope — don't inject one, preserve backward compat
+                    memory.remember_many(extracted, agent_role=self.agent.role)
         except Exception as e:
             self.agent._logger.log("error", f"Failed to save to memory: {e}")

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

@@ -22,6 +22,7 @@ from crewai.cli.replay_from_task import replay_task_command
 from crewai.cli.reset_memories_command import reset_memories_command
 from crewai.cli.run_crew import run_crew
 from crewai.cli.settings.main import SettingsCommand
+from crewai.cli.shared.token_manager import TokenManager
 from crewai.cli.tools.main import ToolCommand
 from crewai.cli.train_crew import train_crew
 from crewai.cli.triggers.main import TriggersCommand
@@ -34,7 +35,7 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
 
 @click.group()
 @click.version_option(get_version("crewai"))
-def crewai():
+def crewai() -> None:
     """Top-level command group for crewai."""
 
 
@@ -45,7 +46,7 @@ def crewai():
     ),
 )
 @click.argument("uv_args", nargs=-1, type=click.UNPROCESSED)
-def uv(uv_args):
+def uv(uv_args: tuple[str, ...]) -> None:
     """A wrapper around uv commands that adds custom tool authentication through env vars."""
     env = os.environ.copy()
     try:
@@ -83,7 +84,9 @@ def uv(uv_args):
 @click.argument("name")
 @click.option("--provider", type=str, help="The provider to use for the crew")
 @click.option("--skip_provider", is_flag=True, help="Skip provider validation")
-def create(type, name, provider, skip_provider=False):
+def create(
+    type: str, name: str, provider: str | None, skip_provider: bool = False
+) -> None:
     """Create a new crew, or flow."""
     if type == "crew":
         create_crew(name, provider, skip_provider)
@@ -97,7 +100,7 @@ def create(type, name, provider, skip_provider=False):
 @click.option(
     "--tools", is_flag=True, help="Show the installed version of crewai tools"
 )
-def version(tools):
+def version(tools: bool) -> None:
     """Show the installed version of crewai."""
     try:
         crewai_version = get_version("crewai")
@@ -128,7 +131,7 @@ def version(tools):
     default="trained_agents_data.pkl",
     help="Path to a custom file for training",
 )
-def train(n_iterations: int, filename: str):
+def train(n_iterations: int, filename: str) -> None:
     """Train the crew."""
     click.echo(f"Training the Crew for {n_iterations} iterations")
     train_crew(n_iterations, filename)
@@ -334,7 +337,7 @@ def memory(
     default="gpt-4o-mini",
     help="LLM Model to run the tests on the Crew. For now only accepting only OpenAI models.",
 )
-def test(n_iterations: int, model: str):
+def test(n_iterations: int, model: str) -> None:
     """Test the crew and evaluate the results."""
     click.echo(f"Testing the crew for {n_iterations} iterations with model {model}")
     evaluate_crew(n_iterations, model)
@@ -347,46 +350,62 @@ def test(n_iterations: int, model: str):
     )
 )
 @click.pass_context
-def install(context):
+def install(context: click.Context) -> None:
     """Install the Crew."""
     install_crew(context.args)
 
 
 @crewai.command()
-def run():
+def run() -> None:
     """Run the Crew."""
     run_crew()
 
 
 @crewai.command()
-def update():
+def update() -> None:
     """Update the pyproject.toml of the Crew project to use uv."""
     update_crew()
 
 
 @crewai.command()
-def login():
+def login() -> None:
     """Sign Up/Login to CrewAI AMP."""
     Settings().clear_user_settings()
     AuthenticationCommand().login()
 
 
+@crewai.command()
+@click.option(
+    "--reset", is_flag=True, help="Also reset all CLI configuration to defaults"
+)
+def logout(reset: bool) -> None:
+    """Logout from CrewAI AMP."""
+    settings = Settings()
+    if reset:
+        settings.reset()
+        click.echo("Successfully logged out and reset all CLI configuration.")
+    else:
+        TokenManager().clear_tokens()
+        settings.clear_user_settings()
+        click.echo("Successfully logged out from CrewAI AMP.")
+
+
 # DEPLOY CREWAI+ COMMANDS
 @crewai.group()
-def deploy():
+def deploy() -> None:
     """Deploy the Crew CLI group."""
 
 
 @deploy.command(name="create")
 @click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
-def deploy_create(yes: bool):
+def deploy_create(yes: bool) -> None:
     """Create a Crew deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.create_crew(yes)
 
 
 @deploy.command(name="list")
-def deploy_list():
+def deploy_list() -> None:
     """List all deployments."""
     deploy_cmd = DeployCommand()
     deploy_cmd.list_crews()
@@ -394,7 +413,7 @@ def deploy_list():
 
 @deploy.command(name="push")
 @click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_push(uuid: str | None):
+def deploy_push(uuid: str | None) -> None:
     """Deploy the Crew."""
     deploy_cmd = DeployCommand()
     deploy_cmd.deploy(uuid=uuid)
@@ -402,7 +421,7 @@ def deploy_push(uuid: str | None):
 
 @deploy.command(name="status")
 @click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deply_status(uuid: str | None):
+def deply_status(uuid: str | None) -> None:
     """Get the status of a deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.get_crew_status(uuid=uuid)
@@ -410,7 +429,7 @@ def deply_status(uuid: str | None):
 
 @deploy.command(name="logs")
 @click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_logs(uuid: str | None):
+def deploy_logs(uuid: str | None) -> None:
     """Get the logs of a deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.get_crew_logs(uuid=uuid)
@@ -418,27 +437,27 @@ def deploy_logs(uuid: str | None):
 
 @deploy.command(name="remove")
 @click.option("-u", "--uuid", type=str, help="Crew UUID parameter")
-def deploy_remove(uuid: str | None):
+def deploy_remove(uuid: str | None) -> None:
     """Remove a deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.remove_crew(uuid=uuid)
 
 
 @crewai.group()
-def tool():
+def tool() -> None:
     """Tool Repository related commands."""
 
 
 @tool.command(name="create")
 @click.argument("handle")
-def tool_create(handle: str):
+def tool_create(handle: str) -> None:
     tool_cmd = ToolCommand()
     tool_cmd.create(handle)
 
 
 @tool.command(name="install")
 @click.argument("handle")
-def tool_install(handle: str):
+def tool_install(handle: str) -> None:
     tool_cmd = ToolCommand()
     tool_cmd.login()
     tool_cmd.install(handle)
@@ -454,26 +473,26 @@ def tool_install(handle: str):
 )
 @click.option("--public", "is_public", flag_value=True, default=False)
 @click.option("--private", "is_public", flag_value=False)
-def tool_publish(is_public: bool, force: bool):
+def tool_publish(is_public: bool, force: bool) -> None:
     tool_cmd = ToolCommand()
     tool_cmd.login()
     tool_cmd.publish(is_public, force)
 
 
 @crewai.group()
-def flow():
+def flow() -> None:
     """Flow related commands."""
 
 
 @flow.command(name="kickoff")
-def flow_run():
+def flow_run() -> None:
     """Kickoff the Flow."""
     click.echo("Running the Flow")
     kickoff_flow()
 
 
 @flow.command(name="plot")
-def flow_plot():
+def flow_plot() -> None:
     """Plot the Flow."""
     click.echo("Plotting the Flow")
     plot_flow()
@@ -481,19 +500,19 @@ def flow_plot():
 
 @flow.command(name="add-crew")
 @click.argument("crew_name")
-def flow_add_crew(crew_name):
+def flow_add_crew(crew_name: str) -> None:
     """Add a crew to an existing flow."""
     click.echo(f"Adding crew {crew_name} to the flow")
     add_crew_to_flow(crew_name)
 
 
 @crewai.group()
-def triggers():
+def triggers() -> None:
     """Trigger related commands. Use 'crewai triggers list' to see available triggers, or 'crewai triggers run app_slug/trigger_slug' to execute."""
 
 
 @triggers.command(name="list")
-def triggers_list():
+def triggers_list() -> None:
     """List all available triggers from integrations."""
     triggers_cmd = TriggersCommand()
     triggers_cmd.list_triggers()
@@ -501,14 +520,14 @@ def triggers_list():
 
 @triggers.command(name="run")
 @click.argument("trigger_path")
-def triggers_run(trigger_path: str):
+def triggers_run(trigger_path: str) -> None:
     """Execute crew with trigger payload. Format: app_slug/trigger_slug"""
     triggers_cmd = TriggersCommand()
     triggers_cmd.execute_with_trigger(trigger_path)
 
 
 @crewai.command()
-def chat():
+def chat() -> None:
     """
     Start a conversation with the Crew, collecting user-supplied inputs,
     and using the Chat LLM to generate responses.
@@ -521,12 +540,12 @@ def chat():
 
 
 @crewai.group(invoke_without_command=True)
-def org():
+def org() -> None:
     """Organization management commands."""
 
 
 @org.command("list")
-def org_list():
+def org_list() -> None:
     """List available organizations."""
     org_command = OrganizationCommand()
     org_command.list()
@@ -534,39 +553,39 @@ def org_list():
 
 @org.command()
 @click.argument("id")
-def switch(id):
+def switch(id: str) -> None:
     """Switch to a specific organization."""
     org_command = OrganizationCommand()
     org_command.switch(id)
 
 
 @org.command()
-def current():
+def current() -> None:
     """Show current organization when 'crewai org' is called without subcommands."""
     org_command = OrganizationCommand()
     org_command.current()
 
 
 @crewai.group()
-def enterprise():
+def enterprise() -> None:
     """Enterprise Configuration commands."""
 
 
 @enterprise.command("configure")
 @click.argument("enterprise_url")
-def enterprise_configure(enterprise_url: str):
+def enterprise_configure(enterprise_url: str) -> None:
     """Configure CrewAI AMP OAuth2 settings from the provided Enterprise URL."""
     enterprise_command = EnterpriseConfigureCommand()
     enterprise_command.configure(enterprise_url)
 
 
 @crewai.group()
-def config():
+def config() -> None:
     """CLI Configuration commands."""
 
 
 @config.command("list")
-def config_list():
+def config_list() -> None:
     """List all CLI configuration parameters."""
     config_command = SettingsCommand()
     config_command.list()
@@ -575,26 +594,26 @@ def config_list():
 @config.command("set")
 @click.argument("key")
 @click.argument("value")
-def config_set(key: str, value: str):
+def config_set(key: str, value: str) -> None:
     """Set a CLI configuration parameter."""
     config_command = SettingsCommand()
     config_command.set(key, value)
 
 
 @config.command("reset")
-def config_reset():
+def config_reset() -> None:
     """Reset all CLI configuration parameters to default values."""
     config_command = SettingsCommand()
     config_command.reset_all_settings()
 
 
 @crewai.group()
-def env():
+def env() -> None:
     """Environment variable commands."""
 
 
 @env.command("view")
-def env_view():
+def env_view() -> None:
     """View tracing-related environment variables."""
     import os
     from pathlib import Path
@@ -672,12 +691,12 @@ def env_view():
 
 
 @crewai.group()
-def traces():
+def traces() -> None:
     """Trace collection management commands."""
 
 
 @traces.command("enable")
-def traces_enable():
+def traces_enable() -> None:
     """Enable trace collection for crew/flow executions."""
     from rich.console import Console
     from rich.panel import Panel
@@ -700,7 +719,7 @@ def traces_enable():
 
 
 @traces.command("disable")
-def traces_disable():
+def traces_disable() -> None:
     """Disable trace collection for crew/flow executions."""
     from rich.console import Console
     from rich.panel import Panel
@@ -723,7 +742,7 @@ def traces_disable():
 
 
 @traces.command("status")
-def traces_status():
+def traces_status() -> None:
     """Show current trace collection status."""
     import os
 

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

@@ -6,7 +6,7 @@ import click
 from crewai.telemetry import Telemetry
 
 
-def create_flow(name):
+def create_flow(name: str) -> None:
     """Create a new flow."""
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
@@ -49,7 +49,7 @@ def create_flow(name):
         "poem_crew",
     ]
 
-    def process_file(src_file, dst_file):
+    def process_file(src_file: Path, dst_file: Path) -> None:
         if src_file.suffix in [".pyc", ".pyo", ".pyd"]:
             return
 

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

@@ -15,7 +15,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
     A class to handle deployment-related operations for CrewAI projects.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         """
         Initialize the DeployCommand with project name and API client.
         """
@@ -67,7 +67,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         Args:
             uuid (Optional[str]): The UUID of the crew to deploy.
         """
-        self._start_deployment_span = self._telemetry.start_deployment_span(uuid)
+        self._telemetry.start_deployment_span(uuid)
         console.print("Starting deployment...", style="bold blue")
         if uuid:
             response = self.plus_api_client.deploy_by_uuid(uuid)
@@ -84,9 +84,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         """
         Create a new crew deployment.
         """
-        self._create_crew_deployment_span = (
-            self._telemetry.create_crew_deployment_span()
-        )
+        self._telemetry.create_crew_deployment_span()
         console.print("Creating deployment...", style="bold blue")
         env_vars = fetch_and_json_env_file()
 
@@ -236,7 +234,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
             uuid (Optional[str]): The UUID of the crew to get logs for.
             log_type (str): The type of logs to retrieve (default: "deployment").
         """
-        self._get_crew_logs_span = self._telemetry.get_crew_logs_span(uuid, log_type)
+        self._telemetry.get_crew_logs_span(uuid, log_type)
         console.print(f"Fetching {log_type} logs...", style="bold blue")
 
         if uuid:
@@ -257,7 +255,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin):
         Args:
             uuid (Optional[str]): The UUID of the crew to remove.
         """
-        self._remove_crew_span = self._telemetry.remove_crew_span(uuid)
+        self._telemetry.remove_crew_span(uuid)
         console.print("Removing deployment...", style="bold blue")
 
         if uuid:

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]==1.11.0"
+    "crewai[tools]==1.11.1"
 ]
 
 [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]==1.11.0"
+    "crewai[tools]==1.11.1"
 ]
 
 [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]==1.11.0"
+    "crewai[tools]==1.11.1"
 ]
 
 [tool.crewai]

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

@@ -16,7 +16,7 @@ class TriggersCommand(BaseCommand, PlusAPIMixin):
     A class to handle trigger-related operations for CrewAI projects.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         BaseCommand.__init__(self)
         PlusAPIMixin.__init__(self, telemetry=self._telemetry)
 

lib/crewai/src/crewai/crew.py

@@ -6,6 +6,7 @@ from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
 import json
+from pathlib import Path
 import re
 from typing import (
     TYPE_CHECKING,
@@ -91,6 +92,7 @@ from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.rag.types import SearchResult
 from crewai.security.fingerprint import Fingerprint
 from crewai.security.security_config import SecurityConfig
+from crewai.skills.models import Skill
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
@@ -294,6 +296,11 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Knowledge for the crew.",
     )
+    skills: list[Path | Skill] | None = Field(
+        default=None,
+        description="Skill search paths or pre-loaded Skill objects applied to all agents in the crew.",
+    )
+
     security_config: SecurityConfig = Field(
         default_factory=SecurityConfig,
         description="Security configuration for the crew, including fingerprinting.",
@@ -357,7 +364,18 @@ class Crew(FlowTrackable, BaseModel):
 
     @model_validator(mode="after")
     def create_crew_memory(self) -> Crew:
-        """Initialize unified memory, respecting crew embedder config."""
+        """Initialize unified memory, respecting crew embedder config.
+
+        When memory is enabled, sets a hierarchical root_scope based on the
+        crew name (e.g. '/crew/research-crew') so that all memories saved by
+        this crew and its agents are organized under a consistent namespace.
+        """
+        from crewai.memory.utils import sanitize_scope_name
+
+        # Compute sanitized crew name for root_scope
+        crew_name = sanitize_scope_name(self.name or "crew")
+        crew_root_scope = f"/crew/{crew_name}"
+
         if self.memory is True:
             from crewai.memory.unified_memory import Memory
 
@@ -365,10 +383,11 @@ class Crew(FlowTrackable, BaseModel):
             if self.embedder is not None:
                 from crewai.rag.embeddings.factory import build_embedder
 
-                embedder = build_embedder(self.embedder)  # type: ignore[arg-type]
-            self._memory = Memory(embedder=embedder)
+                embedder = build_embedder(cast(dict[str, Any], self.embedder))
+            self._memory = Memory(embedder=embedder, root_scope=crew_root_scope)
         elif self.memory:
             # User passed a Memory / MemoryScope / MemorySlice instance
+            # Respect user's configuration — don't auto-set root_scope
             self._memory = self.memory
         else:
             self._memory = None

lib/crewai/src/crewai/crews/utils.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import asyncio
 from collections.abc import Callable, Coroutine, Iterable, Mapping
+from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 from opentelemetry import baggage
@@ -11,6 +12,8 @@ from opentelemetry import baggage
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.crews.crew_output import CrewOutput
 from crewai.rag.embeddings.types import EmbedderConfig
+from crewai.skills.loader import activate_skill, discover_skills
+from crewai.skills.models import INSTRUCTIONS, Skill as SkillModel
 from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
 from crewai.utilities.file_store import store_files
 from crewai.utilities.streaming import (
@@ -51,6 +54,30 @@ def enable_agent_streaming(agents: Iterable[BaseAgent]) -> None:
             agent.llm.stream = True
 
 
+def _resolve_crew_skills(crew: Crew) -> list[SkillModel] | None:
+    """Resolve crew-level skill paths once so agents don't repeat the work."""
+    if not isinstance(crew.skills, list) or not crew.skills:
+        return None
+
+    resolved: list[SkillModel] = []
+    seen: set[str] = set()
+    for item in crew.skills:
+        if isinstance(item, Path):
+            for skill in discover_skills(item):
+                if skill.name not in seen:
+                    seen.add(skill.name)
+                    resolved.append(activate_skill(skill))
+        elif isinstance(item, SkillModel):
+            if item.name not in seen:
+                seen.add(item.name)
+                resolved.append(
+                    activate_skill(item)
+                    if item.disclosure_level < INSTRUCTIONS
+                    else item
+                )
+    return resolved
+
+
 def setup_agents(
     crew: Crew,
     agents: Iterable[BaseAgent],
@@ -67,9 +94,12 @@ def setup_agents(
         function_calling_llm: Default function calling LLM for agents.
         step_callback: Default step callback for agents.
     """
+    resolved_crew_skills = _resolve_crew_skills(crew)
+
     for agent in agents:
         agent.crew = crew
         agent.set_knowledge(crew_embedder=embedder)
+        agent.set_skills(resolved_crew_skills=resolved_crew_skills)
         if not agent.function_calling_llm:  # type: ignore[attr-defined]
             agent.function_calling_llm = function_calling_llm  # type: ignore[attr-defined]
         if not agent.step_callback:  # type: ignore[attr-defined]

lib/crewai/src/crewai/events/__init__.py

@@ -88,6 +88,14 @@ from crewai.events.types.reasoning_events import (
     AgentReasoningStartedEvent,
     ReasoningEvent,
 )
+from crewai.events.types.skill_events import (
+    SkillActivatedEvent,
+    SkillDiscoveryCompletedEvent,
+    SkillDiscoveryStartedEvent,
+    SkillEvent,
+    SkillLoadFailedEvent,
+    SkillLoadedEvent,
+)
 from crewai.events.types.task_events import (
     TaskCompletedEvent,
     TaskEvaluationEvent,
@@ -186,6 +194,12 @@ __all__ = [
     "MethodExecutionFinishedEvent",
     "MethodExecutionStartedEvent",
     "ReasoningEvent",
+    "SkillActivatedEvent",
+    "SkillDiscoveryCompletedEvent",
+    "SkillDiscoveryStartedEvent",
+    "SkillEvent",
+    "SkillLoadFailedEvent",
+    "SkillLoadedEvent",
     "TaskCompletedEvent",
     "TaskEvaluationEvent",
     "TaskFailedEvent",

lib/crewai/src/crewai/events/types/skill_events.py

@@ -0,0 +1,62 @@
+"""Skill lifecycle events for the Agent Skills standard.
+
+Events emitted during skill discovery, loading, and activation.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from crewai.events.base_events import BaseEvent
+
+
+class SkillEvent(BaseEvent):
+    """Base event for skill operations."""
+
+    skill_name: str = ""
+    skill_path: Path | None = None
+    from_agent: Any | None = None
+    from_task: Any | None = None
+
+    def __init__(self, **data: Any) -> None:
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class SkillDiscoveryStartedEvent(SkillEvent):
+    """Event emitted when skill discovery begins."""
+
+    type: str = "skill_discovery_started"
+    search_path: Path
+
+
+class SkillDiscoveryCompletedEvent(SkillEvent):
+    """Event emitted when skill discovery completes."""
+
+    type: str = "skill_discovery_completed"
+    search_path: Path
+    skills_found: int
+    skill_names: list[str]
+
+
+class SkillLoadedEvent(SkillEvent):
+    """Event emitted when a skill is loaded at metadata level."""
+
+    type: str = "skill_loaded"
+    disclosure_level: int = 1
+
+
+class SkillActivatedEvent(SkillEvent):
+    """Event emitted when a skill is activated (promoted to instructions level)."""
+
+    type: str = "skill_activated"
+    disclosure_level: int = 2
+
+
+class SkillLoadFailedEvent(SkillEvent):
+    """Event emitted when skill loading fails."""
+
+    type: str = "skill_load_failed"
+    error: str

lib/crewai/src/crewai/flow/__init__.py

@@ -6,6 +6,7 @@ from crewai.flow.async_feedback import (
 )
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 from crewai.flow.flow_config import flow_config
+from crewai.flow.flow_serializer import flow_structure
 from crewai.flow.human_feedback import HumanFeedbackResult, human_feedback
 from crewai.flow.input_provider import InputProvider, InputResponse
 from crewai.flow.persistence import persist
@@ -29,6 +30,7 @@ __all__ = [
     "and_",
     "build_flow_structure",
     "flow_config",
+    "flow_structure",
     "human_feedback",
     "listen",
     "or_",

lib/crewai/src/crewai/flow/async_feedback/types.py

@@ -60,7 +60,7 @@ class PendingFeedbackContext:
     emit: list[str] | None = None
     default_outcome: str | None = None
     metadata: dict[str, Any] = field(default_factory=dict)
-    llm: str | None = None
+    llm: dict[str, Any] | str | None = None
     requested_at: datetime = field(default_factory=datetime.now)
 
     def to_dict(self) -> dict[str, Any]:

lib/crewai/src/crewai/flow/flow.py

@@ -905,7 +905,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # Internal flows (RecallFlow, EncodingFlow) set _skip_auto_memory
         # to avoid creating a wasteful standalone Memory instance.
         if self.memory is None and not getattr(self, "_skip_auto_memory", False):
-            self.memory = Memory()
+            from crewai.memory.utils import sanitize_scope_name
+
+            flow_name = sanitize_scope_name(self.name or self.__class__.__name__)
+            self.memory = Memory(root_scope=f"/flow/{flow_name}")
 
         # Register all flow-related methods
         for method_name in dir(self):
@@ -1315,7 +1318,25 @@ class Flow(Generic[T], metaclass=FlowMeta):
         context = self._pending_feedback_context
         emit = context.emit
         default_outcome = context.default_outcome
-        llm = context.llm
+
+        # Try to get the live LLM from the re-imported decorator first.
+        # This preserves the fully-configured object (credentials, safety_settings, etc.)
+        # for same-process resume. For cross-process resume, fall back to the
+        # serialized context.llm which is now a dict with full config (or a legacy string).
+        from crewai.flow.human_feedback import _deserialize_llm_from_context
+
+        llm = None
+        method = self._methods.get(FlowMethodName(context.method_name))
+        if method is not None:
+            live_llm = getattr(method, "_hf_llm", None)
+            if live_llm is not None:
+                from crewai.llms.base_llm import BaseLLM as BaseLLMClass
+
+                if isinstance(live_llm, BaseLLMClass):
+                    llm = live_llm
+
+        if llm is None:
+            llm = _deserialize_llm_from_context(context.llm)
 
         # Determine outcome
         collapsed_outcome: str | None = None

lib/crewai/src/crewai/flow/flow_serializer.py

@@ -0,0 +1,619 @@
+"""Flow structure serializer for introspecting Flow classes.
+
+This module provides the flow_structure() function that analyzes a Flow class
+and returns a JSON-serializable dictionary describing its graph structure.
+This is used by Studio UI to render a visual flow graph.
+
+Example:
+    >>> from crewai.flow import Flow, start, listen
+    >>> from crewai.flow.flow_serializer import flow_structure
+    >>>
+    >>> class MyFlow(Flow):
+    ...     @start()
+    ...     def begin(self):
+    ...         return "started"
+    ...
+    ...     @listen(begin)
+    ...     def process(self):
+    ...         return "done"
+    >>>
+    >>> structure = flow_structure(MyFlow)
+    >>> print(structure["name"])
+    'MyFlow'
+"""
+
+from __future__ import annotations
+
+import inspect
+import logging
+import re
+import textwrap
+from typing import Any, TypedDict, get_args, get_origin
+
+from pydantic import BaseModel
+from pydantic_core import PydanticUndefined
+
+from crewai.flow.flow_wrappers import (
+    FlowCondition,
+    FlowMethod,
+    ListenMethod,
+    RouterMethod,
+    StartMethod,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class MethodInfo(TypedDict, total=False):
+    """Information about a single flow method.
+
+    Attributes:
+        name: The method name.
+        type: Method type - start, listen, router, or start_router.
+        trigger_methods: List of method names that trigger this method.
+        condition_type: 'AND' or 'OR' for composite conditions, null otherwise.
+        router_paths: For routers, the possible route names returned.
+        has_human_feedback: Whether the method has @human_feedback decorator.
+        has_crew: Whether the method body references a Crew.
+    """
+
+    name: str
+    type: str
+    trigger_methods: list[str]
+    condition_type: str | None
+    router_paths: list[str]
+    has_human_feedback: bool
+    has_crew: bool
+
+
+class EdgeInfo(TypedDict, total=False):
+    """Information about an edge between flow methods.
+
+    Attributes:
+        from_method: Source method name.
+        to_method: Target method name.
+        edge_type: Type of edge - 'listen' or 'route'.
+        condition: Route name for router edges, null for listen edges.
+    """
+
+    from_method: str
+    to_method: str
+    edge_type: str
+    condition: str | None
+
+
+class StateFieldInfo(TypedDict, total=False):
+    """Information about a state field.
+
+    Attributes:
+        name: Field name.
+        type: Field type as string.
+        default: Default value if any.
+    """
+
+    name: str
+    type: str
+    default: Any
+
+
+class StateSchemaInfo(TypedDict, total=False):
+    """Information about the flow's state schema.
+
+    Attributes:
+        fields: List of field information.
+    """
+
+    fields: list[StateFieldInfo]
+
+
+class FlowStructureInfo(TypedDict, total=False):
+    """Complete flow structure information.
+
+    Attributes:
+        name: Flow class name.
+        description: Flow docstring if available.
+        methods: List of method information.
+        edges: List of edge information.
+        state_schema: State schema if typed, null otherwise.
+        inputs: Detected flow inputs if available.
+    """
+
+    name: str
+    description: str | None
+    methods: list[MethodInfo]
+    edges: list[EdgeInfo]
+    state_schema: StateSchemaInfo | None
+    inputs: list[str]
+
+
+def _get_method_type(
+    method_name: str,
+    method: Any,
+    start_methods: list[str],
+    routers: set[str],
+) -> str:
+    """Determine the type of a flow method.
+
+    Args:
+        method_name: Name of the method.
+        method: The method object.
+        start_methods: List of start method names.
+        routers: Set of router method names.
+
+    Returns:
+        One of: 'start', 'listen', 'router', or 'start_router'.
+    """
+    is_start = method_name in start_methods or getattr(
+        method, "__is_start_method__", False
+    )
+    is_router = method_name in routers or getattr(method, "__is_router__", False)
+
+    if is_start and is_router:
+        return "start_router"
+    if is_start:
+        return "start"
+    if is_router:
+        return "router"
+    return "listen"
+
+
+def _has_human_feedback(method: Any) -> bool:
+    """Check if a method has the @human_feedback decorator.
+
+    Args:
+        method: The method object to check.
+
+    Returns:
+        True if the method has __human_feedback_config__ attribute.
+    """
+    return hasattr(method, "__human_feedback_config__")
+
+
+def _detect_crew_reference(method: Any) -> bool:
+    """Detect if a method body references a Crew.
+
+    Checks for patterns like:
+    - .crew() method calls
+    - Crew( instantiation
+    - References to Crew class in type hints
+
+    Note:
+        This is a **best-effort heuristic for UI hints**, not a guarantee.
+        Uses inspect.getsource + regex which can false-positive on comments
+        or string literals, and may fail on dynamically generated methods
+        or lambdas. Do not rely on this for correctness-critical logic.
+
+    Args:
+        method: The method object to inspect.
+
+    Returns:
+        True if crew reference detected, False otherwise.
+    """
+    try:
+        # Get the underlying function from wrapper
+        func = method
+        if hasattr(method, "_meth"):
+            func = method._meth
+        elif hasattr(method, "__wrapped__"):
+            func = method.__wrapped__
+
+        source = inspect.getsource(func)
+        source = textwrap.dedent(source)
+
+        # Patterns that indicate Crew usage
+        crew_patterns = [
+            r"\.crew\(\)",  # .crew() method call
+            r"Crew\s*\(",  # Crew( instantiation
+            r":\s*Crew\b",  # Type hint with Crew
+            r"->.*Crew",  # Return type hint with Crew
+        ]
+
+        for pattern in crew_patterns:
+            if re.search(pattern, source):
+                return True
+
+        return False
+    except (OSError, TypeError):
+        # Can't get source code - assume no crew reference
+        return False
+
+
+def _extract_trigger_methods(method: Any) -> tuple[list[str], str | None]:
+    """Extract trigger methods and condition type from a method.
+
+    Args:
+        method: The method object to inspect.
+
+    Returns:
+        Tuple of (trigger_methods list, condition_type or None).
+    """
+    trigger_methods: list[str] = []
+    condition_type: str | None = None
+
+    # First try __trigger_methods__ (populated for simple conditions)
+    if hasattr(method, "__trigger_methods__") and method.__trigger_methods__:
+        trigger_methods = [str(m) for m in method.__trigger_methods__]
+
+    # For complex conditions (or_/and_ combinators), extract from __trigger_condition__
+    if (
+        not trigger_methods
+        and hasattr(method, "__trigger_condition__")
+        and method.__trigger_condition__
+    ):
+        trigger_condition = method.__trigger_condition__
+        trigger_methods = _extract_all_methods_from_condition(trigger_condition)
+
+    if hasattr(method, "__condition_type__") and method.__condition_type__:
+        condition_type = str(method.__condition_type__)
+
+    return trigger_methods, condition_type
+
+
+def _extract_router_paths(
+    method: Any, router_paths_registry: dict[str, list[str]]
+) -> list[str]:
+    """Extract router paths for a router method.
+
+    Args:
+        method: The method object.
+        router_paths_registry: The class-level _router_paths dict.
+
+    Returns:
+        List of possible route names.
+    """
+    method_name = getattr(method, "__name__", "")
+
+    # First check if there are __router_paths__ on the method itself
+    if hasattr(method, "__router_paths__") and method.__router_paths__:
+        return [str(p) for p in method.__router_paths__]
+
+    # Then check the class-level registry
+    if method_name in router_paths_registry:
+        return [str(p) for p in router_paths_registry[method_name]]
+
+    return []
+
+
+def _extract_all_methods_from_condition(
+    condition: str | FlowCondition | dict[str, Any] | list[Any],
+) -> list[str]:
+    """Extract all method names from a condition tree recursively.
+
+    Args:
+        condition: Can be a string, FlowCondition tuple, dict, or list.
+
+    Returns:
+        List of all method names found in the condition.
+    """
+    if isinstance(condition, str):
+        return [condition]
+    if isinstance(condition, tuple) and len(condition) == 2:
+        # FlowCondition: (condition_type, methods_list)
+        _, methods = condition
+        if isinstance(methods, list):
+            result: list[str] = []
+            for m in methods:
+                result.extend(_extract_all_methods_from_condition(m))
+            return result
+        return []
+    if isinstance(condition, dict):
+        conditions_list = condition.get("conditions", [])
+        methods: list[str] = []
+        for sub_cond in conditions_list:
+            methods.extend(_extract_all_methods_from_condition(sub_cond))
+        return methods
+    if isinstance(condition, list):
+        methods = []
+        for item in condition:
+            methods.extend(_extract_all_methods_from_condition(item))
+        return methods
+    return []
+
+
+def _generate_edges(
+    listeners: dict[str, tuple[str, list[str]] | FlowCondition],
+    routers: set[str],
+    router_paths: dict[str, list[str]],
+    all_methods: set[str],
+) -> list[EdgeInfo]:
+    """Generate edges from listeners and routers.
+
+    Args:
+        listeners: Map of listener_name -> (condition_type, trigger_methods) or FlowCondition.
+        routers: Set of router method names.
+        router_paths: Map of router_name -> possible return values.
+        all_methods: Set of all method names in the flow.
+
+    Returns:
+        List of EdgeInfo dictionaries.
+    """
+    edges: list[EdgeInfo] = []
+
+    # Generate edges from listeners (listen edges)
+    for listener_name, condition_data in listeners.items():
+        trigger_methods: list[str] = []
+
+        if isinstance(condition_data, tuple) and len(condition_data) == 2:
+            _condition_type, methods = condition_data
+            trigger_methods = [str(m) for m in methods]
+        elif isinstance(condition_data, dict):
+            trigger_methods = _extract_all_methods_from_condition(condition_data)
+
+        # Create edges from each trigger to the listener
+        edges.extend(
+            EdgeInfo(
+                from_method=trigger,
+                to_method=listener_name,
+                edge_type="listen",
+                condition=None,
+            )
+            for trigger in trigger_methods
+            if trigger in all_methods
+        )
+
+    # Generate edges from routers (route edges)
+    for router_name, paths in router_paths.items():
+        for path in paths:
+            # Find listeners that listen to this path
+            for listener_name, condition_data in listeners.items():
+                path_triggers: list[str] = []
+
+                if isinstance(condition_data, tuple) and len(condition_data) == 2:
+                    _, methods = condition_data
+                    path_triggers = [str(m) for m in methods]
+                elif isinstance(condition_data, dict):
+                    path_triggers = _extract_all_methods_from_condition(condition_data)
+
+                if str(path) in path_triggers:
+                    edges.append(
+                        EdgeInfo(
+                            from_method=router_name,
+                            to_method=listener_name,
+                            edge_type="route",
+                            condition=str(path),
+                        )
+                    )
+
+    return edges
+
+
+def _extract_state_schema(flow_class: type) -> StateSchemaInfo | None:
+    """Extract state schema from a Flow class.
+
+    Checks for:
+    - Generic type parameter (Flow[MyState])
+    - initial_state class attribute
+
+    Args:
+        flow_class: The Flow class to inspect.
+
+    Returns:
+        StateSchemaInfo if a Pydantic model state is detected, None otherwise.
+    """
+    state_type: type | None = None
+
+    # Check for _initial_state_t set by __class_getitem__
+    if hasattr(flow_class, "_initial_state_t"):
+        state_type = flow_class._initial_state_t
+
+    # Check initial_state class attribute
+    if state_type is None and hasattr(flow_class, "initial_state"):
+        initial_state = flow_class.initial_state
+        if isinstance(initial_state, type) and issubclass(initial_state, BaseModel):
+            state_type = initial_state
+        elif isinstance(initial_state, BaseModel):
+            state_type = type(initial_state)
+
+    # Check __orig_bases__ for generic parameters
+    if state_type is None and hasattr(flow_class, "__orig_bases__"):
+        for base in flow_class.__orig_bases__:
+            origin = get_origin(base)
+            if origin is not None:
+                args = get_args(base)
+                if args:
+                    candidate = args[0]
+                    if isinstance(candidate, type) and issubclass(candidate, BaseModel):
+                        state_type = candidate
+                        break
+
+    if state_type is None or not issubclass(state_type, BaseModel):
+        return None
+
+    # Extract fields from the Pydantic model
+    fields: list[StateFieldInfo] = []
+    try:
+        model_fields = state_type.model_fields
+        for field_name, field_info in model_fields.items():
+            field_type_str = "Any"
+            if field_info.annotation is not None:
+                field_type_str = str(field_info.annotation)
+                # Clean up the type string
+                field_type_str = field_type_str.replace("typing.", "")
+                field_type_str = field_type_str.replace("<class '", "").replace(
+                    "'>", ""
+                )
+
+            default_value = None
+            if (
+                field_info.default is not PydanticUndefined
+                and field_info.default is not None
+                and not callable(field_info.default)
+            ):
+                try:
+                    # Try to serialize the default value
+                    default_value = field_info.default
+                except Exception:
+                    default_value = str(field_info.default)
+
+            fields.append(
+                StateFieldInfo(
+                    name=field_name,
+                    type=field_type_str,
+                    default=default_value,
+                )
+            )
+    except Exception:
+        logger.debug(
+            "Failed to extract state schema fields for %s", flow_class.__name__
+        )
+
+    return StateSchemaInfo(fields=fields) if fields else None
+
+
+def _detect_flow_inputs(flow_class: type) -> list[str]:
+    """Detect flow input parameters.
+
+    Inspects the __init__ signature for custom parameters beyond standard Flow params.
+
+    Args:
+        flow_class: The Flow class to inspect.
+
+    Returns:
+        List of detected input names.
+    """
+    inputs: list[str] = []
+
+    # Check for inputs in __init__ signature beyond standard Flow params
+    try:
+        init_sig = inspect.signature(flow_class.__init__)
+        standard_params = {
+            "self",
+            "persistence",
+            "tracing",
+            "suppress_flow_events",
+            "max_method_calls",
+            "kwargs",
+        }
+        inputs.extend(
+            param_name
+            for param_name in init_sig.parameters
+            if param_name not in standard_params and not param_name.startswith("_")
+        )
+    except Exception:
+        logger.debug(
+            "Failed to detect inputs from __init__ for %s", flow_class.__name__
+        )
+
+    return inputs
+
+
+def flow_structure(flow_class: type) -> FlowStructureInfo:
+    """Introspect a Flow class and return its structure as a JSON-serializable dict.
+
+    This function analyzes a Flow CLASS (not instance) and returns complete
+    information about its graph structure including methods, edges, and state.
+
+    Args:
+        flow_class: A Flow class (not an instance) to introspect.
+
+    Returns:
+        FlowStructureInfo dictionary containing:
+        - name: Flow class name
+        - description: Docstring if available
+        - methods: List of method info dicts
+        - edges: List of edge info dicts
+        - state_schema: State schema if typed, None otherwise
+        - inputs: Detected input names
+
+    Raises:
+        TypeError: If flow_class is not a class.
+
+    Example:
+        >>> structure = flow_structure(MyFlow)
+        >>> print(structure["name"])
+        'MyFlow'
+        >>> for method in structure["methods"]:
+        ...     print(method["name"], method["type"])
+    """
+    if not isinstance(flow_class, type):
+        raise TypeError(
+            f"flow_structure requires a Flow class, not an instance. "
+            f"Got {type(flow_class).__name__}"
+        )
+
+    # Get class-level metadata set by FlowMeta
+    start_methods: list[str] = getattr(flow_class, "_start_methods", [])
+    listeners: dict[str, Any] = getattr(flow_class, "_listeners", {})
+    routers: set[str] = getattr(flow_class, "_routers", set())
+    router_paths_registry: dict[str, list[str]] = getattr(
+        flow_class, "_router_paths", {}
+    )
+
+    # Collect all flow methods
+    methods: list[MethodInfo] = []
+    all_method_names: set[str] = set()
+
+    for attr_name in dir(flow_class):
+        if attr_name.startswith("_"):
+            continue
+
+        try:
+            attr = getattr(flow_class, attr_name)
+        except AttributeError:
+            continue
+
+        # Check if it's a flow method
+        is_flow_method = (
+            isinstance(attr, (FlowMethod, StartMethod, ListenMethod, RouterMethod))
+            or hasattr(attr, "__is_flow_method__")
+            or hasattr(attr, "__is_start_method__")
+            or hasattr(attr, "__trigger_methods__")
+            or hasattr(attr, "__is_router__")
+        )
+
+        if not is_flow_method:
+            continue
+
+        all_method_names.add(attr_name)
+
+        # Get method type
+        method_type = _get_method_type(attr_name, attr, start_methods, routers)
+
+        # Get trigger methods and condition type
+        trigger_methods, condition_type = _extract_trigger_methods(attr)
+
+        # Get router paths if applicable
+        router_paths_list: list[str] = []
+        if method_type in ("router", "start_router"):
+            router_paths_list = _extract_router_paths(attr, router_paths_registry)
+
+        # Check for human feedback
+        has_hf = _has_human_feedback(attr)
+
+        # Check for crew reference
+        has_crew = _detect_crew_reference(attr)
+
+        method_info = MethodInfo(
+            name=attr_name,
+            type=method_type,
+            trigger_methods=trigger_methods,
+            condition_type=condition_type,
+            router_paths=router_paths_list,
+            has_human_feedback=has_hf,
+            has_crew=has_crew,
+        )
+        methods.append(method_info)
+
+    # Generate edges
+    edges = _generate_edges(listeners, routers, router_paths_registry, all_method_names)
+
+    # Extract state schema
+    state_schema = _extract_state_schema(flow_class)
+
+    # Detect inputs
+    inputs = _detect_flow_inputs(flow_class)
+
+    # Get flow description from docstring
+    description: str | None = None
+    if flow_class.__doc__:
+        description = flow_class.__doc__.strip()
+
+    return FlowStructureInfo(
+        name=flow_class.__name__,
+        description=description,
+        methods=methods,
+        edges=edges,
+        state_schema=state_schema,
+        inputs=inputs,
+    )

lib/crewai/src/crewai/flow/flow_wrappers.py

@@ -75,6 +75,7 @@ class FlowMethod(Generic[P, R]):
             "__is_router__",
             "__router_paths__",
             "__human_feedback_config__",
+            "_hf_llm",  # Live LLM object for HITL resume
         ]:
             if hasattr(meth, attr):
                 setattr(self, attr, getattr(meth, attr))

lib/crewai/src/crewai/flow/human_feedback.py

@@ -76,22 +76,48 @@ if TYPE_CHECKING:
 F = TypeVar("F", bound=Callable[..., Any])
 
 
-def _serialize_llm_for_context(llm: Any) -> str | None:
-    """Serialize a BaseLLM object to a model string with provider prefix.
+def _serialize_llm_for_context(llm: Any) -> dict[str, Any] | str | None:
+    """Serialize a BaseLLM object to a dict preserving full config.
 
-    When persisting the LLM for HITL resume, we need to store enough info
-    to reconstruct a working LLM on the resume worker. Just storing the bare
-    model name (e.g. "gemini-3-flash-preview") causes provider inference to
-    fail — it defaults to OpenAI. Including the provider prefix (e.g.
-    "gemini/gemini-3-flash-preview") allows LLM() to correctly route.
+    Delegates to ``llm.to_config_dict()`` when available (BaseLLM and
+    subclasses). Falls back to extracting the model string with provider
+    prefix for unknown LLM types.
     """
+    if hasattr(llm, "to_config_dict"):
+        return llm.to_config_dict()
+
+    # Fallback for non-BaseLLM objects: just extract model + provider prefix
     model = getattr(llm, "model", None)
     if not model:
         return None
     provider = getattr(llm, "provider", None)
-    if provider and "/" not in model:
-        return f"{provider}/{model}"
-    return model
+    return f"{provider}/{model}" if provider and "/" not in model else model
+
+
+def _deserialize_llm_from_context(
+    llm_data: dict[str, Any] | str | None,
+) -> BaseLLM | None:
+    """Reconstruct an LLM instance from serialized context data.
+
+    Handles both the new dict format (with full config) and the legacy
+    string format (model name only) for backward compatibility.
+
+    Returns a BaseLLM instance, or None if llm_data is None.
+    """
+    if llm_data is None:
+        return None
+
+    from crewai.llm import LLM
+
+    if isinstance(llm_data, str):
+        return LLM(model=llm_data)
+
+    if isinstance(llm_data, dict):
+        model = llm_data.pop("model", None)
+        if not model:
+            return None
+        return LLM(model=model, **llm_data)
+    return None
 
 
 @dataclass
@@ -572,6 +598,14 @@ def human_feedback(
             wrapper.__is_router__ = True
             wrapper.__router_paths__ = list(emit)
 
+        # Stash the live LLM object for HITL resume to retrieve.
+        # When a flow pauses for human feedback and later resumes (possibly in a
+        # different process), the serialized context only contains a model string.
+        # By storing the original LLM on the wrapper, resume_async can retrieve
+        # the fully-configured LLM (with credentials, project, safety_settings, etc.)
+        # instead of creating a bare LLM from just the model string.
+        wrapper._hf_llm = llm
+
         return wrapper  # type: ignore[no-any-return]
 
     return decorator

lib/crewai/src/crewai/llm.py

@@ -62,18 +62,6 @@ except ImportError:
 
 
 if TYPE_CHECKING:
-    from litellm.exceptions import ContextWindowExceededError
-    from litellm.litellm_core_utils.get_supported_openai_params import (
-        get_supported_openai_params,
-    )
-    from litellm.types.utils import (
-        ChatCompletionDeltaToolCall,
-        Choices,
-        Function,
-        ModelResponse,
-    )
-    from litellm.utils import supports_response_schema
-
     from crewai.agent.core import Agent
     from crewai.llms.hooks.base import BaseInterceptor
     from crewai.llms.providers.anthropic.completion import AnthropicThinkingConfig
@@ -83,8 +71,6 @@ if TYPE_CHECKING:
 
 try:
     import litellm
-    from litellm.exceptions import ContextWindowExceededError
-    from litellm.integrations.custom_logger import CustomLogger
     from litellm.litellm_core_utils.get_supported_openai_params import (
         get_supported_openai_params,
     )
@@ -99,15 +85,13 @@ try:
     LITELLM_AVAILABLE = True
 except ImportError:
     LITELLM_AVAILABLE = False
-    litellm = None  # type: ignore
-    Choices = None  # type: ignore
-    ContextWindowExceededError = Exception  # type: ignore
-    get_supported_openai_params = None  # type: ignore
-    ChatCompletionDeltaToolCall = None  # type: ignore
-    Function = None  # type: ignore
-    ModelResponse = None  # type: ignore
-    supports_response_schema = None  # type: ignore
-    CustomLogger = None  # type: ignore
+    litellm = None  # type: ignore[assignment]
+    Choices = 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]
+    ModelResponse = None  # type: ignore[assignment, misc]
+    supports_response_schema = None  # type: ignore[assignment]
 
 
 load_dotenv()
@@ -1009,12 +993,15 @@ class LLM(BaseLLM):
             )
             return full_response
 
-        except ContextWindowExceededError as e:
-            # Catch context window errors from litellm and convert them to our own exception type.
-            # This exception is handled by CrewAgentExecutor._invoke_loop() which can then
-            # decide whether to summarize the content or abort based on the respect_context_window flag.
-            raise LLMContextLengthExceededError(str(e)) from e
+        except LLMContextLengthExceededError:
+            # Re-raise our own context length error
+            raise
         except Exception as e:
+            # Check if this is a context window error and convert to our exception type
+            error_msg = str(e)
+            if LLMContextLengthExceededError._is_context_limit_error(error_msg):
+                raise LLMContextLengthExceededError(error_msg) from e
+
             logging.error(f"Error in streaming response: {e!s}")
             if full_response.strip():
                 logging.warning(f"Returning partial response despite error: {e!s}")
@@ -1195,10 +1182,15 @@ class LLM(BaseLLM):
                 usage_info = response.usage
                 self._track_token_usage_internal(usage_info)
 
-        except ContextWindowExceededError as e:
-            # Convert litellm's context window error to our own exception type
-            # for consistent handling in the rest of the codebase
-            raise LLMContextLengthExceededError(str(e)) from e
+        except LLMContextLengthExceededError:
+            # Re-raise our own context length error
+            raise
+        except Exception as e:
+            # Check if this is a context window error and convert to our exception type
+            error_msg = str(e)
+            if LLMContextLengthExceededError._is_context_limit_error(error_msg):
+                raise LLMContextLengthExceededError(error_msg) from e
+            raise
 
         # --- 2) Handle structured output response (when response_model is provided)
         if response_model is not None:
@@ -1330,8 +1322,15 @@ class LLM(BaseLLM):
                 usage_info = response.usage
                 self._track_token_usage_internal(usage_info)
 
-        except ContextWindowExceededError as e:
-            raise LLMContextLengthExceededError(str(e)) from e
+        except LLMContextLengthExceededError:
+            # Re-raise our own context length error
+            raise
+        except Exception as e:
+            # Check if this is a context window error and convert to our exception type
+            error_msg = str(e)
+            if LLMContextLengthExceededError._is_context_limit_error(error_msg):
+                raise LLMContextLengthExceededError(error_msg) from e
+            raise
 
         if response_model is not None:
             if isinstance(response, BaseModel):
@@ -1548,9 +1547,15 @@ class LLM(BaseLLM):
             )
             return full_response
 
-        except ContextWindowExceededError as e:
-            raise LLMContextLengthExceededError(str(e)) from e
-        except Exception:
+        except LLMContextLengthExceededError:
+            # Re-raise our own context length error
+            raise
+        except Exception as e:
+            # Check if this is a context window error and convert to our exception type
+            error_msg = str(e)
+            if LLMContextLengthExceededError._is_context_limit_error(error_msg):
+                raise LLMContextLengthExceededError(error_msg) from e
+
             if chunk_count == 0:
                 raise
             if full_response:
@@ -1984,7 +1989,16 @@ class LLM(BaseLLM):
         Returns:
             Messages with files formatted into content blocks.
         """
-        if not HAS_CREWAI_FILES or not self.supports_multimodal():
+        if not HAS_CREWAI_FILES:
+            return messages
+
+        if not self.supports_multimodal():
+            if any(msg.get("files") for msg in messages):
+                raise ValueError(
+                    f"Model '{self.model}' does not support multimodal input, "
+                    "but files were provided via 'input_files'. "
+                    "Use a vision-capable model or remove the file inputs."
+                )
             return messages
 
         provider = getattr(self, "provider", None) or self.model
@@ -2026,7 +2040,16 @@ class LLM(BaseLLM):
         Returns:
             Messages with files formatted into content blocks.
         """
-        if not HAS_CREWAI_FILES or not self.supports_multimodal():
+        if not HAS_CREWAI_FILES:
+            return messages
+
+        if not self.supports_multimodal():
+            if any(msg.get("files") for msg in messages):
+                raise ValueError(
+                    f"Model '{self.model}' does not support multimodal input, "
+                    "but files were provided via 'input_files'. "
+                    "Use a vision-capable model or remove the file inputs."
+                )
             return messages
 
         provider = getattr(self, "provider", None) or self.model
@@ -2139,7 +2162,15 @@ class LLM(BaseLLM):
           - E.g., "openrouter/deepseek/deepseek-chat" yields "openrouter"
           - "gemini/gemini-1.5-pro" yields "gemini"
           - If no slash is present, "openai" is assumed.
+
+        Note: This validation only applies to the litellm fallback path.
+        Native providers have their own validation.
         """
+        if not LITELLM_AVAILABLE or supports_response_schema is None:
+            # When litellm is not available, skip validation
+            # (this path should only be reached for litellm fallback models)
+            return
+
         provider = self._get_custom_llm_provider()
         if self.response_format is not None and not supports_response_schema(
             model=self.model,
@@ -2151,6 +2182,16 @@ class LLM(BaseLLM):
             )
 
     def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling.
+
+        Note: This method is only used by the litellm fallback path.
+        Native providers override this method with their own implementation.
+        """
+        if not LITELLM_AVAILABLE:
+            # When litellm is not available, assume function calling is supported
+            # (all modern models support it)
+            return True
+
         try:
             provider = self._get_custom_llm_provider()
             return litellm.utils.supports_function_calling(
@@ -2158,15 +2199,24 @@ class LLM(BaseLLM):
             )
         except Exception as e:
             logging.error(f"Failed to check function calling support: {e!s}")
-            return False
+            return True  # Default to True for modern models
 
     def supports_stop_words(self) -> bool:
+        """Check if the model supports stop words.
+
+        Note: This method is only used by the litellm fallback path.
+        Native providers override this method with their own implementation.
+        """
+        if not LITELLM_AVAILABLE or get_supported_openai_params is None:
+            # When litellm is not available, assume stop words are supported
+            return True
+
         try:
             params = get_supported_openai_params(model=self.model)
             return params is not None and "stop" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {e!s}")
-            return False
+            return True  # Default to True
 
     def get_context_window_size(self) -> int:
         """
@@ -2202,7 +2252,15 @@ class LLM(BaseLLM):
         """
         Attempt to keep a single set of callbacks in litellm by removing old
         duplicates and adding new ones.
+
+        Note: This only affects the litellm fallback path. Native providers
+        don't use litellm callbacks - they emit events via base_llm.py.
         """
+        if not LITELLM_AVAILABLE:
+            # When litellm is not available, callbacks are still stored
+            # but not registered with litellm globals
+            return
+
         with suppress_warnings():
             callback_types = [type(callback) for callback in callbacks]
             for callback in litellm.success_callback[:]:
@@ -2227,6 +2285,9 @@ class LLM(BaseLLM):
         If the environment variables are not set or are empty, the corresponding callback lists
         will be set to empty lists.
 
+        Note: This only affects the litellm fallback path. Native providers
+        don't use litellm callbacks - they emit events via base_llm.py.
+
         Examples:
             LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
             LITELLM_FAILURE_CALLBACKS="langfuse"
@@ -2234,9 +2295,13 @@ class LLM(BaseLLM):
         This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
         `litellm.failure_callback` to ["langfuse"].
         """
+        if not LITELLM_AVAILABLE:
+            # When litellm is not available, env callbacks have no effect
+            return
+
         with suppress_warnings():
             success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
+            success_callbacks: list[str | Callable[..., Any]] = []
             if success_callbacks_str:
                 success_callbacks = [
                     cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
@@ -2244,7 +2309,7 @@ class LLM(BaseLLM):
 
             failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
             if failure_callbacks_str:
-                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
+                failure_callbacks: list[str | Callable[..., Any]] = [
                     cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
                 ]
 
@@ -2398,6 +2463,9 @@ class LLM(BaseLLM):
             "gpt-4.1",
             "claude-3",
             "claude-4",
+            "claude-sonnet-4",
+            "claude-opus-4",
+            "claude-haiku-4",
             "gemini",
         )
         model_lower = self.model.lower()

lib/crewai/src/crewai/llms/base_llm.py

@@ -152,6 +152,28 @@ class BaseLLM(ABC):
             "cached_prompt_tokens": 0,
         }
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Serialize this LLM to a dict that can reconstruct it via ``LLM(**config)``.
+
+        Returns the core fields that BaseLLM owns. Provider subclasses should
+        override this (calling ``super().to_config_dict()``) to add their own
+        fields (e.g. ``project``, ``location``, ``safety_settings``).
+        """
+        model = self.model
+        provider = self.provider
+        model_str = f"{provider}/{model}" if provider and "/" not in model else model
+
+        config: dict[str, Any] = {"model": model_str}
+
+        if self.temperature is not None:
+            config["temperature"] = self.temperature
+        if self.base_url is not None:
+            config["base_url"] = self.base_url
+        if self.stop:
+            config["stop"] = self.stop
+
+        return config
+
     @property
     def provider(self) -> str:
         """Get the provider of the LLM."""
@@ -619,7 +641,16 @@ class BaseLLM(ABC):
         Returns:
             Messages with files formatted into content blocks.
         """
-        if not HAS_CREWAI_FILES or not self.supports_multimodal():
+        if not HAS_CREWAI_FILES:
+            return messages
+
+        if not self.supports_multimodal():
+            if any(msg.get("files") for msg in messages):
+                raise ValueError(
+                    f"Model '{self.model}' does not support multimodal input, "
+                    "but files were provided via 'input_files'. "
+                    "Use a vision-capable model or remove the file inputs."
+                )
             return messages
 
         provider = getattr(self, "provider", None) or getattr(self, "model", "openai")

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

@@ -256,6 +256,19 @@ class AnthropicCompletion(BaseLLM):
         else:
             self.stop_sequences = []
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Anthropic-specific fields."""
+        config = super().to_config_dict()
+        if self.max_tokens != 4096:  # non-default
+            config["max_tokens"] = self.max_tokens
+        if self.max_retries != 2:  # non-default
+            config["max_retries"] = self.max_retries
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.timeout is not None:
+            config["timeout"] = self.timeout
+        return config
+
     def _get_client_params(self) -> dict[str, Any]:
         """Get client parameters."""
 
@@ -1753,7 +1766,14 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             True if the model supports images and PDFs.
         """
-        return "claude-3" in self.model.lower() or "claude-4" in self.model.lower()
+        model_lower = self.model.lower()
+        return (
+            "claude-3" in model_lower
+            or "claude-4" in model_lower
+            or "claude-sonnet-4" in model_lower
+            or "claude-opus-4" in model_lower
+            or "claude-haiku-4" in model_lower
+        )
 
     def get_file_uploader(self) -> Any:
         """Get an Anthropic file uploader using this LLM's clients.

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

@@ -180,6 +180,27 @@ class AzureCompletion(BaseLLM):
             and "/openai/deployments/" in self.endpoint
         )
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Azure-specific fields."""
+        config = super().to_config_dict()
+        if self.endpoint:
+            config["endpoint"] = self.endpoint
+        if self.api_version and self.api_version != "2024-06-01":
+            config["api_version"] = self.api_version
+        if self.timeout is not None:
+            config["timeout"] = self.timeout
+        if self.max_retries != 2:
+            config["max_retries"] = self.max_retries
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.frequency_penalty is not None:
+            config["frequency_penalty"] = self.frequency_penalty
+        if self.presence_penalty is not None:
+            config["presence_penalty"] = self.presence_penalty
+        if self.max_tokens is not None:
+            config["max_tokens"] = self.max_tokens
+        return config
+
     @staticmethod
     def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
         """Validate and fix Azure endpoint URL format.

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

@@ -346,6 +346,23 @@ class BedrockCompletion(BaseLLM):
         # Handle inference profiles for newer models
         self.model_id = model
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Bedrock-specific fields."""
+        config = super().to_config_dict()
+        # NOTE: AWS credentials (access_key, secret_key, session_token) are
+        # intentionally excluded — they must come from env on resume.
+        if self.region_name and self.region_name != "us-east-1":
+            config["region_name"] = self.region_name
+        if self.max_tokens is not None:
+            config["max_tokens"] = self.max_tokens
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.top_k is not None:
+            config["top_k"] = self.top_k
+        if self.guardrail_config:
+            config["guardrail_config"] = self.guardrail_config
+        return config
+
     @property
     def stop(self) -> list[str]:
         """Get stop sequences sent to the API."""
@@ -1880,7 +1897,9 @@ class BedrockCompletion(BaseLLM):
                 # Anthropic (Claude) models reject assistant-last messages when
                 # tools are in the request. Append a user message so the
                 # Converse API accepts the payload.
-                elif "anthropic" in self.model.lower() or "claude" in self.model.lower():
+                elif (
+                    "anthropic" in self.model.lower() or "claude" in self.model.lower()
+                ):
                     converse_messages.append(
                         {
                             "role": "user",
@@ -2100,12 +2119,18 @@ class BedrockCompletion(BaseLLM):
         model_lower = self.model.lower()
         vision_models = (
             "anthropic.claude-3",
+            "anthropic.claude-sonnet-4",
+            "anthropic.claude-opus-4",
+            "anthropic.claude-haiku-4",
             "amazon.nova-lite",
             "amazon.nova-pro",
             "amazon.nova-premier",
             "us.amazon.nova-lite",
             "us.amazon.nova-pro",
             "us.amazon.nova-premier",
+            "us.anthropic.claude-sonnet-4",
+            "us.anthropic.claude-opus-4",
+            "us.anthropic.claude-haiku-4",
         )
         return any(model_lower.startswith(m) for m in vision_models)
 

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

@@ -176,6 +176,28 @@ class GeminiCompletion(BaseLLM):
         else:
             self.stop_sequences = []
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Gemini/Vertex-specific fields."""
+        config = super().to_config_dict()
+        if self.project:
+            config["project"] = self.project
+        if self.location and self.location != "us-central1":
+            config["location"] = self.location
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.top_k is not None:
+            config["top_k"] = self.top_k
+        if self.max_output_tokens is not None:
+            config["max_output_tokens"] = self.max_output_tokens
+        if self.safety_settings:
+            config["safety_settings"] = [
+                {"category": str(s.category), "threshold": str(s.threshold)}
+                if hasattr(s, "category") and hasattr(s, "threshold")
+                else s
+                for s in self.safety_settings
+            ]
+        return config
+
     def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:
         """Initialize the Google Gen AI client with proper parameter handling.
 

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

@@ -329,6 +329,35 @@ class OpenAICompletion(BaseLLM):
         """
         self._last_reasoning_items = None
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with OpenAI-specific fields."""
+        config = super().to_config_dict()
+        # Client-level params (from OpenAI SDK)
+        if self.organization:
+            config["organization"] = self.organization
+        if self.project:
+            config["project"] = self.project
+        if self.timeout is not None:
+            config["timeout"] = self.timeout
+        if self.max_retries != 2:
+            config["max_retries"] = self.max_retries
+        # Completion params
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.frequency_penalty is not None:
+            config["frequency_penalty"] = self.frequency_penalty
+        if self.presence_penalty is not None:
+            config["presence_penalty"] = self.presence_penalty
+        if self.max_tokens is not None:
+            config["max_tokens"] = self.max_tokens
+        if self.max_completion_tokens is not None:
+            config["max_completion_tokens"] = self.max_completion_tokens
+        if self.seed is not None:
+            config["seed"] = self.seed
+        if self.reasoning_effort is not None:
+            config["reasoning_effort"] = self.reasoning_effort
+        return config
+
     def _get_client_params(self) -> dict[str, Any]:
         """Get OpenAI client parameters."""
 

lib/crewai/src/crewai/memory/encoding_flow.py

@@ -28,6 +28,7 @@ from crewai.memory.analyze import (
     analyze_for_save,
 )
 from crewai.memory.types import MemoryConfig, MemoryRecord, embed_texts
+from crewai.memory.utils import join_scope_paths
 
 
 logger = logging.getLogger(__name__)
@@ -48,6 +49,8 @@ class ItemState(BaseModel):
     importance: float | None = None
     source: str | None = None
     private: bool = False
+    # Structural root scope prefix for hierarchical scoping
+    root_scope: str | None = None
     # Resolved values
     resolved_scope: str = "/"
     resolved_categories: list[str] = Field(default_factory=list)
@@ -104,6 +107,14 @@ class EncodingFlow(Flow[EncodingState]):
         embedder: Any,
         config: MemoryConfig | None = None,
     ) -> None:
+        """Initialize the encoding flow.
+
+        Args:
+            storage: Storage backend for persisting memories.
+            llm: LLM instance for analysis.
+            embedder: Embedder for generating vectors.
+            config: Optional memory configuration.
+        """
         super().__init__(suppress_flow_events=True)
         self._storage = storage
         self._llm = llm
@@ -180,10 +191,18 @@ class EncodingFlow(Flow[EncodingState]):
         def _search_one(
             item: ItemState,
         ) -> list[tuple[MemoryRecord, float]]:
-            scope_prefix = item.scope if item.scope and item.scope.strip("/") else None
+            # Use root_scope as the search boundary, then narrow by explicit scope if provided
+            effective_prefix = None
+            if item.root_scope:
+                effective_prefix = item.root_scope.rstrip("/")
+                if item.scope and item.scope.strip("/"):
+                    effective_prefix = effective_prefix + "/" + item.scope.strip("/")
+            elif item.scope and item.scope.strip("/"):
+                effective_prefix = item.scope
+
             return self._storage.search(  # type: ignore[no-any-return]
                 item.embedding,
-                scope_prefix=scope_prefix,
+                scope_prefix=effective_prefix,
                 categories=None,
                 limit=self._config.consolidation_limit,
                 min_score=0.0,
@@ -253,9 +272,16 @@ class EncodingFlow(Flow[EncodingState]):
         existing_scopes: list[str] = []
         existing_categories: list[str] = []
         if any_needs_fields:
-            existing_scopes = self._storage.list_scopes("/") or ["/"]
+            # Constrain scope/category suggestions to root_scope boundary
+            # Check if any active item has root_scope
+            active_root = next(
+                (it.root_scope for it in items if not it.dropped and it.root_scope),
+                None,
+            )
+            scope_search_root = active_root if active_root else "/"
+            existing_scopes = self._storage.list_scopes(scope_search_root) or ["/"]
             existing_categories = list(
-                self._storage.list_categories(scope_prefix=None).keys()
+                self._storage.list_categories(scope_prefix=active_root).keys()
             )
 
         # Classify items and submit LLM calls
@@ -321,7 +347,13 @@ class EncodingFlow(Flow[EncodingState]):
             for i, future in save_futures.items():
                 analysis = future.result()
                 item = items[i]
-                item.resolved_scope = item.scope or analysis.suggested_scope or "/"
+                # Determine inner scope from explicit scope or LLM-inferred
+                inner_scope = item.scope or analysis.suggested_scope or "/"
+                # Join root_scope with inner scope if root_scope is set
+                if item.root_scope:
+                    item.resolved_scope = join_scope_paths(item.root_scope, inner_scope)
+                else:
+                    item.resolved_scope = inner_scope
                 item.resolved_categories = (
                     item.categories
                     if item.categories is not None
@@ -353,8 +385,18 @@ class EncodingFlow(Flow[EncodingState]):
             pool.shutdown(wait=False)
 
     def _apply_defaults(self, item: ItemState) -> None:
-        """Apply caller values with config defaults (fast path)."""
-        item.resolved_scope = item.scope or "/"
+        """Apply caller values with config defaults (fast path).
+
+        If root_scope is set, prepends it to the inner scope to create the
+        final resolved_scope.
+        """
+        inner_scope = item.scope or "/"
+        # Join root_scope with inner scope if root_scope is set
+        if item.root_scope:
+            item.resolved_scope = join_scope_paths(item.root_scope, inner_scope)
+        else:
+            item.resolved_scope = inner_scope if inner_scope != "/" else "/"
+
         item.resolved_categories = item.categories or []
         item.resolved_metadata = item.metadata or {}
         item.resolved_importance = (

lib/crewai/src/crewai/memory/unified_memory.py

@@ -31,6 +31,7 @@ from crewai.memory.types import (
     compute_composite_score,
     embed_text,
 )
+from crewai.memory.utils import join_scope_paths
 from crewai.rag.embeddings.factory import build_embedder
 from crewai.rag.embeddings.providers.openai.types import OpenAIProviderSpec
 
@@ -126,6 +127,14 @@ class Memory(BaseModel):
         default=False,
         description="If True, remember() and remember_many() are silent no-ops.",
     )
+    root_scope: str | None = Field(
+        default=None,
+        description=(
+            "Structural root scope prefix. When set, LLM-inferred or explicit scopes "
+            "are nested under this root. For example, a crew with root_scope='/crew/research' "
+            "will store memories at '/crew/research/<inferred_scope>'."
+        ),
+    )
 
     _config: MemoryConfig = PrivateAttr()
     _llm_instance: BaseLLM | None = PrivateAttr(default=None)
@@ -297,11 +306,26 @@ class Memory(BaseModel):
         importance: float | None = None,
         source: str | None = None,
         private: bool = False,
+        root_scope: str | None = None,
     ) -> list[MemoryRecord]:
         """Run the batch EncodingFlow for one or more items. No event emission.
 
         This is the core encoding logic shared by ``remember()`` and
         ``remember_many()``. Events are managed by the calling method.
+
+        Args:
+            contents: List of text content to encode and store.
+            scope: Optional explicit scope (inner scope, nested under root_scope).
+            categories: Optional categories for all items.
+            metadata: Optional metadata for all items.
+            importance: Optional importance score for all items.
+            source: Optional source identifier for all items.
+            private: Whether items are private.
+            root_scope: Structural root scope prefix. LLM-inferred or explicit
+                scopes are nested under this root.
+
+        Returns:
+            List of created MemoryRecord instances.
         """
         from crewai.memory.encoding_flow import EncodingFlow
 
@@ -320,6 +344,7 @@ class Memory(BaseModel):
                 "importance": importance,
                 "source": source,
                 "private": private,
+                "root_scope": root_scope,
             }
             for c in contents
         ]
@@ -340,6 +365,7 @@ class Memory(BaseModel):
         source: str | None = None,
         private: bool = False,
         agent_role: str | None = None,
+        root_scope: str | None = None,
     ) -> MemoryRecord | None:
         """Store a single item in memory (synchronous).
 
@@ -349,13 +375,15 @@ class Memory(BaseModel):
 
         Args:
             content: Text to remember.
-            scope: Optional scope path; inferred if None.
+            scope: Optional scope path (inner scope); inferred if None.
             categories: Optional categories; inferred if None.
             metadata: Optional metadata; merged with LLM-extracted if inferred.
             importance: Optional importance 0-1; inferred if None.
             source: Optional provenance identifier (e.g. user ID, session ID).
             private: If True, only visible to recall from the same source.
             agent_role: Optional agent role for event metadata.
+            root_scope: Optional root scope override. If provided, this overrides
+                the instance-level root_scope for this call only.
 
         Returns:
             The created MemoryRecord, or None if this memory is read-only.
@@ -365,6 +393,10 @@ class Memory(BaseModel):
         """
         if self.read_only:
             return None
+
+        # Determine effective root_scope: per-call override takes precedence
+        effective_root = root_scope if root_scope is not None else self.root_scope
+
         _source_type = "unified_memory"
         try:
             crewai_event_bus.emit(
@@ -388,6 +420,7 @@ class Memory(BaseModel):
                 importance,
                 source,
                 private,
+                effective_root,
             )
             records = future.result()
             record = records[0] if records else None
@@ -426,6 +459,7 @@ class Memory(BaseModel):
         source: str | None = None,
         private: bool = False,
         agent_role: str | None = None,
+        root_scope: str | None = None,
     ) -> list[MemoryRecord]:
         """Store multiple items in memory (non-blocking).
 
@@ -440,13 +474,15 @@ class Memory(BaseModel):
 
         Args:
             contents: List of text items to remember.
-            scope: Optional scope applied to all items.
+            scope: Optional scope (inner scope) applied to all items.
             categories: Optional categories applied to all items.
             metadata: Optional metadata applied to all items.
             importance: Optional importance applied to all items.
             source: Optional provenance identifier applied to all items.
             private: Privacy flag applied to all items.
             agent_role: Optional agent role for event metadata.
+            root_scope: Optional root scope override. If provided, this overrides
+                the instance-level root_scope for this call only.
 
         Returns:
             Empty list (records are not available until the background save completes).
@@ -454,6 +490,9 @@ class Memory(BaseModel):
         if not contents or self.read_only:
             return []
 
+        # Determine effective root_scope: per-call override takes precedence
+        effective_root = root_scope if root_scope is not None else self.root_scope
+
         self._submit_save(
             self._background_encode_batch,
             contents,
@@ -464,6 +503,7 @@ class Memory(BaseModel):
             source,
             private,
             agent_role,
+            effective_root,
         )
         return []
 
@@ -477,6 +517,7 @@ class Memory(BaseModel):
         source: str | None,
         private: bool,
         agent_role: str | None,
+        root_scope: str | None = None,
     ) -> list[MemoryRecord]:
         """Run the encoding pipeline in a background thread with event emission.
 
@@ -486,6 +527,20 @@ class Memory(BaseModel):
         All ``emit`` calls are wrapped in try/except to handle the case where
         the event bus shuts down before the background save finishes (e.g.
         during process exit).
+
+        Args:
+            contents: List of text content to encode.
+            scope: Optional inner scope for all items.
+            categories: Optional categories for all items.
+            metadata: Optional metadata for all items.
+            importance: Optional importance for all items.
+            source: Optional source identifier for all items.
+            private: Whether items are private.
+            agent_role: Optional agent role for event metadata.
+            root_scope: Optional root scope prefix for hierarchical scoping.
+
+        Returns:
+            List of created MemoryRecord instances.
         """
         try:
             crewai_event_bus.emit(
@@ -502,7 +557,14 @@ class Memory(BaseModel):
         try:
             start = time.perf_counter()
             records = self._encode_batch(
-                contents, scope, categories, metadata, importance, source, private
+                contents,
+                scope,
+                categories,
+                metadata,
+                importance,
+                source,
+                private,
+                root_scope,
             )
             elapsed_ms = (time.perf_counter() - start) * 1000
         except RuntimeError:
@@ -575,6 +637,14 @@ class Memory(BaseModel):
         # so that the search sees all persisted records.
         self.drain_writes()
 
+        # Apply root_scope as default scope_prefix for read isolation
+        effective_scope = scope
+        if effective_scope is None and self.root_scope:
+            effective_scope = self.root_scope
+        elif effective_scope is not None and self.root_scope:
+            # Nest provided scope under root
+            effective_scope = join_scope_paths(self.root_scope, effective_scope)
+
         _source = "unified_memory"
         try:
             crewai_event_bus.emit(
@@ -595,7 +665,7 @@ class Memory(BaseModel):
                 else:
                     raw = self._storage.search(
                         embedding,
-                        scope_prefix=scope,
+                        scope_prefix=effective_scope,
                         categories=categories,
                         limit=limit,
                         min_score=0.0,
@@ -630,7 +700,7 @@ class Memory(BaseModel):
                 flow.kickoff(
                     inputs={
                         "query": query,
-                        "scope": scope,
+                        "scope": effective_scope,
                         "categories": categories or [],
                         "limit": limit,
                         "source": source,
@@ -684,11 +754,24 @@ class Memory(BaseModel):
     ) -> int:
         """Delete memories matching criteria.
 
+        Args:
+            scope: Scope to delete from. If None and root_scope is set, deletes
+                only within root_scope.
+            categories: Filter by categories.
+            older_than: Delete records older than this datetime.
+            metadata_filter: Filter by metadata fields.
+            record_ids: Specific record IDs to delete.
+
         Returns:
             Number of records deleted.
         """
+        effective_scope = scope
+        if effective_scope is None and self.root_scope:
+            effective_scope = self.root_scope
+        elif effective_scope is not None and self.root_scope:
+            effective_scope = join_scope_paths(self.root_scope, effective_scope)
         return self._storage.delete(
-            scope_prefix=scope,
+            scope_prefix=effective_scope,
             categories=categories,
             record_ids=record_ids,
             older_than=older_than,
@@ -763,9 +846,21 @@ class Memory(BaseModel):
             read_only=read_only,
         )
 
-    def list_scopes(self, path: str = "/") -> list[str]:
-        """List immediate child scopes under path."""
-        return self._storage.list_scopes(path)
+    def list_scopes(self, path: str | None = None) -> list[str]:
+        """List immediate child scopes under path.
+
+        Args:
+            path: Scope path to list children of. If None and root_scope is set,
+                defaults to root_scope. Otherwise defaults to '/'.
+        """
+        effective_path = path
+        if effective_path is None and self.root_scope:
+            effective_path = self.root_scope
+        elif effective_path is not None and self.root_scope:
+            effective_path = join_scope_paths(self.root_scope, effective_path)
+        elif effective_path is None:
+            effective_path = "/"
+        return self._storage.list_scopes(effective_path)
 
     def list_records(
         self, scope: str | None = None, limit: int = 200, offset: int = 0
@@ -773,20 +868,52 @@ class Memory(BaseModel):
         """List records in a scope, newest first.
 
         Args:
-            scope: Optional scope path prefix to filter by.
+            scope: Optional scope path prefix to filter by. If None and root_scope
+                is set, defaults to root_scope.
             limit: Maximum number of records to return.
             offset: Number of records to skip (for pagination).
         """
+        effective_scope = scope
+        if effective_scope is None and self.root_scope:
+            effective_scope = self.root_scope
+        elif effective_scope is not None and self.root_scope:
+            effective_scope = join_scope_paths(self.root_scope, effective_scope)
         return self._storage.list_records(
-            scope_prefix=scope, limit=limit, offset=offset
+            scope_prefix=effective_scope, limit=limit, offset=offset
         )
 
-    def info(self, path: str = "/") -> ScopeInfo:
-        """Return scope info for path."""
-        return self._storage.get_scope_info(path)
+    def info(self, path: str | None = None) -> ScopeInfo:
+        """Return scope info for path.
+
+        Args:
+            path: Scope path to get info for. If None and root_scope is set,
+                defaults to root_scope. Otherwise defaults to '/'.
+        """
+        effective_path = path
+        if effective_path is None and self.root_scope:
+            effective_path = self.root_scope
+        elif effective_path is not None and self.root_scope:
+            effective_path = join_scope_paths(self.root_scope, effective_path)
+        elif effective_path is None:
+            effective_path = "/"
+        return self._storage.get_scope_info(effective_path)
+
+    def tree(self, path: str | None = None, max_depth: int = 3) -> str:
+        """Return a formatted tree of scopes (string).
+
+        Args:
+            path: Root path for the tree. If None and root_scope is set,
+                defaults to root_scope. Otherwise defaults to '/'.
+            max_depth: Maximum depth to traverse.
+        """
+        effective_path = path
+        if effective_path is None and self.root_scope:
+            effective_path = self.root_scope
+        elif effective_path is not None and self.root_scope:
+            effective_path = join_scope_paths(self.root_scope, effective_path)
+        elif effective_path is None:
+            effective_path = "/"
 
-    def tree(self, path: str = "/", max_depth: int = 3) -> str:
-        """Return a formatted tree of scopes (string)."""
         lines: list[str] = []
 
         def _walk(p: str, depth: int, prefix: str) -> None:
@@ -797,16 +924,36 @@ class Memory(BaseModel):
             for child in info.child_scopes[:20]:
                 _walk(child, depth + 1, prefix + "  ")
 
-        _walk(path.rstrip("/") or "/", 0, "")
-        return "\n".join(lines) if lines else f"{path or '/'} (0 records)"
+        _walk(effective_path.rstrip("/") or "/", 0, "")
+        return "\n".join(lines) if lines else f"{effective_path or '/'} (0 records)"
 
     def list_categories(self, path: str | None = None) -> dict[str, int]:
-        """List categories and counts; path=None means global."""
-        return self._storage.list_categories(scope_prefix=path)
+        """List categories and counts.
+
+        Args:
+            path: Scope path to filter categories by. If None and root_scope is set,
+                defaults to root_scope.
+        """
+        effective_path = path
+        if effective_path is None and self.root_scope:
+            effective_path = self.root_scope
+        elif effective_path is not None and self.root_scope:
+            effective_path = join_scope_paths(self.root_scope, effective_path)
+        return self._storage.list_categories(scope_prefix=effective_path)
 
     def reset(self, scope: str | None = None) -> None:
-        """Reset (delete all) memories in scope. None = all."""
-        self._storage.reset(scope_prefix=scope)
+        """Reset (delete all) memories in scope.
+
+        Args:
+            scope: Scope to reset. If None and root_scope is set, resets only
+                within root_scope. If None and no root_scope, resets all.
+        """
+        effective_scope = scope
+        if effective_scope is None and self.root_scope:
+            effective_scope = self.root_scope
+        elif effective_scope is not None and self.root_scope:
+            effective_scope = join_scope_paths(self.root_scope, effective_scope)
+        self._storage.reset(scope_prefix=effective_scope)
 
     async def aextract_memories(self, content: str) -> list[str]:
         """Async variant of extract_memories."""

lib/crewai/src/crewai/memory/utils.py

@@ -0,0 +1,110 @@
+"""Utility functions for the unified memory system."""
+
+from __future__ import annotations
+
+import re
+
+
+def sanitize_scope_name(name: str) -> str:
+    """Sanitize a name for use in hierarchical scope paths.
+
+    Converts to lowercase, replaces non-alphanumeric chars (except underscore
+    and hyphen) with hyphens, collapses multiple hyphens, strips leading/trailing
+    hyphens.
+
+    Args:
+        name: The raw name to sanitize (e.g. crew name, agent role, flow class name).
+
+    Returns:
+        A sanitized string safe for use in scope paths. Returns 'unknown' if the
+        result would be empty.
+
+    Examples:
+        >>> sanitize_scope_name("Research Crew")
+        'research-crew'
+        >>> sanitize_scope_name("Agent #1 (Main)")
+        'agent-1-main'
+        >>> sanitize_scope_name("café_worker")
+        'caf-_worker'
+    """
+    if not name:
+        return "unknown"
+    name = name.lower().strip()
+    # Replace any character that's not alphanumeric, underscore, or hyphen with hyphen
+    name = re.sub(r"[^a-z0-9_-]", "-", name)
+    # Collapse multiple hyphens into one
+    name = re.sub(r"-+", "-", name)
+    # Strip leading/trailing hyphens
+    name = name.strip("-")
+    return name or "unknown"
+
+
+def normalize_scope_path(path: str) -> str:
+    """Normalize a scope path by removing double slashes and ensuring proper format.
+
+    Args:
+        path: The raw scope path (e.g. '/crew/MyCrewName//agent//role').
+
+    Returns:
+        A normalized path with leading slash, no trailing slash, no double slashes.
+        Returns '/' for empty or root-only paths.
+
+    Examples:
+        >>> normalize_scope_path("/crew/test//agent//")
+        '/crew/test/agent'
+        >>> normalize_scope_path("")
+        '/'
+        >>> normalize_scope_path("crew/test")
+        '/crew/test'
+    """
+    if not path or path == "/":
+        return "/"
+    # Collapse multiple slashes
+    path = re.sub(r"/+", "/", path)
+    # Ensure leading slash
+    if not path.startswith("/"):
+        path = "/" + path
+    # Remove trailing slash (unless it's just '/')
+    if len(path) > 1:
+        path = path.rstrip("/")
+    return path
+
+
+def join_scope_paths(root: str | None, inner: str | None) -> str:
+    """Join a root scope with an inner scope, handling edge cases properly.
+
+    Args:
+        root: The root scope prefix (e.g. '/crew/research-crew').
+        inner: The inner scope (e.g. '/market-trends' or 'market-trends').
+
+    Returns:
+        The combined, normalized scope path.
+
+    Examples:
+        >>> join_scope_paths("/crew/test", "/market-trends")
+        '/crew/test/market-trends'
+        >>> join_scope_paths("/crew/test", "market-trends")
+        '/crew/test/market-trends'
+        >>> join_scope_paths("/crew/test", "/")
+        '/crew/test'
+        >>> join_scope_paths("/crew/test", None)
+        '/crew/test'
+        >>> join_scope_paths(None, "/market-trends")
+        '/market-trends'
+        >>> join_scope_paths(None, None)
+        '/'
+    """
+    # Normalize both parts
+    root = root.rstrip("/") if root else ""
+    inner = inner.strip("/") if inner else ""
+
+    if root and inner:
+        result = f"{root}/{inner}"
+    elif root:
+        result = root
+    elif inner:
+        result = f"/{inner}"
+    else:
+        result = "/"
+
+    return normalize_scope_path(result)

lib/crewai/src/crewai/skills/__init__.py

@@ -0,0 +1,17 @@
+"""Agent Skills standard implementation for crewAI.
+
+Provides filesystem-based skill packaging with progressive disclosure.
+"""
+
+from crewai.skills.loader import activate_skill, discover_skills
+from crewai.skills.models import Skill, SkillFrontmatter
+from crewai.skills.parser import SkillParseError
+
+
+__all__ = [
+    "Skill",
+    "SkillFrontmatter",
+    "SkillParseError",
+    "activate_skill",
+    "discover_skills",
+]

lib/crewai/src/crewai/skills/loader.py

@@ -0,0 +1,184 @@
+"""Filesystem discovery and progressive loading for Agent Skills.
+
+Provides functions to discover skills in directories, activate them
+for agent use, and format skill context for prompt injection.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.skill_events import (
+    SkillActivatedEvent,
+    SkillDiscoveryCompletedEvent,
+    SkillDiscoveryStartedEvent,
+    SkillLoadFailedEvent,
+    SkillLoadedEvent,
+)
+from crewai.skills.models import INSTRUCTIONS, RESOURCES, Skill
+from crewai.skills.parser import (
+    SKILL_FILENAME,
+    load_skill_instructions,
+    load_skill_metadata,
+    load_skill_resources,
+)
+
+
+if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+_logger = logging.getLogger(__name__)
+
+
+def discover_skills(
+    search_path: Path,
+    source: BaseAgent | None = None,
+) -> list[Skill]:
+    """Scan a directory for skill directories containing SKILL.md.
+
+    Loads each discovered skill at METADATA disclosure level.
+
+    Args:
+        search_path: Directory to scan for skill subdirectories.
+        source: Optional event source (agent or crew) for event emission.
+
+    Returns:
+        List of Skill instances at METADATA level.
+    """
+    if not search_path.is_dir():
+        msg = f"Skill search path does not exist or is not a directory: {search_path}"
+        raise FileNotFoundError(msg)
+
+    skills: list[Skill] = []
+
+    if source is not None:
+        crewai_event_bus.emit(
+            source,
+            event=SkillDiscoveryStartedEvent(
+                from_agent=source,
+                search_path=search_path,
+            ),
+        )
+
+    for child in sorted(search_path.iterdir()):
+        if not child.is_dir():
+            continue
+        skill_md = child / SKILL_FILENAME
+        if not skill_md.is_file():
+            continue
+        try:
+            skill = load_skill_metadata(child)
+            skills.append(skill)
+            if source is not None:
+                crewai_event_bus.emit(
+                    source,
+                    event=SkillLoadedEvent(
+                        from_agent=source,
+                        skill_name=skill.name,
+                        skill_path=skill.path,
+                        disclosure_level=skill.disclosure_level,
+                    ),
+                )
+        except Exception as e:
+            _logger.warning("Failed to load skill from %s: %s", child, e)
+            if source is not None:
+                crewai_event_bus.emit(
+                    source,
+                    event=SkillLoadFailedEvent(
+                        from_agent=source,
+                        skill_name=child.name,
+                        skill_path=child,
+                        error=str(e),
+                    ),
+                )
+
+    if source is not None:
+        crewai_event_bus.emit(
+            source,
+            event=SkillDiscoveryCompletedEvent(
+                from_agent=source,
+                search_path=search_path,
+                skills_found=len(skills),
+                skill_names=[s.name for s in skills],
+            ),
+        )
+
+    return skills
+
+
+def activate_skill(
+    skill: Skill,
+    source: BaseAgent | None = None,
+) -> Skill:
+    """Promote a skill to INSTRUCTIONS disclosure level.
+
+    Idempotent: returns the skill unchanged if already at or above INSTRUCTIONS.
+
+    Args:
+        skill: Skill to activate.
+        source: Optional event source for event emission.
+
+    Returns:
+        Skill at INSTRUCTIONS level or higher.
+    """
+    if skill.disclosure_level >= INSTRUCTIONS:
+        return skill
+
+    activated = load_skill_instructions(skill)
+
+    if source is not None:
+        crewai_event_bus.emit(
+            source,
+            event=SkillActivatedEvent(
+                from_agent=source,
+                skill_name=activated.name,
+                skill_path=activated.path,
+                disclosure_level=activated.disclosure_level,
+            ),
+        )
+
+    return activated
+
+
+def load_resources(skill: Skill) -> Skill:
+    """Promote a skill to RESOURCES disclosure level.
+
+    Args:
+        skill: Skill to promote.
+
+    Returns:
+        Skill at RESOURCES level.
+    """
+    return load_skill_resources(skill)
+
+
+def format_skill_context(skill: Skill) -> str:
+    """Format skill information for agent prompt injection.
+
+    At METADATA level: returns name and description only.
+    At INSTRUCTIONS level or above: returns full SKILL.md body.
+
+    Args:
+        skill: The skill to format.
+
+    Returns:
+        Formatted skill context string.
+    """
+    if skill.disclosure_level >= INSTRUCTIONS and skill.instructions:
+        parts = [
+            f"## Skill: {skill.name}",
+            skill.description,
+            "",
+            skill.instructions,
+        ]
+        if skill.disclosure_level >= RESOURCES and skill.resource_files:
+            parts.append("")
+            parts.append("### Available Resources")
+            for dir_name, files in sorted(skill.resource_files.items()):
+                if files:
+                    parts.append(f"- **{dir_name}/**: {', '.join(files)}")
+        return "\n".join(parts)
+    return f"## Skill: {skill.name}\n{skill.description}"

lib/crewai/src/crewai/skills/models.py

@@ -0,0 +1,175 @@
+"""Pydantic data models for the Agent Skills standard.
+
+Defines DisclosureLevel, SkillFrontmatter, and Skill models for
+progressive disclosure of skill information.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Annotated, Any, Final, Literal
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from crewai.skills.validation import (
+    MAX_SKILL_NAME_LENGTH,
+    MIN_SKILL_NAME_LENGTH,
+    SKILL_NAME_PATTERN,
+)
+
+
+MAX_DESCRIPTION_LENGTH: Final[int] = 1024
+ResourceDirName = Literal["scripts", "references", "assets"]
+
+
+DisclosureLevel = Annotated[
+    Literal[1, 2, 3], "Progressive disclosure levels for skill loading."
+]
+
+METADATA: Final[
+    Annotated[
+        DisclosureLevel, "Only frontmatter metadata is loaded (name, description)."
+    ]
+] = 1
+INSTRUCTIONS: Final[Annotated[DisclosureLevel, "Full SKILL.md body is loaded."]] = 2
+RESOURCES: Final[
+    Annotated[
+        DisclosureLevel,
+        "Resource directories (scripts, references, assets) are cataloged.",
+    ]
+] = 3
+
+
+class SkillFrontmatter(BaseModel):
+    """YAML frontmatter from a SKILL.md file.
+
+    Attributes:
+        name: Unique skill identifier (1-64 chars, lowercase alphanumeric + hyphens).
+        description: Human-readable description (1-1024 chars).
+        license: Optional license name or reference.
+        compatibility: Optional compatibility information (max 500 chars).
+        metadata: Optional additional metadata as string key-value pairs.
+        allowed_tools: Optional space-delimited list of pre-approved tools.
+    """
+
+    model_config = ConfigDict(frozen=True, populate_by_name=True)
+
+    name: str = Field(
+        min_length=MIN_SKILL_NAME_LENGTH,
+        max_length=MAX_SKILL_NAME_LENGTH,
+        pattern=SKILL_NAME_PATTERN,
+    )
+    description: str = Field(min_length=1, max_length=MAX_DESCRIPTION_LENGTH)
+    license: str | None = Field(
+        default=None,
+        description="SPDX license identifier or free-text license reference, e.g. 'MIT', 'Apache-2.0'.",
+    )
+    compatibility: str | None = Field(
+        default=None,
+        max_length=500,
+        description="Version or platform constraints for the skill, e.g. 'crewai >= 0.80'.",
+    )
+    metadata: dict[str, str] | None = Field(
+        default=None,
+        description="Arbitrary string key-value pairs for custom skill metadata.",
+    )
+    allowed_tools: list[str] | None = Field(
+        default=None,
+        alias="allowed-tools",
+        description="Pre-approved tool names the skill may use, parsed from a space-delimited string in frontmatter.",
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def parse_allowed_tools(cls, values: dict[str, Any]) -> dict[str, Any]:
+        """Parse space-delimited allowed-tools string into a list."""
+        key = "allowed-tools"
+        alt_key = "allowed_tools"
+        raw = values.get(key) or values.get(alt_key)
+        if isinstance(raw, str):
+            values[key] = raw.split()
+        return values
+
+
+class Skill(BaseModel):
+    """A loaded Agent Skill with progressive disclosure support.
+
+    Attributes:
+        frontmatter: Parsed YAML frontmatter.
+        instructions: Full SKILL.md body text (populated at INSTRUCTIONS level).
+        path: Filesystem path to the skill directory.
+        disclosure_level: Current disclosure level of the skill.
+        resource_files: Cataloged resource files (populated at RESOURCES level).
+    """
+
+    frontmatter: SkillFrontmatter = Field(
+        description="Parsed YAML frontmatter from SKILL.md.",
+    )
+    instructions: str | None = Field(
+        default=None,
+        description="Full SKILL.md body text, populated at INSTRUCTIONS level.",
+    )
+    path: Path = Field(
+        description="Filesystem path to the skill directory.",
+    )
+    disclosure_level: DisclosureLevel = Field(
+        default=METADATA,
+        description="Current progressive disclosure level of the skill.",
+    )
+    resource_files: dict[ResourceDirName, list[str]] | None = Field(
+        default=None,
+        description="Cataloged resource files by directory, populated at RESOURCES level.",
+    )
+
+    @property
+    def name(self) -> str:
+        """Skill name from frontmatter."""
+        return self.frontmatter.name
+
+    @property
+    def description(self) -> str:
+        """Skill description from frontmatter."""
+        return self.frontmatter.description
+
+    @property
+    def scripts_dir(self) -> Path:
+        """Path to the scripts directory."""
+        return self.path / "scripts"
+
+    @property
+    def references_dir(self) -> Path:
+        """Path to the references directory."""
+        return self.path / "references"
+
+    @property
+    def assets_dir(self) -> Path:
+        """Path to the assets directory."""
+        return self.path / "assets"
+
+    def with_disclosure_level(
+        self,
+        level: DisclosureLevel,
+        instructions: str | None = None,
+        resource_files: dict[ResourceDirName, list[str]] | None = None,
+    ) -> Skill:
+        """Create a new Skill at a different disclosure level.
+
+        Args:
+            level: The new disclosure level.
+            instructions: Optional instructions body text.
+            resource_files: Optional cataloged resource files.
+
+        Returns:
+            A new Skill instance at the specified disclosure level.
+        """
+        return Skill(
+            frontmatter=self.frontmatter,
+            instructions=instructions
+            if instructions is not None
+            else self.instructions,
+            path=self.path,
+            disclosure_level=level,
+            resource_files=(
+                resource_files if resource_files is not None else self.resource_files
+            ),
+        )

lib/crewai/src/crewai/skills/parser.py

@@ -0,0 +1,194 @@
+"""SKILL.md file parsing for the Agent Skills standard.
+
+Parses YAML frontmatter and markdown body from SKILL.md files,
+and provides progressive loading functions for skill data.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+import re
+from typing import Any, Final
+
+import yaml
+
+from crewai.skills.models import (
+    INSTRUCTIONS,
+    METADATA,
+    RESOURCES,
+    ResourceDirName,
+    Skill,
+    SkillFrontmatter,
+)
+from crewai.skills.validation import validate_directory_name
+
+
+_logger = logging.getLogger(__name__)
+
+
+SKILL_FILENAME: Final[str] = "SKILL.md"
+_CLOSING_DELIMITER: Final[re.Pattern[str]] = re.compile(r"\n---[ \t]*(?:\n|$)")
+_MAX_BODY_CHARS: Final[int] = 50_000
+
+
+class SkillParseError(ValueError):
+    """Error raised when SKILL.md parsing fails."""
+
+
+def parse_frontmatter(content: str) -> tuple[dict[str, Any], str]:
+    """Split SKILL.md content into frontmatter dict and body text.
+
+    Args:
+        content: Raw SKILL.md file content.
+
+    Returns:
+        Tuple of (frontmatter dict, body text).
+
+    Raises:
+        SkillParseError: If frontmatter delimiters are missing or YAML is invalid.
+    """
+    if not content.startswith("---"):
+        msg = "SKILL.md must start with '---' frontmatter delimiter"
+        raise SkillParseError(msg)
+
+    match = _CLOSING_DELIMITER.search(content, pos=3)
+    if match is None:
+        msg = "SKILL.md missing closing '---' frontmatter delimiter"
+        raise SkillParseError(msg)
+
+    yaml_content = content[3 : match.start()].strip()
+    body = content[match.end() :].strip()
+
+    try:
+        frontmatter = yaml.safe_load(yaml_content)
+    except yaml.YAMLError as e:
+        msg = f"Invalid YAML in frontmatter: {e}"
+        raise SkillParseError(msg) from e
+
+    if not isinstance(frontmatter, dict):
+        msg = "Frontmatter must be a YAML mapping"
+        raise SkillParseError(msg)
+
+    return frontmatter, body
+
+
+def parse_skill_md(path: Path) -> tuple[SkillFrontmatter, str]:
+    """Read and parse a SKILL.md file.
+
+    Args:
+        path: Path to the SKILL.md file.
+
+    Returns:
+        Tuple of (SkillFrontmatter, body text).
+
+    Raises:
+        FileNotFoundError: If the file does not exist.
+        SkillParseError: If parsing fails.
+    """
+    content = path.read_text(encoding="utf-8")
+    frontmatter_dict, body = parse_frontmatter(content)
+    frontmatter = SkillFrontmatter(**frontmatter_dict)
+    return frontmatter, body
+
+
+def load_skill_metadata(skill_dir: Path) -> Skill:
+    """Load a skill at METADATA disclosure level.
+
+    Parses SKILL.md frontmatter only and validates directory name.
+
+    Args:
+        skill_dir: Path to the skill directory.
+
+    Returns:
+        Skill instance at METADATA level.
+
+    Raises:
+        FileNotFoundError: If SKILL.md is missing.
+        SkillParseError: If parsing fails.
+        ValueError: If directory name doesn't match skill name.
+    """
+    skill_md_path = skill_dir / SKILL_FILENAME
+    frontmatter, body = parse_skill_md(skill_md_path)
+    validate_directory_name(skill_dir, frontmatter.name)
+    if len(body) > _MAX_BODY_CHARS:
+        _logger.warning(
+            "SKILL.md body for '%s' is %d chars (threshold: %d). "
+            "Large bodies may consume significant context window when injected into prompts.",
+            frontmatter.name,
+            len(body),
+            _MAX_BODY_CHARS,
+        )
+    return Skill(
+        frontmatter=frontmatter,
+        path=skill_dir,
+        disclosure_level=METADATA,
+    )
+
+
+def load_skill_instructions(skill: Skill) -> Skill:
+    """Promote a skill to INSTRUCTIONS disclosure level.
+
+    Reads the full SKILL.md body text.
+
+    Args:
+        skill: Skill at METADATA level.
+
+    Returns:
+        New Skill instance at INSTRUCTIONS level.
+    """
+    if skill.disclosure_level >= INSTRUCTIONS:
+        return skill
+
+    skill_md_path = skill.path / SKILL_FILENAME
+    _, body = parse_skill_md(skill_md_path)
+    if len(body) > _MAX_BODY_CHARS:
+        _logger.warning(
+            "SKILL.md body for '%s' is %d chars (threshold: %d). "
+            "Large bodies may consume significant context window when injected into prompts.",
+            skill.name,
+            len(body),
+            _MAX_BODY_CHARS,
+        )
+    return skill.with_disclosure_level(
+        level=INSTRUCTIONS,
+        instructions=body,
+    )
+
+
+def load_skill_resources(skill: Skill) -> Skill:
+    """Promote a skill to RESOURCES disclosure level.
+
+    Catalogs available resource directories (scripts, references, assets).
+
+    Args:
+        skill: Skill at any level.
+
+    Returns:
+        New Skill instance at RESOURCES level.
+    """
+    if skill.disclosure_level >= RESOURCES:
+        return skill
+
+    if skill.disclosure_level < INSTRUCTIONS:
+        skill = load_skill_instructions(skill)
+
+    resource_dirs: list[tuple[ResourceDirName, Path]] = [
+        ("scripts", skill.scripts_dir),
+        ("references", skill.references_dir),
+        ("assets", skill.assets_dir),
+    ]
+    resource_files: dict[ResourceDirName, list[str]] = {}
+    for dir_name, resource_dir in resource_dirs:
+        if resource_dir.is_dir():
+            resource_files[dir_name] = sorted(
+                str(f.relative_to(resource_dir))
+                for f in resource_dir.rglob("*")
+                if f.is_file()
+            )
+
+    return skill.with_disclosure_level(
+        level=RESOURCES,
+        instructions=skill.instructions,
+        resource_files=resource_files,
+    )

lib/crewai/src/crewai/skills/validation.py

@@ -0,0 +1,31 @@
+"""Validation functions for Agent Skills specification constraints.
+
+Validates skill names and directory structures per the Agent Skills standard.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+import re
+from typing import Final
+
+
+MAX_SKILL_NAME_LENGTH: Final[int] = 64
+MIN_SKILL_NAME_LENGTH: Final[int] = 1
+SKILL_NAME_PATTERN: Final[re.Pattern[str]] = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$")
+
+
+def validate_directory_name(skill_dir: Path, skill_name: str) -> None:
+    """Validate that a directory name matches the skill name.
+
+    Args:
+        skill_dir: Path to the skill directory.
+        skill_name: The declared skill name from frontmatter.
+
+    Raises:
+        ValueError: If the directory name does not match the skill name.
+    """
+    dir_name = skill_dir.name
+    if dir_name != skill_name:
+        msg = f"Directory name '{dir_name}' does not match skill name '{skill_name}'"
+        raise ValueError(msg)

lib/crewai/src/crewai/utilities/token_counter_callback.py

@@ -1,37 +1,40 @@
 """Token counting callback handler for LLM interactions.
 
 This module provides a callback handler that tracks token usage
-for LLM API calls through the litellm library.
+for LLM API calls. Works standalone and also integrates with litellm
+when available (for the litellm fallback path).
 """
 
-from typing import TYPE_CHECKING, Any
-
-
-if TYPE_CHECKING:
-    from litellm.integrations.custom_logger import CustomLogger
-    from litellm.types.utils import Usage
-else:
-    try:
-        from litellm.integrations.custom_logger import CustomLogger
-        from litellm.types.utils import Usage
-    except ImportError:
-
-        class CustomLogger:
-            """Fallback CustomLogger when litellm is not available."""
-
-        class Usage:
-            """Fallback Usage when litellm is not available."""
-
+from typing import Any
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.utilities.logger_utils import suppress_warnings
 
 
-class TokenCalcHandler(CustomLogger):
+# Check if litellm is available for callback integration
+try:
+    from litellm.integrations.custom_logger import CustomLogger as LiteLLMCustomLogger
+
+    LITELLM_AVAILABLE = True
+except ImportError:
+    LiteLLMCustomLogger = None  # type: ignore[misc, assignment]
+    LITELLM_AVAILABLE = False
+
+
+# Create a base class that conditionally inherits from litellm's CustomLogger
+# when available, or from object when not available
+if LITELLM_AVAILABLE and LiteLLMCustomLogger is not None:
+    _BaseClass: type = LiteLLMCustomLogger
+else:
+    _BaseClass = object
+
+
+class TokenCalcHandler(_BaseClass):  # type: ignore[misc]
     """Handler for calculating and tracking token usage in LLM calls.
 
-    This handler integrates with litellm's logging system to track
-    prompt tokens, completion tokens, and cached tokens across requests.
+    This handler tracks prompt tokens, completion tokens, and cached tokens
+    across requests. It works standalone and also integrates with litellm's
+    logging system when litellm is installed (for the fallback path).
 
     Attributes:
         token_cost_process: The token process tracker to accumulate usage metrics.
@@ -43,7 +46,9 @@ class TokenCalcHandler(CustomLogger):
         Args:
             token_cost_process: Optional token process tracker for accumulating metrics.
         """
-        super().__init__(**kwargs)
+        # Only call super().__init__ if we have a real parent class with __init__
+        if LITELLM_AVAILABLE and LiteLLMCustomLogger is not None:
+            super().__init__(**kwargs)
         self.token_cost_process = token_cost_process
 
     def log_success_event(
@@ -55,6 +60,10 @@ class TokenCalcHandler(CustomLogger):
     ) -> None:
         """Log successful LLM API call and track token usage.
 
+        This method has the same interface as litellm's CustomLogger.log_success_event()
+        so it can be used as a litellm callback when litellm is installed, or called
+        directly when litellm is not installed.
+
         Args:
             kwargs: The arguments passed to the LLM call.
             response_obj: The response object from the LLM API.
@@ -66,7 +75,7 @@ class TokenCalcHandler(CustomLogger):
 
         with suppress_warnings():
             if isinstance(response_obj, dict) and "usage" in response_obj:
-                usage: Usage = response_obj["usage"]
+                usage = response_obj["usage"]
                 if usage:
                     self.token_cost_process.sum_successful_requests(1)
                     if hasattr(usage, "prompt_tokens"):

lib/crewai/tests/skills/fixtures/invalid-name/SKILL.md

@@ -0,0 +1,4 @@
+---
+name: Invalid--Name
+description: This skill has an invalid name.
+---

lib/crewai/tests/skills/fixtures/minimal-skill/SKILL.md

@@ -0,0 +1,4 @@
+---
+name: minimal-skill
+description: A minimal skill with only required fields.
+---

lib/crewai/tests/skills/fixtures/valid-skill/SKILL.md

@@ -0,0 +1,22 @@
+---
+name: valid-skill
+description: A complete test skill with all optional directories.
+license: Apache-2.0
+compatibility: crewai>=0.1.0
+metadata:
+  author: test
+  version: "1.0"
+allowed-tools: web-search file-read
+---
+
+## Instructions
+
+This skill provides comprehensive instructions for the agent.
+
+### Usage
+
+Follow these steps to use the skill effectively.
+
+### Notes
+
+Additional context for the agent.

lib/crewai/tests/skills/fixtures/valid-skill/assets/config.json

@@ -0,0 +1 @@
+{"key": "value"}

lib/crewai/tests/skills/fixtures/valid-skill/references/guide.md

@@ -0,0 +1,3 @@
+# Reference Guide
+
+This is a reference document for the skill.

lib/crewai/tests/skills/fixtures/valid-skill/scripts/setup.sh

@@ -0,0 +1,2 @@
+#!/bin/bash
+echo "setup"

lib/crewai/tests/skills/test_integration.py

@@ -0,0 +1,78 @@
+"""Integration tests for the skills system."""
+
+from pathlib import Path
+
+import pytest
+
+from crewai.skills.loader import activate_skill, discover_skills, format_skill_context
+from crewai.skills.models import INSTRUCTIONS, METADATA
+
+
+def _create_skill_dir(parent: Path, name: str, body: str = "Body.") -> Path:
+    """Helper to create a skill directory with SKILL.md."""
+    skill_dir = parent / name
+    skill_dir.mkdir()
+    (skill_dir / "SKILL.md").write_text(
+        f"---\nname: {name}\ndescription: Skill {name}\n---\n{body}"
+    )
+    return skill_dir
+
+
+class TestSkillDiscoveryAndActivation:
+    """End-to-end tests for discover + activate workflow."""
+
+    def test_discover_and_activate(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "my-skill", body="Use this skill.")
+        skills = discover_skills(tmp_path)
+        assert len(skills) == 1
+        assert skills[0].disclosure_level == METADATA
+
+        activated = activate_skill(skills[0])
+        assert activated.disclosure_level == INSTRUCTIONS
+        assert activated.instructions == "Use this skill."
+
+        context = format_skill_context(activated)
+        assert "## Skill: my-skill" in context
+        assert "Use this skill." in context
+
+    def test_filter_by_skill_names(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "alpha")
+        _create_skill_dir(tmp_path, "beta")
+        _create_skill_dir(tmp_path, "gamma")
+
+        all_skills = discover_skills(tmp_path)
+        wanted = {"alpha", "gamma"}
+        filtered = [s for s in all_skills if s.name in wanted]
+        assert {s.name for s in filtered} == {"alpha", "gamma"}
+
+    def test_full_fixture_skill(self) -> None:
+        fixtures = Path(__file__).parent / "fixtures"
+        valid_dir = fixtures / "valid-skill"
+        if not valid_dir.exists():
+            pytest.skip("Fixture not found")
+
+        skills = discover_skills(fixtures)
+        valid_skills = [s for s in skills if s.name == "valid-skill"]
+        assert len(valid_skills) == 1
+
+        skill = valid_skills[0]
+        assert skill.frontmatter.license == "Apache-2.0"
+        assert skill.frontmatter.allowed_tools == ["web-search", "file-read"]
+
+        activated = activate_skill(skill)
+        assert "Instructions" in (activated.instructions or "")
+
+    def test_multiple_search_paths(self, tmp_path: Path) -> None:
+        path_a = tmp_path / "a"
+        path_a.mkdir()
+        _create_skill_dir(path_a, "skill-a")
+
+        path_b = tmp_path / "b"
+        path_b.mkdir()
+        _create_skill_dir(path_b, "skill-b")
+
+        all_skills = []
+        for search_path in [path_a, path_b]:
+            all_skills.extend(discover_skills(search_path))
+        names = {s.name for s in all_skills}
+        assert names == {"skill-a", "skill-b"}

lib/crewai/tests/skills/test_loader.py

@@ -0,0 +1,161 @@
+"""Tests for skills/loader.py."""
+
+from pathlib import Path
+
+import pytest
+
+from crewai.skills.loader import (
+    activate_skill,
+    discover_skills,
+    format_skill_context,
+    load_resources,
+)
+from crewai.skills.models import INSTRUCTIONS, METADATA, RESOURCES, Skill, SkillFrontmatter
+from crewai.skills.parser import load_skill_metadata
+
+
+def _create_skill_dir(parent: Path, name: str, body: str = "Body.") -> Path:
+    """Helper to create a skill directory with SKILL.md."""
+    skill_dir = parent / name
+    skill_dir.mkdir()
+    (skill_dir / "SKILL.md").write_text(
+        f"---\nname: {name}\ndescription: Skill {name}\n---\n{body}"
+    )
+    return skill_dir
+
+
+class TestDiscoverSkills:
+    """Tests for discover_skills."""
+
+    def test_finds_valid_skills(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "alpha")
+        _create_skill_dir(tmp_path, "beta")
+        skills = discover_skills(tmp_path)
+        names = {s.name for s in skills}
+        assert names == {"alpha", "beta"}
+
+    def test_skips_dirs_without_skill_md(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "valid")
+        (tmp_path / "no-skill").mkdir()
+        skills = discover_skills(tmp_path)
+        assert len(skills) == 1
+        assert skills[0].name == "valid"
+
+    def test_skips_invalid_skills(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "good-skill")
+        bad_dir = tmp_path / "bad-skill"
+        bad_dir.mkdir()
+        (bad_dir / "SKILL.md").write_text(
+            "---\nname: Wrong-Name\ndescription: bad\n---\n"
+        )
+        skills = discover_skills(tmp_path)
+        assert len(skills) == 1
+
+    def test_empty_directory(self, tmp_path: Path) -> None:
+        skills = discover_skills(tmp_path)
+        assert skills == []
+
+    def test_nonexistent_path(self, tmp_path: Path) -> None:
+        with pytest.raises(FileNotFoundError):
+            discover_skills(tmp_path / "nonexistent")
+
+    def test_sorted_by_name(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "zebra")
+        _create_skill_dir(tmp_path, "alpha")
+        skills = discover_skills(tmp_path)
+        assert [s.name for s in skills] == ["alpha", "zebra"]
+
+
+class TestActivateSkill:
+    """Tests for activate_skill."""
+
+    def test_promotes_to_instructions(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "my-skill", body="Instructions.")
+        skill = load_skill_metadata(tmp_path / "my-skill")
+        activated = activate_skill(skill)
+        assert activated.disclosure_level == INSTRUCTIONS
+        assert activated.instructions == "Instructions."
+
+    def test_idempotent(self, tmp_path: Path) -> None:
+        _create_skill_dir(tmp_path, "my-skill")
+        skill = load_skill_metadata(tmp_path / "my-skill")
+        activated = activate_skill(skill)
+        again = activate_skill(activated)
+        assert again is activated
+
+
+class TestLoadResources:
+    """Tests for load_resources."""
+
+    def test_promotes_to_resources(self, tmp_path: Path) -> None:
+        skill_dir = _create_skill_dir(tmp_path, "my-skill")
+        (skill_dir / "scripts").mkdir()
+        (skill_dir / "scripts" / "run.sh").write_text("#!/bin/bash")
+        skill = load_skill_metadata(skill_dir)
+        full = load_resources(skill)
+        assert full.disclosure_level == RESOURCES
+
+
+class TestFormatSkillContext:
+    """Tests for format_skill_context."""
+
+    def test_metadata_level(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="A skill")
+        skill = Skill(
+            frontmatter=fm, path=tmp_path, disclosure_level=METADATA
+        )
+        ctx = format_skill_context(skill)
+        assert "## Skill: test-skill" in ctx
+        assert "A skill" in ctx
+
+    def test_instructions_level(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="A skill")
+        skill = Skill(
+            frontmatter=fm,
+            path=tmp_path,
+            disclosure_level=INSTRUCTIONS,
+            instructions="Do these things.",
+        )
+        ctx = format_skill_context(skill)
+        assert "## Skill: test-skill" in ctx
+        assert "Do these things." in ctx
+
+    def test_no_instructions_at_instructions_level(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="A skill")
+        skill = Skill(
+            frontmatter=fm,
+            path=tmp_path,
+            disclosure_level=INSTRUCTIONS,
+            instructions=None,
+        )
+        ctx = format_skill_context(skill)
+        assert ctx == "## Skill: test-skill\nA skill"
+
+    def test_resources_level(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="A skill")
+        skill = Skill(
+            frontmatter=fm,
+            path=tmp_path,
+            disclosure_level=RESOURCES,
+            instructions="Do things.",
+            resource_files={
+                "scripts": ["run.sh"],
+                "assets": ["data.json", "config.yaml"],
+            },
+        )
+        ctx = format_skill_context(skill)
+        assert "### Available Resources" in ctx
+        assert "**assets/**: data.json, config.yaml" in ctx
+        assert "**scripts/**: run.sh" in ctx
+
+    def test_resources_level_empty_files(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="A skill")
+        skill = Skill(
+            frontmatter=fm,
+            path=tmp_path,
+            disclosure_level=RESOURCES,
+            instructions="Do things.",
+            resource_files={},
+        )
+        ctx = format_skill_context(skill)
+        assert "### Available Resources" not in ctx

lib/crewai/tests/skills/test_models.py

@@ -0,0 +1,91 @@
+"""Tests for skills/models.py."""
+
+from pathlib import Path
+
+import pytest
+
+from crewai.skills.models import (
+    INSTRUCTIONS,
+    METADATA,
+    RESOURCES,
+    Skill,
+    SkillFrontmatter,
+)
+
+
+class TestDisclosureLevel:
+    """Tests for DisclosureLevel constants."""
+
+    def test_ordering(self) -> None:
+        assert METADATA < INSTRUCTIONS
+        assert INSTRUCTIONS < RESOURCES
+
+    def test_values(self) -> None:
+        assert METADATA == 1
+        assert INSTRUCTIONS == 2
+        assert RESOURCES == 3
+
+
+class TestSkillFrontmatter:
+    """Tests for SkillFrontmatter model."""
+
+    def test_required_fields(self) -> None:
+        fm = SkillFrontmatter(name="my-skill", description="A test skill")
+        assert fm.name == "my-skill"
+        assert fm.description == "A test skill"
+        assert fm.license is None
+        assert fm.metadata is None
+        assert fm.allowed_tools is None
+
+    def test_all_fields(self) -> None:
+        fm = SkillFrontmatter(
+            name="web-search",
+            description="Search the web",
+            license="Apache-2.0",
+            compatibility="crewai>=0.1.0",
+            metadata={"author": "test"},
+            allowed_tools=["browser"],
+        )
+        assert fm.license == "Apache-2.0"
+        assert fm.metadata == {"author": "test"}
+        assert fm.allowed_tools == ["browser"]
+
+    def test_frozen(self) -> None:
+        fm = SkillFrontmatter(name="my-skill", description="desc")
+        with pytest.raises(Exception):
+            fm.name = "other"  # type: ignore[misc]
+
+    def test_invalid_name_rejected(self) -> None:
+        with pytest.raises(ValueError):
+            SkillFrontmatter(name="Invalid--Name", description="bad")
+
+
+class TestSkill:
+    """Tests for Skill model."""
+
+    def test_properties(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="desc")
+        skill = Skill(frontmatter=fm, path=tmp_path / "test-skill")
+        assert skill.name == "test-skill"
+        assert skill.description == "desc"
+        assert skill.disclosure_level == METADATA
+
+    def test_resource_dirs(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "test-skill"
+        skill_dir.mkdir()
+        fm = SkillFrontmatter(name="test-skill", description="desc")
+        skill = Skill(frontmatter=fm, path=skill_dir)
+        assert skill.scripts_dir == skill_dir / "scripts"
+        assert skill.references_dir == skill_dir / "references"
+        assert skill.assets_dir == skill_dir / "assets"
+
+    def test_with_disclosure_level(self, tmp_path: Path) -> None:
+        fm = SkillFrontmatter(name="test-skill", description="desc")
+        skill = Skill(frontmatter=fm, path=tmp_path)
+        promoted = skill.with_disclosure_level(
+            INSTRUCTIONS,
+            instructions="Do this.",
+        )
+        assert promoted.disclosure_level == INSTRUCTIONS
+        assert promoted.instructions == "Do this."
+        assert skill.disclosure_level == METADATA

lib/crewai/tests/skills/test_parser.py

@@ -0,0 +1,167 @@
+"""Tests for skills/parser.py."""
+
+from pathlib import Path
+
+import pytest
+
+from crewai.skills.models import INSTRUCTIONS, METADATA, RESOURCES
+from crewai.skills.parser import (
+    SkillParseError,
+    load_skill_instructions,
+    load_skill_metadata,
+    load_skill_resources,
+    parse_frontmatter,
+    parse_skill_md,
+)
+
+
+class TestParseFrontmatter:
+    """Tests for parse_frontmatter."""
+
+    def test_valid_frontmatter_and_body(self) -> None:
+        content = "---\nname: test\ndescription: A test\n---\n\nBody text here."
+        fm, body = parse_frontmatter(content)
+        assert fm["name"] == "test"
+        assert fm["description"] == "A test"
+        assert body == "Body text here."
+
+    def test_empty_body(self) -> None:
+        content = "---\nname: test\ndescription: A test\n---"
+        fm, body = parse_frontmatter(content)
+        assert fm["name"] == "test"
+        assert body == ""
+
+    def test_missing_opening_delimiter(self) -> None:
+        with pytest.raises(SkillParseError, match="must start with"):
+            parse_frontmatter("name: test\n---\nBody")
+
+    def test_missing_closing_delimiter(self) -> None:
+        with pytest.raises(SkillParseError, match="missing closing"):
+            parse_frontmatter("---\nname: test\n")
+
+    def test_invalid_yaml(self) -> None:
+        with pytest.raises(SkillParseError, match="Invalid YAML"):
+            parse_frontmatter("---\n: :\n  bad: [yaml\n---\nBody")
+
+    def test_triple_dash_in_body(self) -> None:
+        content = "---\nname: test\ndescription: desc\n---\n\nBody with --- inside."
+        fm, body = parse_frontmatter(content)
+        assert "---" in body
+
+    def test_inline_triple_dash_in_yaml_value(self) -> None:
+        content = '---\nname: test\ndescription: "Use---carefully"\n---\n\nBody.'
+        fm, body = parse_frontmatter(content)
+        assert fm["description"] == "Use---carefully"
+        assert body == "Body."
+
+    def test_unicode_content(self) -> None:
+        content = "---\nname: test\ndescription: Beschreibung\n---\n\nUnicode: \u00e4\u00f6\u00fc\u00df"
+        fm, body = parse_frontmatter(content)
+        assert fm["description"] == "Beschreibung"
+        assert "\u00e4\u00f6\u00fc\u00df" in body
+
+    def test_non_mapping_frontmatter(self) -> None:
+        with pytest.raises(SkillParseError, match="must be a YAML mapping"):
+            parse_frontmatter("---\n- item1\n- item2\n---\nBody")
+
+
+class TestParseSkillMd:
+    """Tests for parse_skill_md."""
+
+    def test_valid_file(self, tmp_path: Path) -> None:
+        skill_md = tmp_path / "SKILL.md"
+        skill_md.write_text(
+            "---\nname: my-skill\ndescription: desc\n---\nInstructions here."
+        )
+        fm, body = parse_skill_md(skill_md)
+        assert fm.name == "my-skill"
+        assert body == "Instructions here."
+
+    def test_file_not_found(self, tmp_path: Path) -> None:
+        with pytest.raises(FileNotFoundError):
+            parse_skill_md(tmp_path / "nonexistent" / "SKILL.md")
+
+
+class TestLoadSkillMetadata:
+    """Tests for load_skill_metadata."""
+
+    def test_valid_skill(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "my-skill"
+        skill_dir.mkdir()
+        (skill_dir / "SKILL.md").write_text(
+            "---\nname: my-skill\ndescription: Test skill\n---\nBody"
+        )
+        skill = load_skill_metadata(skill_dir)
+        assert skill.name == "my-skill"
+        assert skill.disclosure_level == METADATA
+        assert skill.instructions is None
+
+    def test_directory_name_mismatch(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "wrong-name"
+        skill_dir.mkdir()
+        (skill_dir / "SKILL.md").write_text(
+            "---\nname: my-skill\ndescription: Test skill\n---\n"
+        )
+        with pytest.raises(ValueError, match="does not match"):
+            load_skill_metadata(skill_dir)
+
+
+class TestLoadSkillInstructions:
+    """Tests for load_skill_instructions."""
+
+    def test_promotes_to_instructions(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "my-skill"
+        skill_dir.mkdir()
+        (skill_dir / "SKILL.md").write_text(
+            "---\nname: my-skill\ndescription: Test\n---\nFull body."
+        )
+        skill = load_skill_metadata(skill_dir)
+        promoted = load_skill_instructions(skill)
+        assert promoted.disclosure_level == INSTRUCTIONS
+        assert promoted.instructions == "Full body."
+
+    def test_idempotent(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "my-skill"
+        skill_dir.mkdir()
+        (skill_dir / "SKILL.md").write_text(
+            "---\nname: my-skill\ndescription: Test\n---\nBody."
+        )
+        skill = load_skill_metadata(skill_dir)
+        promoted = load_skill_instructions(skill)
+        again = load_skill_instructions(promoted)
+        assert again is promoted
+
+
+class TestLoadSkillResources:
+    """Tests for load_skill_resources."""
+
+    def test_catalogs_resources(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "my-skill"
+        skill_dir.mkdir()
+        (skill_dir / "SKILL.md").write_text(
+            "---\nname: my-skill\ndescription: Test\n---\nBody."
+        )
+        (skill_dir / "scripts").mkdir()
+        (skill_dir / "scripts" / "run.sh").write_text("#!/bin/bash")
+        (skill_dir / "assets").mkdir()
+        (skill_dir / "assets" / "data.json").write_text("{}")
+
+        skill = load_skill_metadata(skill_dir)
+        full = load_skill_resources(skill)
+        assert full.disclosure_level == RESOURCES
+        assert full.instructions == "Body."
+        assert full.resource_files is not None
+        assert "scripts" in full.resource_files
+        assert "run.sh" in full.resource_files["scripts"]
+        assert "assets" in full.resource_files
+        assert "data.json" in full.resource_files["assets"]
+
+    def test_no_resource_dirs(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "my-skill"
+        skill_dir.mkdir()
+        (skill_dir / "SKILL.md").write_text(
+            "---\nname: my-skill\ndescription: Test\n---\nBody."
+        )
+        skill = load_skill_metadata(skill_dir)
+        full = load_skill_resources(skill)
+        assert full.resource_files == {}

lib/crewai/tests/skills/test_validation.py

@@ -0,0 +1,93 @@
+"""Tests for skills validation."""
+
+from pathlib import Path
+
+import pytest
+
+from crewai.skills.models import SkillFrontmatter
+from crewai.skills.validation import (
+    MAX_SKILL_NAME_LENGTH,
+    validate_directory_name,
+)
+
+
+def _make(name: str) -> SkillFrontmatter:
+    """Create a SkillFrontmatter with the given name."""
+    return SkillFrontmatter(name=name, description="desc")
+
+
+class TestSkillNameValidation:
+    """Tests for skill name constraints via SkillFrontmatter."""
+
+    def test_simple_name(self) -> None:
+        assert _make("web-search").name == "web-search"
+
+    def test_single_word(self) -> None:
+        assert _make("search").name == "search"
+
+    def test_numeric(self) -> None:
+        assert _make("tool3").name == "tool3"
+
+    def test_all_digits(self) -> None:
+        assert _make("123").name == "123"
+
+    def test_single_char(self) -> None:
+        assert _make("a").name == "a"
+
+    def test_max_length(self) -> None:
+        name = "a" * MAX_SKILL_NAME_LENGTH
+        assert _make(name).name == name
+
+    def test_multi_hyphen_segments(self) -> None:
+        assert _make("my-cool-skill").name == "my-cool-skill"
+
+    def test_empty_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("")
+
+    def test_too_long_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("a" * (MAX_SKILL_NAME_LENGTH + 1))
+
+    def test_uppercase_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("MySkill")
+
+    def test_leading_hyphen_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("-skill")
+
+    def test_trailing_hyphen_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("skill-")
+
+    def test_consecutive_hyphens_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("my--skill")
+
+    def test_underscore_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("my_skill")
+
+    def test_space_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("my skill")
+
+    def test_special_chars_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _make("skill@v1")
+
+
+class TestValidateDirectoryName:
+    """Tests for validate_directory_name."""
+
+    def test_matching_names(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "my-skill"
+        skill_dir.mkdir()
+        validate_directory_name(skill_dir, "my-skill")
+
+    def test_mismatched_names(self, tmp_path: Path) -> None:
+        skill_dir = tmp_path / "other-name"
+        skill_dir.mkdir()
+        with pytest.raises(ValueError, match="does not match"):
+            validate_directory_name(skill_dir, "my-skill")

lib/crewai/tests/test_async_human_feedback.py

@@ -988,11 +988,9 @@ class TestLLMObjectPreservedInContext:
             db_path = os.path.join(tmpdir, "test_flows.db")
             persistence = SQLiteFlowPersistence(db_path)
 
-            # Create a mock BaseLLM object (not a string)
-            # Simulates LLM(model="gemini-2.0-flash", provider="gemini")
-            mock_llm_obj = MagicMock()
-            mock_llm_obj.model = "gemini-2.0-flash"
-            mock_llm_obj.provider = "gemini"
+            # Create a real LLM object (not a string)
+            from crewai.llm import LLM
+            mock_llm_obj = LLM(model="gemini-2.0-flash", provider="gemini")
 
             class PausingProvider:
                 def __init__(self, persistence: SQLiteFlowPersistence):
@@ -1041,32 +1039,37 @@ class TestLLMObjectPreservedInContext:
             result = flow1.kickoff()
             assert isinstance(result, HumanFeedbackPending)
 
-            # Verify the context stored the model STRING, not None
+            # Verify the context stored the model config dict, not None
             assert provider.captured_context is not None
-            assert provider.captured_context.llm == "gemini/gemini-2.0-flash"
+            assert isinstance(provider.captured_context.llm, dict)
+            assert provider.captured_context.llm["model"] == "gemini/gemini-2.0-flash"
 
             # Verify it survives persistence roundtrip
             flow_id = result.context.flow_id
             loaded = persistence.load_pending_feedback(flow_id)
             assert loaded is not None
             _, loaded_context = loaded
-            assert loaded_context.llm == "gemini/gemini-2.0-flash"
+            assert isinstance(loaded_context.llm, dict)
+            assert loaded_context.llm["model"] == "gemini/gemini-2.0-flash"
 
             # Phase 2: Resume with positive feedback - should use LLM to classify
             flow2 = TestFlow.from_pending(flow_id, persistence)
             assert flow2._pending_feedback_context is not None
-            assert flow2._pending_feedback_context.llm == "gemini/gemini-2.0-flash"
+            assert isinstance(flow2._pending_feedback_context.llm, dict)
+            assert flow2._pending_feedback_context.llm["model"] == "gemini/gemini-2.0-flash"
 
             # Mock _collapse_to_outcome to verify it gets called (not skipped)
             with patch.object(flow2, "_collapse_to_outcome", return_value="approved") as mock_collapse:
                 flow2.resume("this looks good, proceed!")
 
             # The key assertion: _collapse_to_outcome was called (not skipped due to llm=None)
-            mock_collapse.assert_called_once_with(
-                feedback="this looks good, proceed!",
-                outcomes=["needs_changes", "approved"],
-                llm="gemini/gemini-2.0-flash",
-            )
+            mock_collapse.assert_called_once()
+            call_kwargs = mock_collapse.call_args
+            assert call_kwargs.kwargs["feedback"] == "this looks good, proceed!"
+            assert call_kwargs.kwargs["outcomes"] == ["needs_changes", "approved"]
+            # LLM should be a live object (from _hf_llm) or reconstructed, not None
+            assert call_kwargs.kwargs["llm"] is not None
+            assert getattr(call_kwargs.kwargs["llm"], "model", None) == "gemini-2.0-flash"
             assert flow2.last_human_feedback.outcome == "approved"
             assert flow2.result_path == "approved"
 
@@ -1096,23 +1099,25 @@ class TestLLMObjectPreservedInContext:
     def test_provider_prefix_added_to_bare_model(self) -> None:
         """Test that provider prefix is added when model has no slash."""
         from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
 
-        mock_obj = MagicMock()
-        mock_obj.model = "gemini-3-flash-preview"
-        mock_obj.provider = "gemini"
-        assert _serialize_llm_for_context(mock_obj) == "gemini/gemini-3-flash-preview"
+        llm = LLM(model="gemini-2.0-flash", provider="gemini")
+        result = _serialize_llm_for_context(llm)
+        assert isinstance(result, dict)
+        assert result["model"] == "gemini/gemini-2.0-flash"
 
     def test_provider_prefix_not_doubled_when_already_present(self) -> None:
         """Test that provider prefix is not added when model already has a slash."""
         from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
 
-        mock_obj = MagicMock()
-        mock_obj.model = "gemini/gemini-2.0-flash"
-        mock_obj.provider = "gemini"
-        assert _serialize_llm_for_context(mock_obj) == "gemini/gemini-2.0-flash"
+        llm = LLM(model="gemini/gemini-2.0-flash")
+        result = _serialize_llm_for_context(llm)
+        assert isinstance(result, dict)
+        assert result["model"] == "gemini/gemini-2.0-flash"
 
     def test_no_provider_attr_falls_back_to_bare_model(self) -> None:
-        """Test that bare model is used when no provider attribute exists."""
+        """Test that objects without to_config_dict fall back to model string."""
         from crewai.flow.human_feedback import _serialize_llm_for_context
 
         mock_obj = MagicMock(spec=[])
@@ -1216,3 +1221,279 @@ class TestAsyncHumanFeedbackEdgeCases:
 
             assert flow.last_human_feedback.outcome == "approved"
             assert flow.last_human_feedback.feedback == ""
+
+
+# =============================================================================
+# Tests for _hf_llm attribute and live LLM resolution on resume
+# =============================================================================
+
+
+class TestLiveLLMPreservationOnResume:
+    """Tests for preserving the full LLM config across HITL resume."""
+
+    def test_hf_llm_attribute_set_on_wrapper_with_basellm(self) -> None:
+        """Test that _hf_llm is set on the wrapper when llm is a BaseLLM instance."""
+        from crewai.llms.base_llm import BaseLLM
+
+        # Create a mock BaseLLM object
+        mock_llm = MagicMock(spec=BaseLLM)
+        mock_llm.model = "gemini/gemini-3-flash"
+
+        class TestFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm=mock_llm,
+            )
+            def review(self):
+                return "content"
+
+        flow = TestFlow()
+        method = flow._methods.get("review")
+        assert method is not None
+        assert hasattr(method, "_hf_llm")
+        assert method._hf_llm is mock_llm
+
+    def test_hf_llm_attribute_set_on_wrapper_with_string(self) -> None:
+        """Test that _hf_llm is set on the wrapper even when llm is a string."""
+
+        class TestFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            def review(self):
+                return "content"
+
+        flow = TestFlow()
+        method = flow._methods.get("review")
+        assert method is not None
+        assert hasattr(method, "_hf_llm")
+        assert method._hf_llm == "gpt-4o-mini"
+
+    @patch("crewai.flow.flow.crewai_event_bus.emit")
+    def test_resume_async_uses_live_basellm_over_serialized_string(
+        self, mock_emit: MagicMock
+    ) -> None:
+        """Test that resume_async uses the live BaseLLM from decorator instead of serialized string.
+
+        This is the main bug fix: when a flow resumes, it should use the fully-configured
+        LLM from the re-imported decorator (with credentials, project, etc.) instead of
+        creating a new LLM from just the model string.
+        """
+        with tempfile.TemporaryDirectory() as tmpdir:
+            db_path = os.path.join(tmpdir, "test_flows.db")
+            persistence = SQLiteFlowPersistence(db_path)
+
+            from crewai.llms.base_llm import BaseLLM
+
+            # Create a mock BaseLLM with full config (simulating Gemini with service account)
+            live_llm = MagicMock(spec=BaseLLM)
+            live_llm.model = "gemini/gemini-3-flash"
+
+            class TestFlow(Flow):
+                result_path: str = ""
+
+                @start()
+                @human_feedback(
+                    message="Approve?",
+                    emit=["approved", "rejected"],
+                    llm=live_llm,  # Full LLM object with credentials
+                )
+                def review(self):
+                    return "content"
+
+                @listen("approved")
+                def handle_approved(self):
+                    self.result_path = "approved"
+                    return "Approved!"
+
+            # Save pending feedback with just a model STRING (simulating serialization)
+            context = PendingFeedbackContext(
+                flow_id="live-llm-test",
+                flow_class="TestFlow",
+                method_name="review",
+                method_output="content",
+                message="Approve?",
+                emit=["approved", "rejected"],
+                llm="gemini/gemini-3-flash",  # Serialized string, NOT the live object
+            )
+            persistence.save_pending_feedback(
+                flow_uuid="live-llm-test",
+                context=context,
+                state_data={"id": "live-llm-test"},
+            )
+
+            # Restore flow - this re-imports the class with the live LLM
+            flow = TestFlow.from_pending("live-llm-test", persistence)
+
+            # Mock _collapse_to_outcome to capture what LLM it receives
+            captured_llm = []
+
+            def capture_llm(feedback, outcomes, llm):
+                captured_llm.append(llm)
+                return "approved"
+
+            with patch.object(flow, "_collapse_to_outcome", side_effect=capture_llm):
+                flow.resume("looks good!")
+
+            # The key assertion: _collapse_to_outcome received the LIVE BaseLLM object,
+            # NOT the serialized string. The live_llm was captured at class definition
+            # time and stored on the method wrapper as _hf_llm.
+            assert len(captured_llm) == 1
+            # Verify it's the same object that was passed to the decorator
+            # (which is stored on the method's _hf_llm attribute)
+            method = flow._methods.get("review")
+            assert method is not None
+            assert captured_llm[0] is method._hf_llm
+            # And verify it's a BaseLLM instance, not a string
+            assert isinstance(captured_llm[0], BaseLLM)
+
+    @patch("crewai.flow.flow.crewai_event_bus.emit")
+    def test_resume_async_falls_back_to_serialized_string_when_no_hf_llm(
+        self, mock_emit: MagicMock
+    ) -> None:
+        """Test that resume_async falls back to context.llm when _hf_llm is not available.
+
+        This ensures backward compatibility with flows that were paused before this fix.
+        """
+        with tempfile.TemporaryDirectory() as tmpdir:
+            db_path = os.path.join(tmpdir, "test_flows.db")
+            persistence = SQLiteFlowPersistence(db_path)
+
+            class TestFlow(Flow):
+                @start()
+                @human_feedback(
+                    message="Approve?",
+                    emit=["approved", "rejected"],
+                    llm="gpt-4o-mini",
+                )
+                def review(self):
+                    return "content"
+
+            # Save pending feedback
+            context = PendingFeedbackContext(
+                flow_id="fallback-test",
+                flow_class="TestFlow",
+                method_name="review",
+                method_output="content",
+                message="Approve?",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            persistence.save_pending_feedback(
+                flow_uuid="fallback-test",
+                context=context,
+                state_data={"id": "fallback-test"},
+            )
+
+            flow = TestFlow.from_pending("fallback-test", persistence)
+
+            # Remove _hf_llm to simulate old decorator without this attribute
+            method = flow._methods.get("review")
+            if hasattr(method, "_hf_llm"):
+                delattr(method, "_hf_llm")
+
+            # Mock _collapse_to_outcome to capture what LLM it receives
+            captured_llm = []
+
+            def capture_llm(feedback, outcomes, llm):
+                captured_llm.append(llm)
+                return "approved"
+
+            with patch.object(flow, "_collapse_to_outcome", side_effect=capture_llm):
+                flow.resume("looks good!")
+
+            # Should fall back to deserialized LLM from context string
+            assert len(captured_llm) == 1
+            from crewai.llms.base_llm import BaseLLM as BaseLLMClass
+            assert isinstance(captured_llm[0], BaseLLMClass)
+            assert captured_llm[0].model == "gpt-4o-mini"
+
+    @patch("crewai.flow.flow.crewai_event_bus.emit")
+    def test_resume_async_uses_string_from_context_when_hf_llm_is_string(
+        self, mock_emit: MagicMock
+    ) -> None:
+        """Test that when _hf_llm is a string (not BaseLLM), we still use context.llm.
+
+        String LLM values offer no benefit over the serialized context.llm,
+        so we don't prefer them.
+        """
+        with tempfile.TemporaryDirectory() as tmpdir:
+            db_path = os.path.join(tmpdir, "test_flows.db")
+            persistence = SQLiteFlowPersistence(db_path)
+
+            class TestFlow(Flow):
+                @start()
+                @human_feedback(
+                    message="Approve?",
+                    emit=["approved", "rejected"],
+                    llm="gpt-4o-mini",  # String LLM
+                )
+                def review(self):
+                    return "content"
+
+            # Save pending feedback
+            context = PendingFeedbackContext(
+                flow_id="string-llm-test",
+                flow_class="TestFlow",
+                method_name="review",
+                method_output="content",
+                message="Approve?",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            persistence.save_pending_feedback(
+                flow_uuid="string-llm-test",
+                context=context,
+                state_data={"id": "string-llm-test"},
+            )
+
+            flow = TestFlow.from_pending("string-llm-test", persistence)
+
+            # Verify _hf_llm is a string
+            method = flow._methods.get("review")
+            assert method._hf_llm == "gpt-4o-mini"
+
+            # Mock _collapse_to_outcome to capture what LLM it receives
+            captured_llm = []
+
+            def capture_llm(feedback, outcomes, llm):
+                captured_llm.append(llm)
+                return "approved"
+
+            with patch.object(flow, "_collapse_to_outcome", side_effect=capture_llm):
+                flow.resume("looks good!")
+
+            # _hf_llm is a string, so resume deserializes context.llm into an LLM instance
+            assert len(captured_llm) == 1
+            from crewai.llms.base_llm import BaseLLM as BaseLLMClass
+            assert isinstance(captured_llm[0], BaseLLMClass)
+            assert captured_llm[0].model == "gpt-4o-mini"
+
+    def test_hf_llm_set_for_async_wrapper(self) -> None:
+        """Test that _hf_llm is set on async wrapper functions."""
+        import asyncio
+        from crewai.llms.base_llm import BaseLLM
+
+        mock_llm = MagicMock(spec=BaseLLM)
+        mock_llm.model = "gemini/gemini-3-flash"
+
+        class TestFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm=mock_llm,
+            )
+            async def async_review(self):
+                return "content"
+
+        flow = TestFlow()
+        method = flow._methods.get("async_review")
+        assert method is not None
+        assert hasattr(method, "_hf_llm")
+        assert method._hf_llm is mock_llm

lib/crewai/tests/test_flow_serializer.py

@@ -0,0 +1,795 @@
+"""Tests for flow_serializer.py - Flow structure serialization for Studio UI."""
+
+from typing import Literal
+
+import pytest
+from pydantic import BaseModel, Field
+
+from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.flow_serializer import flow_structure
+from crewai.flow.human_feedback import human_feedback
+
+
+class TestSimpleLinearFlow:
+    """Test simple linear flow (start → listen → listen)."""
+
+    def test_linear_flow_structure(self):
+        """Test a simple sequential flow structure."""
+
+        class LinearFlow(Flow):
+            """A simple linear flow for testing."""
+
+            @start()
+            def begin(self):
+                return "started"
+
+            @listen(begin)
+            def process(self):
+                return "processed"
+
+            @listen(process)
+            def finalize(self):
+                return "done"
+
+        structure = flow_structure(LinearFlow)
+
+        assert structure["name"] == "LinearFlow"
+        assert structure["description"] == "A simple linear flow for testing."
+        assert len(structure["methods"]) == 3
+
+        # Check method types
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        assert method_map["begin"]["type"] == "start"
+        assert method_map["process"]["type"] == "listen"
+        assert method_map["finalize"]["type"] == "listen"
+
+        # Check edges
+        assert len(structure["edges"]) == 2
+
+        edge_pairs = [(e["from_method"], e["to_method"]) for e in structure["edges"]]
+        assert ("begin", "process") in edge_pairs
+        assert ("process", "finalize") in edge_pairs
+
+        # All edges should be listen type
+        for edge in structure["edges"]:
+            assert edge["edge_type"] == "listen"
+            assert edge["condition"] is None
+
+
+class TestRouterFlow:
+    """Test flow with router branching."""
+
+    def test_router_flow_structure(self):
+        """Test a flow with router that branches to different paths."""
+
+        class BranchingFlow(Flow):
+            @start()
+            def init(self):
+                return "initialized"
+
+            @router(init)
+            def decide(self) -> Literal["path_a", "path_b"]:
+                return "path_a"
+
+            @listen("path_a")
+            def handle_a(self):
+                return "handled_a"
+
+            @listen("path_b")
+            def handle_b(self):
+                return "handled_b"
+
+        structure = flow_structure(BranchingFlow)
+
+        assert structure["name"] == "BranchingFlow"
+        assert len(structure["methods"]) == 4
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        # Check method types
+        assert method_map["init"]["type"] == "start"
+        assert method_map["decide"]["type"] == "router"
+        assert method_map["handle_a"]["type"] == "listen"
+        assert method_map["handle_b"]["type"] == "listen"
+
+        # Check router paths
+        assert "path_a" in method_map["decide"]["router_paths"]
+        assert "path_b" in method_map["decide"]["router_paths"]
+
+        # Check edges
+        # Should have: init -> decide (listen), decide -> handle_a (route), decide -> handle_b (route)
+        listen_edges = [e for e in structure["edges"] if e["edge_type"] == "listen"]
+        route_edges = [e for e in structure["edges"] if e["edge_type"] == "route"]
+
+        assert len(listen_edges) == 1
+        assert listen_edges[0]["from_method"] == "init"
+        assert listen_edges[0]["to_method"] == "decide"
+
+        assert len(route_edges) == 2
+        route_targets = {e["to_method"] for e in route_edges}
+        assert "handle_a" in route_targets
+        assert "handle_b" in route_targets
+
+        # Check route conditions
+        route_conditions = {e["to_method"]: e["condition"] for e in route_edges}
+        assert route_conditions["handle_a"] == "path_a"
+        assert route_conditions["handle_b"] == "path_b"
+
+
+class TestAndOrConditions:
+    """Test flow with AND/OR conditions."""
+
+    def test_and_condition_flow(self):
+        """Test a flow where a method waits for multiple methods (AND)."""
+
+        class AndConditionFlow(Flow):
+            @start()
+            def step_a(self):
+                return "a"
+
+            @start()
+            def step_b(self):
+                return "b"
+
+            @listen(and_(step_a, step_b))
+            def converge(self):
+                return "converged"
+
+        structure = flow_structure(AndConditionFlow)
+
+        assert len(structure["methods"]) == 3
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        assert method_map["step_a"]["type"] == "start"
+        assert method_map["step_b"]["type"] == "start"
+        assert method_map["converge"]["type"] == "listen"
+
+        # Check condition type
+        assert method_map["converge"]["condition_type"] == "AND"
+
+        # Check trigger methods
+        triggers = method_map["converge"]["trigger_methods"]
+        assert "step_a" in triggers
+        assert "step_b" in triggers
+
+        # Check edges - should have 2 edges to converge
+        converge_edges = [e for e in structure["edges"] if e["to_method"] == "converge"]
+        assert len(converge_edges) == 2
+
+    def test_or_condition_flow(self):
+        """Test a flow where a method is triggered by any of multiple methods (OR)."""
+
+        class OrConditionFlow(Flow):
+            @start()
+            def path_1(self):
+                return "1"
+
+            @start()
+            def path_2(self):
+                return "2"
+
+            @listen(or_(path_1, path_2))
+            def handle_any(self):
+                return "handled"
+
+        structure = flow_structure(OrConditionFlow)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        assert method_map["handle_any"]["condition_type"] == "OR"
+
+        triggers = method_map["handle_any"]["trigger_methods"]
+        assert "path_1" in triggers
+        assert "path_2" in triggers
+
+
+class TestHumanFeedbackMethods:
+    """Test flow with @human_feedback decorated methods."""
+
+    def test_human_feedback_detection(self):
+        """Test that human feedback methods are correctly identified."""
+
+        class HumanFeedbackFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Please review:",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            def review_step(self):
+                return "content to review"
+
+            @listen("approved")
+            def handle_approved(self):
+                return "approved"
+
+            @listen("rejected")
+            def handle_rejected(self):
+                return "rejected"
+
+        structure = flow_structure(HumanFeedbackFlow)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        # review_step should have human feedback
+        assert method_map["review_step"]["has_human_feedback"] is True
+        # It's a start+router (due to emit)
+        assert method_map["review_step"]["type"] == "start_router"
+        assert "approved" in method_map["review_step"]["router_paths"]
+        assert "rejected" in method_map["review_step"]["router_paths"]
+
+        # Other methods should not have human feedback
+        assert method_map["handle_approved"]["has_human_feedback"] is False
+        assert method_map["handle_rejected"]["has_human_feedback"] is False
+
+
+class TestCrewReferences:
+    """Test detection of Crew references in method bodies."""
+
+    def test_crew_detection_with_crew_call(self):
+        """Test that .crew() calls are detected."""
+
+        class FlowWithCrew(Flow):
+            @start()
+            def run_crew(self):
+                # Simulating crew usage pattern
+                # result = MyCrew().crew().kickoff()
+                return "result"
+
+            @listen(run_crew)
+            def no_crew(self):
+                return "done"
+
+        structure = flow_structure(FlowWithCrew)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        # Note: Since the actual .crew() call is in a comment/string,
+        # the detection might not trigger. In real code it would.
+        # We're testing the mechanism exists.
+        assert "has_crew" in method_map["run_crew"]
+        assert "has_crew" in method_map["no_crew"]
+
+    def test_no_crew_when_absent(self):
+        """Test that methods without Crew refs return has_crew=False."""
+
+        class SimpleNonCrewFlow(Flow):
+            @start()
+            def calculate(self):
+                return 1 + 1
+
+            @listen(calculate)
+            def display(self):
+                return "result"
+
+        structure = flow_structure(SimpleNonCrewFlow)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        assert method_map["calculate"]["has_crew"] is False
+        assert method_map["display"]["has_crew"] is False
+
+
+class TestTypedStateSchema:
+    """Test flow with typed Pydantic state."""
+
+    def test_pydantic_state_schema_extraction(self):
+        """Test extracting state schema from a Flow with Pydantic state."""
+
+        class MyState(BaseModel):
+            counter: int = 0
+            message: str = ""
+            items: list[str] = Field(default_factory=list)
+
+        class TypedStateFlow(Flow[MyState]):
+            initial_state = MyState
+
+            @start()
+            def increment(self):
+                self.state.counter += 1
+                return self.state.counter
+
+            @listen(increment)
+            def display(self):
+                return f"Count: {self.state.counter}"
+
+        structure = flow_structure(TypedStateFlow)
+
+        assert structure["state_schema"] is not None
+        fields = structure["state_schema"]["fields"]
+
+        field_names = {f["name"] for f in fields}
+        assert "counter" in field_names
+        assert "message" in field_names
+        assert "items" in field_names
+
+        # Check types
+        field_map = {f["name"]: f for f in fields}
+        assert "int" in field_map["counter"]["type"]
+        assert "str" in field_map["message"]["type"]
+
+        # Check defaults
+        assert field_map["counter"]["default"] == 0
+        assert field_map["message"]["default"] == ""
+
+    def test_dict_state_returns_none(self):
+        """Test that flows using dict state return None for state_schema."""
+
+        class DictStateFlow(Flow):
+            @start()
+            def begin(self):
+                self.state["count"] = 1
+                return "started"
+
+        structure = flow_structure(DictStateFlow)
+
+        assert structure["state_schema"] is None
+
+
+class TestEdgeCases:
+    """Test edge cases and special scenarios."""
+
+    def test_start_router_combo(self):
+        """Test a method that is both @start and a router (via human_feedback emit)."""
+
+        class StartRouterFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["continue", "stop"],
+                llm="gpt-4o-mini",
+            )
+            def entry_point(self):
+                return "data"
+
+            @listen("continue")
+            def proceed(self):
+                return "proceeding"
+
+            @listen("stop")
+            def halt(self):
+                return "halted"
+
+        structure = flow_structure(StartRouterFlow)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        assert method_map["entry_point"]["type"] == "start_router"
+        assert method_map["entry_point"]["has_human_feedback"] is True
+        assert "continue" in method_map["entry_point"]["router_paths"]
+        assert "stop" in method_map["entry_point"]["router_paths"]
+
+    def test_multiple_start_methods(self):
+        """Test a flow with multiple start methods."""
+
+        class MultiStartFlow(Flow):
+            @start()
+            def start_a(self):
+                return "a"
+
+            @start()
+            def start_b(self):
+                return "b"
+
+            @listen(and_(start_a, start_b))
+            def combine(self):
+                return "combined"
+
+        structure = flow_structure(MultiStartFlow)
+
+        start_methods = [m for m in structure["methods"] if m["type"] == "start"]
+        assert len(start_methods) == 2
+
+        start_names = {m["name"] for m in start_methods}
+        assert "start_a" in start_names
+        assert "start_b" in start_names
+
+    def test_orphan_methods(self):
+        """Test that orphan methods (not connected to flow) are still captured."""
+
+        class FlowWithOrphan(Flow):
+            @start()
+            def begin(self):
+                return "started"
+
+            @listen(begin)
+            def connected(self):
+                return "connected"
+
+            @listen("never_triggered")
+            def orphan(self):
+                return "orphan"
+
+        structure = flow_structure(FlowWithOrphan)
+
+        method_names = {m["name"] for m in structure["methods"]}
+        assert "orphan" in method_names
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+        assert method_map["orphan"]["trigger_methods"] == ["never_triggered"]
+
+    def test_empty_flow(self):
+        """Test building structure for a flow with no methods."""
+
+        class EmptyFlow(Flow):
+            pass
+
+        structure = flow_structure(EmptyFlow)
+
+        assert structure["name"] == "EmptyFlow"
+        assert structure["methods"] == []
+        assert structure["edges"] == []
+        assert structure["state_schema"] is None
+
+    def test_flow_with_docstring(self):
+        """Test that flow docstring is captured."""
+
+        class DocumentedFlow(Flow):
+            """This is a well-documented flow.
+
+            It has multiple lines of documentation.
+            """
+
+            @start()
+            def begin(self):
+                return "started"
+
+        structure = flow_structure(DocumentedFlow)
+
+        assert structure["description"] is not None
+        assert "well-documented flow" in structure["description"]
+
+    def test_flow_without_docstring(self):
+        """Test that missing docstring returns None."""
+
+        class UndocumentedFlow(Flow):
+            @start()
+            def begin(self):
+                return "started"
+
+        structure = flow_structure(UndocumentedFlow)
+
+        assert structure["description"] is None
+
+    def test_nested_conditions(self):
+        """Test flow with nested AND/OR conditions."""
+
+        class NestedConditionFlow(Flow):
+            @start()
+            def a(self):
+                return "a"
+
+            @start()
+            def b(self):
+                return "b"
+
+            @start()
+            def c(self):
+                return "c"
+
+            @listen(or_(and_(a, b), c))
+            def complex_trigger(self):
+                return "triggered"
+
+        structure = flow_structure(NestedConditionFlow)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        # Should have triggers for a, b, and c
+        triggers = method_map["complex_trigger"]["trigger_methods"]
+        assert len(triggers) == 3
+        assert "a" in triggers
+        assert "b" in triggers
+        assert "c" in triggers
+
+
+class TestErrorHandling:
+    """Test error handling and validation."""
+
+    def test_instance_raises_type_error(self):
+        """Test that passing an instance raises TypeError."""
+
+        class TestFlow(Flow):
+            @start()
+            def begin(self):
+                return "started"
+
+        flow_instance = TestFlow()
+
+        with pytest.raises(TypeError) as exc_info:
+            flow_structure(flow_instance)
+
+        assert "requires a Flow class, not an instance" in str(exc_info.value)
+
+    def test_non_class_raises_type_error(self):
+        """Test that passing non-class raises TypeError."""
+
+        with pytest.raises(TypeError):
+            flow_structure("not a class")
+
+        with pytest.raises(TypeError):
+            flow_structure(123)
+
+
+class TestEdgeGeneration:
+    """Test edge generation in various scenarios."""
+
+    def test_all_edges_generated_correctly(self):
+        """Verify all edges are correctly generated for a complex flow."""
+
+        class ComplexFlow(Flow):
+            @start()
+            def entry(self):
+                return "started"
+
+            @listen(entry)
+            def step_1(self):
+                return "step_1"
+
+            @router(step_1)
+            def branch(self) -> Literal["left", "right"]:
+                return "left"
+
+            @listen("left")
+            def left_path(self):
+                return "left_done"
+
+            @listen("right")
+            def right_path(self):
+                return "right_done"
+
+            @listen(or_(left_path, right_path))
+            def converge(self):
+                return "done"
+
+        structure = flow_structure(ComplexFlow)
+
+        # Build edge map for easier checking
+        edges = structure["edges"]
+
+        # Check listen edges
+        listen_edges = [(e["from_method"], e["to_method"]) for e in edges if e["edge_type"] == "listen"]
+
+        assert ("entry", "step_1") in listen_edges
+        assert ("step_1", "branch") in listen_edges
+        assert ("left_path", "converge") in listen_edges
+        assert ("right_path", "converge") in listen_edges
+
+        # Check route edges
+        route_edges = [(e["from_method"], e["to_method"], e["condition"]) for e in edges if e["edge_type"] == "route"]
+
+        assert ("branch", "left_path", "left") in route_edges
+        assert ("branch", "right_path", "right") in route_edges
+
+    def test_router_edge_conditions(self):
+        """Test that router edge conditions are properly set."""
+
+        class RouterConditionFlow(Flow):
+            @start()
+            def begin(self):
+                return "start"
+
+            @router(begin)
+            def route(self) -> Literal["option_1", "option_2", "option_3"]:
+                return "option_1"
+
+            @listen("option_1")
+            def handle_1(self):
+                return "1"
+
+            @listen("option_2")
+            def handle_2(self):
+                return "2"
+
+            @listen("option_3")
+            def handle_3(self):
+                return "3"
+
+        structure = flow_structure(RouterConditionFlow)
+
+        route_edges = [e for e in structure["edges"] if e["edge_type"] == "route"]
+
+        # Should have 3 route edges
+        assert len(route_edges) == 3
+
+        conditions = {e["to_method"]: e["condition"] for e in route_edges}
+        assert conditions["handle_1"] == "option_1"
+        assert conditions["handle_2"] == "option_2"
+        assert conditions["handle_3"] == "option_3"
+
+
+class TestMethodTypeClassification:
+    """Test method type classification."""
+
+    def test_all_method_types(self):
+        """Test classification of all method types."""
+
+        class AllTypesFlow(Flow):
+            @start()
+            def start_only(self):
+                return "start"
+
+            @listen(start_only)
+            def listen_only(self):
+                return "listen"
+
+            @router(listen_only)
+            def router_only(self) -> Literal["path"]:
+                return "path"
+
+            @listen("path")
+            def after_router(self):
+                return "after"
+
+            @start()
+            @human_feedback(
+                message="Review",
+                emit=["yes", "no"],
+                llm="gpt-4o-mini",
+            )
+            def start_and_router(self):
+                return "data"
+
+        structure = flow_structure(AllTypesFlow)
+
+        method_map = {m["name"]: m for m in structure["methods"]}
+
+        assert method_map["start_only"]["type"] == "start"
+        assert method_map["listen_only"]["type"] == "listen"
+        assert method_map["router_only"]["type"] == "router"
+        assert method_map["after_router"]["type"] == "listen"
+        assert method_map["start_and_router"]["type"] == "start_router"
+
+
+class TestInputDetection:
+    """Test flow input detection."""
+
+    def test_inputs_list_exists(self):
+        """Test that inputs list is always present."""
+
+        class SimpleFlow(Flow):
+            @start()
+            def begin(self):
+                return "started"
+
+        structure = flow_structure(SimpleFlow)
+
+        assert "inputs" in structure
+        assert isinstance(structure["inputs"], list)
+
+
+class TestJsonSerializable:
+    """Test that output is JSON serializable."""
+
+    def test_structure_is_json_serializable(self):
+        """Test that the entire structure can be JSON serialized."""
+        import json
+
+        class MyState(BaseModel):
+            value: int = 0
+
+        class SerializableFlow(Flow[MyState]):
+            """Test flow for JSON serialization."""
+
+            initial_state = MyState
+
+            @start()
+            @human_feedback(
+                message="Review",
+                emit=["ok", "not_ok"],
+                llm="gpt-4o-mini",
+            )
+            def begin(self):
+                return "data"
+
+            @listen("ok")
+            def proceed(self):
+                return "done"
+
+        structure = flow_structure(SerializableFlow)
+
+        # Should not raise
+        json_str = json.dumps(structure)
+        assert json_str is not None
+
+        # Should round-trip
+        parsed = json.loads(json_str)
+        assert parsed["name"] == "SerializableFlow"
+        assert len(parsed["methods"]) > 0
+
+
+class TestFlowInheritance:
+    """Test flow inheritance scenarios."""
+
+    def test_child_flow_inherits_parent_methods(self):
+        """Test that FlowB inheriting from FlowA includes methods from both.
+
+        Note: FlowMeta propagates methods but does NOT fully propagate the
+        _listeners registry from parent classes. This means edges defined
+        in the parent class (e.g., parent_start -> parent_process) may not
+        appear in the child's structure. This is a known FlowMeta limitation.
+        """
+
+        class FlowA(Flow):
+            """Parent flow with start method."""
+
+            @start()
+            def parent_start(self):
+                return "parent started"
+
+            @listen(parent_start)
+            def parent_process(self):
+                return "parent processed"
+
+        class FlowB(FlowA):
+            """Child flow with additional methods."""
+
+            @listen(FlowA.parent_process)
+            def child_continue(self):
+                return "child continued"
+
+            @listen(child_continue)
+            def child_finalize(self):
+                return "child finalized"
+
+        structure = flow_structure(FlowB)
+
+        assert structure["name"] == "FlowB"
+
+        # Check all methods are present (from both parent and child)
+        method_names = {m["name"] for m in structure["methods"]}
+        assert "parent_start" in method_names
+        assert "parent_process" in method_names
+        assert "child_continue" in method_names
+        assert "child_finalize" in method_names
+
+        # Check method types
+        method_map = {m["name"]: m for m in structure["methods"]}
+        assert method_map["parent_start"]["type"] == "start"
+        assert method_map["parent_process"]["type"] == "listen"
+        assert method_map["child_continue"]["type"] == "listen"
+        assert method_map["child_finalize"]["type"] == "listen"
+
+        # Check edges defined in child class exist
+        edge_pairs = [(e["from_method"], e["to_method"]) for e in structure["edges"]]
+        assert ("parent_process", "child_continue") in edge_pairs
+        assert ("child_continue", "child_finalize") in edge_pairs
+
+        # KNOWN LIMITATION: Edges defined in parent class (parent_start -> parent_process)
+        # are NOT propagated to child's _listeners registry by FlowMeta.
+        # The edge (parent_start, parent_process) will NOT be in edge_pairs.
+        # This is a FlowMeta limitation, not a serializer bug.
+
+    def test_child_flow_can_override_parent_method(self):
+        """Test that child can override parent methods."""
+
+        class BaseFlow(Flow):
+            @start()
+            def begin(self):
+                return "base begin"
+
+            @listen(begin)
+            def process(self):
+                return "base process"
+
+        class ExtendedFlow(BaseFlow):
+            @listen(BaseFlow.begin)
+            def process(self):
+                # Override parent's process method
+                return "extended process"
+
+            @listen(process)
+            def finalize(self):
+                return "extended finalize"
+
+        structure = flow_structure(ExtendedFlow)
+
+        method_names = {m["name"] for m in structure["methods"]}
+        assert "begin" in method_names
+        assert "process" in method_names
+        assert "finalize" in method_names
+
+        # Should have 3 methods total (not 4, since process is overridden)
+        assert len(structure["methods"]) == 3

lib/crewai/tests/test_human_feedback_integration.py

@@ -772,3 +772,204 @@ class TestEdgeCases:
         assert result.output == "content"
         assert result.feedback == "feedback"
         assert result.outcome is None  # No routing, no outcome
+
+
+class TestLLMConfigPreservation:
+    """Tests that LLM config is preserved through @human_feedback serialization.
+
+    PR #4970 introduced _hf_llm stashing so the live LLM object survives
+    decorator wrapping for same-process resume. The serialization path
+    (_serialize_llm_for_context / _deserialize_llm_from_context) preserves
+    config for cross-process resume.
+    """
+
+    def test_hf_llm_stashed_on_wrapper_with_llm_instance(self):
+        """Test that passing an LLM instance stashes it on the wrapper as _hf_llm."""
+        from crewai.llm import LLM
+
+        llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)
+
+        class ConfigFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm=llm_instance,
+            )
+            def review(self):
+                return "content"
+
+        method = ConfigFlow.review
+        assert hasattr(method, "_hf_llm"), "_hf_llm not found on wrapper"
+        assert method._hf_llm is llm_instance, "_hf_llm is not the same object"
+
+    def test_hf_llm_preserved_on_listen_method(self):
+        """Test that _hf_llm is preserved when @human_feedback is on a @listen method."""
+        from crewai.llm import LLM
+
+        llm_instance = LLM(model="gpt-4o-mini", temperature=0.7)
+
+        class ListenConfigFlow(Flow):
+            @start()
+            def generate(self):
+                return "draft"
+
+            @listen("generate")
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm=llm_instance,
+            )
+            def review(self):
+                return "content"
+
+        method = ListenConfigFlow.review
+        assert hasattr(method, "_hf_llm")
+        assert method._hf_llm is llm_instance
+
+    def test_hf_llm_accessible_on_instance(self):
+        """Test that _hf_llm survives Flow instantiation (bound method access)."""
+        from crewai.llm import LLM
+
+        llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)
+
+        class InstanceFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm=llm_instance,
+            )
+            def review(self):
+                return "content"
+
+        flow = InstanceFlow()
+        instance_method = flow.review
+        assert hasattr(instance_method, "_hf_llm")
+        assert instance_method._hf_llm is llm_instance
+
+    def test_serialize_llm_preserves_config_fields(self):
+        """Test that _serialize_llm_for_context captures temperature, base_url, etc."""
+        from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
+
+        llm = LLM(
+            model="gpt-4o-mini",
+            temperature=0.42,
+            base_url="https://custom.example.com/v1",
+        )
+
+        serialized = _serialize_llm_for_context(llm)
+
+        assert isinstance(serialized, dict), f"Expected dict, got {type(serialized)}"
+        assert serialized["model"] == "openai/gpt-4o-mini"
+        assert serialized["temperature"] == 0.42
+        assert serialized["base_url"] == "https://custom.example.com/v1"
+
+    def test_serialize_llm_excludes_api_key(self):
+        """Test that api_key is NOT included in serialized output (security)."""
+        from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
+
+        llm = LLM(model="gpt-4o-mini")
+
+        serialized = _serialize_llm_for_context(llm)
+        assert isinstance(serialized, dict)
+        assert "api_key" not in serialized
+
+    def test_deserialize_round_trip_preserves_config(self):
+        """Test that serialize → deserialize round-trip preserves all config."""
+        from crewai.flow.human_feedback import (
+            _deserialize_llm_from_context,
+            _serialize_llm_for_context,
+        )
+        from crewai.llm import LLM
+
+        original = LLM(
+            model="gpt-4o-mini",
+            temperature=0.42,
+            base_url="https://custom.example.com/v1",
+        )
+
+        serialized = _serialize_llm_for_context(original)
+        reconstructed = _deserialize_llm_from_context(serialized)
+
+        assert reconstructed is not None
+        assert reconstructed.model == original.model
+        assert reconstructed.temperature == original.temperature
+        assert reconstructed.base_url == original.base_url
+
+    def test_deserialize_handles_legacy_string_format(self):
+        """Test backward compat: plain string still reconstructs an LLM."""
+        from crewai.flow.human_feedback import _deserialize_llm_from_context
+
+        reconstructed = _deserialize_llm_from_context("openai/gpt-4o-mini")
+
+        assert reconstructed is not None
+        assert reconstructed.model == "gpt-4o-mini"
+
+    def test_deserialize_returns_none_for_none(self):
+        """Test that None input returns None."""
+        from crewai.flow.human_feedback import _deserialize_llm_from_context
+
+        assert _deserialize_llm_from_context(None) is None
+
+    def test_serialize_llm_preserves_provider_specific_fields(self):
+        """Test that provider-specific fields like project/location are serialized."""
+        from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
+
+        # Create a Gemini-style LLM with project and non-default location
+        llm = LLM(
+            model="gemini-2.0-flash",
+            provider="gemini",
+            project="my-project",
+            location="europe-west1",
+            temperature=0.3,
+        )
+
+        serialized = _serialize_llm_for_context(llm)
+
+        assert isinstance(serialized, dict)
+        assert serialized.get("project") == "my-project"
+        assert serialized.get("location") == "europe-west1"
+        assert serialized.get("temperature") == 0.3
+
+    def test_config_preserved_through_full_flow_execution(self):
+        """Test that the LLM with custom config is used during outcome collapsing."""
+        from crewai.llm import LLM
+
+        llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)
+        collapse_calls = []
+
+        class FullFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm=llm_instance,
+            )
+            def review(self):
+                return "content"
+
+            @listen("approved")
+            def on_approved(self):
+                return "done"
+
+        flow = FullFlow()
+
+        original_collapse = flow._collapse_to_outcome
+
+        def spy_collapse(feedback, outcomes, llm):
+            collapse_calls.append(llm)
+            return "approved"
+
+        with (
+            patch.object(flow, "_request_human_feedback", return_value="looks good"),
+            patch.object(flow, "_collapse_to_outcome", side_effect=spy_collapse),
+        ):
+            flow.kickoff()
+
+        assert len(collapse_calls) == 1
+        # The LLM passed to _collapse_to_outcome should be the original instance
+        assert collapse_calls[0] is llm_instance

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.11.0"
+__version__ = "1.11.1"

pyproject.toml

@@ -29,6 +29,7 @@ dev = [
     "types-psycopg2==2.9.21.20251012",
     "types-pymysql==1.1.0.20250916",
     "types-aiofiles~=25.1.0",
+    "commitizen>=4.13.9",
 ]
 
 
@@ -142,17 +143,33 @@ python_files = "test_*.py"
 python_classes = "Test*"
 python_functions = "test_*"
 
+[tool.commitizen]
+name = "cz_customize"
+version_provider = "scm"
+tag_format = "$version"
+allowed_prefixes = ["Merge", "Revert"]
+changelog_incremental = true
+update_changelog_on_bump = false
+
+[tool.commitizen.customize]
+schema = "<type>(<scope>): <description>"
+schema_pattern = "^(feat|fix|refactor|perf|test|docs|chore|ci|style|revert)(\\(.+\\))?!?: .{1,72}"
+bump_pattern = "^(feat|fix|perf|refactor|revert)"
+bump_map = { feat = "MINOR", fix = "PATCH", perf = "PATCH", refactor = "PATCH", revert = "PATCH" }
+info = "Commits must follow Conventional Commits 1.0.0."
+
+
 [tool.uv]
 
 # 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.
 # fastembed 0.7.x and docling 2.63 cap pillow<12; the removed APIs don't affect them.
-# langchain-core 0.3.76 has a template-injection vuln (GHSA); force >=0.3.80.
+# langchain-core <1.2.11 has SSRF via image_url token counting (CVE-2026-26013).
 override-dependencies = [
     "rich>=13.7.1",
     "onnxruntime<1.24; python_version < '3.11'",
     "pillow>=12.1.1",
-    "langchain-core>=0.3.80,<1",
+    "langchain-core>=1.2.11,<2",
     "urllib3>=2.6.3",
 ]
 

uv.lock

@@ -20,7 +20,7 @@ members = [
     "crewai-tools",
 ]
 overrides = [
-    { name = "langchain-core", specifier = ">=0.3.80,<1" },
+    { name = "langchain-core", specifier = ">=1.2.11,<2" },
     { name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.24" },
     { name = "pillow", specifier = ">=12.1.1" },
     { name = "rich", specifier = ">=13.7.1" },
@@ -31,6 +31,7 @@ overrides = [
 dev = [
     { name = "bandit", specifier = "==1.9.2" },
     { name = "boto3-stubs", extras = ["bedrock-runtime"], specifier = "==1.42.40" },
+    { name = "commitizen", specifier = ">=4.13.9" },
     { name = "mypy", specifier = "==1.19.1" },
     { name = "pre-commit", specifier = "==4.5.1" },
     { name = "pytest", specifier = "==8.4.2" },
@@ -370,6 +371,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "argcomplete"
+version = "3.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/38/61/0b9ae6399dd4a58d8c1b1dc5a27d6f2808023d0b5dd3104bb99f45a33ff6/argcomplete-3.6.3.tar.gz", hash = "sha256:62e8ed4fd6a45864acc8235409461b72c9a28ee785a2011cc5eb78318786c89c", size = 73754, upload-time = "2025-10-20T03:33:34.741Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/74/f5/9373290775639cb67a2fce7f629a1c240dce9f12fe927bc32b2736e16dfc/argcomplete-3.6.3-py3-none-any.whl", hash = "sha256:f5007b3a600ccac5d25bbce33089211dfd49eab4a7718da3f10e3082525a92ce", size = 43846, upload-time = "2025-10-20T03:33:33.021Z" },
+]
+
 [[package]]
 name = "asn1crypto"
 version = "1.5.1"
@@ -944,6 +954,30 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" },
 ]
 
+[[package]]
+name = "commitizen"
+version = "4.13.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "argcomplete" },
+    { name = "charset-normalizer" },
+    { name = "colorama" },
+    { name = "decli" },
+    { name = "deprecated" },
+    { name = "jinja2" },
+    { name = "packaging" },
+    { name = "prompt-toolkit" },
+    { name = "pyyaml" },
+    { name = "questionary" },
+    { name = "termcolor" },
+    { name = "tomlkit" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a6/44/10f95e8178ab5a584298726a4a94ceb83a7f77e00741fec4680df05fedd5/commitizen-4.13.9.tar.gz", hash = "sha256:2b4567ed50555e10920e5bd804a6a4e2c42ec70bb74f14a83f2680fe9eaf9727", size = 64145, upload-time = "2026-02-25T02:40:05.326Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/28/22/9b14ee0f17f0aad219a2fb37a293a57b8324d9d195c6ef6807bcd0bf2055/commitizen-4.13.9-py3-none-any.whl", hash = "sha256:d2af3d6a83cacec9d5200e17768942c5de6266f93d932c955986c60c4285f2db", size = 85373, upload-time = "2026-02-25T02:40:03.83Z" },
+]
+
 [[package]]
 name = "composio-core"
 version = "0.7.21"
@@ -1115,6 +1149,7 @@ dependencies = [
     { name = "pydantic-settings" },
     { name = "pyjwt" },
     { name = "python-dotenv" },
+    { name = "pyyaml" },
     { name = "regex" },
     { name = "textual" },
     { name = "tokenizers" },
@@ -1222,6 +1257,7 @@ requires-dist = [
     { name = "pydantic-settings", specifier = "~=2.10.1" },
     { name = "pyjwt", specifier = ">=2.9.0,<3" },
     { name = "python-dotenv", specifier = "~=1.1.1" },
+    { name = "pyyaml", specifier = "~=6.0" },
     { name = "qdrant-client", extras = ["fastembed"], marker = "extra == 'qdrant'", specifier = "~=1.14.3" },
     { name = "regex", specifier = "~=2026.1.15" },
     { name = "textual", specifier = ">=7.5.0" },
@@ -1275,9 +1311,9 @@ requires-dist = [
     { name = "aiofiles", specifier = "~=24.1.0" },
     { name = "av", specifier = "~=13.0.0" },
     { name = "pillow", specifier = "~=12.1.1" },
-    { name = "pypdf", specifier = "~=6.7.5" },
+    { name = "pypdf", specifier = "~=6.9.1" },
     { name = "python-magic", specifier = ">=0.4.27" },
-    { name = "tinytag", specifier = "~=1.10.0" },
+    { name = "tinytag", specifier = "~=2.2.1" },
 ]
 
 [[package]]
@@ -1573,6 +1609,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "decli"
+version = "0.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0c/59/d4ffff1dee2c8f6f2dd8f87010962e60f7b7847504d765c91ede5a466730/decli-0.6.3.tar.gz", hash = "sha256:87f9d39361adf7f16b9ca6e3b614badf7519da13092f2db3c80ca223c53c7656", size = 7564, upload-time = "2025-06-01T15:23:41.25Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d8/fa/ec878c28bc7f65b77e7e17af3522c9948a9711b9fa7fc4c5e3140a7e3578/decli-0.6.3-py3-none-any.whl", hash = "sha256:5152347c7bb8e3114ad65db719e5709b28d7f7f45bdb709f70167925e55640f3", size = 7989, upload-time = "2025-06-01T15:23:40.228Z" },
+]
+
 [[package]]
 name = "decorator"
 version = "5.2.1"
@@ -3295,7 +3340,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.83"
+version = "1.2.20"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -3307,9 +3352,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/21/a4/24f2d787bfcf56e5990924cacefe6f6e7971a3629f97c8162fc7a2a3d851/langchain_core-0.3.83.tar.gz", hash = "sha256:a0a4c7b6ea1c446d3b432116f405dc2afa1fe7891c44140d3d5acca221909415", size = 597965, upload-time = "2026-01-13T01:19:23.854Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/db/41/6552a419fe549a79601e5a698d1d5ee2ca7fe93bb87fd624a16a8c1bdee3/langchain_core-1.2.20.tar.gz", hash = "sha256:c7ac8b976039b5832abb989fef058b88c270594ba331efc79e835df046e7dc44", size = 838330, upload-time = "2026-03-18T17:34:45.522Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d9/06/08c88ddd4d6766de4e6c43111ae8f3025df383d2a4379cb938fc571b49d4/langchain_core-1.2.20-py3-none-any.whl", hash = "sha256:b65ff678f3c3dc1f1b4d03a3af5ee3b8d51f9be5181d74eb53c6c11cd9dd5e68", size = 504215, upload-time = "2026-03-18T17:34:44.087Z" },
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 [[package]]
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 ]
 
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.51"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bb/6e/9d084c929dfe9e3bfe0c6a47e31f78a25c54627d64a66e884a8bf5474f1c/prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed", size = 428940, upload-time = "2025-04-15T09:18:47.731Z" }
+wheels = [
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+]
+
 [[package]]
 name = "propcache"
 version = "0.4.1"
@@ -6174,14 +6231,14 @@ wheels = [
 
 [[package]]
 name = "pypdf"
-version = "6.7.5"
+version = "6.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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-sdist = { url = "https://files.pythonhosted.org/packages/f6/52/37cc0aa9e9d1bf7729a737a0d83f8b3f851c8eb137373d9f71eafb0a3405/pypdf-6.7.5.tar.gz", hash = "sha256:40bb2e2e872078655f12b9b89e2f900888bb505e88a82150b64f9f34fa25651d", size = 5304278, upload-time = "2026-03-02T09:05:21.464Z" }
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+    { url = "https://files.pythonhosted.org/packages/f9/f4/75543fa802b86e72f87e9395440fe1a89a6d149887e3e55745715c3352ac/pypdf-6.9.1-py3-none-any.whl", hash = "sha256:f35a6a022348fae47e092a908339a8f3dc993510c026bb39a96718fc7185e89f", size = 333661, upload-time = "2026-03-17T10:46:06.286Z" },
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 [[package]]
@@ -6556,6 +6613,18 @@ fastembed = [
     { name = "fastembed", version = "0.7.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
 ]
 
+[[package]]
+name = "questionary"
+version = "2.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "prompt-toolkit" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f6/45/eafb0bba0f9988f6a2520f9ca2df2c82ddfa8d67c95d6625452e97b204a5/questionary-2.1.1.tar.gz", hash = "sha256:3d7e980292bb0107abaa79c68dd3eee3c561b83a0f89ae482860b181c8bd412d", size = 25845, upload-time = "2025-08-28T19:00:20.851Z" }
+wheels = [
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+]
+
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 version = "3.14.3"
@@ -7555,6 +7624,15 @@ wheels = [
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+[[package]]
+name = "termcolor"
+version = "3.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/46/79/cf31d7a93a8fdc6aa0fbb665be84426a8c5a557d9240b6239e9e11e35fc5/termcolor-3.3.0.tar.gz", hash = "sha256:348871ca648ec6a9a983a13ab626c0acce02f515b9e1983332b17af7979521c5", size = 14434, upload-time = "2025-12-29T12:55:21.882Z" }
+wheels = [
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+
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 name = "textual"
 version = "7.5.0"
@@ -7626,11 +7704,11 @@ wheels = [
 
 [[package]]
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-version = "1.10.1"
+version = "2.2.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/59/b5/ff5e5f9ca9677be7272260f67c87f7e8e885babc7ce94604e837dcfd8d76/tinytag-1.10.1.tar.gz", hash = "sha256:122a63b836f85094aacca43fc807aaee3290be3de17d134f5f4a08b509ae268f", size = 40906, upload-time = "2023-10-26T19:30:38.791Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/59/8a8cb2331e2602b53e4dc06960f57d1387a2b18e7efd24e5f9cb60ea4925/tinytag-2.2.1.tar.gz", hash = "sha256:e6d06610ebe7cd66fd07be2d3b9495914ab32654a5e47657bb8cd44c2484523c", size = 38214, upload-time = "2026-03-15T18:48:01.11Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2f/04/ef783cbc4aa3a5ed75969e300b3e3929daf3d1b52fe80e950c63e0d66d95/tinytag-1.10.1-py3-none-any.whl", hash = "sha256:e437654d04c966fbbbdbf807af61eb9759f1d80e4173a7d26202506b37cfdaf0", size = 37900, upload-time = "2023-10-26T19:30:36.724Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/34/d50e338631baaf65ec5396e70085e5de0b52b24b28db1ffbc1c6e82190dc/tinytag-2.2.1-py3-none-any.whl", hash = "sha256:ed8b1e6d25367937e3321e054f4974f9abfde1a3e0a538824c87da377130c2b6", size = 32927, upload-time = "2026-03-15T18:47:59.613Z" },
 ]
 
 [[package]]
@@ -8565,6 +8643,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6e/d4/ed38dd3b1767193de971e694aa544356e63353c33a85d948166b5ff58b9e/watchfiles-1.1.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3e6f39af2eab0118338902798b5aa6664f46ff66bc0280de76fca67a7f262a49", size = 457546, upload-time = "2025-10-14T15:06:13.372Z" },
 ]
 
+[[package]]
+name = "wcwidth"
+version = "0.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/a2/8e3becb46433538a38726c948d3399905a4c7cabd0df578ede5dc51f0ec2/wcwidth-0.6.0.tar.gz", hash = "sha256:cdc4e4262d6ef9a1a57e018384cbeb1208d8abbc64176027e2c2455c81313159", size = 159684, upload-time = "2026-02-06T19:19:40.919Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/68/5a/199c59e0a824a3db2b89c5d2dade7ab5f9624dbf6448dc291b46d5ec94d3/wcwidth-0.6.0-py3-none-any.whl", hash = "sha256:1a3a1e510b553315f8e146c54764f4fb6264ffad731b3d78088cdb1478ffbdad", size = 94189, upload-time = "2026-02-06T19:19:39.646Z" },
+]
+
 [[package]]
 name = "weaviate-client"
 version = "4.18.3"