Merge branch 'main' into lorenze/trace-improvements-3

- Changed type hints from `Dict` and `List` to built-in `dict` and `list` for consistency with modern Python standards.
- Refactored the trace batch finalization display logic to improve user experience by introducing a new method `_display_traces_events_link`.
- Enhanced error handling in the display method to fallback to a simpler display in case of exceptions.
- Cleaned up unused imports and organized the import statements for better readability.

.github/workflows/codeql.yml

@@ -1,102 +0,0 @@
-# For most projects, this workflow file will not need changing; you simply need
-# to commit it to your repository.
-#
-# You may wish to alter this file to override the set of languages analyzed,
-# or to provide custom queries or build logic.
-#
-# ******** NOTE ********
-# We have attempted to detect the languages in your repository. Please check
-# the `language` matrix defined below to confirm you have the correct set of
-# supported CodeQL languages.
-#
-name: "CodeQL Advanced"
-
-on:
-  push:
-    branches: [ "main" ]
-    paths-ignore:
-      - "src/crewai/cli/templates/**"
-  pull_request:
-    branches: [ "main" ]
-    paths-ignore:
-      - "src/crewai/cli/templates/**"
-
-jobs:
-  analyze:
-    name: Analyze (${{ matrix.language }})
-    # Runner size impacts CodeQL analysis time. To learn more, please see:
-    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
-    #   - https://gh.io/supported-runners-and-hardware-resources
-    #   - https://gh.io/using-larger-runners (GitHub.com only)
-    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
-    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
-    permissions:
-      # required for all workflows
-      security-events: write
-
-      # required to fetch internal or private CodeQL packs
-      packages: read
-
-      # only required for workflows in private repositories
-      actions: read
-      contents: read
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-        - language: actions
-          build-mode: none
-        - language: python
-          build-mode: none
-        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
-        # Use `c-cpp` to analyze code written in C, C++ or both
-        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
-        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
-        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
-        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
-        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
-        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
-
-    # Add any setup steps before running the `github/codeql-action/init` action.
-    # This includes steps like installing compilers or runtimes (`actions/setup-node`
-    # or others). This is typically only required for manual builds.
-    # - name: Setup runtime (example)
-    #   uses: actions/setup-example@v1
-
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@v3
-      with:
-        languages: ${{ matrix.language }}
-        build-mode: ${{ matrix.build-mode }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
-
-        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
-        # queries: security-extended,security-and-quality
-
-    # If the analyze step fails for one of the languages you are analyzing with
-    # "We were unable to automatically build your code", modify the matrix above
-    # to set the build mode to "manual" for that language. Then modify this step
-    # to build your code.
-    # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
-    - if: matrix.build-mode == 'manual'
-      shell: bash
-      run: |
-        echo 'If you are using a "manual" build mode for one or more of the' \
-          'languages you are analyzing, replace this with the commands to build' \
-          'your code, for example:'
-        echo '  make bootstrap'
-        echo '  make release'
-        exit 1
-
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v3
-      with:
-        category: "/language:${{matrix.language}}"

docs/en/changelog.mdx

@@ -5,82 +5,6 @@ icon: "clock"
 mode: "wide"
 ---
 
-<Update label="Sep 20, 2025">
-  ## v0.193.2
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.2)
-
-  ## What's Changed
-
-  - Updated pyproject templates to use the right version
-
-</Update>
-
-<Update label="Sep 20, 2025">
-  ## v0.193.1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.1)
-
-  ## What's Changed
-
-  - Series of minor fixes and linter improvements
-
-</Update>
-
-<Update label="Sep 19, 2025">
-  ## v0.193.0
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.0)
-
-  ## Core Improvements & Fixes
-  
-  - Fixed handling of the `model` parameter during OpenAI adapter initialization  
-  - Resolved test duration cache issues in CI workflows  
-  - Fixed flaky test related to repeated tool usage by agents  
-  - Added missing event exports to `__init__.py` for consistent module behavior  
-  - Dropped message storage from metadata in Mem0 to reduce bloat  
-  - Fixed L2 distance metric support for backward compatibility in vector search  
-
-  ## New Features & Enhancements
-  
-  - Introduced thread-safe platform context management  
-  - Added test duration caching for optimized `pytest-split` runs  
-  - Added ephemeral trace improvements for better trace control  
-  - Made search parameters for RAG, knowledge, and memory fully configurable  
-  - Enabled ChromaDB to use OpenAI API for embedding functions  
-  - Added deeper observability tools for user-level insights  
-  - Unified RAG storage system with instance-specific client support  
-
-  ## Documentation & Guides
-  
-  - Updated `RagTool` references to reflect CrewAI native RAG implementation  
-  - Improved internal docs for `langgraph` and `openai` agent adapters with type annotations and docstrings  
-
-</Update>
-
-<Update label="Sep 11, 2025">
-  ## v0.186.1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.186.1)
-
-  ## What's Changed
-
-  - Fixed version not being found and silently failing reversion  
-  - Bumped CrewAI version to 0.186.1 and updated dependencies in the CLI  
-
-</Update>
-
-<Update label="Sep 10, 2025">
-  ## v0.186.0
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.186.0)
-
-  ## What's Changed
-
-  - Refer to the GitHub release notes for detailed changes
-
-</Update>
-
 <Update label="Sep 04, 2025">
   ## v0.177.0
 

docs/en/concepts/cli.mdx

@@ -404,10 +404,6 @@ crewai config reset
 After resetting configuration, re-run `crewai login` to authenticate again.
 </Tip>
 
-<Tip>
-CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
-</Tip>
-
 <Note>
 Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
 </Note>

docs/en/enterprise/features/tool-repository.mdx

@@ -52,36 +52,6 @@ researcher = Agent(
 )
 ```
 
-## Adding other packages after installing a tool
-
-After installing a tool from the CrewAI Enterprise Tool Repository, you need to use the `crewai uv` command to add other packages to your project.
-Using pure `uv` commands will fail due to authentication to tool repository being handled by the CLI. By using the `crewai uv` command, you can add other packages to your project without having to worry about authentication.
-Any `uv` command can be used with the `crewai uv` command, making it a powerful tool for managing your project's dependencies without the hassle of managing authentication through environment variables or other methods.
-
-Say that you have installed a custom tool from the CrewAI Enterprise Tool Repository called "my-tool":
-
-```bash
-crewai tool install my-tool
-```
-
-And now you want to add another package to your project, you can use the following command:
-
-```bash
-crewai uv add requests
-```
-
-Other commands like `uv sync` or `uv remove` can also be used with the `crewai uv` command:
-
-```bash
-crewai uv sync
-```
-
-```bash
-crewai uv remove requests
-```
-
-This will add the package to your project and update `pyproject.toml` accordingly.
-
 ## Creating and Publishing Tools
 
 To create a new tool project:

docs/en/learn/human-in-the-loop.mdx

@@ -22,45 +22,6 @@ Human-in-the-Loop (HITL) is a powerful approach that combines artificial intelli
         <Frame>
             <img src="/images/enterprise/crew-webhook-url.png" alt="Crew Webhook URL" />
         </Frame>
-
-        Example with Bearer authentication:
-        ```bash
-        curl -X POST {BASE_URL}/kickoff \
-          -H "Authorization: Bearer YOUR_API_TOKEN" \
-          -H "Content-Type: application/json" \
-          -d '{
-            "inputs": {
-              "topic": "AI Research"
-            },
-            "humanInputWebhook": {
-              "url": "https://your-webhook.com/hitl",
-              "authentication": {
-                "strategy": "bearer",
-                "token": "your-webhook-secret-token"
-              }
-            }
-          }'
-        ```
-
-        Or with Basic authentication:
-        ```bash
-        curl -X POST {BASE_URL}/kickoff \
-          -H "Authorization: Bearer YOUR_API_TOKEN" \
-          -H "Content-Type: application/json" \
-          -d '{
-            "inputs": {
-              "topic": "AI Research"
-            },
-            "humanInputWebhook": {
-              "url": "https://your-webhook.com/hitl",
-              "authentication": {
-                "strategy": "basic",
-                "username": "your-username",
-                "password": "your-password"
-              }
-            }
-          }'
-        ```
     </Step>
 
     <Step title="Receive Webhook Notification">
@@ -115,4 +76,4 @@ HITL workflows are particularly valuable for:
 - Complex decision-making scenarios
 - Sensitive or high-stakes operations
 - Creative tasks requiring human judgment
-- Compliance and regulatory reviews
+- Compliance and regulatory reviews 

docs/en/quickstart.mdx

@@ -27,7 +27,7 @@ Follow the steps below to get Crewing! 🚣‍♂️
   <Step title="Navigate to your new crew project">
     <CodeGroup>
       ```shell Terminal
-      cd latest_ai_development
+      cd latest-ai-development
       ```
     </CodeGroup>
   </Step>

docs/en/tools/ai-ml/ragtool.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 ## Description
 
-The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through CrewAI's native RAG system.
+The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
 It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
 This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
 
@@ -76,8 +76,8 @@ The `RagTool` can be used with a wide variety of data sources, including:
 The `RagTool` accepts the following parameters:
 
 - **summarize**: Optional. Whether to summarize the retrieved content. Default is `False`.
-- **adapter**: Optional. A custom adapter for the knowledge base. If not provided, a CrewAIRagAdapter will be used.
-- **config**: Optional. Configuration for the underlying CrewAI RAG system.
+- **adapter**: Optional. A custom adapter for the knowledge base. If not provided, an EmbedchainAdapter will be used.
+- **config**: Optional. Configuration for the underlying EmbedChain App.
 
 ## Adding Content
 
@@ -130,23 +130,44 @@ from crewai_tools import RagTool
 
 # Create a RAG tool with custom configuration
 config = {
-    "vectordb": {
-        "provider": "qdrant",
+    "app": {
+        "name": "custom_app",
+    },
+    "llm": {
+        "provider": "openai",
         "config": {
-            "collection_name": "my-collection"
+            "model": "gpt-4",
         }
     },
     "embedding_model": {
         "provider": "openai",
         "config": {
-            "model": "text-embedding-3-small"
+            "model": "text-embedding-ada-002"
         }
+    },
+    "vectordb": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "my-collection",
+            "cloud_id": "deployment-name:xxxx",
+            "api_key": "your-key",
+            "verify_certs": False
+        }
+    },
+    "chunker": {
+        "chunk_size": 400,
+        "chunk_overlap": 100,
+        "length_function": "len",
+        "min_chunk_size": 0
     }
 }
 
 rag_tool = RagTool(config=config, summarize=True)
 ```
 
+The internal RAG tool utilizes the Embedchain adapter, allowing you to pass any configuration options that are supported by Embedchain.
+You can refer to the [Embedchain documentation](https://docs.embedchain.ai/components/introduction) for details.
+Make sure to review the configuration options available in the .yaml file.
 
 ## Conclusion
 The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.

docs/ko/changelog.mdx

@@ -5,82 +5,6 @@ icon: "clock"
 mode: "wide"
 ---
 
-<Update label="2025년 9월 20일">
-  ## v0.193.2
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/0.193.2)
-
-  ## 변경 사항
-  
-  - 올바른 버전을 사용하도록 pyproject 템플릿 업데이트
-
-</Update>
-
-<Update label="2025년 9월 20일">
-  ## v0.193.1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/0.193.1)
-
-  ## 변경 사항
-  
-  - 일련의 사소한 수정 및 린터 개선
-
-</Update>
-
-<Update label="2025년 9월 19일">
-  ## v0.193.0
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/0.193.0)
-
-  ## 핵심 개선 사항 및 수정 사항
-  
-  - OpenAI 어댑터 초기화 중 `model` 매개변수 처리 수정  
-  - CI 워크플로에서 테스트 소요 시간 캐시 문제 해결  
-  - 에이전트의 반복 도구 사용과 관련된 불안정한 테스트 수정  
-  - 일관된 모듈 동작을 위해 누락된 이벤트 내보내기를 `__init__.py`에 추가  
-  - 메타데이터 부하를 줄이기 위해 Mem0에서 메시지 저장 제거  
-  - 벡터 검색의 하위 호환성을 위해 L2 거리 메트릭 지원 수정  
-
-  ## 새로운 기능 및 향상 사항
-  
-  - 스레드 안전한 플랫폼 컨텍스트 관리 도입  
-  - `pytest-split` 실행 최적화를 위한 테스트 소요 시간 캐싱 추가  
-  - 더 나은 추적 제어를 위한 일시적(trace) 개선  
-  - RAG, 지식, 메모리 검색 매개변수를 완전 구성 가능하게 변경  
-  - ChromaDB가 임베딩 함수에 OpenAI API를 사용할 수 있도록 지원  
-  - 사용자 수준 인사이트를 위한 심화된 관찰 가능성 도구 추가  
-  - 인스턴스별 클라이언트를 지원하는 통합 RAG 스토리지 시스템  
-
-  ## 문서 및 가이드
-  
-  - CrewAI 네이티브 RAG 구현을 반영하도록 `RagTool` 참조 업데이트  
-  - 타입 주석과 도크스트링을 포함해 `langgraph` 및 `openai` 에이전트 어댑터 내부 문서 개선  
-
-</Update>
-
-<Update label="2025년 9월 11일">
-  ## v0.186.1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/0.186.1)
-
-  ## 변경 사항
-  
-  - 버전을 찾지 못해 조용히 되돌리는(reversion) 문제 수정  
-  - CLI에서 CrewAI 버전을 0.186.1로 올리고 의존성 업데이트  
-
-</Update>
-
-<Update label="2025년 9월 10일">
-  ## v0.186.0
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/0.186.0)
-
-  ## 변경 사항
-  
-  - 자세한 변경 사항은 GitHub 릴리스 노트를 참조하세요
-
-</Update>
-
 <Update label="2025년 9월 4일">
   ## v0.177.0
 

docs/ko/quickstart.mdx

@@ -27,7 +27,7 @@ mode: "wide"
   <Step title="새로운 crew 프로젝트로 이동하기">
     <CodeGroup>
       ```shell Terminal
-      cd latest_ai_development
+      cd latest-ai-development
       ```
     </CodeGroup>
   </Step>

docs/pt-BR/changelog.mdx

@@ -5,82 +5,6 @@ icon: "clock"
 mode: "wide"
 ---
 
-<Update label="20 set 2025">
-  ## v0.193.2
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.2)
-
-  ## O que Mudou
-
-  - Atualizados templates do pyproject para usar a versão correta
-
-</Update>
-
-<Update label="20 set 2025">
-  ## v0.193.1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.1)
-
-  ## O que Mudou
-
-  - Série de pequenas correções e melhorias de linter
-
-</Update>
-
-<Update label="19 set 2025">
-  ## v0.193.0
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.0)
-
-  ## Melhorias e Correções Principais
-  
-  - Corrigido manuseio do parâmetro `model` durante a inicialização do adaptador OpenAI  
-  - Resolvidos problemas de cache da duração de testes nos fluxos de CI  
-  - Corrigido teste instável relacionado ao uso repetido de ferramentas pelos agentes  
-  - Adicionadas exportações de eventos ausentes no `__init__.py` para comportamento consistente do módulo  
-  - Removido armazenamento de mensagem dos metadados no Mem0 para reduzir inchaço  
-  - Corrigido suporte à métrica de distância L2 para compatibilidade retroativa na busca vetorial  
-
-  ## Novos Recursos e Melhorias
-  
-  - Introduzida gestão de contexto de plataforma com segurança de threads  
-  - Adicionado cache da duração de testes para execuções otimizadas do `pytest-split`  
-  - Melhorias de traces efêmeros para melhor controle de rastreamento  
-  - Parâmetros de busca para RAG, conhecimento e memória totalmente configuráveis  
-  - Habilitado ChromaDB para usar a OpenAI API para funções de embedding  
-  - Adicionadas ferramentas de observabilidade mais profundas para insights ao nível do usuário  
-  - Sistema de armazenamento RAG unificado com suporte a cliente específico por instância  
-
-  ## Documentação e Guias
-  
-  - Atualizadas referências do `RagTool` para refletir a implementação nativa de RAG do CrewAI  
-  - Melhorada documentação interna para adaptadores de agente `langgraph` e `openai` com anotações de tipo e docstrings  
-
-</Update>
-
-<Update label="11 set 2025">
-  ## v0.186.1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.186.1)
-
-  ## O que Mudou
-
-  - Corrigida falha silenciosa de reversão quando a versão não era encontrada  
-  - Versão do CrewAI atualizada para 0.186.1 e dependências do CLI atualizadas  
-
-</Update>
-
-<Update label="10 set 2025">
-  ## v0.186.0
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.186.0)
-
-  ## O que Mudou
-
-  - Consulte as notas de lançamento no GitHub para detalhes completos
-
-</Update>
-
 <Update label="04 set 2025">
   ## v0.177.0
 

docs/pt-BR/quickstart.mdx

@@ -27,7 +27,7 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
   <Step title="Navegue até o novo projeto da sua tripulação">
     <CodeGroup>
       ```shell Terminal
-      cd latest_ai_development
+      cd latest-ai-development
       ```
     </CodeGroup>
   </Step>

pyproject.toml

@@ -9,7 +9,7 @@ authors = [
 ]
 dependencies = [
     # Core Dependencies
-    "pydantic>=2.11.9",
+    "pydantic>=2.4.2",
     "openai>=1.13.3",
     "litellm==1.74.9",
     "instructor>=1.3.3",
@@ -21,12 +21,13 @@ dependencies = [
     "opentelemetry-sdk>=1.30.0",
     "opentelemetry-exporter-otlp-proto-http>=1.30.0",
     # Data Handling
-    "chromadb~=1.1.0",
+    "chromadb>=0.5.23",
     "tokenizers>=0.20.3",
+    "onnxruntime==1.22.0",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
     # Authentication and Security
-    "python-dotenv>=1.1.1",
+    "python-dotenv>=1.0.0",
     "pyjwt>=2.9.0",
     # Configuration and Utils
     "click>=8.1.7",
@@ -39,7 +40,6 @@ dependencies = [
     "blinker>=1.9.0",
     "json5>=0.10.0",
     "portalocker==2.7.0",
-    "pydantic-settings>=2.10.1",
 ]
 
 [project.urls]
@@ -48,9 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = [
-    "crewai-tools>=0.74.0",
-]
+tools = ["crewai-tools~=0.71.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
@@ -73,30 +71,24 @@ aisuite = [
 qdrant = [
     "qdrant-client[fastembed]>=1.14.3",
 ]
-aws = [
-    "boto3>=1.40.38",
-]
-watson = [
-    "ibm-watsonx-ai>=1.3.39",
-]
-voyageai = [
-    "voyageai>=0.3.5",
-]
 
-[dependency-groups]
-dev = [
-    "ruff>=0.13.1",
-    "mypy>=1.18.2",
+[tool.uv]
+dev-dependencies = [
+    "ruff>=0.12.11",
+    "mypy>=1.17.1",
     "pre-commit>=4.3.0",
     "bandit>=1.8.6",
-    "pytest>=8.4.2",
-    "pytest-asyncio>=1.2.0",
-    "pytest-subprocess>=1.5.3",
-    "pytest-recording>=0.13.4",
-    "pytest-randomly>=4.0.1",
-    "pytest-timeout>=2.4.0",
-    "pytest-xdist>=3.8.0",
-    "pytest-split>=0.10.0",
+    "pillow>=10.2.0",
+    "cairosvg>=2.7.1",
+    "pytest>=8.0.0",
+    "python-dotenv>=1.0.0",
+    "pytest-asyncio>=0.23.7",
+    "pytest-subprocess>=1.5.2",
+    "pytest-recording>=0.13.2",
+    "pytest-randomly>=3.16.0",
+    "pytest-timeout>=2.3.1",
+    "pytest-xdist>=3.6.1",
+    "pytest-split>=0.9.0",
     "types-requests==2.32.*",
     "types-pyyaml==6.0.*",
     "types-regex==2024.11.6.*",
@@ -139,15 +131,13 @@ select = [
     "I001",   # sort imports
     "I002",   # remove unused imports
 ]
-ignore = ["E501"]  # ignore line too long globally
+ignore = ["E501"]  # ignore line too long
 
 [tool.ruff.lint.per-file-ignores]
-"tests/**/*.py" = ["S101", "RET504"]  # Allow assert statements and unnecessary assignments before return in tests
+"tests/**/*.py" = ["S101"]  # Allow assert statements in tests
 
 [tool.mypy]
-exclude = ["src/crewai/cli/templates", "tests/"]
-plugins = ["pydantic.mypy"]
-
+exclude = ["src/crewai/cli/templates", "tests"]
 
 [tool.bandit]
 exclude_dirs = ["src/crewai/cli/templates"]

src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "0.201.1"
+__version__ = "0.186.1"
 _telemetry_submitted = False
 
 

src/crewai/agent.py

@@ -1,10 +1,17 @@
 import shutil
 import subprocess
 import time
-from collections.abc import Callable, Sequence
 from typing import (
     Any,
+    Callable,
+    Dict,
+    List,
     Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
 )
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -12,31 +19,12 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-from crewai.events.types.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
-from crewai.events.types.memory_events import (
-    MemoryRetrievalCompletedEvent,
-    MemoryRetrievalStartedEvent,
-)
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security import Fingerprint
 from crewai.task import Task
 from crewai.tools import BaseTool
@@ -50,6 +38,24 @@ from crewai.utilities.agent_utils import (
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
+from crewai.events.types.agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+    AgentExecutionStartedEvent,
+)
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.memory_events import (
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
+)
+from crewai.events.types.knowledge_events import (
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -81,36 +87,36 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
-    max_execution_time: int | None = Field(
+    max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
     )
     agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
-    step_callback: Any | None = Field(
+    step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    use_system_prompt: bool | None = Field(
+    use_system_prompt: Optional[bool] = Field(
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: str | InstanceOf[BaseLLM] | Any = Field(
+    llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
-    system_template: str | None = Field(
+    system_template: Optional[str] = Field(
         default=None, description="System format for the agent."
     )
-    prompt_template: str | None = Field(
+    prompt_template: Optional[str] = Field(
         default=None, description="Prompt format for the agent."
     )
-    response_template: str | None = Field(
+    response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
-    allow_code_execution: bool | None = Field(
+    allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
     respect_context_window: bool = Field(
@@ -141,31 +147,31 @@ class Agent(BaseAgent):
         default=False,
         description="Whether the agent should reflect and create a plan before executing a task.",
     )
-    max_reasoning_attempts: int | None = Field(
+    max_reasoning_attempts: Optional[int] = Field(
         default=None,
         description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
-    embedder: EmbedderConfig | None = Field(
+    embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
-    agent_knowledge_context: str | None = Field(
+    agent_knowledge_context: Optional[str] = Field(
         default=None,
         description="Knowledge context for the agent.",
     )
-    crew_knowledge_context: str | None = Field(
+    crew_knowledge_context: Optional[str] = Field(
         default=None,
         description="Knowledge context for the crew.",
     )
-    knowledge_search_query: str | None = Field(
+    knowledge_search_query: Optional[str] = Field(
         default=None,
         description="Knowledge search query for the agent dynamically generated by the agent.",
     )
-    from_repository: str | None = Field(
+    from_repository: Optional[str] = Field(
         default=None,
         description="The Agent's role to be used from your repository.",
     )
-    guardrail: Callable[[Any], tuple[bool, Any]] | str | None = Field(
+    guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
         default=None,
         description="Function or string description of a guardrail to validate agent output",
     )
@@ -174,7 +180,7 @@ class Agent(BaseAgent):
     )
 
     @model_validator(mode="before")
-    def validate_from_repository(cls, v): # noqa: N805
+    def validate_from_repository(cls, v):
         if v is not None and (from_repository := v.get("from_repository")):
             return load_agent_from_repository(from_repository) | v
         return v
@@ -202,7 +208,7 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
+    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
         try:
             if self.embedder is None and crew_embedder:
                 self.embedder = crew_embedder
@@ -218,7 +224,7 @@ class Agent(BaseAgent):
                     )
                     self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
-            raise ValueError(f"Invalid Knowledge Configuration: {e!s}") from e
+            raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
 
     def _is_any_available_memory(self) -> bool:
         """Check if any memory is available."""
@@ -238,8 +244,8 @@ class Agent(BaseAgent):
     def execute_task(
         self,
         task: Task,
-        context: str | None = None,
-        tools: list[BaseTool] | None = None,
+        context: Optional[str] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -272,9 +278,11 @@ class Agent(BaseAgent):
                 task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
             except Exception as e:
                 if hasattr(self, "_logger"):
-                    self._logger.log("error", f"Error during reasoning process: {e!s}")
+                    self._logger.log(
+                        "error", f"Error during reasoning process: {str(e)}"
+                    )
                 else:
-                    print(f"Error during reasoning process: {e!s}")
+                    print(f"Error during reasoning process: {str(e)}")
 
         self._inject_date_to_task(task)
 
@@ -327,7 +335,7 @@ class Agent(BaseAgent):
                 agent=self,
                 task=task,
             )
-            memory = contextual_memory.build_context_for_task(task, context)  # type: ignore[arg-type]
+            memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
@@ -517,14 +525,14 @@ class Agent(BaseAgent):
 
             try:
                 return future.result(timeout=timeout)
-            except concurrent.futures.TimeoutError as e:
+            except concurrent.futures.TimeoutError:
                 future.cancel()
                 raise TimeoutError(
                     f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task."
-                ) from e
+                )
             except Exception as e:
                 future.cancel()
-                raise RuntimeError(f"Task execution failed: {e!s}") from e
+                raise RuntimeError(f"Task execution failed: {str(e)}")
 
     def _execute_without_timeout(self, task_prompt: str, task: Task) -> str:
         """Execute a task without a timeout.
@@ -546,14 +554,14 @@ class Agent(BaseAgent):
         )["output"]
 
     def create_agent_executor(
-        self, tools: list[BaseTool] | None = None, task=None
+        self, tools: Optional[List[BaseTool]] = None, task=None
     ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        raw_tools: list[BaseTool] = tools or self.tools or []
+        raw_tools: List[BaseTool] = tools or self.tools or []
         parsed_tools = parse_tools(raw_tools)
 
         prompt = Prompts(
@@ -579,7 +587,7 @@ class Agent(BaseAgent):
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
-            prompt=prompt,  # type: ignore[arg-type]
+            prompt=prompt,
             original_tools=raw_tools,
             stop_words=stop_words,
             max_iter=self.max_iter,
@@ -595,9 +603,10 @@ class Agent(BaseAgent):
             callbacks=[TokenCalcHandler(self._token_process)],
         )
 
-    def get_delegation_tools(self, agents: list[BaseAgent]):
+    def get_delegation_tools(self, agents: List[BaseAgent]):
         agent_tools = AgentTools(agents=agents)
-        return agent_tools.tools()
+        tools = agent_tools.tools()
+        return tools
 
     def get_multimodal_tools(self) -> Sequence[BaseTool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
@@ -645,7 +654,7 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
-    def _render_text_description(self, tools: list[Any]) -> str:
+    def _render_text_description(self, tools: List[Any]) -> str:
         """Render the tool name and description in plain text.
 
         Output will be in the format of:
@@ -655,13 +664,15 @@ class Agent(BaseAgent):
             search: This tool is used for search
             calculator: This tool is used for math
         """
-        return "\n".join(
+        description = "\n".join(
             [
                 f"Tool name: {tool.name}\nTool description:\n{tool.description}"
                 for tool in tools
             ]
         )
 
+        return description
+
     def _inject_date_to_task(self, task):
         """Inject the current date into the task description if inject_date is enabled."""
         if self.inject_date:
@@ -685,13 +696,13 @@ class Agent(BaseAgent):
                 if not is_valid:
                     raise ValueError(f"Invalid date format: {self.date_format}")
 
-                current_date = datetime.now().strftime(self.date_format)
+                current_date: str = datetime.now().strftime(self.date_format)
                 task.description += f"\n\nCurrent Date: {current_date}"
             except Exception as e:
                 if hasattr(self, "_logger"):
-                    self._logger.log("warning", f"Failed to inject date: {e!s}")
+                    self._logger.log("warning", f"Failed to inject date: {str(e)}")
                 else:
-                    print(f"Warning: Failed to inject date: {e!s}")
+                    print(f"Warning: Failed to inject date: {str(e)}")
 
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
@@ -702,15 +713,15 @@ class Agent(BaseAgent):
 
         try:
             subprocess.run(
-                ["/usr/bin/docker", "info"],
+                ["docker", "info"],
                 check=True,
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
             )
-        except subprocess.CalledProcessError as e:
+        except subprocess.CalledProcessError:
             raise RuntimeError(
                 f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
-            ) from e
+            )
 
     def __repr__(self):
         return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"
@@ -785,8 +796,8 @@ class Agent(BaseAgent):
 
     def kickoff(
         self,
-        messages: str | list[dict[str, str]],
-        response_format: type[Any] | None = None,
+        messages: Union[str, List[Dict[str, str]]],
+        response_format: Optional[Type[Any]] = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent with the given messages using a LiteAgent instance.
@@ -825,8 +836,8 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: str | list[dict[str, str]],
-        response_format: type[Any] | None = None,
+        messages: Union[str, List[Dict[str, str]]],
+        response_format: Optional[Type[Any]] = None,
     ) -> LiteAgentOutput:
         """
         Execute the agent asynchronously with the given messages using a LiteAgent instance.

src/crewai/agents/__init__.py

@@ -1,12 +1,5 @@
 from crewai.agents.cache.cache_handler import CacheHandler
-from crewai.agents.parser import AgentAction, AgentFinish, OutputParserError, parse
+from crewai.agents.parser import parse, AgentAction, AgentFinish, OutputParserException
 from crewai.agents.tools_handler import ToolsHandler
 
-__all__ = [
-    "AgentAction",
-    "AgentFinish",
-    "CacheHandler",
-    "OutputParserError",
-    "ToolsHandler",
-    "parse",
-]
+__all__ = ["CacheHandler", "parse", "AgentAction", "AgentFinish", "OutputParserException", "ToolsHandler"]

src/crewai/agents/agent_adapters/base_agent_adapter.py

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Dict, List, Optional
 
-from pydantic import ConfigDict, PrivateAttr
+from pydantic import PrivateAttr
 
 from crewai.agent import BaseAgent
 from crewai.tools import BaseTool
@@ -16,21 +16,22 @@ class BaseAgentAdapter(BaseAgent, ABC):
     """
 
     adapted_structured_output: bool = False
-    _agent_config: dict[str, Any] | None = PrivateAttr(default=None)
+    _agent_config: Optional[Dict[str, Any]] = PrivateAttr(default=None)
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}
 
-    def __init__(self, agent_config: dict[str, Any] | None = None, **kwargs: Any):
+    def __init__(self, agent_config: Optional[Dict[str, Any]] = None, **kwargs: Any):
         super().__init__(adapted_agent=True, **kwargs)
         self._agent_config = agent_config
 
     @abstractmethod
-    def configure_tools(self, tools: list[BaseTool] | None = None) -> None:
+    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
         """Configure and adapt tools for the specific agent implementation.
 
         Args:
             tools: Optional list of BaseTool instances to be configured
         """
+        pass
 
     def configure_structured_output(self, structured_output: Any) -> None:
         """Configure the structured output for the specific agent implementation.
@@ -38,3 +39,4 @@ class BaseAgentAdapter(BaseAgent, ABC):
         Args:
             structured_output: The structured output to be configured
         """
+        pass

src/crewai/agents/agent_adapters/base_tool_adapter.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, List, Optional
 
 from crewai.tools.base_tool import BaseTool
 
@@ -12,22 +12,23 @@ class BaseToolAdapter(ABC):
     different frameworks and platforms.
     """
 
-    original_tools: list[BaseTool]
-    converted_tools: list[Any]
+    original_tools: List[BaseTool]
+    converted_tools: List[Any]
 
-    def __init__(self, tools: list[BaseTool] | None = None):
+    def __init__(self, tools: Optional[List[BaseTool]] = None):
         self.original_tools = tools or []
         self.converted_tools = []
 
     @abstractmethod
-    def configure_tools(self, tools: list[BaseTool]) -> None:
+    def configure_tools(self, tools: List[BaseTool]) -> None:
         """Configure and convert tools for the specific implementation.
 
         Args:
             tools: List of BaseTool instances to be configured and converted
         """
+        pass
 
-    def tools(self) -> list[Any]:
+    def tools(self) -> List[Any]:
         """Return all converted tools."""
         return self.converted_tools
 

src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -77,9 +77,9 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         Raises:
             ImportError: If OpenAI agent dependencies are not installed.
         """
-        self.llm = kwargs.pop("model", "gpt-4o-mini")
         super().__init__(**kwargs)
         self._tool_adapter = OpenAIAgentToolAdapter(tools=kwargs.get("tools"))
+        self.llm = kwargs.get("model", "gpt-4o-mini")
         self._converter_adapter = OpenAIConverterAdapter(agent_adapter=self)
 
     def _build_system_prompt(self) -> str:

src/crewai/agents/agent_builder/base_agent.py

@@ -1,9 +1,8 @@
 import uuid
 from abc import ABC, abstractmethod
-from collections.abc import Callable
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, TypeVar
+from typing import Any, Callable, Dict, List, Optional, TypeVar
 
 from pydantic import (
     UUID4,
@@ -22,11 +21,11 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
+from crewai.utilities.converter import Converter
 from crewai.utilities.string_utils import interpolate_only
 
 T = TypeVar("T", bound="BaseAgent")
@@ -82,17 +81,17 @@ class BaseAgent(ABC, BaseModel):
 
     __hash__ = object.__hash__  # type: ignore
     _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
-    _rpm_controller: RPMController | None = PrivateAttr(default=None)
+    _rpm_controller: Optional[RPMController] = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    _original_role: str | None = PrivateAttr(default=None)
-    _original_goal: str | None = PrivateAttr(default=None)
-    _original_backstory: str | None = PrivateAttr(default=None)
+    _original_role: Optional[str] = PrivateAttr(default=None)
+    _original_goal: Optional[str] = PrivateAttr(default=None)
+    _original_backstory: Optional[str] = PrivateAttr(default=None)
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
     backstory: str = Field(description="Backstory of the agent")
-    config: dict[str, Any] | None = Field(
+    config: Optional[Dict[str, Any]] = Field(
         description="Configuration for the agent", default=None, exclude=True
     )
     cache: bool = Field(
@@ -101,7 +100,7 @@ class BaseAgent(ABC, BaseModel):
     verbose: bool = Field(
         default=False, description="Verbose mode for the Agent Execution"
     )
-    max_rpm: int | None = Field(
+    max_rpm: Optional[int] = Field(
         default=None,
         description="Maximum number of requests per minute for the agent execution to be respected.",
     )
@@ -109,7 +108,7 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: list[BaseTool] | None = Field(
+    tools: Optional[List[BaseTool]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(
@@ -123,27 +122,27 @@ class BaseAgent(ABC, BaseModel):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-    cache_handler: InstanceOf[CacheHandler] | None = Field(
+    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
     tools_handler: InstanceOf[ToolsHandler] = Field(
         default_factory=ToolsHandler,
         description="An instance of the ToolsHandler class.",
     )
-    tools_results: list[dict[str, Any]] = Field(
+    tools_results: List[Dict[str, Any]] = Field(
         default=[], description="Results of the tools used by the agent."
     )
-    max_tokens: int | None = Field(
+    max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
     )
-    knowledge: Knowledge | None = Field(
+    knowledge: Optional[Knowledge] = Field(
         default=None, description="Knowledge for the agent."
     )
-    knowledge_sources: list[BaseKnowledgeSource] | None = Field(
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
         default=None,
         description="Knowledge sources for the agent.",
     )
-    knowledge_storage: Any | None = Field(
+    knowledge_storage: Optional[Any] = Field(
         default=None,
         description="Custom knowledge storage for the agent.",
     )
@@ -151,13 +150,13 @@ class BaseAgent(ABC, BaseModel):
         default_factory=SecurityConfig,
         description="Security configuration for the agent, including fingerprinting.",
     )
-    callbacks: list[Callable] = Field(
+    callbacks: List[Callable] = Field(
         default=[], description="Callbacks to be used for the agent"
     )
     adapted_agent: bool = Field(
         default=False, description="Whether the agent is adapted"
     )
-    knowledge_config: KnowledgeConfig | None = Field(
+    knowledge_config: Optional[KnowledgeConfig] = Field(
         default=None,
         description="Knowledge configuration for the agent such as limits and threshold",
     )
@@ -169,7 +168,7 @@ class BaseAgent(ABC, BaseModel):
 
     @field_validator("tools")
     @classmethod
-    def validate_tools(cls, tools: list[Any]) -> list[BaseTool]:
+    def validate_tools(cls, tools: List[Any]) -> List[BaseTool]:
         """Validate and process the tools provided to the agent.
 
         This method ensures that each tool is either an instance of BaseTool
@@ -222,7 +221,7 @@ class BaseAgent(ABC, BaseModel):
 
     @field_validator("id", mode="before")
     @classmethod
-    def _deny_user_set_id(cls, v: UUID4 | None) -> None:
+    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
         if v:
             raise PydanticCustomError(
                 "may_not_set_field", "This field is not to be set by the user.", {}
@@ -253,8 +252,8 @@ class BaseAgent(ABC, BaseModel):
     def execute_task(
         self,
         task: Any,
-        context: str | None = None,
-        tools: list[BaseTool] | None = None,
+        context: Optional[str] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         pass
 
@@ -263,8 +262,9 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: list["BaseAgent"]) -> list[BaseTool]:
+    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
         """Set the task tools that init BaseAgenTools class."""
+        pass
 
     def copy(self: T) -> T:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
         """Create a deep copy of the Agent."""
@@ -309,7 +309,7 @@ class BaseAgent(ABC, BaseModel):
 
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
-        return type(self)(
+        copied_agent = type(self)(
             **copied_data,
             llm=existing_llm,
             tools=self.tools,
@@ -318,7 +318,9 @@ class BaseAgent(ABC, BaseModel):
             knowledge_storage=copied_knowledge_storage,
         )
 
-    def interpolate_inputs(self, inputs: dict[str, Any]) -> None:
+        return copied_agent
+
+    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
         """Interpolate inputs into the agent description and backstory."""
         if self._original_role is None:
             self._original_role = self.role
@@ -360,5 +362,5 @@ class BaseAgent(ABC, BaseModel):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
+    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
         pass

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -1,13 +1,13 @@
 import time
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Dict, List
 
-from crewai.events.event_listener import event_listener
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.utilities import I18N
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.printer import Printer
+from crewai.events.event_listener import event_listener
 
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -21,7 +21,7 @@ class CrewAgentExecutorMixin:
     task: "Task"
     iterations: int
     max_iter: int
-    messages: list[dict[str, str]]
+    messages: List[Dict[str, str]]
     _i18n: I18N
     _printer: Printer = Printer()
 
@@ -46,6 +46,7 @@ class CrewAgentExecutorMixin:
                     )
             except Exception as e:
                 print(f"Failed to add to short term memory: {e}")
+                pass
 
     def _create_external_memory(self, output) -> None:
         """Create and save a external-term memory item if conditions are met."""
@@ -66,6 +67,7 @@ class CrewAgentExecutorMixin:
                 )
             except Exception as e:
                 print(f"Failed to add to external memory: {e}")
+                pass
 
     def _create_long_term_memory(self, output) -> None:
         """Create and save long-term and entity memory items based on evaluation."""
@@ -111,8 +113,10 @@ class CrewAgentExecutorMixin:
                     self.crew._entity_memory.save(entity_memories)
             except AttributeError as e:
                 print(f"Missing attributes for long term memory: {e}")
+                pass
             except Exception as e:
                 print(f"Failed to add to long term memory: {e}")
+                pass
         elif (
             self.crew
             and self.crew._long_term_memory

src/crewai/agents/crew_agent_executor.py

@@ -12,7 +12,7 @@ from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecu
 from crewai.agents.parser import (
     AgentAction,
     AgentFinish,
-    OutputParserError,
+    OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.events.event_bus import crewai_event_bus
@@ -228,7 +228,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._invoke_step_callback(formatted_answer)
                 self._append_message(formatted_answer.text)
 
-            except OutputParserError as e:  # noqa: PERF203
+            except OutputParserException as e:
                 formatted_answer = handle_output_parser_exception(
                     e=e,
                     messages=self.messages,
@@ -251,20 +251,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         i18n=self._i18n,
                     )
                     continue
-                handle_unknown_error(self._printer, e)
-                raise e
+                else:
+                    handle_unknown_error(self._printer, e)
+                    raise e
             finally:
                 self.iterations += 1
 
         # During the invoke loop, formatted_answer alternates between AgentAction
         # (when the agent is using tools) and eventually becomes AgentFinish
-        # (when the agent reaches a final answer). This check confirms we've
+        # (when the agent reaches a final answer). This assertion confirms we've
         # reached a final answer and helps type checking understand this transition.
-        if not isinstance(formatted_answer, AgentFinish):
-            raise RuntimeError(
-                "Agent execution ended without reaching a final answer. "
-                f"Got {type(formatted_answer).__name__} instead of AgentFinish."
-            )
+        assert isinstance(formatted_answer, AgentFinish)
         self._show_logs(formatted_answer)
         return formatted_answer
 
@@ -327,7 +324,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             self.agent,
             AgentLogsStartedEvent(
                 agent_role=self.agent.role,
-                task_description=(self.task.description if self.task else "Not Found"),
+                task_description=(
+                    getattr(self.task, "description") if self.task else "Not Found"
+                ),
                 verbose=self.agent.verbose
                 or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
             ),
@@ -416,7 +415,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         prompt = prompt.replace("{input}", inputs["input"])
         prompt = prompt.replace("{tool_names}", inputs["tool_names"])
-        return prompt.replace("{tools}", inputs["tools"])
+        prompt = prompt.replace("{tools}", inputs["tools"])
+        return prompt
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
         """Process human feedback.

src/crewai/agents/parser.py

@@ -7,18 +7,18 @@ AgentAction or AgentFinish objects.
 
 from dataclasses import dataclass
 
-from json_repair import repair_json  # type: ignore[import-untyped]
+from json_repair import repair_json
 
 from crewai.agents.constants import (
-    ACTION_INPUT_ONLY_REGEX,
     ACTION_INPUT_REGEX,
     ACTION_REGEX,
+    ACTION_INPUT_ONLY_REGEX,
     FINAL_ANSWER_ACTION,
     MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
     MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
     UNABLE_TO_REPAIR_JSON_RESULTS,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities import I18N
 
 _I18N = I18N()
 
@@ -43,7 +43,7 @@ class AgentFinish:
     text: str
 
 
-class OutputParserError(Exception):
+class OutputParserException(Exception):
     """Exception raised when output parsing fails.
 
     Attributes:
@@ -51,7 +51,7 @@ class OutputParserError(Exception):
     """
 
     def __init__(self, error: str) -> None:
-        """Initialize OutputParserError.
+        """Initialize OutputParserException.
 
         Args:
             error: The error message.
@@ -87,7 +87,7 @@ def parse(text: str) -> AgentAction | AgentFinish:
         AgentAction or AgentFinish based on the content.
 
     Raises:
-        OutputParserError: If the text format is invalid.
+        OutputParserException: If the text format is invalid.
     """
     thought = _extract_thought(text)
     includes_answer = FINAL_ANSWER_ACTION in text
@@ -104,7 +104,7 @@ def parse(text: str) -> AgentAction | AgentFinish:
                 final_answer = final_answer[:-3].rstrip()
         return AgentFinish(thought=thought, output=final_answer, text=text)
 
-    if action_match:
+    elif action_match:
         action = action_match.group(1)
         clean_action = _clean_action(action)
 
@@ -118,18 +118,19 @@ def parse(text: str) -> AgentAction | AgentFinish:
         )
 
     if not ACTION_REGEX.search(text):
-        raise OutputParserError(
+        raise OutputParserException(
             f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{_I18N.slice('final_answer_format')}",
         )
-    if not ACTION_INPUT_ONLY_REGEX.search(text):
-        raise OutputParserError(
+    elif not ACTION_INPUT_ONLY_REGEX.search(text):
+        raise OutputParserException(
             MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
         )
-    err_format = _I18N.slice("format_without_tools")
-    error = f"{err_format}"
-    raise OutputParserError(
-        error,
-    )
+    else:
+        err_format = _I18N.slice("format_without_tools")
+        error = f"{err_format}"
+        raise OutputParserException(
+            error,
+        )
 
 
 def _extract_thought(text: str) -> str:
@@ -148,7 +149,8 @@ def _extract_thought(text: str) -> str:
         return ""
     thought = text[:thought_index].strip()
     # Remove any triple backticks from the thought string
-    return thought.replace("```", "").strip()
+    thought = thought.replace("```", "").strip()
+    return thought
 
 
 def _clean_action(text: str) -> str:

src/crewai/agents/tools_handler.py

@@ -1,10 +1,8 @@
 """Tools handler for managing tool execution and caching."""
 
-import json
-
-from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.tools.cache_tools.cache_tools import CacheTools
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
+from crewai.agents.cache.cache_handler import CacheHandler
 
 
 class ToolsHandler:
@@ -39,16 +37,8 @@ class ToolsHandler:
         """
         self.last_used_tool = calling
         if self.cache and should_cache and calling.tool_name != CacheTools().name:
-            # Convert arguments to string for cache
-            input_str = ""
-            if calling.arguments:
-                if isinstance(calling.arguments, dict):
-                    input_str = json.dumps(calling.arguments)
-                else:
-                    input_str = str(calling.arguments)
-
             self.cache.add(
                 tool=calling.tool_name,
-                input=input_str,
+                input=calling.arguments,
                 output=output,
             )

src/crewai/cli/authentication/providers/auth0.py

@@ -1,6 +1,5 @@
 from crewai.cli.authentication.providers.base_provider import BaseProvider
 
-
 class Auth0Provider(BaseProvider):
     def get_authorize_url(self) -> str:
         return f"https://{self._get_domain()}/oauth/device/code"
@@ -15,20 +14,13 @@ class Auth0Provider(BaseProvider):
         return f"https://{self._get_domain()}/"
 
     def get_audience(self) -> str:
-        if self.settings.audience is None:
-            raise ValueError(
-                "Audience is required. Please set it in the configuration."
-            )
+        assert self.settings.audience is not None, "Audience is required"
         return self.settings.audience
 
     def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
+        assert self.settings.client_id is not None, "Client ID is required"
         return self.settings.client_id
 
     def _get_domain(self) -> str:
-        if self.settings.domain is None:
-            raise ValueError("Domain is required. Please set it in the configuration.")
+        assert self.settings.domain is not None, "Domain is required"
         return self.settings.domain

src/crewai/cli/authentication/providers/base_provider.py

@@ -1,26 +1,30 @@
 from abc import ABC, abstractmethod
-
 from crewai.cli.authentication.main import Oauth2Settings
 
-
 class BaseProvider(ABC):
     def __init__(self, settings: Oauth2Settings):
         self.settings = settings
 
     @abstractmethod
-    def get_authorize_url(self) -> str: ...
+    def get_authorize_url(self) -> str:
+        ...
 
     @abstractmethod
-    def get_token_url(self) -> str: ...
+    def get_token_url(self) -> str:
+        ...
 
     @abstractmethod
-    def get_jwks_url(self) -> str: ...
+    def get_jwks_url(self) -> str:
+        ...
 
     @abstractmethod
-    def get_issuer(self) -> str: ...
+    def get_issuer(self) -> str:
+        ...
 
     @abstractmethod
-    def get_audience(self) -> str: ...
+    def get_audience(self) -> str:
+        ...
 
     @abstractmethod
-    def get_client_id(self) -> str: ...
+    def get_client_id(self) -> str:
+        ...

src/crewai/cli/authentication/providers/okta.py

@@ -1,6 +1,5 @@
 from crewai.cli.authentication.providers.base_provider import BaseProvider
 
-
 class OktaProvider(BaseProvider):
     def get_authorize_url(self) -> str:
         return f"https://{self.settings.domain}/oauth2/default/v1/device/authorize"
@@ -15,15 +14,9 @@ class OktaProvider(BaseProvider):
         return f"https://{self.settings.domain}/oauth2/default"
 
     def get_audience(self) -> str:
-        if self.settings.audience is None:
-            raise ValueError(
-                "Audience is required. Please set it in the configuration."
-            )
+        assert self.settings.audience is not None
         return self.settings.audience
 
     def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
+        assert self.settings.client_id is not None
         return self.settings.client_id

src/crewai/cli/authentication/providers/workos.py

@@ -1,6 +1,5 @@
 from crewai.cli.authentication.providers.base_provider import BaseProvider
 
-
 class WorkosProvider(BaseProvider):
     def get_authorize_url(self) -> str:
         return f"https://{self._get_domain()}/oauth2/device_authorization"
@@ -18,13 +17,9 @@ class WorkosProvider(BaseProvider):
         return self.settings.audience or ""
 
     def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
+        assert self.settings.client_id is not None, "Client ID is required"
         return self.settings.client_id
 
     def _get_domain(self) -> str:
-        if self.settings.domain is None:
-            raise ValueError("Domain is required. Please set it in the configuration.")
+        assert self.settings.domain is not None, "Domain is required"
         return self.settings.domain

src/crewai/cli/authentication/utils.py

@@ -17,6 +17,8 @@ def validate_jwt_token(
                        missing required claims).
     """
 
+    decoded_token = None
+
     try:
         jwk_client = PyJWKClient(jwks_url)
         signing_key = jwk_client.get_signing_key_from_jwt(jwt_token)
@@ -24,7 +26,7 @@ def validate_jwt_token(
         _unverified_decoded_token = jwt.decode(
             jwt_token, options={"verify_signature": False}
         )
-        return jwt.decode(
+        decoded_token = jwt.decode(
             jwt_token,
             signing_key.key,
             algorithms=["RS256"],
@@ -38,22 +40,23 @@ def validate_jwt_token(
                 "require": ["exp", "iat", "iss", "aud", "sub"],
             },
         )
+        return decoded_token
 
-    except jwt.ExpiredSignatureError as e:
-        raise Exception("Token has expired.") from e
-    except jwt.InvalidAudienceError as e:
+    except jwt.ExpiredSignatureError:
+        raise Exception("Token has expired.")
+    except jwt.InvalidAudienceError:
         actual_audience = _unverified_decoded_token.get("aud", "[no audience found]")
         raise Exception(
             f"Invalid token audience. Got: '{actual_audience}'. Expected: '{audience}'"
-        ) from e
-    except jwt.InvalidIssuerError as e:
+        )
+    except jwt.InvalidIssuerError:
         actual_issuer = _unverified_decoded_token.get("iss", "[no issuer found]")
         raise Exception(
             f"Invalid token issuer. Got: '{actual_issuer}'. Expected: '{issuer}'"
-        ) from e
+        )
     except jwt.MissingRequiredClaimError as e:
-        raise Exception(f"Token is missing required claims: {e!s}") from e
+        raise Exception(f"Token is missing required claims: {str(e)}")
     except jwt.exceptions.PyJWKClientError as e:
-        raise Exception(f"JWKS or key processing error: {e!s}") from e
+        raise Exception(f"JWKS or key processing error: {str(e)}")
     except jwt.InvalidTokenError as e:
-        raise Exception(f"Invalid token: {e!s}") from e
+        raise Exception(f"Invalid token: {str(e)}")

src/crewai/cli/cli.py

@@ -1,16 +1,13 @@
-import os
-import subprocess
 from importlib.metadata import version as get_version
+from typing import Optional
 
 import click
-
-from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.config import Settings
+from crewai.cli.settings.main import SettingsCommand
+from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
 from crewai.cli.crew_chat import run_chat
-from crewai.cli.settings.main import SettingsCommand
-from crewai.cli.utils import build_env_with_tool_repository_credentials, read_toml
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -37,46 +34,6 @@ def crewai():
     """Top-level command group for crewai."""
 
 
-@crewai.command(
-    name="uv",
-    context_settings=dict(
-        ignore_unknown_options=True,
-    ),
-)
-@click.argument("uv_args", nargs=-1, type=click.UNPROCESSED)
-def uv(uv_args):
-    """A wrapper around uv commands that adds custom tool authentication through env vars."""
-    env = os.environ.copy()
-    try:
-        pyproject_data = read_toml()
-        sources = pyproject_data.get("tool", {}).get("uv", {}).get("sources", {})
-
-        for source_config in sources.values():
-            if isinstance(source_config, dict):
-                index = source_config.get("index")
-                if index:
-                    index_env = build_env_with_tool_repository_credentials(index)
-                    env.update(index_env)
-    except (FileNotFoundError, KeyError) as e:
-        raise SystemExit(
-            "Error. A valid pyproject.toml file is required. Check that a valid pyproject.toml file exists in the current directory."
-        ) from e
-    except Exception as e:
-        raise SystemExit(f"Error: {e}") from e
-
-    try:
-        subprocess.run(  # noqa: S603
-            ["uv", *uv_args],  # noqa: S607
-            capture_output=False,
-            env=env,
-            text=True,
-            check=True,
-        )
-    except subprocess.CalledProcessError as e:
-        click.secho(f"uv command failed with exit code {e.returncode}", fg="red")
-        raise SystemExit(e.returncode) from e
-
-
 @crewai.command()
 @click.argument("type", type=click.Choice(["crew", "flow"]))
 @click.argument("name")
@@ -280,6 +237,13 @@ def login():
 @crewai.group()
 def deploy():
     """Deploy the Crew CLI group."""
+    pass
+
+
+@crewai.group()
+def tool():
+    """Tool Repository related commands."""
+    pass
 
 
 @deploy.command(name="create")
@@ -299,7 +263,7 @@ 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: Optional[str]):
     """Deploy the Crew."""
     deploy_cmd = DeployCommand()
     deploy_cmd.deploy(uuid=uuid)
@@ -307,7 +271,7 @@ 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: Optional[str]):
     """Get the status of a deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.get_crew_status(uuid=uuid)
@@ -315,7 +279,7 @@ 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: Optional[str]):
     """Get the logs of a deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.get_crew_logs(uuid=uuid)
@@ -323,17 +287,12 @@ 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: Optional[str]):
     """Remove a deployment."""
     deploy_cmd = DeployCommand()
     deploy_cmd.remove_crew(uuid=uuid)
 
 
-@crewai.group()
-def tool():
-    """Tool Repository related commands."""
-
-
 @tool.command(name="create")
 @click.argument("handle")
 def tool_create(handle: str):
@@ -368,6 +327,7 @@ def tool_publish(is_public: bool, force: bool):
 @crewai.group()
 def flow():
     """Flow related commands."""
+    pass
 
 
 @flow.command(name="kickoff")
@@ -399,7 +359,7 @@ def chat():
     and using the Chat LLM to generate responses.
     """
     click.secho(
-        "\nStarting a conversation with the Crew\nType 'exit' or Ctrl+C to quit.\n",
+        "\nStarting a conversation with the Crew\n" "Type 'exit' or Ctrl+C to quit.\n",
     )
 
     run_chat()
@@ -408,6 +368,7 @@ def chat():
 @crewai.group(invoke_without_command=True)
 def org():
     """Organization management commands."""
+    pass
 
 
 @org.command("list")
@@ -435,6 +396,7 @@ def current():
 @crewai.group()
 def enterprise():
     """Enterprise Configuration commands."""
+    pass
 
 
 @enterprise.command("configure")
@@ -448,6 +410,7 @@ def enterprise_configure(enterprise_url: str):
 @crewai.group()
 def config():
     """CLI Configuration commands."""
+    pass
 
 
 @config.command("list")

src/crewai/cli/config.py

@@ -1,61 +1,20 @@
 import json
-import tempfile
-from logging import getLogger
 from pathlib import Path
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
 from crewai.cli.constants import (
+    DEFAULT_CREWAI_ENTERPRISE_URL,
+    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_PROVIDER,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_PROVIDER,
-    DEFAULT_CREWAI_ENTERPRISE_URL,
 )
 from crewai.cli.shared.token_manager import TokenManager
 
-logger = getLogger(__name__)
-
 DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
 
-
-def get_writable_config_path() -> Path | None:
-    """
-    Find a writable location for the config file with fallback options.
-
-    Tries in order:
-    1. Default: ~/.config/crewai/settings.json
-    2. Temp directory: /tmp/crewai_settings.json (or OS equivalent)
-    3. Current directory: ./crewai_settings.json
-    4. In-memory only (returns None)
-
-    Returns:
-        Path object for writable config location, or None if no writable location found
-    """
-    fallback_paths = [
-        DEFAULT_CONFIG_PATH,  # Default location
-        Path(tempfile.gettempdir()) / "crewai_settings.json",  # Temporary directory
-        Path.cwd() / "crewai_settings.json",  # Current working directory
-    ]
-
-    for config_path in fallback_paths:
-        try:
-            config_path.parent.mkdir(parents=True, exist_ok=True)
-            test_file = config_path.parent / ".crewai_write_test"
-            try:
-                test_file.write_text("test")
-                test_file.unlink()  # Clean up test file
-                logger.info(f"Using config path: {config_path}")
-                return config_path
-            except Exception:  # noqa: S112
-                continue
-
-        except Exception:  # noqa: S112
-            continue
-
-    return None
-
-
 # Settings that are related to the user's account
 USER_SETTINGS_KEYS = [
     "tool_repository_username",
@@ -97,20 +56,20 @@ HIDDEN_SETTINGS_KEYS = [
 
 
 class Settings(BaseModel):
-    enterprise_base_url: str | None = Field(
+    enterprise_base_url: Optional[str] = Field(
         default=DEFAULT_CLI_SETTINGS["enterprise_base_url"],
         description="Base URL of the CrewAI Enterprise instance",
     )
-    tool_repository_username: str | None = Field(
+    tool_repository_username: Optional[str] = Field(
         None, description="Username for interacting with the Tool Repository"
     )
-    tool_repository_password: str | None = Field(
+    tool_repository_password: Optional[str] = Field(
         None, description="Password for interacting with the Tool Repository"
     )
-    org_name: str | None = Field(
+    org_name: Optional[str] = Field(
         None, description="Name of the currently active organization"
     )
-    org_uuid: str | None = Field(
+    org_uuid: Optional[str] = Field(
         None, description="UUID of the currently active organization"
     )
     config_path: Path = Field(default=DEFAULT_CONFIG_PATH, frozen=True, exclude=True)
@@ -120,7 +79,7 @@ class Settings(BaseModel):
         default=DEFAULT_CLI_SETTINGS["oauth2_provider"],
     )
 
-    oauth2_audience: str | None = Field(
+    oauth2_audience: Optional[str] = Field(
         description="OAuth2 audience value, typically used to identify the target API or resource.",
         default=DEFAULT_CLI_SETTINGS["oauth2_audience"],
     )
@@ -135,32 +94,16 @@ class Settings(BaseModel):
         default=DEFAULT_CLI_SETTINGS["oauth2_domain"],
     )
 
-    def __init__(self, config_path: Path | None = None, **data):
-        """Load Settings from config path with fallback support"""
-        if config_path is None:
-            config_path = get_writable_config_path()
-
-        # If config_path is None, we're in memory-only mode
-        if config_path is None:
-            merged_data = {**data}
-            # Dummy path for memory-only mode
-            super().__init__(config_path=Path("/dev/null"), **merged_data)
-            return
-
-        try:
-            config_path.parent.mkdir(parents=True, exist_ok=True)
-        except Exception:
-            merged_data = {**data}
-            # Dummy path for memory-only mode
-            super().__init__(config_path=Path("/dev/null"), **merged_data)
-            return
+    def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):
+        """Load Settings from config path"""
+        config_path.parent.mkdir(parents=True, exist_ok=True)
 
         file_data = {}
         if config_path.is_file():
             try:
                 with config_path.open("r") as f:
                     file_data = json.load(f)
-            except Exception:
+            except json.JSONDecodeError:
                 file_data = {}
 
         merged_data = {**file_data, **data}
@@ -180,22 +123,15 @@ class Settings(BaseModel):
 
     def dump(self) -> None:
         """Save current settings to settings.json"""
-        if str(self.config_path) == "/dev/null":
-            return
+        if self.config_path.is_file():
+            with self.config_path.open("r") as f:
+                existing_data = json.load(f)
+        else:
+            existing_data = {}
 
-        try:
-            if self.config_path.is_file():
-                with self.config_path.open("r") as f:
-                    existing_data = json.load(f)
-            else:
-                existing_data = {}
-
-            updated_data = {**existing_data, **self.model_dump(exclude_unset=True)}
-            with self.config_path.open("w") as f:
-                json.dump(updated_data, f, indent=4)
-
-        except Exception:  # noqa: S110
-            pass
+        updated_data = {**existing_data, **self.model_dump(exclude_unset=True)}
+        with self.config_path.open("w") as f:
+            json.dump(updated_data, f, indent=4)
 
     def _reset_user_settings(self) -> None:
         """Reset all user settings to default values"""

src/crewai/cli/create_crew.py

@@ -16,72 +16,48 @@ from crewai.cli.utils import copy_template, load_env_vars, write_env_file
 def create_folder_structure(name, parent_folder=None):
     import keyword
     import re
-
-    name = name.rstrip("/")
-
+    
+    name = name.rstrip('/')
+    
     if not name.strip():
         raise ValueError("Project name cannot be empty or contain only whitespace")
-
+    
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
-    folder_name = re.sub(r"[^a-zA-Z0-9_]", "", folder_name)
-
+    folder_name = re.sub(r'[^a-zA-Z0-9_]', '', folder_name)
+    
     # Check if the name starts with invalid characters or is primarily invalid
-    if re.match(r"^[^a-zA-Z0-9_-]+", name):
-        raise ValueError(
-            f"Project name '{name}' contains no valid characters for a Python module name"
-        )
-
+    if re.match(r'^[^a-zA-Z0-9_-]+', name):
+        raise ValueError(f"Project name '{name}' contains no valid characters for a Python module name")
+    
     if not folder_name:
-        raise ValueError(
-            f"Project name '{name}' contains no valid characters for a Python module name"
-        )
-
+        raise ValueError(f"Project name '{name}' contains no valid characters for a Python module name")
+    
     if folder_name[0].isdigit():
-        raise ValueError(
-            f"Project name '{name}' would generate folder name '{folder_name}' which cannot start with a digit (invalid Python module name)"
-        )
-
+        raise ValueError(f"Project name '{name}' would generate folder name '{folder_name}' which cannot start with a digit (invalid Python module name)")
+    
     if keyword.iskeyword(folder_name):
-        raise ValueError(
-            f"Project name '{name}' would generate folder name '{folder_name}' which is a reserved Python keyword"
-        )
-
+        raise ValueError(f"Project name '{name}' would generate folder name '{folder_name}' which is a reserved Python keyword")
+    
     if not folder_name.isidentifier():
-        raise ValueError(
-            f"Project name '{name}' would generate invalid Python module name '{folder_name}'"
-        )
-
+        raise ValueError(f"Project name '{name}' would generate invalid Python module name '{folder_name}'")
+    
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
-
-    class_name = re.sub(r"[^a-zA-Z0-9_]", "", class_name)
-
+    
+    class_name = re.sub(r'[^a-zA-Z0-9_]', '', class_name)
+    
     if not class_name:
-        raise ValueError(
-            f"Project name '{name}' contains no valid characters for a Python class name"
-        )
-
+        raise ValueError(f"Project name '{name}' contains no valid characters for a Python class name")
+    
     if class_name[0].isdigit():
-        raise ValueError(
-            f"Project name '{name}' would generate class name '{class_name}' which cannot start with a digit"
-        )
-
+        raise ValueError(f"Project name '{name}' would generate class name '{class_name}' which cannot start with a digit")
+    
     # Check if the original name (before title casing) is a keyword
-    original_name_clean = re.sub(
-        r"[^a-zA-Z0-9_]", "", name.replace("_", "").replace("-", "").lower()
-    )
-    if (
-        keyword.iskeyword(original_name_clean)
-        or keyword.iskeyword(class_name)
-        or class_name in ("True", "False", "None")
-    ):
-        raise ValueError(
-            f"Project name '{name}' would generate class name '{class_name}' which is a reserved Python keyword"
-        )
-
+    original_name_clean = re.sub(r'[^a-zA-Z0-9_]', '', name.replace("_", "").replace("-", "").lower())
+    if keyword.iskeyword(original_name_clean) or keyword.iskeyword(class_name) or class_name in ('True', 'False', 'None'):
+        raise ValueError(f"Project name '{name}' would generate class name '{class_name}' which is a reserved Python keyword")
+    
     if not class_name.isidentifier():
-        raise ValueError(
-            f"Project name '{name}' would generate invalid Python class name '{class_name}'"
-        )
+        raise ValueError(f"Project name '{name}' would generate invalid Python class name '{class_name}'")
 
     if parent_folder:
         folder_path = Path(parent_folder) / folder_name
@@ -196,7 +172,7 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
             )
 
         # Check if the selected provider has predefined models
-        if MODELS.get(selected_provider):
+        if selected_provider in MODELS and MODELS[selected_provider]:
             while True:
                 selected_model = select_model(selected_provider, provider_models)
                 if selected_model is None:  # User typed 'q'

src/crewai/cli/crew_chat.py

@@ -5,7 +5,7 @@ import sys
 import threading
 import time
 from pathlib import Path
-from typing import Any
+from typing import Any, Dict, List, Optional, Set, Tuple
 
 import click
 import tomli
@@ -116,7 +116,7 @@ def show_loading(event: threading.Event):
     print()
 
 
-def initialize_chat_llm(crew: Crew) -> LLM | BaseLLM | None:
+def initialize_chat_llm(crew: Crew) -> Optional[LLM | BaseLLM]:
     """Initializes the chat LLM and handles exceptions."""
     try:
         return create_llm(crew.chat_llm)
@@ -157,7 +157,7 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
     )
 
 
-def create_tool_function(crew: Crew, messages: list[dict[str, str]]) -> Any:
+def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
     """Creates a wrapper function for running the crew tool with messages."""
 
     def run_crew_tool_with_messages(**kwargs):
@@ -193,7 +193,7 @@ def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
                 user_input, chat_llm, messages, crew_tool_schema, available_functions
             )
 
-        except KeyboardInterrupt:  # noqa: PERF203
+        except KeyboardInterrupt:
             click.echo("\nExiting chat. Goodbye!")
             break
         except Exception as e:
@@ -221,9 +221,9 @@ def get_user_input() -> str:
 def handle_user_input(
     user_input: str,
     chat_llm: LLM,
-    messages: list[dict[str, str]],
-    crew_tool_schema: dict[str, Any],
-    available_functions: dict[str, Any],
+    messages: List[Dict[str, str]],
+    crew_tool_schema: Dict[str, Any],
+    available_functions: Dict[str, Any],
 ) -> None:
     if user_input.strip().lower() == "exit":
         click.echo("Exiting chat. Goodbye!")
@@ -281,7 +281,7 @@ def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
     }
 
 
-def run_crew_tool(crew: Crew, messages: list[dict[str, str]], **kwargs):
+def run_crew_tool(crew: Crew, messages: List[Dict[str, str]], **kwargs):
     """
     Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
 
@@ -304,8 +304,9 @@ def run_crew_tool(crew: Crew, messages: list[dict[str, str]], **kwargs):
         crew_output = crew.kickoff(inputs=kwargs)
 
         # Convert CrewOutput to a string to send back to the user
-        return str(crew_output)
+        result = str(crew_output)
 
+        return result
     except Exception as e:
         # Exit the chat and show the error message
         click.secho("An error occurred while running the crew:", fg="red")
@@ -313,7 +314,7 @@ def run_crew_tool(crew: Crew, messages: list[dict[str, str]], **kwargs):
         sys.exit(1)
 
 
-def load_crew_and_name() -> tuple[Crew, str]:
+def load_crew_and_name() -> Tuple[Crew, str]:
     """
     Loads the crew by importing the crew class from the user's project.
 
@@ -350,17 +351,15 @@ def load_crew_and_name() -> tuple[Crew, str]:
     try:
         crew_module = __import__(crew_module_name, fromlist=[crew_class_name])
     except ImportError as e:
-        raise ImportError(
-            f"Failed to import crew module {crew_module_name}: {e}"
-        ) from e
+        raise ImportError(f"Failed to import crew module {crew_module_name}: {e}")
 
     # Get the crew class from the module
     try:
         crew_class = getattr(crew_module, crew_class_name)
-    except AttributeError as e:
+    except AttributeError:
         raise AttributeError(
             f"Crew class {crew_class_name} not found in module {crew_module_name}"
-        ) from e
+        )
 
     # Instantiate the crew
     crew_instance = crew_class().crew()
@@ -396,7 +395,7 @@ def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInput
     )
 
 
-def fetch_required_inputs(crew: Crew) -> set[str]:
+def fetch_required_inputs(crew: Crew) -> Set[str]:
     """
     Extracts placeholders from the crew's tasks and agents.
 
@@ -406,8 +405,8 @@ def fetch_required_inputs(crew: Crew) -> set[str]:
     Returns:
         Set[str]: A set of placeholder names.
     """
-    placeholder_pattern = re.compile(r"\{(.+?)}")
-    required_inputs: set[str] = set()
+    placeholder_pattern = re.compile(r"\{(.+?)\}")
+    required_inputs: Set[str] = set()
 
     # Scan tasks
     for task in crew.tasks:
@@ -436,7 +435,7 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
     """
     # Gather context from tasks and agents where the input is used
     context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)}")
+    placeholder_pattern = re.compile(r"\{(.+?)\}")
 
     for task in crew.tasks:
         if (
@@ -480,7 +479,9 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
         f"{context}"
     )
     response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    return response.strip()
+    description = response.strip()
+
+    return description
 
 
 def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
@@ -496,7 +497,7 @@ def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
     """
     # Gather context from tasks and agents
     context_texts = []
-    placeholder_pattern = re.compile(r"\{(.+?)}")
+    placeholder_pattern = re.compile(r"\{(.+?)\}")
 
     for task in crew.tasks:
         # Replace placeholders with input names
@@ -530,4 +531,6 @@ def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
         f"{context}"
     )
     response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    return response.strip()
+    crew_description = response.strip()
+
+    return crew_description

src/crewai/cli/git.py

@@ -14,15 +14,11 @@ class Repository:
 
         self.fetch()
 
-    @staticmethod
-    def is_git_installed() -> bool:
+    def is_git_installed(self) -> bool:
         """Check if Git is installed and available in the system."""
         try:
             subprocess.run(
-                ["git", "--version"],  # noqa: S607
-                capture_output=True,
-                check=True,
-                text=True,
+                ["git", "--version"], capture_output=True, check=True, text=True
             )
             return True
         except (subprocess.CalledProcessError, FileNotFoundError):
@@ -30,26 +26,22 @@ class Repository:
 
     def fetch(self) -> None:
         """Fetch latest updates from the remote."""
-        subprocess.run(["git", "fetch"], cwd=self.path, check=True)  # noqa: S607
+        subprocess.run(["git", "fetch"], cwd=self.path, check=True)
 
     def status(self) -> str:
         """Get the git status in porcelain format."""
         return subprocess.check_output(
-            ["git", "status", "--branch", "--porcelain"],  # noqa: S607
+            ["git", "status", "--branch", "--porcelain"],
             cwd=self.path,
             encoding="utf-8",
         ).strip()
 
-    @lru_cache(maxsize=None)  # noqa: B019
+    @lru_cache(maxsize=None)
     def is_git_repo(self) -> bool:
-        """Check if the current directory is a git repository.
-
-        Notes:
-          - TODO: This method is cached to avoid redundant checks, but using lru_cache on methods can lead to memory leaks
-        """
+        """Check if the current directory is a git repository."""
         try:
             subprocess.check_output(
-                ["git", "rev-parse", "--is-inside-work-tree"],  # noqa: S607
+                ["git", "rev-parse", "--is-inside-work-tree"],
                 cwd=self.path,
                 encoding="utf-8",
             )
@@ -72,13 +64,14 @@ class Repository:
         """Return True if the Git repository is fully synced with the remote, False otherwise."""
         if self.has_uncommitted_changes() or self.is_ahead_or_behind():
             return False
-        return True
+        else:
+            return True
 
     def origin_url(self) -> str | None:
         """Get the Git repository's remote URL."""
         try:
             result = subprocess.run(
-                ["git", "remote", "get-url", "origin"],  # noqa: S607
+                ["git", "remote", "get-url", "origin"],
                 cwd=self.path,
                 capture_output=True,
                 text=True,

src/crewai/cli/install_crew.py

@@ -12,8 +12,8 @@ def install_crew(proxy_options: list[str]) -> None:
     Install the crew by running the UV command to lock and install.
     """
     try:
-        command = ["uv", "sync", *proxy_options]
-        subprocess.run(command, check=True, capture_output=False, text=True)  # noqa: S603
+        command = ["uv", "sync"] + proxy_options
+        subprocess.run(command, check=True, capture_output=False, text=True)
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while running the crew: {e}", err=True)

src/crewai/cli/plus_api.py

@@ -1,10 +1,11 @@
+from typing import List, Optional
 from urllib.parse import urljoin
 
 import requests
 
 from crewai.cli.config import Settings
-from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
 from crewai.cli.version import get_crewai_version
+from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
 
 
 class PlusAPI:
@@ -55,9 +56,9 @@ class PlusAPI:
         handle: str,
         is_public: bool,
         version: str,
-        description: str | None,
+        description: Optional[str],
         encoded_file: str,
-        available_exports: list[str] | None = None,
+        available_exports: Optional[List[str]] = None,
     ):
         params = {
             "handle": handle,
@@ -166,13 +167,3 @@ class PlusAPI:
             json=payload,
             timeout=30,
         )
-
-    def mark_trace_batch_as_failed(
-        self, trace_batch_id: str, error_message: str
-    ) -> requests.Response:
-        return self._make_request(
-            "PATCH",
-            f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}",
-            json={"status": "failed", "failure_reason": error_message},
-            timeout=30,
-        )

src/crewai/cli/provider.py

@@ -1,10 +1,10 @@
-import json
 import os
+import certifi
+import json
 import time
 from collections import defaultdict
 from pathlib import Path
 
-import certifi
 import click
 import requests
 
@@ -25,7 +25,7 @@ def select_choice(prompt_message, choices):
 
     provider_models = get_provider_data()
     if not provider_models:
-        return None
+        return
     click.secho(prompt_message, fg="cyan")
     for idx, choice in enumerate(choices, start=1):
         click.secho(f"{idx}. {choice}", fg="cyan")
@@ -67,7 +67,7 @@ def select_provider(provider_models):
     all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
 
     provider = select_choice(
-        "Select a provider to set up:", [*predefined_providers, "other"]
+        "Select a provider to set up:", predefined_providers + ["other"]
     )
     if provider is None:  # User typed 'q'
         return None
@@ -102,9 +102,10 @@ def select_model(provider, provider_models):
         click.secho(f"No models available for provider '{provider}'.", fg="red")
         return None
 
-    return select_choice(
+    selected_model = select_choice(
         f"Select a model to use for {provider.capitalize()}:", available_models
     )
+    return selected_model
 
 
 def load_provider_data(cache_file, cache_expiry):
@@ -164,7 +165,7 @@ def fetch_provider_data(cache_file):
     Returns:
     - dict or None: The fetched provider data or None if the operation fails.
     """
-    ssl_config = os.environ["SSL_CERT_FILE"] = certifi.where()
+    ssl_config = os.environ['SSL_CERT_FILE'] = certifi.where()
 
     try:
         response = requests.get(JSON_URL, stream=True, timeout=60, verify=ssl_config)

src/crewai/cli/run_crew.py

@@ -1,5 +1,6 @@
 import subprocess
 from enum import Enum
+from typing import List, Optional
 
 import click
 from packaging import version
@@ -56,7 +57,7 @@ def execute_command(crew_type: CrewType) -> None:
     command = ["uv", "run", "kickoff" if crew_type == CrewType.FLOW else "run_crew"]
 
     try:
-        subprocess.run(command, capture_output=False, text=True, check=True)  # noqa: S603
+        subprocess.run(command, capture_output=False, text=True, check=True)
 
     except subprocess.CalledProcessError as e:
         handle_error(e, crew_type)

src/crewai/cli/shared/token_manager.py

@@ -3,7 +3,7 @@ import os
 import sys
 from datetime import datetime
 from pathlib import Path
-
+from typing import Optional
 from cryptography.fernet import Fernet
 
 
@@ -49,7 +49,7 @@ class TokenManager:
         encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
         self.save_secure_file(self.file_path, encrypted_data)
 
-    def get_token(self) -> str | None:
+    def get_token(self) -> Optional[str]:
         """
         Get the access token if it is valid and not expired.
 
@@ -113,7 +113,7 @@ class TokenManager:
         # Set appropriate permissions (read/write for owner only)
         os.chmod(file_path, 0o600)
 
-    def read_secure_file(self, filename: str) -> bytes | None:
+    def read_secure_file(self, filename: str) -> Optional[bytes]:
         """
         Read the content of a secure file.
 

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]>=0.201.1,<1.0.0"
+    "crewai[tools]>=0.186.1,<1.0.0"
 ]
 
 [project.scripts]

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]>=0.201.1,<1.0.0",
+    "crewai[tools]>=0.186.1,<1.0.0",
 ]
 
 [project.scripts]

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]>=0.201.1"
+    "crewai[tools]>=0.186.1"
 ]
 
 [tool.crewai]

src/crewai/cli/tools/main.py

@@ -12,7 +12,6 @@ from crewai.cli import git
 from crewai.cli.command import BaseCommand, PlusAPIMixin
 from crewai.cli.config import Settings
 from crewai.cli.utils import (
-    build_env_with_tool_repository_credentials,
     extract_available_exports,
     get_project_description,
     get_project_name,
@@ -43,7 +42,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         if project_root.exists():
             click.secho(f"Folder {folder_name} already exists.", fg="red")
             raise SystemExit
-        os.makedirs(project_root)
+        else:
+            os.makedirs(project_root)
 
         click.secho(f"Creating custom tool {folder_name}...", fg="green", bold=True)
 
@@ -56,7 +56,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         os.chdir(project_root)
         try:
             self.login()
-            subprocess.run(["git", "init"], check=True)  # noqa: S607
+            subprocess.run(["git", "init"], check=True)
             console.print(
                 f"[green]Created custom tool [bold]{folder_name}[/bold]. Run [bold]cd {project_root}[/bold] to start working.[/green]"
             )
@@ -76,10 +76,10 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             raise SystemExit()
 
         project_name = get_project_name(require=True)
-        assert isinstance(project_name, str)  # noqa: S101
+        assert isinstance(project_name, str)
 
         project_version = get_project_version(require=True)
-        assert isinstance(project_version, str)  # noqa: S101
+        assert isinstance(project_version, str)
 
         project_description = get_project_description(require=False)
         encoded_tarball = None
@@ -94,8 +94,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         self._print_current_organization()
 
         with tempfile.TemporaryDirectory() as temp_build_dir:
-            subprocess.run(  # noqa: S603
-                ["uv", "build", "--sdist", "--out-dir", temp_build_dir],  # noqa: S607
+            subprocess.run(
+                ["uv", "build", "--sdist", "--out-dir", temp_build_dir],
                 check=True,
                 capture_output=False,
             )
@@ -146,7 +146,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
                 style="bold red",
             )
             raise SystemExit
-        if get_response.status_code != 200:
+        elif get_response.status_code != 200:
             console.print(
                 "Failed to get tool details. Please try again later.", style="bold red"
             )
@@ -196,10 +196,10 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         else:
             add_package_command.extend(["--index", index, tool_handle])
 
-        add_package_result = subprocess.run(  # noqa: S603
+        add_package_result = subprocess.run(
             add_package_command,
             capture_output=False,
-            env=build_env_with_tool_repository_credentials(repository_handle),
+            env=self._build_env_with_credentials(repository_handle),
             text=True,
             check=True,
         )
@@ -221,6 +221,20 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             )
             raise SystemExit
 
+    def _build_env_with_credentials(self, repository_handle: str):
+        repository_handle = repository_handle.upper().replace("-", "_")
+        settings = Settings()
+
+        env = os.environ.copy()
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
+            settings.tool_repository_username or ""
+        )
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
+            settings.tool_repository_password or ""
+        )
+
+        return env
+
     def _print_current_organization(self) -> None:
         settings = Settings()
         if settings.org_uuid:

src/crewai/cli/utils.py

@@ -5,13 +5,12 @@ import sys
 from functools import reduce
 from inspect import getmro, isclass, isfunction, ismethod
 from pathlib import Path
-from typing import Any, get_type_hints
+from typing import Any, Dict, List, get_type_hints
 
 import click
 import tomli
 from rich.console import Console
 
-from crewai.cli.config import Settings
 from crewai.cli.constants import ENV_VARS
 from crewai.crew import Crew
 from crewai.flow import Flow
@@ -42,7 +41,8 @@ def copy_template(src, dst, name, class_name, folder_name):
 def read_toml(file_path: str = "pyproject.toml"):
     """Read the content of a TOML file and return it as a dictionary."""
     with open(file_path, "rb") as f:
-        return tomli.load(f)
+        toml_dict = tomli.load(f)
+    return toml_dict
 
 
 def parse_toml(content):
@@ -77,7 +77,7 @@ def get_project_description(
 
 
 def _get_project_attribute(
-    pyproject_path: str, keys: list[str], require: bool
+    pyproject_path: str, keys: List[str], require: bool
 ) -> Any | None:
     """Get an attribute from the pyproject.toml file."""
     attribute = None
@@ -96,20 +96,16 @@ def _get_project_attribute(
     except FileNotFoundError:
         console.print(f"Error: {pyproject_path} not found.", style="bold red")
     except KeyError:
+        console.print(f"Error: {pyproject_path} is not a valid pyproject.toml file.", style="bold red")
+    except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e:  # type: ignore
         console.print(
-            f"Error: {pyproject_path} is not a valid pyproject.toml file.",
+            f"Error: {pyproject_path} is not a valid TOML file."
+            if sys.version_info >= (3, 11)
+            else f"Error reading the pyproject.toml file: {e}",
             style="bold red",
         )
     except Exception as e:
-        # Handle TOML decode errors for Python 3.11+
-        if sys.version_info >= (3, 11) and isinstance(e, tomllib.TOMLDecodeError):  # type: ignore
-            console.print(
-                f"Error: {pyproject_path} is not a valid TOML file.", style="bold red"
-            )
-        else:
-            console.print(
-                f"Error reading the pyproject.toml file: {e}", style="bold red"
-            )
+        console.print(f"Error reading the pyproject.toml file: {e}", style="bold red")
 
     if require and not attribute:
         console.print(
@@ -121,7 +117,7 @@ def _get_project_attribute(
     return attribute
 
 
-def _get_nested_value(data: dict[str, Any], keys: list[str]) -> Any:
+def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any:
     return reduce(dict.__getitem__, keys, data)
 
 
@@ -300,10 +296,7 @@ def get_crews(crew_path: str = "crew.py", require: bool = False) -> list[Crew]:
                                 try:
                                     crew_instances.extend(fetch_crews(module_attr))
                                 except Exception as e:
-                                    console.print(
-                                        f"Error processing attribute {attr_name}: {e}",
-                                        style="bold red",
-                                    )
+                                    console.print(f"Error processing attribute {attr_name}: {e}", style="bold red")
                                     continue
 
                             # If we found crew instances, break out of the loop
@@ -311,15 +304,12 @@ def get_crews(crew_path: str = "crew.py", require: bool = False) -> list[Crew]:
                                 break
 
                         except Exception as exec_error:
-                            console.print(
-                                f"Error executing module: {exec_error}",
-                                style="bold red",
-                            )
+                            console.print(f"Error executing module: {exec_error}", style="bold red")
 
                     except (ImportError, AttributeError) as e:
                         if require:
                             console.print(
-                                f"Error importing crew from {crew_path}: {e!s}",
+                                f"Error importing crew from {crew_path}: {str(e)}",
                                 style="bold red",
                             )
                         continue
@@ -335,9 +325,9 @@ def get_crews(crew_path: str = "crew.py", require: bool = False) -> list[Crew]:
     except Exception as e:
         if require:
             console.print(
-                f"Unexpected error while loading crew: {e!s}", style="bold red"
+                f"Unexpected error while loading crew: {str(e)}", style="bold red"
             )
-            raise SystemExit from e
+            raise SystemExit
     return crew_instances
 
 
@@ -358,7 +348,8 @@ def get_crew_instance(module_attr) -> Crew | None:
 
     if isinstance(module_attr, Crew):
         return module_attr
-    return None
+    else:
+        return None
 
 
 def fetch_crews(module_attr) -> list[Crew]:
@@ -411,26 +402,11 @@ def extract_available_exports(dir_path: str = "src"):
         return available_exports
 
     except Exception as e:
-        console.print(f"[red]Error: Could not extract tool classes: {e!s}[/red]")
+        console.print(f"[red]Error: Could not extract tool classes: {str(e)}[/red]")
         console.print(
             "Please ensure your project contains valid tools (classes inheriting from BaseTool or functions with @tool decorator)."
         )
-        raise SystemExit(1) from e
-
-
-def build_env_with_tool_repository_credentials(repository_handle: str):
-    repository_handle = repository_handle.upper().replace("-", "_")
-    settings = Settings()
-
-    env = os.environ.copy()
-    env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
-        settings.tool_repository_username or ""
-    )
-    env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
-        settings.tool_repository_password or ""
-    )
-
-    return env
+        raise SystemExit(1)
 
 
 def _load_tools_from_init(init_file: Path) -> list[dict[str, Any]]:
@@ -464,8 +440,8 @@ def _load_tools_from_init(init_file: Path) -> list[dict[str, Any]]:
         ]
 
     except Exception as e:
-        console.print(f"[red]Warning: Could not load {init_file}: {e!s}[/red]")
-        raise SystemExit(1) from e
+        console.print(f"[red]Warning: Could not load {init_file}: {str(e)}[/red]")
+        raise SystemExit(1)
 
     finally:
         sys.modules.pop("temp_module", None)

src/crewai/crew.py

@@ -3,17 +3,26 @@ import json
 import re
 import uuid
 import warnings
-from collections.abc import Callable
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
 from typing import (
     Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Union,
     cast,
 )
 
 from opentelemetry import baggage
 from opentelemetry.context import attach, detach
+
+from crewai.utilities.crew.models import CrewContext
+
 from pydantic import (
     UUID4,
     BaseModel,
@@ -30,15 +39,26 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
 from crewai.crews.crew_output import CrewOutput
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.event_listener import EventListener
-from crewai.events.listeners.tracing.trace_listener import (
-    TraceCollectionListener,
-)
-from crewai.events.listeners.tracing.utils import (
-    is_tracing_enabled,
-    should_auto_collect_first_time_traces,
-)
+from crewai.flow.flow_trackable import FlowTrackable
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.llm import LLM, BaseLLM
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.external.external_memory import ExternalMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.process import Process
+from crewai.security import Fingerprint, SecurityConfig
+from crewai.task import Task
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
+from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.tools.base_tool import BaseTool, Tool
+from crewai.types.usage_metrics import UsageMetrics
+from crewai.utilities import I18N, FileHandler, Logger, RPMController
+from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
+from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
+from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
@@ -50,29 +70,16 @@ from crewai.events.types.crew_events import (
     CrewTrainFailedEvent,
     CrewTrainStartedEvent,
 )
-from crewai.flow.flow_trackable import FlowTrackable
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.llm import LLM, BaseLLM
-from crewai.memory.entity.entity_memory import EntityMemory
-from crewai.memory.external.external_memory import ExternalMemory
-from crewai.memory.long_term.long_term_memory import LongTermMemory
-from crewai.memory.short_term.short_term_memory import ShortTermMemory
-from crewai.process import Process
-from crewai.rag.embeddings.types import EmbedderConfig
-from crewai.rag.types import SearchResult
-from crewai.security import Fingerprint, SecurityConfig
-from crewai.task import Task
-from crewai.tasks.conditional_task import ConditionalTask
-from crewai.tasks.task_output import TaskOutput
-from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import BaseTool, Tool
-from crewai.types.usage_metrics import UsageMetrics
-from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
-from crewai.utilities.crew.models import CrewContext
-from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
-from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.event_listener import EventListener
+from crewai.events.listeners.tracing.trace_listener import (
+    TraceCollectionListener,
+)
+
+
+from crewai.events.listeners.tracing.utils import (
+    is_tracing_enabled,
+)
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
@@ -87,40 +94,28 @@ warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 class Crew(FlowTrackable, BaseModel):
     """
-    Represents a group of agents, defining how they should collaborate and the
-    tasks they should perform.
+    Represents a group of agents, defining how they should collaborate and the tasks they should perform.
 
     Attributes:
-        tasks: list of tasks assigned to the crew.
-        agents: list of agents part of this crew.
+        tasks: List of tasks assigned to the crew.
+        agents: List of agents part of this crew.
         manager_llm: The language model that will run manager agent.
         manager_agent: Custom agent that will be used as manager.
-        memory: Whether the crew should use memory to store memories of it's
-            execution.
-        cache: Whether the crew should use a cache to store the results of the
-            tools execution.
-        function_calling_llm: The language model that will run the tool calling
-            for all the agents.
-        process: The process flow that the crew will follow (e.g., sequential,
-            hierarchical).
+        memory: Whether the crew should use memory to store memories of it's execution.
+        cache: Whether the crew should use a cache to store the results of the tools execution.
+        function_calling_llm: The language model that will run the tool calling for all the agents.
+        process: The process flow that the crew will follow (e.g., sequential, hierarchical).
         verbose: Indicates the verbosity level for logging during execution.
         config: Configuration settings for the crew.
-        max_rpm: Maximum number of requests per minute for the crew execution to
-            be respected.
+        max_rpm: Maximum number of requests per minute for the crew execution to be respected.
         prompt_file: Path to the prompt json file to be used for the crew.
         id: A unique identifier for the crew instance.
-        task_callback: Callback to be executed after each task for every agents
-            execution.
-        step_callback: Callback to be executed after each step for every agents
-            execution.
-        share_crew: Whether you want to share the complete crew information and
-            execution with crewAI to make the library better, and allow us to
-            train models.
+        task_callback: Callback to be executed after each task for every agents execution.
+        step_callback: Callback to be executed after each step for every agents execution.
+        share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
         planning: Plan the crew execution and add the plan to the crew.
-        chat_llm: The language model used for orchestrating chat interactions
-            with the crew.
-        security_config: Security configuration for the crew, including
-            fingerprinting.
+        chat_llm: The language model used for orchestrating chat interactions with the crew.
+        security_config: Security configuration for the crew, including fingerprinting.
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -129,13 +124,13 @@ class Crew(FlowTrackable, BaseModel):
     _logger: Logger = PrivateAttr()
     _file_handler: FileHandler = PrivateAttr()
     _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default=CacheHandler())
-    _short_term_memory: InstanceOf[ShortTermMemory] | None = PrivateAttr()
-    _long_term_memory: InstanceOf[LongTermMemory] | None = PrivateAttr()
-    _entity_memory: InstanceOf[EntityMemory] | None = PrivateAttr()
-    _external_memory: InstanceOf[ExternalMemory] | None = PrivateAttr()
-    _train: bool | None = PrivateAttr(default=False)
-    _train_iteration: int | None = PrivateAttr()
-    _inputs: dict[str, Any] | None = PrivateAttr(default=None)
+    _short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
+    _long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
+    _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
+    _external_memory: Optional[InstanceOf[ExternalMemory]] = PrivateAttr()
+    _train: Optional[bool] = PrivateAttr(default=False)
+    _train_iteration: Optional[int] = PrivateAttr()
+    _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
     _logging_color: str = PrivateAttr(
         default="bold_purple",
     )
@@ -143,121 +138,107 @@ class Crew(FlowTrackable, BaseModel):
         default_factory=TaskOutputStorageHandler
     )
 
-    name: str | None = Field(default="crew")
+    name: Optional[str] = Field(default="crew")
     cache: bool = Field(default=True)
-    tasks: list[Task] = Field(default_factory=list)
-    agents: list[BaseAgent] = Field(default_factory=list)
+    tasks: List[Task] = Field(default_factory=list)
+    agents: List[BaseAgent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
     verbose: bool = Field(default=False)
     memory: bool = Field(
         default=False,
-        description="If crew should use memory to store memories of it's execution",
+        description="Whether the crew should use memory to store memories of it's execution",
     )
-    short_term_memory: InstanceOf[ShortTermMemory] | None = Field(
+    short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
         default=None,
         description="An Instance of the ShortTermMemory to be used by the Crew",
     )
-    long_term_memory: InstanceOf[LongTermMemory] | None = Field(
+    long_term_memory: Optional[InstanceOf[LongTermMemory]] = Field(
         default=None,
         description="An Instance of the LongTermMemory to be used by the Crew",
     )
-    entity_memory: InstanceOf[EntityMemory] | None = Field(
+    entity_memory: Optional[InstanceOf[EntityMemory]] = Field(
         default=None,
         description="An Instance of the EntityMemory to be used by the Crew",
     )
-    external_memory: InstanceOf[ExternalMemory] | None = Field(
+    external_memory: Optional[InstanceOf[ExternalMemory]] = Field(
         default=None,
         description="An Instance of the ExternalMemory to be used by the Crew",
     )
-    embedder: EmbedderConfig | None = Field(
+    embedder: Optional[dict] = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
-    usage_metrics: UsageMetrics | None = Field(
+    usage_metrics: Optional[UsageMetrics] = Field(
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    manager_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
+    manager_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
-    manager_agent: BaseAgent | None = Field(
+    manager_agent: Optional[BaseAgent] = Field(
         description="Custom agent that will be used as manager.", default=None
     )
-    function_calling_llm: str | InstanceOf[LLM] | Any | None = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
-    config: Json | dict[str, Any] | None = Field(default=None)
+    config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
-    share_crew: bool | None = Field(default=False)
-    step_callback: Any | None = Field(
+    share_crew: Optional[bool] = Field(default=False)
+    step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step for all agents execution.",
     )
-    task_callback: Any | None = Field(
+    task_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each task for all agents execution.",
     )
-    before_kickoff_callbacks: list[
-        Callable[[dict[str, Any] | None], dict[str, Any] | None]
+    before_kickoff_callbacks: List[
+        Callable[[Optional[Dict[str, Any]]], Optional[Dict[str, Any]]]
     ] = Field(
         default_factory=list,
-        description=(
-            "List of callbacks to be executed before crew kickoff. "
-            "It may be used to adjust inputs before the crew is executed."
-        ),
+        description="List of callbacks to be executed before crew kickoff. It may be used to adjust inputs before the crew is executed.",
     )
-    after_kickoff_callbacks: list[Callable[[CrewOutput], CrewOutput]] = Field(
+    after_kickoff_callbacks: List[Callable[[CrewOutput], CrewOutput]] = Field(
         default_factory=list,
-        description=(
-            "List of callbacks to be executed after crew kickoff. "
-            "It may be used to adjust the output of the crew."
-        ),
+        description="List of callbacks to be executed after crew kickoff. It may be used to adjust the output of the crew.",
     )
-    max_rpm: int | None = Field(
+    max_rpm: Optional[int] = Field(
         default=None,
-        description=(
-            "Maximum number of requests per minute for the crew execution "
-            "to be respected."
-        ),
+        description="Maximum number of requests per minute for the crew execution to be respected.",
     )
-    prompt_file: str | None = Field(
+    prompt_file: Optional[str] = Field(
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
-    output_log_file: bool | str | None = Field(
+    output_log_file: Optional[Union[bool, str]] = Field(
         default=None,
         description="Path to the log file to be saved",
     )
-    planning: bool | None = Field(
+    planning: Optional[bool] = Field(
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
+    planning_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         default=None,
-        description=(
-            "Language model that will run the AgentPlanner if planning is True."
-        ),
+        description="Language model that will run the AgentPlanner if planning is True.",
     )
-    task_execution_output_json_files: list[str] | None = Field(
+    task_execution_output_json_files: Optional[List[str]] = Field(
         default=None,
-        description="list of file paths for task execution JSON files.",
+        description="List of file paths for task execution JSON files.",
     )
-    execution_logs: list[dict[str, Any]] = Field(
+    execution_logs: List[Dict[str, Any]] = Field(
         default=[],
-        description="list of execution logs for tasks",
+        description="List of execution logs for tasks",
     )
-    knowledge_sources: list[BaseKnowledgeSource] | None = Field(
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
         default=None,
-        description=(
-            "Knowledge sources for the crew. Add knowledge sources to the "
-            "knowledge object."
-        ),
+        description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.",
     )
-    chat_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
+    chat_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
-    knowledge: Knowledge | None = Field(
+    knowledge: Optional[Knowledge] = Field(
         default=None,
         description="Knowledge for the crew.",
     )
@@ -265,18 +246,18 @@ class Crew(FlowTrackable, BaseModel):
         default_factory=SecurityConfig,
         description="Security configuration for the crew, including fingerprinting.",
     )
-    token_usage: UsageMetrics | None = Field(
+    token_usage: Optional[UsageMetrics] = Field(
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    tracing: bool | None = Field(
+    tracing: Optional[bool] = Field(
         default=False,
         description="Whether to enable tracing for the crew.",
     )
 
     @field_validator("id", mode="before")
     @classmethod
-    def _deny_user_set_id(cls, v: UUID4 | None) -> None:
+    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
         """Prevent manual setting of the 'id' field by users."""
         if v:
             raise PydanticCustomError(
@@ -285,7 +266,9 @@ class Crew(FlowTrackable, BaseModel):
 
     @field_validator("config", mode="before")
     @classmethod
-    def check_config_type(cls, v: Json | dict[str, Any]) -> Json | dict[str, Any]:
+    def check_config_type(
+        cls, v: Union[Json, Dict[str, Any]]
+    ) -> Union[Json, Dict[str, Any]]:
         """Validates that the config is a valid type.
         Args:
             v: The config to be validated.
@@ -298,16 +281,12 @@ class Crew(FlowTrackable, BaseModel):
 
     @model_validator(mode="after")
     def set_private_attrs(self) -> "Crew":
-        """set private attributes."""
+        """Set private attributes."""
 
         self._cache_handler = CacheHandler()
         event_listener = EventListener()
 
-        if (
-            is_tracing_enabled()
-            or self.tracing
-            or should_auto_collect_first_time_traces()
-        ):
+        if is_tracing_enabled() or self.tracing:
             trace_listener = TraceCollectionListener()
             trace_listener.setup_listeners(crewai_event_bus)
         event_listener.verbose = self.verbose
@@ -335,8 +314,7 @@ class Crew(FlowTrackable, BaseModel):
     def create_crew_memory(self) -> "Crew":
         """Initialize private memory attributes."""
         self._external_memory = (
-            # External memory does not support a default value since it was
-            # designed to be managed entirely externally
+            # External memory doesn’t support a default value since it was designed to be managed entirely externally
             self.external_memory.set_crew(self) if self.external_memory else None
         )
 
@@ -377,10 +355,7 @@ class Crew(FlowTrackable, BaseModel):
             if not self.manager_llm and not self.manager_agent:
                 raise PydanticCustomError(
                     "missing_manager_llm_or_manager_agent",
-                    (
-                        "Attribute `manager_llm` or `manager_agent` is required "
-                        "when using hierarchical process."
-                    ),
+                    "Attribute `manager_llm` or `manager_agent` is required when using hierarchical process.",
                     {},
                 )
 
@@ -423,10 +398,7 @@ class Crew(FlowTrackable, BaseModel):
                 if task.agent is None:
                     raise PydanticCustomError(
                         "missing_agent_in_task",
-                        (
-                            f"Sequential process error: Agent is missing in the task "
-                            f"with the following description: {task.description}"
-                        ),  # type: ignore # Dynamic string in error message
+                        f"Sequential process error: Agent is missing in the task with the following description: {task.description}",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
                         {},
                     )
 
@@ -487,10 +459,7 @@ class Crew(FlowTrackable, BaseModel):
             if task.async_execution and isinstance(task, ConditionalTask):
                 raise PydanticCustomError(
                     "invalid_async_conditional_task",
-                    (
-                        f"Conditional Task: {task.description}, "
-                        f"cannot be executed asynchronously."
-                    ),
+                    f"Conditional Task: {task.description} , cannot be executed asynchronously.",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
                     {},
                 )
         return self
@@ -509,9 +478,7 @@ class Crew(FlowTrackable, BaseModel):
                         for j in range(i - 1, -1, -1):
                             if self.tasks[j] == context_task:
                                 raise ValueError(
-                                    f"Task '{task.description}' is asynchronous and "
-                                    f"cannot include other sequential asynchronous "
-                                    f"tasks in its context."
+                                    f"Task '{task.description}' is asynchronous and cannot include other sequential asynchronous tasks in its context."
                                 )
                             if not self.tasks[j].async_execution:
                                 break
@@ -529,15 +496,13 @@ class Crew(FlowTrackable, BaseModel):
                         continue  # Skip context tasks not in the main tasks list
                     if task_indices[id(context_task)] > task_indices[id(task)]:
                         raise ValueError(
-                            f"Task '{task.description}' has a context dependency "
-                            f"on a future task '{context_task.description}', "
-                            f"which is not allowed."
+                            f"Task '{task.description}' has a context dependency on a future task '{context_task.description}', which is not allowed."
                         )
         return self
 
     @property
     def key(self) -> str:
-        source: list[str] = [agent.key for agent in self.agents] + [
+        source: List[str] = [agent.key for agent in self.agents] + [
             task.key for task in self.tasks
         ]
         return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
@@ -553,9 +518,9 @@ class Crew(FlowTrackable, BaseModel):
         return self.security_config.fingerprint
 
     def _setup_from_config(self):
+        assert self.config is not None, "Config should not be None."
+
         """Initializes agents and tasks from the provided config."""
-        if self.config is None:
-            raise ValueError("Config should not be None.")
         if not self.config.get("agents") or not self.config.get("tasks"):
             raise PydanticCustomError(
                 "missing_keys_in_config", "Config should have 'agents' and 'tasks'.", {}
@@ -565,7 +530,7 @@ class Crew(FlowTrackable, BaseModel):
         self.agents = [Agent(**agent) for agent in self.config["agents"]]
         self.tasks = [self._create_task(task) for task in self.config["tasks"]]
 
-    def _create_task(self, task_config: dict[str, Any]) -> Task:
+    def _create_task(self, task_config: Dict[str, Any]) -> Task:
         """Creates a task instance from its configuration.
 
         Args:
@@ -594,7 +559,7 @@ class Crew(FlowTrackable, BaseModel):
         CrewTrainingHandler(filename).initialize_file()
 
     def train(
-        self, n_iterations: int, filename: str, inputs: dict[str, Any] | None = None
+        self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = None
     ) -> None:
         """Trains the crew for a given number of iterations."""
         inputs = inputs or {}
@@ -623,8 +588,7 @@ class Crew(FlowTrackable, BaseModel):
                         training_data=training_data, agent_id=str(agent.id)
                     )
                     CrewTrainingHandler(filename).save_trained_data(
-                        agent_id=str(agent.role),
-                        trained_data=result.model_dump(),  # type: ignore[arg-type]
+                        agent_id=str(agent.role), trained_data=result.model_dump()
                     )
 
             crewai_event_bus.emit(
@@ -647,7 +611,7 @@ class Crew(FlowTrackable, BaseModel):
 
     def kickoff(
         self,
-        inputs: dict[str, Any] | None = None,
+        inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
         ctx = baggage.set_baggage(
             "crew_context", CrewContext(id=str(self.id), key=self.key)
@@ -718,9 +682,9 @@ class Crew(FlowTrackable, BaseModel):
         finally:
             detach(token)
 
-    def kickoff_for_each(self, inputs: list[dict[str, Any]]) -> list[CrewOutput]:
-        """Executes the Crew's workflow for each input and aggregates results."""
-        results: list[CrewOutput] = []
+    def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
+        """Executes the Crew's workflow for each input in the list and aggregates results."""
+        results: List[CrewOutput] = []
 
         # Initialize the parent crew's usage metrics
         total_usage_metrics = UsageMetrics()
@@ -739,12 +703,14 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return results
 
-    async def kickoff_async(self, inputs: dict[str, Any] | None = None) -> CrewOutput:
+    async def kickoff_async(
+        self, inputs: Optional[Dict[str, Any]] = None
+    ) -> CrewOutput:
         """Asynchronous kickoff method to start the crew execution."""
         inputs = inputs or {}
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: list[dict]) -> list[CrewOutput]:
+    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[CrewOutput]:
         crew_copies = [self.copy() for _ in inputs]
 
         async def run_crew(crew, input_data):
@@ -773,9 +739,7 @@ class Crew(FlowTrackable, BaseModel):
             tasks=self.tasks, planning_agent_llm=self.planning_llm
         )._handle_crew_planning()
 
-        for task, step_plan in zip(
-            self.tasks, result.list_of_plans_per_task, strict=False
-        ):
+        for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
             task.description += step_plan.plan
 
     def _store_execution_log(
@@ -812,7 +776,7 @@ class Crew(FlowTrackable, BaseModel):
         return self._execute_tasks(self.tasks)
 
     def _run_hierarchical_process(self) -> CrewOutput:
-        """Creates and assigns a manager agent to complete the tasks."""
+        """Creates and assigns a manager agent to make sure the crew completes the tasks."""
         self._create_manager_agent()
         return self._execute_tasks(self.tasks)
 
@@ -843,24 +807,23 @@ class Crew(FlowTrackable, BaseModel):
 
     def _execute_tasks(
         self,
-        tasks: list[Task],
-        start_index: int | None = 0,
+        tasks: List[Task],
+        start_index: Optional[int] = 0,
         was_replayed: bool = False,
     ) -> CrewOutput:
         """Executes tasks sequentially and returns the final output.
 
         Args:
             tasks (List[Task]): List of tasks to execute
-            manager (Optional[BaseAgent], optional): Manager agent to use for
-                delegation. Defaults to None.
+            manager (Optional[BaseAgent], optional): Manager agent to use for delegation. Defaults to None.
 
         Returns:
             CrewOutput: Final output of the crew
         """
 
-        task_outputs: list[TaskOutput] = []
-        futures: list[tuple[Task, Future[TaskOutput], int]] = []
-        last_sync_output: TaskOutput | None = None
+        task_outputs: List[TaskOutput] = []
+        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
+        last_sync_output: Optional[TaskOutput] = None
 
         for task_index, task in enumerate(tasks):
             if start_index is not None and task_index < start_index:
@@ -875,9 +838,7 @@ class Crew(FlowTrackable, BaseModel):
             agent_to_use = self._get_agent_to_use(task)
             if agent_to_use is None:
                 raise ValueError(
-                    f"No agent available for task: {task.description}. "
-                    f"Ensure that either the task has an assigned agent "
-                    f"or a manager agent is provided."
+                    f"No agent available for task: {task.description}. Ensure that either the task has an assigned agent or a manager agent is provided."
                 )
 
             # Determine which tools to use - task tools take precedence over agent tools
@@ -886,7 +847,7 @@ class Crew(FlowTrackable, BaseModel):
             tools_for_task = self._prepare_tools(
                 agent_to_use,
                 task,
-                cast(list[Tool] | list[BaseTool], tools_for_task),
+                cast(Union[List[Tool], List[BaseTool]], tools_for_task),
             )
 
             self._log_task_start(task, agent_to_use.role)
@@ -906,7 +867,7 @@ class Crew(FlowTrackable, BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(list[BaseTool], tools_for_task),
+                    tools=cast(List[BaseTool], tools_for_task),
                 )
                 futures.append((task, future, task_index))
             else:
@@ -918,7 +879,7 @@ class Crew(FlowTrackable, BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(list[BaseTool], tools_for_task),
+                    tools=cast(List[BaseTool], tools_for_task),
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -932,11 +893,11 @@ class Crew(FlowTrackable, BaseModel):
     def _handle_conditional_task(
         self,
         task: ConditionalTask,
-        task_outputs: list[TaskOutput],
-        futures: list[tuple[Task, Future[TaskOutput], int]],
+        task_outputs: List[TaskOutput],
+        futures: List[Tuple[Task, Future[TaskOutput], int]],
         task_index: int,
         was_replayed: bool,
-    ) -> TaskOutput | None:
+    ) -> Optional[TaskOutput]:
         if futures:
             task_outputs = self._process_async_tasks(futures, was_replayed)
             futures.clear()
@@ -956,8 +917,8 @@ class Crew(FlowTrackable, BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+        self, agent: BaseAgent, task: Task, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         # Add delegation tools if agent allows delegation
         if hasattr(agent, "allow_delegation") and getattr(
             agent, "allow_delegation", False
@@ -986,22 +947,22 @@ class Crew(FlowTrackable, BaseModel):
         ):
             tools = self._add_multimodal_tools(agent, tools)
 
-        # Return a List[BaseTool] compatible with Task.execute_sync and execute_async
-        return cast(list[BaseTool], tools)
+        # Return a List[BaseTool] which is compatible with both Task.execute_sync and Task.execute_async
+        return cast(List[BaseTool], tools)
 
-    def _get_agent_to_use(self, task: Task) -> BaseAgent | None:
+    def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
         if self.process == Process.hierarchical:
             return self.manager_agent
         return task.agent
 
     def _merge_tools(
         self,
-        existing_tools: list[Tool] | list[BaseTool],
-        new_tools: list[Tool] | list[BaseTool],
-    ) -> list[BaseTool]:
-        """Merge new tools into existing tools list, avoiding duplicates."""
+        existing_tools: Union[List[Tool], List[BaseTool]],
+        new_tools: Union[List[Tool], List[BaseTool]],
+    ) -> List[BaseTool]:
+        """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
-            return cast(list[BaseTool], existing_tools)
+            return cast(List[BaseTool], existing_tools)
 
         # Create mapping of tool names to new tools
         new_tool_map = {tool.name: tool for tool in new_tools}
@@ -1012,41 +973,41 @@ class Crew(FlowTrackable, BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return cast(list[BaseTool], tools)
+        return cast(List[BaseTool], tools)
 
     def _inject_delegation_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: Union[List[Tool], List[BaseTool]],
         task_agent: BaseAgent,
-        agents: list[BaseAgent],
-    ) -> list[BaseTool]:
+        agents: List[BaseAgent],
+    ) -> List[BaseTool]:
         if hasattr(task_agent, "get_delegation_tools"):
             delegation_tools = task_agent.get_delegation_tools(agents)
             # Cast delegation_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(list[BaseTool], delegation_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, cast(List[BaseTool], delegation_tools))
+        return cast(List[BaseTool], tools)
 
     def _add_multimodal_tools(
-        self, agent: BaseAgent, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+        self, agent: BaseAgent, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         if hasattr(agent, "get_multimodal_tools"):
             multimodal_tools = agent.get_multimodal_tools()
             # Cast multimodal_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(list[BaseTool], multimodal_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, cast(List[BaseTool], multimodal_tools))
+        return cast(List[BaseTool], tools)
 
     def _add_code_execution_tools(
-        self, agent: BaseAgent, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+        self, agent: BaseAgent, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         if hasattr(agent, "get_code_execution_tools"):
             code_tools = agent.get_code_execution_tools()
             # Cast code_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(list[BaseTool], code_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, cast(List[BaseTool], code_tools))
+        return cast(List[BaseTool], tools)
 
     def _add_delegation_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
             if not tools:
@@ -1054,20 +1015,17 @@ class Crew(FlowTrackable, BaseModel):
             tools = self._inject_delegation_tools(
                 tools, task.agent, agents_for_delegation
             )
-        return cast(list[BaseTool], tools)
+        return cast(List[BaseTool], tools)
 
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
             self._file_handler.log(
-                task_name=task.name,  # type: ignore[arg-type]
-                task=task.description,
-                agent=role,
-                status="started",
+                task_name=task.name, task=task.description, agent=role, status="started"
             )
 
     def _update_manager_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+        self, task: Task, tools: Union[List[Tool], List[BaseTool]]
+    ) -> List[BaseTool]:
         if self.manager_agent:
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
@@ -1075,30 +1033,31 @@ class Crew(FlowTrackable, BaseModel):
                 tools = self._inject_delegation_tools(
                     tools, self.manager_agent, self.agents
                 )
-        return cast(list[BaseTool], tools)
+        return cast(List[BaseTool], tools)
 
-    def _get_context(self, task: Task, task_outputs: list[TaskOutput]) -> str:
+    def _get_context(self, task: Task, task_outputs: List[TaskOutput]) -> str:
         if not task.context:
             return ""
 
-        return (
+        context = (
             aggregate_raw_outputs_from_task_outputs(task_outputs)
             if task.context is NOT_SPECIFIED
             else aggregate_raw_outputs_from_tasks(task.context)
         )
+        return context
 
     def _process_task_result(self, task: Task, output: TaskOutput) -> None:
         role = task.agent.role if task.agent is not None else "None"
         if self.output_log_file:
             self._file_handler.log(
-                task_name=task.name,  # type: ignore[arg-type]
+                task_name=task.name,
                 task=task.description,
                 agent=role,
                 status="completed",
                 output=output.raw,
             )
 
-    def _create_crew_output(self, task_outputs: list[TaskOutput]) -> CrewOutput:
+    def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
         if not task_outputs:
             raise ValueError("No task outputs available to create crew output.")
 
@@ -1129,10 +1088,10 @@ class Crew(FlowTrackable, BaseModel):
 
     def _process_async_tasks(
         self,
-        futures: list[tuple[Task, Future[TaskOutput], int]],
+        futures: List[Tuple[Task, Future[TaskOutput], int]],
         was_replayed: bool = False,
-    ) -> list[TaskOutput]:
-        task_outputs: list[TaskOutput] = []
+    ) -> List[TaskOutput]:
+        task_outputs: List[TaskOutput] = []
         for future_task, future, task_index in futures:
             task_output = future.result()
             task_outputs.append(task_output)
@@ -1142,7 +1101,9 @@ class Crew(FlowTrackable, BaseModel):
             )
         return task_outputs
 
-    def _find_task_index(self, task_id: str, stored_outputs: list[Any]) -> int | None:
+    def _find_task_index(
+        self, task_id: str, stored_outputs: List[Any]
+    ) -> Optional[int]:
         return next(
             (
                 index
@@ -1152,8 +1113,9 @@ class Crew(FlowTrackable, BaseModel):
             None,
         )
 
-    def replay(self, task_id: str, inputs: dict[str, Any] | None = None) -> CrewOutput:
-        """Replay the crew execution from a specific task."""
+    def replay(
+        self, task_id: str, inputs: Optional[Dict[str, Any]] = None
+    ) -> CrewOutput:
         stored_outputs = self._task_output_handler.load()
         if not stored_outputs:
             raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
@@ -1189,19 +1151,19 @@ class Crew(FlowTrackable, BaseModel):
             self.tasks[i].output = task_output
 
         self._logging_color = "bold_blue"
-        return self._execute_tasks(self.tasks, start_index, True)
+        result = self._execute_tasks(self.tasks, start_index, True)
+        return result
 
     def query_knowledge(
-        self, query: list[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> list[SearchResult] | None:
-        """Query the crew's knowledge base for relevant information."""
+        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
+    ) -> Union[List[Dict[str, Any]], None]:
         if self.knowledge:
             return self.knowledge.query(
                 query, results_limit=results_limit, score_threshold=score_threshold
             )
         return None
 
-    def fetch_inputs(self) -> set[str]:
+    def fetch_inputs(self) -> Set[str]:
         """
         Gathers placeholders (e.g., {something}) referenced in tasks or agents.
         Scans each task's 'description' + 'expected_output', and each agent's
@@ -1210,11 +1172,11 @@ class Crew(FlowTrackable, BaseModel):
         Returns a set of all discovered placeholder names.
         """
         placeholder_pattern = re.compile(r"\{(.+?)\}")
-        required_inputs: set[str] = set()
+        required_inputs: Set[str] = set()
 
         # Scan tasks for inputs
         for task in self.tasks:
-            # description and expected_output might contain e.g. {topic}, {user_name}
+            # description and expected_output might contain e.g. {topic}, {user_name}, etc.
             text = f"{task.description or ''} {task.expected_output or ''}"
             required_inputs.update(placeholder_pattern.findall(text))
 
@@ -1268,7 +1230,7 @@ class Crew(FlowTrackable, BaseModel):
             cloned_tasks.append(cloned_task)
             task_mapping[task.key] = cloned_task
 
-        for cloned_task, original_task in zip(cloned_tasks, self.tasks, strict=False):
+        for cloned_task, original_task in zip(cloned_tasks, self.tasks):
             if isinstance(original_task.context, list):
                 cloned_context = [
                     task_mapping[context_task.key]
@@ -1294,7 +1256,7 @@ class Crew(FlowTrackable, BaseModel):
         copied_data.pop("agents", None)
         copied_data.pop("tasks", None)
 
-        return Crew(
+        copied_crew = Crew(
             **copied_data,
             agents=cloned_agents,
             tasks=cloned_tasks,
@@ -1304,13 +1266,15 @@ class Crew(FlowTrackable, BaseModel):
             manager_llm=manager_llm,
         )
 
+        return copied_crew
+
     def _set_tasks_callbacks(self) -> None:
         """Sets callback for every task suing task_callback"""
         for task in self.tasks:
             if not task.callback:
                 task.callback = self.task_callback
 
-    def _interpolate_inputs(self, inputs: dict[str, Any]) -> None:
+    def _interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
         """Interpolates the inputs in the tasks and agents."""
         [
             task.interpolate_inputs_and_add_conversation_history(
@@ -1343,13 +1307,10 @@ class Crew(FlowTrackable, BaseModel):
     def test(
         self,
         n_iterations: int,
-        eval_llm: str | InstanceOf[BaseLLM],
-        inputs: dict[str, Any] | None = None,
+        eval_llm: Union[str, InstanceOf[BaseLLM]],
+        inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations.
-
-        Uses concurrent.futures for concurrent execution.
-        """
+        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         try:
             # Create LLM instance and ensure it's of type LLM for CrewEvaluator
             llm_instance = create_llm(eval_llm)
@@ -1389,11 +1350,7 @@ class Crew(FlowTrackable, BaseModel):
             raise
 
     def __repr__(self):
-        return (
-            f"Crew(id={self.id}, process={self.process}, "
-            f"number_of_agents={len(self.agents)}, "
-            f"number_of_tasks={len(self.tasks)})"
-        )
+        return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
 
     def reset_memories(self, command_type: str) -> None:
         """Reset specific or all memories for the crew.
@@ -1407,7 +1364,7 @@ class Crew(FlowTrackable, BaseModel):
             ValueError: If an invalid command type is provided.
             RuntimeError: If memory reset operation fails.
         """
-        valid_types = frozenset(
+        VALID_TYPES = frozenset(
             [
                 "long",
                 "short",
@@ -1420,10 +1377,9 @@ class Crew(FlowTrackable, BaseModel):
             ]
         )
 
-        if command_type not in valid_types:
+        if command_type not in VALID_TYPES:
             raise ValueError(
-                f"Invalid command type. Must be one of: "
-                f"{', '.join(sorted(valid_types))}"
+                f"Invalid command type. Must be one of: {', '.join(sorted(VALID_TYPES))}"
             )
 
         try:
@@ -1433,7 +1389,7 @@ class Crew(FlowTrackable, BaseModel):
                 self._reset_specific_memory(command_type)
 
         except Exception as e:
-            error_msg = f"Failed to reset {command_type} memory: {e!s}"
+            error_msg = f"Failed to reset {command_type} memory: {str(e)}"
             self._logger.log("error", error_msg)
             raise RuntimeError(error_msg) from e
 
@@ -1441,7 +1397,7 @@ class Crew(FlowTrackable, BaseModel):
         """Reset all available memory systems."""
         memory_systems = self._get_memory_systems()
 
-        for config in memory_systems.values():
+        for memory_type, config in memory_systems.items():
             if (system := config.get("system")) is not None:
                 name = config.get("name")
                 try:
@@ -1449,13 +1405,11 @@ class Crew(FlowTrackable, BaseModel):
                     reset_fn(system)
                     self._logger.log(
                         "info",
-                        f"[Crew ({self.name if self.name else self.id})] "
-                        f"{name} memory has been reset",
+                        f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
                     )
                 except Exception as e:
                     raise RuntimeError(
-                        f"[Crew ({self.name if self.name else self.id})] "
-                        f"Failed to reset {name} memory: {e!s}"
+                        f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
                     ) from e
 
     def _reset_specific_memory(self, memory_type: str) -> None:
@@ -1480,21 +1434,18 @@ class Crew(FlowTrackable, BaseModel):
             reset_fn(system)
             self._logger.log(
                 "info",
-                f"[Crew ({self.name if self.name else self.id})] "
-                f"{name} memory has been reset",
+                f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
             )
         except Exception as e:
             raise RuntimeError(
-                f"[Crew ({self.name if self.name else self.id})] "
-                f"Failed to reset {name} memory: {e!s}"
+                f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
             ) from e
 
     def _get_memory_systems(self):
         """Get all available memory systems with their configuration.
 
         Returns:
-            Dict containing all memory systems with their reset functions and
-            display names.
+            Dict containing all memory systems with their reset functions and display names.
         """
 
         def default_reset(memory):
@@ -1555,7 +1506,7 @@ class Crew(FlowTrackable, BaseModel):
             },
         }
 
-    def reset_knowledge(self, knowledges: list[Knowledge]) -> None:
+    def reset_knowledge(self, knowledges: List[Knowledge]) -> None:
         """Reset crew and agent knowledge storage."""
         for ks in knowledges:
             ks.reset()

src/crewai/crews/crew_output.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any
+from typing import Any, Dict, Optional
 
 from pydantic import BaseModel, Field
 
@@ -12,21 +12,19 @@ class CrewOutput(BaseModel):
     """Class that represents the result of a crew."""
 
     raw: str = Field(description="Raw output of crew", default="")
-    pydantic: BaseModel | None = Field(
+    pydantic: Optional[BaseModel] = Field(
         description="Pydantic output of Crew", default=None
     )
-    json_dict: dict[str, Any] | None = Field(
+    json_dict: Optional[Dict[str, Any]] = Field(
         description="JSON dict output of Crew", default=None
     )
     tasks_output: list[TaskOutput] = Field(
         description="Output of each task", default=[]
     )
-    token_usage: UsageMetrics = Field(
-        description="Processed token summary", default_factory=UsageMetrics
-    )
+    token_usage: UsageMetrics = Field(description="Processed token summary", default={})
 
     @property
-    def json(self) -> str | None:  # type: ignore[override]
+    def json(self) -> Optional[str]:
         if self.tasks_output[-1].output_format != OutputFormat.JSON:
             raise ValueError(
                 "No JSON output found in the final task. Please make sure to set the output_json property in the final task in your crew."
@@ -34,7 +32,7 @@ class CrewOutput(BaseModel):
 
         return json.dumps(self.json_dict)
 
-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> Dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""
         output_dict = {}
         if self.json_dict:
@@ -46,9 +44,10 @@ class CrewOutput(BaseModel):
     def __getitem__(self, key):
         if self.pydantic and hasattr(self.pydantic, key):
             return getattr(self.pydantic, key)
-        if self.json_dict and key in self.json_dict:
+        elif self.json_dict and key in self.json_dict:
             return self.json_dict[key]
-        raise KeyError(f"Key '{key}' not found in CrewOutput.")
+        else:
+            raise KeyError(f"Key '{key}' not found in CrewOutput.")
 
     def __str__(self):
         if self.pydantic:

src/crewai/events/__init__.py

@@ -9,158 +9,48 @@ This module provides the event infrastructure that allows users to:
 
 from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.agent_events import (
-    AgentEvaluationCompletedEvent,
-    AgentEvaluationFailedEvent,
-    AgentEvaluationStartedEvent,
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent,
-    LiteAgentExecutionErrorEvent,
-    LiteAgentExecutionStartedEvent,
-)
-from crewai.events.types.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestResultEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTrainStartedEvent,
-)
-from crewai.events.types.flow_events import (
-    FlowCreatedEvent,
-    FlowEvent,
-    FlowFinishedEvent,
-    FlowPlotEvent,
-    FlowStartedEvent,
-    MethodExecutionFailedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
-from crewai.events.types.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
-from crewai.events.types.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMStreamChunkEvent,
-)
-from crewai.events.types.llm_guardrail_events import (
-    LLMGuardrailCompletedEvent,
-    LLMGuardrailStartedEvent,
-)
-from crewai.events.types.logging_events import (
-    AgentLogsExecutionEvent,
-    AgentLogsStartedEvent,
-)
+
 from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
-    MemoryQueryFailedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveStartedEvent,
     MemoryQueryStartedEvent,
     MemoryRetrievalCompletedEvent,
-    MemoryRetrievalStartedEvent,
-    MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
-    MemorySaveStartedEvent,
+    MemoryQueryFailedEvent,
 )
-from crewai.events.types.reasoning_events import (
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-    AgentReasoningStartedEvent,
-    ReasoningEvent,
+
+from crewai.events.types.knowledge_events import (
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
 )
-from crewai.events.types.task_events import (
-    TaskCompletedEvent,
-    TaskEvaluationEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
+
+from crewai.events.types.crew_events import (
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
 )
-from crewai.events.types.tool_usage_events import (
-    ToolExecutionErrorEvent,
-    ToolSelectionErrorEvent,
-    ToolUsageErrorEvent,
-    ToolUsageEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-    ToolValidateInputErrorEvent,
+from crewai.events.types.agent_events import (
+    AgentExecutionCompletedEvent,
+)
+
+from crewai.events.types.llm_events import (
+    LLMStreamChunkEvent,
 )
 
 __all__ = [
-    "AgentEvaluationCompletedEvent",
-    "AgentEvaluationFailedEvent",
-    "AgentEvaluationStartedEvent",
-    "AgentExecutionCompletedEvent",
-    "AgentExecutionErrorEvent",
-    "AgentExecutionStartedEvent",
-    "AgentLogsExecutionEvent",
-    "AgentLogsStartedEvent",
-    "AgentReasoningCompletedEvent",
-    "AgentReasoningFailedEvent",
-    "AgentReasoningStartedEvent",
     "BaseEventListener",
-    "CrewKickoffCompletedEvent",
-    "CrewKickoffFailedEvent",
-    "CrewKickoffStartedEvent",
-    "CrewTestCompletedEvent",
-    "CrewTestFailedEvent",
-    "CrewTestResultEvent",
-    "CrewTestStartedEvent",
-    "CrewTrainCompletedEvent",
-    "CrewTrainFailedEvent",
-    "CrewTrainStartedEvent",
-    "FlowCreatedEvent",
-    "FlowEvent",
-    "FlowFinishedEvent",
-    "FlowPlotEvent",
-    "FlowStartedEvent",
-    "KnowledgeQueryCompletedEvent",
-    "KnowledgeQueryFailedEvent",
-    "KnowledgeQueryStartedEvent",
-    "KnowledgeRetrievalCompletedEvent",
-    "KnowledgeRetrievalStartedEvent",
-    "KnowledgeSearchQueryFailedEvent",
-    "LLMCallCompletedEvent",
-    "LLMCallFailedEvent",
-    "LLMCallStartedEvent",
-    "LLMGuardrailCompletedEvent",
-    "LLMGuardrailStartedEvent",
-    "LLMStreamChunkEvent",
-    "LiteAgentExecutionCompletedEvent",
-    "LiteAgentExecutionErrorEvent",
-    "LiteAgentExecutionStartedEvent",
+    "crewai_event_bus",
     "MemoryQueryCompletedEvent",
-    "MemoryQueryFailedEvent",
+    "MemorySaveCompletedEvent",
+    "MemorySaveStartedEvent",
     "MemoryQueryStartedEvent",
     "MemoryRetrievalCompletedEvent",
-    "MemoryRetrievalStartedEvent",
-    "MemorySaveCompletedEvent",
     "MemorySaveFailedEvent",
-    "MemorySaveStartedEvent",
-    "MethodExecutionFailedEvent",
-    "MethodExecutionFinishedEvent",
-    "MethodExecutionStartedEvent",
-    "ReasoningEvent",
-    "TaskCompletedEvent",
-    "TaskEvaluationEvent",
-    "TaskFailedEvent",
-    "TaskStartedEvent",
-    "ToolExecutionErrorEvent",
-    "ToolSelectionErrorEvent",
-    "ToolUsageErrorEvent",
-    "ToolUsageEvent",
-    "ToolUsageFinishedEvent",
-    "ToolUsageStartedEvent",
-    "ToolValidateInputErrorEvent",
-    "crewai_event_bus",
-]
+    "MemoryQueryFailedEvent",
+    "KnowledgeRetrievalStartedEvent",
+    "KnowledgeRetrievalCompletedEvent",
+    "CrewKickoffStartedEvent",
+    "CrewKickoffCompletedEvent",
+    "AgentExecutionCompletedEvent",
+    "LLMStreamChunkEvent",
+]

src/crewai/events/base_events.py

@@ -1,6 +1,5 @@
 from datetime import datetime, timezone
-from typing import Any
-
+from typing import Any, Dict, Optional
 from pydantic import BaseModel, Field
 
 from crewai.utilities.serialization import to_serializable
@@ -11,11 +10,11 @@ class BaseEvent(BaseModel):
 
     timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     type: str
-    source_fingerprint: str | None = None  # UUID string of the source entity
-    source_type: str | None = (
+    source_fingerprint: Optional[str] = None  # UUID string of the source entity
+    source_type: Optional[str] = (
         None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
     )
-    fingerprint_metadata: dict[str, Any] | None = None  # Any relevant metadata
+    fingerprint_metadata: Optional[Dict[str, Any]] = None  # Any relevant metadata
 
     def to_json(self, exclude: set[str] | None = None):
         """
@@ -29,13 +28,13 @@ class BaseEvent(BaseModel):
         """
         return to_serializable(self, exclude=exclude)
 
-    def _set_task_params(self, data: dict[str, Any]):
+    def _set_task_params(self, data: Dict[str, Any]):
         if "from_task" in data and (task := data["from_task"]):
             self.task_id = task.id
             self.task_name = task.name or task.description
             self.from_task = None
 
-    def _set_agent_params(self, data: dict[str, Any]):
+    def _set_agent_params(self, data: Dict[str, Any]):
         task = data.get("from_task", None)
         agent = task.agent if task else data.get("from_agent", None)
 

src/crewai/events/event_bus.py

@@ -1,9 +1,8 @@
 from __future__ import annotations
 
 import threading
-from collections.abc import Callable
 from contextlib import contextmanager
-from typing import Any, TypeVar, cast
+from typing import Any, Callable, Dict, List, Type, TypeVar, cast
 
 from blinker import Signal
 
@@ -26,17 +25,17 @@ class CrewAIEventsBus:
         if cls._instance is None:
             with cls._lock:
                 if cls._instance is None:  # prevent race condition
-                    cls._instance = super().__new__(cls)
+                    cls._instance = super(CrewAIEventsBus, cls).__new__(cls)
                     cls._instance._initialize()
         return cls._instance
 
     def _initialize(self) -> None:
         """Initialize the event bus internal state"""
         self._signal = Signal("crewai_event_bus")
-        self._handlers: dict[type[BaseEvent], list[Callable]] = {}
+        self._handlers: Dict[Type[BaseEvent], List[Callable]] = {}
 
     def on(
-        self, event_type: type[EventT]
+        self, event_type: Type[EventT]
     ) -> Callable[[Callable[[Any, EventT], None]], Callable[[Any, EventT], None]]:
         """
         Decorator to register an event handler for a specific event type.
@@ -62,18 +61,6 @@ class CrewAIEventsBus:
 
         return decorator
 
-    @staticmethod
-    def _call_handler(
-        handler: Callable, source: Any, event: BaseEvent, event_type: type
-    ) -> None:
-        """Call a single handler with error handling."""
-        try:
-            handler(source, event)
-        except Exception as e:
-            print(
-                f"[EventBus Error] Handler '{handler.__name__}' failed for event '{event_type.__name__}': {e}"
-            )
-
     def emit(self, source: Any, event: BaseEvent) -> None:
         """
         Emit an event to all registered handlers
@@ -85,12 +72,17 @@ class CrewAIEventsBus:
         for event_type, handlers in self._handlers.items():
             if isinstance(event, event_type):
                 for handler in handlers:
-                    self._call_handler(handler, source, event, event_type)
+                    try:
+                        handler(source, event)
+                    except Exception as e:
+                        print(
+                            f"[EventBus Error] Handler '{handler.__name__}' failed for event '{event_type.__name__}': {e}"
+                        )
 
         self._signal.send(source, event=event)
 
     def register_handler(
-        self, event_type: type[EventTypes], handler: Callable[[Any, EventTypes], None]
+        self, event_type: Type[EventTypes], handler: Callable[[Any, EventTypes], None]
     ) -> None:
         """Register an event handler for a specific event type"""
         if event_type not in self._handlers:

src/crewai/events/event_listener.py

@@ -1,30 +1,15 @@
 from __future__ import annotations
 
 from io import StringIO
-from typing import Any
+from typing import Any, Dict
 
 from pydantic import Field, PrivateAttr
-
+from crewai.llm import LLM
+from crewai.task import Task
+from crewai.telemetry.telemetry import Telemetry
+from crewai.utilities import Logger
+from crewai.utilities.constants import EMITTER_COLOR
 from crewai.events.base_event_listener import BaseEventListener
-from crewai.events.types.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent,
-    LiteAgentExecutionErrorEvent,
-    LiteAgentExecutionStartedEvent,
-)
-from crewai.events.types.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestResultEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTrainStartedEvent,
-)
 from crewai.events.types.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
@@ -40,21 +25,34 @@ from crewai.events.types.llm_events import (
     LLMStreamChunkEvent,
 )
 from crewai.events.types.llm_guardrail_events import (
-    LLMGuardrailCompletedEvent,
     LLMGuardrailStartedEvent,
-)
-from crewai.events.types.logging_events import (
-    AgentLogsExecutionEvent,
-    AgentLogsStartedEvent,
+    LLMGuardrailCompletedEvent,
 )
 from crewai.events.utils.console_formatter import ConsoleFormatter
-from crewai.llm import LLM
-from crewai.task import Task
-from crewai.telemetry.telemetry import Telemetry
-from crewai.utilities import Logger
-from crewai.utilities.constants import EMITTER_COLOR
 
-from .listeners.memory_listener import MemoryListener
+from crewai.events.types.agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionStartedEvent,
+    LiteAgentExecutionCompletedEvent,
+    LiteAgentExecutionErrorEvent,
+    LiteAgentExecutionStartedEvent,
+)
+from crewai.events.types.logging_events import (
+    AgentLogsStartedEvent,
+    AgentLogsExecutionEvent,
+)
+from crewai.events.types.crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTestResultEvent,
+    CrewTestStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    CrewTrainStartedEvent,
+)
 from .types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
@@ -63,24 +61,26 @@ from .types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from .types.reasoning_events import (
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-    AgentReasoningStartedEvent,
-)
 from .types.task_events import TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent
 from .types.tool_usage_events import (
     ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from .types.reasoning_events import (
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+)
+
+from .listeners.memory_listener import MemoryListener
 
 
 class EventListener(BaseEventListener):
     _instance = None
     _telemetry: Telemetry = PrivateAttr(default_factory=lambda: Telemetry())
     logger = Logger(verbose=True, default_color=EMITTER_COLOR)
-    execution_spans: dict[Task, Any] = Field(default_factory=dict)
+    execution_spans: Dict[Task, Any] = Field(default_factory=dict)
     next_chunk = 0
     text_stream = StringIO()
     knowledge_retrieval_in_progress = False

src/crewai/events/event_types.py

@@ -1,10 +1,11 @@
+from typing import Union
+
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
     LiteAgentExecutionCompletedEvent,
 )
-
 from .types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
@@ -23,14 +24,6 @@ from .types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from .types.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 from .types.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
@@ -41,21 +34,6 @@ from .types.llm_guardrail_events import (
     LLMGuardrailCompletedEvent,
     LLMGuardrailStartedEvent,
 )
-from .types.memory_events import (
-    MemoryQueryCompletedEvent,
-    MemoryQueryFailedEvent,
-    MemoryQueryStartedEvent,
-    MemoryRetrievalCompletedEvent,
-    MemoryRetrievalStartedEvent,
-    MemorySaveCompletedEvent,
-    MemorySaveFailedEvent,
-    MemorySaveStartedEvent,
-)
-from .types.reasoning_events import (
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-    AgentReasoningStartedEvent,
-)
 from .types.task_events import (
     TaskCompletedEvent,
     TaskFailedEvent,
@@ -66,53 +44,77 @@ from .types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-
-EventTypes = (
-    CrewKickoffStartedEvent
-    | CrewKickoffCompletedEvent
-    | CrewKickoffFailedEvent
-    | CrewTestStartedEvent
-    | CrewTestCompletedEvent
-    | CrewTestFailedEvent
-    | CrewTrainStartedEvent
-    | CrewTrainCompletedEvent
-    | CrewTrainFailedEvent
-    | AgentExecutionStartedEvent
-    | AgentExecutionCompletedEvent
-    | LiteAgentExecutionCompletedEvent
-    | TaskStartedEvent
-    | TaskCompletedEvent
-    | TaskFailedEvent
-    | FlowStartedEvent
-    | FlowFinishedEvent
-    | MethodExecutionStartedEvent
-    | MethodExecutionFinishedEvent
-    | MethodExecutionFailedEvent
-    | AgentExecutionErrorEvent
-    | ToolUsageFinishedEvent
-    | ToolUsageErrorEvent
-    | ToolUsageStartedEvent
-    | LLMCallStartedEvent
-    | LLMCallCompletedEvent
-    | LLMCallFailedEvent
-    | LLMStreamChunkEvent
-    | LLMGuardrailStartedEvent
-    | LLMGuardrailCompletedEvent
-    | AgentReasoningStartedEvent
-    | AgentReasoningCompletedEvent
-    | AgentReasoningFailedEvent
-    | KnowledgeRetrievalStartedEvent
-    | KnowledgeRetrievalCompletedEvent
-    | KnowledgeQueryStartedEvent
-    | KnowledgeQueryCompletedEvent
-    | KnowledgeQueryFailedEvent
-    | KnowledgeSearchQueryFailedEvent
-    | MemorySaveStartedEvent
-    | MemorySaveCompletedEvent
-    | MemorySaveFailedEvent
-    | MemoryQueryStartedEvent
-    | MemoryQueryCompletedEvent
-    | MemoryQueryFailedEvent
-    | MemoryRetrievalStartedEvent
-    | MemoryRetrievalCompletedEvent
+from .types.reasoning_events import (
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
 )
+from .types.knowledge_events import (
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
+
+from .types.memory_events import (
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
+)
+
+EventTypes = Union[
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewTestStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTrainStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    AgentExecutionStartedEvent,
+    AgentExecutionCompletedEvent,
+    LiteAgentExecutionCompletedEvent,
+    TaskStartedEvent,
+    TaskCompletedEvent,
+    TaskFailedEvent,
+    FlowStartedEvent,
+    FlowFinishedEvent,
+    MethodExecutionStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionFailedEvent,
+    AgentExecutionErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+    LLMCallStartedEvent,
+    LLMCallCompletedEvent,
+    LLMCallFailedEvent,
+    LLMStreamChunkEvent,
+    LLMGuardrailStartedEvent,
+    LLMGuardrailCompletedEvent,
+    AgentReasoningStartedEvent,
+    AgentReasoningCompletedEvent,
+    AgentReasoningFailedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeSearchQueryFailedEvent,
+    MemorySaveStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveFailedEvent,
+    MemoryQueryStartedEvent,
+    MemoryQueryCompletedEvent,
+    MemoryQueryFailedEvent,
+    MemoryRetrievalStartedEvent,
+    MemoryRetrievalCompletedEvent,
+]

src/crewai/events/listeners/__init__.py

@@ -2,4 +2,4 @@
 
 This module contains various event listener implementations
 for handling memory, tracing, and other event-driven functionality.
-"""
+"""

src/crewai/events/listeners/memory_listener.py

@@ -1,12 +1,12 @@
 from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.types.memory_events import (
-    MemoryQueryCompletedEvent,
-    MemoryQueryFailedEvent,
     MemoryRetrievalCompletedEvent,
     MemoryRetrievalStartedEvent,
+    MemoryQueryFailedEvent,
+    MemoryQueryCompletedEvent,
+    MemorySaveStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
-    MemorySaveStartedEvent,
 )
 
 

src/crewai/events/listeners/tracing/first_time_trace_handler.py

@@ -1,229 +0,0 @@
-import logging
-import uuid
-import webbrowser
-from pathlib import Path
-
-from rich.console import Console
-from rich.panel import Panel
-
-from crewai.events.listeners.tracing.trace_batch_manager import TraceBatchManager
-from crewai.events.listeners.tracing.utils import (
-    mark_first_execution_completed,
-    prompt_user_for_trace_viewing,
-    should_auto_collect_first_time_traces,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def _update_or_create_env_file():
-    """Update or create .env file with CREWAI_TRACING_ENABLED=true."""
-    env_path = Path(".env")
-    env_content = ""
-    variable_name = "CREWAI_TRACING_ENABLED"
-    variable_value = "true"
-
-    # Read existing content if file exists
-    if env_path.exists():
-        with open(env_path, "r") as f:
-            env_content = f.read()
-
-    # Check if CREWAI_TRACING_ENABLED is already set
-    lines = env_content.splitlines()
-    variable_exists = False
-    updated_lines = []
-
-    for line in lines:
-        if line.strip().startswith(f"{variable_name}="):
-            # Update existing variable
-            updated_lines.append(f"{variable_name}={variable_value}")
-            variable_exists = True
-        else:
-            updated_lines.append(line)
-
-    # Add variable if it doesn't exist
-    if not variable_exists:
-        if updated_lines and not updated_lines[-1].strip():
-            # If last line is empty, replace it
-            updated_lines[-1] = f"{variable_name}={variable_value}"
-        else:
-            # Add new line and then the variable
-            updated_lines.append(f"{variable_name}={variable_value}")
-
-    # Write updated content
-    with open(env_path, "w") as f:
-        f.write("\n".join(updated_lines))
-        if updated_lines:  # Add final newline if there's content
-            f.write("\n")
-
-
-class FirstTimeTraceHandler:
-    """Handles the first-time user trace collection and display flow."""
-
-    def __init__(self):
-        self.is_first_time: bool = False
-        self.collected_events: bool = False
-        self.trace_batch_id: str | None = None
-        self.ephemeral_url: str | None = None
-        self.batch_manager: TraceBatchManager | None = None
-
-    def initialize_for_first_time_user(self) -> bool:
-        """Check if this is first time and initialize collection."""
-        self.is_first_time = should_auto_collect_first_time_traces()
-        return self.is_first_time
-
-    def set_batch_manager(self, batch_manager: TraceBatchManager):
-        """Set reference to batch manager for sending events."""
-        self.batch_manager = batch_manager
-
-    def mark_events_collected(self):
-        """Mark that events have been collected during execution."""
-        self.collected_events = True
-
-    def handle_execution_completion(self):
-        """Handle the completion flow as shown in your diagram."""
-        if not self.is_first_time or not self.collected_events:
-            return
-
-        try:
-            user_wants_traces = prompt_user_for_trace_viewing(timeout_seconds=20)
-
-            if user_wants_traces:
-                self._initialize_backend_and_send_events()
-
-                # Enable tracing for future runs by updating .env file
-                try:
-                    _update_or_create_env_file()
-                except Exception:  # noqa: S110
-                    pass
-
-                if self.ephemeral_url:
-                    self._display_ephemeral_trace_link()
-
-            mark_first_execution_completed()
-
-        except Exception as e:
-            self._gracefully_fail(f"Error in trace handling: {e}")
-            mark_first_execution_completed()
-
-    def _initialize_backend_and_send_events(self):
-        """Initialize backend batch and send collected events."""
-        if not self.batch_manager:
-            return
-
-        try:
-            if not self.batch_manager.backend_initialized:
-                original_metadata = (
-                    self.batch_manager.current_batch.execution_metadata
-                    if self.batch_manager.current_batch
-                    else {}
-                )
-
-                user_context = {
-                    "privacy_level": "standard",
-                    "user_id": "first_time_user",
-                    "session_id": str(uuid.uuid4()),
-                    "trace_id": self.batch_manager.trace_batch_id,
-                }
-
-                execution_metadata = {
-                    "execution_type": original_metadata.get("execution_type", "crew"),
-                    "crew_name": original_metadata.get(
-                        "crew_name", "First Time Execution"
-                    ),
-                    "flow_name": original_metadata.get("flow_name"),
-                    "agent_count": original_metadata.get("agent_count", 1),
-                    "task_count": original_metadata.get("task_count", 1),
-                    "crewai_version": original_metadata.get("crewai_version"),
-                }
-
-                self.batch_manager._initialize_backend_batch(
-                    user_context=user_context,
-                    execution_metadata=execution_metadata,
-                    use_ephemeral=True,
-                )
-                self.batch_manager.backend_initialized = True
-
-            if self.batch_manager.event_buffer:
-                self.batch_manager._send_events_to_backend()
-
-            self.batch_manager.finalize_batch()
-            self.ephemeral_url = self.batch_manager.ephemeral_trace_url
-
-            if not self.ephemeral_url:
-                self._show_local_trace_message()
-
-        except Exception as e:
-            self._gracefully_fail(f"Backend initialization failed: {e}")
-
-    def _display_ephemeral_trace_link(self):
-        """Display the ephemeral trace link to the user and automatically open browser."""
-        console = Console()
-
-        try:
-            webbrowser.open(self.ephemeral_url)
-        except Exception:  # noqa: S110
-            pass
-
-        panel_content = f"""
-🎉 Your First CrewAI Execution Trace is Ready!
-
-View your execution details here:
-{self.ephemeral_url}
-
-This trace shows:
-• Agent decisions and interactions
-• Task execution timeline
-• Tool usage and results
-• LLM calls and responses
-
-✅ Tracing has been enabled for future runs! (CREWAI_TRACING_ENABLED=true added to .env)
-You can also add tracing=True to your Crew(tracing=True) / Flow(tracing=True) for more control.
-
-📝 Note: This link will expire in 24 hours.
-        """.strip()
-
-        panel = Panel(
-            panel_content,
-            title="🔍 Execution Trace Generated",
-            border_style="bright_green",
-            padding=(1, 2),
-        )
-
-        console.print("\n")
-        console.print(panel)
-        console.print()
-
-    def _gracefully_fail(self, error_message: str):
-        """Handle errors gracefully without disrupting user experience."""
-        console = Console()
-        console.print(f"[yellow]Note: {error_message}[/yellow]")
-
-        logger.debug(f"First-time trace error: {error_message}")
-
-    def _show_local_trace_message(self):
-        """Show message when traces were collected locally but couldn't be uploaded."""
-        console = Console()
-
-        panel_content = f"""
-📊 Your execution traces were collected locally!
-
-Unfortunately, we couldn't upload them to the server right now, but here's what we captured:
-• {len(self.batch_manager.event_buffer)} trace events
-• Execution duration: {self.batch_manager.calculate_duration("execution")}ms
-• Batch ID: {self.batch_manager.trace_batch_id}
-
-Tracing has been enabled for future runs! (CREWAI_TRACING_ENABLED=true added to .env)
-The traces include agent decisions, task execution, and tool usage.
-    """.strip()
-
-        panel = Panel(
-            panel_content,
-            title="🔍 Local Traces Collected",
-            border_style="yellow",
-            padding=(1, 2),
-        )
-
-        console.print("\n")
-        console.print(panel)
-        console.print()

src/crewai/events/listeners/tracing/trace_batch_manager.py

@@ -2,16 +2,17 @@ import uuid
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from logging import getLogger
-from typing import Any
+from typing import Any, ClassVar
 
+from rich.align import Align
 from rich.console import Console
 from rich.panel import Panel
+from rich.text import Text
 
 from crewai.cli.authentication.token import AuthError, get_auth_token
 from crewai.cli.plus_api import PlusAPI
 from crewai.cli.version import get_crewai_version
 from crewai.events.listeners.tracing.types import TraceEvent
-from crewai.events.listeners.tracing.utils import should_auto_collect_first_time_traces
 from crewai.utilities.constants import CREWAI_BASE_URL
 
 logger = getLogger(__name__)
@@ -40,23 +41,21 @@ class TraceBatch:
 class TraceBatchManager:
     """Single responsibility: Manage batches and event buffering"""
 
+    is_current_batch_ephemeral: bool = False
+    trace_batch_id: str | None = None
+    current_batch: TraceBatch | None = None
+    event_buffer: ClassVar[list[TraceEvent]] = []
+    execution_start_times: ClassVar[dict[str, datetime]] = {}
+    batch_owner_type: str | None = None
+    batch_owner_id: str | None = None
+
     def __init__(self):
-        self.is_current_batch_ephemeral: bool = False
-        self.trace_batch_id: str | None = None
-        self.current_batch: TraceBatch | None = None
-        self.event_buffer: list[TraceEvent] = []
-        self.execution_start_times: dict[str, datetime] = {}
-        self.batch_owner_type: str | None = None
-        self.batch_owner_id: str | None = None
-        self.backend_initialized: bool = False
-        self.ephemeral_trace_url: str | None = None
         try:
             self.plus_api = PlusAPI(
                 api_key=get_auth_token(),
             )
         except AuthError:
             self.plus_api = PlusAPI(api_key="")
-        self.ephemeral_trace_url = None
 
     def initialize_batch(
         self,
@@ -72,14 +71,7 @@ class TraceBatchManager:
         self.is_current_batch_ephemeral = use_ephemeral
 
         self.record_start_time("execution")
-
-        if should_auto_collect_first_time_traces():
-            self.trace_batch_id = self.current_batch.batch_id
-        else:
-            self._initialize_backend_batch(
-                user_context, execution_metadata, use_ephemeral
-            )
-            self.backend_initialized = True
+        self._initialize_backend_batch(user_context, execution_metadata, use_ephemeral)
 
         return self.current_batch
 
@@ -138,6 +130,13 @@ class TraceBatchManager:
                     if not use_ephemeral
                     else response_data["ephemeral_trace_id"]
                 )
+                console = Console()
+                panel = Panel(
+                    f"✅ Trace batch initialized with session ID: {self.trace_batch_id}",
+                    title="Trace Batch Initialization",
+                    border_style="green",
+                )
+                console.print(panel)
             else:
                 logger.warning(
                     f"Trace batch initialization returned status {response.status_code}. Continuing without tracing."
@@ -145,7 +144,7 @@ class TraceBatchManager:
 
         except Exception as e:
             logger.warning(
-                f"Error initializing trace batch: {e}. Continuing without tracing."
+                f"Error initializing trace batch: {e!s}. Continuing without tracing."
             )
 
     def add_event(self, trace_event: TraceEvent):
@@ -156,6 +155,7 @@ class TraceBatchManager:
         """Send buffered events to backend with graceful failure handling"""
         if not self.plus_api or not self.trace_batch_id or not self.event_buffer:
             return 500
+
         try:
             payload = {
                 "events": [event.to_dict() for event in self.event_buffer],
@@ -179,7 +179,6 @@ class TraceBatchManager:
             if response.status_code in [200, 201]:
                 self.event_buffer.clear()
                 return 200
-
             logger.warning(
                 f"Failed to send events: {response.status_code}. Events will be lost."
             )
@@ -187,7 +186,7 @@ class TraceBatchManager:
 
         except Exception as e:
             logger.warning(
-                f"Error sending events to backend: {e}. Events will be lost."
+                f"Error sending events to backend: {e!s}. Events will be lost."
             )
             return 500
 
@@ -200,9 +199,6 @@ class TraceBatchManager:
         if self.event_buffer:
             events_sent_to_backend_status = self._send_events_to_backend()
             if events_sent_to_backend_status == 500:
-                self.plus_api.mark_trace_batch_as_failed(
-                    self.trace_batch_id, "Error sending events to backend"
-                )
                 return None
         self._finalize_backend_batch()
 
@@ -250,39 +246,67 @@ class TraceBatchManager:
                     if not self.is_current_batch_ephemeral and access_code is None
                     else f"{CREWAI_BASE_URL}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
                 )
-
-                if self.is_current_batch_ephemeral:
-                    self.ephemeral_trace_url = return_link
-
-                # Create a properly formatted message with URL on its own line
-                message_parts = [
-                    f"✅ Trace batch finalized with session ID: {self.trace_batch_id}",
-                    "",
-                    f"🔗 View here: {return_link}",
-                ]
-
-                if access_code:
-                    message_parts.append(f"🔑 Access Code: {access_code}")
-
-                panel = Panel(
-                    "\n".join(message_parts),
-                    title="Trace Batch Finalization",
-                    border_style="green",
-                )
-                if not should_auto_collect_first_time_traces():
-                    console.print(panel)
+                self._display_traces_events_link(console, return_link, access_code)
 
             else:
                 logger.error(
                     f"❌ Failed to finalize trace batch: {response.status_code} - {response.text}"
                 )
-                self.plus_api.mark_trace_batch_as_failed(
-                    self.trace_batch_id, response.text
-                )
 
         except Exception as e:
-            logger.error(f"❌ Error finalizing trace batch: {e}")
-            self.plus_api.mark_trace_batch_as_failed(self.trace_batch_id, str(e))
+            logger.error(f"❌ Error finalizing trace batch: {e!s}")
+            # TODO: send error to app
+
+    def _display_traces_events_link(
+        self, console: Console, return_link: str, access_code: str | None = None
+    ):
+        """Display trace batch finalization information"""
+        try:
+            final_text = Text()
+            final_text.append("🎊", style="bold bright_yellow")
+            final_text.append(" TRACES READY FOR VIEWING! ", style="bold bright_green")
+            final_text.append("🎊", style="bold bright_yellow")
+            final_text.append("\n\n")
+
+            final_text.append("Trace ID: ", style="bold bright_cyan")
+            final_text.append(
+                f"{self.trace_batch_id}",
+                style="bright_blue",
+            )
+            final_text.append("\n\n")
+
+            final_text.append("View Your Traces: ", style="bold bright_cyan")
+            final_text.append(f"{return_link}", style="bright_white on red")
+
+            if access_code:
+                final_text.append("\n\n")
+                final_text.append("Access Code: ", style="bold bright_cyan")
+                final_text.append(f"{access_code}", style="bright_blue")
+
+            final_text.append("\n\n")
+            final_text.append("💡 ", style="bright_yellow")
+            final_text.append(
+                "Click the link above to dive into your agentic automation traces!",
+                style="italic bright_white",
+            )
+
+            final_panel = Panel(
+                Align.center(final_text),
+                title="🎊 Your Traces Are Ready! 🎊",
+                style="bright_green",
+                expand=True,
+                padding=(2, 4),
+            )
+            console.print(final_panel)
+
+        except Exception as e:
+            logger.warning(f"Display failed, falling back to simple display: {e!s}")
+            fallback_panel = Panel(
+                f"✅ Trace batch finalized with session ID: {self.trace_batch_id}. View here: {return_link} {f', Access Code: {access_code}' if access_code else ''}",
+                title="Trace Batch Finalization",
+                border_style="green",
+            )
+            console.print(fallback_panel)
 
     def _cleanup_batch_data(self):
         """Clean up batch data after successful finalization to free memory"""
@@ -299,7 +323,7 @@ class TraceBatchManager:
                 self.batch_sequence = 0
 
         except Exception as e:
-            logger.error(f"Warning: Error during cleanup: {e}")
+            logger.error(f"Warning: Error during cleanup: {e!s}")
 
     def has_events(self) -> bool:
         """Check if there are events in the buffer"""

src/crewai/events/listeners/tracing/trace_listener.py

@@ -5,11 +5,7 @@ from typing import Any, ClassVar
 from crewai.cli.authentication.token import AuthError, get_auth_token
 from crewai.cli.version import get_crewai_version
 from crewai.events.base_event_listener import BaseEventListener
-from crewai.events.listeners.tracing.first_time_trace_handler import (
-    FirstTimeTraceHandler,
-)
 from crewai.events.listeners.tracing.types import TraceEvent
-from crewai.events.listeners.tracing.utils import safe_serialize_to_dict
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
@@ -64,6 +60,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from crewai.utilities.serialization import to_serializable
 
 from .trace_batch_manager import TraceBatchManager
 
@@ -86,7 +83,7 @@ class TraceCollectionListener(BaseEventListener):
     _initialized = False
     _listeners_setup = False
 
-    def __new__(cls, batch_manager: TraceBatchManager | None = None):
+    def __new__(cls, batch_manager=None):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
         return cls._instance
@@ -101,10 +98,6 @@ class TraceCollectionListener(BaseEventListener):
         super().__init__()
         self.batch_manager = batch_manager or TraceBatchManager()
         self._initialized = True
-        self.first_time_handler = FirstTimeTraceHandler()
-
-        if self.first_time_handler.initialize_for_first_time_user():
-            self.first_time_handler.set_batch_manager(self.batch_manager)
 
     def _check_authenticated(self) -> bool:
         """Check if tracing should be enabled"""
@@ -162,14 +155,8 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(FlowFinishedEvent)
         def on_flow_finished(source, event):
             self._handle_trace_event("flow_finished", source, event)
-
             if self.batch_manager.batch_owner_type == "flow":
-                if self.first_time_handler.is_first_time:
-                    self.first_time_handler.mark_events_collected()
-                    self.first_time_handler.handle_execution_completion()
-                else:
-                    # Normal flow finalization
-                    self.batch_manager.finalize_batch()
+                self.batch_manager.finalize_batch()
 
         @event_bus.on(FlowPlotEvent)
         def on_flow_plot(source, event):
@@ -188,20 +175,12 @@ class TraceCollectionListener(BaseEventListener):
         def on_crew_completed(source, event):
             self._handle_trace_event("crew_kickoff_completed", source, event)
             if self.batch_manager.batch_owner_type == "crew":
-                if self.first_time_handler.is_first_time:
-                    self.first_time_handler.mark_events_collected()
-                    self.first_time_handler.handle_execution_completion()
-                else:
-                    self.batch_manager.finalize_batch()
+                self.batch_manager.finalize_batch()
 
         @event_bus.on(CrewKickoffFailedEvent)
         def on_crew_failed(source, event):
             self._handle_trace_event("crew_kickoff_failed", source, event)
-            if self.first_time_handler.is_first_time:
-                self.first_time_handler.mark_events_collected()
-                self.first_time_handler.handle_execution_completion()
-            else:
-                self.batch_manager.finalize_batch()
+            self.batch_manager.finalize_batch()
 
         @event_bus.on(TaskStartedEvent)
         def on_task_started(source, event):
@@ -342,17 +321,15 @@ class TraceCollectionListener(BaseEventListener):
     def _initialize_batch(
         self, user_context: dict[str, str], execution_metadata: dict[str, Any]
     ):
-        """Initialize trace batch - auto-enable ephemeral for first-time users."""
-
-        if self.first_time_handler.is_first_time:
-            return self.batch_manager.initialize_batch(
+        """Initialize trace batch if ephemeral"""
+        if not self._check_authenticated():
+            self.batch_manager.initialize_batch(
                 user_context, execution_metadata, use_ephemeral=True
             )
-
-        use_ephemeral = not self._check_authenticated()
-        return self.batch_manager.initialize_batch(
-            user_context, execution_metadata, use_ephemeral=use_ephemeral
-        )
+        else:
+            self.batch_manager.initialize_batch(
+                user_context, execution_metadata, use_ephemeral=False
+            )
 
     def _handle_trace_event(self, event_type: str, source: Any, event: Any):
         """Generic handler for context end events"""
@@ -391,7 +368,7 @@ class TraceCollectionListener(BaseEventListener):
     ) -> dict[str, Any]:
         """Build event data"""
         if event_type not in self.complex_events:
-            return safe_serialize_to_dict(event)
+            return self._safe_serialize_to_dict(event)
         if event_type == "task_started":
             return {
                 "task_description": event.task.description,
@@ -427,7 +404,7 @@ class TraceCollectionListener(BaseEventListener):
                 "agent_backstory": event.agent.backstory,
             }
         if event_type == "llm_call_started":
-            event_data = safe_serialize_to_dict(event)
+            event_data = self._safe_serialize_to_dict(event)
             event_data["task_name"] = (
                 event.task_name or event.task_description
                 if hasattr(event, "task_name") and event.task_name
@@ -435,10 +412,22 @@ class TraceCollectionListener(BaseEventListener):
             )
             return event_data
         if event_type == "llm_call_completed":
-            return safe_serialize_to_dict(event)
-
+            return self._safe_serialize_to_dict(event)
         return {
             "event_type": event_type,
-            "event": safe_serialize_to_dict(event),
+            "event": self._safe_serialize_to_dict(event),
             "source": source,
         }
+
+    # TODO: move to utils
+    def _safe_serialize_to_dict(
+        self, obj, exclude: set[str] | None = None
+    ) -> dict[str, Any]:
+        """Safely serialize an object to a dictionary for event data."""
+        try:
+            serialized = to_serializable(obj, exclude)
+            if isinstance(serialized, dict):
+                return serialized
+            return {"serialized_data": serialized}
+        except Exception as e:
+            return {"serialization_error": str(e), "object_type": type(obj).__name__}

src/crewai/events/listeners/tracing/types.py

@@ -1,7 +1,7 @@
-import uuid
-from dataclasses import asdict, dataclass, field
+from dataclasses import dataclass, field, asdict
 from datetime import datetime, timezone
-from typing import Any
+from typing import Dict, Any
+import uuid
 
 
 @dataclass
@@ -13,7 +13,7 @@ class TraceEvent:
         default_factory=lambda: datetime.now(timezone.utc).isoformat()
     )
     type: str = ""
-    event_data: dict[str, Any] = field(default_factory=dict)
+    event_data: Dict[str, Any] = field(default_factory=dict)
 
-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> Dict[str, Any]:
         return asdict(self)

src/crewai/events/listeners/tracing/utils.py

@@ -1,25 +1,17 @@
-import getpass
-import hashlib
-import json
-import logging
 import os
 import platform
-import re
-import subprocess
 import uuid
-from datetime import datetime
+import hashlib
+import subprocess
+import getpass
 from pathlib import Path
-from typing import Any
+from datetime import datetime
+import re
+import json
 
 import click
-from rich.console import Console
-from rich.panel import Panel
-from rich.text import Text
 
 from crewai.utilities.paths import db_storage_path
-from crewai.utilities.serialization import to_serializable
-
-logger = logging.getLogger(__name__)
 
 
 def is_tracing_enabled() -> bool:
@@ -51,167 +43,49 @@ def _get_machine_id() -> str:
 
     try:
         mac = ":".join(
-            [f"{(uuid.getnode() >> b) & 0xFF:02x}" for b in range(0, 12, 2)][::-1]
+            ["{:02x}".format((uuid.getnode() >> b) & 0xFF) for b in range(0, 12, 2)][
+                ::-1
+            ]
         )
         parts.append(mac)
-    except Exception:  # noqa: S110
+    except Exception:
         pass
 
-    try:
-        sysname = platform.system()
-        parts.append(sysname)
-    except Exception:
-        sysname = "unknown"
-        parts.append(sysname)
+    sysname = platform.system()
+    parts.append(sysname)
 
     try:
         if sysname == "Darwin":
-            try:
-                res = subprocess.run(
-                    ["/usr/sbin/system_profiler", "SPHardwareDataType"],
-                    capture_output=True,
-                    text=True,
-                    timeout=2,
-                )
-                m = re.search(r"Hardware UUID:\s*([A-Fa-f0-9\-]+)", res.stdout)
-                if m:
-                    parts.append(m.group(1))
-            except Exception:  # noqa: S110
-                pass
-
+            res = subprocess.run(
+                ["system_profiler", "SPHardwareDataType"],
+                capture_output=True,
+                text=True,
+                timeout=2,
+            )
+            m = re.search(r"Hardware UUID:\s*([A-Fa-f0-9\-]+)", res.stdout)
+            if m:
+                parts.append(m.group(1))
         elif sysname == "Linux":
-            linux_id = _get_linux_machine_id()
-            if linux_id:
-                parts.append(linux_id)
-
-        elif sysname == "Windows":
             try:
-                res = subprocess.run(
-                    [
-                        "C:\\Windows\\System32\\wbem\\wmic.exe",
-                        "csproduct",
-                        "get",
-                        "UUID",
-                    ],
-                    capture_output=True,
-                    text=True,
-                    timeout=2,
-                )
-                lines = [
-                    line.strip() for line in res.stdout.splitlines() if line.strip()
-                ]
-                if len(lines) >= 2:
-                    parts.append(lines[1])
-            except Exception:  # noqa: S110
-                pass
-        else:
-            generic_id = _get_generic_system_id()
-            if generic_id:
-                parts.append(generic_id)
-
-    except Exception:  # noqa: S110
+                parts.append(Path("/etc/machine-id").read_text().strip())
+            except Exception:
+                parts.append(Path("/sys/class/dmi/id/product_uuid").read_text().strip())
+        elif sysname == "Windows":
+            res = subprocess.run(
+                ["wmic", "csproduct", "get", "UUID"],
+                capture_output=True,
+                text=True,
+                timeout=2,
+            )
+            lines = [line.strip() for line in res.stdout.splitlines() if line.strip()]
+            if len(lines) >= 2:
+                parts.append(lines[1])
+    except Exception:
         pass
 
-    if len(parts) <= 1:
-        try:
-            import socket
-
-            parts.append(socket.gethostname())
-        except Exception:  # noqa: S110
-            pass
-
-        try:
-            parts.append(getpass.getuser())
-        except Exception:  # noqa: S110
-            pass
-
-        try:
-            parts.append(platform.machine())
-            parts.append(platform.processor())
-        except Exception:  # noqa: S110
-            pass
-
-    if not parts:
-        parts.append("unknown-system")
-        parts.append(str(uuid.uuid4()))
-
     return hashlib.sha256("".join(parts).encode()).hexdigest()
 
 
-def _get_linux_machine_id() -> str | None:
-    linux_id_sources = [
-        "/etc/machine-id",
-        "/sys/class/dmi/id/product_uuid",
-        "/proc/sys/kernel/random/boot_id",
-        "/sys/class/dmi/id/board_serial",
-        "/sys/class/dmi/id/chassis_serial",
-    ]
-
-    for source in linux_id_sources:
-        try:
-            path = Path(source)
-            if path.exists() and path.is_file():
-                content = path.read_text().strip()
-                if content and content.lower() not in [
-                    "unknown",
-                    "to be filled by o.e.m.",
-                    "",
-                ]:
-                    return content
-        except Exception:  # noqa: S112, PERF203
-            continue
-
-    try:
-        import socket
-
-        hostname = socket.gethostname()
-        arch = platform.machine()
-        if hostname and arch:
-            return f"{hostname}-{arch}"
-    except Exception:  # noqa: S110
-        pass
-
-    return None
-
-
-def _get_generic_system_id() -> str | None:
-    try:
-        parts = []
-
-        try:
-            import socket
-
-            hostname = socket.gethostname()
-            if hostname:
-                parts.append(hostname)
-        except Exception:  # noqa: S110
-            pass
-
-        try:
-            parts.append(platform.machine())
-            parts.append(platform.processor())
-            parts.append(platform.architecture()[0])
-        except Exception:  # noqa: S110
-            pass
-
-        try:
-            container_id = os.environ.get(
-                "HOSTNAME", os.environ.get("CONTAINER_ID", "")
-            )
-            if container_id:
-                parts.append(container_id)
-        except Exception:  # noqa: S110
-            pass
-
-        if parts:
-            return "-".join(filter(None, parts))
-
-    except Exception:  # noqa: S110
-        pass
-
-    return None
-
-
 def _user_data_file() -> Path:
     base = Path(db_storage_path())
     base.mkdir(parents=True, exist_ok=True)
@@ -223,8 +97,8 @@ def _load_user_data() -> dict:
     if p.exists():
         try:
             return json.loads(p.read_text())
-        except (json.JSONDecodeError, OSError, PermissionError) as e:
-            logger.warning(f"Failed to load user data: {e}")
+        except Exception:
+            pass
     return {}
 
 
@@ -232,8 +106,8 @@ def _save_user_data(data: dict) -> None:
     try:
         p = _user_data_file()
         p.write_text(json.dumps(data, indent=2))
-    except (OSError, PermissionError) as e:
-        logger.warning(f"Failed to save user data: {e}")
+    except Exception:
+        pass
 
 
 def get_user_id() -> str:
@@ -277,103 +151,3 @@ def mark_first_execution_done() -> None:
         }
     )
     _save_user_data(data)
-
-
-def safe_serialize_to_dict(obj, exclude: set[str] | None = None) -> dict[str, Any]:
-    """Safely serialize an object to a dictionary for event data."""
-    try:
-        serialized = to_serializable(obj, exclude)
-        if isinstance(serialized, dict):
-            return serialized
-        return {"serialized_data": serialized}
-    except Exception as e:
-        return {"serialization_error": str(e), "object_type": type(obj).__name__}
-
-
-def truncate_messages(messages, max_content_length=500, max_messages=5):
-    """Truncate message content and limit number of messages"""
-    if not messages or not isinstance(messages, list):
-        return messages
-
-    limited_messages = messages[:max_messages]
-
-    for msg in limited_messages:
-        if isinstance(msg, dict) and "content" in msg:
-            content = msg["content"]
-            if len(content) > max_content_length:
-                msg["content"] = content[:max_content_length] + "..."
-
-    return limited_messages
-
-
-def should_auto_collect_first_time_traces() -> bool:
-    """True if we should auto-collect traces for first-time user."""
-    if _is_test_environment():
-        return False
-    return is_first_execution()
-
-
-def prompt_user_for_trace_viewing(timeout_seconds: int = 20) -> bool:
-    """
-    Prompt user if they want to see their traces with timeout.
-    Returns True if user wants to see traces, False otherwise.
-    """
-    if _is_test_environment():
-        return False
-
-    try:
-        import threading
-
-        console = Console()
-
-        content = Text()
-        content.append("🔍 ", style="cyan bold")
-        content.append(
-            "Detailed execution traces are available!\n\n", style="cyan bold"
-        )
-        content.append("View insights including:\n", style="white")
-        content.append("  • Agent decision-making process\n", style="bright_blue")
-        content.append("  • Task execution flow and timing\n", style="bright_blue")
-        content.append("  • Tool usage details", style="bright_blue")
-
-        panel = Panel(
-            content,
-            title="[bold cyan]Execution Traces[/bold cyan]",
-            border_style="cyan",
-            padding=(1, 2),
-        )
-        console.print("\n")
-        console.print(panel)
-
-        prompt_text = click.style(
-            f"Would you like to view your execution traces? [y/N] ({timeout_seconds}s timeout): ",
-            fg="white",
-            bold=True,
-        )
-        click.echo(prompt_text, nl=False)
-
-        result = [False]
-
-        def get_input():
-            try:
-                response = input().strip().lower()
-                result[0] = response in ["y", "yes"]
-            except (EOFError, KeyboardInterrupt):
-                result[0] = False
-
-        input_thread = threading.Thread(target=get_input, daemon=True)
-        input_thread.start()
-        input_thread.join(timeout=timeout_seconds)
-
-        if input_thread.is_alive():
-            return False
-
-        return result[0]
-
-    except Exception:
-        return False
-
-
-def mark_first_execution_completed() -> None:
-    """Mark first execution as completed (called after trace prompt)."""
-    mark_first_execution_done()

src/crewai/events/types/__init__.py

@@ -2,4 +2,4 @@
 
 This module contains all event types used throughout the CrewAI system
 for monitoring and extending agent, crew, task, and tool execution.
-"""
+"""

src/crewai/events/types/agent_events.py

@@ -2,15 +2,14 @@
 
 from __future__ import annotations
 
-from collections.abc import Sequence
-from typing import Any
+from typing import Any, Dict, List, Optional, Sequence, Union
 
-from pydantic import ConfigDict, model_validator
+from pydantic import model_validator
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.events.base_events import BaseEvent
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
+from crewai.events.base_events import BaseEvent
 
 
 class AgentExecutionStartedEvent(BaseEvent):
@@ -18,11 +17,11 @@ class AgentExecutionStartedEvent(BaseEvent):
 
     agent: BaseAgent
     task: Any
-    tools: Sequence[BaseTool | CrewStructuredTool] | None
+    tools: Optional[Sequence[Union[BaseTool, CrewStructuredTool]]]
     task_prompt: str
     type: str = "agent_execution_started"
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}
 
     @model_validator(mode="after")
     def set_fingerprint_data(self):
@@ -46,7 +45,7 @@ class AgentExecutionCompletedEvent(BaseEvent):
     output: str
     type: str = "agent_execution_completed"
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}
 
     @model_validator(mode="after")
     def set_fingerprint_data(self):
@@ -70,7 +69,7 @@ class AgentExecutionErrorEvent(BaseEvent):
     error: str
     type: str = "agent_execution_error"
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}
 
     @model_validator(mode="after")
     def set_fingerprint_data(self):
@@ -90,18 +89,18 @@ class AgentExecutionErrorEvent(BaseEvent):
 class LiteAgentExecutionStartedEvent(BaseEvent):
     """Event emitted when a LiteAgent starts executing"""
 
-    agent_info: dict[str, Any]
-    tools: Sequence[BaseTool | CrewStructuredTool] | None
-    messages: str | list[dict[str, str]]
+    agent_info: Dict[str, Any]
+    tools: Optional[Sequence[Union[BaseTool, CrewStructuredTool]]]
+    messages: Union[str, List[Dict[str, str]]]
     type: str = "lite_agent_execution_started"
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}
 
 
 class LiteAgentExecutionCompletedEvent(BaseEvent):
     """Event emitted when a LiteAgent completes execution"""
 
-    agent_info: dict[str, Any]
+    agent_info: Dict[str, Any]
     output: str
     type: str = "lite_agent_execution_completed"
 
@@ -109,7 +108,7 @@ class LiteAgentExecutionCompletedEvent(BaseEvent):
 class LiteAgentExecutionErrorEvent(BaseEvent):
     """Event emitted when a LiteAgent encounters an error during execution"""
 
-    agent_info: dict[str, Any]
+    agent_info: Dict[str, Any]
     error: str
     type: str = "lite_agent_execution_error"
 

src/crewai/events/types/crew_events.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
 
 from crewai.events.base_events import BaseEvent
 
@@ -11,8 +11,8 @@ else:
 class CrewBaseEvent(BaseEvent):
     """Base class for crew events with fingerprint handling"""
 
-    crew_name: str | None
-    crew: Crew | None = None
+    crew_name: Optional[str]
+    crew: Optional[Crew] = None
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -38,7 +38,7 @@ class CrewBaseEvent(BaseEvent):
 class CrewKickoffStartedEvent(CrewBaseEvent):
     """Event emitted when a crew starts execution"""
 
-    inputs: dict[str, Any] | None
+    inputs: Optional[Dict[str, Any]]
     type: str = "crew_kickoff_started"
 
 
@@ -62,7 +62,7 @@ class CrewTrainStartedEvent(CrewBaseEvent):
 
     n_iterations: int
     filename: str
-    inputs: dict[str, Any] | None
+    inputs: Optional[Dict[str, Any]]
     type: str = "crew_train_started"
 
 
@@ -85,8 +85,8 @@ class CrewTestStartedEvent(CrewBaseEvent):
     """Event emitted when a crew starts testing"""
 
     n_iterations: int
-    eval_llm: str | Any | None
-    inputs: dict[str, Any] | None
+    eval_llm: Optional[Union[str, Any]]
+    inputs: Optional[Dict[str, Any]]
     type: str = "crew_test_started"
 
 

src/crewai/events/types/flow_events.py

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Dict, Optional, Union
 
 from pydantic import BaseModel, ConfigDict
 
@@ -16,7 +16,7 @@ class FlowStartedEvent(FlowEvent):
     """Event emitted when a flow starts execution"""
 
     flow_name: str
-    inputs: dict[str, Any] | None = None
+    inputs: Optional[Dict[str, Any]] = None
     type: str = "flow_started"
 
 
@@ -32,8 +32,8 @@ class MethodExecutionStartedEvent(FlowEvent):
 
     flow_name: str
     method_name: str
-    state: dict[str, Any] | BaseModel
-    params: dict[str, Any] | None = None
+    state: Union[Dict[str, Any], BaseModel]
+    params: Optional[Dict[str, Any]] = None
     type: str = "method_execution_started"
 
 
@@ -43,7 +43,7 @@ class MethodExecutionFinishedEvent(FlowEvent):
     flow_name: str
     method_name: str
     result: Any = None
-    state: dict[str, Any] | BaseModel
+    state: Union[Dict[str, Any], BaseModel]
     type: str = "method_execution_finished"
 
 
@@ -62,7 +62,7 @@ class FlowFinishedEvent(FlowEvent):
     """Event emitted when a flow completes execution"""
 
     flow_name: str
-    result: Any | None = None
+    result: Optional[Any] = None
     type: str = "flow_finished"
 
 

src/crewai/events/types/knowledge_events.py

@@ -1,6 +1,7 @@
-from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.events.base_events import BaseEvent
 
+from crewai.agents.agent_builder.base_agent import BaseAgent
+
 
 class KnowledgeRetrievalStartedEvent(BaseEvent):
     """Event emitted when a knowledge retrieval is started."""

src/crewai/events/types/llm_events.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any
+from typing import Any, Dict, List, Optional, Union
 
 from pydantic import BaseModel
 
@@ -7,14 +7,14 @@ from crewai.events.base_events import BaseEvent
 
 
 class LLMEventBase(BaseEvent):
-    task_name: str | None = None
-    task_id: str | None = None
+    task_name: Optional[str] = None
+    task_id: Optional[str] = None
 
-    agent_id: str | None = None
-    agent_role: str | None = None
+    agent_id: Optional[str] = None
+    agent_role: Optional[str] = None
 
-    from_task: Any | None = None
-    from_agent: Any | None = None
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -38,11 +38,11 @@ class LLMCallStartedEvent(LLMEventBase):
     """
 
     type: str = "llm_call_started"
-    model: str | None = None
-    messages: str | list[dict[str, Any]] | None = None
-    tools: list[dict[str, Any]] | None = None
-    callbacks: list[Any] | None = None
-    available_functions: dict[str, Any] | None = None
+    model: Optional[str] = None
+    messages: Optional[Union[str, List[Dict[str, Any]]]] = None
+    tools: Optional[List[dict[str, Any]]] = None
+    callbacks: Optional[List[Any]] = None
+    available_functions: Optional[Dict[str, Any]] = None
 
 
 class LLMCallCompletedEvent(LLMEventBase):
@@ -52,7 +52,7 @@ class LLMCallCompletedEvent(LLMEventBase):
     messages: str | list[dict[str, Any]] | None = None
     response: Any
     call_type: LLMCallType
-    model: str | None = None
+    model: Optional[str] = None
 
 
 class LLMCallFailedEvent(LLMEventBase):
@@ -64,13 +64,13 @@ class LLMCallFailedEvent(LLMEventBase):
 
 class FunctionCall(BaseModel):
     arguments: str
-    name: str | None = None
+    name: Optional[str] = None
 
 
 class ToolCall(BaseModel):
-    id: str | None = None
+    id: Optional[str] = None
     function: FunctionCall
-    type: str | None = None
+    type: Optional[str] = None
     index: int
 
 
@@ -79,4 +79,4 @@ class LLMStreamChunkEvent(LLMEventBase):
 
     type: str = "llm_stream_chunk"
     chunk: str
-    tool_call: ToolCall | None = None
+    tool_call: Optional[ToolCall] = None

src/crewai/events/types/llm_guardrail_events.py

@@ -1,6 +1,5 @@
-from collections.abc import Callable
 from inspect import getsource
-from typing import Any
+from typing import Any, Callable, Optional, Union
 
 from crewai.events.base_events import BaseEvent
 
@@ -14,12 +13,12 @@ class LLMGuardrailStartedEvent(BaseEvent):
     """
 
     type: str = "llm_guardrail_started"
-    guardrail: str | Callable
+    guardrail: Union[str, Callable]
     retry_count: int
 
     def __init__(self, **data):
-        from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
         from crewai.tasks.llm_guardrail import LLMGuardrail
+        from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 
         super().__init__(**data)
 
@@ -42,5 +41,5 @@ class LLMGuardrailCompletedEvent(BaseEvent):
     type: str = "llm_guardrail_completed"
     success: bool
     result: Any
-    error: str | None = None
+    error: Optional[str] = None
     retry_count: int

src/crewai/events/types/logging_events.py

@@ -1,8 +1,6 @@
 """Agent logging events that don't reference BaseAgent to avoid circular imports."""
 
-from typing import Any
-
-from pydantic import ConfigDict
+from typing import Any, Optional
 
 from crewai.events.base_events import BaseEvent
 
@@ -11,7 +9,7 @@ class AgentLogsStartedEvent(BaseEvent):
     """Event emitted when agent logs should be shown at start"""
 
     agent_role: str
-    task_description: str | None = None
+    task_description: Optional[str] = None
     verbose: bool = False
     type: str = "agent_logs_started"
 
@@ -24,4 +22,4 @@ class AgentLogsExecutionEvent(BaseEvent):
     verbose: bool = False
     type: str = "agent_logs_execution"
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}

src/crewai/events/types/memory_events.py

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Dict, Optional
 
 from crewai.events.base_events import BaseEvent
 
@@ -7,12 +7,12 @@ class MemoryBaseEvent(BaseEvent):
     """Base event for memory operations"""
 
     type: str
-    task_id: str | None = None
-    task_name: str | None = None
-    from_task: Any | None = None
-    from_agent: Any | None = None
-    agent_role: str | None = None
-    agent_id: str | None = None
+    task_id: Optional[str] = None
+    task_name: Optional[str] = None
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
+    agent_role: Optional[str] = None
+    agent_id: Optional[str] = None
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -26,7 +26,7 @@ class MemoryQueryStartedEvent(MemoryBaseEvent):
     type: str = "memory_query_started"
     query: str
     limit: int
-    score_threshold: float | None = None
+    score_threshold: Optional[float] = None
 
 
 class MemoryQueryCompletedEvent(MemoryBaseEvent):
@@ -36,7 +36,7 @@ class MemoryQueryCompletedEvent(MemoryBaseEvent):
     query: str
     results: Any
     limit: int
-    score_threshold: float | None = None
+    score_threshold: Optional[float] = None
     query_time_ms: float
 
 
@@ -46,7 +46,7 @@ class MemoryQueryFailedEvent(MemoryBaseEvent):
     type: str = "memory_query_failed"
     query: str
     limit: int
-    score_threshold: float | None = None
+    score_threshold: Optional[float] = None
     error: str
 
 
@@ -54,9 +54,9 @@ class MemorySaveStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is started"""
 
     type: str = "memory_save_started"
-    value: str | None = None
-    metadata: dict[str, Any] | None = None
-    agent_role: str | None = None
+    value: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+    agent_role: Optional[str] = None
 
 
 class MemorySaveCompletedEvent(MemoryBaseEvent):
@@ -64,8 +64,8 @@ class MemorySaveCompletedEvent(MemoryBaseEvent):
 
     type: str = "memory_save_completed"
     value: str
-    metadata: dict[str, Any] | None = None
-    agent_role: str | None = None
+    metadata: Optional[Dict[str, Any]] = None
+    agent_role: Optional[str] = None
     save_time_ms: float
 
 
@@ -73,9 +73,9 @@ class MemorySaveFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation fails"""
 
     type: str = "memory_save_failed"
-    value: str | None = None
-    metadata: dict[str, Any] | None = None
-    agent_role: str | None = None
+    value: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+    agent_role: Optional[str] = None
     error: str
 
 
@@ -83,13 +83,13 @@ class MemoryRetrievalStartedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt starts"""
 
     type: str = "memory_retrieval_started"
-    task_id: str | None = None
+    task_id: Optional[str] = None
 
 
 class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt completes successfully"""
 
     type: str = "memory_retrieval_completed"
-    task_id: str | None = None
+    task_id: Optional[str] = None
     memory_content: str
     retrieval_time_ms: float

src/crewai/events/types/reasoning_events.py

@@ -1,6 +1,5 @@
-from typing import Any
-
 from crewai.events.base_events import BaseEvent
+from typing import Any, Optional
 
 
 class ReasoningEvent(BaseEvent):
@@ -10,10 +9,10 @@ class ReasoningEvent(BaseEvent):
     attempt: int = 1
     agent_role: str
     task_id: str
-    task_name: str | None = None
-    from_task: Any | None = None
-    agent_id: str | None = None
-    from_agent: Any | None = None
+    task_name: Optional[str] = None
+    from_task: Optional[Any] = None
+    agent_id: Optional[str] = None
+    from_agent: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)

src/crewai/events/types/task_events.py

@@ -1,15 +1,15 @@
-from typing import Any
+from typing import Any, Optional
 
-from crewai.events.base_events import BaseEvent
 from crewai.tasks.task_output import TaskOutput
+from crewai.events.base_events import BaseEvent
 
 
 class TaskStartedEvent(BaseEvent):
     """Event emitted when a task starts"""
 
     type: str = "task_started"
-    context: str | None
-    task: Any | None = None
+    context: Optional[str]
+    task: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -29,7 +29,7 @@ class TaskCompletedEvent(BaseEvent):
 
     output: TaskOutput
     type: str = "task_completed"
-    task: Any | None = None
+    task: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -49,7 +49,7 @@ class TaskFailedEvent(BaseEvent):
 
     error: str
     type: str = "task_failed"
-    task: Any | None = None
+    task: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -69,7 +69,7 @@ class TaskEvaluationEvent(BaseEvent):
 
     type: str = "task_evaluation"
     evaluation_type: str
-    task: Any | None = None
+    task: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)

src/crewai/events/types/tool_usage_events.py

@@ -1,8 +1,5 @@
-from collections.abc import Callable
 from datetime import datetime
-from typing import Any
-
-from pydantic import ConfigDict
+from typing import Any, Callable, Dict, Optional
 
 from crewai.events.base_events import BaseEvent
 
@@ -10,21 +7,21 @@ from crewai.events.base_events import BaseEvent
 class ToolUsageEvent(BaseEvent):
     """Base event for tool usage tracking"""
 
-    agent_key: str | None = None
-    agent_role: str | None = None
-    agent_id: str | None = None
+    agent_key: Optional[str] = None
+    agent_role: Optional[str] = None
+    agent_id: Optional[str] = None
     tool_name: str
-    tool_args: dict[str, Any] | str
-    tool_class: str | None = None
+    tool_args: Dict[str, Any] | str
+    tool_class: Optional[str] = None
     run_attempts: int | None = None
     delegations: int | None = None
-    agent: Any | None = None
-    task_name: str | None = None
-    task_id: str | None = None
-    from_task: Any | None = None
-    from_agent: Any | None = None
+    agent: Optional[Any] = None
+    task_name: Optional[str] = None
+    task_id: Optional[str] = None
+    from_task: Optional[Any] = None
+    from_agent: Optional[Any] = None
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = {"arbitrary_types_allowed": True}
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -84,9 +81,9 @@ class ToolExecutionErrorEvent(BaseEvent):
     error: Any
     type: str = "tool_execution_error"
     tool_name: str
-    tool_args: dict[str, Any]
+    tool_args: Dict[str, Any]
     tool_class: Callable
-    agent: Any | None = None
+    agent: Optional[Any] = None
 
     def __init__(self, **data):
         super().__init__(**data)

src/crewai/events/utils/console_formatter.py

@@ -1,25 +1,25 @@
-from typing import Any, ClassVar
+from typing import Any, Dict, Optional
 
 from rich.console import Console
-from rich.live import Live
 from rich.panel import Panel
-from rich.syntax import Syntax
 from rich.text import Text
 from rich.tree import Tree
+from rich.live import Live
+from rich.syntax import Syntax
 
 
 class ConsoleFormatter:
-    current_crew_tree: Tree | None = None
-    current_task_branch: Tree | None = None
-    current_agent_branch: Tree | None = None
-    current_tool_branch: Tree | None = None
-    current_flow_tree: Tree | None = None
-    current_method_branch: Tree | None = None
-    current_lite_agent_branch: Tree | None = None
-    tool_usage_counts: ClassVar[dict[str, int]] = {}
-    current_reasoning_branch: Tree | None = None  # Track reasoning status
+    current_crew_tree: Optional[Tree] = None
+    current_task_branch: Optional[Tree] = None
+    current_agent_branch: Optional[Tree] = None
+    current_tool_branch: Optional[Tree] = None
+    current_flow_tree: Optional[Tree] = None
+    current_method_branch: Optional[Tree] = None
+    current_lite_agent_branch: Optional[Tree] = None
+    tool_usage_counts: Dict[str, int] = {}
+    current_reasoning_branch: Optional[Tree] = None  # Track reasoning status
     _live_paused: bool = False
-    current_llm_tool_tree: Tree | None = None
+    current_llm_tool_tree: Optional[Tree] = None
 
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
@@ -29,7 +29,7 @@ class ConsoleFormatter:
         # instance so the previous render is replaced instead of writing a new one.
         # Once any non-Tree renderable is printed we stop the Live session so the
         # final Tree persists on the terminal.
-        self._live: Live | None = None
+        self._live: Optional[Live] = None
 
     def create_panel(self, content: Text, title: str, style: str = "blue") -> Panel:
         """Create a standardized panel with consistent styling."""
@@ -45,7 +45,7 @@ class ConsoleFormatter:
         title: str,
         name: str,
         status_style: str = "blue",
-        tool_args: dict[str, Any] | str = "",
+        tool_args: Dict[str, Any] | str = "",
         **fields,
     ) -> Text:
         """Create standardized status content with consistent formatting."""
@@ -70,7 +70,7 @@ class ConsoleFormatter:
         prefix: str,
         name: str,
         style: str = "blue",
-        status: str | None = None,
+        status: Optional[str] = None,
     ) -> None:
         """Update tree label with consistent formatting."""
         label = Text()
@@ -115,7 +115,7 @@ class ConsoleFormatter:
                 self._live.update(tree, refresh=True)
             return  # Nothing else to do
 
-        # Case 2: blank line while a live session is running - ignore so we
+        # Case 2: blank line while a live session is running – ignore so we
         # don't break the in-place rendering behaviour
         if len(args) == 0 and self._live:
             return
@@ -156,7 +156,7 @@ class ConsoleFormatter:
 
     def update_crew_tree(
         self,
-        tree: Tree | None,
+        tree: Optional[Tree],
         crew_name: str,
         source_id: str,
         status: str = "completed",
@@ -196,7 +196,7 @@ class ConsoleFormatter:
 
         self.print_panel(content, title, style)
 
-    def create_crew_tree(self, crew_name: str, source_id: str) -> Tree | None:
+    def create_crew_tree(self, crew_name: str, source_id: str) -> Optional[Tree]:
         """Create and initialize a new crew tree with initial status."""
         if not self.verbose:
             return None
@@ -220,8 +220,8 @@ class ConsoleFormatter:
         return tree
 
     def create_task_branch(
-        self, crew_tree: Tree | None, task_id: str, task_name: str | None = None
-    ) -> Tree | None:
+        self, crew_tree: Optional[Tree], task_id: str, task_name: Optional[str] = None
+    ) -> Optional[Tree]:
         """Create and initialize a task branch."""
         if not self.verbose:
             return None
@@ -255,11 +255,11 @@ class ConsoleFormatter:
 
     def update_task_status(
         self,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
         task_id: str,
         agent_role: str,
         status: str = "completed",
-        task_name: str | None = None,
+        task_name: Optional[str] = None,
     ) -> None:
         """Update task status in the tree."""
         if not self.verbose or crew_tree is None:
@@ -306,8 +306,8 @@ class ConsoleFormatter:
         self.print_panel(content, panel_title, style)
 
     def create_agent_branch(
-        self, task_branch: Tree | None, agent_role: str, crew_tree: Tree | None
-    ) -> Tree | None:
+        self, task_branch: Optional[Tree], agent_role: str, crew_tree: Optional[Tree]
+    ) -> Optional[Tree]:
         """Create and initialize an agent branch."""
         if not self.verbose or not task_branch or not crew_tree:
             return None
@@ -325,9 +325,9 @@ class ConsoleFormatter:
 
     def update_agent_status(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         agent_role: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
         status: str = "completed",
     ) -> None:
         """Update agent status in the tree."""
@@ -336,7 +336,7 @@ class ConsoleFormatter:
         # altering the tree. Keeping it a no-op avoids duplicate status lines.
         return
 
-    def create_flow_tree(self, flow_name: str, flow_id: str) -> Tree | None:
+    def create_flow_tree(self, flow_name: str, flow_id: str) -> Optional[Tree]:
         """Create and initialize a flow tree."""
         content = self.create_status_content(
             "Starting Flow Execution", flow_name, "blue", ID=flow_id
@@ -356,7 +356,7 @@ class ConsoleFormatter:
 
         return flow_tree
 
-    def start_flow(self, flow_name: str, flow_id: str) -> Tree | None:
+    def start_flow(self, flow_name: str, flow_id: str) -> Optional[Tree]:
         """Initialize a flow execution tree."""
         flow_tree = Tree("")
         flow_label = Text()
@@ -376,7 +376,7 @@ class ConsoleFormatter:
 
     def update_flow_status(
         self,
-        flow_tree: Tree | None,
+        flow_tree: Optional[Tree],
         flow_name: str,
         flow_id: str,
         status: str = "completed",
@@ -423,11 +423,11 @@ class ConsoleFormatter:
 
     def update_method_status(
         self,
-        method_branch: Tree | None,
-        flow_tree: Tree | None,
+        method_branch: Optional[Tree],
+        flow_tree: Optional[Tree],
         method_name: str,
         status: str = "running",
-    ) -> Tree | None:
+    ) -> Optional[Tree]:
         """Update method status in the flow tree."""
         if not flow_tree:
             return None
@@ -480,7 +480,7 @@ class ConsoleFormatter:
     def handle_llm_tool_usage_started(
         self,
         tool_name: str,
-        tool_args: dict[str, Any] | str,
+        tool_args: Dict[str, Any] | str,
     ):
         # Create status content for the tool usage
         content = self.create_status_content(
@@ -520,11 +520,11 @@ class ConsoleFormatter:
 
     def handle_tool_usage_started(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         tool_name: str,
-        crew_tree: Tree | None,
-        tool_args: dict[str, Any] | str = "",
-    ) -> Tree | None:
+        crew_tree: Optional[Tree],
+        tool_args: Dict[str, Any] | str = "",
+    ) -> Optional[Tree]:
         """Handle tool usage started event."""
         if not self.verbose:
             return None
@@ -569,9 +569,9 @@ class ConsoleFormatter:
 
     def handle_tool_usage_finished(
         self,
-        tool_branch: Tree | None,
+        tool_branch: Optional[Tree],
         tool_name: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle tool usage finished event."""
         if not self.verbose or tool_branch is None:
@@ -600,10 +600,10 @@ class ConsoleFormatter:
 
     def handle_tool_usage_error(
         self,
-        tool_branch: Tree | None,
+        tool_branch: Optional[Tree],
         tool_name: str,
         error: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle tool usage error event."""
         if not self.verbose:
@@ -631,9 +631,9 @@ class ConsoleFormatter:
 
     def handle_llm_call_started(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
-    ) -> Tree | None:
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+    ) -> Optional[Tree]:
         """Handle LLM call started event."""
         if not self.verbose:
             return None
@@ -672,9 +672,9 @@ class ConsoleFormatter:
 
     def handle_llm_call_completed(
         self,
-        tool_branch: Tree | None,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
+        tool_branch: Optional[Tree],
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle LLM call completed event."""
         if not self.verbose:
@@ -736,7 +736,7 @@ class ConsoleFormatter:
                 self.print()
 
     def handle_llm_call_failed(
-        self, tool_branch: Tree | None, error: str, crew_tree: Tree | None
+        self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
     ) -> None:
         """Handle LLM call failed event."""
         if not self.verbose:
@@ -789,7 +789,7 @@ class ConsoleFormatter:
 
     def handle_crew_test_started(
         self, crew_name: str, source_id: str, n_iterations: int
-    ) -> Tree | None:
+    ) -> Optional[Tree]:
         """Handle crew test started event."""
         if not self.verbose:
             return None
@@ -823,7 +823,7 @@ class ConsoleFormatter:
         return test_tree
 
     def handle_crew_test_completed(
-        self, flow_tree: Tree | None, crew_name: str
+        self, flow_tree: Optional[Tree], crew_name: str
     ) -> None:
         """Handle crew test completed event."""
         if not self.verbose:
@@ -913,7 +913,7 @@ class ConsoleFormatter:
         self.print_panel(failure_content, "Test Failure", "red")
         self.print()
 
-    def create_lite_agent_branch(self, lite_agent_role: str) -> Tree | None:
+    def create_lite_agent_branch(self, lite_agent_role: str) -> Optional[Tree]:
         """Create and initialize a lite agent branch."""
         if not self.verbose:
             return None
@@ -935,10 +935,10 @@ class ConsoleFormatter:
 
     def update_lite_agent_status(
         self,
-        lite_agent_branch: Tree | None,
+        lite_agent_branch: Optional[Tree],
         lite_agent_role: str,
         status: str = "completed",
-        **fields: dict[str, Any],
+        **fields: Dict[str, Any],
     ) -> None:
         """Update lite agent status in the tree."""
         if not self.verbose or lite_agent_branch is None:
@@ -981,7 +981,7 @@ class ConsoleFormatter:
         lite_agent_role: str,
         status: str = "started",
         error: Any = None,
-        **fields: dict[str, Any],
+        **fields: Dict[str, Any],
     ) -> None:
         """Handle lite agent execution events with consistent formatting."""
         if not self.verbose:
@@ -1006,9 +1006,9 @@ class ConsoleFormatter:
 
     def handle_knowledge_retrieval_started(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
-    ) -> Tree | None:
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+    ) -> Optional[Tree]:
         """Handle knowledge retrieval started event."""
         if not self.verbose:
             return None
@@ -1034,13 +1034,13 @@ class ConsoleFormatter:
 
     def handle_knowledge_retrieval_completed(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
         retrieved_knowledge: Any,
     ) -> None:
         """Handle knowledge retrieval completed event."""
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = self.current_lite_agent_branch or agent_branch
         tree_to_use = branch_to_use or crew_tree
@@ -1062,7 +1062,7 @@ class ConsoleFormatter:
                 )
                 self.print(knowledge_panel)
                 self.print()
-            return
+            return None
 
         knowledge_branch_found = False
         for child in branch_to_use.children:
@@ -1111,18 +1111,18 @@ class ConsoleFormatter:
 
     def handle_knowledge_query_started(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         task_prompt: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle knowledge query generated event."""
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = self.current_lite_agent_branch or agent_branch
         tree_to_use = branch_to_use or crew_tree
         if branch_to_use is None or tree_to_use is None:
-            return
+            return None
 
         query_branch = branch_to_use.add("")
         self.update_tree_label(
@@ -1134,9 +1134,9 @@ class ConsoleFormatter:
 
     def handle_knowledge_query_failed(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         error: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle knowledge query failed event."""
         if not self.verbose:
@@ -1159,18 +1159,18 @@ class ConsoleFormatter:
 
     def handle_knowledge_query_completed(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle knowledge query completed event."""
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = self.current_lite_agent_branch or agent_branch
         tree_to_use = branch_to_use or crew_tree
 
         if branch_to_use is None or tree_to_use is None:
-            return
+            return None
 
         query_branch = branch_to_use.add("")
         self.update_tree_label(query_branch, "✅", "Knowledge Query Completed", "green")
@@ -1180,9 +1180,9 @@ class ConsoleFormatter:
 
     def handle_knowledge_search_query_failed(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         error: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle knowledge search query failed event."""
         if not self.verbose:
@@ -1207,10 +1207,10 @@ class ConsoleFormatter:
 
     def handle_reasoning_started(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         attempt: int,
-        crew_tree: Tree | None,
-    ) -> Tree | None:
+        crew_tree: Optional[Tree],
+    ) -> Optional[Tree]:
         """Handle agent reasoning started (or refinement) event."""
         if not self.verbose:
             return None
@@ -1249,7 +1249,7 @@ class ConsoleFormatter:
         self,
         plan: str,
         ready: bool,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle agent reasoning completed event."""
         if not self.verbose:
@@ -1292,7 +1292,7 @@ class ConsoleFormatter:
     def handle_reasoning_failed(
         self,
         error: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         """Handle agent reasoning failure event."""
         if not self.verbose:
@@ -1329,7 +1329,7 @@ class ConsoleFormatter:
     def handle_agent_logs_started(
         self,
         agent_role: str,
-        task_description: str | None = None,
+        task_description: Optional[str] = None,
         verbose: bool = False,
     ) -> None:
         """Handle agent logs started event."""
@@ -1367,11 +1367,10 @@ class ConsoleFormatter:
         if not verbose:
             return
 
+        from crewai.agents.parser import AgentAction, AgentFinish
         import json
         import re
 
-        from crewai.agents.parser import AgentAction, AgentFinish
-
         agent_role = agent_role.partition("\n")[0]
 
         if isinstance(formatted_answer, AgentAction):
@@ -1474,9 +1473,9 @@ class ConsoleFormatter:
 
     def handle_memory_retrieval_started(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
-    ) -> Tree | None:
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
+    ) -> Optional[Tree]:
         if not self.verbose:
             return None
 
@@ -1498,13 +1497,13 @@ class ConsoleFormatter:
 
     def handle_memory_retrieval_completed(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
         memory_content: str,
         retrieval_time_ms: float,
     ) -> None:
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = self.current_lite_agent_branch or agent_branch
         tree_to_use = branch_to_use or crew_tree
@@ -1529,7 +1528,7 @@ class ConsoleFormatter:
 
         if branch_to_use is None or tree_to_use is None:
             add_panel()
-            return
+            return None
 
         memory_branch_found = False
         for child in branch_to_use.children:
@@ -1566,13 +1565,13 @@ class ConsoleFormatter:
 
     def handle_memory_query_completed(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         source_type: str,
         query_time_ms: float,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = self.current_lite_agent_branch or agent_branch
         tree_to_use = branch_to_use or crew_tree
@@ -1581,15 +1580,15 @@ class ConsoleFormatter:
             branch_to_use = tree_to_use
 
         if branch_to_use is None:
-            return
+            return None
 
         memory_type = source_type.replace("_", " ").title()
 
         for child in branch_to_use.children:
             if "Memory Retrieval" in str(child.label):
-                for inner_child in child.children:
-                    sources_branch = inner_child
-                    if "Sources Used" in str(inner_child.label):
+                for child in child.children:
+                    sources_branch = child
+                    if "Sources Used" in str(child.label):
                         sources_branch.add(f"✅ {memory_type} ({query_time_ms:.2f}ms)")
                         break
                 else:
@@ -1599,13 +1598,13 @@ class ConsoleFormatter:
 
     def handle_memory_query_failed(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
         error: str,
         source_type: str,
     ) -> None:
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = self.current_lite_agent_branch or agent_branch
         tree_to_use = branch_to_use or crew_tree
@@ -1614,15 +1613,15 @@ class ConsoleFormatter:
             branch_to_use = tree_to_use
 
         if branch_to_use is None:
-            return
+            return None
 
         memory_type = source_type.replace("_", " ").title()
 
         for child in branch_to_use.children:
             if "Memory Retrieval" in str(child.label):
-                for inner_child in child.children:
-                    sources_branch = inner_child
-                    if "Sources Used" in str(inner_child.label):
+                for child in child.children:
+                    sources_branch = child
+                    if "Sources Used" in str(child.label):
                         sources_branch.add(f"❌ {memory_type} - Error: {error}")
                         break
                 else:
@@ -1631,16 +1630,16 @@ class ConsoleFormatter:
                     break
 
     def handle_memory_save_started(
-        self, agent_branch: Tree | None, crew_tree: Tree | None
+        self, agent_branch: Optional[Tree], crew_tree: Optional[Tree]
     ) -> None:
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = agent_branch or self.current_lite_agent_branch
         tree_to_use = branch_to_use or crew_tree
 
         if tree_to_use is None:
-            return
+            return None
 
         for child in tree_to_use.children:
             if "Memory Update" in str(child.label):
@@ -1656,19 +1655,19 @@ class ConsoleFormatter:
 
     def handle_memory_save_completed(
         self,
-        agent_branch: Tree | None,
-        crew_tree: Tree | None,
+        agent_branch: Optional[Tree],
+        crew_tree: Optional[Tree],
         save_time_ms: float,
         source_type: str,
     ) -> None:
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = agent_branch or self.current_lite_agent_branch
         tree_to_use = branch_to_use or crew_tree
 
         if tree_to_use is None:
-            return
+            return None
 
         memory_type = source_type.replace("_", " ").title()
         content = f"✅ {memory_type} Memory Saved ({save_time_ms:.2f}ms)"
@@ -1686,19 +1685,19 @@ class ConsoleFormatter:
 
     def handle_memory_save_failed(
         self,
-        agent_branch: Tree | None,
+        agent_branch: Optional[Tree],
         error: str,
         source_type: str,
-        crew_tree: Tree | None,
+        crew_tree: Optional[Tree],
     ) -> None:
         if not self.verbose:
-            return
+            return None
 
         branch_to_use = agent_branch or self.current_lite_agent_branch
         tree_to_use = branch_to_use or crew_tree
 
         if branch_to_use is None or tree_to_use is None:
-            return
+            return None
 
         memory_type = source_type.replace("_", " ").title()
         content = f"❌ {memory_type} Memory Save Failed"
@@ -1739,7 +1738,7 @@ class ConsoleFormatter:
     def handle_guardrail_completed(
         self,
         success: bool,
-        error: str | None,
+        error: Optional[str],
         retry_count: int,
     ) -> None:
         """Display guardrail evaluation result.

src/crewai/experimental/__init__.py

@@ -1,39 +1,40 @@
 from crewai.experimental.evaluation import (
-    AgentEvaluationResult,
-    AgentEvaluator,
     BaseEvaluator,
     EvaluationScore,
-    EvaluationTraceCallback,
-    ExperimentResult,
-    ExperimentResults,
-    ExperimentRunner,
-    GoalAlignmentEvaluator,
     MetricCategory,
-    ParameterExtractionEvaluator,
-    ReasoningEfficiencyEvaluator,
+    AgentEvaluationResult,
     SemanticQualityEvaluator,
-    ToolInvocationEvaluator,
+    GoalAlignmentEvaluator,
+    ReasoningEfficiencyEvaluator,
     ToolSelectionEvaluator,
-    create_default_evaluator,
+    ParameterExtractionEvaluator,
+    ToolInvocationEvaluator,
+    EvaluationTraceCallback,
     create_evaluation_callbacks,
+    AgentEvaluator,
+    create_default_evaluator,
+    ExperimentRunner,
+    ExperimentResults,
+    ExperimentResult,
 )
 
+
 __all__ = [
-    "AgentEvaluationResult",
-    "AgentEvaluator",
     "BaseEvaluator",
     "EvaluationScore",
-    "EvaluationTraceCallback",
-    "ExperimentResult",
-    "ExperimentResults",
-    "ExperimentRunner",
-    "GoalAlignmentEvaluator",
     "MetricCategory",
-    "ParameterExtractionEvaluator",
-    "ReasoningEfficiencyEvaluator",
+    "AgentEvaluationResult",
     "SemanticQualityEvaluator",
-    "ToolInvocationEvaluator",
+    "GoalAlignmentEvaluator",
+    "ReasoningEfficiencyEvaluator",
     "ToolSelectionEvaluator",
-    "create_default_evaluator",
+    "ParameterExtractionEvaluator",
+    "ToolInvocationEvaluator",
+    "EvaluationTraceCallback",
     "create_evaluation_callbacks",
-]
+    "AgentEvaluator",
+    "create_default_evaluator",
+    "ExperimentRunner",
+    "ExperimentResults",
+    "ExperimentResult"
+]

src/crewai/experimental/evaluation/__init__.py

@@ -1,47 +1,51 @@
-from crewai.experimental.evaluation.agent_evaluator import (
-    AgentEvaluator,
-    create_default_evaluator,
-)
 from crewai.experimental.evaluation.base_evaluator import (
-    AgentEvaluationResult,
     BaseEvaluator,
     EvaluationScore,
     MetricCategory,
+    AgentEvaluationResult
 )
+
+from crewai.experimental.evaluation.metrics import (
+    SemanticQualityEvaluator,
+    GoalAlignmentEvaluator,
+    ReasoningEfficiencyEvaluator,
+    ToolSelectionEvaluator,
+    ParameterExtractionEvaluator,
+    ToolInvocationEvaluator
+)
+
 from crewai.experimental.evaluation.evaluation_listener import (
     EvaluationTraceCallback,
-    create_evaluation_callbacks,
+    create_evaluation_callbacks
 )
+
+from crewai.experimental.evaluation.agent_evaluator import (
+    AgentEvaluator,
+    create_default_evaluator
+)
+
 from crewai.experimental.evaluation.experiment import (
-    ExperimentResult,
-    ExperimentResults,
     ExperimentRunner,
-)
-from crewai.experimental.evaluation.metrics import (
-    GoalAlignmentEvaluator,
-    ParameterExtractionEvaluator,
-    ReasoningEfficiencyEvaluator,
-    SemanticQualityEvaluator,
-    ToolInvocationEvaluator,
-    ToolSelectionEvaluator,
+    ExperimentResults,
+    ExperimentResult
 )
 
 __all__ = [
-    "AgentEvaluationResult",
-    "AgentEvaluator",
     "BaseEvaluator",
     "EvaluationScore",
-    "EvaluationTraceCallback",
-    "ExperimentResult",
-    "ExperimentResults",
-    "ExperimentRunner",
-    "GoalAlignmentEvaluator",
     "MetricCategory",
-    "ParameterExtractionEvaluator",
-    "ReasoningEfficiencyEvaluator",
+    "AgentEvaluationResult",
     "SemanticQualityEvaluator",
-    "ToolInvocationEvaluator",
+    "GoalAlignmentEvaluator",
+    "ReasoningEfficiencyEvaluator",
     "ToolSelectionEvaluator",
-    "create_default_evaluator",
+    "ParameterExtractionEvaluator",
+    "ToolInvocationEvaluator",
+    "EvaluationTraceCallback",
     "create_evaluation_callbacks",
+    "AgentEvaluator",
+    "create_default_evaluator",
+    "ExperimentRunner",
+    "ExperimentResults",
+    "ExperimentResult"
 ]

src/crewai/experimental/evaluation/agent_evaluator.py

@@ -1,36 +1,34 @@
 import threading
-from collections.abc import Sequence
-from typing import Any
+from typing import Any, Optional
 
-from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.agent_events import (
-    AgentEvaluationCompletedEvent,
-    AgentEvaluationFailedEvent,
-    AgentEvaluationStartedEvent,
-    LiteAgentExecutionCompletedEvent,
-)
-from crewai.events.types.task_events import TaskCompletedEvent
-from crewai.events.utils.console_formatter import ConsoleFormatter
 from crewai.experimental.evaluation.base_evaluator import (
-    AgentAggregatedEvaluationResult,
     AgentEvaluationResult,
     AggregationStrategy,
-    BaseEvaluator,
+)
+from crewai.agent import Agent
+from crewai.task import Task
+from crewai.experimental.evaluation.evaluation_display import EvaluationDisplayFormatter
+from crewai.events.types.agent_events import (
+    AgentEvaluationStartedEvent,
+    AgentEvaluationCompletedEvent,
+    AgentEvaluationFailedEvent,
+)
+from crewai.experimental.evaluation import BaseEvaluator, create_evaluation_callbacks
+from collections.abc import Sequence
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.utils.console_formatter import ConsoleFormatter
+from crewai.events.types.task_events import TaskCompletedEvent
+from crewai.events.types.agent_events import LiteAgentExecutionCompletedEvent
+from crewai.experimental.evaluation.base_evaluator import (
+    AgentAggregatedEvaluationResult,
     EvaluationScore,
     MetricCategory,
 )
-from crewai.experimental.evaluation.evaluation_display import EvaluationDisplayFormatter
-from crewai.experimental.evaluation.evaluation_listener import (
-    create_evaluation_callbacks,
-)
-from crewai.task import Task
 
 
 class ExecutionState:
-    current_agent_id: str | None = None
-    current_task_id: str | None = None
+    current_agent_id: Optional[str] = None
+    current_task_id: Optional[str] = None
 
     def __init__(self):
         self.traces = {}
@@ -42,10 +40,10 @@ class ExecutionState:
 class AgentEvaluator:
     def __init__(
         self,
-        agents: list[Agent] | list[BaseAgent],
+        agents: list[Agent],
         evaluators: Sequence[BaseEvaluator] | None = None,
     ):
-        self.agents: list[Agent] | list[BaseAgent] = agents
+        self.agents: list[Agent] = agents
         self.evaluators: Sequence[BaseEvaluator] | None = evaluators
 
         self.callback = create_evaluation_callbacks()
@@ -77,8 +75,7 @@ class AgentEvaluator:
         )
 
     def _handle_task_completed(self, source: Any, event: TaskCompletedEvent) -> None:
-        if event.task is None:
-            raise ValueError("TaskCompletedEvent must have a task")
+        assert event.task is not None
         agent = event.task.agent
         if (
             agent
@@ -95,8 +92,9 @@ class AgentEvaluator:
             state.current_agent_id = str(agent.id)
             state.current_task_id = str(event.task.id)
 
-            if state.current_agent_id is None or state.current_task_id is None:
-                raise ValueError("Agent ID and Task ID must not be None")
+            assert (
+                state.current_agent_id is not None and state.current_task_id is not None
+            )
             trace = self.callback.get_trace(
                 state.current_agent_id, state.current_task_id
             )
@@ -148,8 +146,9 @@ class AgentEvaluator:
             if not target_agent:
                 return
 
-            if state.current_agent_id is None or state.current_task_id is None:
-                raise ValueError("Agent ID and Task ID must not be None")
+            assert (
+                state.current_agent_id is not None and state.current_task_id is not None
+            )
             trace = self.callback.get_trace(
                 state.current_agent_id, state.current_task_id
             )
@@ -245,7 +244,7 @@ class AgentEvaluator:
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
+        agent: Agent,
         execution_trace: dict[str, Any],
         final_output: Any,
         state: ExecutionState,
@@ -256,8 +255,7 @@ class AgentEvaluator:
             task_id=state.current_task_id or (str(task.id) if task else "unknown_task"),
         )
 
-        if self.evaluators is None:
-            raise ValueError("Evaluators must be initialized")
+        assert self.evaluators is not None
         task_id = str(task.id) if task else None
         for evaluator in self.evaluators:
             try:
@@ -278,7 +276,7 @@ class AgentEvaluator:
                     metric_category=evaluator.metric_category,
                     score=score,
                 )
-            except Exception as e:  # noqa: PERF203
+            except Exception as e:
                 self.emit_evaluation_failed_event(
                     agent_role=agent.role,
                     agent_id=str(agent.id),
@@ -286,7 +284,7 @@ class AgentEvaluator:
                     error=str(e),
                 )
                 self.console_formatter.print(
-                    f"Error in {evaluator.metric_category.value} evaluator: {e!s}"
+                    f"Error in {evaluator.metric_category.value} evaluator: {str(e)}"
                 )
 
         return result
@@ -339,14 +337,14 @@ class AgentEvaluator:
         )
 
 
-def create_default_evaluator(agents: list[Agent] | list[BaseAgent], llm: None = None):
+def create_default_evaluator(agents: list[Agent], llm: None = None):
     from crewai.experimental.evaluation import (
         GoalAlignmentEvaluator,
-        ParameterExtractionEvaluator,
-        ReasoningEfficiencyEvaluator,
         SemanticQualityEvaluator,
-        ToolInvocationEvaluator,
         ToolSelectionEvaluator,
+        ParameterExtractionEvaluator,
+        ToolInvocationEvaluator,
+        ReasoningEfficiencyEvaluator,
     )
 
     evaluators = [

src/crewai/experimental/evaluation/base_evaluator.py

@@ -1,17 +1,15 @@
 import abc
 import enum
 from enum import Enum
-from typing import Any
+from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.llm import BaseLLM
 from crewai.task import Task
+from crewai.llm import BaseLLM
 from crewai.utilities.llm_utils import create_llm
 
-
 class MetricCategory(enum.Enum):
     GOAL_ALIGNMENT = "goal_alignment"
     SEMANTIC_QUALITY = "semantic_quality"
@@ -21,7 +19,7 @@ class MetricCategory(enum.Enum):
     TOOL_INVOCATION = "tool_invocation"
 
     def title(self):
-        return self.value.replace("_", " ").title()
+        return self.value.replace('_', ' ').title()
 
 
 class EvaluationScore(BaseModel):
@@ -29,13 +27,15 @@ class EvaluationScore(BaseModel):
         default=5.0,
         description="Numeric score from 0-10 where 0 is worst and 10 is best, None if not applicable",
         ge=0.0,
-        le=10.0,
+        le=10.0
     )
     feedback: str = Field(
-        default="", description="Detailed feedback explaining the evaluation score"
+        default="",
+        description="Detailed feedback explaining the evaluation score"
     )
     raw_response: str | None = Field(
-        default=None, description="Raw response from the evaluator (e.g., LLM)"
+        default=None,
+        description="Raw response from the evaluator (e.g., LLM)"
     )
 
     def __str__(self) -> str:
@@ -56,8 +56,8 @@ class BaseEvaluator(abc.ABC):
     @abc.abstractmethod
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: Any,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -67,8 +67,9 @@ class BaseEvaluator(abc.ABC):
 class AgentEvaluationResult(BaseModel):
     agent_id: str = Field(description="ID of the evaluated agent")
     task_id: str = Field(description="ID of the task that was executed")
-    metrics: dict[MetricCategory, EvaluationScore] = Field(
-        default_factory=dict, description="Evaluation scores for each metric category"
+    metrics: Dict[MetricCategory, EvaluationScore] = Field(
+        default_factory=dict,
+        description="Evaluation scores for each metric category"
     )
 
 
@@ -80,23 +81,33 @@ class AggregationStrategy(Enum):
 
 
 class AgentAggregatedEvaluationResult(BaseModel):
-    agent_id: str = Field(default="", description="ID of the agent")
-    agent_role: str = Field(default="", description="Role of the agent")
+    agent_id: str = Field(
+        default="",
+        description="ID of the agent"
+    )
+    agent_role: str = Field(
+        default="",
+        description="Role of the agent"
+    )
     task_count: int = Field(
-        default=0, description="Number of tasks included in this aggregation"
+        default=0,
+        description="Number of tasks included in this aggregation"
     )
     aggregation_strategy: AggregationStrategy = Field(
         default=AggregationStrategy.SIMPLE_AVERAGE,
-        description="Strategy used for aggregation",
+        description="Strategy used for aggregation"
     )
-    metrics: dict[MetricCategory, EvaluationScore] = Field(
-        default_factory=dict, description="Aggregated metrics across all tasks"
+    metrics: Dict[MetricCategory, EvaluationScore] = Field(
+        default_factory=dict,
+        description="Aggregated metrics across all tasks"
     )
-    task_results: list[str] = Field(
-        default_factory=list, description="IDs of tasks included in this aggregation"
+    task_results: List[str] = Field(
+        default_factory=list,
+        description="IDs of tasks included in this aggregation"
     )
-    overall_score: float | None = Field(
-        default=None, description="Overall score for this agent"
+    overall_score: Optional[float] = Field(
+        default=None,
+        description="Overall score for this agent"
     )
 
     def __str__(self) -> str:
@@ -108,7 +119,7 @@ class AgentAggregatedEvaluationResult(BaseModel):
             result += f"\n\n- {category.value.upper()}: {score.score}/10\n"
 
             if score.feedback:
-                detailed_feedback = "\n  ".join(score.feedback.split("\n"))
+                detailed_feedback = "\n  ".join(score.feedback.split('\n'))
                 result += f"  {detailed_feedback}\n"
 
-        return result
+        return result

src/crewai/experimental/evaluation/evaluation_display.py

@@ -1,18 +1,16 @@
 from collections import defaultdict
-from collections.abc import Sequence
-from typing import Any
-
-from rich.box import HEAVY_EDGE, ROUNDED
+from typing import Dict, Any, List
 from rich.table import Table
-
-from crewai.events.utils.console_formatter import ConsoleFormatter
+from rich.box import HEAVY_EDGE, ROUNDED
+from collections.abc import Sequence
 from crewai.experimental.evaluation.base_evaluator import (
     AgentAggregatedEvaluationResult,
-    AgentEvaluationResult,
     AggregationStrategy,
-    EvaluationScore,
+    AgentEvaluationResult,
     MetricCategory,
 )
+from crewai.experimental.evaluation import EvaluationScore
+from crewai.events.utils.console_formatter import ConsoleFormatter
 from crewai.utilities.llm_utils import create_llm
 
 
@@ -21,7 +19,7 @@ class EvaluationDisplayFormatter:
         self.console_formatter = ConsoleFormatter()
 
     def display_evaluation_with_feedback(
-        self, iterations_results: dict[int, dict[str, list[Any]]]
+        self, iterations_results: Dict[int, Dict[str, List[Any]]]
     ):
         if not iterations_results:
             self.console_formatter.print(
@@ -101,7 +99,7 @@ class EvaluationDisplayFormatter:
 
     def display_summary_results(
         self,
-        iterations_results: dict[int, dict[str, list[AgentEvaluationResult]]],
+        iterations_results: Dict[int, Dict[str, List[AgentAggregatedEvaluationResult]]],
     ):
         if not iterations_results:
             self.console_formatter.print(
@@ -282,7 +280,7 @@ class EvaluationDisplayFormatter:
                     feedback_summary = feedbacks[0]
 
             aggregated_metrics[category] = EvaluationScore(
-                score=avg_score, feedback=feedback_summary or ""
+                score=avg_score, feedback=feedback_summary
             )
 
         overall_score = None
@@ -306,25 +304,25 @@ class EvaluationDisplayFormatter:
         self,
         agent_role: str,
         metric: str,
-        feedbacks: list[str],
-        scores: list[float | None],
+        feedbacks: List[str],
+        scores: List[float | None],
         strategy: AggregationStrategy,
     ) -> str:
         if len(feedbacks) <= 2 and all(len(fb) < 200 for fb in feedbacks):
             return "\n\n".join(
-                [f"Feedback {i + 1}: {fb}" for i, fb in enumerate(feedbacks)]
+                [f"Feedback {i+1}: {fb}" for i, fb in enumerate(feedbacks)]
             )
 
         try:
             llm = create_llm()
 
             formatted_feedbacks = []
-            for i, (feedback, score) in enumerate(zip(feedbacks, scores, strict=False)):
+            for i, (feedback, score) in enumerate(zip(feedbacks, scores)):
                 if len(feedback) > 500:
                     feedback = feedback[:500] + "..."
                 score_text = f"{score:.1f}" if score is not None else "N/A"
                 formatted_feedbacks.append(
-                    f"Feedback #{i + 1} (Score: {score_text}):\n{feedback}"
+                    f"Feedback #{i+1} (Score: {score_text}):\n{feedback}"
                 )
 
             all_feedbacks = "\n\n" + "\n\n---\n\n".join(formatted_feedbacks)
@@ -367,9 +365,10 @@ class EvaluationDisplayFormatter:
                 """,
                 },
             ]
-            if llm is None:
-                raise ValueError("LLM must be initialized")
-            return llm.call(prompt)
+            assert llm is not None
+            response = llm.call(prompt)
+
+            return response
 
         except Exception:
             return "Synthesized from multiple tasks: " + "\n\n".join(

src/crewai/experimental/evaluation/evaluation_listener.py

@@ -1,25 +1,26 @@
-from collections.abc import Sequence
 from datetime import datetime
-from typing import Any
+from typing import Any, Dict, Optional
 
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from collections.abc import Sequence
+
+from crewai.agent import Agent
+from crewai.task import Task
 from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.event_bus import CrewAIEventsBus
 from crewai.events.types.agent_events import (
-    AgentExecutionCompletedEvent,
     AgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent,
+    AgentExecutionCompletedEvent,
     LiteAgentExecutionStartedEvent,
+    LiteAgentExecutionCompletedEvent,
 )
-from crewai.events.types.llm_events import LLMCallCompletedEvent, LLMCallStartedEvent
 from crewai.events.types.tool_usage_events import (
+    ToolUsageFinishedEvent,
+    ToolUsageErrorEvent,
     ToolExecutionErrorEvent,
     ToolSelectionErrorEvent,
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
     ToolValidateInputErrorEvent,
 )
-from crewai.task import Task
+from crewai.events.types.llm_events import LLMCallStartedEvent, LLMCallCompletedEvent
 
 
 class EvaluationTraceCallback(BaseEventListener):
@@ -135,7 +136,7 @@ class EvaluationTraceCallback(BaseEventListener):
     def _init_trace(self, trace_key: str, **kwargs: Any):
         self.traces[trace_key] = kwargs
 
-    def on_agent_start(self, agent: BaseAgent, task: Task):
+    def on_agent_start(self, agent: Agent, task: Task):
         self.current_agent_id = agent.id
         self.current_task_id = task.id
 
@@ -150,7 +151,7 @@ class EvaluationTraceCallback(BaseEventListener):
             final_output=None,
         )
 
-    def on_agent_finish(self, agent: BaseAgent, task: Task, output: Any):
+    def on_agent_finish(self, agent: Agent, task: Task, output: Any):
         trace_key = f"{agent.id}_{task.id}"
         if trace_key in self.traces:
             self.traces[trace_key]["final_output"] = output
@@ -252,7 +253,7 @@ class EvaluationTraceCallback(BaseEventListener):
         if hasattr(self, "current_llm_call"):
             self.current_llm_call = {}
 
-    def get_trace(self, agent_id: str, task_id: str) -> dict[str, Any] | None:
+    def get_trace(self, agent_id: str, task_id: str) -> Optional[Dict[str, Any]]:
         trace_key = f"{agent_id}_{task_id}"
         return self.traces.get(trace_key)
 

src/crewai/experimental/evaluation/experiment/__init__.py

@@ -1,7 +1,8 @@
-from crewai.experimental.evaluation.experiment.result import (
-    ExperimentResult,
-    ExperimentResults,
-)
 from crewai.experimental.evaluation.experiment.runner import ExperimentRunner
+from crewai.experimental.evaluation.experiment.result import ExperimentResults, ExperimentResult
 
-__all__ = ["ExperimentResult", "ExperimentResults", "ExperimentRunner"]
+__all__ = [
+    "ExperimentRunner",
+    "ExperimentResults",
+    "ExperimentResult"
+]

src/crewai/experimental/evaluation/experiment/result.py

@@ -2,60 +2,45 @@ import json
 import os
 from datetime import datetime, timezone
 from typing import Any
-
 from pydantic import BaseModel
 
-
 class ExperimentResult(BaseModel):
     identifier: str
     inputs: dict[str, Any]
-    score: float | dict[str, float]
-    expected_score: float | dict[str, float]
+    score: int | dict[str, int | float]
+    expected_score: int | dict[str, int | float]
     passed: bool
     agent_evaluations: dict[str, Any] | None = None
 
-
 class ExperimentResults:
-    def __init__(
-        self, results: list[ExperimentResult], metadata: dict[str, Any] | None = None
-    ):
+    def __init__(self, results: list[ExperimentResult], metadata: dict[str, Any] | None = None):
         self.results = results
         self.metadata = metadata or {}
         self.timestamp = datetime.now(timezone.utc)
 
-        from crewai.experimental.evaluation.experiment.result_display import (
-            ExperimentResultsDisplay,
-        )
-
+        from crewai.experimental.evaluation.experiment.result_display import ExperimentResultsDisplay
         self.display = ExperimentResultsDisplay()
 
     def to_json(self, filepath: str | None = None) -> dict[str, Any]:
         data = {
             "timestamp": self.timestamp.isoformat(),
             "metadata": self.metadata,
-            "results": [
-                r.model_dump(exclude={"agent_evaluations"}) for r in self.results
-            ],
+            "results": [r.model_dump(exclude={"agent_evaluations"}) for r in self.results]
         }
 
         if filepath:
-            with open(filepath, "w") as f:
+            with open(filepath, 'w') as f:
                 json.dump(data, f, indent=2)
             self.display.console.print(f"[green]Results saved to {filepath}[/green]")
 
         return data
 
-    def compare_with_baseline(
-        self,
-        baseline_filepath: str,
-        save_current: bool = True,
-        print_summary: bool = False,
-    ) -> dict[str, Any]:
+    def compare_with_baseline(self, baseline_filepath: str, save_current: bool = True, print_summary: bool = False) -> dict[str, Any]:
         baseline_runs = []
 
         if os.path.exists(baseline_filepath) and os.path.getsize(baseline_filepath) > 0:
             try:
-                with open(baseline_filepath, "r") as f:
+                with open(baseline_filepath, 'r') as f:
                     baseline_data = json.load(f)
 
                 if isinstance(baseline_data, dict) and "timestamp" in baseline_data:
@@ -63,18 +48,14 @@ class ExperimentResults:
                 elif isinstance(baseline_data, list):
                     baseline_runs = baseline_data
             except (json.JSONDecodeError, FileNotFoundError) as e:
-                self.display.console.print(
-                    f"[yellow]Warning: Could not load baseline file: {e!s}[/yellow]"
-                )
+                self.display.console.print(f"[yellow]Warning: Could not load baseline file: {str(e)}[/yellow]")
 
         if not baseline_runs:
             if save_current:
                 current_data = self.to_json()
-                with open(baseline_filepath, "w") as f:
+                with open(baseline_filepath, 'w') as f:
                     json.dump([current_data], f, indent=2)
-                self.display.console.print(
-                    f"[green]Saved current results as new baseline to {baseline_filepath}[/green]"
-                )
+                self.display.console.print(f"[green]Saved current results as new baseline to {baseline_filepath}[/green]")
             return {"is_baseline": True, "changes": {}}
 
         baseline_runs.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
@@ -88,11 +69,9 @@ class ExperimentResults:
         if save_current:
             current_data = self.to_json()
             baseline_runs.append(current_data)
-            with open(baseline_filepath, "w") as f:
+            with open(baseline_filepath, 'w') as f:
                 json.dump(baseline_runs, f, indent=2)
-            self.display.console.print(
-                f"[green]Added current results to baseline file {baseline_filepath}[/green]"
-            )
+            self.display.console.print(f"[green]Added current results to baseline file {baseline_filepath}[/green]")
 
         return comparison
 
@@ -139,5 +118,5 @@ class ExperimentResults:
             "new_tests": new_tests,
             "missing_tests": missing_tests,
             "total_compared": len(improved) + len(regressed) + len(unchanged),
-            "baseline_timestamp": baseline_run.get("timestamp", "unknown"),
+            "baseline_timestamp": baseline_run.get("timestamp", "unknown")
         }

src/crewai/experimental/evaluation/experiment/result_display.py

@@ -1,12 +1,9 @@
-from typing import Any
-
+from typing import Dict, Any
 from rich.console import Console
-from rich.panel import Panel
 from rich.table import Table
-
+from rich.panel import Panel
 from crewai.experimental.evaluation.experiment.result import ExperimentResults
 
-
 class ExperimentResultsDisplay:
     def __init__(self):
         self.console = Console()
@@ -22,19 +19,13 @@ class ExperimentResultsDisplay:
         table.add_row("Total Test Cases", str(total))
         table.add_row("Passed", str(passed))
         table.add_row("Failed", str(total - passed))
-        table.add_row(
-            "Success Rate", f"{(passed / total * 100):.1f}%" if total > 0 else "N/A"
-        )
+        table.add_row("Success Rate", f"{(passed / total * 100):.1f}%" if total > 0 else "N/A")
 
         self.console.print(table)
 
-    def comparison_summary(self, comparison: dict[str, Any], baseline_timestamp: str):
-        self.console.print(
-            Panel(
-                f"[bold]Comparison with baseline run from {baseline_timestamp}[/bold]",
-                expand=False,
-            )
-        )
+    def comparison_summary(self, comparison: Dict[str, Any], baseline_timestamp: str):
+        self.console.print(Panel(f"[bold]Comparison with baseline run from {baseline_timestamp}[/bold]",
+                                 expand=False))
 
         table = Table(title="Results Comparison")
         table.add_column("Metric", style="cyan")
@@ -43,9 +34,7 @@ class ExperimentResultsDisplay:
 
         improved = comparison.get("improved", [])
         if improved:
-            details = ", ".join(
-                [f"{test_identifier}" for test_identifier in improved[:3]]
-            )
+            details = ", ".join([f"{test_identifier}" for test_identifier in improved[:3]])
             if len(improved) > 3:
                 details += f" and {len(improved) - 3} more"
             table.add_row("✅ Improved", str(len(improved)), details)
@@ -54,9 +43,7 @@ class ExperimentResultsDisplay:
 
         regressed = comparison.get("regressed", [])
         if regressed:
-            details = ", ".join(
-                [f"{test_identifier}" for test_identifier in regressed[:3]]
-            )
+            details = ", ".join([f"{test_identifier}" for test_identifier in regressed[:3]])
             if len(regressed) > 3:
                 details += f" and {len(regressed) - 3} more"
             table.add_row("❌ Regressed", str(len(regressed)), details, style="red")
@@ -71,13 +58,13 @@ class ExperimentResultsDisplay:
             details = ", ".join(new_tests[:3])
             if len(new_tests) > 3:
                 details += f" and {len(new_tests) - 3} more"
-            table.add_row("+ New Tests", str(len(new_tests)), details)
+            table.add_row("➕ New Tests", str(len(new_tests)), details)
 
         missing_tests = comparison.get("missing_tests", [])
         if missing_tests:
             details = ", ".join(missing_tests[:3])
             if len(missing_tests) > 3:
                 details += f" and {len(missing_tests) - 3} more"
-            table.add_row("- Missing Tests", str(len(missing_tests)), details)
+            table.add_row("➖ Missing Tests", str(len(missing_tests)), details)
 
         self.console.print(table)

src/crewai/experimental/evaluation/experiment/runner.py

@@ -2,20 +2,11 @@ from collections import defaultdict
 from hashlib import md5
 from typing import Any
 
-from crewai import Agent, Crew
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai import Crew, Agent
 from crewai.experimental.evaluation import AgentEvaluator, create_default_evaluator
-from crewai.experimental.evaluation.evaluation_display import (
-    AgentAggregatedEvaluationResult,
-)
-from crewai.experimental.evaluation.experiment.result import (
-    ExperimentResult,
-    ExperimentResults,
-)
-from crewai.experimental.evaluation.experiment.result_display import (
-    ExperimentResultsDisplay,
-)
-
+from crewai.experimental.evaluation.experiment.result_display import ExperimentResultsDisplay
+from crewai.experimental.evaluation.experiment.result import ExperimentResults, ExperimentResult
+from crewai.experimental.evaluation.evaluation_display import AgentAggregatedEvaluationResult
 
 class ExperimentRunner:
     def __init__(self, dataset: list[dict[str, Any]]):
@@ -23,17 +14,11 @@ class ExperimentRunner:
         self.evaluator: AgentEvaluator | None = None
         self.display = ExperimentResultsDisplay()
 
-    def run(
-        self,
-        crew: Crew | None = None,
-        agents: list[Agent] | list[BaseAgent] | None = None,
-        print_summary: bool = False,
-    ) -> ExperimentResults:
+    def run(self, crew: Crew | None = None, agents: list[Agent] | None = None, print_summary: bool = False) -> ExperimentResults:
         if crew and not agents:
             agents = crew.agents
 
-        if agents is None:
-            raise ValueError("Agents must be provided either directly or via a crew")
+        assert agents is not None
         self.evaluator = create_default_evaluator(agents=agents)
 
         results = []
@@ -50,37 +35,21 @@ class ExperimentRunner:
 
         return experiment_results
 
-    def _run_test_case(
-        self,
-        test_case: dict[str, Any],
-        agents: list[Agent] | list[BaseAgent],
-        crew: Crew | None = None,
-    ) -> ExperimentResult:
+    def _run_test_case(self, test_case: dict[str, Any], agents: list[Agent], crew: Crew | None = None) -> ExperimentResult:
         inputs = test_case["inputs"]
         expected_score = test_case["expected_score"]
-        identifier = (
-            test_case.get("identifier")
-            or md5(str(test_case).encode(), usedforsecurity=False).hexdigest()
-        )
+        identifier = test_case.get("identifier") or md5(str(test_case).encode(), usedforsecurity=False).hexdigest()
 
         try:
-            self.display.console.print(
-                f"[dim]Running crew with input: {str(inputs)[:50]}...[/dim]"
-            )
+            self.display.console.print(f"[dim]Running crew with input: {str(inputs)[:50]}...[/dim]")
             self.display.console.print("\n")
             if crew:
                 crew.kickoff(inputs=inputs)
             else:
                 for agent in agents:
-                    if isinstance(agent, Agent):
-                        agent.kickoff(**inputs)
-                    else:
-                        raise TypeError(
-                            f"Agent {agent} is not an instance of Agent and cannot be kicked off directly"
-                        )
+                    agent.kickoff(**inputs)
 
-            if self.evaluator is None:
-                raise ValueError("Evaluator must be initialized")
+            assert self.evaluator is not None
             agent_evaluations = self.evaluator.get_agent_evaluation()
 
             actual_score = self._extract_scores(agent_evaluations)
@@ -92,38 +61,35 @@ class ExperimentRunner:
                 score=actual_score,
                 expected_score=expected_score,
                 passed=passed,
-                agent_evaluations=agent_evaluations,
+                agent_evaluations=agent_evaluations
             )
 
         except Exception as e:
-            self.display.console.print(f"[red]Error running test case: {e!s}[/red]")
+            self.display.console.print(f"[red]Error running test case: {str(e)}[/red]")
             return ExperimentResult(
                 identifier=identifier,
                 inputs=inputs,
-                score=0.0,
+                score=0,
                 expected_score=expected_score,
-                passed=False,
+                passed=False
             )
 
-    def _extract_scores(
-        self, agent_evaluations: dict[str, AgentAggregatedEvaluationResult]
-    ) -> float | dict[str, float]:
+    def _extract_scores(self, agent_evaluations: dict[str, AgentAggregatedEvaluationResult]) -> float | dict[str,  float]:
         all_scores: dict[str, list[float]] = defaultdict(list)
         for evaluation in agent_evaluations.values():
             for metric_name, score in evaluation.metrics.items():
                 if score.score is not None:
                     all_scores[metric_name.value].append(score.score)
 
-        avg_scores = {m: sum(s) / len(s) for m, s in all_scores.items()}
+        avg_scores = {m: sum(s)/len(s) for m, s in all_scores.items()}
 
         if len(avg_scores) == 1:
-            return next(iter(avg_scores.values()))
+            return list(avg_scores.values())[0]
 
         return avg_scores
 
-    def _assert_scores(
-        self, expected: float | dict[str, float], actual: float | dict[str, float]
-    ) -> bool:
+    def _assert_scores(self, expected: float | dict[str, float],
+                        actual: float | dict[str, float]) -> bool:
         """
         Compare expected and actual scores, and return whether the test case passed.
 
@@ -156,4 +122,4 @@ class ExperimentRunner:
             # All matching keys must have actual >= expected
             return all(actual[key] >= expected[key] for key in matching_keys)
 
-        return False
+        return False

src/crewai/experimental/evaluation/json_parser.py

@@ -13,11 +13,11 @@ def extract_json_from_llm_response(text: str) -> dict[str, Any]:
 
     json_patterns = [
         # Standard markdown code blocks with json
-        r"```json\s*([\s\S]*?)\s*```",
+        r'```json\s*([\s\S]*?)\s*```',
         # Code blocks without language specifier
-        r"```\s*([\s\S]*?)\s*```",
+        r'```\s*([\s\S]*?)\s*```',
         # Inline code with JSON
-        r"`([{\\[].*[}\]])`",
+        r'`([{\\[].*[}\]])`',
     ]
 
     for pattern in json_patterns:
@@ -25,6 +25,6 @@ def extract_json_from_llm_response(text: str) -> dict[str, Any]:
         for match in matches:
             try:
                 return json.loads(match.strip())
-            except json.JSONDecodeError:  # noqa: PERF203
+            except json.JSONDecodeError:
                 continue
     raise ValueError("No valid JSON found in the response")

src/crewai/experimental/evaluation/metrics/__init__.py

@@ -1,21 +1,26 @@
-from crewai.experimental.evaluation.metrics.goal_metrics import GoalAlignmentEvaluator
 from crewai.experimental.evaluation.metrics.reasoning_metrics import (
-    ReasoningEfficiencyEvaluator,
-)
-from crewai.experimental.evaluation.metrics.semantic_quality_metrics import (
-    SemanticQualityEvaluator,
+    ReasoningEfficiencyEvaluator
 )
+
 from crewai.experimental.evaluation.metrics.tools_metrics import (
-    ParameterExtractionEvaluator,
-    ToolInvocationEvaluator,
     ToolSelectionEvaluator,
+    ParameterExtractionEvaluator,
+    ToolInvocationEvaluator
+)
+
+from crewai.experimental.evaluation.metrics.goal_metrics import (
+    GoalAlignmentEvaluator
+)
+
+from crewai.experimental.evaluation.metrics.semantic_quality_metrics import (
+    SemanticQualityEvaluator
 )
 
 __all__ = [
-    "GoalAlignmentEvaluator",
-    "ParameterExtractionEvaluator",
     "ReasoningEfficiencyEvaluator",
-    "SemanticQualityEvaluator",
-    "ToolInvocationEvaluator",
     "ToolSelectionEvaluator",
-]
+    "ParameterExtractionEvaluator",
+    "ToolInvocationEvaluator",
+    "GoalAlignmentEvaluator",
+    "SemanticQualityEvaluator"
+]

src/crewai/experimental/evaluation/metrics/goal_metrics.py

@@ -1,15 +1,10 @@
-from typing import Any
+from typing import Any, Dict
 
 from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.experimental.evaluation.base_evaluator import (
-    BaseEvaluator,
-    EvaluationScore,
-    MetricCategory,
-)
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 from crewai.task import Task
 
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 
 class GoalAlignmentEvaluator(BaseEvaluator):
     @property
@@ -18,8 +13,8 @@ class GoalAlignmentEvaluator(BaseEvaluator):
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: Any,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -28,9 +23,7 @@ class GoalAlignmentEvaluator(BaseEvaluator):
             task_context = f"Task description: {task.description}\nExpected output: {task.expected_output}\n"
 
         prompt = [
-            {
-                "role": "system",
-                "content": """You are an expert evaluator assessing how well an AI agent's output aligns with its assigned task goal.
+            {"role": "system", "content": """You are an expert evaluator assessing how well an AI agent's output aligns with its assigned task goal.
 
 Score the agent's goal alignment on a scale from 0-10 where:
 - 0: Complete misalignment, agent did not understand or attempt the task goal
@@ -44,11 +37,8 @@ Consider:
 4. Did the agent provide all requested information or deliverables?
 
 Return your evaluation as JSON with fields 'score' (number) and 'feedback' (string).
-""",
-            },
-            {
-                "role": "user",
-                "content": f"""
+"""},
+            {"role": "user", "content": f"""
 Agent role: {agent.role}
 Agent goal: {agent.goal}
 {task_context}
@@ -57,26 +47,23 @@ Agent's final output:
 {final_output}
 
 Evaluate how well the agent's output aligns with the assigned task goal.
-""",
-            },
+"""}
         ]
-        if self.llm is None:
-            raise ValueError("LLM must be initialized")
+        assert self.llm is not None
         response = self.llm.call(prompt)
 
         try:
             evaluation_data: dict[str, Any] = extract_json_from_llm_response(response)
-            if evaluation_data is None:
-                raise ValueError("Failed to extract evaluation data from LLM response")
+            assert evaluation_data is not None
 
             return EvaluationScore(
                 score=evaluation_data.get("score", 0),
                 feedback=evaluation_data.get("feedback", response),
-                raw_response=response,
+                raw_response=response
             )
         except Exception:
             return EvaluationScore(
                 score=None,
                 feedback=f"Failed to parse evaluation. Raw response: {response}",
-                raw_response=response,
+                raw_response=response
             )

src/crewai/experimental/evaluation/metrics/reasoning_metrics.py

@@ -8,23 +8,17 @@ This module provides evaluator implementations for:
 
 import logging
 import re
-from collections.abc import Sequence
 from enum import Enum
-from typing import Any
-
+from typing import Any, Dict, List, Tuple
 import numpy as np
+from collections.abc import Sequence
 
 from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.experimental.evaluation.base_evaluator import (
-    BaseEvaluator,
-    EvaluationScore,
-    MetricCategory,
-)
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
 
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
+from crewai.tasks.task_output import TaskOutput
 
 class ReasoningPatternType(Enum):
     EFFICIENT = "efficient"  # Good reasoning flow
@@ -41,8 +35,8 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: TaskOutput | str,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -55,7 +49,7 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
         if not llm_calls or len(llm_calls) < 2:
             return EvaluationScore(
                 score=None,
-                feedback="Insufficient LLM calls to evaluate reasoning efficiency.",
+                feedback="Insufficient LLM calls to evaluate reasoning efficiency."
             )
 
         total_calls = len(llm_calls)
@@ -64,16 +58,12 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
         time_intervals = []
         has_reliable_timing = True
         for i in range(1, len(llm_calls)):
-            start_time = llm_calls[i - 1].get("end_time")
+            start_time = llm_calls[i-1].get("end_time")
             end_time = llm_calls[i].get("start_time")
             if start_time and end_time and start_time != end_time:
                 try:
                     interval = end_time - start_time
-                    time_intervals.append(
-                        interval.total_seconds()
-                        if hasattr(interval, "total_seconds")
-                        else 0
-                    )
+                    time_intervals.append(interval.total_seconds() if hasattr(interval, 'total_seconds') else 0)
                 except Exception:
                     has_reliable_timing = False
             else:
@@ -93,22 +83,14 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
         if has_reliable_timing and time_intervals:
             efficiency_metrics["avg_time_between_calls"] = np.mean(time_intervals)
 
-        loop_info = (
-            f"Detected {len(loop_details)} potential reasoning loops."
-            if loop_detected
-            else "No significant reasoning loops detected."
-        )
+        loop_info = f"Detected {len(loop_details)} potential reasoning loops." if loop_detected else "No significant reasoning loops detected."
 
         call_samples = self._get_call_samples(llm_calls)
 
-        final_output = (
-            final_output.raw if isinstance(final_output, TaskOutput) else final_output
-        )
+        final_output = final_output.raw if isinstance(final_output, TaskOutput) else final_output
 
         prompt = [
-            {
-                "role": "system",
-                "content": """You are an expert evaluator assessing the reasoning efficiency of an AI agent's thought process.
+            {"role": "system", "content": """You are an expert evaluator assessing the reasoning efficiency of an AI agent's thought process.
 
 Evaluate the agent's reasoning efficiency across these five key subcategories:
 
@@ -138,11 +120,8 @@ Return your evaluation as JSON with the following structure:
     "feedback": string (general feedback about overall reasoning efficiency),
     "optimization_suggestions": string (concrete suggestions for improving reasoning efficiency),
     "detected_patterns": string (describe any inefficient reasoning patterns you observe)
-}""",
-            },
-            {
-                "role": "user",
-                "content": f"""
+}"""},
+            {"role": "user", "content": f"""
 Agent role: {agent.role}
 {task_context}
 
@@ -161,12 +140,10 @@ Agent's final output:
 
 Evaluate the reasoning efficiency of this agent based on these interaction patterns.
 Identify any inefficient reasoning patterns and provide specific suggestions for optimization.
-""",
-            },
+"""}
         ]
 
-        if self.llm is None:
-            raise ValueError("LLM must be initialized")
+        assert self.llm is not None
         response = self.llm.call(prompt)
 
         try:
@@ -179,46 +156,34 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
             conciseness = scores.get("conciseness", 5.0)
             loop_avoidance = scores.get("loop_avoidance", 5.0)
 
-            overall_score = evaluation_data.get(
-                "overall_score", evaluation_data.get("score", 5.0)
-            )
+            overall_score = evaluation_data.get("overall_score", evaluation_data.get("score", 5.0))
             feedback = evaluation_data.get("feedback", "No detailed feedback provided.")
-            optimization_suggestions = evaluation_data.get(
-                "optimization_suggestions", "No specific suggestions provided."
-            )
+            optimization_suggestions = evaluation_data.get("optimization_suggestions", "No specific suggestions provided.")
 
             detailed_feedback = "Reasoning Efficiency Evaluation:\n"
-            detailed_feedback += (
-                f"• Focus: {focus}/10 - Staying on topic without tangents\n"
-            )
-            detailed_feedback += (
-                f"• Progression: {progression}/10 - Building on previous thinking\n"
-            )
+            detailed_feedback += f"• Focus: {focus}/10 - Staying on topic without tangents\n"
+            detailed_feedback += f"• Progression: {progression}/10 - Building on previous thinking\n"
             detailed_feedback += f"• Decision Quality: {decision_quality}/10 - Making appropriate decisions\n"
-            detailed_feedback += (
-                f"• Conciseness: {conciseness}/10 - Communicating efficiently\n"
-            )
+            detailed_feedback += f"• Conciseness: {conciseness}/10 - Communicating efficiently\n"
             detailed_feedback += f"• Loop Avoidance: {loop_avoidance}/10 - Avoiding repetitive patterns\n\n"
 
             detailed_feedback += f"Feedback:\n{feedback}\n\n"
-            detailed_feedback += (
-                f"Optimization Suggestions:\n{optimization_suggestions}"
-            )
+            detailed_feedback += f"Optimization Suggestions:\n{optimization_suggestions}"
 
             return EvaluationScore(
                 score=float(overall_score),
                 feedback=detailed_feedback,
-                raw_response=response,
+                raw_response=response
             )
         except Exception as e:
             logging.warning(f"Failed to parse reasoning efficiency evaluation: {e}")
             return EvaluationScore(
                 score=None,
                 feedback=f"Failed to parse reasoning efficiency evaluation. Raw response: {response[:200]}...",
-                raw_response=response,
+                raw_response=response
             )
 
-    def _detect_loops(self, llm_calls: list[dict]) -> tuple[bool, list[dict]]:
+    def _detect_loops(self, llm_calls: List[Dict]) -> Tuple[bool, List[Dict]]:
         loop_details = []
 
         messages = []
@@ -228,11 +193,9 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
                 messages.append(content)
             elif isinstance(content, list) and len(content) > 0:
                 # Handle message list format
-                messages.extend(
-                    msg["content"]
-                    for msg in content
-                    if isinstance(msg, dict) and "content" in msg
-                )
+                for msg in content:
+                    if isinstance(msg, dict) and "content" in msg:
+                        messages.append(msg["content"])
 
         # Simple n-gram based similarity detection
         # For a more robust implementation, consider using embedding-based similarity
@@ -242,20 +205,18 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
                 # A more sophisticated approach would use semantic similarity
                 similarity = self._calculate_text_similarity(messages[i], messages[j])
                 if similarity > 0.7:  # Arbitrary threshold
-                    loop_details.append(
-                        {
-                            "first_occurrence": i,
-                            "second_occurrence": j,
-                            "similarity": similarity,
-                            "snippet": messages[i][:100] + "...",
-                        }
-                    )
+                    loop_details.append({
+                        "first_occurrence": i,
+                        "second_occurrence": j,
+                        "similarity": similarity,
+                        "snippet": messages[i][:100] + "..."
+                    })
 
         return len(loop_details) > 0, loop_details
 
     def _calculate_text_similarity(self, text1: str, text2: str) -> float:
-        text1 = re.sub(r"\s+", " ", text1.lower()).strip()
-        text2 = re.sub(r"\s+", " ", text2.lower()).strip()
+        text1 = re.sub(r'\s+', ' ', text1.lower()).strip()
+        text2 = re.sub(r'\s+', ' ', text2.lower()).strip()
 
         # Simple Jaccard similarity on word sets
         words1 = set(text1.split())
@@ -266,7 +227,7 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
 
         return intersection / union if union > 0 else 0.0
 
-    def _analyze_reasoning_patterns(self, llm_calls: list[dict]) -> dict[str, Any]:
+    def _analyze_reasoning_patterns(self, llm_calls: List[Dict]) -> Dict[str, Any]:
         call_lengths = []
         response_times = []
 
@@ -287,8 +248,8 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
             if start_time and end_time:
                 try:
                     response_times.append(end_time - start_time)
-                except Exception as e:
-                    logging.debug(f"Failed to calculate response time: {e}")
+                except Exception:
+                    pass
 
         avg_length = np.mean(call_lengths) if call_lengths else 0
         std_length = np.std(call_lengths) if call_lengths else 0
@@ -306,9 +267,7 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
             details = "Agent is consistently verbose across interactions."
         elif len(llm_calls) > 10 and length_trend > 0.5:
             primary_pattern = ReasoningPatternType.INDECISIVE
-            details = (
-                "Agent shows signs of indecisiveness with increasing message lengths."
-            )
+            details = "Agent shows signs of indecisiveness with increasing message lengths."
         elif std_length / avg_length > 0.8:
             primary_pattern = ReasoningPatternType.SCATTERED
             details = "Agent shows inconsistent reasoning flow with highly variable responses."
@@ -320,8 +279,8 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
                 "avg_length": avg_length,
                 "std_length": std_length,
                 "length_trend": length_trend,
-                "loop_score": loop_score,
-            },
+                "loop_score": loop_score
+            }
         }
 
     def _calculate_trend(self, values: Sequence[float | int]) -> float:
@@ -344,9 +303,7 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
         except Exception:
             return 0.0
 
-    def _calculate_loop_likelihood(
-        self, call_lengths: Sequence[float], response_times: Sequence[float]
-    ) -> float:
+    def _calculate_loop_likelihood(self, call_lengths: Sequence[float], response_times: Sequence[float]) -> float:
         if not call_lengths or len(call_lengths) < 3:
             return 0.0
 
@@ -355,11 +312,7 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
         if len(call_lengths) >= 4:
             repeated_lengths = 0
             for i in range(len(call_lengths) - 2):
-                ratio = (
-                    call_lengths[i] / call_lengths[i + 2]
-                    if call_lengths[i + 2] > 0
-                    else 0
-                )
+                ratio = call_lengths[i] / call_lengths[i + 2] if call_lengths[i + 2] > 0 else 0
                 if 0.85 <= ratio <= 1.15:
                     repeated_lengths += 1
 
@@ -371,27 +324,21 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
                 std_time = np.std(response_times)
                 mean_time = np.mean(response_times)
                 if mean_time > 0:
-                    time_consistency = 1.0 - (float(std_time) / float(mean_time))
-                    indicators.append(max(0.0, float(time_consistency - 0.3)) * 1.5)
-            except Exception as e:
-                logging.debug(f"Time consistency calculation failed: {e}")
+                    time_consistency = 1.0 - (std_time / mean_time)
+                    indicators.append(max(0, time_consistency - 0.3) * 1.5)
+            except Exception:
+                pass
 
-        return float(np.mean(indicators)) if indicators else 0.0
+        return np.mean(indicators) if indicators else 0.0
 
-    def _get_call_samples(self, llm_calls: list[dict]) -> str:
+    def _get_call_samples(self, llm_calls: List[Dict]) -> str:
         samples = []
 
         if len(llm_calls) <= 6:
             sample_indices = list(range(len(llm_calls)))
         else:
-            sample_indices = [
-                0,
-                1,
-                len(llm_calls) // 2 - 1,
-                len(llm_calls) // 2,
-                len(llm_calls) - 2,
-                len(llm_calls) - 1,
-            ]
+            sample_indices = [0, 1, len(llm_calls) // 2 - 1, len(llm_calls) // 2,
+                             len(llm_calls) - 2, len(llm_calls) - 1]
 
         for idx in sample_indices:
             call = llm_calls[idx]
@@ -400,11 +347,10 @@ Identify any inefficient reasoning patterns and provide specific suggestions for
             if isinstance(content, str):
                 sample = content
             elif isinstance(content, list) and len(content) > 0:
-                sample_parts = [
-                    msg["content"]
-                    for msg in content
-                    if isinstance(msg, dict) and "content" in msg
-                ]
+                sample_parts = []
+                for msg in content:
+                    if isinstance(msg, dict) and "content" in msg:
+                        sample_parts.append(msg["content"])
                 sample = "\n".join(sample_parts)
             else:
                 sample = str(content)

src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py

@@ -1,15 +1,10 @@
-from typing import Any
+from typing import Any, Dict
 
 from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.experimental.evaluation.base_evaluator import (
-    BaseEvaluator,
-    EvaluationScore,
-    MetricCategory,
-)
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 from crewai.task import Task
 
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 
 class SemanticQualityEvaluator(BaseEvaluator):
     @property
@@ -18,8 +13,8 @@ class SemanticQualityEvaluator(BaseEvaluator):
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: Any,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -27,9 +22,7 @@ class SemanticQualityEvaluator(BaseEvaluator):
         if task is not None:
             task_context = f"Task description: {task.description}"
         prompt = [
-            {
-                "role": "system",
-                "content": """You are an expert evaluator assessing the semantic quality of an AI agent's output.
+            {"role": "system", "content": """You are an expert evaluator assessing the semantic quality of an AI agent's output.
 
 Score the semantic quality on a scale from 0-10 where:
 - 0: Completely incoherent, confusing, or logically flawed output
@@ -44,11 +37,8 @@ Consider:
 5. Is the output free from contradictions and logical fallacies?
 
 Return your evaluation as JSON with fields 'score' (number) and 'feedback' (string).
-""",
-            },
-            {
-                "role": "user",
-                "content": f"""
+"""},
+            {"role": "user", "content": f"""
 Agent role: {agent.role}
 {task_context}
 
@@ -56,28 +46,23 @@ Agent's final output:
 {final_output}
 
 Evaluate the semantic quality and reasoning of this output.
-""",
-            },
+"""}
         ]
 
-        if self.llm is None:
-            raise ValueError("LLM must be initialized")
+        assert self.llm is not None
         response = self.llm.call(prompt)
 
         try:
             evaluation_data: dict[str, Any] = extract_json_from_llm_response(response)
-            if evaluation_data is None:
-                raise ValueError("Failed to extract evaluation data from LLM response")
+            assert evaluation_data is not None
             return EvaluationScore(
-                score=float(evaluation_data["score"])
-                if evaluation_data.get("score") is not None
-                else None,
+                score=float(evaluation_data["score"]) if evaluation_data.get("score") is not None else None,
                 feedback=evaluation_data.get("feedback", response),
-                raw_response=response,
+                raw_response=response
             )
         except Exception:
             return EvaluationScore(
                 score=None,
                 feedback=f"Failed to parse evaluation. Raw response: {response}",
-                raw_response=response,
-            )
+                raw_response=response
+            )

src/crewai/experimental/evaluation/metrics/tools_metrics.py

@@ -1,26 +1,22 @@
 import json
-from typing import Any
+from typing import Dict, Any
 
-from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.experimental.evaluation.base_evaluator import (
-    BaseEvaluator,
-    EvaluationScore,
-    MetricCategory,
-)
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
 from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
+from crewai.agent import Agent
 from crewai.task import Task
 
 
 class ToolSelectionEvaluator(BaseEvaluator):
+
     @property
     def metric_category(self) -> MetricCategory:
         return MetricCategory.TOOL_SELECTION
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: str,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -30,18 +26,19 @@ class ToolSelectionEvaluator(BaseEvaluator):
 
         tool_uses = execution_trace.get("tool_uses", [])
         tool_count = len(tool_uses)
-        unique_tool_types = set(
-            [tool.get("tool", "Unknown tool") for tool in tool_uses]
-        )
+        unique_tool_types = set([tool.get("tool", "Unknown tool") for tool in tool_uses])
 
         if tool_count == 0:
             if not agent.tools:
                 return EvaluationScore(
-                    score=None, feedback="Agent had no tools available to use."
+                    score=None,
+                    feedback="Agent had no tools available to use."
+                )
+            else:
+                return EvaluationScore(
+                    score=None,
+                    feedback="Agent had tools available but didn't use any."
                 )
-            return EvaluationScore(
-                score=None, feedback="Agent had tools available but didn't use any."
-            )
 
         available_tools_info = ""
         if agent.tools:
@@ -55,9 +52,7 @@ class ToolSelectionEvaluator(BaseEvaluator):
             tool_types_summary += f"- {tool_type}\n"
 
         prompt = [
-            {
-                "role": "system",
-                "content": """You are an expert evaluator assessing if an AI agent selected the most appropriate tools for a given task.
+            {"role": "system", "content": """You are an expert evaluator assessing if an AI agent selected the most appropriate tools for a given task.
 
 You must evaluate based on these 2 criteria:
 1. Relevance (0-10): Were the tools chosen directly aligned with the task's goals?
@@ -78,11 +73,8 @@ Return your evaluation as JSON with these fields:
 - overall_score: number (average of all scores, 0-10)
 - feedback: string (focused ONLY on tool selection decisions from available tools)
 - improvement_suggestions: string (ONLY suggest better selection from the AVAILABLE tools list, NOT new tools)
-""",
-            },
-            {
-                "role": "user",
-                "content": f"""
+"""},
+            {"role": "user", "content": f"""
 Agent role: {agent.role}
 {task_context}
 
@@ -97,17 +89,14 @@ IMPORTANT:
 - ONLY evaluate selection from tools listed as available
 - DO NOT suggest new tools that aren't in the available tools list
 - DO NOT evaluate tool usage or results
-""",
-            },
+"""}
         ]
-        if self.llm is None:
-            raise ValueError("LLM must be initialized")
+        assert self.llm is not None
         response = self.llm.call(prompt)
 
         try:
             evaluation_data = extract_json_from_llm_response(response)
-            if evaluation_data is None:
-                raise ValueError("Failed to extract evaluation data from LLM response")
+            assert evaluation_data is not None
 
             scores = evaluation_data.get("scores", {})
             relevance = scores.get("relevance", 5.0)
@@ -116,24 +105,22 @@ IMPORTANT:
 
             feedback = "Tool Selection Evaluation:\n"
             feedback += f"• Relevance: {relevance}/10 - Selection of appropriate tool types for the task\n"
-            feedback += (
-                f"• Coverage: {coverage}/10 - Selection of all necessary tool types\n"
-            )
+            feedback += f"• Coverage: {coverage}/10 - Selection of all necessary tool types\n"
             if "improvement_suggestions" in evaluation_data:
                 feedback += f"Improvement Suggestions:\n{evaluation_data['improvement_suggestions']}"
             else:
-                feedback += evaluation_data.get(
-                    "feedback", "No detailed feedback available."
-                )
+                feedback += evaluation_data.get("feedback", "No detailed feedback available.")
 
             return EvaluationScore(
-                score=overall_score, feedback=feedback, raw_response=response
+                score=overall_score,
+                feedback=feedback,
+                raw_response=response
             )
         except Exception as e:
             return EvaluationScore(
                 score=None,
                 feedback=f"Error evaluating tool selection: {e}",
-                raw_response=response,
+                raw_response=response
             )
 
 
@@ -144,8 +131,8 @@ class ParameterExtractionEvaluator(BaseEvaluator):
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: str,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -158,23 +145,19 @@ class ParameterExtractionEvaluator(BaseEvaluator):
         if tool_count == 0:
             return EvaluationScore(
                 score=None,
-                feedback="No tool usage detected. Cannot evaluate parameter extraction.",
+                feedback="No tool usage detected. Cannot evaluate parameter extraction."
             )
 
-        validation_errors = [
-            {
-                "tool": tool_use.get("tool", "Unknown tool"),
-                "error": tool_use.get("result"),
-                "args": tool_use.get("args", {}),
-            }
-            for tool_use in tool_uses
-            if not tool_use.get("success", True)
-            and tool_use.get("error_type") == "validation_error"
-        ]
+        validation_errors = []
+        for tool_use in tool_uses:
+            if not tool_use.get("success", True) and tool_use.get("error_type") == "validation_error":
+                validation_errors.append({
+                    "tool": tool_use.get("tool", "Unknown tool"),
+                    "error": tool_use.get("result"),
+                    "args": tool_use.get("args", {})
+                })
 
-        validation_error_rate = (
-            len(validation_errors) / tool_count if tool_count > 0 else 0
-        )
+        validation_error_rate = len(validation_errors) / tool_count if tool_count > 0 else 0
 
         param_samples = []
         for i, tool_use in enumerate(tool_uses[:5]):
@@ -185,7 +168,7 @@ class ParameterExtractionEvaluator(BaseEvaluator):
 
             is_validation_error = error_type == "validation_error"
 
-            sample = f"Tool use #{i + 1} - {tool_name}:\n"
+            sample = f"Tool use #{i+1} - {tool_name}:\n"
             sample += f"- Parameters: {json.dumps(tool_args, indent=2)}\n"
             sample += f"- Success: {'No' if not success else 'Yes'}"
 
@@ -204,17 +187,13 @@ class ParameterExtractionEvaluator(BaseEvaluator):
                 tool_name = err.get("tool", "Unknown tool")
                 error_msg = err.get("error", "Unknown error")
                 args = err.get("args", {})
-                validation_errors_info += f"\nValidation Error #{i + 1}:\n- Tool: {tool_name}\n- Args: {json.dumps(args, indent=2)}\n- Error: {error_msg}"
+                validation_errors_info += f"\nValidation Error #{i+1}:\n- Tool: {tool_name}\n- Args: {json.dumps(args, indent=2)}\n- Error: {error_msg}"
 
             if len(validation_errors) > 3:
-                validation_errors_info += (
-                    f"\n...and {len(validation_errors) - 3} more validation errors."
-                )
+                validation_errors_info += f"\n...and {len(validation_errors) - 3} more validation errors."
         param_samples_text = "\n\n".join(param_samples)
         prompt = [
-            {
-                "role": "system",
-                "content": """You are an expert evaluator assessing how well an AI agent extracts and formats PARAMETER VALUES for tool calls.
+            {"role": "system", "content": """You are an expert evaluator assessing how well an AI agent extracts and formats PARAMETER VALUES for tool calls.
 
 Your job is to evaluate ONLY whether the agent used the correct parameter VALUES, not whether the right tools were selected or how the tools were invoked.
 
@@ -237,11 +216,8 @@ Return your evaluation as JSON with these fields:
 - overall_score: number (average of all scores, 0-10)
 - feedback: string (focused ONLY on parameter value extraction quality)
 - improvement_suggestions: string (concrete suggestions for better parameter VALUE extraction)
-""",
-            },
-            {
-                "role": "user",
-                "content": f"""
+"""},
+            {"role": "user", "content": f"""
 Agent role: {agent.role}
 {task_context}
 
@@ -250,18 +226,15 @@ Parameter extraction examples:
 {validation_errors_info}
 
 Evaluate the quality of the agent's parameter extraction for this task.
-""",
-            },
+"""}
         ]
 
-        if self.llm is None:
-            raise ValueError("LLM must be initialized")
+        assert self.llm is not None
         response = self.llm.call(prompt)
 
         try:
             evaluation_data = extract_json_from_llm_response(response)
-            if evaluation_data is None:
-                raise ValueError("Failed to extract evaluation data from LLM response")
+            assert evaluation_data is not None
 
             scores = evaluation_data.get("scores", {})
             accuracy = scores.get("accuracy", 5.0)
@@ -278,18 +251,18 @@ Evaluate the quality of the agent's parameter extraction for this task.
             if "improvement_suggestions" in evaluation_data:
                 feedback += f"Improvement Suggestions:\n{evaluation_data['improvement_suggestions']}"
             else:
-                feedback += evaluation_data.get(
-                    "feedback", "No detailed feedback available."
-                )
+                feedback += evaluation_data.get("feedback", "No detailed feedback available.")
 
             return EvaluationScore(
-                score=overall_score, feedback=feedback, raw_response=response
+                score=overall_score,
+                feedback=feedback,
+                raw_response=response
             )
         except Exception as e:
             return EvaluationScore(
                 score=None,
                 feedback=f"Error evaluating parameter extraction: {e}",
-                raw_response=response,
+                raw_response=response
             )
 
 
@@ -300,8 +273,8 @@ class ToolInvocationEvaluator(BaseEvaluator):
 
     def evaluate(
         self,
-        agent: Agent | BaseAgent,
-        execution_trace: dict[str, Any],
+        agent: Agent,
+        execution_trace: Dict[str, Any],
         final_output: str,
         task: Task | None = None,
     ) -> EvaluationScore:
@@ -315,7 +288,7 @@ class ToolInvocationEvaluator(BaseEvaluator):
         if tool_count == 0:
             return EvaluationScore(
                 score=None,
-                feedback="No tool usage detected. Cannot evaluate tool invocation.",
+                feedback="No tool usage detected. Cannot evaluate tool invocation."
             )
 
         for tool_use in tool_uses:
@@ -323,7 +296,7 @@ class ToolInvocationEvaluator(BaseEvaluator):
                 error_info = {
                     "tool": tool_use.get("tool", "Unknown tool"),
                     "error": tool_use.get("result"),
-                    "error_type": tool_use.get("error_type", "unknown_error"),
+                    "error_type": tool_use.get("error_type", "unknown_error")
                 }
                 tool_errors.append(error_info)
 
@@ -342,11 +315,9 @@ class ToolInvocationEvaluator(BaseEvaluator):
             tool_args = tool_use.get("args", {})
             success = tool_use.get("success", True) and not tool_use.get("error", False)
             error_type = tool_use.get("error_type", "") if not success else ""
-            error_msg = (
-                tool_use.get("result", "No error") if not success else "No error"
-            )
+            error_msg = tool_use.get("result", "No error") if not success else "No error"
 
-            sample = f"Tool invocation #{i + 1}:\n"
+            sample = f"Tool invocation #{i+1}:\n"
             sample += f"- Tool: {tool_name}\n"
             sample += f"- Parameters: {json.dumps(tool_args, indent=2)}\n"
             sample += f"- Success: {'No' if not success else 'Yes'}\n"
@@ -359,13 +330,11 @@ class ToolInvocationEvaluator(BaseEvaluator):
         if error_types:
             error_type_summary = "Error type breakdown:\n"
             for error_type, count in error_types.items():
-                error_type_summary += f"- {error_type}: {count} occurrences ({(count / tool_count):.1%})\n"
+                error_type_summary += f"- {error_type}: {count} occurrences ({(count/tool_count):.1%})\n"
 
         invocation_samples_text = "\n\n".join(invocation_samples)
         prompt = [
-            {
-                "role": "system",
-                "content": """You are an expert evaluator assessing how correctly an AI agent's tool invocations are STRUCTURED.
+            {"role": "system", "content": """You are an expert evaluator assessing how correctly an AI agent's tool invocations are STRUCTURED.
 
 Your job is to evaluate ONLY the structural and syntactical aspects of how the agent called tools, NOT which tools were selected or what parameter values were used.
 
@@ -390,11 +359,8 @@ Return your evaluation as JSON with these fields:
 - overall_score: number (average of all scores, 0-10)
 - feedback: string (focused ONLY on structural aspects of tool invocation)
 - improvement_suggestions: string (concrete suggestions for better structuring of tool calls)
-""",
-            },
-            {
-                "role": "user",
-                "content": f"""
+"""},
+            {"role": "user", "content": f"""
 Agent role: {agent.role}
 {task_context}
 
@@ -405,18 +371,15 @@ Tool error rate: {error_rate:.2%} ({len(tool_errors)} errors out of {tool_count}
 {error_type_summary}
 
 Evaluate the quality of the agent's tool invocation structure during this task.
-""",
-            },
+"""}
         ]
 
-        if self.llm is None:
-            raise ValueError("LLM must be initialized")
+        assert self.llm is not None
         response = self.llm.call(prompt)
 
         try:
             evaluation_data = extract_json_from_llm_response(response)
-            if evaluation_data is None:
-                raise ValueError("Failed to extract evaluation data from LLM response")
+            assert evaluation_data is not None
             scores = evaluation_data.get("scores", {})
             structure = scores.get("structure", 5.0)
             error_handling = scores.get("error_handling", 5.0)
@@ -425,25 +388,23 @@ Evaluate the quality of the agent's tool invocation structure during this task.
             overall_score = float(evaluation_data.get("overall_score", 5.0))
 
             feedback = "Tool Invocation Evaluation:\n"
-            feedback += (
-                f"• Structure: {structure}/10 - Following proper syntax and format\n"
-            )
+            feedback += f"• Structure: {structure}/10 - Following proper syntax and format\n"
             feedback += f"• Error Handling: {error_handling}/10 - Appropriately handling tool errors\n"
             feedback += f"• Invocation Patterns: {invocation_patterns}/10 - Proper sequencing and management of calls\n\n"
 
             if "improvement_suggestions" in evaluation_data:
                 feedback += f"Improvement Suggestions:\n{evaluation_data['improvement_suggestions']}"
             else:
-                feedback += evaluation_data.get(
-                    "feedback", "No detailed feedback available."
-                )
+                feedback += evaluation_data.get("feedback", "No detailed feedback available.")
 
             return EvaluationScore(
-                score=overall_score, feedback=feedback, raw_response=response
+                score=overall_score,
+                feedback=feedback,
+                raw_response=response
             )
         except Exception as e:
             return EvaluationScore(
                 score=None,
                 feedback=f"Error evaluating tool invocation: {e}",
-                raw_response=response,
+                raw_response=response
             )

src/crewai/experimental/evaluation/testing.py

@@ -1,21 +1,12 @@
 import inspect
-import warnings
 
 from typing_extensions import Any
+import warnings
+from crewai.experimental.evaluation.experiment import ExperimentResults, ExperimentRunner
+from crewai import Crew, Agent
 
-from crewai import Agent, Crew
-from crewai.experimental.evaluation.experiment import (
-    ExperimentResults,
-    ExperimentRunner,
-)
-
-
-def assert_experiment_successfully(
-    experiment_results: ExperimentResults, baseline_filepath: str | None = None
-) -> None:
-    failed_tests = [
-        result for result in experiment_results.results if not result.passed
-    ]
+def assert_experiment_successfully(experiment_results: ExperimentResults, baseline_filepath: str | None = None) -> None:
+    failed_tests = [result for result in experiment_results.results if not result.passed]
 
     if failed_tests:
         detailed_failures: list[str] = []
@@ -23,54 +14,39 @@ def assert_experiment_successfully(
         for result in failed_tests:
             expected = result.expected_score
             actual = result.score
-            detailed_failures.append(
-                f"- {result.identifier}: expected {expected}, got {actual}"
-            )
+            detailed_failures.append(f"- {result.identifier}: expected {expected}, got {actual}")
 
         failure_details = "\n".join(detailed_failures)
         raise AssertionError(f"The following test cases failed:\n{failure_details}")
 
     baseline_filepath = baseline_filepath or _get_baseline_filepath_fallback()
-    comparison = experiment_results.compare_with_baseline(
-        baseline_filepath=baseline_filepath
-    )
+    comparison = experiment_results.compare_with_baseline(baseline_filepath=baseline_filepath)
     assert_experiment_no_regression(comparison)
 
-
 def assert_experiment_no_regression(comparison_result: dict[str, list[str]]) -> None:
     regressed = comparison_result.get("regressed", [])
     if regressed:
-        raise AssertionError(
-            f"Regression detected! The following tests that previously passed now fail: {regressed}"
-        )
+        raise AssertionError(f"Regression detected! The following tests that previously passed now fail: {regressed}")
 
     missing_tests = comparison_result.get("missing_tests", [])
     if missing_tests:
         warnings.warn(
             f"Warning: {len(missing_tests)} tests from the baseline are missing in the current run: {missing_tests}",
-            UserWarning,
-            stacklevel=2,
+            UserWarning
         )
 
-
-def run_experiment(
-    dataset: list[dict[str, Any]],
-    crew: Crew | None = None,
-    agents: list[Agent] | None = None,
-    verbose: bool = False,
-) -> ExperimentResults:
+def run_experiment(dataset: list[dict[str, Any]], crew: Crew | None = None, agents: list[Agent] | None = None, verbose: bool = False) -> ExperimentResults:
     runner = ExperimentRunner(dataset=dataset)
 
     return runner.run(agents=agents, crew=crew, print_summary=verbose)
 
-
 def _get_baseline_filepath_fallback() -> str:
     test_func_name = "experiment_fallback"
 
     try:
         current_frame = inspect.currentframe()
         if current_frame is not None:
-            test_func_name = current_frame.f_back.f_back.f_code.co_name  # type: ignore[union-attr]
+            test_func_name = current_frame.f_back.f_back.f_code.co_name # type: ignore[union-attr]
     except Exception:
         ...
-    return f"{test_func_name}_results.json"
+    return f"{test_func_name}_results.json"

src/crewai/flow/__init__.py

@@ -1,4 +1,5 @@
-from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.flow import Flow, start, listen, or_, and_, router
 from crewai.flow.persistence import persist
 
-__all__ = ["Flow", "and_", "listen", "or_", "persist", "router", "start"]
+__all__ = ["Flow", "start", "listen", "or_", "and_", "router", "persist"]
+

src/crewai/flow/flow.py

@@ -2,22 +2,30 @@ import asyncio
 import copy
 import inspect
 import logging
-from collections.abc import Callable
-from typing import Any, ClassVar, Generic, TypeVar, cast
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Set,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
 from uuid import uuid4
 
 from opentelemetry import baggage
 from opentelemetry.context import attach, detach
 from pydantic import BaseModel, Field, ValidationError
 
+from crewai.flow.flow_visualizer import plot_flow
+from crewai.flow.persistence.base import FlowPersistence
+from crewai.flow.types import FlowExecutionData
+from crewai.flow.utils import get_possible_return_constants
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.listeners.tracing.trace_listener import (
-    TraceCollectionListener,
-)
-from crewai.events.listeners.tracing.utils import (
-    is_tracing_enabled,
-    should_auto_collect_first_time_traces,
-)
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
@@ -27,11 +35,13 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from crewai.flow.flow_visualizer import plot_flow
-from crewai.flow.persistence.base import FlowPersistence
-from crewai.flow.types import FlowExecutionData
-from crewai.flow.utils import get_possible_return_constants
-from crewai.utilities.printer import Printer, PrinterColor
+from crewai.events.listeners.tracing.trace_listener import (
+    TraceCollectionListener,
+)
+from crewai.events.listeners.tracing.utils import (
+    is_tracing_enabled,
+)
+from crewai.utilities.printer import Printer
 
 logger = logging.getLogger(__name__)
 
@@ -45,14 +55,16 @@ class FlowState(BaseModel):
     )
 
 
-# type variables with explicit bounds
-T = TypeVar("T", bound=dict[str, Any] | BaseModel)  # Generic flow state type parameter
+# Type variables with explicit bounds
+T = TypeVar(
+    "T", bound=Union[Dict[str, Any], BaseModel]
+)  # Generic flow state type parameter
 StateT = TypeVar(
-    "StateT", bound=dict[str, Any] | BaseModel
+    "StateT", bound=Union[Dict[str, Any], BaseModel]
 )  # State validation type parameter
 
 
-def ensure_state_type(state: Any, expected_type: type[StateT]) -> StateT:
+def ensure_state_type(state: Any, expected_type: Type[StateT]) -> StateT:
     """Ensure state matches expected type with proper validation.
 
     Args:
@@ -92,7 +104,7 @@ def ensure_state_type(state: Any, expected_type: type[StateT]) -> StateT:
     raise TypeError(f"Invalid expected_type: {expected_type}")
 
 
-def start(condition: str | dict | Callable | None = None) -> Callable:
+def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
     """
     Marks a method as a flow's starting point.
 
@@ -159,7 +171,7 @@ def start(condition: str | dict | Callable | None = None) -> Callable:
     return decorator
 
 
-def listen(condition: str | dict | Callable) -> Callable:
+def listen(condition: Union[str, dict, Callable]) -> Callable:
     """
     Creates a listener that executes when specified conditions are met.
 
@@ -219,7 +231,7 @@ def listen(condition: str | dict | Callable) -> Callable:
     return decorator
 
 
-def router(condition: str | dict | Callable) -> Callable:
+def router(condition: Union[str, dict, Callable]) -> Callable:
     """
     Creates a routing method that directs flow execution based on conditions.
 
@@ -285,7 +297,7 @@ def router(condition: str | dict | Callable) -> Callable:
     return decorator
 
 
-def or_(*conditions: str | dict | Callable) -> dict:
+def or_(*conditions: Union[str, dict, Callable]) -> dict:
     """
     Combines multiple conditions with OR logic for flow control.
 
@@ -331,7 +343,7 @@ def or_(*conditions: str | dict | Callable) -> dict:
     return {"type": "OR", "methods": methods}
 
 
-def and_(*conditions: str | dict | Callable) -> dict:
+def and_(*conditions: Union[str, dict, Callable]) -> dict:
     """
     Combines multiple conditions with AND logic for flow control.
 
@@ -413,10 +425,10 @@ class FlowMeta(type):
                         if possible_returns:
                             router_paths[attr_name] = possible_returns
 
-        cls._start_methods = start_methods
-        cls._listeners = listeners
-        cls._routers = routers
-        cls._router_paths = router_paths
+        setattr(cls, "_start_methods", start_methods)
+        setattr(cls, "_listeners", listeners)
+        setattr(cls, "_routers", routers)
+        setattr(cls, "_router_paths", router_paths)
 
         return cls
 
@@ -424,29 +436,29 @@ class FlowMeta(type):
 class Flow(Generic[T], metaclass=FlowMeta):
     """Base class for all flows.
 
-    type parameter T must be either dict[str, Any] or a subclass of BaseModel."""
+    Type parameter T must be either Dict[str, Any] or a subclass of BaseModel."""
 
     _printer = Printer()
 
-    _start_methods: ClassVar[list[str]] = []
-    _listeners: ClassVar[dict[str, tuple[str, list[str]]]] = {}
-    _routers: ClassVar[set[str]] = set()
-    _router_paths: ClassVar[dict[str, list[str]]] = {}
-    initial_state: type[T] | T | None = None
-    name: str | None = None
-    tracing: bool | None = False
+    _start_methods: List[str] = []
+    _listeners: Dict[str, tuple[str, List[str]]] = {}
+    _routers: Set[str] = set()
+    _router_paths: Dict[str, List[str]] = {}
+    initial_state: Union[Type[T], T, None] = None
+    name: Optional[str] = None
+    tracing: Optional[bool] = False
 
-    def __class_getitem__(cls: type["Flow"], item: type[T]) -> type["Flow"]:
+    def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
-            _initial_state_t = item  # type: ignore
+            _initial_state_T = item  # type: ignore
 
         _FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]"
         return _FlowGeneric
 
     def __init__(
         self,
-        persistence: FlowPersistence | None = None,
-        tracing: bool | None = False,
+        persistence: Optional[FlowPersistence] = None,
+        tracing: Optional[bool] = False,
         **kwargs: Any,
     ) -> None:
         """Initialize a new Flow instance.
@@ -456,24 +468,23 @@ class Flow(Generic[T], metaclass=FlowMeta):
             **kwargs: Additional state values to initialize or override
         """
         # Initialize basic instance attributes
-        self._methods: dict[str, Callable] = {}
-        self._method_execution_counts: dict[str, int] = {}
-        self._pending_and_listeners: dict[str, set[str]] = {}
-        self._method_outputs: list[Any] = []  # list to store all method outputs
-        self._completed_methods: set[str] = set()  # Track completed methods for reload
-        self._persistence: FlowPersistence | None = persistence
+        self._methods: Dict[str, Callable] = {}
+        self._method_execution_counts: Dict[str, int] = {}
+        self._pending_and_listeners: Dict[str, Set[str]] = {}
+        self._method_outputs: List[Any] = []  # List to store all method outputs
+        self._completed_methods: Set[str] = set()  # Track completed methods for reload
+        self._persistence: Optional[FlowPersistence] = persistence
         self._is_execution_resuming: bool = False
 
         # Initialize state with initial values
-        self._state = self._create_initial_state(kwargs)
+        self._state = self._create_initial_state()
         self.tracing = tracing
-        if (
-            is_tracing_enabled()
-            or self.tracing
-            or should_auto_collect_first_time_traces()
-        ):
+        if is_tracing_enabled() or self.tracing:
             trace_listener = TraceCollectionListener()
             trace_listener.setup_listeners(crewai_event_bus)
+        # Apply any additional kwargs
+        if kwargs:
+            self._initialize_state(kwargs)
 
         crewai_event_bus.emit(
             self,
@@ -499,12 +510,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         method = method.__get__(self, self.__class__)
                     self._methods[method_name] = method
 
-    def _create_initial_state(self, kwargs: dict[str, Any] | None = None) -> T:
+    def _create_initial_state(self) -> T:
         """Create and initialize flow state with UUID and default values.
 
-        Args:
-            kwargs: Optional initial values for state fields
-
         Returns:
             New state instance with UUID and default values initialized
 
@@ -513,27 +521,25 @@ class Flow(Generic[T], metaclass=FlowMeta):
             TypeError: If state is neither BaseModel nor dictionary
         """
         # Handle case where initial_state is None but we have a type parameter
-        if self.initial_state is None and hasattr(self, "_initial_state_t"):
-            state_type = self._initial_state_t
+        if self.initial_state is None and hasattr(self, "_initial_state_T"):
+            state_type = getattr(self, "_initial_state_T")
             if isinstance(state_type, type):
                 if issubclass(state_type, FlowState):
                     # Create instance without id, then set it
-                    init_kwargs = kwargs or {}
-                    instance = state_type(**init_kwargs)
+                    instance = state_type()
                     if not hasattr(instance, "id"):
-                        instance.id = str(uuid4())
+                        setattr(instance, "id", str(uuid4()))
                     return cast(T, instance)
-                if issubclass(state_type, BaseModel):
+                elif issubclass(state_type, BaseModel):
                     # Create a new type that includes the ID field
                     class StateWithId(state_type, FlowState):  # type: ignore
                         pass
 
-                    init_kwargs = kwargs or {}
-                    instance = StateWithId(**init_kwargs)
+                    instance = StateWithId()
                     if not hasattr(instance, "id"):
-                        instance.id = str(uuid4())
+                        setattr(instance, "id", str(uuid4()))
                     return cast(T, instance)
-                if state_type is dict:
+                elif state_type is dict:
                     return cast(T, {"id": str(uuid4())})
 
         # Handle case where no initial state is provided
@@ -543,14 +549,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # Handle case where initial_state is a type (class)
         if isinstance(self.initial_state, type):
             if issubclass(self.initial_state, FlowState):
-                return cast(T, self.initial_state(**(kwargs or {})))
-            if issubclass(self.initial_state, BaseModel):
+                return cast(T, self.initial_state())  # Uses model defaults
+            elif issubclass(self.initial_state, BaseModel):
                 # Validate that the model has an id field
                 model_fields = getattr(self.initial_state, "model_fields", None)
                 if not model_fields or "id" not in model_fields:
                     raise ValueError("Flow state model must have an 'id' field")
-                return cast(T, self.initial_state(**(kwargs or {})))
-            if self.initial_state is dict:
+                return cast(T, self.initial_state())  # Uses model defaults
+            elif self.initial_state is dict:
                 return cast(T, {"id": str(uuid4())})
 
         # Handle dictionary instance case
@@ -594,7 +600,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return self._state
 
     @property
-    def method_outputs(self) -> list[Any]:
+    def method_outputs(self) -> List[Any]:
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
@@ -625,13 +631,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             if isinstance(self._state, dict):
                 return str(self._state.get("id", ""))
-            if isinstance(self._state, BaseModel):
+            elif isinstance(self._state, BaseModel):
                 return str(getattr(self._state, "id", ""))
             return ""
         except (AttributeError, TypeError):
             return ""  # Safely handle any unexpected attribute access issues
 
-    def _initialize_state(self, inputs: dict[str, Any]) -> None:
+    def _initialize_state(self, inputs: Dict[str, Any]) -> None:
         """Initialize or update flow state with new inputs.
 
         Args:
@@ -685,7 +691,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         else:
             raise TypeError("State must be a BaseModel instance or a dictionary.")
 
-    def _restore_state(self, stored_state: dict[str, Any]) -> None:
+    def _restore_state(self, stored_state: Dict[str, Any]) -> None:
         """Restore flow state from persistence.
 
         Args:
@@ -729,7 +735,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             execution_data: Flow execution data containing:
                 - id: Flow execution ID
                 - flow: Flow structure
-                - completed_methods: list of successfully completed methods
+                - completed_methods: List of successfully completed methods
                 - execution_methods: All execution methods with their status
         """
         flow_id = execution_data.get("id")
@@ -765,7 +771,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if state_to_apply:
             self._apply_state_updates(state_to_apply)
 
-        for method in sorted_methods[:-1]:
+        for i, method in enumerate(sorted_methods[:-1]):
             method_name = method.get("flow_method", {}).get("name")
             if method_name:
                 self._completed_methods.add(method_name)
@@ -777,7 +783,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         elif hasattr(self._state, field_name):
             object.__setattr__(self._state, field_name, value)
 
-    def _apply_state_updates(self, updates: dict[str, Any]) -> None:
+    def _apply_state_updates(self, updates: Dict[str, Any]) -> None:
         """Apply multiple state updates efficiently."""
         if isinstance(self._state, dict):
             self._state.update(updates)
@@ -786,7 +792,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if hasattr(self._state, key):
                     object.__setattr__(self._state, key, value)
 
-    def kickoff(self, inputs: dict[str, Any] | None = None) -> Any:
+    def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
         """
         Start the flow execution in a synchronous context.
 
@@ -799,7 +805,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         return asyncio.run(run_flow())
 
-    async def kickoff_async(self, inputs: dict[str, Any] | None = None) -> Any:
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
         """
         Start the flow execution asynchronously.
 
@@ -834,7 +840,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     if isinstance(self._state, dict):
                         self._state["id"] = inputs["id"]
                     elif isinstance(self._state, BaseModel):
-                        setattr(self._state, "id", inputs["id"])  # noqa: B010
+                        setattr(self._state, "id", inputs["id"])
 
                 # If persistence is enabled, attempt to restore the stored state using the provided id.
                 if "id" in inputs and self._persistence is not None:
@@ -1069,7 +1075,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 )
 
         # Now execute normal listeners for all router results and the original trigger
-        all_triggers = [trigger_method, *router_results]
+        all_triggers = [trigger_method] + router_results
 
         for current_trigger in all_triggers:
             if current_trigger:  # Skip None results
@@ -1088,7 +1094,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     for method_name in self._start_methods:
                         # Check if this start method is triggered by the current trigger
                         if method_name in self._listeners:
-                            _, trigger_methods = self._listeners[
+                            condition_type, trigger_methods = self._listeners[
                                 method_name
                             ]
                             if current_trigger in trigger_methods:
@@ -1103,7 +1109,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
     def _find_triggered_methods(
         self, trigger_method: str, router_only: bool
-    ) -> list[str]:
+    ) -> List[str]:
         """
         Finds all methods that should be triggered based on conditions.
 
@@ -1120,7 +1126,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         Returns
         -------
-        list[str]
+        List[str]
             Names of methods that should be triggered.
 
         Notes
@@ -1220,7 +1226,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             raise
 
     def _log_flow_event(
-        self, message: str, color: PrinterColor = "yellow", level: str = "info"
+        self, message: str, color: str = "yellow", level: str = "info"
     ) -> None:
         """Centralized logging method for flow events.
 

src/crewai/flow/flow_trackable.py

@@ -1,4 +1,5 @@
 import inspect
+from typing import Optional
 
 from pydantic import BaseModel, Field, InstanceOf, model_validator
 
@@ -13,7 +14,7 @@ class FlowTrackable(BaseModel):
     inspecting the call stack.
     """
 
-    parent_flow: InstanceOf[Flow] | None = Field(
+    parent_flow: Optional[InstanceOf[Flow]] = Field(
         default=None,
         description="The parent flow of the instance, if it was created inside a flow.",
     )

src/crewai/flow/flow_visualizer.py

@@ -1,13 +1,14 @@
 # flow_visualizer.py
 
 import os
+from pathlib import Path
 
-from pyvis.network import Network  # type: ignore[import-untyped]
+from pyvis.network import Network
 
 from crewai.flow.config import COLORS, NODE_STYLES
 from crewai.flow.html_template_handler import HTMLTemplateHandler
 from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items
-from crewai.flow.path_utils import safe_path_join
+from crewai.flow.path_utils import safe_path_join, validate_path_exists
 from crewai.flow.utils import calculate_node_levels
 from crewai.flow.visualization_utils import (
     add_edges,
@@ -33,13 +34,13 @@ class FlowPlot:
         ValueError
             If flow object is invalid or missing required attributes.
         """
-        if not hasattr(flow, "_methods"):
+        if not hasattr(flow, '_methods'):
             raise ValueError("Invalid flow object: missing '_methods' attribute")
-        if not hasattr(flow, "_listeners"):
+        if not hasattr(flow, '_listeners'):
             raise ValueError("Invalid flow object: missing '_listeners' attribute")
-        if not hasattr(flow, "_start_methods"):
+        if not hasattr(flow, '_start_methods'):
             raise ValueError("Invalid flow object: missing '_start_methods' attribute")
-
+            
         self.flow = flow
         self.colors = COLORS
         self.node_styles = NODE_STYLES
@@ -64,7 +65,7 @@ class FlowPlot:
         """
         if not filename or not isinstance(filename, str):
             raise ValueError("Filename must be a non-empty string")
-
+            
         try:
             # Initialize network
             net = Network(
@@ -95,34 +96,32 @@ class FlowPlot:
             try:
                 node_levels = calculate_node_levels(self.flow)
             except Exception as e:
-                raise ValueError(f"Failed to calculate node levels: {e!s}") from e
+                raise ValueError(f"Failed to calculate node levels: {str(e)}")
 
             # Compute positions
             try:
                 node_positions = compute_positions(self.flow, node_levels)
             except Exception as e:
-                raise ValueError(f"Failed to compute node positions: {e!s}") from e
+                raise ValueError(f"Failed to compute node positions: {str(e)}")
 
             # Add nodes to the network
             try:
                 add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
             except Exception as e:
-                raise RuntimeError(f"Failed to add nodes to network: {e!s}") from e
+                raise RuntimeError(f"Failed to add nodes to network: {str(e)}")
 
             # Add edges to the network
             try:
                 add_edges(net, self.flow, node_positions, self.colors)
             except Exception as e:
-                raise RuntimeError(f"Failed to add edges to network: {e!s}") from e
+                raise RuntimeError(f"Failed to add edges to network: {str(e)}")
 
             # Generate HTML
             try:
                 network_html = net.generate_html()
                 final_html_content = self._generate_final_html(network_html)
             except Exception as e:
-                raise RuntimeError(
-                    f"Failed to generate network visualization: {e!s}"
-                ) from e
+                raise RuntimeError(f"Failed to generate network visualization: {str(e)}")
 
             # Save the final HTML content to the file
             try:
@@ -130,16 +129,12 @@ class FlowPlot:
                     f.write(final_html_content)
                 print(f"Plot saved as {filename}.html")
             except IOError as e:
-                raise IOError(
-                    f"Failed to save flow visualization to {filename}.html: {e!s}"
-                ) from e
+                raise IOError(f"Failed to save flow visualization to {filename}.html: {str(e)}")
 
         except (ValueError, RuntimeError, IOError) as e:
             raise e
         except Exception as e:
-            raise RuntimeError(
-                f"Unexpected error during flow visualization: {e!s}"
-            ) from e
+            raise RuntimeError(f"Unexpected error during flow visualization: {str(e)}")
         finally:
             self._cleanup_pyvis_lib()
 
@@ -170,9 +165,7 @@ class FlowPlot:
         try:
             # Extract just the body content from the generated HTML
             current_dir = os.path.dirname(__file__)
-            template_path = safe_path_join(
-                "assets", "crewai_flow_visual_template.html", root=current_dir
-            )
+            template_path = safe_path_join("assets", "crewai_flow_visual_template.html", root=current_dir)
             logo_path = safe_path_join("assets", "crewai_logo.svg", root=current_dir)
 
             if not os.path.exists(template_path):
@@ -186,9 +179,12 @@ class FlowPlot:
             # Generate the legend items HTML
             legend_items = get_legend_items(self.colors)
             legend_items_html = generate_legend_items_html(legend_items)
-            return html_handler.generate_final_html(network_body, legend_items_html)
+            final_html_content = html_handler.generate_final_html(
+                network_body, legend_items_html
+            )
+            return final_html_content
         except Exception as e:
-            raise IOError(f"Failed to generate visualization HTML: {e!s}") from e
+            raise IOError(f"Failed to generate visualization HTML: {str(e)}")
 
     def _cleanup_pyvis_lib(self):
         """
@@ -201,7 +197,6 @@ class FlowPlot:
             lib_folder = safe_path_join("lib", root=os.getcwd())
             if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
                 import shutil
-
                 shutil.rmtree(lib_folder)
         except ValueError as e:
             print(f"Error validating lib folder path: {e}")

src/crewai/flow/html_template_handler.py

@@ -1,7 +1,8 @@
 import base64
 import re
+from pathlib import Path
 
-from crewai.flow.path_utils import validate_path_exists
+from crewai.flow.path_utils import safe_path_join, validate_path_exists
 
 
 class HTMLTemplateHandler:
@@ -27,7 +28,7 @@ class HTMLTemplateHandler:
             self.template_path = validate_path_exists(template_path, "file")
             self.logo_path = validate_path_exists(logo_path, "file")
         except ValueError as e:
-            raise ValueError(f"Invalid template or logo path: {e}") from e
+            raise ValueError(f"Invalid template or logo path: {e}")
 
     def read_template(self):
         """Read and return the HTML template file contents."""
@@ -52,23 +53,23 @@ class HTMLTemplateHandler:
             if "border" in item:
                 legend_items_html += f"""
                 <div class="legend-item">
-                <div class="legend-color-box" style="background-color: {item["color"]}; border: 2px dashed {item["border"]};"></div>
-                <div>{item["label"]}</div>
+                <div class="legend-color-box" style="background-color: {item['color']}; border: 2px dashed {item['border']};"></div>
+                <div>{item['label']}</div>
                 </div>
                 """
             elif item.get("dashed") is not None:
                 style = "dashed" if item["dashed"] else "solid"
                 legend_items_html += f"""
                 <div class="legend-item">
-                <div class="legend-{style}" style="border-bottom: 2px {style} {item["color"]};"></div>
-                <div>{item["label"]}</div>
+                <div class="legend-{style}" style="border-bottom: 2px {style} {item['color']};"></div>
+                <div>{item['label']}</div>
                 </div>
                 """
             else:
                 legend_items_html += f"""
                 <div class="legend-item">
-                <div class="legend-color-box" style="background-color: {item["color"]};"></div>
-                <div>{item["label"]}</div>
+                <div class="legend-color-box" style="background-color: {item['color']};"></div>
+                <div>{item['label']}</div>
                 </div>
                 """
         return legend_items_html
@@ -78,9 +79,15 @@ class HTMLTemplateHandler:
         html_template = self.read_template()
         logo_svg_base64 = self.encode_logo()
 
-        return (
-            html_template.replace("{{ title }}", title)
-            .replace("{{ network_content }}", network_body)
-            .replace("{{ logo_svg_base64 }}", logo_svg_base64)
-            .replace("<!-- LEGEND_ITEMS_PLACEHOLDER -->", legend_items_html)
+        final_html_content = html_template.replace("{{ title }}", title)
+        final_html_content = final_html_content.replace(
+            "{{ network_content }}", network_body
         )
+        final_html_content = final_html_content.replace(
+            "{{ logo_svg_base64 }}", logo_svg_base64
+        )
+        final_html_content = final_html_content.replace(
+            "<!-- LEGEND_ITEMS_PLACEHOLDER -->", legend_items_html
+        )
+
+        return final_html_content

src/crewai/flow/path_utils.py

@@ -5,10 +5,12 @@ This module provides utilities for secure path handling to prevent directory
 traversal attacks and ensure paths remain within allowed boundaries.
 """
 
+import os
 from pathlib import Path
+from typing import List, Union
 
 
-def safe_path_join(*parts: str, root: str | Path | None = None) -> str:
+def safe_path_join(*parts: str, root: Union[str, Path, None] = None) -> str:
     """
     Safely join path components and ensure the result is within allowed boundaries.
 
@@ -41,25 +43,25 @@ def safe_path_join(*parts: str, root: str | Path | None = None) -> str:
 
         # Establish root directory
         root_path = Path(root).resolve() if root else Path.cwd()
-
+        
         # Join and resolve the full path
         full_path = Path(root_path, *clean_parts).resolve()
-
+        
         # Check if the resolved path is within root
         if not str(full_path).startswith(str(root_path)):
             raise ValueError(
                 f"Invalid path: Potential directory traversal. Path must be within {root_path}"
             )
-
+            
         return str(full_path)
-
+        
     except Exception as e:
         if isinstance(e, ValueError):
             raise
-        raise ValueError(f"Invalid path components: {e!s}") from e
+        raise ValueError(f"Invalid path components: {str(e)}")
 
 
-def validate_path_exists(path: str | Path, file_type: str = "file") -> str:
+def validate_path_exists(path: Union[str, Path], file_type: str = "file") -> str:
     """
     Validate that a path exists and is of the expected type.
 
@@ -82,24 +84,24 @@ def validate_path_exists(path: str | Path, file_type: str = "file") -> str:
     """
     try:
         path_obj = Path(path).resolve()
-
+        
         if not path_obj.exists():
             raise ValueError(f"Path does not exist: {path}")
-
+            
         if file_type == "file" and not path_obj.is_file():
             raise ValueError(f"Path is not a file: {path}")
-        if file_type == "directory" and not path_obj.is_dir():
+        elif file_type == "directory" and not path_obj.is_dir():
             raise ValueError(f"Path is not a directory: {path}")
-
+            
         return str(path_obj)
-
+        
     except Exception as e:
         if isinstance(e, ValueError):
             raise
-        raise ValueError(f"Invalid path: {e!s}") from e
+        raise ValueError(f"Invalid path: {str(e)}")
 
 
-def list_files(directory: str | Path, pattern: str = "*") -> list[str]:
+def list_files(directory: Union[str, Path], pattern: str = "*") -> List[str]:
     """
     Safely list files in a directory matching a pattern.
 
@@ -124,10 +126,10 @@ def list_files(directory: str | Path, pattern: str = "*") -> list[str]:
         dir_path = Path(directory).resolve()
         if not dir_path.is_dir():
             raise ValueError(f"Not a directory: {directory}")
-
+            
         return [str(p) for p in dir_path.glob(pattern) if p.is_file()]
-
+        
     except Exception as e:
         if isinstance(e, ValueError):
             raise
-        raise ValueError(f"Error listing files: {e!s}") from e
+        raise ValueError(f"Error listing files: {str(e)}")

src/crewai/flow/persistence/__init__.py

@@ -4,7 +4,7 @@ CrewAI Flow Persistence.
 This module provides interfaces and implementations for persisting flow states.
 """
 
-from typing import Any, TypeVar
+from typing import Any, Dict, TypeVar, Union
 
 from pydantic import BaseModel
 
@@ -12,7 +12,7 @@ from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.persistence.decorators import persist
 from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
 
-__all__ = ["FlowPersistence", "SQLiteFlowPersistence", "persist"]
+__all__ = ["FlowPersistence", "persist", "SQLiteFlowPersistence"]
 
-StateType = TypeVar("StateType", bound=dict[str, Any] | BaseModel)
-DictStateType = dict[str, Any]
+StateType = TypeVar('StateType', bound=Union[Dict[str, Any], BaseModel])
+DictStateType = Dict[str, Any]

src/crewai/flow/persistence/base.py

@@ -1,47 +1,53 @@
 """Base class for flow state persistence."""
 
 import abc
-from typing import Any
+from typing import Any, Dict, Optional, Union
 
 from pydantic import BaseModel
 
 
 class FlowPersistence(abc.ABC):
     """Abstract base class for flow state persistence.
-
+    
     This class defines the interface that all persistence implementations must follow.
     It supports both structured (Pydantic BaseModel) and unstructured (dict) states.
     """
-
+    
     @abc.abstractmethod
     def init_db(self) -> None:
         """Initialize the persistence backend.
-
+        
         This method should handle any necessary setup, such as:
         - Creating tables
         - Establishing connections
         - Setting up indexes
         """
-
+        pass
+    
     @abc.abstractmethod
     def save_state(
-        self, flow_uuid: str, method_name: str, state_data: dict[str, Any] | BaseModel
+        self,
+        flow_uuid: str,
+        method_name: str,
+        state_data: Union[Dict[str, Any], BaseModel]
     ) -> None:
         """Persist the flow state after method completion.
-
+        
         Args:
             flow_uuid: Unique identifier for the flow instance
             method_name: Name of the method that just completed
             state_data: Current state data (either dict or Pydantic model)
         """
-
+        pass
+    
     @abc.abstractmethod
-    def load_state(self, flow_uuid: str) -> dict[str, Any] | None:
+    def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
         """Load the most recent state for a given flow UUID.
-
+        
         Args:
             flow_uuid: Unique identifier for the flow instance
-
+            
         Returns:
             The most recent state as a dictionary, or None if no state exists
         """
+        pass

src/crewai/flow/persistence/decorators.py

@@ -24,10 +24,13 @@ Example:
 import asyncio
 import functools
 import logging
-from collections.abc import Callable
 from typing import (
     Any,
+    Callable,
+    Optional,
+    Type,
     TypeVar,
+    Union,
     cast,
 )
 
@@ -45,7 +48,7 @@ LOG_MESSAGES = {
     "save_state": "Saving flow state to memory for ID: {}",
     "save_error": "Failed to persist state for method {}: {}",
     "state_missing": "Flow instance has no state",
-    "id_missing": "Flow state must have an 'id' field for persistence",
+    "id_missing": "Flow state must have an 'id' field for persistence"
 }
 
 
@@ -55,13 +58,7 @@ class PersistenceDecorator:
     _printer = Printer()  # Class-level printer instance
 
     @classmethod
-    def persist_state(
-        cls,
-        flow_instance: Any,
-        method_name: str,
-        persistence_instance: FlowPersistence,
-        verbose: bool = False,
-    ) -> None:
+    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence, verbose: bool = False) -> None:
         """Persist flow state with proper error handling and logging.
 
         This method handles the persistence of flow state data, including proper
@@ -79,24 +76,22 @@ class PersistenceDecorator:
             AttributeError: If flow instance lacks required state attributes
         """
         try:
-            state = getattr(flow_instance, "state", None)
+            state = getattr(flow_instance, 'state', None)
             if state is None:
                 raise ValueError("Flow instance has no state")
 
-            flow_uuid: str | None = None
+            flow_uuid: Optional[str] = None
             if isinstance(state, dict):
-                flow_uuid = state.get("id")
+                flow_uuid = state.get('id')
             elif isinstance(state, BaseModel):
-                flow_uuid = getattr(state, "id", None)
+                flow_uuid = getattr(state, 'id', None)
 
             if not flow_uuid:
                 raise ValueError("Flow state must have an 'id' field for persistence")
 
             # Log state saving only if verbose is True
             if verbose:
-                cls._printer.print(
-                    LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan"
-                )
+                cls._printer.print(LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan")
                 logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
 
             try:
@@ -109,12 +104,12 @@ class PersistenceDecorator:
                 error_msg = LOG_MESSAGES["save_error"].format(method_name, str(e))
                 cls._printer.print(error_msg, color="red")
                 logger.error(error_msg)
-                raise RuntimeError(f"State persistence failed: {e!s}") from e
-        except AttributeError as e:
+                raise RuntimeError(f"State persistence failed: {str(e)}") from e
+        except AttributeError:
             error_msg = LOG_MESSAGES["state_missing"]
             cls._printer.print(error_msg, color="red")
             logger.error(error_msg)
-            raise ValueError(error_msg) from e
+            raise ValueError(error_msg)
         except (TypeError, ValueError) as e:
             error_msg = LOG_MESSAGES["id_missing"]
             cls._printer.print(error_msg, color="red")
@@ -122,7 +117,7 @@ class PersistenceDecorator:
             raise ValueError(error_msg) from e
 
 
-def persist(persistence: FlowPersistence | None = None, verbose: bool = False):
+def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False):
     """Decorator to persist flow state.
 
     This decorator can be applied at either the class level or method level.
@@ -149,151 +144,111 @@ def persist(persistence: FlowPersistence | None = None, verbose: bool = False):
             def begin(self):
                 pass
     """
-
-    def decorator(target: type | Callable[..., T]) -> type | Callable[..., T]:
+    def decorator(target: Union[Type, Callable[..., T]]) -> Union[Type, Callable[..., T]]:
         """Decorator that handles both class and method decoration."""
         actual_persistence = persistence or SQLiteFlowPersistence()
 
         if isinstance(target, type):
             # Class decoration
-            original_init = target.__init__  # type: ignore[misc]
+            original_init = getattr(target, "__init__")
 
             @functools.wraps(original_init)
             def new_init(self: Any, *args: Any, **kwargs: Any) -> None:
-                if "persistence" not in kwargs:
-                    kwargs["persistence"] = actual_persistence
+                if 'persistence' not in kwargs:
+                    kwargs['persistence'] = actual_persistence
                 original_init(self, *args, **kwargs)
 
-            target.__init__ = new_init  # type: ignore[misc]
+            setattr(target, "__init__", new_init)
 
             # Store original methods to preserve their decorators
-            original_methods = {
-                name: method
-                for name, method in target.__dict__.items()
-                if callable(method)
-                and (
-                    hasattr(method, "__is_start_method__")
-                    or hasattr(method, "__trigger_methods__")
-                    or hasattr(method, "__condition_type__")
-                    or hasattr(method, "__is_flow_method__")
-                    or hasattr(method, "__is_router__")
-                )
-            }
+            original_methods = {}
+
+            for name, method in target.__dict__.items():
+                if callable(method) and (
+                    hasattr(method, "__is_start_method__") or
+                    hasattr(method, "__trigger_methods__") or
+                    hasattr(method, "__condition_type__") or
+                    hasattr(method, "__is_flow_method__") or
+                    hasattr(method, "__is_router__")
+                ):
+                    original_methods[name] = method
 
             # Create wrapped versions of the methods that include persistence
             for name, method in original_methods.items():
                 if asyncio.iscoroutinefunction(method):
                     # Create a closure to capture the current name and method
-                    def create_async_wrapper(
-                        method_name: str, original_method: Callable
-                    ):
+                    def create_async_wrapper(method_name: str, original_method: Callable):
                         @functools.wraps(original_method)
-                        async def method_wrapper(
-                            self: Any, *args: Any, **kwargs: Any
-                        ) -> Any:
+                        async def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = await original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose
-                            )
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
                             return result
-
                         return method_wrapper
 
                     wrapped = create_async_wrapper(name, method)
 
                     # Preserve all original decorators and attributes
-                    for attr in [
-                        "__is_start_method__",
-                        "__trigger_methods__",
-                        "__condition_type__",
-                        "__is_router__",
-                    ]:
+                    for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                         if hasattr(method, attr):
                             setattr(wrapped, attr, getattr(method, attr))
-                    wrapped.__is_flow_method__ = True  # type: ignore[attr-defined]
+                    setattr(wrapped, "__is_flow_method__", True)
 
                     # Update the class with the wrapped method
                     setattr(target, name, wrapped)
                 else:
                     # Create a closure to capture the current name and method
-                    def create_sync_wrapper(
-                        method_name: str, original_method: Callable
-                    ):
+                    def create_sync_wrapper(method_name: str, original_method: Callable):
                         @functools.wraps(original_method)
                         def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose
-                            )
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
                             return result
-
                         return method_wrapper
 
                     wrapped = create_sync_wrapper(name, method)
 
                     # Preserve all original decorators and attributes
-                    for attr in [
-                        "__is_start_method__",
-                        "__trigger_methods__",
-                        "__condition_type__",
-                        "__is_router__",
-                    ]:
+                    for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                         if hasattr(method, attr):
                             setattr(wrapped, attr, getattr(method, attr))
-                    wrapped.__is_flow_method__ = True  # type: ignore[attr-defined]
+                    setattr(wrapped, "__is_flow_method__", True)
 
                     # Update the class with the wrapped method
                     setattr(target, name, wrapped)
 
             return target
-        # Method decoration
-        method = target
-        method.__is_flow_method__ = True  # type: ignore[attr-defined]
+        else:
+            # Method decoration
+            method = target
+            setattr(method, "__is_flow_method__", True)
 
-        if asyncio.iscoroutinefunction(method):
+            if asyncio.iscoroutinefunction(method):
+                @functools.wraps(method)
+                async def method_async_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
+                    method_coro = method(flow_instance, *args, **kwargs)
+                    if asyncio.iscoroutine(method_coro):
+                        result = await method_coro
+                    else:
+                        result = method_coro
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
+                    return result
 
-            @functools.wraps(method)
-            async def method_async_wrapper(
-                flow_instance: Any, *args: Any, **kwargs: Any
-            ) -> T:
-                method_coro = method(flow_instance, *args, **kwargs)
-                if asyncio.iscoroutine(method_coro):
-                    result = await method_coro
-                else:
-                    result = method_coro
-                PersistenceDecorator.persist_state(
-                    flow_instance, method.__name__, actual_persistence, verbose
-                )
-                return result
+                for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
+                    if hasattr(method, attr):
+                        setattr(method_async_wrapper, attr, getattr(method, attr))
+                setattr(method_async_wrapper, "__is_flow_method__", True)
+                return cast(Callable[..., T], method_async_wrapper)
+            else:
+                @functools.wraps(method)
+                def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
+                    result = method(flow_instance, *args, **kwargs)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
+                    return result
 
-            for attr in [
-                "__is_start_method__",
-                "__trigger_methods__",
-                "__condition_type__",
-                "__is_router__",
-            ]:
-                if hasattr(method, attr):
-                    setattr(method_async_wrapper, attr, getattr(method, attr))
-            method_async_wrapper.__is_flow_method__ = True  # type: ignore[attr-defined]
-            return cast(Callable[..., T], method_async_wrapper)
-
-        @functools.wraps(method)
-        def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
-            result = method(flow_instance, *args, **kwargs)
-            PersistenceDecorator.persist_state(
-                flow_instance, method.__name__, actual_persistence, verbose
-            )
-            return result
-
-        for attr in [
-            "__is_start_method__",
-            "__trigger_methods__",
-            "__condition_type__",
-            "__is_router__",
-        ]:
-            if hasattr(method, attr):
-                setattr(method_sync_wrapper, attr, getattr(method, attr))
-        method_sync_wrapper.__is_flow_method__ = True  # type: ignore[attr-defined]
-        return cast(Callable[..., T], method_sync_wrapper)
+                for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
+                    if hasattr(method, attr):
+                        setattr(method_sync_wrapper, attr, getattr(method, attr))
+                setattr(method_sync_wrapper, "__is_flow_method__", True)
+                return cast(Callable[..., T], method_sync_wrapper)
 
     return decorator

src/crewai/flow/persistence/sqlite.py

@@ -6,7 +6,7 @@ import json
 import sqlite3
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any
+from typing import Any, Dict, Optional, Union
 
 from pydantic import BaseModel
 
@@ -23,7 +23,7 @@ class SQLiteFlowPersistence(FlowPersistence):
 
     db_path: str
 
-    def __init__(self, db_path: str | None = None):
+    def __init__(self, db_path: Optional[str] = None):
         """Initialize SQLite persistence.
 
         Args:
@@ -70,7 +70,7 @@ class SQLiteFlowPersistence(FlowPersistence):
         self,
         flow_uuid: str,
         method_name: str,
-        state_data: dict[str, Any] | BaseModel,
+        state_data: Union[Dict[str, Any], BaseModel],
     ) -> None:
         """Save the current flow state to SQLite.
 
@@ -107,7 +107,7 @@ class SQLiteFlowPersistence(FlowPersistence):
                 ),
             )
 
-    def load_state(self, flow_uuid: str) -> dict[str, Any] | None:
+    def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
         """Load the most recent state for a given flow UUID.
 
         Args:

src/crewai/flow/types.py

@@ -5,7 +5,6 @@ the Flow system.
 """
 
 from typing import Any, TypedDict
-
 from typing_extensions import NotRequired, Required
 
 

src/crewai/flow/utils.py

@@ -17,10 +17,10 @@ import ast
 import inspect
 import textwrap
 from collections import defaultdict, deque
-from typing import Any
+from typing import Any, Deque, Dict, List, Optional, Set, Union
 
 
-def get_possible_return_constants(function: Any) -> list[str] | None:
+def get_possible_return_constants(function: Any) -> Optional[List[str]]:
     try:
         source = inspect.getsource(function)
     except OSError:
@@ -58,12 +58,12 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
                 target = node.targets[0]
                 if isinstance(target, ast.Name):
                     var_name = target.id
+                    dict_values = []
                     # Extract string values from the dictionary
-                    dict_values = [
-                        val.value
-                        for val in node.value.values
-                        if isinstance(val, ast.Constant) and isinstance(val.value, str)
-                    ]
+                    for val in node.value.values:
+                        if isinstance(val, ast.Constant) and isinstance(val.value, str):
+                            dict_values.append(val.value)
+                        # If non-string, skip or just ignore
                     if dict_values:
                         dict_definitions[var_name] = dict_values
             self.generic_visit(node)
@@ -94,7 +94,7 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
     return list(return_values) if return_values else None
 
 
-def calculate_node_levels(flow: Any) -> dict[str, int]:
+def calculate_node_levels(flow: Any) -> Dict[str, int]:
     """
     Calculate the hierarchical level of each node in the flow.
 
@@ -118,10 +118,10 @@ def calculate_node_levels(flow: Any) -> dict[str, int]:
     - Handles both OR and AND conditions for listeners
     - Processes router paths separately
     """
-    levels: dict[str, int] = {}
-    queue: deque[str] = deque()
-    visited: set[str] = set()
-    pending_and_listeners: dict[str, set[str]] = {}
+    levels: Dict[str, int] = {}
+    queue: Deque[str] = deque()
+    visited: Set[str] = set()
+    pending_and_listeners: Dict[str, Set[str]] = {}
 
     # Make all start methods at level 0
     for method_name, method in flow._methods.items():
@@ -172,7 +172,7 @@ def calculate_node_levels(flow: Any) -> dict[str, int]:
     return levels
 
 
-def count_outgoing_edges(flow: Any) -> dict[str, int]:
+def count_outgoing_edges(flow: Any) -> Dict[str, int]:
     """
     Count the number of outgoing edges for each method in the flow.
 
@@ -197,7 +197,7 @@ def count_outgoing_edges(flow: Any) -> dict[str, int]:
     return counts
 
 
-def build_ancestor_dict(flow: Any) -> dict[str, set[str]]:
+def build_ancestor_dict(flow: Any) -> Dict[str, Set[str]]:
     """
     Build a dictionary mapping each node to its ancestor nodes.
 
@@ -211,8 +211,8 @@ def build_ancestor_dict(flow: Any) -> dict[str, set[str]]:
     Dict[str, Set[str]]
         Dictionary mapping each node to a set of its ancestor nodes.
     """
-    ancestors: dict[str, set[str]] = {node: set() for node in flow._methods}
-    visited: set[str] = set()
+    ancestors: Dict[str, Set[str]] = {node: set() for node in flow._methods}
+    visited: Set[str] = set()
     for node in flow._methods:
         if node not in visited:
             dfs_ancestors(node, ancestors, visited, flow)
@@ -220,7 +220,7 @@ def build_ancestor_dict(flow: Any) -> dict[str, set[str]]:
 
 
 def dfs_ancestors(
-    node: str, ancestors: dict[str, set[str]], visited: set[str], flow: Any
+    node: str, ancestors: Dict[str, Set[str]], visited: Set[str], flow: Any
 ) -> None:
     """
     Perform depth-first search to build ancestor relationships.
@@ -265,7 +265,7 @@ def dfs_ancestors(
 
 
 def is_ancestor(
-    node: str, ancestor_candidate: str, ancestors: dict[str, set[str]]
+    node: str, ancestor_candidate: str, ancestors: Dict[str, Set[str]]
 ) -> bool:
     """
     Check if one node is an ancestor of another.
@@ -287,7 +287,7 @@ def is_ancestor(
     return ancestor_candidate in ancestors.get(node, set())
 
 
-def build_parent_children_dict(flow: Any) -> dict[str, list[str]]:
+def build_parent_children_dict(flow: Any) -> Dict[str, List[str]]:
     """
     Build a dictionary mapping parent nodes to their children.
 
@@ -307,7 +307,7 @@ def build_parent_children_dict(flow: Any) -> dict[str, list[str]]:
     - Maps router methods to their paths and listeners
     - Children lists are sorted for consistent ordering
     """
-    parent_children: dict[str, list[str]] = {}
+    parent_children: Dict[str, List[str]] = {}
 
     # Map listeners to their trigger methods
     for listener_name, (_, trigger_methods) in flow._listeners.items():
@@ -332,7 +332,7 @@ def build_parent_children_dict(flow: Any) -> dict[str, list[str]]:
 
 
 def get_child_index(
-    parent: str, child: str, parent_children: dict[str, list[str]]
+    parent: str, child: str, parent_children: Dict[str, List[str]]
 ) -> int:
     """
     Get the index of a child node in its parent's sorted children list.
@@ -364,7 +364,7 @@ def process_router_paths(flow, current, current_level, levels, queue):
         paths = flow._router_paths.get(current, [])
         for path in paths:
             for listener_name, (
-                _condition_type,
+                condition_type,
                 trigger_methods,
             ) in flow._listeners.items():
                 if path in trigger_methods: